SAN FRANCISCO, July 21, 2020 (GLOBE NEWSWIRE) -- Sisu, the fastest and most comprehensive diagnostic analytics platform, has been named a 2020 Hot Vendor in Artificial Intelligence by Aragon Research, Inc.

Aragon Researchs Hot Vendor Report identifies noteworthy and innovative vendors transforming their markets and driving change for their customers. Sisu was founded by Peter Bailis, associate professor at Stanford and co-leader of the DAWN lab, whose research is focused on making it dramatically easier to build and deploy machine learning-enabled applications.

Theres a disconnect between a companys ability to collect incredibly rich data and their ability to extract real value from it, said Peter Bailis, CEO and Founder of Sisu. We built Sisu to help data-driven businesses do one thing incredibly well - understand why their metrics are changing, fast enough to inform daily operational decisions.

Sisu is built on a novel machine-learning engine, purpose-built to handle the kind of high-volume, high-dimensional data enterprises are collecting in cloud data warehouses. Sisus cloud-native platform enables unmatched analytics speed and result quality, processing hundreds of columns and millions of records in seconds. Unlike conventional BI tools, Sisu automates the manual, rote work of data exploration and surfaces useful explanations in even the most complex data sets.

Read more about the Aragon Research Hot Vendor Report here: https://aragonresearch.com/special-report-aragon-research-hot-vendors-for-2020-part-iii/

About SisuSisu is the fastest and most comprehensive diagnostic analytics platform, helping analysts rapidly diagnose why critical business metrics are changing. Based on years of research at Stanford University and proven at scale at Microsoft, Facebook and Google, Sisus diagnostic analytics platform combines machine learning and powerful statistical analysis to help anyone get answers to their toughest business questions. To learn more about Sisu, visit https://sisudata.com/.

Required Disclaimer:Aragon Research does not endorse vendors, or their products or services that are referenced in its research publications, and does not advise users to select those vendors that are rated the highest. Aragon Research publications consist of the opinions of Aragon Research and Advisory Services organization and should not be construed as statements of fact. Aragon Research provides its research publications and the information contained in them "AS IS," without warranty of any kind.

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Sisu Identified as a 2020 Hot Vendor in Artificial Intelligence by Aragon Research - GlobeNewswire

(Bloomberg Opinion) -- This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the U.K. tweeted: I just got access to gpt-3 and I can't stop smiling, i am so excited. Venture capitalist Paul Graham noted coyly: Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone? Venture capitalist and AI expert Daniel Gross referred to GPT-3 as a landmark moment in the field of AI.

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the U.S. economy. The innovation came to the U.S. before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to Is this a verified email conversation? Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where Skynet goes live, to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include "Big Business: A Love Letter to an American Anti-Hero."

For more articles like this, please visit us at bloomberg.com/opinion

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2020 Bloomberg L.P.

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Artificial Intelligence Is the Hope 2020 Needs - Yahoo Finance

As the world grapples with a new way of life and the COVID-19 crisis peaks, its impact on everyones mental and physical health is indescribable.

The pandemic is not only creating a major impact on the global economy, it is also helping to accelerate the development and commercialisation of several emerging technologies that previously received lukewarm consumer response. This is predominantly accurate for innovations that reduce human-to-human contact, automate processes, and increase productivity amid social distancing.

With Artificial intelligence (AI) and Internet of Things (IoT) rapidly gaining ground in varying forms and degrees, the use of these innovation has begun to appear in a wide spectrum of technologies - from the phones we use to communicate to the supply chains that bring goods to market. It is modifying the way we interact, consume information, and obtain goods and services.

Healthcare is no exception to this new disruption. In the medical industry, the impact of AI, IoT, and other technologies through natural language processing (NLP) and machine learning (ML), is transforming care delivery.

Data suggests that AI simplifies the lives of patients, doctors, and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.

A major trend in medicine, when it comes to AI, is using deep learning in medical diagnosis to detect cancer. A recent study published in the Journal of the National Cancer Institute shows that the AI system has achieved a breast cancer detection accuracy comparable to an average breast radiologist.

With the ability of AI networks to train radiologists, there are chances that their performance will be significantly improved in the nearest future.

Another promising implementation is the use of AI and the Internet of Medical Things in consumer health applications, which allows them to gather healthcare data and process the information and offer adjustments to the current lifestyle of a patient.

When it comes to medical diagnosis, doctors have seen that applying AI & IoT to medical diagnosis provides numerous benefits to the healthcare industry. AI and IoT based software can tell whether a patient has a certain disease even before evident symptoms appear.

But what AI is extensively helping doctors in is the ease that it is providing in classifying diseases. With deep learning technologies that analyse images and recognise patterns, it is creating a huge potential in generating algorithms that are helping healthcare officials in diagnosing diseases faster.

Moreover, research suggests that AI-driven software can be programmed to accurately spot signs of a certain disease in medical images such as MRIs, X-rays, and CT scans. Existing similar solutions already use AI for cancer diagnosis by processing photos of skin lesions.

By using such tools, doctors are able to diagnose patients more accurately and prescribe the most suitable treatment for them at an earlier stage, resulting in increasing the chances of cancer prevention.

Henceforth, we can say that from patient and self-service to chatbots, computer-aided detection (CAD) systems for diagnosis, and image data analysis to identify candidate molecules in drug discovery, AI, IoT and other technologies are already at work.

They are swiftly helping in increasing convenience and efficiency, reducing costs and errors, and generally making it easier for more patients to receive the healthcare they need. While each technology can contribute significant value alone, the larger potential lies in the synergies generated by using them together across the entire patient journey, from diagnoses to treatment, to ongoing health maintenance.

It can easily be said that AI and IoT solutions can lead to better care outcomes and improve the productivity and efficiency of care delivery. They can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients, and in doing so, raise staff morale and improve retention.

It can even offer life-saving treatments to markets faster. With the increased use of AI and IoT in healthcare, it will certainly influence the types of new entrants into the healthcare industry as well as influence how providers, clinicians, and other staff will work in the future.

In India, the last five years have seen consumer-facing health tech being talked about and embraced by investors, government, and gradually by the public. Among educated consumers in urban areas, technology is largely gaining traction through online health service aggregators, telemedicine, e-pharmacies, and a few fitness apps. Existing methods are also being used to reinvent healthcare delivery in the form of online consults or chat-based basic healthcare service apps, especially during these unprecedented times.

For our country, we can conclude that these advanced technologies in healthcare are helping expand the human capacity rather than replacing human labour altogether. Putting us in a unique position to be the driver for AI and IoT technologies in healthcare space for national and international companies.

With large amounts of data and a burgeoning startup community, India has the opportunity to address many healthcare-related problems by using them. With new disruptions in healthcare innovations, we will soon be in a position to realise the benefits of these technologies on health outcomes.

Irrespective of a patients location or condition, an evolution of the AI, IoT, IoMT ecosystem will become progressively impactful. And even the most remote locations will benefit from better access to care as connected medical devices continue to find their way into the hands of both patients and clinicians.

Connected health and Artificial intelligence in healthcare is no more a thing of the future, it is slowly transforming the now that we are living in.

