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Computer Fraud Laws are Flawed, this Lawyer is Fighting Against Them

Tor Ekeland, hacker lawyer, fights back against the harsh punishments decreed using the Computer Fraud and Abuse Act. And one of those fights can be seen in “Trust Machine,” available now at Breaker.io.

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NASA Engineer: Humans Should Consider Settling Saturn’s Moon Titan

nasa-engineer-humans-settling-titan

Destination: Titan

If the Earth becomes inhabitable, a NASA engineer named Janelle Wellons says we should think about settling Saturn’s moon Titan, and she has a laundry list of reasons why — including that you might be able to fly by flapping your arms.

“It has a thick atmosphere that could help protect us from space radiation,” Wellons wrote on Reddit. “It is so dense that we could actually attach wings to our arms and fly on this moon. I don’t know, it just seems like an awesome place to live.”

Largest Moon

Wellon’s comments came in a Reddit appearance in which NASA engineers, scientists and pilots fielded questions from the public. One Redditor asked where the team would recommend settling if conditions on Earth became untenable, and Wellons chimed in with what she said was a “more interesting answer than the standard Mars or Moon response.”

“How about we consider one of the water worlds in our solar system — Titan,” she wrote. “Titan is the largest moon of Saturn, larger than the planet Mercury even, so I think we could settle with plenty room.”

Swim Good

In spite of Wellon’s enthusiasm, there are definite downsides to Titan. It only gets about one percent of the sunlight Earth does, and according to NASA’s research its maximum temperature is a wintry -292 degrees Fahrenheit.

But Wellon is still a fan.

“Now as for the conditions on the surface — not as rough as you may think,” she wrote. “Titan is the only place besides Earth known to have liquids in the form of lakes and seas on its surface. These liquids are made of methane but, armed with the right kind of protective gear, one could theoretically be able to swim without harm!”

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NASA Engineer: Humans Should Consider Settling Saturn’s Moon Titan

New Rocket Engine Could Whip You From London to Sydney in 4 Hours

The makers of a new hypersonic rocket engine say it could whip flights from London to Sydney in just four hours, traveling at five times the speed of sound.

Rocket Plane

The makers of a new hypersonic rocket engine say it could whisk flights from London to Sydney in just four hours, traveling at five times the speed of sound. That’s a flight that can take 20 hours on a conventional jetliner.

According to the BBCUK company Reaction Engines says it’s gearing up to test the futuristic craft in Colorado — a startling vision of the future of transportation that could also, if the engine lives up to the hype, inform the future of spaceflight.

Screaming Fist

Reaction Engines, which has backing from the Rolls-Royce and Boeing, calls the new rocket engine the Sabre. It inhales air at lower altitudes, but works more like a rocket when it gets higher up.

“The core can be tested on the ground, but it’s the core that gets you air-breathing from the ground up to the edge of space, at which point there is no more oxygen to breathe and the system transitions to the pure rocket mode,” said Shaun Driscoll, Reaction Engines’ program director, according to the BBC.

Orbiter

The company also says the Sabre engine could push the frontiers of spaceflight, by sending crafts straight into orbit without multiple propellant stages, according to the BBC, which also reported that the the European Space Agency recently signed off on a design review for the Sabre engine.

“The positive conclusion of our Preliminary Design Review marks a major milestone in Sabre development,” ESA’s head of propulsion engineering Mark Ford told the broadcaster. “It confirms the test version of this revolutionary new class of engine is ready for implementation.”

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Astronomers Just Found 83 Giant Black Holes at Universe’s Edge

An international team of researchers says it's found 83 new supermassive black holes at extreme end of the visible universe.

Hole Story

An international team of researchers says it’s found 83 new supermassive black holes at extreme end of the visible universe — by looking at light that took so long to reach Earth that it dates from the early universe.

“It is remarkable that such massive dense objects were able to form so soon after the Big Bang,” said Michael Strauss, a professor of astrophysical sciences at Princeton University involved in the research, in a press release. “Understanding how black holes can form in the early universe, and just how common they are, is a challenge for our cosmological models.”

Squad Goals

The discovery was made by 48 astronomers around the world who described the findings in five new papers in The Astrophysical Journal and the Publications of the Astronomical Observatory of Japan.

The finding was based on data taken with the Hyper Suprime-Cam, a “cutting-edge instrument” at the Subaru Telescope at the National Astronomical Observatory of Japan, in Hawaii, which the researchers combined with readings from three more powerful telescopes around the world.

Quasar Theory

The newly-discovered black holes are quasars, which shoot out matter in powerful jets. The researchers are hoping that more datagathering and analysis will shed light onto how some of the earliest quasars in the universe formed.

“The quasars we discovered will be an interesting subject for further follow-up observations with current and future facilities,” said Yoshiki Matsuok, a researcher at Ehime University who worked on the discovery. “We will also learn about the formation and early evolution of [super massive black holes], by comparing the measured number density and luminosity distribution with predictions from theoretical models.”

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Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] More specifically, Kaplan and Haenlein define AI as a systems ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.[2] Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.”[4] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.[5] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[7] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[2] Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[8][9] followed by disappointment and the loss of funding (known as an “AI winter”),[10][11] followed by new approaches, success and renewed funding.[9][12] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[13] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[14] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[15][16][17] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[13]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14] General intelligence is among the field’s long-term goals.[18] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[19] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[20] Some people also consider AI to be a danger to humanity if it progresses unabated.[21] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[22]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[23][12]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[24] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[25] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[20]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[26] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[27] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[29] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[30] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[32] (and by 1959 were reportedly playing better than the average human),[33] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[34] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[35] and laboratories had been established around the world.[36] AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation… the problem of creating ‘artificial intelligence’ will substantially be solved”.[8]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”,[10] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[38] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S and British governments to restore funding for academic research.[9] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[11]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[23] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[39] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[42] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[43] as do intelligent personal assistants in smartphones.[44] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.[7][45] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[46] who at the time continuously held the world No. 1 ranking for two years.[47][48] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[49] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[12] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[49] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[50][51] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[52][53]

A typical AI analyses its environment and takes actions that maximize its chance of success.[1] An AI’s intended utility function (or goal) can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do mathematically similar actions to the ones succeeded in the past”). Goals can be explicitly defined, or induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[56]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.[58] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[60]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[61] the best approach is often different depending on the problem.[63]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][66][67][68]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[71][72][73] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[74][75][76]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[14]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[77] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[78]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[58] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[79]

Knowledge representation[80] and knowledge engineering[81] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[82] situations, events, states and time;[83] causes and effects;[84] knowledge about knowledge (what we know about what other people know);[85] and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[86] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[87] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[88] scene interpretation,[89] clinical decision support,[90] knowledge discovery (mining “interesting” and actionable inferences from large databases),[91] and other areas.[92]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[99] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[100]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[101] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[102]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[103]

Machine learning, a fundamental concept of AI research since the field’s inception,[104] is the study of computer algorithms that improve automatically through experience.[105][106]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[107] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[106] Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[108] In reinforcement learning[109] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[110] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[111] and machine translation.[112] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[113]

Machine perception[114] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[115] facial recognition, and object recognition.[116] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[117]

AI is heavily used in robotics.[118] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[119] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[121][122] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[123][124] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[125]

Moravec’s paradox can be extended to many forms of social intelligence.[127][128] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[129] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[133]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[134] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[135]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[136] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[18][137] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[138][139][140] Besides transfer learning,[141] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[143][144]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[145] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[15]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[16]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[146] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[147] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[148]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[149][150]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless of whether people used the same algorithms.[15] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[151] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[152]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[153] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[16] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[154]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[155] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[38] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[156] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[17] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[157] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[158][159]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[162] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[163]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[39][164] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[173] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[174] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[175] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[119] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[176] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use “heuristics” or “rules of thumb” that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for the path on which the solution lies.[177] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[178]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[179] Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[180][181]

