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For decades, scientists have been giddy and citizens have been fearful of the power of computers. In 1965 Herbert Simon, a Nobel laureate in economics and also a winner of the Turing Award (considered “The Nobel Prize of computing”), predicted that “machines will be capable, within 20 years, of doing any work a man can do.” His misplaced faith in computers is hardly unique. Sixty-seven years later, we are still waiting for computers to become our slaves and masters.

Businesses have spent hundreds of billions of dollars on AI moonshots that have crashed and burned. IBM’s “Dr. Watson” was supposed to revolutionize health care and “eradicate cancer.” Eight years later, after burning through $15 billion with no demonstrable successes, IBM fired Dr. Watson.

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In 2016 Turing Award Winner Geoffrey Hinton advised that “We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.” Six years later, the number of radiologists has gone up, not down. Researchers have spent billions of dollars working on thousands of radiology image-recognition algorithms that are not as good as human radiologists.

What about those self-driving vehicles, promised by many including Elon Musk in his 2016 boast that “I really consider autonomous driving a solved problem. I think we are probably less than two years away.” Six years later, the most advanced self-driving vehicles are arguably Waymos in San Francisco, which only operate between 10 p.m. and 6 a.m. on the least crowded roads and still have accidents and cause traffic tie-ups. They are a long way from successfully operating in downtown traffic during the middle of the day at a required 99.9999% level of proficiency.

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The list goes on. Zillow’s house-flipping misadventure lost billions of dollars trying to revolutionize home-buying before they shuttered it. Carvana’s car-flipping gambit still loses billions.

We have argued for years that we should be developing AI that makes people more productive instead of trying to replace people. Computers have wondrous memories, make calculations that are lightning-fast and error-free, and are tireless, but humans have the real-world experience, common sense, wisdom and critical thinking skills that computers lack. Together, they can do more than either could do on their own.

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Effective augmentation appears to be finally happening with medical images. A large-scale study just published in Lancet Digital Health is the first to directly compare AI cancer screening when used alone or to assist humans. The software comes from a German startup, Vara, whose AI is already used in more than 25% of Germany’s breast cancer screening centers.

Researchers from Vara, Essen University and the Memorial Sloan Kettering Cancer Center trained the algorithm on more than 367,000 mammograms, and then tested it on 82,851 mammograms that had been held back for that purpose.

In the first strategy, the algorithm was used alone to analyze the 82,851 mammograms. In the second strategy, the algorithm separated the mammograms into three groups: clearly cancer, clearly no cancer, and uncertain. The uncertain mammograms were then sent to board-certified radiologists who were given no information about the AI diagnosis.

Doctors and AI working together turned out to be better than either working alone. The AI pre-screening reduced the number of images the doctors examined by 37% while lowering the false-positive and false-negative rates by about a third compared to AI alone and by 14%-20% compared to doctors alone. Less work and better results!

As machine learning improves, the AI analysis of X-rays will no doubt become more efficient and accurate. There will come a time when AI can be trusted to work alone. However, that time is likely to be decades in the future and attempts to jump directly to that point are dangerous.

We are optimistic that the productivity of many workers can be improved by similar augmentation strategies — not to mention the fact that many of the tasks that computers excel at are dreadful drudgery; e.g., legal research, inventory control and statistical calculations. But far too many attempts to replace humans entirely have not only been an enormous waste of resources but have also undermined the credibility of AI research. The last thing we need is another AI winter where funding dries up, resources are diverted and the tremendous potential of these technologies are put on hold. We are optimistic that the accumulating failures of moonshots and successes of augmentation strategies will change the way that we think about AI.

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Funk is an independent technology consultant who previously taught at National University of Singapore, Hitotsubashi and Kobe Universities in Japan, and Penn State, where he taught courses on the economics of new technologies. Smith is the author of ”The AI Delusion” and co-author (with Jay Cordes) of ”The 9 Pitfalls of Data Science” and ”The Phantom Pattern Problem.”

