A couple of years ago, Vladimir Putin warned Russians that the country that led in technologies using artificial intelligence will dominate the globe. He was right to be worried. Russia is now a minor player, and the race seems now to be mainly between the United States and China. But dont count out the European Union just yet; the EU is still a fifth of the world economy, and it has underappreciated strengths. Technological leadership will require big digital investments, rapid business process innovation, and efficient tax and transfer systems. China appears to have the edge in the first, the U.S. in the second, and Western Europe in the third. One out of three wont do, and even two out three will not be enough; whoever does all three best will dominate the rest.

We are on the cusp of colossal changes. But you dont have to take Mr. Putins word for it, nor mine. This is what Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy and a serious student of the effects of digital technologies, says:

This is a moment of choice and opportunity. It could be the best 10 years ahead of us that we have ever had in human history or one of the worst, because we have more power than we have ever had before.

To understand why this is a special time, we need to know how this wave of technologies is different from the ones that came before and how it is the same. We need to know what these technologies mean for people and businesses. And we need to know what governments can do and what theyve been doing. With my colleagues Wolfgang Fengler, Kenan Karaklah, and Ravtosh Bal, I have been trying to whittle the research of scholars such as David Autor, Erik Brynjolfsson, and Diego Comin down to its lessons for laymen. This blog utilizes the work to forecast trends during the next decade.

It is useful to think of technical change as having come in four waves since the 1800s, brought about by a sequence of general purpose technologies (GPTs). GPTs are best described by economists as changes that transform both household life and the ways in which firms conduct business. The four most important GPTs of the last two centuries were the steam engine, electric power, information technology (IT), and artificial intelligence (AI).

All these GPTs inspired complementary innovations and changes in business processes. The robust and most relevant facts about technological progress have to do with its pace, prerequisites, and problems:

Source: Comin and Mestieri (2017).

Putin is not the first Russian leader to understand the importance of breakthrough general purpose technologies. A hundred years ago, Vladimir Lenins Communist Party invented the Five-Year Plan to exploit electric power. Indeed, it wouldnt be an exaggeration to say that modern planning practices originated with Lenins plan for the electrification of the Soviet Union. To appreciate the importance of electrification, it is worth reading Lenins short Report on the Work of the Council of Peoples Commissars. Here are extracts from that speech, delivered in 1920 to stormy and prolonged applause:

You will hear the report of the State Electrification Commission, which was set up by the All-Russia Central Executive Committee of February 7, 1920.Communism is Soviet power plus the electrification of the whole country. We are weaker than capitalism, not only on the world scale, but also within the country. Only when the country has been electrified, and industry, agriculture and transport have been placed on the technical basis of modern large-scale industry, only then shall we be fully victorious. We have a plan which gives us estimates of materials and finances covering a long period, not less than a decade. We must fulfill this plan at all costs, and the period of its fulfillment must be reduced.

Today, the most serious practitioner of Soviet-style planning is the Chinese Communist Party. In 2015, it announced the $1.68 trillion Made in China 2025 plan, to do with artificial intelligence. The plan is to transform the Chinese economy and dominate global manufacturing by 2030. China has neither the entrepreneurial nimbleness of America nor the capable public finance systems of Western Europe, but it is putting a lot of money into digital dominance. The question is whether this will be enough.

The last two decades witnessed the rise of China as an economic power; the next 10 years will decide whether it will eventually become a superpower. For now, President Xis approach could be summed up much as Lenins strategy was in 1920: State capitalism is the Peoples Party plus artificial intelligence.

The story goes that in 2018, President Donald Trump complained to President Xi Jinping that Made in China 2025 was insulting to the U.S. because it aimed to make China the global leader in technology. Since then, there are no official references to it. No point taunting the worlds technology leader into doing more, the Chinese government reckons.

But the real advantage of the U.S. is that government exercises a lighter touch than in China or Europe, leading to shorter lags from invention to market and quicker adaptation by businesses so that productivity gains are realized more quickly than in competing countries. Notice the relatively rapid diffusion of computersavailable for use simultaneously in all rich economiesin the U.S., as compared with Canada, Japan, Germany, and France (Figure 3).

Sources: Historical Cross-Country Technology Adoption Dataset by Comin and Hobijn (2004) and the Maddison Project Database.

The regulatory, infrastructural, and cultural conditions that lead to quicker business process innovation require tight industry-academic linkages, a welcoming environment for high-skilled immigrants, sound product-market regulations, and sensible hiring and firing rules. These will be not easy for either China or Europe to institute, and the U.S. will have this edge for a while.