Logic[182] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[183] and inductive logic programming is a method for learning.[184]

Several different forms of logic are used in AI research. Propositional logic[185] involves truth functions such as “or” and “not”. First-order logic[186] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a “degree of truth” (between 0 and 1) to vague statements such as “Alice is old” (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][188][189]

Default logics, non-monotonic logics and circumscription[94] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[82] situation calculus, event calculus and fluent calculus (for representing events and time);[83] causal calculus;[84] belief calculus;[190] and modal logics.[85]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[192]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[193]

Bayesian networks[194] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[195] learning (using the expectation-maximization algorithm),[f][197] planning (using decision networks)[198] and perception (using dynamic Bayesian networks).[199] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[199] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[200] and information value theory.[100] These tools include models such as Markov decision processes,[201] dynamic decision networks,[199] game theory and mechanism design.[202]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[203]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[204] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[206]k-nearest neighbor algorithm,[g][208]kernel methods such as the support vector machine (SVM),[h][210]Gaussian mixture model,[211] and the extremely popular naive Bayes classifier.[i][213] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as “naive Bayes” on most practical data sets.[214]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[217][218]

The study of non-learning artificial neural networks[206] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[219] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[220]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[221][222] and was introduced to neural networks by Paul Werbos.[223][224][225]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[226]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[227]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[228] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[229][230][228]

According to one overview,[231] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[232] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[233] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[234][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[235] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[237]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[238] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[239]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[228]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[240]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[241] which are in theory Turing complete[242] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[228] RNNs can be trained by gradient descent[243][244][245] but suffer from the vanishing gradient problem.[229][246] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[247]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[248] LSTM is often trained by Connectionist Temporal Classification (CTC).[249] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[250][251][252] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[253] Google also used LSTM to improve machine translation,[254] Language Modeling[255] and Multilingual Language Processing.[256] LSTM combined with CNNs also improved automatic image captioning[257] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[258] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[259][260] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[261] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[125]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[262][263] E-sports such as StarCraft continue to provide additional public benchmarks.[264][265] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[266]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[267] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[269][270]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[273] prediction of judicial decisions[274] and targeting online advertisements.[275][276]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[277] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[278]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[279]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[280] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[281] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[282] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[283]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[284] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[285] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[286]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[287]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[288]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[289] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[290]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[291] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[292]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[293] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[294] In August 2001, robots beat humans in a simulated financial trading competition.[295] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[296]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[297] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[298][299]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[300][301] Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI.[302]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[303]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[304] A documented case reports that online gambling companies were using AI to improve customer targeting.[305]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[306]

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Artificial intelligence – Wikipedia

Benefits & Risks of Artificial Intelligence – Future of …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence – Future of …

What is Artificial Intelligence? How Does AI Work? | Built In

Can machines think? Alan Turing, 1950

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?”

Turing’s paper “Computing Machinery and Intelligence” (1950), and it’s subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.

At it’s core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to manyquestions and debates. So much so, that no singular definition of the field is universally accepted.

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive percepts from the environment and perform actions.” (Russel and Norvig viii)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.” (Russel and Norvig 4).

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with machine learning and other subsets of artificial intelligence.

While addressing a crowd at the Japan AI Experience in 2017, DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:

“AI is a computer system able to perform tasks that ordinarily require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.”

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What is Artificial Intelligence? How Does AI Work? | Built In

What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

Psoriasis: Pictures, Symptoms, Causes, Diagnosis, Treatment

Articles OnPsoriasis Psoriasis Psoriasis – Psoriasis What Is Psoriasis?

Psoriasis is a skin disorder that causes skin cells to multiply up to 10 times faster than normal. This makes the skin build up into bumpy red patches covered with white scales. They can grow anywhere, but most appear on the scalp, elbows, knees, and lower back. Psoriasis can’t be passed from person to person. It does sometimes happen in members of the same family.

Psoriasis usually appears in early adulthood. For most people, it affects just a few areas. In severe cases, psoriasis can cover large parts of the body. The patches can heal and then come back throughout a person’s life.

The symptoms of psoriasis vary depending on the type you have. Some common symptoms for plaque psoriasis — the most common variety of the condition — include:

People with psoriasis can also get a type of arthritis called psoriatic arthritis. It causes pain and swelling in the joints. The National Psoriasis Foundation estimates that between 10% to 30% of people with psoriasis also have psoriatic arthritis.

Other types of psoriasis include:

No one knows the exact cause of psoriasis, but experts believe that its a combination of things. Something wrong with the immune system causes inflammation, triggering new skin cells to form too quickly. Normally, skin cells are replaced every 10 to 30 days. With psoriasis, new cells grow every 3 to 4 days. The buildup of old cells being replaced by new ones creates those silver scales.

Psoriasis tends to run in families, but it may be skip generations. For instance, a grandfather and his grandson may be affected, but not the child’s mother.

Things that can trigger an outbreak of psoriasis include:

Physical exam. Its usually easy for your doctor to diagnose psoriasis, especially if you have plaques on areas such as your:

Your doctor will give you a full physical exam and ask if people in your family have psoriasis.

Lab tests. The doctor might do a biopsy — remove a small piece of skin and test it to make sure you dont have a skin infection. Theres no other test to confirm or rule out psoriasis.

Luckily, there are many treatments. Some slow the growth of new skin cells, and others relieve itching and dry skin. Your doctor will select a treatment plan that is right for you based on the size of your rash, where it is on your body, your age, your overall health, and other things. Common treatments include:

Treatments for moderate to severe psoriasis include:

Theres no cure, but treatment greatly reduces symptoms, even in serious cases. Recent studies have suggested that when you better control the inflammation of psoriasis, your risk of heart disease, stroke, metabolic syndrome, and other diseases associated with inflammation go down.

Psoriasis affects:

SOURCES:

National Institute of Arthritis and Musculoskeletal and Skin Disease.

National Psoriasis Foundation.

The Psoriasis Foundation.

American Academy of Dermatology.

UpToDate: Epidemiology, clinical manifestations, and diagnosis of psoriasis.

FDA: “FDA approves new psoriasis drug Taltz,” FDA approves Amjevita, a biosimilar to Humira.

Medscape: “FDA OKs Biologic Guselkumab (Tremfya) for Plaque Psoriasis.”

National Psoriasis Foundation: Statistics.

PubMed Health: “Plaque Psoriasis.”

World Health Organization: Global report on psoriasis.

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Psoriasis: Pictures, Symptoms, Causes, Diagnosis, Treatment

Psoriasis – Symptoms and causes – Mayo Clinic

Overview

Psoriasis is a common skin condition that speeds up the life cycle of skin cells. It causes cells to build up rapidly on the surface of the skin. The extra skin cells form scales and red patches that are itchy and sometimes painful.

Psoriasis is a chronic disease that often comes and goes. The main goal of treatment is to stop the skin cells from growing so quickly.

There is no cure for psoriasis, but you can manage symptoms. Lifestyle measures, such as moisturizing, quitting smoking and managing stress, may help.

Psoriasis care at Mayo Clinic

Psoriasis signs and symptoms are different for everyone. Common signs and symptoms include:

Psoriasis patches can range from a few spots of dandruff-like scaling to major eruptions that cover large areas.

Most types of psoriasis go through cycles, flaring for a few weeks or months, then subsiding for a time or even going into complete remission.

There are several types of psoriasis. These include:

Guttate psoriasis. This type primarily affects young adults and children. It’s usually triggered by a bacterial infection such as strep throat. It’s marked by small, water-drop-shaped, scaling lesions on your trunk, arms, legs and scalp.