Fri, 05 Aug 2022 21:00:00 -0500 en-US text/html https://www.nydailynews.com/opinion/ny-oped-the-right-and-wrong-way-to-use-artificial-intelligence-20220806-txybtmlcwfgddnfdozvynz5u64-story.html
Killexams : Machine Learning Artificial intelligence Market Size from 2022 to 2028

The MarketWatch News Department was not involved in the creation of this content.

Jul 26, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

The "Machine Learning Artificial intelligenceMarket research report" profile investigates the company's business structure, operations, important products and services, prospects, locations and subsidiaries, key executives' biographies, and key rivals 2022 to 2028. The revenue trends, profitability, growth, margins and returns, liquidity and leverage, financial condition, and efficiency ratios are all included in this Machine Learning Artificial intelligence Market study. For this study on the Machine Learning Artificial intelligence market, the researchers used modern primary and secondary research approaches and technologies.Here is a list of the best key players Listed in the Machine Learning Artificial intelligence Market report is AIBrain,Amazon,Anki,CloudMinds,Deepmind,Google,Facebook,IBM,Iris AI,Apple,Luminoso,Qualcomm.

The global Machine Learning Artificial intelligence market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for trial Report).

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The assembly, revenue, price, market share, and rate of growth of each type are displayed in the Machine Learning Artificial intelligence market industry research report, which is divided into Deep Learning,Natural Language Processing,Machine Vision,Others. The status and outlook for main applications/end users, consumption (sales), market share, and rate of growth for each application, including Automotive & Transportation,Agriculture,Manufacturing,Others, are all included in this Machine Learning Artificial intelligence market research report. North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea is a list of regions covered in the Machine Learning Artificial intelligence Market Report. The report has a page count of 129.

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The top competitors in the Machine Learning Artificial intelligence Market, as highlighted in the report, are:

  • AIBrain
  • Amazon
  • Anki
  • CloudMinds
  • Deepmind
  • Google
  • Facebook
  • IBM
  • Iris AI
  • Apple
  • Luminoso
  • Qualcomm

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Market Segmentation

The worldwide Machine Learning Artificial intelligence Market is categorized on Component, Deployment, Application, and Region.

The Machine Learning Artificial intelligence Market Analysis by types is segmented into:

  • Deep Learning
  • Natural Language Processing
  • Machine Vision
  • Others

The Machine Learning Artificial intelligence Market Industry Research by Application is segmented into:

  • Automotive & Transportation
  • Agriculture
  • Manufacturing
  • Others

In terms of Region, the Machine Learning Artificial intelligence Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

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The Machine Learning Artificial intelligence market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

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Highlights of The Machine Learning Artificial intelligence Market Report

The Machine Learning Artificial intelligence Market Industry Research Report contains:

  • International and domestic market segmentation
  • Major changes in the Machine Learning Artificial intelligence market research structure
  • Regional and country-level competitive analysis
  • The Machine Learning Artificial intelligence market share, size, and growth comprehension analysis
  • The most common growth strategies employed by business owners

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COVID 19 Impact Analysis:

The Machine Learning Artificial intelligence market industry trend segmentation is based on various characteristics such as kinds, applications, and regions. In addition, the Machine Learning Artificial intelligence market research report evaluates the impact of the new COVID-19 pandemic on the Machine Learning Artificial intelligence market research and provides a unique assessment of the predicted market changes during the forecasted time frame. Machine Learning Artificial intelligence Market Share, Stock Determinations and Figures, contact information, Sales, Capacity, Production, Price, Cost, Revenue, and Business Profiles are all encased within the Machine Learning Artificial intelligence market report. Over the forecast period, the Machine Learning Artificial intelligence market research report comprises market segmentation, competitive analysis, and regional study.

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Machine Learning Artificial intelligence Market Size and Industry Challenges:

The Machine Learning Artificial intelligence market research report includes Company Profile, Product Specifications, Revenue, Price, and Gross Margin Sales, as well as a comprehensive analysis of the market competitive landscape and detailed information on vendors, as well as comprehensive details of factors that will challenge the growth of major market vendors.

