Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasksas, for example, discovering proofs for mathematical theorems or playing chesswith great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

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Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.Although there are no AIs that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks.

No, artificial intelligence and machine learning are not the same, but they are closely related. Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. Machine learning helps a computer to achieve artificial intelligence.

All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasps instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligenceconspicuously absent in the case of Sphexmust include the ability to adapt to new circumstances.

Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and proceduresknown as rote learningis relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the add ed rule and so form the past tense of jump based on experience with similar verbs.

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artificial intelligence | Definition, Examples, and ...

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

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

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

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

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

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

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

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Benefits & Risks of Artificial Intelligence - Future of ...

The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage.

Early AI research in the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names.

This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search systems that can be designed to complement and augment human abilities.

While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isnt that scary or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in health care, retail and more.

Why is artificial intelligence important?

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Artificial Intelligence What it is and why it matters | SAS

Moving up the charts with AI

There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate.

Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

In an MIT Sloan Management Review/BCG survey, seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.

NewVantage Partners 2019 Big Data and AI Executive surveyFirms report ongoing interest and an active embrace of AI technologies and solutions, with 91.5% of firms reporting ongoing investment in AI. But only 14.6% of firms report that they have deployed AI capabilities into widespread production. Perhaps as a result, the percentage of respondents agreeing that their pace of investment in AI and big data was accelerating fell from 92% in 2018 to 52% in 2019.

Deloitte 2018 State of Enterprise AI surveyThe top 3 challenges with AI were implementation issues, integrating AI into the companys roles and functions, and data issuesall factors involved in large-scale deployment.

In a 2018 McKinsey Global Survey of AI, most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions.

In short, AI has not yet achieved much return on investment. It has yet to substantially improve the lives of workers, the productivity and performance of organizations, or the effective functions of societies. It is capable of doing all these things, but is being held back from its potential impact by a series of factors I will describe below.

Whats Holding AI Back

Ill describe the factors that are preventing AI from having a substantial return in terms of the letters of our new organization: the ROAI Institute. Although it primarily stands for return on artificial intelligence, it also works to describe the missing or critical ingredients for a successful return:

ReengineeringThe business process reengineering movement of the 1980s and early 90s, in which I wrote the first article and book (admittedly by only a few weeks in both cases) described an opportunity for substantial change in broad business processes based on the capabilities of information technology. Then the technology catalyst was enterprise systems and the Internet; now its artificial intelligence and business analytics.

There is a great opportunitythus far only rarely pursuedto redesign business processes and tasks around AI. Since AI thus far is a relatively narrow technology, task redesign is more feasible now, and essential if organizations are to derive value from AI. Process and task design has become a question of what machines will do vs. what tasks are best suited to humans.

We are not condemned to narrow task redesign forever, however. Combinations of multiple AI technologies can lead to change in entire end to end processesnew product and service development, customer service, order management, procure to pay, and the like.

Organizations need to embrace this new form of reengineering while avoiding the problems that derailed the movement in the past; I called it The Fad that Forgot People. Forgetting people, and their interactions with AI, would also lead to the derailing of AI technology as a vehicle for positive change.

Organization and CultureAI is the child of big data and analytics, and is likely to be subject to the same organization and culture issues as the parent. Unfortunately, there are plenty of survey results suggesting that firms are struggling to achieve data-driven cultures.

The 2019 NewVantage Partners survey of large U.S. firms I cite above found that only 31.0% of companies say they are data-driven. This number has declined from 37.1% in 2017 and 32.4% in 2018. 28% said in 2019 that they have a data culture. 77% reported that business adoption of big data and AI initiatives remains a major challenge. Executives cited multiple factors (organizational alignment, agility, resistance), with 95% stemming from cultural challenges (people and process), and only 5% relating to technology.

A 2019 Deloitte survey of US executives on their perspectives on analytical insights found that most executives63%do not believe their companies are analytics-driven. 37% say their companies are either analytical competitors (10%) or analytical companies (27%). 67% of executives say they are not comfortable accessing or using data from their tools and resources; even 37% of companies with strong data-driven cultures express discomfort.

The absence of a data-driven culture affects AI as much as any technology. It means that the company and its leaders are unlikely to be motivated or knowledgeable about AI, and hence unlikely to build the necessary AI capabilities to succeed. Even if AI applications are successfully developed, they may not be broadly implemented or adopted by users. In addition to culture, AI systems may be a poor fit with an organization for reasons of organizational structure, strategy, or badly-executed change management. In short, the organizational and cultural dimension is critical for any firm seeking to achieve return on AI.

Algorithms and DataAlgorithms are, of course, the key technical feature of most AI systemsat least those based on machine learning. And its impossible to separate data from algorithms, since machine learning algorithms learn from data. In fact, the greatest impediment to effective algorithms is insufficient, poor quality, or unlabeled data. Other algorithm-related challenges for AI implementation include:

InvestmentOne key driver of lack of return from AI is the simple failure to invest enough. Survey data suggest most companies dont invest much yet, and I mentioned one above suggesting that investment levels have peaked in many large firms. And the issue is not just the level of investment, but also how the investments are being managed. Few companies are demanding ROI analysis both before and after implementation; they apparently view AI as experimental, even though the most common version of it (supervised machine learning) has been available for over fifty years. The same companies may not plan for increased investment at the deployment stagetypically one or two orders of magnitude more than a pilotonly focusing on pre-deployment AI applications.

Of course, with any technology it can be difficult to attribute revenue or profit gains to the application. Smart companies seek intermediate measures of effectiveness, including user behavior changes, task performance, process changes, and so forththat would precede improvements in financial outcomes. But its rare for these to be measured by companies either.

A Program of Research and Structured Action

Along with several other veterans of big data and AI, I am forming the Return on AI Institute, which will carry out programs of research and structured action, including surveys, case studies, workshops, methodologies, and guidelines for projects and programs. The ROAI Institute is a benefit corporation that will be supported by companies and organizations who desire to get more value out of their AI investments

Our focus will be less on AI technology-though technological breakthroughs and trends will be considered for their potential to improve returnsand more on the factors defined in this article that improve deployment, organizational change, and financial and social returns. We will focus on the important social dimension of AI in our work as wellis it improving work or the quality of life, solving social or healthcare problems, or making government bodies more responsive? Those types of benefits will be described in our work in addition to the financial ones.