The lesions are covered by a fine scale and aren’t as thick as typical plaques are. You may have a single outbreak that goes away on its own, or you may have repeated episodes.

Pustular psoriasis. This uncommon form of psoriasis can occur in widespread patches (generalized pustular psoriasis) or in smaller areas on your hands, feet or fingertips.

It generally develops quickly, with pus-filled blisters appearing just hours after your skin becomes red and tender. The blisters may come and go frequently. Generalized pustular psoriasis can also cause fever, chills, severe itching and diarrhea.

If you suspect that you may have psoriasis, see your doctor for an examination. Also, talk to your doctor if your psoriasis:

Seek medical advice if your signs and symptoms worsen or don’t improve with treatment. You may need a different medication or a combination of treatments to manage the psoriasis.

Viven Williams: Your fingernails are clues to your overall health. Many people develop lines or ridges from the cuticle to the tip.

Rachel Miest, M.D.: Those are actually completely fine and just a part of normal aging.

Viven Williams: But Dr. Rachel Miest says there are other nail changes you should not ignore that may indicate

Rachel Miest, M.D.: liver problems, kidney problems, nutritional deficiencies …

Viven Williams: and other issues. Here are six examples: No. 1 is pitting. This could be a sign of psoriasis. Two is clubbing. Clubbing happens when your oxygen is low and could be a sign of lung issues. Three is spooning. It can happen if you have iron-deficient anemia or liver disease. Four is called “a Beau’s line.” It’s a horizontal line that indicates a previous injury or infection. Five is nail separation. This may happen as a result of injury, infection or a medication. And six is yellowing of the nails, which may be the result of chronic bronchitis.

For the Mayo Clinic News Network, I’m Vivien Williams.

The cause of psoriasis isn’t fully understood, but it’s thought to be related to an immune system problem with T cells and other white blood cells, called neutrophils, in your body.

T cells normally travel through the body to defend against foreign substances, such as viruses or bacteria.

But if you have psoriasis, the T cells attack healthy skin cells by mistake, as if to heal a wound or to fight an infection.

Overactive T cells also trigger increased production of healthy skin cells, more T cells and other white blood cells, especially neutrophils. These travel into the skin causing redness and sometimes pus in pustular lesions. Dilated blood vessels in psoriasis-affected areas create warmth and redness in the skin lesions.

The process becomes an ongoing cycle in which new skin cells move to the outermost layer of skin too quickly in days rather than weeks. Skin cells build up in thick, scaly patches on the skin’s surface, continuing until treatment stops the cycle.

Just what causes T cells to malfunction in people with psoriasis isn’t entirely clear. Researchers believe both genetics and environmental factors play a role.

Psoriasis typically starts or worsens because of a trigger that you may be able to identify and avoid. Factors that may trigger psoriasis include:

Anyone can develop psoriasis, but these factors can increase your risk of developing the disease:

If you have psoriasis, you’re at greater risk of developing certain diseases. These include:

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Psoriasis – Symptoms and causes – Mayo Clinic

Psoriasis Treatment, Causes, Pictures, Symptoms, Types & Diet

Psoriasis facts

What is psoriasis?

Psoriasis is a noncontagious, chronic skin disease that produces plaques of thickened, scaly skin. The dry flakes of silvery-white skin scales result from the excessively rapid proliferation of skin cells. Psoriasis is fundamentally an immune system problem. The proliferation of skin cells is triggered by inflammatory chemicals produced by specialized white blood cells called T-cells. Psoriasis commonly affects the skin of the elbows, knees, and scalp.

The spectrum of this autoimmune disease ranges from mild with limited involvement of small areas of skin to severe psoriasis with large, thick plaques to red inflamed skin affecting the entire body surface.

Psoriasis is considered an incurable, long-term (chronic) inflammatory skin condition. It has a variable course, periodically improving and worsening. It is not unusual for psoriasis to spontaneously clear for years and stay in remission. Many people note a worsening of their symptoms in the colder winter months.

Psoriasis, an immune-mediated inflammatory disease, affects all races and both sexes. Although psoriasis can be seen in people of any age, from babies to seniors, most commonly patients are first diagnosed in their early adult years. The self-esteem and quality of life of patients with psoriasis is often diminished because of the appearance of their skin. Recently, it has become clear that people with psoriasis are more likely to have diabetes, high blood lipids, cardiovascular disease, and a variety of other inflammatory diseases. This may reflect an inability to control inflammation. Caring for psoriasis takes medical teamwork.

No. Psoriasis is not contagious. Psoriasis is not transmitted sexually or by physical contact. Psoriasis is not caused by lifestyle, diet, or bad hygiene.

While the exact cause of psoriasis is unknown, researchers consider environmental, genetic, and immune system factors as playing roles in the establishment of the disease.

What are psoriasis causes and risk factors?

The exact cause remains unknown. A combination of elements, including genetic predisposition and environmental factors, are involved. It is common for psoriasis to be found in members of the same family. Defects in the immune system and the control of inflammation are thought to play major roles. Certain medications like beta-blockers have been linked to psoriasis. Despite research over the past 30 years, the “master switch” that turns on psoriasis is still a mystery.

What are the different types of psoriasis?

There are several different forms of psoriasis, including plaque psoriasis or psoriasis vulgaris (common type), guttate psoriasis (small, drop-like spots), inverse psoriasis (in the folds like of the underarms, navel, groin, and buttocks), and pustular psoriasis (small pus-filled yellowish blisters). When the palms and the soles are involved, this is known as palmoplantar psoriasis. In erythrodermic psoriasis, the entire skin surface is involved with the disease. Patients with this form of psoriasis often feel cold and may develop congestive heart failure if they have a preexisting heart problem. Nail psoriasis produces yellow pitted nails that can be confused with nail fungus. Scalp psoriasis can be severe enough to produce localized hair loss, plenty of dandruff, and severe itching.

Can psoriasis affect my joints?

Yes, psoriasis is associated with inflamed joints in about one-third of those affected. In fact, sometimes joint pains may be the only sign of the disorder, with completely clear skin. The joint disease associated with psoriasis is referred to as psoriatic arthritis. Patients may have inflammation of any joints (arthritis), although the joints of the hands, knees, and ankles tend to be most commonly affected. Psoriatic arthritis is an inflammatory, destructive form of arthritis and needs to be treated with medications in order to stop the disease progression.

The average age for onset of psoriatic arthritis is 30-40 years of age. Usually, the skin symptoms and signs precede the onset of the arthritis.

Can psoriasis affect only my nails?

Yes, psoriasis may involve solely the nails in a limited number of patients. Usually, the nail signs accompany the skin and arthritis symptoms and signs. Nail psoriasis is typically very difficult to treat. Treatment options are somewhat limited and include potent topical steroids applied at the nail-base cuticle, injection of steroids at the nail-base cuticle, and oral or systemic medications as described below for the treatment of psoriasis.

What are psoriasis symptoms and signs? What does psoriasis look like?

Plaque psoriasis signs and symptoms appear as red or pink small scaly bumps that merge into plaques of raised skin. Plaque psoriasis classically affects skin over the elbows, knees, and scalp and is often itchy. Although any area may be involved, plaque psoriasis tends to be more common at sites of friction, scratching, or abrasion. Sometimes pulling off one of these small dry white flakes of skin causes a tiny blood spot on the skin. This is a special diagnostic sign in psoriasis called the Auspitz sign.

Fingernails and toenails often exhibit small pits (pinpoint depressions) and/or larger yellowish-brown separations of the nail from the nail bed at the fingertip called distal onycholysis. Nail psoriasis may be confused with and incorrectly diagnosed as a fungal nail infection.