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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Heres how connected health and artificial intelligence is transforming healthcare industry - YourStory

There are more signs that Ohio once again is in play for the presidential election. The Columbus Dispatch reports that The Lincoln Project super PAC "is aligning with another group, Republican Voters Against Trump, for what they are calling 'Operation Grant,' a nod to Ohio native Ulysses S. Grant. That alliance's plan kicks off with a Lincoln Project advertisement attacking Trump's response to the COVID-19 pandemic that will air on broadcast and cable television from Friday through Monday in Columbus, Cleveland, Akron and Canton." The paper says the efforts "also will include a ground campaign that has had to move onto the web during the pandemic," according to John Weaver, a co-founder of the Lincoln Project and former top political adviser to former Ohio Gov. John Kasich. Weaver said the groups have 20,000 volunteers in Ohio and are planning a town hall meeting for next week. Meanwhile, the New York Times reports that two prominent polls of Ohio last month "showed the presidential race in a statistical tie. Turnout in the Ohio primary elections in April was higher for Democrats than Republicans for the first time in a dozen years, evidence of enthusiasm in the Democratic base. And the Trump campaign recently booked $18.4 million in fall TV ads in Ohio, more than in any state besides Florida a sign that (President Donald) Trump is on the defensive in a state that until recently seemed locked down for Republicans."

MLive Media Group in Michigan this week announced it will transfer production of its eight newspapers to Cleveland and close its printing facility outside Grand Rapids, Mich. The media company's eight newspapers currently printed at the production facility in Walker, Mich., will instead be printed in Cleveland beginning Oct. 5, said Tim Gruber, president and chief revenue officer of MLive Media Group. The newspapers will be printed at the same facility that prints The Plain Dealer. It's a case of corporate efficiencies, as Cleveland.com and MLive Media Group are owned by Advance Local.

Ohio's a great place to live if (when times are normal) you enjoy a good bar, according to Esquire. The magazine's new list of the best bars in America has no less than four Ohio spots: The Happy Dog and the Spotted Owl, both in Cleveland, plus Wdka Bar in Cincinnati and Law Bird in Columbus. Esquire calls The Happy Dog "a rock 'n' roll bar to its bones, with vinyl booths, Christmas lights, a no-bullshit beer list, and mics already set up for any ragged busker who's brave or drunk enough to climb onstage." The Spotted Owl won over Esquire's writer, who notes, "I was staring at a paper wheel that looked like a scrap of Ouija board. The wheel had words on it: bitter, potent, fruity, tropical, etc. Instead of ordering from a cocktail menu, I was instructed to select my desired mood (I went with relax) and a range of flavors (I went with umami and ginger) from this wheel. The bartender would then conjure something for me to drink. I figured this was all some sort of gimmick until I tasted my cocktail, which had been made with gin, lime, and a pho syrup yes, the Vietnamese soup. It was absurdly delicious, and it was then I decided the Spotted Owl is a next-wave mystic temple of cocktailing."

Grooming might not be all that high on your list of priorities during the pandemic, but if you're a man with a beard, you might want to check out this piece from The New York Times that offers tips for getting your bear under control and takes note of a product favored by Cavaliers center Andre Drummond. That product is a Kuschelbr, a heated beard-straightening brush made by Masc by Jeff Chastain. It has heated teeth that emerge from a heated plate, a compact version of the full-size hair-straightening brushes marketed to women. The Times notes that Drummond made a video of himself straightening his beard with it.

You also can follow me on Twitter for more news about business and Northeast Ohio.

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Artificial intelligence data centers take on greater importance in facing the very real threat of COVID-19 - Crain's Cleveland Business

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the worlds leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics, says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mdry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimass project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglars research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahlehs research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapidstart and significant progress of the C3.ai Digital Transformation Institute, says Thomas Siebel, chair and CEO of C3.ai. It is a privilege to be working with such an accomplished team.

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards:

"Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions" Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

"Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data" Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

"Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic" Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

"Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak" Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

"Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections" David Gifford, professor of biological engineering and of electrical engineering and computer science

"Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19" Aleksander Mdry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

"Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic" Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

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Faculty receive funding to develop artificial intelligence techniques to combat Covid-19 - MIT News

The new research report is entitled Global Artificial Intelligence (AI) in Agriculture Market Growth (Status and Outlook) 2020-2025 assists players in strengthening their overall growth and establishes a strong position. The report showcases a comprehensive study of the overall industry along with key industry drivers and restraints. The report in-depth analysis of critical subjects of the global Artificial Intelligence (AI) in Agriculture industry involving consumption, revenue, sales, production, trends, opportunities, geographic expansion, competition, segmentation, and challenges. The report is divided into product types, applications, and regions. These segments provide accurate calculations and forecasts for sales in terms of volume and value. This analysis can help customers increase their business and make calculated decisions.

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Global Artificial Intelligence (AI) in Agriculture Market 2020: by Top-Vendors, Products, Applications, Growth Strategies and Forecast 2025 - Cole of...

In 2018, I wrote about a research project that was focused on building a large dataset around unstructured, everyday cooking actions and behaviors called EPIC-KITCHENS.

The project, which is being led by a group of university researchers, is designed to provide an open-source set of data that will allow artificial intelligence research teams to build better computer models that can better understand and identify behavior in the kitchen.

From the post in 2018:

The ultimate goal behind EPIC-KITCHENS is to create an open dataset about kitchen-centric objects, behavior, and interactions upon which researchers across the world can then focus their deep-learning algorithms on in the hope of advancing artificial intelligence in the kitchen.

Since those early days, the project has continued to progress, recently releasing a newly expanded dataset and publishing the results from the second annual challenge. The first research challenge, completed in 2019, was focused researchers building models that can recognize actions in the kitchen. The recently completed challenge focused on action anticipation, where they asked researchers to predict what action would take place after one second of video.

Researchers who competed in the most research challenge include teams from a variety of universities spanning the globe from Cambridge to Georgia to Singapore as well as some corporate research labs such as the AI team from Facebook.

I recently caught up with the resesarch lead for EPIC-KITCHENS, Dr. Dima Damen from the University of Bristol in the United Kingdom, who told me that the various research teams competing used a variety of approaches to help make their systems better at recognizing and predicting actions based on the information from the video.

There are some people whove used audio, said Damen. So theyve used the audio from the video to identify something like opening the tap versus closing the tap. Traditionally, computer vision has relied on just images without like videos without sound.

There are some people who looked on a very big set of things, at what happened the past minute, because thats helping them. And there are people who said, no, Ill focus on the objects, like where the hand is, where the object is, thats a better approach.'

For the next set of challenges, the group is providing a newly expanded set of data and asking them to focus on things such as test of time, where they ask if models trained two years ago still perform well and scalabilty, where they will have researchers look at whether more data is better.

Part of the expanded data will be a newly broadened dataset called EPIC-KITCHEN-100, where new footage brings the total number of hours of video captured to 100. According to Damen, the new video is from a cohort that included participants from both the previous study (half of the original 32 participants agreed to participate again) and 8 new participants.

According to Damen, by bringing back past participants, it will allow the computer models to better understand kitchen behavior by factoring in what happens with the passage of time, like in real life, but also better understanding how small changes can impact the results.