Our research and recommendations will address topics such as:

Please contact me at tdavenport@babson.edu if you care about these issues with regard to your own organization and are interested in approaches to them. AI is a powerful and potentially beneficial technology, but its benefits wont be realized without considerable attention to ROAI.

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Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes

Artificial intelligence (AI) is informing just about every facet of society, from detecting fraud and surveillanceto helping countries battle the current COVID-19 pandemic. But AI is a thorny subject, fraught with complex terminology, contradictory information, and general confusion about what it is at its most fundamental level. This is why the Oxford Internet Institute (OII), the University of Oxfords research and teaching department specializing in the social science of the internet, has partnered with Google to launch a portal with a series of explainers outlining what AI actually is including the fundamentals, ethics, its impact on society, and how its created.

The Oxford Internet Institute is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet.

At launch, the A-Z of AI covers 26 topics, including bias and how AI is used in climate science, ethics, machine learning, human-in-the-loop, and Generative adversarial networks (GANs).

Googles People and AI Research team (PAIR) worked with Gina Neff, a senior research fellow and associate professor at OII, and her team to select the subjects they felt were pivotal to understanding AI and its role today.

The 26 topics chosen are by no means an exhaustive list, but they are a great place for first-timers to start, the guides FAQ section explains. The team carefully balanced their selections across a spectrum of technical understanding, production techniques, use cases, societal implications, and ethical considerations.

For example, bias in data sets is a well-documented issue in the development of AI algorithms, and the guide briefly explains how the problem is created and how it can be addressed.

Typically, AI forms a bias when the data its given to learn from isnt fully comprehensive and, therefore, starts leading it toward certain outcomes, the guide reads. Because data is an AI systems only means of learning, it could end up reproducing any imbalances or biases found within the original information. For example, if you were teaching AI to recognize shoes and only showed it imagery of sneakers, it wouldnt learn to recognize high heels, sandals, or boots as shoes.

You can peruse the guide in its full A-Z form or filter content by one of four categories: AI fundamentals, Making AI, Society and AI, and Using AI.

Those with a decent background in AI will find this guide simplistic, but its a good starting point for anyone looking to grasp the key points they will be hearing about as AI continues to shape society in the years to come.

Its also worth noting that this isnt a static resource the plan is to update it as AI evolves.

The A-Z will be refreshed periodically as new technologies come into play and existing technologies evolve, the guide explains.

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Google and the Oxford Internet Institute explain artificial intelligence basics with the A-Z of AI - VentureBeat

The agency is looking at how to best apply curated data sets to new use cases.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.

Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.

When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded

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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media

The Global Artificial Intelligence in Healthcare Market is expected to grow from USD 2,178. 37 Million in 2018 to USD 10,578. 45 Million by the end of 2025 at a Compound Annual Growth Rate (CAGR) of 25.

New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Healthcare Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871979/?utm_source=GNW 32%.

The positioning of the Global Artificial Intelligence in Healthcare Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).

The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Healthcare Market including are Google, IBM, Intel, Microsoft, NVIDIA, Amazon Web Services, General Electric Company, Medtronic, Micron Technology, and Siemens Healthineers.

On the basis of Offering, the Global Artificial Intelligence in Healthcare Market is studied across Hardware, Services, and Software.

On the basis of Technology, the Global Artificial Intelligence in Healthcare Market is studied across Computer Vision, Context-Aware Computing, Machine Learning, Natural Language Processing, and Querying Method.

On the basis of Application, the Global Artificial Intelligence in Healthcare Market is studied across Clinical Trial Participant Identifier, Cybersecurity, Drug Discovery, Emergency Room & Robot-Assisted Surgery, Fraud Detection, Healthcare Assistance Robots, Inpatient Care & Hospital Management, Lifestyle Management & Monitoring, Medical Imaging & Diagnostics, Patient Data and Risk Analysis, Precision Medicine, Research, Virtual Assistant, and Wearables.

On the basis of End User, the Global Artificial Intelligence in Healthcare Market is studied across Healthcare Payers, Hospitals and Providers, Patients, and Pharmaceutical and Biotechnology Companies.

For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.

In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.

Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Healthcare Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Healthcare Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Healthcare Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Healthcare Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Healthcare Market

The report answers questions such as:1. What is the market size of Artificial Intelligence in Healthcare market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Healthcare Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Healthcare Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Healthcare Market?5. What are the opportunities in the Global Artificial Intelligence in Healthcare Market?6. What are the modes of entering the Global Artificial Intelligence in Healthcare Market?Read the full report: https://www.reportlinker.com/p05871979/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Global Artificial Intelligence in Healthcare Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing &...

The positioning of the Global Artificial Intelligence in Aviation Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).

New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Aviation Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871978/?utm_source=GNW

The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Aviation Market including are Intel, Micron, Nvidia, Samsung Electronics, Xilinx, Airbus, Amazon, Boeing, Garmin, GE, IBM, Lockheed Martin, Microsoft, and Thales.

On the basis of Technology, the Global Artificial Intelligence in Aviation Market is studied across Computer Vision, Context Awareness Computing, Machine Learning, and Natural Language Processing (Nlp).

On the basis of Offering, the Global Artificial Intelligence in Aviation Market is studied across Hardware, Services, and Software.

On the basis of Application, the Global Artificial Intelligence in Aviation Market is studied across Dynamic Pricing, Flight Operations, Manufacturing, Smart Maintenance, Surveillance, Training, and Virtual Assistants.

For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.

In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.

Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Aviation Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Aviation Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Aviation Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Aviation Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Aviation Market

The report answers questions such as:1. What is the market size of Artificial Intelligence in Aviation market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Aviation Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Aviation Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Aviation Market?5. What are the opportunities in the Global Artificial Intelligence in Aviation Market?6. What are the modes of entering the Global Artificial Intelligence in Aviation Market?Read the full report: https://www.reportlinker.com/p05871978/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The Global Artificial Intelligence in Aviation Market is expected to grow from USD 214.36 Million in 2018 to USD 1,824.46 Million by the end of 2025...