Guttate psoriasis symptoms and signs include bumps or small plaques ( inch or less) of red itchy, scaling skin that may appear explosively, affecting large parts of the skin surface simultaneously, after a sore throat.

In inverse psoriasis, genital lesions, especially in the groin and on the head of the penis, are common. Psoriasis in moist areas like the navel or the area between the buttocks (intergluteal folds) may look like flat red plaques without much scaling. This may be confused with other skin conditions like fungal infections, yeast infections, allergic rashes, or bacterial infections.

Symptoms and signs of pustular psoriasis include at rapid onset of groups of small bumps filled with pus on the torso. Patients are often systemically ill and may have a fever.

Erythrodermic psoriasis appears as extensive areas of red skin often involving the entire skin surface. Patients may often feel chilled.

Scalp psoriasis may look like severe dandruff with dry flakes and red areas of skin. It can be difficult to differentiate between scalp psoriasis and seborrheic dermatitis when only the scalp is involved. However, the treatment is often very similar for both conditions.

How do health care professionals diagnose psoriasis?

The diagnosis of psoriasis is typically made by obtaining information from the physical examination of the skin, medical history, and relevant family health history.

Sometimes lab tests, including a microscopic examination of skin cells obtained from a skin biopsy, may be necessary.

Eczema vs. psoriasis

Occasionally, it can be difficult to differentiate eczematous dermatitis from psoriasis. This is when a biopsy can be quite valuable to distinguish between the two conditions. Of note, both eczematous dermatitis and psoriasis often respond to similar treatments. Certain types of eczematous dermatitis can be cured where this is not the case for psoriasis.

How many people have psoriasis?

Psoriasis is a fairly common skin condition and is estimated to affect approximately 1%-3% of the U.S. population. It currently affects roughly 7.5 million to 8.5 million people in the U.S. It is seen worldwide in about 125 million people. Interestingly, African Americans have about half the rate of psoriasis as Caucasians.

Is psoriasis contagious?

No. A person cannot catch it from someone else, and one cannot pass it to anyone else by skin-to-skin contact. Directly touching someone with psoriasis every day will never transmit the condition.

Is there a cure for psoriasis?

No, psoriasis is not currently curable. However, it can go into remission, producing an entirely normal skin surface. Ongoing research is actively making progress on finding better treatments and a possible cure in the future.

Is psoriasis hereditary?

Although psoriasis is not contagious from person to person, there is a known hereditary tendency. Therefore, family history is very helpful in making the diagnosis.

What health care specialists treat psoriasis?

Dermatologists are doctors who specialize in the diagnosis and treatment of psoriasis, and rheumatologists specialize in the treatment of joint disorders and psoriatic arthritis. Many kinds of doctors may treat psoriasis, including dermatologists, family physicians, internal medicine physicians, rheumatologists, and other medical doctors. Some patients have also seen other allied health professionals such as acupuncturists, holistic practitioners, chiropractors, and nutritionists.

The American Academy of Dermatology and the National Psoriasis Foundation are excellent sources to help find doctors who specialize in this disease. Not all dermatologists and rheumatologists treat psoriasis. The National Psoriasis Foundation has one of the most up-to-date databases of current psoriasis specialists.

It is now apparent that patients with psoriasis are prone to a variety of other disease conditions, so-called comorbidities. Cardiovascular disease, diabetes, hypertension, inflammatory bowel disease, hyperlipidemia, liver problems, and arthritis are more common in patients with psoriasis. It is very important for all patients with psoriasis to be carefully monitored by their primary care providers for these associated illnesses. The joint inflammation of psoriatic arthritis and its complications are frequently managed by rheumatologists.

What are psoriasis treatment options?

There are many effective psoriasis treatment choices. The best treatment is individually determined by the treating doctor and depends, in part, on the type of disease, the severity, and amount of skin involved and the type of insurance coverage.

For mild disease that involves only small areas of the body (less than 10% of the total skin surface), topical treatments (skin applied), such as creams, lotions, and sprays, may be very effective and safe to use. Occasionally, a small local injection of steroids directly into a tough or resistant isolated psoriatic plaque may be helpful.

For moderate to severe psoriasis that involves much larger areas of the body (>10% or more of the total skin surface), topical products may not be effective or practical to apply. This may require ultraviolet light treatments or systemic (total body treatments such as pills or injections) medicines. Internal medications usually have greater risks. Because topical therapy has no effect on psoriatic arthritis, systemic medications are generally required to stop the progression to permanent joint destruction.

It is important to keep in mind that as with any medical condition, all medicines carry possible side effects. No medication is 100% effective for everyone, and no medication is 100% safe. The decision to use any medication requires thorough consideration and discussion with your health care provider. The risks and potential benefit of medications have to be considered for each type of psoriasis and the individual. Of two patients with precisely the same amount of disease, one may tolerate it with very little treatment, while the other may become incapacitated and require treatment internally.

A proposal to minimize the toxicity of some of these medicines has been commonly called “rotational” therapy. The idea is to change the anti-psoriasis drugs every six to 24 months in order to minimize the toxicity of one medication. Depending on the medications selected, this proposal can be an option. An exception to this proposal is the use of the newer biologic medications as described below. An individual who has been using strong topical steroids over large areas of their body for prolonged periods may benefit from stopping the steroids for a while and rotating onto a different therapy.

What creams, lotions, and home remedies are available for psoriasis?

Topical (skin applied) treatments include topical corticosteroids, vitamin D analogue creams like calcipotriene (Calcitrene, Dovonex, Sorilux), topical retinoids (tazarotene [Tazorac]), moisturizers, topical immunomodulators (tacrolimus and pimecrolimus), coal tar, anthralin, and others.

Are psoriasis shampoos available?

Coal tar shampoos are very useful in controlling psoriasis of the scalp. Using the shampoo daily can be very beneficial adjunctive therapy. There are a variety of over-the-counter shampoos available without a prescription. There is no evidence that one shampoo is superior to another. Generally, the selection of a tar shampoo is simply a matter of personal preference.

What oral medications are available for psoriasis?

Oral medications include methotrexate (Trexall), acitretin (Soriatane), cyclosporine (Neoral), apremilast (Otezla), and others. Oral prednisone (corticosteroid) is generally not used in psoriasis and may cause a disease flare-up if administered.

What injections or infusions are available for psoriasis?

Recently, a new group of drugs called biologics have become available to treat psoriasis and psoriatic arthritis. They are produced by living cells cultures in an industrial setting. They are all proteins and therefore must be administered through the skin because they would otherwise be degraded during digestion. All biologics work by suppressing certain specific portions of the immune inflammatory response that are overactive in psoriasis. A convenient method of categorizing these drugs is on the basis of their site of action:

Drug choice can be complicated, and your physician will help in selecting the best option. In some patients. it may be possible to predict drug efficacy on the basis of a prospective patient’s genetics. It appears that the presence of the HLA-Cw6 gene is correlated with a beneficial response to ustekinumab.

Newer drugs are in development and no doubt will be available in the near future. As this class of drugs is fairly new, ongoing monitoring and adverse effect reporting continues and long-term safety continues to be monitored. Biologics are all comparatively expensive especially in view of the fact they none of them are curative. Recently, the FDA has attempted to address this problem by permitting the use of “biosimilar” drugs. These drugs are structurally identical to a specific biologic drug and are presumed to produce identical therapeutic responses in human beings to the original, but are produced using different methodology. Biosimilars ought to be available at some fraction of the cost of the original. If this will be an effective approach remains to be seen. The only biosimilar available currently is infliximab (Inflectra). Two other biosimilar drugs have been accepted by the FDA, an etanercept equivalent (Erelzi) and an adalimumab equivalent (Amjevita) — but currently, neither are available.