Its the natural progression, like how life will be, said Damen. The question is what happens to computer vision in the meanwhile? So its tiny tiny changes, right? Its a slightly new camera, people might have moved home, and then were asking more questions that we believe would be interest to the community.

Damen said she hopes that her technology can help build better technology and systems that could be of help to humans who need assistance.

So there are new questions that are being asked which, interestingly, even the assistive technology community is not talking about. As in, if you want to help someone, sometimes you can guess what theyre doing, but many times you cant.

Spoon Plus Subscribers can read the full transcript of our conversation and watch my video interview with Daman below.

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The EPIC-KITCHENS Project is Building a Foundation For Artificial Intelligence in the Kitchen - The Spoon

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

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Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.

A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.

According to the father of Artificial Intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, Can a machine think and behave like humans do?

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

To Create Expert Systems The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines Creating systems that understand, think, learn, and behave like humans.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

The programming without and with AI is different in following ways

In the real world, the knowledge has some unwelcomed properties

AI Technique is a manner to organize and use the knowledge efficiently in such a way that

AI techniques elevate the speed of execution of the complex program it is equipped with.

AI has been dominant in various fields such as

Gaming AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems These systems understand, interpret, and comprehend visual input on the computer. For example,

A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

Doctors use clinical expert system to diagnose the patient.

Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

Speech Recognition Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in humans noise due to cold, etc.

Handwriting Recognition The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

Intelligent Robots Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

Here is the history of AI during 20th century

Karel apek play named Rossum's Universal Robots (RUR) opens in London, first use of the word "robot" in English.

Foundations for neural networks laid.

Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.

John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.

John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.

Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.

The first computer-controlled autonomous vehicle, Stanford Cart, was built.

Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI

The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.

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Artificial Intelligence - Overview - Tutorialspoint

While AI often invokes images of the sentient computer overlord of science fiction, the current reality is far different. At its heart, AI uses the same basic algorithmic functions that drive traditional software, but applies them in a different way.

A standard warehouse management system, for example, can show the current levels of various products, while an intelligent one could identify shortages, analyze the cause and its effect on the overall supply chain, and even take steps to correct it.

Artificial intelligence can be allowed to replace a whole system, making all decisions end-to-end, or it can be used to enhance a specific process.

For example, analyzing video footage to recognize gestures, or replacing peripheral devices (keyboard, mouse, touchscreen) with a speech to text system., giving the impression that one is interacting with a sentient being.

Just as philosophers debate the nature of man and the existence of free will, computer science experts debate the various types of AI.

Capable of performing only a limited set of predetermined functions; think, autonomous cars, retail kiosks, etc.;

Said to equal the human minds ability to function autonomously according to a wide set of stimuli;

Which will one day exceed human intelligence (and conceivably take over the world).

At the moment, Narrow AI is only beginning to enter mainstream computing applications.

Can only react to existing situations, not past experiences.

Relies on stored data to learn from recent experiences to make decisions.

Capable of comprehending conversational speech, emotions, non-verbal cues and other intuitive elements;

Human-level consciousness with its own desires, goals and objectives.

A good way to visualize these distinctions would be an AI-driven poker player. A reactive machine would base decisions only on the current hand in play, while a limited memory version would consider past decisions and player profiles.

Using Theory of Mind, however, the program would pick up on speech and facial cues, and a self-aware AI might start to consider if there is something more worthwhile to do than play poker.

AI is currently being applied to a range of functions both in the lab and in commercial/consumer settings:

Allows intelligent systems to convert human speech into text or code.

A subset of speech recognition, enables conversational interaction between humans and computers.

Allows a machine to scan an image and identify it using comparative analysis.

Perhaps the most revolutionary aspect of AI, however, is that it allows software to rewrite itself as it adapts to its environment.

Unlike traditional upgrade programs that take years and are often buggy, or even newer DevOps processes that push changes quickly with less disruption, AI allows a given program to optimize itself to highly specialized use cases.

This should not only lower the cost of software licensing and support, it should provide steadily improving performance and the development of unique processes that deliver crucial advantages in an increasingly competitive economy.

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What is Artificial Intelligence (AI)? - Definition from ...

Currently, artificial intelligence is one of the hottest topics, in the real world and on the internet. On a daily basis, we are witnessing controversial claims about the pros and cons of the technology, ranging from: "it will help us erase all diseases", to "it will erase the human race".

Nowadays, we are leaning towards thinking about the technology like it has some superpowers that can solve all types of issues that we are and will be facing in the future or just the opposite, it will create some of the worst problems that we have ever imagined.

But in reality, is there any truth in these beliefs? Are we about to lose or gain more with the adoption of artificial intelligence?

The opinions are far too polar, but the truth is that the buzz around artificial intelligence is unreasonable. It is not our savior, nor our destroyer. And it doesn't seem it can become such in the near future.

In reality, the idea of a thinking machine is as old as the concept of modern computing, dating back to the 1950s.

However, it was the last decade that saw the biggest progress in the development of artificial intelligence. In finance, artificial intelligence stock trading software became popular, and also the health care sector benefits from AI.

This contributed to the tremendous hype around the technology which painted it in a way-too-perfect or, on the other hand, way-too-unpleasant picture.

Besides, the popularity of artificial intelligence grew at the expense of distorting some of the available information.

Due to this, to most people, the perception of the technology is often confusing. In the following article, we are about to go through the top 20 pros and cons of artificial intelligence to bust the myths, surrounding its adoption, application and advantages.

An undeniable fact is that AI is capable of helping us solve complex tasks and improve our quality of life in a way that was unthinkable, just a few years ago. Here are some of the main advantages of artificial intelligence that prove why it is so important to continue focusing on its development:

Artificial intelligence helps deal with time-consuming and daunting tasks, such as credit scoring, performing background checks, analyzing documentation and drafting conclusions, etc.

High-processing computer power, backed with efficient algorithms allow companies to significantly optimize their processes. For example, JP Morgan's Contract Intelligence system (COIN. is able to significantly shorten the time needed to interpret commercial loan agreements.

The bank reveals that the 12 000 contracts that are being processed on an annual basis are now analyzed in a matter of seconds, instead of the 360 000 hours that employees previously needed in order to fulfil such task.

The chance to eliminate such mundane tasks from human workload schedule helps increase productivity and frees up time for handling more complex tasks.

One of the most daunting processes within financial institutions is fraud detection. Artificial intelligence helps optimize such time-consuming and complex tasks by employing advanced techniques such as pattern-recognition.

That way, oversight authorities and lenders can easily detect frauds and money-laundering.

Due to the high efficiency of the employed algorithms, the several factors (origin/destination of the payment, customer name, business entity details, history and etc.. that should be taken in mind for fraudulent activities to be detected, are processed in a quick and optimized way.

As human's workload increases, the efficiency and accuracy of the performed tasks decreases, thus making the individual's work more prone to errors. With machines, there is no such risk as everything works exactly as programmed.

This is a very important advantage for high-precision works in sectors such as space exploration, where there are no margins for error.

The automation of processes within institutions helps reduce costs and improve operations. By delegating low-level tasks to computers, companies are able to reduce their staff and cut their wage bill.