COVID-19 has taken the world by storm. Lockdowns, quarantines, and shutdowns have created an unpredictable scenario that looks almost apocalyptic. But with the right kind of precautionary measures, knowledge, and resources, we can fight this too. This global crisis is a glaring reminder of the gap between what our current healthcare systems can handle and what they should be equipped to handle.

To bridge this gap and in order to provide the needed care to those affected, Artificial Intelligence (AI) might just be our best bet.

Epidemiology tracks the source of an outbreak and analyzes which sections of the population face the highest risk. With AI, it can become easier to find the pattern of the course of the outbreak and then to predict possibly affected people.

Consider Blue Dot, an Artificial Intelligence agency from Canada that predicted the coronavirus outbreak days before it happened. This AI works by using data from around the world in different languages to comprehensively analyze trends in the disease patterns. This allows it to predict public outbreaks and track infectious diseases before they spread too much.

Using data of population sections, vulnerabilities, and previous diseases, AI can predict the possible turn of events with a pandemic such as a coronavirus. For example, we now know that COVID-19 affects people with respiratory diseases and elderly people more. With this knowledge, AI can use data analysis and predict that areas with larger populations of elderly people or countries with a high number of people with respiratory problems, will be most affected by COVID-19. Military veterans who have been exposed to asbestos become especially susceptible to something like coronavirus because of their compromised respiratory systems. This kind of information can become crucial in controlling COVID-19 from becoming fatal globally.

Currently, there are also cases of hackers stealing information with coronavirus map-tracker malware. Centralized AI performing this activity could have stopped this malware from reaching people. In this moment of widespread anxiety, it is important that we ensure we are reading the correct information and sharing information with safe sources.

In countries such as China and Italy, COVID-19 could only be controlled once its presence became known. Detecting disease before its too late might be one of the most important contributions AI can make to medical science.

An article on GCN by Steve Bennett, former director of the National Biosurveillance Integration Center within the Department of Homeland Security, talks about the potential of AI in terms of coronavirus. He writes that there are pilot approaches that use machine learning to mine social media data for indications of unusual flu symptoms. AI can also be used to examine near-real-time emergency medical services and ambulance data, using ML (machine learning) to look for anomalies in the medical notes as patients were admitted to hospitals. In these instances, AI was able to detect the disease much faster than physical tests saving it from spreading and also ensuring that patients get the treatment in time.

In terms of outbreaks such as COVID-19, early detection is key to both saving lives as well as keeping economies stable. As early as 2009, researchers were using data streams available via internet activity to monitor for listeria outbreaks. Studies like this can be used as roadmaps for AI outbreak detection research.

AI can also be of use in determining which treatments are the most effective for COVID-19. For example, if a treatment helps a patient recover faster in China, then AI can use that information to model and then apply the same treatment in Italy. In turn, AI can also quickly analyze other such cases and reach a possible method of treatment faster than humans alone.

Unfortunately, there is still no reliable vaccine for coronavirus leaving mankind vulnerable to it. It is especially difficult to find preventative and curative alternatives in todays post-antibiotic area. As stated by experts at Sani Professional, superbugs and new diseases are emerging that have greater resistance to common cleaners and chemicals we rely on to sanitize, disinfect, and clean up spaces and tools every day. That being said, there is a lot of research being done on possible cures in the form of antibody research. Since it is still too early to know a specific time when the vaccine or an alternate immediate treatment will be available, the use of AI might help to speed up the process, and possibly highlight other avenues for curative research. Heavy hitters like IBM and Amazon are offering up their supercomputers to help with the research.

Amidst the chaos and the flood of information, it is important that we put our safety first. Getting correct information from trusted sources is the first step towards this. Use updates from National Health Services, the WHO and more to keep yourself abreast of the current situation. It is especially important to comply with any imposed travel restrictions, and take precautions in case youre planning to travel. Unless it is absolutely necessary, it is best to stay at home and wait for this pandemic to pass. Regularly washing your hands for 20 seconds (with soap) and social distancing, are key to protecting yourself and those around you from this disease.

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Is artificial intelligence the answer to disease prevention? - The Burn-In

Artificial intelligence (AI) is inescapable. In our daily lives we probably encounter it and its best friend machine learning much more frequently than we think. Did you buy something online yesterday, use face login on your smartphone, check your Facebook, look for something on Google, or use Google Maps? AI was right there.

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When AI is helping us find the most efficient route home, were often quite happy to let it do its job. But this technology already does so much more, from helping to decide whether to grant us bank loans and diagnose our illnesses, to presenting targeted advertising.

As AI gets more and more embedded in our lives and helps make decisions that are increasingly significant to us, were rightly concerned about transparency. When big new stories like the Cambridge Analytica scandal or ongoing discussion around inherent biases in facial recognition hit the headlines, we are concerned about bias (intentional or otherwise), and our trust in AI takes a hit.

Explainable AI gives us a route to greater trust in AI. It is designed to help us learn more about how AI works in any given situation. So, instead of the AI just giving us an answer to a question, it shows us how it got to the answer. The alternative is the so-called black box situation where an AI uses an unspecified range of information and algorithms to get to an answer, but doesnt make any of this transparent.

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In theory, explainable AI gives us confidence in the conclusions an AI system draws. Dr Terence Tse, Associate Professor of Finance at ESCP Business School, gives the following example: Imagine you want to obtain a loan and the approval is purely determined by an algorithm. Your loan gets rejected. If the algorithm in question is a black box its an issue for all parties. The bank cannot say why this is happening, and you don't know what to do in order to obtain the loan. Having explainable AI will help.

Explainable AI is a vital aspect of understanding an AIs competence in coming up with any particular set of outputs. Mark Stefik, Research Fellow and Lead of Explainable AI at PARC, a Xerox company, tells IT Pro: Typically, when people interact with AIs and the systems do the right thing, then people overestimate the AIs competence. They assume that the machines think like people, which they do not. They assume that machines have common sense, which they do not.

In fact, AI does not think like humans do at all. We use think in relation to AI to describe a way of working that in reality is different to that of our own brains. AI uses algorithms and machine learning to help it draw conclusions from data it is given, or from insights it generates. In showing how an AI has reached its decision, explainable AI can help uncover biases and in doing so not only provide individuals with redress, as in the banking example above, but also help refine the AI system itself.

