‘Citizen K’: Mikhail Khodorkovsky and the birthing of the new Russia – People’s World

Documentary filmmaker Alex Gibneys latest entry takes on the transition from the USSR to shock capitalism, the bargain basement selloff of the Soviet peoples hard-earned material infrastructure to insider cronies who in short time became billionaires, and the way those nouveaux riches became firmly entrenched in an authoritarian oligarchy led by Vladimir Putin.

One of those men, Mikhail Khodorkovsky, no better nor worse than the others, started asking impertinent questions; he was prosecuted on phony embezzlement and tax charges, sent to prison in Siberia for ten years, and is now, from his exile in London, one of Russias most prominent agitators for democratic governance.

The film title Citizen K inevitably recalls such other films about transparency in government, or the lack thereof, as Citizen Kane, a fictionalized life of newspaper titan William Randolph Hearst, and Citizenfour, a documentary about the exploits of the conscientious IT analyst Edward Snowden, still trapped in Russia for fear of severe punishment if and when he ever returns to the U.S.

The film takes us from the Boris Yeltsin years to the present, with Khodorkovsky himself serving as guide and interpreter of the tumultuous events of the past thirty years of cataclysm in Russia as the country jumped off a cliff into the void. Interspersed with interviews with the Great Man himself are numerous business partners, academics, activists and journalists, as well as historical footage illuminating the inflection points in this corrupt saga.

From his start in commercial banking, the brilliant and ruthless young upstart soon joined an elite group of oligarchs who came to control half of the Russian economy. Khodorkovsky got heavily into fossil fuel development, taking over Yukos, the oil empire that was a former state-owned enterprise.

Anyone who rose to such dizzying heights in those years was surely complicit in any number of serious lapses of discretion, not to say lethal crimes. If getting filthy rich was but a rollercoaster game to men like him, well, the sport could get deadly as uncooperative players soon found out. Khodorkovsky could legally return to Russia but he would be subject to a new trial on a murder charge.

For a period in the 1990s Russia became the murder capital of the capitalist world. The legal structure fell far behind the bitter and fast-moving realities on the ground as Wild West capitalism attracted a host of shady personalities.

Power, Khodorkovsky says, in an epigram that seems to sum up the whole socialist collapse as much as the present-day situation, is a question of how much people are willing to defend it.

The social compact, consolidated under Vladimir Putin, came to be defined as an agreement that the oligarchs not interfere with politics, in exchange for which they would be given free rein to do as they pleased without consequence. In one demonstration of his authority, when privately owned TV channels raised criticisms of the government at the time the submarine Kursk sank, an embarrassed Putin simply ordered a government takeover of the stations.

By such measures, Putin appealed to the mass public that had taken a serious hit with the fall of socialism, in lost jobs, security, housing, healthcare, etc., and saw in the autocrat a kind of new Stalin who would restore Russias rightful place in the world.

When Khodorkovsky started taking an interest in politics, Putin did not hesitate to make life difficult for him. Yukos was taken over and eventually became the re-nationalized Rosneft, which is today a leading player in the energy market. The former oil magnate served nine years at a prison camp near the Chinese border, and when the end of his sentence was coming near, he was put on trial again, now on charges of stealing oil. (Where did he put it? curious minds want to know.)

The show trial was patently staged in order to produce a new verdict that would keep Khodorkovsky in prison for 13 more years, But by now a new generation of sympathizers had arisen, fed up with a country not one of laws but of dictatorship. Protesters could begin to imagine a Russia without Putin.

By the time of the 2014 Sochi Winter Olympics, Putin had been pressured to ease up on the persecution of his enemies, and he set a sizable number of prisoners free, including Khodorkovsky, who heard about it on TV. In his case, the condition was that he go abroad, thus becoming an international symbol of the strength of civil society.

Citizen K is totally riveting from beginning to end, assuming a viewer has some interest in the subject. Its documentary filmmaking at its best, without judgment as to the causes of the Soviet collapse in the first place. One need not accept Mikhail Khodorkovsky as Russias savior to appreciate the strange course of his life that has led him to where he is today.