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Killexams : How to Use AI in Video Workflows

The key to understanding how to best use artificial intelligence in video workflows is knowing when to implement it for the appropriate applications. Ethan Drellinger, Client Solutions Engineer, IBM Watson Advertising and The Weather Company, Carlos Hernandez, Chief Revenue Officer, SSIMWAVE, and Gordon Brooks, Executive Chairman and CEO, Zixi, discuss the distinctions between automation and AI, along with how AI can be applied to automation to help maximize and streamline production workflows.

“When I talk to media companies about applying AI, I start at the bottom of a workflow and take those mundane tasks and start to think about how you can express them with AI,” Drellinger says. He believes that letting machines analyze data is the ideal approach, while people such as Producers should handle creative content and cognitive-oriented tasks. “I think in terms of the bottom rung, really start low and build on top of that and take those low-lying tasks and start to automate them,” he says.

Hernandez asks where Drellinger draws the distinction between automation and AI. “Not all automation is AI-driven,” he says. “So where would you say it makes sense?”

“AI can feed automation, certainly, but automation just kind of runs on its own,” Drellinger says. “We have some products in our stack where you can go into an app and the app will start to understand what your preferences are because of AI.” This application of AI can be very useful for end users. “'That should go above this because that person likes pollen count more than precipitation forecast,’” he cites as an example.

Hernandez notes that automation and AI are often confused in the market. “We believe that in some cases, machine learning processes make sense, but in others, knowledge-based [uses] are the most appropriate. The key about artificial intelligence is knowing where it has value and where it doesn't," he says.

“It's a tool, like anything else, right?” Drellinger says. “When you do a home repair job and you need a Phillips head screwdriver, you don't pull out a flathead screwdriver, you get your Phillips head screwdriver. It's a tool to use and you need to know when to use it.”

Gordon Brooks concurs and sums up these points. “It goes through the concept of advanced analytics. It's just one of the tools in that toolbox of doing those advanced analytics, which then drives automation and other things. So, I agree.”

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Biggest Challenges of Moving from On-Prem to Cloud Video Workflows

Some streaming pros say cloud production has made 5-10 years of progress in the two years since the pandemic shook live production to its core, but when will cloud offer the sort of no-latency communication, preview, and replay producers expect and rely on in on-prem workflows? And will it require a paradigm shift in the way producers think about hardware purchases and usage? Live X's Corey Behnke, CNN's Ben Ratner, LiveU's Mike Savello, and Signiant's Jon Finegold discuss the challenges of cloud migration and the current state of cloud in this clip from Streaming Media East 2022.

The Biggest Misconceptions About AI Video Workflow Automation

Zixi VP of Business Development Eric Bolten discusses some of the prevailing misconceptions about AI/ML video workflow automation in this clip from Streaming Media Connect 2022.

Mon, 08 Aug 2022 01:00:00 -0500 text/html https://www.streamingmedia.com/Articles/Editorial/Short-Cuts/How-to-Use-AI-in-Video-Workflows-154284.aspx
Killexams : IBM’s gobbling up AI companies left and right — and we love it

Big Blue’s been on a buying spree lately with Databand.ai, a big data startup, becoming its latest acquisition. Don’t blink. If you do, you might miss another huge IBM buyout.

Up front: Big data is a big deal. Less than a decade ago, many businesses were manually entering data into spreadsheets to meet their insight needs. Today, even the most modest startups can benefit from deep analytics.

Unfortunately, the landscape of companies that provide targeted services for a spectrum of industries is somewhat barren.

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Simply put, you can’t just integrate a bunch of generic AI models into your IT stack and hope to magically pipeline solutions to your company’s problems.

It takes infrastructure and expertise to turn your hoard of data into action points.

Background: IBM’s spent over a century developing the infrastructure. But expertise is a moving target. To keep up with the modern influx of deep learning and data solutions in a period of technological turbulence, the company’s new CEO has opened up the corporate wallet in hopes of building a data-parsing juggernaut.

Per a recent IBM blog post:

Databand.ai is IBM’s fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

This particular acquisition shores up IBM’s ability to provide “data observability” solutions for its clients and customers.

In other words, Databand.ai comes with a suite of products and a team of employees who know how to turn giant troves of data into useful insights.