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Oleg Rogynskyy, Founder and CEO of People.ai says: A lack of explainability on how the machine learning model thinks can result in biases. If there is a bias hidden in the data set a machine learning model is trained on, it will consider the bias a ground truth.

Explainability techniques can be used to detect and then remove biases and ensure a level of trust between the machines and the user.

As AI takes an increasingly important role in our everyday lives, we are getting more and more concerned about whether we can trust it. As Stefik puts it: The need for explainable AI increases if we want to use the systems in critical situations, where there are real consequences for good and bad decisions. People want to know when they can trust the systems before they rely on them.

The IT Pro Podcast: Looking forward to 2020

With 2019 behind us, we predict what trends the IT industry can expect over the next year

The industry recognises this need. In a recent IBM survey of 4,500 IT decision makers, 83% of respondents said being able to explain how AI arrived at a decision was universally important. That number rose to 92% among those already deploying AI, as opposed to 75% of those considering a deployment.

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Rogynskyy is unequivocal in his message, saying: Explainable AI must be prevalent everywhere. Tse was similarly forthright, adding: If we want to gain public trust in the deployment of AI, we have to make explainable AI a priority.

Stefik, however, has reservations, particularly when it comes to how we define terms like trust and explainable, which he argues are nuanced and complex concepts. Nevertheless, he hasnt written explainable AI off completely, saying: It is not ready as a complete (or well-defined) approach to making trustworthy systems, but it will be part of the solution.

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Why transparency is key to promoting trust in artificial intelligence - IT PRO

The Spanish government is planning to test 80,000 people a day for coronavirus with the roll-out of robot testers.

Technology will be used to speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far. According to Bloomberg, Spanish authorities now plan to increase daily testing from about 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing.

Speaking at a conference on Saturday 21 March, Raquel Yotti, head of Madrids health institute, said: A plan to automate tests through robots has already been designed and Spain has committed to buying four robots that will allow us to execute 80,000 tests per day.

Because of the ease that coronavirus spreads from person to person, testing has been identified as one of the best ways to control the disease. But testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners exposure to the virus.

No further details have been given about how the robots will work, but AI is increasingly being designed to work in the healthcare industry by automating some of the work of medical staff, giving them more time to treat patients.

The technology has proved successful in medical trials, including identifying cancer in breast scans.

A research paper from Google Health, published inNaturemagazine, has reported that machine learning, based on Googles TensorFlow algorithm, can be used to reduce false positives in breast cancer scans. A false positive is when a mammogram scan is incorrectly identified as cancerous, and a false negative is when it is wrongly diagnosed as not being cancerous.

In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representativedataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset, while the UK dataset showed a 1.2% reduction in false positives.

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Coronavirus: Spain to use artificial intelligence to automate testing - ComputerWeekly.com

Technology is evolving at a rapid pace, transforming every business sector.

The security industry is no different, as emerging technologies are leveraged to enhance operations.

Much has been made of artificial intelligence (AI) and its potential, with companies of all kinds scrambling to implement it. Whilst the hype may presently outweigh the current benefits, AI in the security sector can be truly beneficial.

The buzz surrounding facial recognition, in particular, has dominated the public perception of AI in the security space. However, there are many applications of this tool which are already delivering benefits to businesses. Deep Learning (DL) is a subcategory of AI, which can empower surveillance technology to achieve unparalleled levels of accuracy. This, in turn, can make security professionals lives easier as they can focus on more pressing tasks, with full reassurance that DL is working in the background, improving protection and efficiency.

Deep Learning precision

In the past, surveillance applications that used video analytics to generate alerts often struggled to differentiate between a human intruder and other objects or wildlife, creating time-consuming false alarms.

However, DL can help overcome this hurdle by enabling users to pre-calibrate the system to detect real threats and ignore false ones. In the context of video analytics, the learning aspect of DL refers to the way that a developer can train an algorithm to only pick up on specific objects and features, much in the same way that a human would visually disseminate a scene and distinguish between objects.

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In a security application, the algorithm can be trained to recognise a person or a vehicle that could pose a threat. This level of sophistication in security tools means that the issue of false alerts is mitigated, and monitoring staff can focus their efforts on less tedious tasks, increasing their productivity and attention span, improving overall performance.

Ultimately, improved alert accuracy leads to a more secure perimeter. By detecting suspicious events in real-time, the technology enables staff to address incidents as they occur, reducing the need to analyse video footage in the wake of a security breach, when very little can be done.

Combining human and artificial intelligence

Its true that AI and automation stand to revolutionise every sector. However, this is not to say that they are always a viable replacement for human intelligence.

AI and DL really excel in the automation of manual tasks and making improvements to operations, but the value of human input cannot be underestimated.

The DL component of security analytics is invaluable for overworked and understaffed monitoring teams it can filter through hundreds of potential alerts and block those that arent useful. Staff are then left with only a handful of unusual situations to evaluate, which they are responsible for resolving. This is where human intelligence is still light years ahead of AI. The most successful businesses across the board are the ones who are able to combine the latest technologies with human intuition.

By Kevin Waterhouse, Managing Director at VCA Technology

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Bridging the gaps: joining human and artificial intelligence | Technology - Business Chief Canada

The classic hockey stick curveits what investors and entrepreneurs desire but what medics despise. In the past week, Italy has seen that kind of curve in its coronavirus case numbers, leaving people and systems overwhelmed. German chancellor Angela Merkel has described coronavirus as Germanys greatest challenge since World War II.

This pandemic is the biggest black swan" event we have witnessed in our lives so far. A black swan event is characterized by a very low probability but extremely high impact. The last one was 9/11 in the US, which some still saw coming. But Covid-19 has taken us all by surprise.

Cases and deaths have had a geometric rise, which defeats understanding, because our minds tend to think in terms of linear progression. Were not programmed to fathom something that multiplies. India hasnt yet seen the ugly tipping point, and I hope we dont. This piece is not about hope against hope, but an earnest call for widespread adoption of artificial intelligence (AI) to counter such unpredictable events.

The initial, and by far most successful, application of AI is on the warfront. Thanks to the deployment of drones, unmanned craft, intelligent machines, humanoid robots and the like, the US has managed to drastically cut its casualties in Afghanistan and Iraq compared to the Vietnam and the Gulf wars. AI has not only lowered collateral damage but also radically increased the accuracy of assault.