While the United States is quick to innovate, Western Europe is intrinsically more equal. Take a look at both the diffusion and penetration of internet use plotted in Figure 4. Europe played catch-up between 1990 and 2010, but internet usage has been more widespread in every European country since then. Greater income inequality in the U.S. surely has something to do with this, but it would be even more worrying if it were also due to more unequal opportunity. There is growing evidence that this is the case, and growing concerns that these gaps will quickly widen as AI-based technologies spread across the economy.

Source: World Banks World Development Indicators and the Maddison Project Database.

Since technological change will exacerbate inequality both of opportunities and outcomes, efficient redistribution will become more necessary during the next decade than it has been in the past. Europe would then have a big advantage: Market income inequality in all but five European countries is lower than the U.S. (Figure 5). After taxes and transfers, every European economy has a lower Gini coefficient than Americas.

Source: Causa and Hermansen (2018).

People who make long-term economic forecasts have a tendency to focus on strengths: China can mobilize a lot of money so it will become a superpower, the U.S. has a good climate for business so it will continue to dominate the world economy, and Europe is more egalitarian so itll get more bang for the buck. But perhaps we should look instead at the willingness of economies to remedy their shortcomings. China has to find ways to encourage entrepreneurship and address the massive disparities in education and wealth. Europe has to mobilize large amounts of money and make it easier for investors anywhere to bring inventions to the Single Market. The United States just has to quickly figure out ways to restore competition in tech, finance, health, and public education, so its redistribution systems are not strained.

So, whos most likely to succeed during the next decade? My money is on the United States. Productivity growth will pick up again as businesses take advantage of new technologies, consumers will take home big price and quality gains, and policy types will stop fretting about fears of secular stagnation. If enough of the tax burden is shifted from labor to capital, the incomes of middle-income households will keep pace. Expect the United States to call the shots for the rest of the century.

Read more here:
Whoever leads in artificial intelligence in 2030 will rule the world until 2100 - Brookings Institution

Two of the most significant predictions for the new decade are that AI will become more pervasive, and the U.S. health-care system will need to evolve. AI can augment and improve the health-care system to serve more patients with fewer doctors.

However, health innovators need to be careful to design a system that enhances doctors capabilities, rather than replace them with technology and also to avoid reproducing human biases.

A recent study published in Nature (in collaboration with Google) reports that Google AI detects breast cancer better than human doctors. Babylon Health, the AI-based mobile primary care system implemented in the United Kingdom in 2013, is coming to the U.S.

Health-care is an industry in need of AI assistance due to a shortage of doctors and physician burnout.

Doctors in the U.S. are experiencing a burnout crisis. Nearly 45 percent of physicians report burnout, and the physician suicide rate is twice that of the general population. Research shows physicians experience burnout because of a poorly designed health care system that isnt intended to protect them or their patients.

Physician burnout has been linked to increased medical errors, unprofessional behavior, early retirement, depression, and racial bias.

In 2019 the Journal of the American Medical Association published a study of 3,392 second-year resident physicians who self-identified as non-Black and found that symptoms of burnout were associated with explicit and implicit racial biases.

A study from the Mayo clinic reported poorly designed electronic health records as a contributor to physician burnout.

Another major contributor to burnout is a shortage of physicians compared to the increased number and needs of patients that require care. The Association of American Medical Collegespredicts a shortageof 21,100 and 55,200 primary care physicians by 2032.

While a possible solution, AI systems can also cause problems. Increased medical error is a real potential consequence of poorly designed AI in medicine.

Medical error is the third leading cause of death in the U.S., attesting to both the need for improving the system but also the fragility of the system and consequences of poor design.

Eliminating the empathetic relationship is another potential consequence of poorly designed and integrated AI. Health care is built on a human-human link.

Humans desire and benefit from the problem-solving that comes from conversations. In clinics with electronic health records, physicians spend about 27 percent of their time on patient care and 52 percent time in the exam room interacting with the patient.

Replacing humans with technology inappropriately could lead to complacency from physicians and reduced engagement from patients.

AI could lead to new inequities and biases. Recent studies have shown that Black people are less likely to get proper treatment for lung cancer and adequate treatment for pain because of false beliefs about differences between Blacks and whites.

While some may conclude that AI would remove the biases that minorities receive by focusing on objective data, new research identifies inequities in AI systems.