According to IBM:

A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time.

Quick take: There’s a lot more to the world of big data than you might think. With this acquisition, IBM not only gets software solutions it can integrate to its current cornucopia of management and analytics tools, but it also gets a team that’s ready to hit the ground running for the company’s clients.

Databand.ai just finished a funding round prior to the acquisition wherein it raised over $14 million — that’s a pretty good indication the company’s on solid footing.

Here at Neural, we love it. Databand’s joining a company whose CEO has their finger firmly on the pulse of big data and IBM’s expanding its already industry-leading portfolio of AI-powered solutions.

Sun, 17 Jul 2022 05:58:00 -0500 en text/html https://thenextweb.com/news/ibm-positions-itself-as-global-big-data-boss-with-latest-acquisition
Killexams : Edology partners with IBM to launch Post Graduate Certificate Program in Data Science

Gurugram (Haryana) [India], July 30 (ANI/NewsVoir): Edology has announced a partnership with IBM, one of the world's top leading and reputed corporations, to introduce its Post Graduate Certificate Program in Data Science for working professionals and everyone wanting to enter the field of Data Science. Developed by IBM inventors and experts who hold numerous patents in the field of Data Science, this is the first IBM programme that has been completely designed by IBM and is being delivered by its faculty.

"The programme for the Edology x IBM Data Science course is a very special offering from IBM, and this is one-of-a-kind initiative," according to Hari Ramasubramanian, Leader, Business Development and Academia Relationships, IBM Expert Labs, India/South Asia. He further added, "There is a strong demand for skilled technology and trained professionals across the industry. Data science is not confined to IT. It includes all the verticals one can imagine-from board meetings to sports, data science brings a lot of value to organizations worldwide. For students, as well as professionals with experience, if you want to fast track your career on to the next level, this is the course you should be doing."

"The IBM Data Science certificate program through the Edology platform, will equip to adapt to the dynamics in the industry and drive technology innovation," said, Vithal Madyalkar, Program Director, IBM Innovation Centre for Education, India/South Asia. "The Data Science course modules will provide deep practical knowledge, coupled with broad-based industry alignment, interaction, talent discoverability as well as excellence in their professional practice."

A global Ed-Tech company, Edology helps students and professionals all around the world advance their careers in a variety of subjects, including data science, artificial intelligence, machine learning, cyber security, and more.

Unique Offerings of the IBM x Edology PG Certificate Programme in Data Science:

- 100+ hours of Live classes by IBM experts

- Globally recognized IBM digital badge

- Job opportunities with 300+ corporate partners

- Edology-IBM Award for Top Performers

- 1 on 1 mentorship from industry experts

- 1 day networking session with IBM team

- Guaranteed interview with IBM for top performers in each cohort

- Dedicated career assistance team

Sumanth Palepu, the Business Head at Edology, states, "Statistical estimates reveal that the worldwide market size for Data Science and analytics is anticipated to reach around a whopping $450 billion by 2025, which also means that the rivalry would be quite severe at the employee level, the competition will be very fierce. Thus, this collaboration with IBM is now more essential than ever, so that we are collectively able to deliver advanced level teaching to the students and working professionals and they get first-hand industry knowledge with our IBM experts."

www.youtube.com/watch?v=rjWGU_k2Dhg

Edology is a Global Ed-Tech Brand that provides industry-powered education and skills to students and professionals across the world, to help them achieve fast-track career growth. Launched in 2017, Edology connects professionals from across the globe with higher education programmes in the fields of law, finance, accounting, business, computing, marketing, fashion, criminology, psychology, and more.

It's a part of Global University Systems (GUS), an international network of higher-education institutions, brought together by a shared passion of providing industry-driven global education accessible and affordable. All the programs of Edology are built with the objective of providing its learners career enhancement and strong CV credentials, along with a quality learning experience.

The courses offered by Edology include Data Science, Certification in AI and Machine Learning, Data Analytics, PGP in International Business, PGP in Renewable Energy Management, PGP in Oil and Gas Management among others. These offerings are done through hands-on industry projects, interactive live classes, global peer-to-peer learning and other facilities.