But AIs applications can be far greater and more useful in humanitarian and disaster relief, conservation, disease control and waste management, among others. Machines have been shown to outperform humans in terms of labour, memory, intelligence and, in some cases even creativity.

At a time when citizens have been advised to practise social distancing, and we are fearfully confined to our homes, who will run the essentials? Someone will have to weather the storm, or perhaps something? We already have so much power offered by the brute force of machines that its up to us to tame it in meaningful ways, and Covid-19 could offer a precise opportunity.

At the time of writing this piece, Summit, the worlds most powerful supercomputer, housed at the US Department of Energys Oak Ridge National Laboratory, had identified 77 drug compounds that might stop coronavirus from infecting cells, a significant step in vaccine development. We are getting to know more about the spread of disease, hotspots and mortality rates on an almost real-time basis, thanks to affordable computing and communication networks. Can we up the ante further by relinquishing more control to machines?

Winston Churchill famously said, Never let a good crisis go to waste", and I think we have a great opportunity at hand. We can make machines take on the more hazardous tasks, while we watch and survive from the sidelines. This is the time for tech startups to leverage the power of general purpose technologies and conceive radical new solutions to address pandemics.

Private Kit: Safe Paths is an app developed by researchers at the Massachusetts Institute of Technology and Harvard. With help from Facebook and Uber, it lets you know if you have crossed paths with someone who is infected while protecting privacy. Its a first step, and like most technologies, it will improve with adoption. OneBreath, a Palo Alto-based medtech startup, has been working on an affordable, reliable ventilator for over a decade now, and should be ready to meet Covid-19.

As geography becomes history, we have become one large family. Our more robust, fast-learning cousins, the machines, must be deployed on the frontlines faster. We are truly at the inflection point towards singularity, and its a choice between speed and accuracy. A useful ethos for the times could be from Mark Twain who reminded us, Continuous improvement is better than delayed perfection."

Pavan Soni is the founder of Inflexion Point, an innovation and strategy consultancy.

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To fight the coronavirus spread, give artificial intelligence a chance - Livemint

Brossard, Quebec, March 25, 2020 (GLOBE NEWSWIRE) -- DIAGNOS Inc. (DIAGNOS or the Corporation) (TSX Venture: ADK) (OTCQB: DGNOF), a leader in early detection of critical health issues through the use of Artificial Intelligence (AI), announces that it is participating in the Call to Action initiative implemented by the White House Office of Science and Technology Policy. DIAGNOS has accessed a significant dataset with the objective of analysing these medical documents with its AI Medical Platform, called FLAIRE, in order to identify key factors that could assist in the battle against the Coronavirus.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 44,000 scholarly articles, including over 29,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

Call to Action (from the White House)

The White House is issuing a call to action to the world's artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions. The CORD-19 dataset represents the most extensive machine-readable coronavirus literature collection available for data mining to date. This allows the worldwide AI research community the opportunity to apply text and data mining approaches to find answers to questions within, and connect insights across, this content in support of the ongoing COVID-19 response efforts worldwide.

Mr. Andr Larente, CEO of Diagnos stated: Diagnos AI platform has been built to address the complexity of multiple sources of data that includes text mining, data mining and medical imaging. The proprietary technology has been developed over a number of years in order to identify medical challenges, for example cardio vascular issues have resulted in new tests to address hypertensive patient complications and to predict a potential stroke. The intention of FLAIRE in response to the White House Call to Action is to assist in resolving some of the issues caused by the virus by analyzing the dataset made available by the US authorities.

The Standing Committee on Emerging Infectious Disease and 21st Century Health Threats of the US and the WHO identified 10 scientific questions that are vital to address this international crisis. These questions include studying the transmission and incubation of the virus, risk factors for getting the COVID-19, the origin of the virus, and the proper medical practice for treating this disease.

Mr. Francis Bellido, PhD in Medical Microbiology and board member at Diagnos added: One outcome that is particularly remarkable in the COVID-19 crisis is that the majority of the deceased victims had one or several pre-condition(s) before the infection struck such as hypertension, diabetes, obesity or other Cardio Vascular issues, which are the sweet spots for the Diagnos diagnostic assisted platform. We believe that this virus could further alter the cartography of the retina in such patients, and if confirmed, creating the possibility of an additional facet to our existing detection tool for our existing patients.

Dr. Hadi Chakor, Chief Medical Officer at Diagnos added: One of the treatments for COVID-19 is the use of chloroquine or hydroxychloroquine. The recommendations of the American Academy of Ophthalmology on the screening of chloroquine (CQ) and hydroxychloroquine (HCQ) are very clear after taking high doses and for a long period of use, a rigorous follow-up with patients is required. These conditions represent the most severe risks of developing morphological alterations in the retina after treatment with chloroquine. Also, previous studies demonstrate clearly that chloroquine disrupts lysosomal function in retinal neurons and RPE. Modern screening should be based on primary AI-based automatic screening tests to assess the fundus plus optical spectral coherence tomography (SD OCT) exams. These investigations should look beyond the central macula to provide objective screening and to detect subtle changes on the retinal membrane.

The Corporation is also announcing a correction to its press release dated March 9th, 2020: The number of common shares that Mr. Tristram Coffin would hold assuming the exercise of stock warrants should read 11,047,561 instead of 10,624,560.About DIAGNOS

DIAGNOS is a publicly-traded Canadian corporation with a mission of early detection of critical health issues through the use of its Artificial Intelligence (AI) platform FLAIRE. Diagnos can build application rapidly using the FLAIRE platform such as CARA (Computer Assisted Retina Analysis). CARA is an application that integrates with existing equipment (hardware and software) and processes at the point of care. CARAs Artificial Intelligence image enhancement algorithms make standard retinal images sharper, clearer and easier to read. CARA is a cost-effective tool for screening large numbers of patients in real-time and has been cleared for commercialization by several regulatory authorities such as Health Canada, the U.S. Food and Drug Administration, European Union and other countries.

Additional information is available at http://www.diagnos.com and http://www.sedar.com.

This news release contains forward-looking information. There can be no assurance that forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated in these statements. DIAGNOS disclaims any intention or obligation to publically update or revise any forward-looking information, whether as a result of new information, future events or otherwise. The forward-looking information contained in this news release is expressly qualified by this cautionary statement.