Artificial intelligence contributes to the financial health of the company in the regard that the only investment that is needed is the initial one for the development and the integration of the system. On the other hand, if the same tasks were performed by humans, the costs will be ongoing each month.

Figures from Stanford University's Annual Artificial Intelligence Index Report highlight the remarkable recent boom in the AI niche with 14x more active US startups focused on AI development since 2000.

Apart from that, the study points out that today there are 6x more annual VC investments into U.S. Artificial Intelligence startups since 2000, as well as 4.5x more jobs requiring Artificial intelligence skills, when compared to the last 5 years.

PwC's Global AI Study estimates that global GDP will be 14% higher by 2030 thanks to AI adoption, which makes it one of the biggest contributors to global growth.

Robots are capable of helping us mitigate the risk for human lives during dangerous missions, such as exploring the ocean, navigating hard-to-reach areas after floods, fires, earthquakes or hurricanes, researching mining areas, etc.

This helps us gain valuable insights and analyze important information, but without having to risk human lives.

Today, the adoption of artificial intelligence in our everyday lives is constantly growing.

From the smartphones that we use, through our cars, classic brokerage accounts, to social media, modern investment apps, e-commerce and even at our work desk; AI is touching almost every aspect of our day-to-day activities and helps us improve our daily routines.

Applying artificial intelligence in healthcare has many benefits. Today, the technology is widely adopted in medicine, mostly for the purpose of diagnosing.

Thanks to its efficiency when scanning large chunks of data, algorithms can quickly, efficiently, and more importantly; correctly analyze the condition of a given patient. This has proven very helpful in the process of predicting diseases.

Apart from that, by employing image recognition techniques, computers can help provide a more accurate diagnose and assign a more comprehensive treatment. Artificial intelligence applications can also help inform the doctors about the side effects of particular drugs or the combination of such.

Robots have proved very successful in conducting test surgeries that are harder-to-perform by human doctors.

Artificial intelligence-based solutions help companies develop products and services that are tailored exactly according to their customers' needs and preferences.

In the digital marketing industry, for example, AI-based solutions help increase users' engagement, as well as improve the customers' loyalty. All this usually results in growing sales and better financial results.

In the finance industry, A.I. assistants, due to the flexibility and the efficiency that they guarantee, are now steadily penetrating the wealth management niche with Robo Advisor investment services along with automated self learning day trading expert advisors.

That way, by requesting basic information about the particular client, the digital consultant can suggest suitable products or portfolio diversification scenarios that can help clients achieve their goals.

AI helps start-ups and existing businesses to operate way more efficiently and at reduced costs. The consequence of this is the company's ability to invest in research and development, marketing or other highly-important business processes, as well as to resist numerous operational risks.

For example, today, a company can start a business with way less investment, when compared to a decade ago, thanks to all the available information, the digitalization of the economy and the connectedness of the world we live in.

Artificial intelligence usually contributes to executing mundane tasks by eliminating the need of hiring people for low-skilled work. Apart from that, it is capable of handling way bigger workload, which contributes to the company's overall productivity and efficiency.

Start-ups and existing companies nowadays usually dedicate their first-level support to bots which are programmed in a way that allows them to fulfil all the tasks, typical for the support staff. A company can run its business with a few employees, by automating most of its processes, from production to customer support.

Apart from all the benefits it brings, artificial intelligence has some negatives as well. This is the reason why it is often described as the villain that will take over our world, steal our jobs and transform our lives for the worse.

Although such statements are way too premature and computers are far off being the main threat for our future wellbeing, it is worth mentioning that artificial intelligence has its cons as well.

The major drawback of artificial intelligence is associated with job losses. Experts suggest as a leading argument the idea that computers will slowly, but surely overtake humans in many sectors.

And the truth is that this has already started to happen with computers becoming ever-present in factories, financial companies, newspapers, shops, etc.

Although for now, A.I. is responsible mostly for basic, straightforward tasks, such as running assembly lines, sorting and analyzing data, etc., in the near future it is expected to take care of way more intuitive and crucial processes.

In fact, computers are already capable of writing articles and processing payments in self-service shops which means that the time when more jobs are lost due to the progress in AI may not be so distant.

Such a scenario has inspired lots of Hollywood productions. Although it is quite exaggerated to believe that computers will become our enemies, it is more reasonable to fear of situations where we can't explain the reasons behind the decisions machines are making.

For example, a few months ago, the CEO of a $97 billion artificial intelligence hedge fund, revealed that even the developers of their propriety system were unable to understand and explain its trading decisions, although they lead to enormous profits.

If such scenarios translate to other industries, the consequences may be far-too-unpredictable.

What differentiates computers and humans (and will continue to do so in the near future. is AI's lack of judgement skills. Computers are unable to contextualize information and take reasonable actions according to a specific situation.

They are programmed to make choices upon pre-programmed rules and scenarios. However, in the real world, some situations require for the information and the circumstances to be taken out of the context and be analyzed in a more untraditional way.

This, for now, remains a hard-to-gain quality.

The truth is that we are yet to reveal how our brain works on practice. However, the one thing that we are certain of is the fact that creativity is common only for humans.

Computers, on the other hand, are unable to act in a creative and unexpected way to solve a certain issue. What they will do is follow common steps and a pre-defined methodology, which in extraordinary situations, will turn out ineffective.

Although computers are able to learn by doing and become better with time, their ability to react in abnormal situations will not get significantly improved. Creativity remains and will continue to remain the main thing that differentiates humans from machines.

The efficiency of the machine learning algorithms behind AI-based solutions is heavily dependable on the quality of information that they are fed with.

Just like the case with financial data, in many industries, the data can sometimes be inadequate, biased or incomplete. This often affects negatively the conclusions of AI-based solutions. Although this is not a problem caused by Artificial intelligence directly, it still limits its efficiency.

Although in the long-term, artificial intelligence becomes cheaper than human labor, in the short term, it often turns out to be way too expensive for some companies.

The development of AI solutions requires significant investments, which, of course, are going to repay themselves in the near future. Still, in the present, they remain financially extensive and may limit the operations of some companies.

This case is often valid for built-from-scratch propriety solutions. When it comes to SaaS (Software-as-a-Service. solutions, the costs are way lower and affordable for all types of companies.

Computers can be as responsible and as intelligent as their algorithms allow them to be. And these algorithms often contain their programmers' biases.

In cases when an important decision should be made, such an issue may affect negatively the outcome of the situation and lead to unnecessary consequences.

This has already proved to be a problem with self-driving cars and the decisions they make in situations when not following the rules can mitigate deadly consequences and save human lives.

If computers continue to develop at a similar pace, they will surely take over low-skilled jobs. This means plenty of people's professions will turn out to be needless for the wellbeing of the economy.

The main issue here is the percentage of these people that will be willing to improve themselves, gain additional skills and re-qualify for other types of jobs.

A big part of those who are replaced by computers will not be motivated enough to find an alternative scenario for their future or even find it difficult to become better versions of themselves. Such an issue has the potential to raise serious social instabilities.