A study published in Science in 2019 found that an algorithm used in U.S. hospitals systematically discriminated against Black patients by allocating less care to them.

Babylons AI-based chatbot sparked concerns as the chatbots safety has been reported; Babylon refutes these claims.

Many of the disruptive aspects of the AI system are unique to the National Health Service, which assigns patients to practitioners. With new funding and support, Babylon will enter the U.S. market. Health safety advocates need to be available to advocate for the unique needs of patients in the American health-care system.

For AI systems to work without harm, a greater understanding of what clinicians do and their current biases is needed. The goal of designing systems that preserves what clinicians do best without the risk of complacency is critical.

Both tech companies and health leadership are primarily comprised of white male staff that may not be trained to think about bias comprehensively. Diversifying the workforce of companies building AI systems and those innovating the health-care system is needed.

A recent report found that people of color and women are underrepresented in the AI field, as about 80 percent of AI professors are men, and people of color are only a fraction of staff at major tech companies.

Diversifying the pipeline of researchers is essential; equally important is building inclusive workplaces and communities that allow under-represented minority researchers to thrive.

Yes, well-designed AI in health-care systems can transform the health and well-being of members of society by allowing healthcare professionals to provide better quality care to more people and restoring balance to the people who dedicate their lives to providing care.

But the danger is that AI systems that pit humans against algorithms will likely introduce new biases and errors into the U.S. health-care system that will not only exacerbate health disparities but also make health care more dangerous for everyone.

Enid Montague, Ph.D. is an expert on human-centered automation in medicine, Associate Professor of Computing at DePaul University, Adjunct Associate Professor of General Internal Medicine at Northwestern University, and a Public Voices Fellow through The OpEd Project.

Link:
Dangers of artificial intelligence in medicine | TheHill - The Hill

The Pentagons nascent center devoted to artificial intelligence research and development wants to learn more about peoples perceptions of the budding technology.

According to a proposed information collection notice published in the Federal Register Thursday, the Defense Departments Joint Artificial Intelligence Center is funding a RAND Corporation-led study exploring civil-military views regarding AI and related technologies.

This data collection will help ensure [Defenses] ability to engage with leading private sector technology corporations and their employees, officials said in the notice.

As is standard with federal information collections, the Pentagon must engage public feedback on whether it is necessary before the study is conducted. Defense will accept comments until March 16 on that specific matter.

While details regarding the direct questions thatll be asked and how the study will inform future Defense efforts are largely absent from the notice, officials make it clear that they hope to hear from members of the software engineering, defense and aerospace communitiesas well as the general public. The notice forecasts that around 5,210 individuals are expected to respond.

The study will also conduct focus groups with members of the software engineering community and students from computer science programs, it said.

Excerpt from:
What Do You Think About Artificial Intelligence? The Pentagon's AI Center Wants to Know. - Nextgov

TAMPA A tech company that works to develop artificial intelligence-enabled systems that learn and collaborate with humans is expanding to Tampa.

TheIncLab, based near Washington D.C., has opened an AI+X lab that is, artificial intelligence plus experience at the Undercroft, a tech development center and membership guild for companies focused on cybersecurity. Along with TheIncLab, the Undercroft provides work space for local offices of BlackHorse Solutions, Sharp Decisions, @Risk Technologies and Bull Horn Communications.

The Undercroft has offices in one of Ybor Citys most historic structures, the El Pasaje building on E Ninth Avenue. Built in 1886, it originally housed the Cherokee Club, a private retreat for for wealthy cigar-makers. The building, with arched porticos reminiscent of Centro Habana in Cuba, also hosted Jos Mart, Theodore Roosevelt and Winston Churchill before going on to become known variously as a bordello, a speak-easy, a casino, a jazzy nightclub and a low-rent hotel.

But when TheIncLab founder and chief executive officer Adriana Avakian describes what made Tampa an attractive place to expand, she talks less about its local color and more about its "burgeoning technology and cybersecurity industry and abundance of highly qualified talent.

"We spent a fair amount of time in the Tampa-St. Petersburg area in the past six months establishing key relationships and meeting with strategic partners to ensure the success of our expansion, she said in an announcement released through the Tampa Bay Economic Development Council.

Since its founding in 2015, TheIncLab has signed up clients from several branches of the U.S. Department of Defense as well as from Fortune 500 companies in the health care, aerospace, manufacturing, consumer electronics and technology spaces. Its services are tailored to help clients accelerate innovation, launch new products and services, open new markets and redefine customer relationships. The firm also has a lab in Nashville.

