Global enterprises collectively spent $34.6 billion on cloud infrastructure services in Q2 2020. Amazon Web Services retained its lead in capturing global cloud infrastructure services spend – but Google Cloud and Azure plan to respond. Global enterprises collectively spent $34.6 billion on cloud infrastructure services in Q2 2020, marking a $3.5 billion increase from the previous quarter, according to Canalys. This represented the largest-ever quarter-over-quarter (QoQ) spending increase for the sector, though the overall growth rate continues tapering off given the overall scale of cloud spending. Here's how the three US cloud titans performed this quarter - and where they see growth opportunities moving forward:  Amazon Web Services (AWS) maintained its position as the cloud leader with 31% share of the market in Q2 2020 — it expects to see growth as companies shift away from on-premise cloud infrastructure to reduce costs. Amazon noted in its Q2 earnings call last week that AWS customers have been aggressively looking for ways to cut expenses. It may seem counterintuitive, then, that it reported a 6% increase in AWS revenue between Q1 2020 and Q2 2020. Amazon CFO Brian Olsavsky said in the earnings call that companies were increasing AWS expenditures as a long-term substitute for on-premise infrastructure, since "on-premise infrastructure is not really flexible to go up or down, and especially in the time of sinking demand, it's a big fixed cost for them." While we think that the vast majority of companies will continue to use multicloud environments — meaning they use both on-premise and public cloud services — AWS and the other public cloud players will likely benefit from a shift toward higher utilization of public cloud services within the hybrid system. Microsoft Azure — which is expanding its infrastructure footprint to reach a wider audience — captured 20% of the cloud infrastructure market in Q2 2020, up from 17% in the previous quarter. Microsoft Azure had the most success of any of the players in stealing market share away from competitors. The wide range of services within the Azure ecosystem undoubtedly helped attract new business — the company noted that 96% of Fortune 500 companies used the Azure Power BI service to gleam insights from internal data. To reach a wider audience, Microsoft is reinvesting in Azure to offer additional availability zones, which will help it appeal to multinational corporations looking to streamline operations across regions. Google Cloud's share remained flat at 6%, but the company is betting on its AI services to be a source of growth. Though Google is still playing catch-up to AWS and Microsoft, it appears the company believes its AI expertise will help it catch up over the long run. Google CEO Sundar Pichai noted on the earnings call that "the longer-run opportunity of actually using AI to truly have business solutions ... for whatever industry you are in, that feels like there's a lot of potential, and we are still very early there." In a recent interview with The New York Times, Elon Musk cited Google's AI project DeepMind as his "top concern" since it "crushes all humans at all games." Whether or not DeepMind represents an existential threat to humanity, it will surely be highly sought after as a cloud service that Google can offer to enterprise customers for solving business analytics. Join the conversation about this story »
Tesla and SpaceX CEO Elon Musk has repeatedly said that he thinks artificial intelligence poses a threat to humanity. Of the companies working on AI technology, Musk is most concerned by the Google-owned DeepMind project, he said in a new interview with the New York Times. "The nature of the AI that they're building is one that crushes all humans at all games," he said. "It's basically the plotline in 'WarGames.'" In the 1983 film "WarGames," starring Matthew Broderick, a supercomputer trained to test wartime scenarios is accidentally triggered to start a nuclear war. Visit Business Insider's homepage for more stories. Billionaire Elon Musk has been sounding the alarm about the potentially dangerous, species-ending future of artificial intelligence for years now. In 2016,  he warned that human beings could become the equivalent of "house cats" to new AI overlords. He has since repeatedly called for regulation and caution when it comes to new AI technology. But, of all the various AI projects currently in the works, none has Musk more worried than Google's DeepMind. "Just the nature of the AI that they're building is one that crushes all humans at all games," Musk told the New York Times in a new interview. "I mean, it's basically the plotline in 'WarGames.'" In "WarGames," a teenage hacker played by Matthew Broderick connects to an AI-controlled government supercomputer trained to run war simulations. In attempting to play a game titled "Global Thermonuclear War," the AI convinces government officials that a nuclear attack from the Soviet Union was imminent.  