Some biologics are to be administered by self-injections for home use while others are given by intravenous infusions in the doctor’s office. Biologics have some screening requirements such as a tuberculosis screening test (TB skin test or PPD test) and other labs prior to starting therapy. As with any drug, side effects are possible with all biologic drugs. Common potential side effects include mild local injection-site reactions (redness and tenderness). There is concern of serious infections and potential malignancy with nearly all biologic drugs. Precautions include patients with known or suspected hepatitis B infection, active tuberculosis, and possibly HIV/AIDS. As a general consideration, these drugs may not be an ideal choice for patients with a history of cancer and patients actively undergoing cancer therapy. In particular, there may be an increased association of lymphoma in patients taking a biologic.

Biologics are expensive medications ranging in price from several to tens of thousands of dollars per year per person. Their use may be limited by availability, cost, and insurance approval. Not all insurance drug plans fully cover these drugs for all conditions. Patients need to check with their insurance and may require a prior authorization request for coverage approval. Some of the biologic manufacturers have patient-assistance programs to help with financial issues. Therefore, choice of the right medication for your condition depends on many factors, not all of them medical. Additionally, convenience of receiving the medication and lifestyle affect the choice of the right biologic medication.

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Is there an anti-psoriasis diet?

Most patients with psoriasis seem to be overweight. Since there is a predisposition for those patients to develop cardiovascular disease and diabetes, it is suggested strongly that they try to maintain a normal body weight. Although evidence is sparse, it has been suggested that slender patients are more likely to respond to treatment.

Although dietary studies are notoriously difficult to perform and interpret, it seems likely that an anti-inflammatorydiet whose fat content is composed of polyunsaturated oils like olive oil and fish oil is beneficial for psoriasis. The so-called Mediterranean diet is an example.

What about light therapy for psoriasis?

Light therapy is also called phototherapy. There are several types of medical light therapies that include PUVA (an acronym for psoralen + UVA), UVB, and narrow-band UVB. These artificial light sources have been used for decades and generally are available in only certain physician’s offices. There are a few companies who may sell light boxes or light bulbs for prescribed home light therapy.

Natural sunlight is also used to treat psoriasis. Daily short, controlled exposures to natural sunlight may help or clear psoriasis in some patients. Skin unaffected by psoriasis and sensitive areas such as the face and hands may need to be protected during sun exposure.

There are also multiple newer light sources like lasers and photodynamic therapy (use of a light activating medication and a special light source) that have been used to treat psoriasis.

PUVA is a special treatment using a photosensitizing drug and timed artificial-light exposure composed of wavelengths of ultraviolet light in the UVA spectrum. The photosensitizing drug in PUVA is called psoralen. Both the psoralen and the UVA light must be administered within one hour of each other for a response to occur. These treatments are usually given in a physician’s office two to three times per week. Several weeks of PUVA is usually required before seeing significant results. The light exposure time is gradually increased during each subsequent treatment. Psoralens may be given orally as a pill or topically as a bath or lotion. After a short incubation period, the skin is exposed to a special wavelength of ultraviolet light called UVA. Patients using PUVA are generally sun sensitive and must avoid sun exposure for a period of time after PUVA. Common side effects with PUVA include burning, aging of the skin, increased brown spots called lentigines, and an increased risk of skin cancer, including melanoma. The relative increase in skin cancer risk with PUVA treatment is controversial. PUVA treatments need to be closely monitored by a physician and discontinued when a maximum number of treatments have been reached.

Narrow-band UVB phototherapy is an artificial light treatment using very limited wavelengths of light. It is frequently given daily or two to three times per week. UVB is also a component of natural sunlight. UVB dosage is based on time and exposure is gradually increased as tolerated. Potential side effects with UVB include skin burning, premature aging, and possible increased risk of skin cancer. The relative increase in skin cancer risk with UVB treatment needs further study but is probably less than PUVA or traditional UVB.

Sometimes UVB is combined with other treatments such as tar application. Goeckerman is a special psoriasis therapy using this combination. Some centers have used this therapy in a “day care” type of setting where patients are in the psoriasis treatment clinic all day for several weeks and go home each night.

Recently, a laser (excimer laser XTRAC) has been developed that generates ultraviolet light in the same range as narrow-band ultraviolet light. This light can be beneficial for psoriasis localized to small areas of skin like the palms, soles, and scalp. It is impractical to use in in extensive disease.

What is the long-term prognosis with psoriasis? What are complications of psoriasis?

Overall, the prognosis for most patients with psoriasis is good. While it is not curable, it is controllable. As described above, recent studies show an association of psoriasis and other medical conditions, including obesity, diabetes, and heart disease.

Is it possible to prevent psoriasis?

Since psoriasis is inherited, it is impossible at this time to suggest anything that is likely to prevent its development aside from indulging in a healthy lifestyle.

What does the future hold for psoriasis?

Psoriasis research is heavily funded and holds great promise for the future. Just the last five to 10 years have produced great improvements in treatment of the disease with medications aimed at controlling precise sites of the process of inflammation. Ongoing research is needed to decipher the ultimate underlying cause of this disease.

Is there a national psoriasis support group?

Yes, the National Psoriasis Foundation (NPF) is an organization dedicated to helping patients with psoriasis and furthering research in this field. They hold national and local chapter meetings. The NPF web site (http://www.psoriasis.org/home/) shares up-to-date reliable medical information and statistics on the condition.

Where can people get more information on psoriasis?

A dermatologist, the American Academy of Dermatology at http://www.AAD.org, and the National Psoriasis Foundation at http://www.psoriasis.org/home/ may be excellent sources of more information.

There are many ongoing clinical trials for psoriasis all over the United States and in the world. Many of these clinical trials are ongoing at academic or university medical centers and are frequently open to patients without cost.

Clinical trials frequently have specific requirements for types and severity of psoriasis that may be enrolled into a specific trial. Patients need to contact these centers and inquire regarding the specific study requirements. Some studies have restrictions on what recent medications have been used for psoriasis, current medication, and overall health.

Some of the many medical centers in the U.S. offering clinical trials for psoriasis include the University of California, San Francisco Department of Dermatology, the University of California, Irvine Department of Dermatology, and the St. Louis University Medical School.

Medically Reviewed on 2/1/2018

References

Alwan, W., and F.O. Nestle. “Pathogenesis and Treatment of Psoriasis: Exploiting Pathophysiological Pathways for Precision Medicine.” Clin Exp Rheumatol 33 (Suppl. 93): S2-S6.

Arndt, Kenneth A., eds., et al. “Topical Therapies for Psoriasis.” Seminars in Cutaneous Medicine and Surgery 35.2S Mar. 2016: S35-S46.

Conrad, Curdin, Michel Gilliet. “Psoriasis: From Pathogenesis to Targeted Therapies.” Clinical Reviews in Allergy & Immunology Jan. 18, 2015.

Dowlatshahi, E.A., E.A.M van der Voort, L.R. Arends, and T. Nijsten. “Markers of Systemic Inflammation in Psoriasis: A Systematic Review and Meta-Analysis.” British Journal of Dermatology 169.2 Aug. 2013: 266282.

Greb, Jacqueline E., et al. “Psoriasis.” Nature Reviews Disease Primers 2 (2016): 1-17.

National Psoriasis Foundation. “Systemic Treatments: Biologics and Oral Treatments.” 1-25.

Ogawa, Eisaku, Yuki Sato, Akane Minagawa, and Ryuhei Okuyama. “Pathogenesis of Psoriasis and Development of Treatment.” The Journal of Dermatology 2017: 1-9.

Villaseor-Park, Jennifer, David Wheeler, and Lisa Grandinetti. “Psoriasis: Evolving Treatment for a Complex Disease.” Cleveland Clinic Journal of Medicine 79.6 June 2012: 413-423.