This, of course, does not mean it isn't the natural way to go or highlights the need of slowing down our progress to serve a certain group of the society. Just the opposite, we must find an appropriate way to navigate the whole transition, which in the short term, may turn out to be difficult.

If Artificial intelligence lives up to the hype and becomes the dominant force of our future, in the end, we will face a significant power overhaul that may destabilize the current structure of our society.

Companies that control and develop AI solutions, as well as those who have the capital to hire them, will become the new power figures in charge. This means that the companies which manage to become the dominant force may shape our future according to their own vision and perception.

In the end, there is the risk that AI may not fulfil the potential it is renowned for. Although the progress in the last few years was a remarkable one, there is no guarantee that we will be able to continue with the same pace and when it will reach its peak.

In a few years from now, we may have progressed significantly, as well as reached a state where we are still using A.I. for time-saving tasks and not some independent and systemically-important operations.

There is also the chance of Artificial intelligence becoming a mainstream thing that may not drive us as further as a society, as expected.

Whether we want it or not, AI is here to stay. The technology has brought so many positives so far, that it is hard to imagine that it will stop driving us forward anytime soon. When I was struggling with coding new day trading systems, I luckily came across the A.I. based stock screener from Trade Ideas. Their newest feature is the automated trading API.

With one mouse click, Trade Ideas can be connected to a brokerage account and trade on auto-pilot. No coding skills needed. In 2017 and 2018 their A.I. based algorithms have beaten the financial indexes like NYSE and NASDAQ in terms of performance. If you are a short term trader or investor, you should read my comprehensive Trade Ideas review.

Apart from all the positives, computers are also raising some alarming signs on the associated risks.

In the end, it is up to us to shape the way artificial intelligence will develop in the future.

If we manage to play our cards right, Artificial intelligence will make us a better society and help us solve some of the most serious problems that we are currently facing. The potential is present, now it is up to us.

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Pros and Cons of Artificial Intelligence 2020 (Top 20)

Eight years ago, I was packing my home and entire life in Mexico to move to the US to pursue a PhD in Ecology and Evolutionary Biology at the University of California-Irvine. Those were easier times, although it did not seem like it at the time. I spent a few months worth of income to pay for paperwork to apply for an F-1 student visa, and to pay for other documents to enroll as a graduate student. This was after I dedicated months to emailing professors everywhere in the US, hoping that one of them would reply to my email and would invite me to apply to join their lab. It was also after spending time and money paying for standardized tests, official document translations, and application fees. It was a one-and-a-half-year process but in July 2012, I was finally moving to the USA to pursue my PhD. It was a dream come true.

It was also a dream come true for the University of California because I had a full scholarship from my home country that paid for the entirety of my international tuition and fees, which were around $35,000 per year. My scholarship allowed me to pursue my PhD in the USA, and to UC Irvine it provided basically free labor as well as prestige.

I paid taxes and did all of the typical graduate student responsibilities. I also dedicated a lot of my time to doing outreach to bring science to underserved communities around Orange County and Southern California. By the time I graduated in 2017, I was a stellar student, with three publications with UC Irvine's name on them. I co-organized summer science camps for middle school girls that brought money and a good reputation to my university and program. I mentored students of all ages. I was a good citizen of my program, of my university, and of Orange County.

Like me, most international students leave their families and everything that they are comfortable with to pursue the dream of graduate school. They bring with them the hope of being welcomed and treated fairly by their American peers. I have experienced this, but I am one of the lucky ones.

It is no secret that international students and postdocs will withstand abuse and other injustices just so they can keep their visa, which is always tied to their university. Many universities receive international students without having a system to deal with the unique challenges that international students face, such as having no credit history, which complicates finding a place to live and leaves international students vulnerable to landlord abuse. Many international students are people of color, and universities, especially predominantly white institutions, do not have resources to ensure safety of these students within the university and in the community at large.

These challenges are further complicated due to a lack of community and support. Making friends in the US, especially if you are coming from Global South countries and/or non-Westernized countries, is extremely challenging. Many times, I have seen how western Europeans, Australians, and Canadians are rapidly accepted in the local community, while many Latinx, Asians, and Middle-Easterners are not.

There are over one million international students in the US. The ICE Student Ban may no longer be a threat, but universities still need to change how they handle international students. We are people too, but many universities have historically valued us only by the amount of money we bring. We improve higher education not only by the money that we bring, but by our unique perspectives, our research productivity, and our willingness to give back to American society.

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An artificial intelligence algorithm designed to beat a video game takes on ecology and evolution - Massive Science

Representative image / Hindi News18.

We see a number of things in everyday life. However, is our vision always similar to what we imagine it to be like?

Well, the researchers beg to differ. A new study has found out an overlap between human and machine, helping us understand how virtual eyes perceive things different as compared to what we process in our brains.

According to research conducted by a team from the Medical University of South Carolina, the brain uses similar visual areas for mental imagery and vision.

However, the low-level visual areas are used less precisely with mental imagery as compared to the vision. The latest study was published in the journal Current Biology on June 22.

Neuroscientist Thomas Naselaris, also a co-author in the paper, revealed, "We know mental imagery is in some ways very similar to vision, but it can't be exactly identical. We wanted to know specifically in which ways it was different."

He works at the Medical University of South Carolina.

The latest findings prove beneficial to study in detail about the mental imagery and vision for mental health disorders, such as post-traumatic stress disorder (PTSD). The new research was conducted using an fMRI scanner and an artificial neural network, which mimicked the human brain.

They can further explore the mental imagery disruptions pattern in other mental health problems such as schizophrenia.

Naselaris added that when a human imagines, the brain activity is less precise. Our brain often misses the details, leading to the fuzziness and blurriness in brain activity.

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Scientists Discover the Correlation Between Our Vision and Imagination Using Artificial Intelligence - News18

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Artificial Intelligence Cars and Light Trucks Market Sets the Table for Continued Growth | Apple, Audi, BAE Systems, BMW - Jewish Life News

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Top Leading Key Players are:

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by Component (Platform, Services), Application (Identity and Access Management, Unified Threat Management, Antivirus/Antimalware, Risk and Compliance Management, Fraud Detection, and others), Industry Vertical (BFSI, retail, IT & Telecommunication, Automotive & Transportation, Manufacturing, Government & Defense, and others)

Based on application, the market has been segmented into:

By Application (Identity and Access Management, Unified Threat Management, Antivirus/Antimalware, Risk and Compliance Management, Fraud Detection, and others), Industry Vertical (BFSI, retail, IT & Telecommunication, Automotive & Transportation, Manufacturing, Government & Defense, and others)

This research documentation built on the basis of in-depth market analysis is aiming at offering report readers with accurate, market specific synopsis of the industry, evaluating it across dynamics and touchpoint analysis, thus shedding ample light on various classifications, industry chain review, dynamic applications, besides harping largely on overall competitive scenario, including leading market players, their growth objectives, long and short term business goals, a thorough evaluation of their tactical business moves, winning business strategies as well as investment details that cohesively influence onward growth trail of the Artificial Intelligence (AI) in Security market besides positioning themselves in an advantageous manner in global Artificial Intelligence (AI) in Security market.