In the end (spoiler for those who haven't seen the 37-year-old movie), the computer runs enough simulations of the potential end results of global thermonuclear war that it declares no winner to be possible, and that the only way to win is to not play. The 1983 film is a direct reflection of its time and place: fear in the US of nuclear war with the Soviet Union still looming, and fear of increasingly advanced technology. But Musk wasn't just talking about old films when he compared DeepMind to "WarGames" – he also said that AI could surpass human intelligence in the next five years, even if we don't see the impact of it immediately. "That doesn't mean that everything goes to hell in five years," he said. "It just means that things get unstable or weird." Musk was an early investor in DeepMind, which sold to Google in 2014 for over $500 million, according to reports. Rather than seeking a return on investment, Musk said in a 2017 interview, he did it to keep an eye on burgeoning AI developments. "It gave me more visibility into the rate at which things were improving, and I think they're really improving at an accelerating rate, far faster than people realize," he said in the 2017 interview. "Mostly because in everyday life you don't see robots walking around. Maybe your Roomba or something. But Roombas aren't going to take over the world." But Musk thinks artificial intelligence should have a different connotation. "I think generally people underestimate the capability of AI — they sort of think it's a smart human," Musk said in a August 2019 talk with Alibaba CEO Jack Ma at the World AI Conference in Shanghai, China. "But it's going to be much more than that. It will be much smarter than the smartest human." Is is "hubris," he said in the Times interview this week, that keeps "very smart people" from realizing the potential dangers of AI. "My assessment about why AI is overlooked by very smart people is that very smart people do not think a computer can ever be as smart as they are. And this is hubris and obviously false." Read the full New York Times interview right here.SEE ALSO: Elon Musk finally took the wraps off his new brain microchip company that plans to connect people's brains to the internet by next year Join the conversation about this story » NOW WATCH: Why thoroughbred horse semen is the world's most expensive liquid
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When asked to think about the effect of Artificial Intelligence on the future, and vice versa, it is nearly impossible for the mind to not instantly travel to Science Fiction works like “Do Androids Dream of Electric Sheep”, “iRobot”, or, “2001: A Space Odyssey”.These works depict Artificial Intelligence in a frightening light and explore topics of autonomy and humanity.These topics and ideas may come to fruition, given enough time of course, but where is the industry of Artificial Intelligence at today?While it is extremely improbable that Google or Apple might make a killer AI that doesn’t adhere to Asimov’s Laws, they have created astonishingly advanced AI.If one thinks back just a decade this could have been just a pipe-dream that few of us could imagine coming to fruition and if one considers the insane speed that this industry has grown and how quickly we are becoming reliant on it those far future, science fiction stories don’t seem so distant, or fictional.Five years will only help further the advancement of Artificial Intelligence, especially with Machine Learning and Google’s DeepMind, at this point we hardly have to teach the AI things, it learns on its own, either through multiple trials or through “reading” other code and taking inspiration from it to better itself, strikingly similar to how humans learn, through experience or though books and other resources.The reasonable conclusion one can draw from this is how much more similar to humanity Artificial Intelligence will grow, perhaps the next iteration will have the ability to teach, first other AI and then humans, maybe it will teach itself more abstract ideas like philosophy, multi-tasking, or anything beyond data comprehension and math, blurring the line of Artificial Intelligence and organic life.Perhaps the next step for Artificial Intelligence isn’t to become more human-like but to advance in another direction and to be used purely as a tool for humanity to use.