Woo, Yu Ri, Dae Ho Cho, and Hyun Jeong Park. “Molecular Mechanisms and Management of a Cutaneous Inflammatory Disorder: Psoriasis.” International Journal of Molecular Sciences 18 Dec. 11, 2017: 1-26.

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Psoriasis Treatment, Causes, Pictures, Symptoms, Types & Diet

Psoriasis Guide: Causes, Symptoms and Treatment Options

Medically reviewed on May 14, 2018

Psoriasis is a chronic skin disorder that causes scaling and inflammation.

Psoriasis may develop as a result of an abnormality in the body’s immune system. The immune system normally fights infection and allergic reactions.

Psoriasis probably has a genetic component. Nearly half of patients have family members with psoriasis.

Certain medications may trigger psoriasis. Other medications seem to make psoriasis worse in people who have the disease.

Psoriasis causes skin scaling and inflammation. It may or may not cause itching. There are several types of psoriasis:

Plaque psoriasis. In plaque psoriasis, there are rounded or oval patches (plaques) of affected skin. These are usually red and covered with a thick silvery scale. The plaques often occur on the elbows, knees, scalp or near the buttocks. They may also appear on the trunk, arms and legs.

Inverse psoriasis. Inverse psoriasis is a plaque type of psoriasis that tends to affect skin creases. Creases in the underarm, groin, buttocks, genital areas or under the breast are particularly affected. The red patches may be moist rather than scaling.

Pustular psoriasis. The skin patches are studded with pimples or pustules.

Guttate psoriasis. In guttate psoriasis, many small, red, scaly patches develop suddenly and simultaneously. Guttate psoriasis often occurs in a young person who has recently had strep throat or a viral upper respiratory infection.

About half of people with skin symptoms of psoriasis also have abnormal fingernails. Their nails are often thick and have small indentations, called pitting.

A type of arthritis called psoriatic arthritis affects some people with psoriasis. Psoriatic arthritis may occur before skin changes appear.

Your doctor will look for the typical skin and nail changes of this disorder. He or she can frequently diagnose psoriasis based on your physical examination.

When skin symptoms are not typical of the disorder, your doctor may recommend a skin biopsy. In a biopsy, a small sample of skin is removed and examined in a laboratory. The biopsy can confirm the diagnosis and rule out other possible skin disorders.

Psoriasis is a long-term disorder. However, symptoms may come and go.

There is no way to prevent psoriasis.

Treatment for psoriasis varies depending on the:

Treatments for psoriasis include:

Topical treatments. These are treatments applied directly to the skin.

Daily skin care with emollients for lubrication. These include petroleum jelly or unscented moisturizers.

Corticosteroid creams, lotions and ointments. These may be prescribed in medium and high-strength forms for stubborn plaques on the hands, feet, arms, legs and trunk. They may be prescribed in low-strength forms for areas of delicate skin such as the face.

Calcipotriol (Dovonex) slows production of skin scales.

Tazarotene (Tazorac) is a synthetic vitamin A derivative.

Coal tar

Salicylic acid to remove scales

Phototherapy. Extensive or widespread psoriasis may be treated with light. Phototherapy uses ultraviolet B or ultraviolet A, alone or in combination with coal tar.

A treatment called PUVA combines ultraviolet A light treatment with an oral medication that improves the effectiveness of the light treatment.

Laser treatment also can be used. It allows treatment to be more focused so that higher amounts of UV light can be used.

Vitamin A derivatives. These are used to treat moderate to severe psoriasis involving large areas of the body. These treatments are very powerful. Some have the potential to cause severe side effects. It’s essential to understand the risks and be monitored closely.

Immunosuppressants. These drugs work by suppressing the immune system. They are used to treat moderate to severe psoriasis involving large areas of the body.

Antineoplastic agents. More rarely, these drugs (which are most often used to treat cancer cells) may be prescribed for severe psoriasis.

Biologic therapies. Biologics are newer agents used for psoriasis that has not responded to other treatments. Psoriasis is caused, in part, by substances made by the immune system that cause inflammation. Biologics act against these substances. Biologic treatments tend to be quite expensive.

If you are unsure whether you have psoriasis, contact your doctor. Also contact your doctor if you have psoriasis and are not doing well with over-the-counter treatment.

For most patients, psoriasis is a long-term condition.

There is no cure. But there are many effective treatments.

In some patients, doctors may switch treatments every 12 to 24 months. This prevents the treatments from losing their effectiveness and decreases the risk of side effects.

National Psoriasis Foundation6600 SW 92nd Ave.Suite 300Portland, OR 97223-7195Phone: 503-244-7404Toll-Free: 1-800-723-9166Fax: 503-245-0626http://www.psoriasis.org/

Always consult your healthcare provider to ensure the information displayed on this page applies to your personal circumstances.

Medical Disclaimer

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Psoriasis Guide: Causes, Symptoms and Treatment Options

Psoriasis – Diagnosis and treatment – Mayo Clinic

Diagnosis

In most cases, diagnosis of psoriasis is fairly straightforward.

Psoriasis treatments reduce inflammation and clear the skin. Treatments can be divided into three main types: topical treatments, light therapy and systemic medications.

Used alone, creams and ointments that you apply to your skin can effectively treat mild to moderate psoriasis. When the disease is more severe, creams are likely to be combined with oral medications or light therapy. Topical psoriasis treatments include:

Topical corticosteroids. These drugs are the most frequently prescribed medications for treating mild to moderate psoriasis. They reduce inflammation and relieve itching and may be used with other treatments.

Mild corticosteroid ointments are usually recommended for sensitive areas, such as your face or skin folds, and for treating widespread patches of damaged skin.

Your doctor may prescribe stronger corticosteroid ointment for smaller, less sensitive or tougher-to-treat areas.

Long-term use or overuse of strong corticosteroids can cause thinning of the skin. Topical corticosteroids may stop working over time. It’s usually best to use topical corticosteroids as a short-term treatment during flares.

Topical retinoids. These are vitamin A derivatives that may decrease inflammation. The most common side effect is skin irritation. These medications may also increase sensitivity to sunlight, so while using the medication apply sunscreen before going outdoors.

The risk of birth defects is far lower for topical retinoids than for oral retinoids. But tazarotene (Tazorac, Avage) isn’t recommended when you’re pregnant or breast-feeding or if you intend to become pregnant.

Calcineurin inhibitors. Calcineurin inhibitors tacrolimus (Prograf) and pimecrolimus (Elidel) reduce inflammation and plaque buildup.

Calcineurin inhibitors are not recommended for long-term or continuous use because of a potential increased risk of skin cancer and lymphoma. They may be especially helpful in areas of thin skin, such as around the eyes, where steroid creams or retinoids are too irritating or may cause harmful effects.

Coal tar. Derived from coal, coal tar reduces scaling, itching and inflammation. Coal tar can irritate the skin. It’s also messy, stains clothing and bedding, and has a strong odor.

Coal tar is available in over-the-counter shampoos, creams and oils. It’s also available in higher concentrations by prescription. This treatment isn’t recommended for women who are pregnant or breast-feeding.

This treatment uses natural or artificial ultraviolet light. The simplest and easiest form of phototherapy involves exposing your skin to controlled amounts of natural sunlight.

Other forms of light therapy include the use of artificial ultraviolet A (UVA) or ultraviolet B (UVB) light, either alone or in combination with medications.

Psoralen plus ultraviolet A (PUVA). This form of photochemotherapy involves taking a light-sensitizing medication (psoralen) before exposure to UVA light. UVA light penetrates deeper into the skin than does UVB light, and psoralen makes the skin more responsive to UVA exposure.