TheIncLab anticipates hiring 15 developers and engineers in the next 12 months and partnering with bay area universities to augment its staff with student interns. Before it decided on Tampa, it talked with University of South Florida administrators about the availability of talent and research expertise.

Artificial intelligence is one of the great frontiers in the innovation economy," USF College of Engineering professor Sudeep Sarkar, who chairs USFs department of computer science and is co-chair of the USF Institute for AI+X, said in a statement released through the Economic Development Council. With young companies, our students and our faculty working together, Tampa is growing to become a promising center of diverse skills and new perspectives that will shape AIs future.

TheIncLab was one of 10 tech companies selected out of 432 applications from across the U.S. to participate in Tampa Bay Waves TechDiversity program last summer, and is the second of the 10 to expand or move to the Tampa Bay area.

Like Drift, a business-to-business marketing tech company that last month opened a Tampa office with plans to hire up to 100 employees by 2021, TheIncLab is not receiving any state or local incentives to expand here, according to Economic Development Council spokeswoman Laura Fontanills.

Go here to read the rest:
Artificial intelligence firm TheIncLab expands to Tampa - Tampa Bay Times

John McCarthy invented the term AI. Getty

John McCarthy was the son of a penniless Irish immigrant from Kerry and maybe the most important Irish American you never heard of. He died in 2011 aged 84.

He was an American computer scientist pioneer and inventor and is known as the father of artificial intelligence (AI) after playing the key role in the development of intelligent machines we now call computers. He won the Turing Prize, one step below the Nobel, in 1971.

He coined the term artificial intelligence for a 1955 Dartmouth College conference he chiefly organized which was the first-ever AI conference.

Decades later his work was critical for the creation of companies like Google as Sebastian Thrun, who currently leads the Google self-driving car project says.

"In my mind, Google is all about artificial intelligencewhat John McCarthy stood for matters, he said.

Read more: 5 discoveries and inventions you never knew were made by the Irish

McCarthy was the son of a union organizer, also John McCarthy, who had left the little fishing village of Cromane in Kerry in the early 1920s. First, he went to London.

He left home when he was 16 and went to London, selling a cow for the fare," his niece Mary Miller told the Irish Independent.

"He came back with a Jewish girl, Ida Glatt, from Lithuania, and they later got married. John Patrick spoke several languages and wrote regularly for The Kerryman before emigrating to Boston.

Both his parents were strong union and communist advocates, and his father found work as a union organizer in the depression in Los Angeles where John grew up. John Junior disavowed communism and became a convert to conservative Republicanism after Russia invaded Czechoslovakia in 1968.

McCarthy stated that his ultimate objective was to make a machine think exactly like a human and also be capable of abstract thought, solving problems and self-improvement.

Read more: Cornelius Ryan, the Irish D-Day reporter who re-invented journalism

That's still some years awayfive to 500 McCarthy once jokedbut AI, even in its current manifestation, has dramatically changed our world. (Isnt that right Alexa?)

Many of his colleagues thought he was nuts, but he persisted in his belief that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

He went on to invent Lisp, acknowledged as one of the world's most influential programming languages which became the standard AI program language and is still used today. Famed computer scientist Alan Kay called Lisp the "greatest single programming language ever designed."

McCarthy also played a major role in the development of time-sharing systems.

"Without time-sharing, you wouldn't have the modern internet," says Lester Earnest, who worked with McCarthy at the Massachusetts Institute of Technology and at the Stanford Artificial Intelligence Lab. (Today we know time-sharing as the cloud.)

He was deeply interested in his Irish heritage and stayed in the homes of relatives during visits to Ireland. In 1992 Trinity College awarded him an honorary doctorate and he gave a lecture entitled The Little Thoughts of Thinking Machines.

McCarthy passed away at age 84 in 2011. He was truly a man who changed the world.

John McCarthy invented the term AI. Getty

The rest is here:
This son of an Irish emigrant invented Artificial Intelligence and changed the world - IrishCentral

Updated: May 25, 2018:

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Recent Federal Government Action on Artificial Intelligence and Next Generation Technologies - JD Supra

Proximity Lab, a leading UX research, strategy, and design agency, released an in-depth research report that studies the impact of artificial intelligence (AI) on digital product design. The report, "Up & Rising: How AI is Transforming Product Design & Creativity," is free and available for download at Proximity Lab's website.