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SummaryWiseGuyReports.com adds “Healthcare AI Market 2019 Global Analysis, Growth, Trends and Opportunities Research Report Forecasting to 2024” reports to its database.This report provides in depth study of “Healthcare AI Market” using SWOT analysis i.e.Strength, Weakness, Opportunities and Threat to the organization.The Healthcare AI Market report also provides an in-depth survey of key players in the market which is based on the various objectives of an organization such as profiling, the product outline, the quantity of production, required raw material, and the financial health of the organization.AI means Artificial intelligence and Healthcare AI is the use of complex algorithms and software to estimate human cognition in the analysis of complicated medical data.This report focuses on the global Healthcare AI status, future forecast, growth opportunity, key market and key players.The study objectives are to present the Healthcare AI development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.The key players covered in this study Apple GE Healthcare Google Deepmind Health IBM Watson Health Imagen Technologies Microsoft Intel Medalogix Lumiata NextHealth Technologies Wellframe Zebra Medical Vision Qventus Sentrian Health FidelityRequest a Free Sample Report @ https://www.wiseguyreports.com/sample-request/4376027-global-healthcare-ai-market-size-status-and-forecast-2019-2025Market segment by Type, the product can be split into Software HardwareMarket segment by Application, split into Diagnostics Robotic Surgeries Virtual Nursing Assistants OtherMarket segment by Regions/Countries, this report covers North America Europe China Japan Southeast Asia India Central & South AmericaAt Any Query @ https://www.wiseguyreports.com/enquiry/4376027-global-healthcare-ai-market-size-status-and-forecast-2019-2025Table of Contents1 Report Overview 1.1 Study Scope 1.2 Key Market Segments 1.3 Players Covered 1.4 Market Analysis by Type 1.4.1 Global Healthcare AI Market Size Growth Rate by Type (2014-2025) 1.4.2 Software 1.4.3 Hardware 1.5 Market by Application 1.5.1 Global Healthcare AI Market Share by Application (2019-2025) 1.5.2 Diagnostics 1.5.3 Robotic Surgeries 1.5.4 Virtual Nursing Assistants 1.5.5 Other 1.6 Study Objectives 1.7 Years Considered2 Global Growth Trends 2.1 Healthcare AI Market Size 2.2 Healthcare AI Growth Trends by Regions 2.2.1 Healthcare AI Market Size by Regions (2019-2025) 2.2.2 Healthcare AI Market Share by Regions (2014-2019) 2.3 Industry Trends 2.3.1 Market Top Trends 2.3.2 Market Drivers 2.3.3 Market Challenges 2.3.4 Porter’s Five Forces Analysis …..12 International Players Profiles 12.1 Apple 12.1.1 Apple Company Details 12.1.2 Company Description and Business Overview 12.1.3 Healthcare AI Introduction 12.1.4 Apple Revenue in Healthcare AI Business (2014-2019)) 12.1.5 Apple Recent Development 12.2 GE Healthcare 12.2.1 GE Healthcare Company Details 12.2.2 Company Description and Business Overview 12.2.3 Healthcare AI Introduction 12.2.4 GE Healthcare Revenue in Healthcare AI Business (2014-2019) 12.2.5 GE Healthcare Recent Development 12.3 Google Deepmind Health 12.3.1 Google Deepmind Health Company Details 12.3.2 Company Description and Business Overview 12.3.3 Healthcare AI Introduction 12.3.4 Google Deepmind Health Revenue in Healthcare AI Business (2014-2019) 12.3.5 Google Deepmind Health Recent Development 12.4 IBM Watson Health 12.4.1 IBM Watson Health Company Details 12.4.2 Company Description and Business Overview 12.4.3 Healthcare AI Introduction 12.4.4 IBM Watson Health Revenue in Healthcare AI Business (2014-2019) 12.4.5 IBM Watson Health Recent Development 12.5 Imagen Technologies 12.5.1 Imagen Technologies Company Details 12.5.2 Company Description and Business Overview 12.5.3 Healthcare AI Introduction 12.5.4 Imagen Technologies Revenue in Healthcare AI Business (2014-2019) 12.5.5 Imagen Technologies Recent Development 12.6 Microsoft 12.6.1 Microsoft Company Details 12.6.2 Company Description and Business Overview 12.6.3 Healthcare AI Introduction 12.6.4 Microsoft Revenue in Healthcare AI Business (2014-2019) 12.6.5 Microsoft Recent Development 12.7 Intel 12.7.1 Intel Company Details 12.7.2 Company Description and Business Overview 12.7.3 Healthcare AI Introduction 12.7.4 Intel Revenue in Healthcare AI Business (2014-2019) 12.7.5 Intel Recent Development 12.8 Medalogix 12.8.1 Medalogix Company Details 12.8.2 Company Description and Business Overview 12.8.3 Healthcare AI Introduction 12.8.4 Medalogix Revenue in Healthcare AI Business (2014-2019) 12.8.5 Medalogix Recent Development 12.9 Lumiata 12.9.1 Lumiata Company Details 12.9.2 Company Description and Business Overview 12.9.3 Healthcare AI Introduction 12.9.4 Lumiata Revenue in Healthcare AI Business (2014-2019) 12.9.5 Lumiata Recent Development 12.10 NextHealth Technologies 12.10.1 NextHealth Technologies Company Details 12.10.2 Company Description and Business Overview 12.10.3 Healthcare AI Introduction 12.10.4 NextHealth Technologies Revenue in Healthcare AI Business (2014-2019) 12.10.5 NextHealth Technologies Recent Development 12.11 Wellframe 12.12 Zebra Medical Vision 12.13 Qventus 12.14 Sentrian 12.15 Health Fidelity Contact Us: [email protected]: +1-646-845-9349 (US); Ph: +44 208 133 9349 (UK)
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Applications interact with each other through a number of APIs, legacy systems, and an increase in complexity from one day to the next.However, the increased complexity leads to a fair share of challenges that can be overcome by machine-based intelligence.As software development life cycles become more complex as day and delivery time decreases, testers need to provide feedback and evaluation to development teams promptly.Given the breakneck pace of new software and product launches, there is no way to test soberly and rigorously in this day and age.To Know More: How Much Does It Cost To Make A Mobile App 2020Releases that happen once a month are now done on a weekly basis and updates are a factor almost every day.After observing the hierarchy of controls, testers can create a technical map, looking at the AI Graphical User Interface (GUI) to obtain labels for various controls.Since testing is about verification of results, access to many areas of test data is essential.Interestingly, Google DeepMind has created an AI program that uses deep reinforcement learning to play video games, thereby generating a lot of test data.Below the line, the Artificial Intelligence test site will be able to track users who are doing exploratory testing, to evaluate and identify applications being tested using the human brain.By automating repetitive test cases and manual testing, testers can focus more on making data-driven connections and decisions.Finally, the limited time to test risk-based automation is a critical factor when it comes to helping users decide which tests to run to get the greatest coverage.
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Researchers from Harvard University and MIT-IBM Watson AI Lab have released Clevrer, a data set for evaluating AI models’ ability to recognize causal relationships and carry out reasoning.A paper sharing initial findings about the CoLlision Events for Video REpresentation and Reasoning (Clevrer) data set was published this week at the entirely digital International Conference of Representation Learning (ICLR).Clevrer builds on Clevr, a data set released in 2016 by a team from Stanford University and Facebook AI Research, including ImageNet creator Dr. Fei-Fei Li, for analyzing the visual reasoning abilities of neural networks.Clevrer cocreators like Chuang Gan of MIT-IBM Watson Lab and Pushmeet Kohli of Deepmind introduced Neuro-Symbolic Concept Learner (NS-DR), a neuralsymbolic model applied to Clevr at ICLR one year ago.“We present a systematic study of temporal and causal reasoning in videos.This profound and challenging problem deeply rooted to the fundamentals of human intelligence has just begun to be studied with ‘modern’ AI tools,” the paper reads.“Our newly introduced Clevrer data set and the NS-DR model are preliminary steps toward this direction.”The data set includes 20,000 synthetic videos of colliding objects on a tabletop created with the Bullet physics simulator, together with a natural language data set of questions and answers about objects in videos.The more than 300,000 questions and answers are categorized as descriptive, explanatory, predictive, and counterfactual.MIT-IBM Watson Lab director David Cox told VentureBeat in an interview that he believes the data set can make progress toward creating hybrid AI that combines neural networks and symbolic AI.