This more aggressive treatment consistently improves skin and is often used for more-severe cases of psoriasis. Short-term side effects include nausea, headache, burning and itching. Long-term side effects include dry and wrinkled skin, freckles, increased sun sensitivity, and increased risk of skin cancer, including melanoma.

If you have severe psoriasis or it’s resistant to other types of treatment, your doctor may prescribe oral or injected drugs. This is known as systemic treatment. Because of severe side effects, some of these medications are used for only brief periods and may be alternated with other forms of treatment.

Although doctors choose treatments based on the type and severity of psoriasis and the areas of skin affected, the traditional approach is to start with the mildest treatments topical creams and ultraviolet light therapy (phototherapy) in those patients with typical skin lesions (plaques) and then progress to stronger ones only if necessary. Patients with pustular or erythrodermic psoriasis or associated arthritis usually need systemic therapy from the beginning of treatment. The goal is to find the most effective way to slow cell turnover with the fewest possible side effects.

There are a number of new medications currently being researched that have the potential to improve psoriasis treatment. These treatments target different proteins that work with the immune system.

A number of alternative therapies claim to ease the symptoms of psoriasis, including special diets, creams, dietary supplements and herbs. None have definitively been proved effective. But some alternative therapies are deemed generally safe, and they may be helpful to some people in reducing signs and symptoms, such as itching and scaling. These treatments would be most appropriate for those with milder, plaque disease and not for those with pustules, erythroderma or arthritis.

If you’re considering dietary supplements or other alternative therapy to ease the symptoms of psoriasis, consult your doctor. He or she can help you weigh the pros and cons of specific alternative therapies.

Explore Mayo Clinic studies testing new treatments, interventions and tests as a means to prevent, detect, treat or manage this disease.

Although self-help measures won’t cure psoriasis, they may help improve the appearance and feel of damaged skin. These measures may benefit you:

Coping with psoriasis can be a challenge, especially if the disease covers large areas of your body or is in places readily seen by other people, such as your face or hands. The ongoing, persistent nature of the disease and the treatment challenges only add to the burden.

Here are some ways to help you cope and to feel more in control:

You’ll likely first see your family doctor or a general practitioner. In some cases, you may be referred directly to a specialist in skin diseases (dermatologist).

Here’s some information to help you prepare for your appointment and to know what to expect from your doctor.

Make a list of the following:

For psoriasis, some basic questions you might ask your doctor include:

Your doctor is likely to ask you several questions, such as:

See original here:

Psoriasis – Diagnosis and treatment – Mayo Clinic

Boeing Is Prepping to Launch Astronauts to Space Station

Commercial Crew Program

SpaceX isn’t the only company attempting to revolutionize the way we send astronauts to space.

Boeing, the largest aerospace company in the world, is looking to send up its own take on a passenger spacecraft, which it calls the CST-100 Starliner, to the International Space Station. Boeing is planning to launch the capsule — uncrewed for now, as a test flight — on an Atlas 5 rocket as early as April, according to NASA.

Starliner

Boeing’s commercial spacecraft shares similarities with SpaceX’s Crew Dragon: it can seat a crew of seven, be operated from a central control panel, dock autonomously with the ISS, and can also be reused multiple times.

Boeing’s Starliner is the result of a $4.2 billion contract signed with NASA in 2014 under the Commercial Crew Program. SpaceX signed a very similar contract for its Crew Dragon mission at the same time, although it paid SpaceX just $2.4 billion.

Race to the ISS

SpaceX successfully launched its passenger spacecraft to the ISS on Saturday, becoming the first ever private American spacecraft to do so. It also marked the first time astronauts launched into space from American soil since the end of NASA’s Space Shuttle program in 2011.

Boeing has tests to complete before it takes off.

“There still are many critical steps to complete before launch and while we eagerly are anticipating these launches, we will step through our test flight preparations and readiness reviews,” Kathy Lueders, Commercial Crew Program manager at NASA said in an official update.

SpaceX is planning a crewed test flight in July of this year. Boeing wants to do the same only a month later — and its first pilots are already on stand-by.

READ MORE: Crew Dragon and Starliner: A Look at the Upcoming Astronaut Taxis [Space.com]

More on Starliner: NASA Announces The First Commercial Astronauts to Pilot The Next Generation of Spacecraft

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Boeing Is Prepping to Launch Astronauts to Space Station

China: New “Artificial Sun” Will Be Completed This Year

A Chinese official claims the nation is poised to wrap up construction on the HL-2M tokamak, a new

On the Horizon

In November, Chinese researchers announced that the Experimental Advanced Superconducting Tokamak (EAST) reactor — an “artificial sun” designed to mimic the nuclear fusion process the real Sun uses to generate energy — had hit a milestone by achieving an electron temperature of 100 million degrees Celsius.

Now, officials are saying they believe they’ll wrap up construction on a new artificial sun this year, and they claim this device will be able to hit a milestone in ion temperature — putting us one step closer to harnessing the power of nuclear fusion.

Hot Tech

On Sunday Duan Xuru, an official at the China National Nuclear Corporation, announced during the annual session of the Chinese People’s Political Consultative Conference that engineers would wrap up construction on the nation’s HL-2M Tokamak in 2019.

“The artificial sun’s plasma is mainly composed of electrons and ions,” Duan told the media, according to the Global Times, “and the country’s existing Tokamak devices have achieved an electron temperature of over 100 million degrees C in its core plasma, and an ion temperature of 50 million C, and it is the ion that generates energy in the device.”

Tokamak

According to Duan, the HL-2M Tokamak will be able to achieve an ion temperature of 100 million degrees Celsius, about seven times hotter than the real Sun’s ion temperature. This meets meeting what the Global Times calls “one of the three challenges to reach the goal of harnessing the nuclear fusion.”

If he’s right, the device could serve as a template for future nuclear fusion reactors, bringing the dream of unlimited clean energy one step closer to reality.

READ MORE: Nation to complete new artificial sun device this year [Global Times]

More on the device: China’s “Artificial Sun” Is Now Hot Enough for Nuclear Fusion

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China: New “Artificial Sun” Will Be Completed This Year

NASA Announces World’s First All-Female Spacewalk

On March 29, NASA astronauts Anne McClain and Christina Koch will leave the ISS to embark on the world's first all-female spacewalk.

The Female Frontier

Two of NASA’s astronauts are scheduled to make history this month.

On March 29, Anne McClain and Christina Koch will leave the relative safety of the International Space Station for a spacewalk to upgrade the craft’s batteries.

Though rare, a spacewalk alone isn’t history-making. What’s exciting is the fact that this spacewalk will be the first to feature only women astronauts — an inspiring sign that women are catching up with men in exploring the final frontier.

Spacewalk This Way

On Wednesday, NASA spokeswoman Stephanie Schierholz confirmed the all-female spacewalk with CNN.

“As currently scheduled, the March 29 spacewalk will be the first with only women,” she told the network.

In addition to McClain and Koch, Schierholz pointed out that two other women will play important roles behind the scenes for this spacewalk — Mary Lawrence and Jackie Kagey will serve as the spacewalk’s lead flight director and lead spacewalk flight controller, respectively.

A third woman, Canadian Space Agency flight controller Kristen Facciol, will support the spacewalk from NASA’s Johnson Space Center in Houston. She’s the one who first broke the news of the all-female spacewalk with an exuberant tweet on March 1.

I just found out that I’ll be on console providing support for the FIRST ALL FEMALE SPACEWALK with @AstroAnnimal and @Astro_Christina and I can not contain my excitement!!!! #WomenInSTEM #WomenInEngineering #WomenInSpace

— Kristen Facciol (@kfacciol) March 1, 2019

As with anything space-related, there is always a chance the spacewalk might not go as planned, with Schierholz telling CNN that “assignments and schedules could always change.”