PORTSMOUTH, N.H. (PRWEB) January 17, 2020

Proximity Lab, a leading UX research, strategy, and design agency, released an in-depth research report that studies the impact of artificial intelligence (AI) on digital product design. The report provides highlights from interviews with AI experts at leading product development companies including Adobe, Alarm.com, Eagle Genomics, MIT Media Lab, Nuance, and Salesforce and summarizes the results of a survey of over 100 digital product designers and knowledge workers.

Key areas of exploration include:

Anthony Finbow, CEO of Eagle Genomics and an expert in AI and ML, was part of the research. "The team at Proximity Lab focused on finding the answer to a critical issue for anyone building products: how to foster the right relationship between AI and the people using it. If you can get these interactions right, you have the opportunity to develop an entirely new conversation that can lead to insights and knowledge that neither a person or machine alone would have uncovered."

The report is divided into the following sections:

The report, "Up & Rising: How AI is Transforming Product Design & Creativity," is free and available for download at Proximity Lab's website.

About Proximity Lab

Proximity Lab is an award-winning interaction design studio with deep experience in research, product strategy and UX design. We are designers, creators and thinkers who bring diverse backgrounds together to develop a common vision to create products that emphasize clarity, simplicity and value. Our team has been helping enterprise software leaders and innovators rethink and reimagine software experiences for over 10 years. Proximity Lab is headquartered in Portsmouth, NH with offices in San Francisco, CA.

Proximity Lab on LinkedIn

For the original version on PRWeb visit: https://www.prweb.com/releases/proximity_lab_releases_research_focused_report_on_the_impact_of_artificial_intelligence_ai_on_product_design_features_insights_from_ai_experts_and_product_designers/prweb16822479.htm

Read more here:
Proximity Lab Releases Research-focused Report on the Impact of Artificial Intelligence (AI) on Product Design: Features Insights from AI Experts and...

The new decade opened with some intriguing news: The journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists. Researchers at Google Health teamed up with academic medical centers in the United States and Britain to train an AI system using tens of thousands of mammograms.

But even the best artificial intelligence system cant fix the uncertainties of early cancer diagnosis.

To understand why, it helps to have a sense of how AI systems learn. In this case, the system was trained with images labeled as either cancer or not cancer. From them, it learned to deduce features such as shape, density and edges that are associated with the cancer label.

Thus, the process is dependent on starting with data that are correctly labeled. In the AI mammography study, the initial diagnoses were determined by a pathologist who examined biopsy specimens under a microscope after an abnormal mammogram. In other words, the pathologist determined whether the mammogram showed cancer.

Unfortunately, this pathologic standard is problematic. Over the last 20 years there has been a growing recognition that screening mammography has led to substantial overdiagnosis the detection of abnormalities that meet the pathological definition of cancer, yet wont ever cause symptoms or death.

Furthermore, pathologists can disagree about who has breast cancer even when presented with the same biopsy specimens under the microscope. The problem is far less for large, obvious cancers far greater for small (even microscopic), early-stage cancers. Thats because there is a gray area between cancer and not cancer. This has important implications for AI technology used for cancer screening.

AI systems will undoubtedly be able to consistently find subtle abnormalities on mammograms, which will lead to more biopsies. This will require pathologists to make judgments on subtler irregularities that may be consistent with cancer under the microscope, but may not represent disease destined to cause symptoms or death. In other words, reliance on pathologists for the ground truth could lead to an increase in cancer overdiagnosis.

The problem is not confined to breast cancer. Overdiagnosis and disagreement over what constitutes cancer are also problems relevant to melanoma, prostate and thyroid cancer. AI systems are already being developed for screening skin moles for melanoma and are likely to be employed in other cancers as well.

In a piece for the New England Journal of Medicine last month, we proposed a better way of deploying AI in cancer detection. Why not make use of the information contained in pathological disagreement? We suggested that each biopsy used in training AI systems be evaluated by a diverse panel of pathologists and labeled with three distinct categories: unanimous agreement of cancer, unanimous agreement of not cancer, and disagreement as to the presence of cancer. This intermediate category of disagreement would not only help researchers understand the natural history of cancer, but could also be used by clinicians and patients to investigate less invasive treatment for cancers in the gray area.