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AI Ops Platform Market is mostly known as an umbrella platform, where it automatically identify and resolves the IT issues by using big data analytics, machine learning and other artificial intelligence technologies.The AI Ops brings three capabilities to the enterprises by bringing down the IT system alerts, recognizes the serious trouble in faster and greater accuracy than humans and brings interaction between the data centers groups and teams.In addition, it is applicable in proper data storage, protection, retention and secure data.The Global AI Ops Platform Market is expected to reach USD 18.51 billion by 2025 from USD 1.76 billion in 2017 and is projected to grow at a CAGR of 34.2% in the forecast period of 2018 to 2025.The upcoming market report contains data for historic year 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025.Get Sample Report at :https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-ai-ops-platform-market Competitive Analysis: Global  AI Ops Platform   MarketThe renowned players in Global AI Ops platform market Micro Focus , CA Technologies , BMC Software, Inc. , Moogsoft, Citrix Systems, Inc., New Relic, Inc., AppDynamics., Dynatrace LLC, SolarWinds Worldwide, LLC,  Sumo Logic, DeepMind Technologies Limited, iCarbonX, Next IT, Salesforce, ViSenze, AIBrain, ANKI., LogRhythm, Inc., TABLEAU SOFTWARE, Datadog, Cloudera, Palo Alto Networks, Inc. and many more.Key Pointers Covered in the Global  AI Ops Platform Market Trends and Forecast to 2026Global  AI Ops Platform Market New Sales VolumesGlobal  AI Ops Platform Market Replacement Sales VolumesGlobal  AI Ops Platform Market Installed BaseGlobal  AI Ops Platform Market By BrandsGlobal  AI Ops Platform Market SizeGlobal  AI Ops Platform Market Procedure VolumesGlobal  AI Ops Platform Market Product Price AnalysisGlobal  AI Ops Platform Market Healthcare OutcomesGlobal  AI Ops Platform Market Cost of Care AnalysisGlobal  AI Ops Platform Market Regulatory Framework and ChangesGlobal  AI Ops Platform Market Prices and Reimbursement AnalysisGlobal  AI Ops Platform Market Shares in Different RegionsRecent Developments for Global  AI Ops Platform Market CompetitorsGlobal  AI Ops Platform Market Upcoming ApplicationsGlobal  AI Ops Platform Market Innovators StudyGet Detailed TOC:https://www.databridgemarketresearch.com/toc/?dbmr=global-ai-ops-platform-market Market Segmentation: Global AI Ops Platform MarketThe global AI ops platform market is segmented based on component, organization size, deployment mode, application and end user and geographical segments.Based on Component, the market is segmented into platforms and services.Based on Organization Size, the market is segmented into small and mid-size companies and large enterprises.Based on Deployment Mode, the market is segmented on premises and cloud.Based on Application, the market is segmented into real-time analytics, application performance management, infrastructure management, network and security management.Based on End User the market is segmented into healthcare and life sciences, retail and consumer goods, IT and telecom, government.Based on geography, the market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific and Middle East & Africa.Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, South Africa, and Brazil among others.
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Coronavirus: DeepMind is currently using AI to study and understand the coronavirus in order to speed up the efforts to develop a vaccine.