Yet even the institution of bourgeois democratic rights, which Russia has never enjoyed at any time in its history, would be a historic advance, and to that extent Khodorkovsky has to be considered as a leader of the forces for good. In the meantime, Vladimir Putin shows few signs of surrendering the reins of power. This story is still playing out and is nowhere near the end.

The trailer can be viewed here. Journalist Christine Amanpour interviews Alex Gibney and Mikhail Khodorkovsky here.

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'Citizen K': Mikhail Khodorkovsky and the birthing of the new Russia - People's World

Asha Curran, CEO of Giving Tuesday, appointed new board chair of theguardian.org; Vivian Schiller, Alice Rhee, and Lois Quam appointed to the board -…

Today, theguardian.org has announced new appointments to its board. Founding Board Member Asha Curran, CEO of Giving Tuesday, has been appointed as board chair.

Other new board appointments include Vivian Schiller, media and technology lead at the Aspen Institute, who has held multiple high-profile media roles including head of news at Twitter, general manager of NYTimes.com and President and CEO of National Public Radio.; Lois Quam, President and CEO of Pathfinder International, a global NGO that champions sexual and reproductive health and rights; and Alice Rhee, Managing Director of Strategic Partnerships and Growth at American Journalism Project, the first venture philanthropy organization focused on local civic news in the U.S.

With these new appointments, theguardian.org further cements its commitment to innovate new models of support for journalism, and to expand generosity and contributions from philanthropic institutions and individuals alike. These appointments also bring fresh board expertise to vital topics like global development and womens health, and affirm commitment to strengthen local and regional news.

Publicly launched in August 2017 and led by Rachel White, theguardian.orgs aim is to advance and inform public discourse around the most pressing issues of our time through the support of independent journalism and journalistic projects at the Guardian. To date, theguardian.org has worked with 12 philanthropic partners to support 20 editorially independent projects most recently The Fight to Vote, a one-year reporting project about voting rights in America, and a global reporting project called The Age of Extinction that focuses on biodiversity and species extinction.

Asha Curran, said:

Its a privilege and honor to become board chair of theguardian.org. High quality, independent journalism has never been more important, nor the need to find new and creative ways to support it.

Vivian Schiller, said:

Im delighted to join the board of theguardian.org, an organization that is leading the news industry in innovating new models to support essential reporting as a pillar of our democracy.

-ends-

For more information please contact:

media.enquiries@theguardian.com

About Guardian News & Media

The Guardian US, with newsrooms in New York, Washington DC and Oakland, California, brings a global perspective to America on issues including inequality, race & immigration, the environment, the role of technology in our lives, national security, womens rights, the rise of the far right, gun control and more.

Guardian News & Media (GNM), publisher of theguardian.com, is one of the largest English-speaking newspaper websites in the world. Since launching its US and Australia digital editions in 2011 and 2013 respectively, traffic from outside of the UK now represents over two-thirds of The Guardians total digital audience.

First published in 1821, The Guardian is renowned for its award-winning investigation, The Counted, which exposed and documented lethal police force across America, its agenda-setting NSA surveillance revelations following disclosures by whistleblower Edward Snowden, its globally acclaimed investigation into phone hacking and most recently the Panama Papers and Paradise Papers investigations.

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Asha Curran, CEO of Giving Tuesday, appointed new board chair of theguardian.org; Vivian Schiller, Alice Rhee, and Lois Quam appointed to the board -...

artificial intelligence | Definition, Examples, and …

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 ...

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

Can machines think? Alan Turing, 1950

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

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

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

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

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

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

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

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

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

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

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

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

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

Benefits & Risks of Artificial Intelligence – Future of …

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

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

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

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

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

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

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

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

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

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

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

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.

Browse key industry insights spread across 180 pages with 167 market data tables & 29 figures & charts from the market research report:https://www.meticulousresearch.com/product/artificial-intelligence-ai-in-supply-chain-market-5064/

Scope of the Report:

AI in Supply Chain Market, by Component

AI in Supply Chain Market, by Technology

AI in Supply Chain Market, by Application

AI in Supply Chain Market, by End User

AI in Supply Chain Market, by Geography

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

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

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

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.

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