In addition, the report include deep dive analysis of the market, which is one of the most important features of the market. Furthermore, the need for making an impact is likely to boost the demand for the experts which are working in the Artificial Intelligence (AI) in Security market. Moreover, an in depth analysis of the competitors is also done to have an estimate for the Artificial Intelligence (AI) in Security market. Moreover, the report provides historical information with future forecast over the forecast period. Various important factors such as market trends, revenue growth patterns market shares and demand and supply are included in almost all the market research report for every industry. Some of the important aspects analysed in the report includes market share, production, key regions, revenue rate as well as key players.

Major Highlights from Table of contents are listed below for quick lookup into Artificial Intelligence (AI) in Security Market reportExecutive SummaryIndustry Overview of Artificial Intelligence (AI) in SecurityManufacturing Cost Structure AnalysisDevelopment and Manufacturing Plants Analysis of Artificial Intelligence (AI) in SecurityMajor Manufacturers Technology Source and Market Position of Artificial Intelligence (AI) in SecurityRecent Development and Expansion PlansKey Figures of Major ManufacturersMarket Concentration DegreeArtificial Intelligence (AI) in Security Regional Market AnalysisArtificial Intelligence (AI) in Security Segment Market Analysis (by Type and by Application)Development Trend of Analysis of Artificial Intelligence (AI) in Security Market

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Artificial Intelligence (AI) in Security Market 2020 Demand, Trend, Latest Techniques, Innovations, Applications, Analysis and 2025 Industry Growth...

This report is well documented to present crucial analytical review affecting the Global Artificial Intelligence in BFSI market amidst COVID-19 outrage. In the light of the lingering COVID-19 pandemic, this mindfully drafted research offering is in complete sync with the current ongoing market developments as well as challenges that together render tangible influence upon the holistic growth trajectory of the Artificial Intelligence in BFSI market. The aim of the report is to equip relevant players in deciphering essential cues about the various real-time market based developments, also drawing significant references from historical data, to eventually present a highly effective market forecast even amidst an unprecedented scenario such as the ongoing COVID-19 pandemic and its subsequent implications.

The report is a holistic, ready-to-use compilation of all major events and developments that replicate growth in the Artificial Intelligence in BFSI market. The report is rightly designed to present multidimensional information about the current and past market occurrences that tend to have a direct implication on onward growth trajectory of the Artificial Intelligence in BFSI market. The report also illustrates minute details in the Artificial Intelligence in BFSI market governing micro and macroeconomic factors that seem to have a dominant and long-term impact, directing the course of popular trends in the global Artificial Intelligence in BFSI market.

The following sections of this versatile report on Artificial Intelligence in BFSI market specifically sheds light on popular industry trends encompassing both market drivers as well as dominant trends that systematically affect the growth trajectory visibly. Each of the market players profiled in the report have been analyzed on the basis of their company and product portfolios, to make logical deductions.

The study encompasses profiles of major companies operating in the Artificial Intelligence in BFSI Market. Key players profiled in the report includes:IBMBaiduBrighterionMicrosoftGoogleSAPIntelIPsoftNVIDIAMicroStrategyIFlyTekInfosysAlbert TechnologiesMegvii Technology

A thorough analytical review of the pertinent growth trends influencing the Artificial Intelligence in BFSI market has been demonstrated in the report to affect unbiased and time-efficient business discretion amongst various leading players. The report also sheds substantial light on all major key producers dominant in the Artificial Intelligence in BFSI market, encompassing versatile details on facets such as production and capacity deductions.

Access Complete Report @ https://www.orbismarketreports.com/global-artificial-intelligence-in-bfsi-market-size-status-and-forecast-2019-2025-2

By the product type, the market is primarily split into On-PremiseCloud-based

By the end-users/application, this report covers the following segments Voice ProcessingText ProcessingImage ProcessingOther

These details are indicated in the report to allow market players undertake a systematic analytical review of the Artificial Intelligence in BFSI market to arrive at logical conclusions governing the growth trajectory of the Artificial Intelligence in BFSI market and their subsequent implications on the growth of the aforementioned market.

Global Artificial Intelligence in BFSI Geographical Segmentation Includes: North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

Details on product portfolios, user application as well as ongoing technical developments concerning the product line have also been touched upon, to derive accurate understanding about the market prognosis and their subsequent implications upon the Artificial Intelligence in BFSI market. The report specifically focuses on market drivers, challenges, threats, and the like that closely manifest market revenue cycle to encourage optimum profit generation in the Artificial Intelligence in BFSI market.

Some Major TOC Points: Chapter 1. Report Overview Chapter 2. Global Growth Trends Chapter 3. Market Share by Key Players Chapter 4. Breakdown Data by Type and Application Chapter 5. Market by End Users/Application Chapter 6. COVID-19 Outbreak: Artificial Intelligence in BFSI Industry Impact Chapter 7. Opportunity Analysis in Covid-19 Crisis Chapter 9. Market Driving ForceAnd Many More

Research Methodology Includes:

The report systematically upholds the current state of dynamic segmentation of the Artificial Intelligence in BFSI market, highlighting major and revenue efficient market segments comprising application, type, technology, and the like that together coin lucrative business returns in the Artificial Intelligence in BFSI market.

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Target Audience:* Artificial Intelligence in BFSI Manufactures* Traders, Importers, and Exporters* Raw Material Suppliers and Distributors* Research and Consulting Firms* Government and Research Organizations* Associations and Industry Bodies

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Global Artificial Intelligence in BFSI Market Expected to reach growth rate of XX% CAGR by 2025 (Pandamic Impact Analysis): IBM, Baidu, Brighterion,...

This research articulation on artificial intelligence (AI) as a service market is a thorough collation of crucial primary and secondary research postulates. This artificial intelligence (AI) as a service market also harps on competitive landscape, accurately identifying and assessing market forerunners in the artificial intelligence (AI) as a service market and their growth rendering initiatives. This thought provoking intricately crafted perspective of the artificial intelligence (AI) as a service market is aimed at offering unfailing cues on market growth as a composite whole that aim at presenting all the nitty gritty of the market to encourage unfaltering growth scope despite stringent competition in the artificial intelligence (AI) as a service market.

Top leading players of the market are:

Alphabet Inc. (Google Inc.),Microsoft Corporation ,Amazon Web Service Inc.,IBM Corporation,Salesforce, Inc.,Apple Inc.,CognitiveScale, Inc.,Intel, Inc.,SAP SE,Fair Isaac Corporation,Others

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Apart from showcasing all the vital details on the artificial intelligence (AI) as a service market determinants that influence onward growth trajectory, the report in its succeeding sections also sheds pertinent details on the artificial intelligence (AI) as a service market, shedding immense light on market segmentation that collectively decide and bolster lush growth in global artificial intelligence (AI) as a service market. Important details on regional diversification is also included in the report unveiling details on core growth propelling geographical pockets highlighting all the vital market decisions that are directed to reap high end growth in the artificial intelligence (AI) as a service market.

In addition to the mentioned factors that decide the growth prospects of the target market, this section of the report also entails details on the available growth prospects and scope , besides also eying details on profit determinants and market break-down that seem to herald excruciating impact on uncompromised growth of the artificial intelligence (AI) as a service market.