The problem of observer disagreement is not confined to pathologists; it also exists with radiologists reading mammograms. Thats the problem AI is trying to solve. Yet, while the notion of disagreement may be unsettling, disagreement also provides important information: Patients diagnosed with an early-stage cancer should be more optimistic about their prognoses if there were some disagreement about whether cancer was present, rather than all pathologists agreeing it was obviously cancer.

Artificial intelligence cant resolve the ambiguities surrounding early cancer diagnosis, but it can help illuminate them. And illuminating these gray areas is the first step in helping patients and their doctors respond wisely to them. We believe that training AI to recognize an intermediate category would be an important advance in the development of this technology.

Adewole S. Adamson is a dermatologist and assistant professor of medicine at Dell Medical School at the University of Texas at Austin. H. Gilbert Welch is a senior researcher in the Center for Surgery and Public Health at Brigham and Womens Hospital in Boston and author of Should I Be Tested for Cancer? Maybe Not and Heres Why. They wrote this piece for the Los Angeles Times.

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Adamson, Welch: Using artificial intelligence to diagnose cancer could mean unnecessary treatments - St. Paul Pioneer Press

Current rapid growth in artificial intelligence (AI) is fuelled predominantly by the rediscovery of deep learning. When it first surfaced 15 years ago, the concept was met with unfavourable conditions and largely abandoned in the aftermath. Nowadays, deep learning is back, circumstances are right and the field flourishes.

The described third boom in artificial AI and subsequent tightening technological and economic competition sent ripples through various aspects of the social realm, including policymaking. Many countries began working on national AI strategies, including current leaders China and U.S. and Japan also followed suit.

However, what distinguishes Japan from other countries is a consistent concept underlying all introduced regulation the vision of Society 5.0. This new form of society is saturated with AI-related technology, which not only improves the lives of its members, but also creates new aspects and new values. Individual needs are met in a timely and proportionate manner allowing for fulfilled and contented lives. With society 5.0 as a targeted outcome, the recently updated AI Strategy 2019 contains a wide spectrum of necessary actions comprising both facilitating the development of AI and utilising it for the advancement of industry and society.

The strength of the strategy lies in its emphasis on the practical application of AI, Japanese policymakers correctly recognised the versatility of AI and its potential to pervade and transform any given field, employing it to improve and lower costs of various processes, perform tasks beyond human capabilities and gain insight where human-operated analysis didnt reach.

The strategy aims not only to improve situation on a national level, targeting five designated priority areas (manufacturing, transportation and logistics, health and medical care, agriculture and disaster response) but also globally by helping solve major societal problems like ageing society or labour shortage, diversification of energy sources, GHG reduction or more efficient waste management, which lines up perfectly with achieving Sustainable Development Goals. According to the strategy, the solutions developed in Japan would then be made available for the world and if realised successfully, it might just be the advantage that Japan needs to win over its competitors.

Still, reaching the targets mentioned above requires major efforts in at least three vital areas: R&D, data and human resources.

At the moment, Japan along with the rest of the world struggles with a lack of educated professionals capable of handling AI-related technologies. The short-term relief could be brought by encouraging more women to participate in the job market and attracting skilled resources form overseas. In the long-term perspective, Japan is preparing for major educational system reform, introducing AI into curricula and making it obligatory part of the university entrance exam, creating a learning inducive environment for students (sufficient network infrastructure and access to communication devices) and facilitating lifelong learning for the existing workforce.

Recognising data as the sine qua non for the development of AI, the Japanese Government is making considerable effort to facilitate data circulation while maintaining its quality. Notable examples include The Basic Act on the Advancement of Utilising Public and Private Sector Data and The Act on the Protection of Personal Information. It is also worth mentioning, that all the practical applications of AI planned in the new AI Strategy are designed as two-way data flows one direction is technology and data deployment into the industry area and the other is the data gathered from users feeding further development of AI. To organise and structure the exchange, the Japanese Government plans to construct a data linkage infrastructure.

Finally, a lot of effort is also directed at the R&D area, with establishing a network of centres of excellence as the first step. In the search for disruptive innovation, the Japanese Government initiated many R&D programmes, the last of which Moonshot R&D positively surprises with scale and forwardness and also offers many opportunities for international collaboration for European partners.

Japan certainly seems determined to embrace and take advantage of AIs potential in transforming its own society and reinforcing its global position. It would, therefore, be beneficial for European companies to closely follow Japans next movements, as the two have a lot in common. With a shared approach to technology and related values (confirmed recently by the European Unions (EU) welcoming Japans Social Principles of Human-Centric AI) and common rivals, Japan and EU are natural allies and its high time to capitalise on cooperation potential.