This story is provided by NewsVoir. ANI will not be responsible in any way for the content of this article. (ANI/NewsVoir)

Fri, 29 Jul 2022 21:31:00 -0500 en text/html https://www.bignewsnetwork.com/news/272637512/edology-partners-with-ibm-to-launch-post-graduate-certificate-program-in-data-science
Killexams : IBM to up skill 100,000 Saudis in Five Years in AI, ML, and more No result found, try new keyword!The initiatives include artificial intelligence, Machine Learning, cybersecurity, joint research, innovation accelerators, policy laboratory, IBM’s cloud and open tech centers for software ... Sun, 17 Jul 2022 17:41:00 -0500 text/html https://www.itp.net/emergent-tech/ibm-to-up-skill-100000-saudis-in-five-years-in-ai-ml-and-more Killexams : Learning with the Internet of Things and Artificial Intelligence: harnessing their potential No result found, try new keyword!According to the e-learning industry, over 47% of learning management tools will incorporate artificial intelligence within the next three years. Fri, 29 Jul 2022 18:19:58 -0500 en-in text/html https://www.msn.com/en-in/news/other/learning-with-the-internet-of-things-and-artificial-intelligence-harnessing-their-potential/ar-AA107Gmq Killexams : IBM extends Power10 server lineup for enterprise use cases

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IBM is looking to grow its enterprise server business with the expansion of its Power10 portfolio announced today.

IBM Power is a RISC (reduced instruction set computer) based chip architecture that is competitive with other chip architectures including x86 from Intel and AMD. IBM’s Power hardware has been used for decades for running IBM’s AIX Unix operating system, as well as the IBM i operating system that was once known as the AS/400. In more recent years, Power has increasingly been used for Linux and specifically in support of Red Hat and its OpenShift Kubernetes platform that enables organizations to run containers and microservices.

The IBM Power10 processor was announced in August 2020, with the first server platform, the E1080 server, coming a year later in September 2021. Now IBM is expanding its Power10 lineup with four new systems, including the Power S1014, S1024, S1022 and E1050, which are being positioned by IBM to help solve enterprise use cases, including the growing need for machine learning (ML) and artificial intelligence (AI).

What runs on IBM Power servers?

Usage of IBM’s Power servers could well be shifting into territory that Intel today still dominates.

Steve Sibley, vp, IBM Power product management, told VentureBeat that approximately 60% of Power workloads are currently running AIX Unix. The IBM i operating system is on approximately 20% of workloads. Linux makes up the remaining 20% and is on a growth trajectory.

IBM owns Red Hat, which has its namesake Linux operating system supported on Power, alongside the OpenShift platform. Sibley noted that IBM has optimized its new Power10 system for Red Hat OpenShift.

“We’ve been able to demonstrate that you can deploy OpenShift on Power at less than half the cost of an Intel stack with OpenShift because of IBM’s container density and throughput that we have within the system,” Sibley said.

A look inside IBM’s four new Power servers

Across the new servers, the ability to access more memory at greater speed than previous generations of Power servers is a key feature. The improved memory is enabled by support of the Open Memory Interface (OMI) specification that IBM helped to develop, and is part of the OpenCAPI Consortium.

“We have Open Memory Interface technology that provides increased bandwidth but also reliability for memory,” Sibley said. “Memory is one of the common areas of failure in a system, particularly when you have lots of it.”

The new servers announced by IBM all use technology from the open-source OpenBMC project that IBM helps to lead. OpenBMC provides secure code for managing the baseboard of the server in an optimized approach for scalability and performance.

E1050

Among the new servers announced today by IBM is the E1050, which is a 4RU (4 rack unit) sized server, with 4 CPU sockets, that can scale up to 16TB of memory, helping to serve large data- and memory-intensive workloads.

S1014 and S1024

The S1014 and the S1024 are also both 4RU systems, with the S1014 providing a single CPU socket and the S1024 integrating a dual-socket design. The S1014 can scale up to 2TB of memory, while the S1024 supports up to 8TB.