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The idea that both computers or software programs can learn and make decisions is very important and is important for us to know as their processes grow exponentially over time.Because of these two skills, AI systems can now accomplish many tasks that were once assigned to humans.Over the past few years, AI technology has been booming.AI-based systems can now help humans benefit from significant improvements and increased efficiency in every area of life.As the development of AI continues to grow, it will change our way of life and work more and more.Now, let’s look at which areas of AI will have a huge impact in 2020.Also Read: Top 46 Artificial Intelligence Companies You Must Know in 2020AI in RetailIn recent years, technology has played a key role in the retail business, as e-commerce solutions have been developed and small, local stores that lack effective online strategies have been forced out of business.In fact, the U.S.In 2015, the bankruptcy of retail stores increased significantly from 2015 to 2019, 81 large retail companies filed for bankruptcy.Amazon is a strong player in the online retail market, but it has begun to enter physical retail stores using new models that incorporate artificial intelligence technologies.Over the next few years, we will see grocery stores offering a blend of automated technology, and a few human helpers to make sure everything runs smoothly for shoppers.Along with automated payment systems and robots, biometrics is another way that AI can change the retail store management process.Currently, biometric technologies are mainly used to scan fingerprints on mobile phones and airports.However, facial recognition tools are gaining momentum in the market and will be implemented in stores in the future, when you examine different products you will analyze your facial expressions and create personalized promotions based on sensor observations.Also Read: What Artificial Intelligence Can Do For Retail Industry?Robotic Process AutomationAutomation, Employment, and Productivity Report, based on some expert's study, predicts that by 2055 half of our work will be done by a robot.This includes jobs in the health care and financial industries, both of which rely on analytics and trends.In finance, the American investment bank Goldman Sachs Group, which once hired 600 merchants in its New York office, now performs the same tasks with only two human traders and a range of AI tools.Also Read: Do You Know The Difference Between Data Analytics & Machine Learning?AI will make healthcare more accurateThe contributions that artificial intelligence brings to the health care industry will change how the medical profession works in groundbreaking ways, enabling people around the world to find safe and effective care and make it easier to prevent and cure diseases.Traditionally, the analysis of health records, medical literature, and historical trends is very time consuming, but these types of tasks are perfectly suited to AI tools.In 2014, Google acquired DeepMind, an AI lab based in the UK, another AI-based assistant used in health care.
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The market value of AI in the healthcare industry is expected to reach 6 6.6 billion by 2021.AI technology is also rapidly entering hospitals.AI applications are concentrated in three major investment fields: digitization, engagement, and diagnostics.Looking at some examples of artificial intelligence in health care, it is clear that there are tremendous advances in the inclusion of AI in medical services.Let’s explore some amazing applications of AI that revolutionize health care.Also Read: The Future of Healthcare Sector Will be Around “AI”Robot DoctorsNothing is more exciting than AI robots.However, these are not human-like droids from sci-fi films.the same year, the first semi-automated surgical robot was used to stitch narrow blood vessels up to 0.03 mm.Clinical DiagnosisAI algorithms diagnose diseases faster and more accurately than physicians.They are particularly successful in detecting diseases from image-based test results.Late last year, Google’s DeepMind trained a neural network to accurately diagnose 50 types of eye diseases by analyzing 3D rental scans.Some types of cancer, such as various types of melanoma, are difficult to detect at an early stage.AI algorithms can scan and analyze biopsy images, and MRI scans 1,000 times faster than doctors.
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Cognitive Computing Technology Market Overview:Market Research Future (MRFR) announces the publication of its half-cooked research report – global cognitive computing technology market 2017-2023According to the analysis of Market Research Future, the global cognitive computing technology market is expected to register a CAGR of 35% over the forecast period, 2017-2023.According to Market Research Future, the global cognitive computing technology market has been segmented into the technology, organization size, end-user, deployment, and regionCompetitive Dashboard:The top players dominating the global Cognitive Computing Technology Market comprises Expert System S.p.A. (Italy), SparkCognition Inc (U.S.), International Business Machines Corporation (U.S.), Microsoft Corporation (U.S.), DeepMind Technologies Limited (U.K), Numenta (U.S.), CustomerMatrix Inc (U.S.), Cisco Systems, Inc (U.S.), Hewlett Packard Enterprise (U.S.), CognitiveScale (U.S.), Google, Inc (U.S.), Saffron Technology, Inc (U.S.), Palantir Technologies (U.S.), Enterra Solutions LLC (U.S.), ColdLight Solutions (U.S.), Airware (U.S.), Vicarious (U.S.), DataRobot (U.S.), DigitalGenius (U.K), Cylance (U.S.), Ross Intelligence (U.S.), Planet Labs (U.S.), Orbital Insight (U.S.), Darktrace (U.K), Vicarious (U.S.), Indico (U.S.), and Cyberlytic (U.K).Request a Free Sample @ https://www.marketresearchfuture.com/sample_request/1533 Market Potential and Pitfalls:The cognitive computing technology market is gaining huge attention in the global market due to the advancements in cognitive computing, which has resulted in its higher adoption.The rising number of large, unstructured, and complex sets of data, and advancements in the computing platforms like mobile, cloud, and data analytics are some of the major factors contributing to the growth of the cognitive computing technology market.The impact of cognitive computing on traditional business applications along with the dearth of awareness among the SMEs, especially in the developing economies are certain factors curbing the growth of the market across the globe.Cognitive Computing Technology Market Segmental Analysis:By Technology, the market is segmented into natural language processing (NLP), machine learning, automated reasoning, and others.Cognitive computing solution helps in maintaining the data loads thereby reducing the operation to a fraction of the time, and with much higher reliability.On the basis of deployment, the market is segmented into on-premises, on-cloud.