Neither the TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

For further information, please contact:

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DIAGNOS Will Utilize its Artificial Intelligence Medical Platform FLAIRE in Response to the US White House - Call to Action to Analyse and Transform...

ATLANTA, March 25, 2020 (GLOBE NEWSWIRE) -- As many government and private institutions scramble to react to the unexpected COVID-19 pandemic, one telehealth company has been prepared for years to respond to this kind of crisis. LiveMD, a leading provider of global telehealth services, has a reliable, established, full-featured telehealth platform that allows anyone to use their phone to check their symptoms, track and self-report viruses such as COVID-19, and talk to a doctor. Plus it offers a verifiable track record of successful service delivery to patients in more than 42 countries worldwide. The company leverages the expertise of doctors in 53 distinct specialties, who are based in 30 different countries.

LiveMD offers an innovative app that can be downloaded directly from the Google Play store at http://bitly.com/livemdapp. With the LiveMD app, anyone can track the COVID-19 corona virus in their local area, regardless of where they live. They can self-report their corona virus status for tracking and covid-19 testing triaging, and connect with local government and medical agencies (such as test labs) for help and guidance. Their personal health information is kept private and secured.

As explained by LiveMD Founder and CEO Emeka Okwara, LiveMD is a global telehealth platform intended to serve anyone on this planet who has a phone. You can think of LiveMD as a global digital hospital on your phone. Anytime and anywhere, virtually anyone can be quickly and safely connected to a certified physician for a live consultation. Anyone can schedule an appointment to talk to a doctor by phone, video, text, or in person. This is our core mission and what we do best.

LiveMD also offers an innovative app that can be securely downloaded directly from the Google Play store. Use the app to talk to a certified doctor in the LiveMD global health network by phone, video, or text from anywhere. With the app, the patient literally has a self-diagnosis tool powered by advanced artificial intelligence capability, in the palm of their hand. After they use the LiveMD app to perform a self-diagnosis, the app then identifies doctors who are available for a consultation and who specialize in that patients specific medical ailment. In the near future, LiveMD will also identify which pharmaceuticals are designed and routinely prescribed to treat that condition.

LiveMD has always advocated for the idea that health care is global and not just local, and must be treated as such. We have led the industry, says Okwara, using our technology with that mindset. This allows us to provide a platform that addresses some of the global health challenges we are experiencing today, and prevent future pandemics.

Organizations such as health insurance companies, business owners and employers, and NGOs can use LiveMD to serve employees and customers. That ensures that those who get the app will receive vital access to the help they need, supported by a global network of doctors.

Okwara adds that, "LiveMD plans to work with governments and provide them with the tools to get real-time COVID-19 updates from their citizens, help triage individuals for covid-19 testing, and help provide citizens the help they desperately need. Our objective is to help governments quickly and efficiently reduce the spread of the virus and save lives. Government institutions can reach LiveMD at gov@mylivemd.com to quickly get onboard the LiveMD Global Health Platform. It takes less than an hour to get onboarded. Because of LiveMDs depth of experience and technological sophistication, the platform can swiftly and strategically respond to individual, local, regional, and global health concerns.

About LiveMD

LiveMD is a Telehealth platform used to increase access to quality healthcare services around the world using its artificial intelligence, big data and telecommunication technologies. Patients across 43 countries use LiveMD to talk to certified doctors by phone, video, and text. They also use LiveMDs artificial intelligence tools for self-diagnosis, virus tracking andmedical testing triaging.You can find more information about LiveMD at http://www.mylivemd.com and you can download the app at http://bitly.com/livemdapp.

LiveMD Media Contact:Emeka Okwarapress@mylivemd.com

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LiveMD Global Telehealth Platform launches artificial intelligence tracking and triaging tools to help combat COVID-19 (CoronaVirus) Pandemic -...

Newark published new research on the Internet of Things (IoT) which confirms strong adoption of Artificial Intelligence (AI) within IoT devices. The companys research showed that 49% of respondents already use AI in their IoT applications, with Machine Learning (ML) the most used technology (28%), followed by cloud-based AI (19%).

In opposition, there were still 51% of respondents that have not adopted or implemented AI technology into IoT applications.

The research survey, which is Newarks second-such report, highlighted security as being the biggest concern for those implementing IoT.

Other statistics that came from the survey are listed:

(All statistics have come directly from Newark)

The survey ran from Sept. Dec. 2019, compiling responses from 2,015 participants. Those who participated came from 67 countries in Europe, North America, and Asia. 59% of respondents were engineers who were working on IoT solutions.

For more information, visit Newark.com

Perry Cohen, associate editor for Embedded Computing Design, is responsible for web content editing and creation in addition to podcast production. He also assists with the publications social media efforts which include strategic posting, follower engagement, and social media analysis. Before joining the ECD editorial team, Perry has been published on both local and national news platforms including KTAR.com (Phoenix), ArizonaSports.com (Phoenix), AZFamily.com, Cronkite News, and MLB/MiLB among others. Perry received a BA in Journalism from the Walter Cronkite School of Journalism and Mass Communications at Arizona State university.He can be reached by email at perry.cohen@opensysmedia.comFollow Perrys work and ECD content on his twitter account @pcohen21

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New Research from Newark Reveals Strong Adoption of Artificial Intelligence within the Internet of Things Ecosystem - Embedded Computing Design

AI tools and services are being used or offered by companies around the world to help fight the coronavirus pandemic.

In a best-case scenario, whereby the virus transmission is massively mitigated, researchers from Imperial College London predict there would still be in the order of 250,000 deaths in GB, and 1.11.2 million in the US resulting from the coronavirus.

Imperial College Londons analysis landed in Washington over the weekend and its said to be the reason behind the US stepping up its response. British PM Johnson warned that further measures in the UK will likely be introduced in the coming days and a coronavirus bill for emergency powers is making its way to the House of Commons.

Much like in wartime, technologies and social experiments that under normal circumstances would take years or decades to be tested and implemented will be rushed into use in days or weeks.

Chinas Tianhe-1 supercomputer is offering doctors around the world free access to an AI diagnosis tool for identifying coronavirus patients based on a chest scan. The supercomputer can sift through hundreds of images generated by computed tomography (CT) and can give a diagnosis in about 10 seconds.