S1022

Rounding out the new services is the S1022, which is a 1RU server that IBM is positioning as an ideal platform for OpenShift container-based workloads.

Bringing more Power to AI and ML

AI and ML workloads are a particularly good use case for all the Power10 systems, thanks to optimizations that IBM has built into the chip architecture.

Sibley explained that all Power10 chips benefit from IBM’s Matrix Match Acceleration (MMA) capability. The enterprise use cases that Power10-based servers can help to support include organizations that are looking to build out risk analytics, fraud detection and supply chain forecasting AI models, among others. 

IBM’s Power10 systems support and have been optimized for multiple popular open-source machine learning frameworks including PyTorch and TensorFlow.

“The way we see AI emerging is that a vast majority of AI in the future will be done on the CPU from an inference standpoint,” Sibley said.

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Mon, 11 Jul 2022 09:01:00 -0500 Sean Michael Kerner en-US text/html https://venturebeat.com/2022/07/11/ibm-extends-power10-server-lineup-for-enterprise-use-cases/
Killexams : How one research center is driving AI innovation for academics and enterprise partners alike

A new research center for artificial intelligence and machine learning has sprung up at the University of Oregon, thanks to a collaboration between IBM and the Oregon Advanced Computing Institute for Science and Society. The Oregon Center for Enterprise AI eXchange (CE-AIX) leverages the university's high-performance computing technology and enterprise servers from IBM to create new training opportunities and collaborations with industry.

"The new lab facility will be a valuable resource for worldwide universities and enterprise companies wanting to take advantage of IBM Enterprise Servers POWER9 and POWER10 combined with IBM Spectrum storage, along with AIX and RHEL with OpenShift," said Ganesan Narayanasamy, IBM's leader for academic and research worldwide.

Narayanasamy said the new center extends state-of-the-art facilities and other Silicon Valley-style services to researchers, system developers, and other users looking to take advantage of open-source high-performance computing resources.  The center has already helped thousands of students gain exposure and practice with its high-performance computing training, and it is expected to serve as a global hub that will help prepare the next generation of computer scientists, according to the center's director Sameer Shende.

"We aim to expand the skillset of researchers and students in the area of commercial application of artificial intelligence and machine learning, as well as high-performance computing technologies," Shende said.

Thanks to a long-term loan agreement with IBM, the center has access to powerful enterprise servers and other capabilities. It was envisioned to bring together data scientists from businesses in different domains, such as financial services, manufacturing, and transportation, along with IBM research and development engineers, IBM partner data scientists, and university students and researchers.

The new center also has the potential to be leveraged by everyone from global transportation companies seeking to design more efficient trucking routes to clean energy firms looking to design better wind turbines based on models of airflow patterns. At the University of Oregon, there are potential applications in data science, machine learning, environmental hazards monitoring, and other emerging areas of research and innovation.

"Enterprise AI is a team sport," said Raj Krishnamurthy, an IBM chief architect for enterprise AI and co-director of the new center. "As businesses continue to operationalize AI in mission-critical systems, the use cases and methodologies developed from collaboration in this center will further promote the adoption of trusted AI techniques in the enterprise."

Ultimately, the center will contribute to the University of Oregon's overall research excellence, said AR Razdan, who serves as the university's vice president for research and innovation.

"The center marks another great step forward in [the university's] ongoing efforts to bring together interdisciplinary teams of researchers and innovators," Razdan said.

This post was created by IBM with Insider Studios.

Sun, 24 Jul 2022 12:00:00 -0500 en-US text/html https://www.businessinsider.com/sc/how-one-tech-partnership-is-making-ai-research-possible
Killexams : Would "artificial superintelligence" lead to the end of life on Earth? It's not a stupid question No result found, try new keyword!A supercharged AI will be so much smarter than us that there's no way to calculate the risk. Let's call time out ... Sat, 06 Aug 2022 04:00:01 -0500 en-us text/html https://www.msn.com/en-us/news/technology/would-artificial-superintelligence-lead-to-the-end-of-life-on-earth-it-s-not-a-stupid-question/ar-AA10nP4x
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