Graphcore, a U.K.-based company developing accelerators for AI workloads, this morning announced a milestone: Its Intelligence Processing Units (IPUs) have launched on Azure.It marks the first time a large-scale cloud vendor — Microsoft — has made publicly available Graphcore’s chips.IPUs on Azure are open for customer sign-up, Graphcore says, with access prioritized for those “focused on pushing the boundaries of [natural language processing]” and “developing new breakthroughs in machine intelligence.”By way of refresher, Graphcore — which was founded in 2016 by Simon Knowles and Nigel Toon — has raised $310 million to date from Robert Bosch Venture Capital, Samsung, Dell Technologies Capital, Amadeus Capital Partners, C4 Ventures, Draper Esprit, Foundation Capital, Pitango Capital, and AI luminaries Arm cofounder Hermann Hauser and DeepMind cofounder Demis Hassabis at a $1.5 billion valuation.Over this period, the Microsoft team, led by Marc Tremblay, distinguished engineer, has been developing systems for Azure and has been enhancing advanced machine vision and natural language processing models on IPUs,” said Toon.“We have been working extensively with a number of leading early-access customers and partners for some time to ensure that [these products are] ready for general release.”
Following a controversial data-sharing project within the National Health Service (NHS) in the UK, the search engine giant has partnered with the second-largest health system in the United States, St Louis-based Ascension, to collect and analyze the health records of millions of patients.According to a report in the Wall Street Journal, which claims to have seen confidential internal documents confirming the move, Google already has the personal health information of millions of Americans across 21 states in a database.The project is codenamed Project Nightingale and according to the WSJ, over 150 Google employees have access to the records of tens of millions of patients.But Google is relying on a legal justification that says hospitals (under the Health Insurance Portability and Accountability Act of 1996) are allowed to share data without telling patients if that data is used to “only to help the covered entity carry out its health care functions.”Google is using the data - which covers everything from lab results to doctor diagnoses to hospitalization records and connects it to patient names and their dates of birth - to develop new software that purports to use artificial intelligence and machine learning to provide valuable insights into health issues and even predict future health issues for individuals.The whole approach may seem oddly familiar to Reg readers: we have extensively covered an almost identical scheme in the UK called DeepMind in which Google was found to be storing and analyzing data on over a million patients following a data-sharing agreement with the Royal Free Hospital.
AlphaStar, an artificial intelligence program powered by Google’s DeepMind, was present at BlizzCon 2019, with the goal of beating any human that tried to go up against it in StarCraft II.Blizzard set up computers at the Blizzard Arcade section of BlizzCon 2019, which ran from November 1 to November 2 at the Anaheim Convention Center, for attendees to try to beat AlphaStar.The catch, however, was that the A.I.program is nearly impossible to beat at the real-time strategy game.AlphaStar has achieved grandmaster status in StarCraft II for all three races of Terran, Protoss, and Zerg, which means that it is capable of beating 99.8% of all ranked human players.Making the feat even more impressive is that the A.I.
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