Read complete report at:https://www.adroitmarketresearch.com/industry-reports/artificial-intelligence-as-a-service-aiaas-market

Global Artificial Intelligence (Ai) As A Service Market is segmented based by type, application and region.

Based on Type, the market has been segmented into:

By Technology (Natural Language Processing (NLP),Machine Learning (ML),Speech Recognition,Computer Vision,Others) By Organization Size (Large Organizations,Small & Medium Organizations) By Industry Vertical (IT & Telecom,Retail,BFSI,Manufacturing,Healthcare,Others)

All the notable artificial intelligence (AI) as a service market specific dimensions are studied and analyzed at length in the report to arrive at conclusive insights. As the report proceeds further, it emphasis relevant development nuances on current, historical, as well as future growth tendencies to make error free growth estimations on crucial parameters.

The high profile research endeavor on artificial intelligence (AI) as a service market offers enough growth impetus and thrust on all round growth brackets based on segmentation of the products, payment module and trade and transaction media, which eventually usher in providing improved service profile, application details and well as technological sophistication that eventually design and propel all round growth in global artificial intelligence (AI) as a service market. Even further in the report emphasis has been lent on current, historical, as well as future growth tendencies to make accurate growth estimations based on market size, value, volume, demand and supply trends as well as growth rate.

The report is a ready to use handbook of all the pertinent market specific developments, highlighting major alterations, dominant trends as well as market forces that collectively render requisite thrust towards unfailing growth in global artificial intelligence (AI) as a service market.

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Adroit Market Research is an India-based business analytics and consulting company. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

Contact Us :

Ryan JohnsonAccount Manager Global3131 McKinney Ave Ste 600, Dallas,TX 75204, U.S.APhone No.: USA: +1 972-362 -8199 / +91 9665341414

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Artificial Intelligence (Ai) As A Service Market Global Industry Analysis, Size, Share, Growth, Trends, and Forecasts 20192025 Post author - Jewish...

In industries where data is key to gaining competitive advantage, artificial intelligence and machine learning have become necessities. This is most definitely the case in the oil and gas industries that ebb and flow over time as market demand waxes and wanes for critical resources weve come to depend on.

In a recent AI Today podcast episode, Tim Custer, Senior Vice president

Tim Custer, Apache

of North America land, business development and real estate with Apache, a major energy firm, shares how AI is impacting the way the energy business operates.. After taking the role of land manager for the past ten years, Custer has shared how tied to real estate and traditional non-energy businesses the oil and gas sector is, and the role that machine learning and AI is playing to greatly change the way that the energy industry deals with documents.

According to Custer, AI and machine learning are extracting valuable data from unstructured data. The oil and gas industry is particularly dependent on an intricate set of processes and document-centric needs for land leases. Gas

leases are vital to the energy industry as they determine legal rights and claims to an oil or gas deposit while regulating the trade and extraction of those resources. At Apache, Custer notes they have around 60,000 paper and document-centric leases which can vary in length from just two pages to over fifty. Moreover, there are provisions contained on each page that must be located and interpreted every time an inquiry is made on a lease. This task can prove quite laborious with the added task of finding the correct hardcopy lease to begin with.

The first step to wrangling control of these leases is to digitize the documents so that machines can understand them. Apache has succeeded in digitizing the majority of their gas leases using Optical Character Recognition (OCR) and natural language processing (NLP). They are capable of searching through these documents for not only the required lease but the provision within it in a matter of seconds. This not only speeds up searching processes at Apache but is also time-saving for teams in need of specific provisions for their projects. Custer continues by describing the optimization of the process as grouping provisions with like wording across vast amounts of data. These digitization and NLP systems ensure higher data integrity by increasing accuracy and removing human interpretation.

One curious particularity of these leases is that they are often old, with the documents done in handwriting, usually in dated calligraphic and handwritten styles. Some of the later documents were hand type-written. As such there is a lot of variability of legibility, fonts, spacing, and overall document quality. Apache has applied machine learning to complete data organizing and searching processes more effectively and quickly than otherwise would be possible with humans having to read and process each document. Custer notes that there are a variety of document insights that must be considered such as letters, correspondence, or internal memos that are attached to the gas lease itself. The AI-enabled systems allow for significantly improved organization of this additional information due to its ability to classify and categorize the documents. In addition to higher efficiency and effectiveness, using a digitization and ML-based approach here also eliminates the need to store documents on-hand in file cabinets. Instead, these documents, once scanned, can be moved to long-term archival storage for use only when necessary as backup.

The energy is heavily regulated and that this can pose a challenge when attempting technology implementation. Custer however sees AI realistically being applied to the energy industry to many unique use-cases that he envisions for the future. In particular, Custer notes the relative inefficiency of logistics and management of the energy industry. He notes that technological advancements have already been made within the industry and give examples such as seismic imaging that can scan for underground reservoirs and drills that can drill both vertically and horizontally into the ground. Custer recognizes these applications as huge technology advancements within the industry. However, Custer notes that there has been minimal advancement in his domain of land management and business development over the years, referring to how people were apprehensive to begin with but have slowly warmed up to the idea of AI within the energy industry. He adds that this acceptance is particularly prominent when provisions and leases are involved due to its time-saving and file organizing abilities. Apache as well as other energy firms are increasingly welcoming the continual technological advancements enjoying the cost and time-saving benefits.

Custer also elaborates on the time-saving aspect of these AI systems, in particular when applied to a provision known as the consent to assign. This provision is involved within a contractual obligation that decides whether ownership can be transferred or not. Custer notes that a consent to assign provision can take hours to review manually while AI-enabled systems can shorten the process to a matter of minutes.

In general, Custer believes that this is just the tip of the iceberg with regards to the ways that AI can dramatically impact the energy industry. He states that there are many possible advancements to be made in the industry that can be learned from other industries such as finance, healthcare, and manufacturing, looking at similar use cases where documents, processes, and data can be effectively leveraged and optimized. Custer notes just how much more efficient data analysis will become and that our ability to extract valuable information from data will only be improved as time goes on.

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AI Fueling The Oil And Gas Industry: Interview With Tim Custer At Apache - Forbes

This versatile research report on Global Artificial Intelligence in Robotics Market presenting crucial details on market relevant information, harping on ample minute details encompassing a multi-dimensional market. This holistic report presented by the report is also determined to cater to all the market specific information and a take on business analysis and key growth steering best industry practices that optimize million-dollar opportunities amidst staggering competition in Artificial Intelligence in Robotics market. The intricately presented market report is in place to unravel all growth steering determinants, presenting a holistic overview and analytical delivery governing the realms of opportunity diversification, a thorough review of challenges and threats to plan and deliver growth driven business strategies.

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This crucial research report is an in-depth and crucial extensive market presentation presented meticulously to derive optimum understanding on market developments as well as the growth factors, dynamics, in the form of growth drivers, restraints, threats, challenges and the like that have a resonating catalytic impact on onward growth trail of global Artificial Intelligence in Robotics market. Proceeding further through the dedicated research report on the Artificial Intelligence in Robotics market, report readers are expected to gain clarity in their comprehension about the key manufacturing landscape, encompassing both historical and current perspectives that eventually enable growth upsurge in the global Artificial Intelligence in Robotics market. Vital analysis of various factors such as pricing matrix and sales figures, sales volume and bulk production, overall growth review and margin, chances of growth in the future and their range amongst other additional growth determinants that influence growth in the Artificial Intelligence in Robotics market.