For more information about AI policy in Japan, see the Analysis of opportunities for EU SMEs in Japans Data Economy and Artificial Intelligence in connection with Robotics report available on the EU-Japan Centre for Industrial Cooperation website.

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Enterprise Artificial Intelligence (AI) Market by Deployment Type (Cloud and On-Premise), Technology (Machine Learning, Natural Language Processing, Image Processing, and Speech Recognition), Organization Size (Large Enterprises and Small & Medium Enterprises), and Industry Vertical (Media & Advertising, BFSI, IT & Telecom, Retail, Healthcare, Automotive & Transportation, and Others): Global Opportunity Analysis And Industry Forecast, 2019-2026

New York, Jan. 17, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Enterprise Artificial Intelligence Market by Deployment Type, Technology, Organization Size, and Industry Vertical : Global Opportunity Analysis And Industry Forecast, 2019-2026" - https://www.reportlinker.com/p05828825/?utm_source=GNW

Artificial intelligence has been one of the fastest-growing technologies in recent years. AI is associated to human intelligence with similar characteristics, such as language understanding, reasoning, learning, problem solving, and others. Manufacturers in the market witness enormous underlying intellectual challenges in the development and revision of such technology. AI is positioned at the core of the next-gen software technologies in the market. Companies, such as Google, IBM, Microsoft, and other leading players, have actively implemented AI as a crucial part of their technologies. The increase in number of innovative start-ups and advancements in technology have led to rise in investment in artificial intelligence technologies. Moreover, escalating demand for analyzing and interpreting large amount of data boosts the requirement of artificial intelligence industry solutions. Moreover, development of more reliable cloud computing infrastructures and improvements in dynamic artificial intelligence solutions have a strong impact on the growth potential of the AI market. However, lack of trained and experienced staff hinders the growth of the enterprise Artificial Intelligence (AI) market. Furthermore, increase in adoption of AI in developing economies, such as China, and India are expected to provide major opportunities for the market growth in the upcoming years. Also, on-going developments in smart virtual assistants and robots are anticipated to be opportunistic for the growth of the enterprise artificial intelligence (AI) market. The global enterprise artificial intelligence (AI) market is segmented on the basis of deployment type, technology, organization size, industry vertical, and region. Based on deployment type, the market is bifurcated into cloud and on-premise. Based on technology, the market is divided into machine learning, natural language processing, image processing, and speech recognition. Based on organization size, the market is classified into large enterprises and small & medium enterprises. Depending on industry vertical, the market is segmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others. Based on region, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The report includes the profiles of key players operating in the market analysis. These include Alphabet Inc. (Google Inc.), Apple Inc., Amazon Web Services, Inc., International Business Machines Corporation, IPsoft Inc., MicroStrategy Incorporated, NVIDIA Corporation, SAP, Verint, and Wipro Limited.

KEY BENEFITS The report provides an in-depth analysis of the global enterprise artificial intelligence (AI) market trends, key driving factors, and potential areas for product investments. Key players are analyzed with respect to their primary offerings, recent investments, and future development strategies. Porters five forces analysis illustrates the potency of buyers and suppliers operating in the industry. The quantitative analysis of the global enterprise artificial intelligence (AI) market share from 2018 to 2026 is provided to determine the market potential.

KEY MARKET SEGMENTS

BY DEPLOYMENT TYPE Cloud On-premise

BY TECHNOLOGY Machine Learning Natural Language Processing Image Processing Speech Recognition

BY ORGANIZATION SIZE Large Enterprises Small & Medium Enterprises

BY INDUSTRY VERTICAL Media & Advertising BFSI IT & Telecom Retail Healthcare Automotive & Transportation Others

BY REGION North America o U.S. o Canada

Europe o UK o Germany o France o Russia o Rest of Europe

Asia-Pacific o China o Japan o India o Australia o Rest of Asia-Pacific

LAMEA o Latin America o Middle East o Africa

KEY MARKET PLAYERS PROFILED IN THE REPORT Alphabet Inc. (Google Inc.) Apple Inc. Amazon Web Services, Inc. International Business Machines Corporation IPsoft Inc. MicroStrategy Incorporated NVIDIA Corporation SAP Verint Wipro Limited

Read the full report: https://www.reportlinker.com/p05828825/?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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