Alibaba Cloud has launched a series of AI technologies including the International Medical Expert Communication Platform on Alibaba Groups enterprise chat and collaboration app, DingTalk. The platform allows verified medical personnel around the world to share their experiences through online messaging and video conferencing.

Another solution from Alibaba estimates the trajectory of a coronavirus outbreak in a specific region using a machine learning algorithm based on public data gathered from 31 provinces in China. Within China, it has a 98 percent accuracy rate.

For researchers and institutions working hard towards a vaccine, Alibaba has opened its AI-powered computational platform to accelerate data transfer and computation time in areas such as virtual drug screening.

Several of the other leading cloud players in China including Baidu and Tencent have opened up specific parts of their solutions for free to qualifying medical personnel. In the US, Microsoft and Google have also done the same.

Last month, scientists from South Korea-based firm Deargen published a paper with the results from a deep learning-based model called MT-DTI which predicted that, of available FDA-approved antiviral medication, the HIV drug atazanavir is the most likely to bind and block a prominent protein on the outside of the virus which causes COVID-19. In early trials, coronavirus sufferers are reportedly improving significantly using HIV drugs.

Hong Kong-based Insilico Medicine also published a paper in February which, instead of seeking to repurpose available drugs, detailed the use of a drug discovery platform which generated tens of thousands of novel molecules with the potential to bind a specific SARS-CoV-2 protein and block the viruss ability to replicate. A deep learning filtering system helped Insilico narrow down the list and the company has synthesised two of the seven molecules and plans to test them in the next two weeks with a pharmaceutical partner.

British AI startup Benevolent AI has also been active in seeking to identify approved drugs that might block the viral replication of COVID-19. The companys AI system examined a large repository of medical information to identify six compounds that effectively block a cellular pathway that appears to allow the virus into cells to make more virus particles. Baricitinib, used for treating rheumatoid arthritis, looks to be the most effective against the virus.

For its part, the White House has urged AI experts to analyse a dataset of 29,000 scholarly articles about coronavirus and use them to develop text and data-mining techniques to help scientists answer the following key questions about COVID-19:

The entire COVID-19 Open Research Dataset (CORD-19) has been made available on SemanticScholar and will be updated whenever new research is published.

While the outlook around the world is currently grim, some of these AI-powered tools and developments offer a glimmer of hope we may be to reduce the viruss spread, improve treatment for patients, and ultimately conquer the coronavirus sooner than otherwise would have been possible.

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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AI vs COVID-19: Here are the AI tools and services fighting coronavirus - AI News

Russ Altman (Image credit: Courtesy Russ Altman)

The impact of COVID-19 on society and the way artificial intelligence can be leveraged to increase understanding of the virus and its spread will be the focus of an April 1 virtual conference sponsored by the Stanford Institute for Human-Centered Artificial Intelligence (HAI).

COVID-19 and AI: A Virtual Conference, which is open to the public, will convene experts from Stanford and beyond. It will be livestreamed to engage the broad research community, government and international organizations, and civil society.

Russ Altman, one of the conference chairs, is an associate director of HAI and the Kenneth Fong Professor and professor of bioengineering, of genetics, of medicine, of biomedical data science, and, by courtesy, of computer science. He is also the host of the Sirius radio show The Future of Everything. He discusses the aims of the conference.

What was the idea behind the conference?

At HAI, we felt this was an opportunity to use our unique focus on AI and humanity to serve the public in a time of crisis. The issues involved in the pandemic are both nuanced and complex. Approaching it from multiple fields of expertise will help speed us toward solutions. The goal is to make leading-edge and interdisciplinary research available, bringing together our network of experts from across different schools and departments.

We have a world-class set of doctors and biological scientists at Stanford Medical School and theyll, of course, be involved. Well also have experts on AI, as well as the social sciences and humanities, to give their scholarly perspective on the implications of this virus, now and over time. The conference will be entirely virtual with every speaker participating remotely, providing an unpolished but authentic window into the minds of thinkers we respect.

What useful information will come out of the conference?

Were asking our speakers to begin their presentation by talking about the problem theyre addressing and why it matters. They will present the methods theyre using, whether scientific or sociological or humanistic, the results theyre seeing even if their work is preliminary and the caveats to their conclusions. Then theyll go into deeper detail that will be very interesting to academic researchers and colleagues. Importantly, we intend to have a summary of key takeaways afterward along with links to information where people can learn more.

We will not give medical advice or information about how to ensure personal safety. The CDC and other public health agencies are mobilized to do that.

What do you think AI has to offer in the fight over viruses like COVID-19?

AI is extremely good at finding patterns across multiple data types. For example, were now able to analyze patterns of human response to the pressures of the pandemic as measured through sentiments on social media, and even patterns in geospatial data to see where social distancing may and may not be working. And, of course, we are using AI to look for patterns in the genome of the virus and its biology to see where we can attack it.

This interdisciplinary conference will show how the availability of molecular, cellular and genomic data, patient and hospital data, population data all of that can be harnessed for insight. Weve always examined these data sources through more traditional methods. But now for the first time, and at a critical time of global crisis, we have the ability to use AI to look deeper into data and see patterns that were otherwise not visible previously, including the social and cultural impact of this pandemic. This is what will enable us to work together as a scholarly, scientific community to help the future of humankind.

Who do you hope will attend?

The core audience is scholars and researchers. We want to have a meaningful discussion about the research challenges and opportunities in the battle against this virus. Having said that, we know that there are many people with an interest in how scientists, researchers, sociologists and humanists are helping in this time of crisis. So were making the conference open to anyone interested in attending. It will be a live video stream from a link on our website, and available as a recording afterward.

What kind of policy effect do you hope the conference can have?

Good policy is always informed by good research. A major goal of HAI is to catalyze high-quality research that we hope will be heeded by policymakers as they work to craft responses to COVID-19 and future pandemic threats. So this will give insights to policymakers on what will be published in the coming months.

Register for the April 1 conference.

Learn more about the Stanford Institute for Human-Centered AI (HAI).

