In the run-up to Ludwig van Beethoven's 250th birthday, a team of musicologists and programmers is using artificial intelligence to complete the composer's unfinished tenth symphony.

The piece was started by Beethoven alongside his famous ninth, which includes the well-known Ode To Joy.

But by the time the German composer died in 1827, there were only a few notes and drafts of the composition.

The experiment risks failing to do justice to the beloved German composer. Tthe team said the first few months yielded results that sounded mechanical and repetitive.

But now the project leader, Matthias Roeder, from the Herbert von Karajan Institute, insists the AI's latest compositions are more promising.

"An AI system learns an unbelievable amount of notes in an extremely short time," said Roeder. "And the first results are a bit like with people, you say 'hmm, maybe it's not so great'. But it keeps going and, at some point, the system really surprises you. And that happened the first time a few weeks ago. We're pleased that it's making such big strides."

The group is in the process of training an algorithm that will produce a completed symphony. They're doing this by playing snippets of Beethoven's work and leaving the computer to improvise the rest of it. Afterwards, they correct the improvisation so it fits with the composer's style.

Similar projects have been undertaken before. Schubert's eighth symphony was finished using AI developed by Huawei. It received mixed reviews.

The final result of the project will be performed by a full orchestra on 28 April next year in Bonn as part of a series of celebrations of Beethoven's work.

The year of celebrations begins on December 16th with the opening of his home in Bonn as a museum after renovation.

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Artificial intelligence is writing the end of Beethoven's unfinished symphony - Euronews

PARIS--(BUSINESS WIRE)--Iktos, a company specialized in Artificial Intelligence for novel drug design and Almirall, S.A. (ALM), a leading skin-health focused global pharmaceutical company, today announced a collaboration agreement in Artificial Intelligence (AI), where Iktos generative modelling technology will be used to design novel optimized compounds, to speed up the identification of promising drug candidates for undisclosed Almirall drug discovery program(s).

Iktos AI technology, based on deep generative models, helps bring speed and efficiency to the drug discovery process, by automatically designing virtual novel molecules that have all desirable characteristics of a novel drug candidate. This tackles one of the key challenges in drug design: rapid and iterative identification of molecules which simultaneously validate multiple bioactive attributes and drug-like criteria for clinical testing.

This partnership is an example of how we intend to explore the enormous possibilities offered by technology to find new molecules and to speed up clinical development, said Dr. Bhushan Hardas, Executive Vice President R&D, Chief Scientific Officer of Almirall. The health sector lags behind others in the digital world. Almirall wants to be at the forefront of innovation to develop holistic and transversal approaches. Artificial Intelligence will provide Almirall a unique opportunity to combine our proficiency with the preciseness and celerity to truly make a difference in patients' lives.

We are thrilled to initiate a new research collaboration with Almirall commented Yann Gaston-Math, President and CEO of Iktos. This new collaboration is further testimony to the leadership position that Iktos has developed in the field of AI for de novo drug design, in little more than two years of existence. We are eager to demonstrate to our collaborators the power of Iktos technology to accelerate their research, and to get the opportunity to further improve by confronting our approach to a new use case, consistently with our strategy to prove our value in real-life projects.

Iktos has recently announced several collaborations with biopharmaceutical companies where Iktos AI technology is used to accelerate the design of promising compounds, and has published, at the EFMC 2018 meeting, an experimental validation of the technology in a real-life drug discovery project. Iktos generative modelling SaaS software, Makya, is now available on the market, and Iktos intends to release its retrosynthesis SaaS platform Spaya as a beta version, before the end of 2019.

About Iktos

Incorporated in October 2016, Iktos is a French start-up company specialized in the development of artificial intelligence solutions applied to chemical research, more specifically medicinal chemistry and new drug design. Iktos is developing a proprietary and innovative solution based on deep learning generative models, which enables, using existing data, to design molecules that are optimized in silico to meet all the success criteria of a small molecule discovery project. The use of Iktos technology enables major productivity gains in upstream pharmaceutical R&D. Iktos offers its technology both as professional services and as a SaaS software platform, Makya.

About Almirall

Almirall is a leading skin-health focused global pharmaceutical company that partners with healthcare professionals, applying Science to provide medical solutions to patients and future generations. Our efforts are focused on fighting against skin health diseases and helping people feel and look their best. We support healthcare professionals by continuous improvement, bringing our innovative solutions where they are needed.