Major companies of this report:

Hanson Robotics LtdNVIDIA CorporationIBM CorporationBlue Frog RoboticsViacrious AINeurala IncVeo Robotics IncAIBrain IncCloudMinds Technology IncUiPathKindred Inc

Browse the complete report @ https://www.orbisresearch.com/reports/index/artificial-intelligence-in-robotics-market-growth-trends-and-forecast-2020-2025

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Key Takeaways:

1. The report endeavors to offer extensive overview of the industry and studies the Artificial Intelligence in Robotics market at a multi-faceted perspective2. Further, the report in order to uphold real time market status is hovering mainly across important areas such as real time market growth status to encourage accurate market specific decisions3. The report is focusing specifically across a range of key development areas such as dynamic segmentation, cross sectional analysis of the target market4. The report also is a ready-to-go market specific document encompassing regional overview, opportunity mapping, and competition analysis.

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Artificial Intelligence in Robotics Market 2020-2025 Growth Analysis, Key Insights and Future Development By Hanson Robotics Ltd, NVIDIA Corporation,...

For some time now, the United Arab Emirates (UAE) has been adopting artificial intelligence (AI) in the public and business sectors. This is part of the Gulf countrys economic diversification strategy, aimed at transforming the UAE away from an oil-dependent economy to a knowledge-based one. AI is generally conceived as human intelligence processes which are simulated by computer systems, including learning, reasoning, problem-solving, planning, predictive analytics, and advanced robotics.

Like other Arab states, the UAE has advanced a public discourse based on a dominant narrative of nationalism which is meant to solidify its image while reinforcing the Emirati rulers power and legitimacy. Its foundational theme is made up of different frames such as diversity, tolerance, moderation, international cooperation, humanitarianism, and modernity. State leaders use narratives not only to persuade and influence a national and international audience of its image and self-perception, but also as a means to determine its understanding of its place and purpose in the international system.

The Path to Digitization

Branding is relevant to the ways in which countries present themselves internationally and how their reputations can be built or enhanced. By highlighting the ideas, concepts, and policies of AI and innovation, the UAE is signaling its active participation in the digital transformation of the region and its status as a progressive, high-tech, and modernized country. The Emirati leadership is driving this process which seeks to construct a narrative based on the UAE being a nation made up of forward-thinking risk-takers, who actively create opportunities and innovate and ensure that their country becomes an increasingly valuable member of the international community.

The UAE has taken numerous steps to bolster its image and commitment to innovation and technology. The UAEs ambition is to become one of the global leaders in the field as well as the regional hub of AI. In 2017, the UAE became the first country in the world to appoint a Minister of State for AI. The UAEs National Artificial Intelligence Strategy 2031 has recognized the growth potential associated with AI and has set out to increase the competitive edge of this sector in the Emirates while establishing itself as an incubator for AI innovation.

The UAE has recognized the importance of creating more specialized AI programs and professionals, as well as the AI Ministrys need for AI consultants, experts, research institutions, and universities that will provide the required skills, knowledge, and expertise. The establishment of the Mohammed bin Zayed University of Artificial Intelligence is an important and innovative step in its quest, which will help facilitate this endeavor. Ajman University has also announced a graduate program in AI, scheduled to commence in the academic year 2020-2021. Dr. Karim Seghir, the Chancellor of Ajman University, said: Its not going to be long before AI takes over many aspects of life, including business (products and services), education, defence, entertainment, medicine, law, government, and even politics and social life.

In March 2014, the government of Dubai announced the Smart Dubai initiative in order to make Dubai a leading smart city. AI has been the trigger and the technology behind the facilitation of Smart Dubai. A smart city requires a digital infrastructure that can link the various sensors, devices, and machines to make up the public system so they can exchange information in real-time. For city governments, the challenge will be to ensure that the huge volumes of data created by smart cities remain safe. Other initiatives created for this purpose are Ibtekr, the first interactive platform designed by the Mohammed bin Rashed Center for Government Innovation (MBRCGI), as well as Dubai Future Foundation, which is focused on innovation. In addition, Mubadala, the UAEs leading Sovereign Wealth Fund, alone has reportedly invested $15 billion in a technology fund to subsidize its efforts.

The UAE has already used AI applications and systems in various sectors. For example, in security and police services, it developed a police officer robot. In Dubai, the Water and Electricity Authority employed a customer service robot and used a pilotless flying taxi, which are not yet in service. The main focus of AI is on radar, radio communication, a variety of aerospace technologies, as well as transport and aviation.

More specifically, the UAE AI strategy aims to increase government performance, productivity, and efficiency at all levels and sectors. AI also has a tremendous economic potential that remains untapped both globally and at the GCC level. AI is predicted to contribute $15.7 trillion to the global economy by 2030 and $320 billion to the Middle East economy. Online shopping has grown tremendously in the last decade. According to one forecast, in 2021, over two billion people around the world are expected to shop online, leading to a 20 percent growth in digital commerce sales. AI models indicate that retailers could save about $340 billion by implementing AI solutions and reduce the cost by implementing AI in supply chains. Another benefit is having direct control over supply chains, plus greater flexibility to respond efficiently to disruptions in local markets and being able to adjust to potential vulnerabilities in global market fluctuations.

Part of the innovation narrative in the UAE is based on the necessity to remain competitive in the midst of the Fourth Industrial Revolution (4IR) and the desire to shift the paradigm of being pure adopters of AI to become developers of this technology. According to the UNs 2019 Global Innovation Index, the UAE ranked first among Arab countries, and 36th worldwide, in terms of overall performance on the index, climbing nine spots from its 2015 ranking.

However, expectations must be realistic. A recent Economist report, entitled An understanding of AIs limitations is starting to sink in, warned that despite major advances in the field, the fact remains that many of the grandest claims made about AI have once again failed to become reality and that the state of AI hype has far exceeded the state of AI science especially in regard to the actual implementation of the technology on the ground.

Conclusion

Prior to the outbreak of coronavirus, new technologies were already making many jobs, business models, and older technologies obsolete. Yet the COVID-19 pandemic has accelerated this trend. Put simply, the pathogen has hot-wired the 4IR, pushing more citizens of the Gulf regionand the world at largetoward the digital sphere. Even after a vaccine for coronavirus is developed, GCC member-states will need to continue innovation-led development as the worlds digital transformation moves forward.

In the Gulf region, where there is a disproportionately young population, the brightest and most innovative citizens have a special role to play as pioneers of the 4IR. Growth of these countries high-tech sectors will pay dividends long into the future as the UAE and other countries in the Arabian Peninsula seek to achieve economic diversification and trim down their bloated public sectors by creating more opportunities for tech-savvy citizens in the private sector.

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Artificial Intelligence and Innovation in the UAE's National Discourse - International Policy Digest