See more here:

Stanford virtual conference to focus on COVID19 and artificial intelligence | Stanford News - Stanford University News

Its an article on the various opportunities and challenges for A.I. to dominate the energy sector

What lies in the store for AI in energy sector: Its potential applications and shortcomings

Artificial Intelligence, Deep Learning and Machine Learning- whatever you are doing, if you dont understand it Learn it. Because you are otherwise going to be a dinosaur in 3 years.

These words by American entrepreneur Marc Cuban can be a bit over-the-top but it puts a strong emphasis on how these modern technologies are gonna dominate almost every industry in the coming years. So today, we are gonna talk about one of these technologies- Artificial Intelligence (AI.) in detail and about its growing importance in specifically, the Energy Sector.

So, lets begin our article with some insights on AI.

Artificial Intelligence: An Introduction

AI is certainly the talk about the hour nowadays. In high-tech industry, AI is the one with most potential. But before proceeding and discussing further on this exciting technology, wed like to first understand what AI means-:

Artificial Intelligence may be defined as a technology which incorporates human intelligence in machines. It provides machines or computer programs the potential of thinking or performing certain tasks which otherwise, wouldnt be possible without human intelligence.

These tasks may include the likes of visual perception or speech recognition for instance.

Artificial intelligence (AI) makes it possible for machines to accomplish specific tasks by processing large amounts of information in form of data and recognizing patterns within the data. Today AI has taken a crucial place in many sectors and with increasing digitisation and increased flow of information everyday, the longer term for AI looks promising.

From retail to banking, from healthcare to manufacturing, AI is resulting in increased efficiency and security by enhancing the speed, precision and effectiveness of human efforts.

AI IN ENERGY SECTOR: AN OVERVIEW

With Artificial Intelligence expanding itself every day, energy sector has also not been left untouched by it.AI and energy sector are a perfect matchto each other. AI thrives on data and the energy resources are flooded with huge chunk of data coming from power grids, wind-farm operations and even oil-companies. AI coupled with other technologies like cloud computing canprocess, stream, analyse and interpret this dataprecisely and with unimaginable speeds to make the energy sector more efficient and secure.

So, lets begin our discussion on what future lies for AI in this ever- changing energy sector and what challenges and opportunities lie ahead to it:

Opportunities for AI in Energy Sector-:

The numerous opportunities for AI can be narrowed down to these five points-

AI in power grids : Smart grids

With time, power grids are becoming more and more decentralized and digital. It is leading to more number of grid participants and hence, more difficult to manage it and keep the grid in balance. This requires evaluating and analyzing a huge chunk of data. AI can help us with quick and efficient processing for this flood of data!

As power is being generated from more volatile sources like solar and wind, the requirement is that power generation must react intelligently to consumption (and vice versa). With AI, we canevaluate, analyse and control participants connected to each othervia these smart grids.

Intelligent Energy Storage(IES)

With modern day emphasis on climate changes and increasing pressure to reduce CO2 emissions, we must find ways to have most of our power generated from renewable resources. The problem with renewable sources of energy are that they areunpredictable, which makes production of energy periodical and sometimes even chaotic. With renewable sources, there can be power outages or too much power generation which needs to be controlled.

Smart storage, also known asIntelligent Energy Storage(IES)can effectively handle these disrupt changes in power supply. If we combine renewable energy with AI-powered storage ,we can greatlyimprove energy storage management, increase business value and minimize power losses.

AI in power trading: AI forecasting

Use of AI in power trading can help improve forecasting. Improving their predictive analysis methods by the use of AI can serve many goals for energy companies:Cost Cutting, Power Saving, Being ready for changing conditionsand also improving their existing customer service. With the help of machine learning and deep learning, its possible to bring forecasting to the next level in the energy industry .The cost of error in energy industry is very high, which means thatprecision of highest levelis required.

Ex- Worlds largest electricity producer company GE Power is working on incorporating AI in its energy supply change to enhance precision and efficiency.

Resource Management

Suppliers can use AI topredict for demandin advance orcheck for problemsto save resources wherever possible. They can therefore haveoptimal utilizationof their resources, hence increasing efficiency.

AI can also enable users to save electricity and reduce their monthly bills. With AI enabled system, the networked devices canreduce power billsby reacting to prices on electricity market.

Preventing disaster

AI can be used topredict system overload or potential transformer breakdowns, thus giving an added layer of security to any disaster sort of mis-happening. Analyzing the available data and coupled with technologies like deep learning, AI can predict corrosion, cracks etc. which pose a threat to the system and can be a cause of future disasters.

Challenges for AI in the energy sector

By learning about so many potential applications of AI. in the energy sector, you would guess that its gonna be a pretty easy path for it in this industry. But turns out, that the path isnt really without its obstacles. So, lets take a look at the major challenges which has to be cleared before AI takes a giant domination in this industry-:

Let us read about each challenge one-by-one:

Lack of expertise and finances

For a shift to AI enabled energy sector, we require a large number of employees withsufficient technical expertiseon AI who could be able to lead this transition, but thats not present. Moreover, theconservative approachof some organisations andhuge risksassociated with data compels many companies to not join this AI revolution.

Moreover, this implementation of technology in the energy sector would requiredeveloping, adjusting and monitoringsoftware which requireslot of resources and finances.

Data Privacy and Security

Data privacy is one of the biggest issues of this century and AI literally thrives on data, so it is natural for data security to be a challenge for AI in the energy sector. Energy supply and entire energy system are prone tocyber-attacks and data theft. Being integral part of a countrys infrastructure,cybersecurity needs to be insuredbefore completely handling over our data to the technology.

Data Consumption by AI itself

Data centers the huge server farms around the world storing users data, now consume 3% of global energy.Processing a lot of data requires large amount of electricity- making it a requirement to have a check on data consumption of AI itself. To make the energy sector artificially intelligent, its integral to ensure that thesedata centres are themselves, energy efficient.

CONCLUSION

Its a no-brainer that the future lies in AI and furthermore, the capability of AI to revolutionize the energy sector must also not be doubted. AI can increase the efficiency, speed and security of energy consumption and generation and could lead the constant transitions in this sector to meet the changing climate needs. But it also goes without saying that even this intelligent technology has its own shortcomings which needs to be taken care of before we can embrace it with open hands.

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Artificial Intelligence in the energy sector: opportunities and challenges - WhaTech