Still, right now, it’s looking like McClain and Koch will spacewalk their way into the history books on March 29.

READ MORE: 2 astronauts are scheduled for the first all-female spacewalk in history [CNN]

More on the ISS: First-Ever 360-Degree Video of Spacewalk Lets You Feel Like an ISS Astronaut

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NASA Announces World’s First All-Female Spacewalk

Stalkers Are Pretending to be Cops to Steal Your Phone’s Location

By telling companies such as Verizon and T-Mobile they're cops, stalkers and scammers can get users' real-time location data.

Just Ask

A loophole designed to protect lives is actually endangering them.

While cell phone companies typically require a court order before they’ll give law enforcement officials a customer’s real-time location data, they will make exceptions under “exigent circumstances” — for example, if turning over the data might prevent someone from being harmed.

Now, Motherboard is reporting that phone companies are also turning over this data to people impersonating officials — another troubling example of how little tech companies are doing to protect your personal data.

Scam Alert

According to Motherboard’s sources — which included Valerie McGilvrey, a skip tracer hired to find people’s locations — Verizon, T-Mobile, and Sprint have all turned over real-time location data to scammers who claimed to be law enforcement officials.

In some instances, the scammers were bounty hunters or debt collectors. In others, they were stalkers and domestic abusers trying to track down their victims. The stories they spin vary, but fake child kidnappings seem to be common approach.

“So many people are doing that and the telcos have been very stupid about it,” McGilvrey told Motherboard. “They have not done due diligence and called the police [departments] directly to verify the case or vet the identity of the person calling.”

Unprotected

This is far from the first example of tech companies inadequately protecting user data — from Facebook to Google, we constantly hear about companies experiencing data breaches, with users’ personal data ending up in the hands of people who were never meant to have access to it.

The issue has now gotten to the point that some legislators are suggesting bills to jail the execs of companies that don’t adequately protect user data — and if there’s one thing more worthy of punishment than accidentally leaking personal data, it might be willingly handing it over like these telephones companies are doing.

READ MORE: Stalkers and Debt Collectors Impersonate Cops to Trick Big Telecom Into Giving Them Cell Phone Location Data [Motherboard]

More on data breaches: New Bill Would Let FTC Jail Execs for Data Breaches

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Stalkers Are Pretending to be Cops to Steal Your Phone’s Location

SpaceX’s Crew Dragon Spacecraft Splashes Down in the Atlantic

Welcome Home

Mission Demo-1 is officially complete.

After successfully undocking from the International Space Station in the early morning hours and burning through Earth’s atmosphere, SpaceX’s passenger spacecraft slowly descended back down to Earth, before safely splashing down into the Atlantic Ocean — and right on schedule at 8:45 am EST.

.@SpaceX’s #CrewDragon returned to Earth with a splash in the Atlantic Ocean off Florida’s eastern shore at 8:45am ET, completing an end-to-end flight test to the @Space_Station and back as part of our @Commercial_Crew Program. Learn more: https://t.co/MFB7dVb60c pic.twitter.com/8lFL6X3Tue

— NASA (@NASA) March 8, 2019

The Descent

Crew Dragon’s descent was slowed thanks to four large parachutes it deployed once it re-entered Earth’s atmosphere.

Astronauts loaded roughly 300 pounds (136 kg) of cargo from the ISS into the spacecraft on Thursday to send back down to Earth.

SpaceX successfully launched the spacecraft on Saturday. It marks the first time a passenger spacecraft launched from American soil to the ISS — and returned safely back down to Earth — since the end of NASA’s Space Shuttle program in 2011.

Mission Accomplished

SpaceX’s Crew Dragon docked itself, with no robotic arm required, to an open port of the International Space Station early Sunday morning. It then spent five days docked to the station while astronauts checked out the inside of what could one day become their ride back home.

A lot could’ve gone wrong. The cargo Dragon variant featured a different parachute system and had a differently shaped hull.

“I see hypersonic re-entry as probably my greatest concern,” SpaceX CEO Elon Musk said during a post-launch press event on Saturday.

In July, SpaceX is hoping to send the Crew Dragon capsule back into space — but this time with NASA astronauts Bob Behnken and Doug Hurley on board.

READ MORE:

More on Crew Dragon: Expert: SpaceX Just Made Russia’s Space Program “Null and Void”

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SpaceX’s Crew Dragon Spacecraft Splashes Down in the Atlantic

New SETI Plan: Detect Alien Starships Powered by Black Holes

A new paper suggests that we could spot an alien civilization by looking for signs of starships powered by the radiation thrown off by small black hole.

Alien Starships

To detect alien civilizations, astronomers need to make some assumptions about the forms they might take — and the traces their technological artifacts could leave behind.

An outrageous new paper by a mathematician at Kansas State University does just that, positing that a sufficiently advanced alien civilization would likely build starships powered by the radiation thrown off by small black holes — and speculating that astronomers could use gamma telescopes to spot evidence of these black hole starships.

Black Holes

The basic idea, according to mathematician Louis Crane, is that a spaceship powered by a black hole would leave distinctive spillover from gamma rays. He suggests astronomers could detect that spillover using a telescope like the orbital Fermi Gamma-ray Space Telescope.

“If some advanced civilization already had such starships, current [very high energy] gamma ray telescopes could detect it out to 100 to 1,000 light years if we were in its beam,” Crane said in a press release. “They could be distinguished from natural sources by their steadily changing redshift over a period of years to decades.”

Game SETI Match

Crane also said, provocatively, that he believes astronomers may have already spotted several gamma ray sources “for which no natural explanation has been given.”

He also speculated about what it would mean for a civilization to be capable of creating an artificial black hole — and it sounds absolutely epic.

“To produce an artificial black hole, we would need to focus a billion-ton gamma ray laser to nuclear dimensions,” Crane said in the press release. “It’s like making as many high-tech nuclear bombs as there are automobiles on Earth. Just the scale of it is beyond the current world economy. A civilization which fully utilized the solar system would have the resources.”

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New SETI Plan: Detect Alien Starships Powered by Black Holes

New Zealand Farmers Are Using Drones to Herd Sheep

Farmers in New Zealand have a new tool to herd sheep and cows, according to Radio New Zealand: drones outfitted with speakers so they can bark like dogs.

Sheep Drones

Farmers in New Zealand have a new tool to herd sheep and cows, according to Radio New Zealand: drones outfitted with speakers that blast the sounds of dogs barking.

“That’s the one thing I’ve noticed when you’re moving cows and calves that the old cows stand up to the dogs, but with the drones, they’ve never done that,” shepherd Corey Lambeth told the station.

Radio New Zealand video shows Lambeth corralling cows and sheep using a drone with a harsh digital bark.

Dog Days

Lambeth’s employer, Ben Crossley, confirmed that his fourth-generation farm is indeed using drones to control sheep. One favored model: the DJI Mavic Enterprise, which is already outfitted to play sounds — such as barking — over a speaker.

The Washington Post noted that farmers are already using drones around the world for a variety of farming tasks, *including* surveying crops.

The Washington Post noted that farmers are already using drones around the world for a variety of farming tasks, including surveying crops. Having the devices deal directly with animals is less common — but it could be a vision of the future of agriculture.

Drone Pups

Dogs, which were already used for herding in New Zealand, are learning to work alongside the drones, according to another story by Radio New Zealand.

“There’s definitely going to be places for dogs always on farm,” Lambeth told the station, but “the one downside of the Mavic [drones] or anything electronic is you still need to bring them in and charge them.”

READ MORE: Barking drones used on farms instead of sheep dogs [Radio New Zealand]

More on drones: Autonomous Drones Are Dropping Rat Poison Bombs on This Island

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