The company, founded almost 75 years ago with headquarters in Barcelona, is listed on the Spanish Stock Exchange (ticker: ALM). Almirall has been key in value creation to society according to its commitment with to major shareholders and through its decision to help others, to understand their challenges and to use Science to provide solutions for real life. Total revenues in 2018 were 811 million euros. More than 1,800 employees are devoted to Science.

For more information, please visit almirall.com

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Iktos and Almirall Announce Research Collaboration in Artificial Intelligence for New Drug Design - Business Wire

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.

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 ...

Bots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying ... and a little racist. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.

In the 2010s, automation got better, cheaper, and way less avoidable. Its still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.

Humans and tech have always coexisted and coevolved, but this decade brought us closer togetherand closer to the futurethan ever. These days, you dont have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as youre constantly offering your digital behavior to train AIs.

So heres how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.

All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. Thats when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.

Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.

In January, Technology Reviews Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video, Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.

Haos report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordinglynot unlike the way dogs and babies learn about the world.

The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a taskespecially if the task is to imitate a human in some capacity.

This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasnt until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.

One function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and rapid worker disenfranchisement as increasingly complex service jobs are doubled up, automated away, or taken over by AI workers.

The Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term surveillance capitalism. This new economic system, she wrote, unilaterally claims human experience as free raw material for translation into behavioural data, in a bid to make profit from informed gambling based on predicted human behavior.

Were already using machine learning to make subjective decisionseven ones that have life-altering consequences. Medical applications are only some of the least controversial uses of artificial intelligence; by the end of the decade, AIs were locating stranded victims of Hurricane Maria, controlling the German power grid, and killing civilians in Pakistan.

The sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci pointed out the presence on the Obama reelection campaign of an unprecedented number of data analysts and social scientists, bringing the traditional confluence of marketing and politics into a new age.

Intelligence that relies on data from an unjust world suffers from the principle of garbage in, garbage out, futurist Cory Doctorow observed in a recent blog post. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:

It doesnt help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.

But the datasets of human faces that the program uses are sourced, according to a report from March, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been arrested on suspicion of criminal activity. None of the millions of faces in the programs data sets belonged to people who had consented to this use of their data.

State-level efforts to regulate AI finally emerged this decade, with some success. The European Unions General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the data subject (read: us); the GDPR also prohibits the black box model for machine learning applications, requiring both transparency and accountability on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI ethics panel a week later, feigning shock at all the negative reception.

Even attempted regulation is a good sign. It means were looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaignsessentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propagandainfiltrated the very mechanics of American democracy.

Keeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.

Tech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural nets name is Grover, and its really good at this.

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The Bot Decade: How AI Took Over Our Lives in the 2010s - Popular Mechanics

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 ...

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

London, Dec. 10, 2019 (GLOBE NEWSWIRE) -- According to a new market research report Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions), Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), & End User - Global Forecast to 2027, published by Meticulous Research, the AI in Supply Chain Market is expected to grow at a CAGR of 39.4% from 2019 to reach $21.8 billion by 2027.

Today supply chain networks are becoming more and more complex owing to progressive globalization. Various well-established supply chain organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in supply chain operations is on the rise in order to create new opportunities & enhance operational capabilities by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future.

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Realizing the fact, various end-use industries are investing heavily in order to reap the profits in highly dynamic and competitive market environments. Organizations are aggressively adopting AI-based solutions for supply chain operations to reshape their business processes and increase profitability. Rapid adoption of AI technology across the supply chain operations, rising awareness about artificial intelligence, and widening implementation of computer vision technologies across several end-use industries are the key factors driving steady growth in the global artificial intelligence in supply chain market.

In recent years, the funding for development and implementation of artificial intelligence solutions for supply chain industry has increased significantly. For instance, in 2018, the Government of Qubec invested $60 million in order to support AI-Powered Supply Chains Supercluster (SCALE.AI). Similar investments were also made by the Government of Canada investing up to nearly $230 million for the AI-Powered Supply Chains Supercluster in 2018. Such initiatives are bringing the manufacturing, retail, and information & communications technology sectors on the same platform, to develop intelligent solutions for supply chain management through incorporation of robotics and AI technologies.

The AI in supply chain market study presents historical market data in terms of value (2017 and 2018), estimated current data (2019), and forecasts for 2027 by component, technology, application, end-user, and geography. The study also evaluates industry competitors and analyzes their market share at the global and regional levels.

Based on component, the software segment is estimated to account for the largest share of the overall artificial intelligence in supply chain market in 2019; and is slated to grow at the fastest CAGR during the forecast period. The large share of this segment is attributed to the supply chain visibility offered by software, including inventory control, warehouse management, order procurement, and reverse logistics and tracking.

Based on technology, the machine learning segment is estimated to account for the largest share of the overall AI in supply chain market, in 2019. This is mainly attributed to the growing demand for AI-based intelligent solutions, increasing government initiatives, and ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies. On the other hand, computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to widening implementation of computer vision across several end-use industries for monitoring operations, spotting suspicious behavior, and preventing thefts.

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Based on the application, supply chain planning is estimated to hold the largest share of the overall AI market in supply chain, in 2019. This is mainly attributed to the ability of AI solutions to optimize supply chain operations and digitize existing processes and workflows by reinventing the supply chain planning. On the other hand, the demand for AI solutions for warehouse management applications is slated to grow at a fastest CAGR during the forecast period, mainly due to benefits offered by AI solutions in the form of optimizing the logistics, spotting & detecting abnormalities, and automated sorting.

Based on end-user, the consumer-packaged-goods (CPG) segment is estimated to hold the largest share of the overall artificial intelligence in supply chain market in 2019, due to expanding e-commerce sector and ability of AI solutions to provide profitable drop-shipping with features like product tracking, inventory management, and warehouse management. On the other hand, the retail segment is slated to grow at the fastest CAGR during the forecast period, mainly due to benefits of AI in the form of addressing issues with stocking inefficiencies, complexity of operations, and high product lead times in supply chain operations of the retail industry.

The report also includes an extensive assessment of the key strategic developments adopted by leading market participants in the AI in supply chain industry over the past 4 years (2016-2019). The artificial intelligence in supply chain market has witnessed number of partnerships & agreements in the recent years. For instance, in December 2018, Google announced a strategic partnership with Iguazio to provide real-time supply chain and inventory management services for the retail sector.

The global artificial intelligence in supply chain market is highly fragmented with the presence of key players, such asIntel Corporation (U.S.), Amazon.com, Inc. (U.S.), Google LLC (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), IBM Corporation (U.S.), Samsung (South Korea), LLamasoft Inc. (U.S.), SAP (Germany), General Electric (U.S.), Deutsche Post AG DHL (Germany), Xilinx (U.S.), Micron Technology, Inc. (U.S.), FedEx (U.S.), and ClearMetal, Inc. (U.S.) along with several local and regional players.

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AI in Supply Chain Market, by Component

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Artificial Intelligence in Healthcare Market by Product (Hardware, Software, Services), Technology (Machine Learning, Context-Aware Computing, NLP), Application (Drug Discovery, Precision Medicine), End User, And Geography - Global Forecast to 2025, read report:https://www.meticulousresearch.com/product/artificial-intelligence-healthcare-market/

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The name of our company defines our services, strengths, and values. Since the inception, we have only thrived to research, analyze and present the critical market data with great attention to details.

Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, with the help of its unique research methodologies, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa regions.

With the meticulous primary and secondary research techniques, we have built strong capabilities in data collection, interpretation, and analysis of data including qualitative and quantitative research with the finest team of analysts. We design our meticulously analyzed intelligent and value-driven syndicate market research reports, custom studies, quick turnaround research, and consulting solutions to address business challenges of sustainable growth.

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Artificial Intelligence (AI) in Supply Chain Market Worth $21.8 billion by 2027- Exclusive Report by Meticulous Research - GlobeNewswire

The Veterans Affairs Department recently launched a National Artificial Intelligence Institute to coordinate and advance strategic vet-focused research and development efforts to harness the budding technology.

VA has a unique opportunity to be a leader in artificial intelligence, Secretary Robert Wilkie said in a statement. VAs artificial intelligence institute will usher in new capabilities and opportunities that will improve health outcomes for our nations heroes.

Home to Americas largest integrated health care system, the VA trains more doctors and nurses than any other entity in the nation and also houses the largest genomic knowledge base linked to health care information in the world. Throughout 2019, the agency unveiled a variety of deliberate investments and projects to leverage artificial intelligence to better meet veterans needs. For example, the agency and tech giant IBM launched an AI-powered mental fitness app to help veterans transitioning to civilian life earlier this year, and VA collaborated with DeepMind Health to develop an AI system that can forecast a life-threatening kidney disease before it appears.

The agency also appointed Dr. Gil Alterovitz as its first-ever national artificial intelligence director this summer. A Harvard Medical professor who has led national and international collaborative initiatives that used data and technology to innovate across the health care landscape, Alterovitz will serve as the NAIIs director and oversee all of its efforts. He told Nextgov Monday that the new institute has been several months in the making and will garner some federal funding for its efforts. Alterovitz also confirmed that the institute will be housed directly at the VA.

There is a special opportunity to work for veteran needs via AI by focusing on improving health and well-being [through research and development], he said. We hope to focus on veteran priorities in such work.

NAII will engage veterans and stakeholders across the health care sector to solicit and execute flagship AI research projects that emphasize topics like deep learning, explainable AI, and privacy-preserving AI. Theyll aim to demonstrate [the] size, scope, and magnitude of capabilities that deliver positive real-world outcomes for Veterans. According to agency insiders, one of the first tasks the NAII took on was surveying the existing use of AI by VA researchers and going forward, the institute will also boost AI-related research projects already underway by offering up fresh resources and forging new possibilities for collaboration.

Medical centers are across the country and new insights can be best done working together, Alterovitz said.

The AI director also has extensive experience leading projects known as tech sprints, which essentially enable outside organizations to test out data in the VA format to develop tools and programs that can lead to new data-driven insightswithout waiting long periods to establish partnership agreements. NAII insiders will lead AI tech sprints to accelerate innovation in the ecosystem and also aim to create an AI Tech Sprint handbook to help new teams orchestrate sprints to introduce health care solutions.

"We envision a future where AI can give us tools to serve Veterans in the best way possible, as they did for our nation," Alterovitz said.

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What Veterans Affairs Aims to Accomplish Through Its Artificial Intelligence Institute - Nextgov

Baidu, Inc. has filed the most AI-related patent applications in China, a recognition of the companys long-term commitment to driving technological advancement, a recent study from the research unit of Chinas Ministry of Industry and Information Technology (MIIT) has shown.

Baidu filed a total of 5,712 AI-related patent applications as of October 2019, ranking No.1 in China for the second consecutive year. Baidus patent applications were followed by Tencent (4,115), Microsoft (3,978), Inspur (3,755), and Huawei (3,656), according to the report issued by the China Industrial Control Systems Cyber Emergency Response Team, a research unit under the MIIT.

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Baidu retained the top spot for AI patent applications in China because of our continuous research and investment in developing AI, as well as our strategic focus on patents, said Victor Liang, Vice President and General Counsel of Baidu.

In the future, we will continue to increase our investments into securing AI patents, especially for high-value and high-quality patents, to provide a solid foundation for Baidus AI business and for our development of world-leading technology, he said.

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The report showed that Baidu is the patent application leader in several key areas of AI. These include deep learning (1,429), natural language processing (938), and speech recognition (933). Baidu also leads in the highly competitive area of intelligent driving, with 1,237 patent applications, a figure that surpasses leading Chinese universities and research institutions, as well as many international automotive companies. With the launch of the Apollo open source autonomous driving platform and other intelligent driving innovations, Baidu has been committed to pioneering the intelligent transformation of the mobility industry.

After years of research, Baidu has developed a comprehensive AI ecosystem and is now at the forefront of the global AI industry. Moving forward, Baidu will continue to conduct research in the core areas of AI, contribute to scientific and technological innovation in China, and actively push forward the application of AI into more vertical industries. Baidu is positioned to be a global leader in a wave of innovation that will transform industries.

Marketing Technology News: Top 5 Technology Trends to Impact the Digital Infrastructure Landscape in 2020

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Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications - MarTech Series

TALLINN (Reuters) - Finland, which holds the rotating EU presidency until the end of the year, said on Tuesday it aims to teach 1% of all Europeans basic skills in artificial intelligence through a free online course it will now translate into all official EU languages.

The European Union is pushing for wide deployment of artificial intelligence across the bloc, to help European companies catch up with rivals in Asia and the United States.

Our investment has three goals: we want to equip EU citizens with digital skills for the future, we wish to increase practical understanding of what artificial intelligence is, and by doing so, we want to give a boost to the digital leadership of Europe, said Finnish Minister of Employment Timo Harakka.

As our Presidency ends, we want to offer something concrete. Its about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy, Harakka said in a statement.

The course, conducted by the University of Helsinki and originally launched in 2018, already has enrolled more than 220,000 students from more than 110 countries.

It includes modules on subjects such as machine learning, neural networks, the philosophy of artificial intelligence and using artificial intelligence to solve problems.

The course is available in English, Finnish, Swedish and Estonian so far, and Finland will translate it to all official EU languages next year.

The original goal to educate 1% of Finns, equalling some 55,000 people, was reached in just a few months.

Reporting by Tarmo Virki, editing by Anne Kauranen

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Finland seeks to teach 1% of Europeans basics on artificial intelligence - Reuters UK