Los Angeles, United State: QY Research recently published a research report titled, Global Machine Learning Chips Market Research Report 2020-2026. The research report attempts to give a holistic overview of the Machine Learning Chips market by keeping the information simple, relevant, accurate, and to the point. The researchers have explained each aspect of the market thoroughmeticulous research and undivided attention to every topic. They have also provided data in statistical data to help readers understand the whole market. The Machine Learning Chips Market report further provides historic and forecast data generated through primary and secondary research of the region and their respective manufacturers.

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Global Machine Learning Chips Market report section gives special attention to the manufacturers in different regions that are expected to show a considerable expansion in their market share. Additionally, it underlines all the current and future trends that are being adopted by these manufacturers to boost their current market shares. This Machine Learning Chips Market report Understanding the various strategies being carried out by various manufacturers will help reader make right business decisions.

Key Players Mentioned in the Global Machine Learning Chips Market Research Report: Wave Computing, Graphcore, Google Inc, Intel Corporation, IBM Corporation, Nvidia Corporation, Qualcomm, Taiwan Semiconductor Manufacturing Machine Learning Chips

Global Machine Learning Chips Market Segmentation by Product: Neuromorphic Chip, Graphics Processing Unit (GPU) Chip, Flash Based Chip, Field Programmable Gate Array (FPGA) Chip, Other Machine Learning Chips

Global Machine Learning Chips Market Segmentation by Application: , Robotics Industry, Consumer Electronics, Automotive, Healthcare, Other

The Machine Learning Chips market is divided into the two important segments, product type segment and end user segment. In the product type segment it lists down all the products currently manufactured by the companies and their economic role in the Machine Learning Chips market. It also reports the new products that are currently being developed and their scope. Further, it presents a detailed understanding of the end users that are a governing force of the Machine Learning Chips market.

In this chapter of the Machine Learning Chips Market report, the researchers have explored the various regions that are expected to witness fruitful developments and make serious contributions to the markets burgeoning growth. Along with general statistical information, the Machine Learning Chips Market report has provided data of each region with respect to its revenue, productions, and presence of major manufacturers. The major regions which are covered in the Machine Learning Chips Market report includes North America, Europe, Central and South America, Asia Pacific, South Asia, the Middle East and Africa, GCC countries, and others.

Key questions answered in the report:

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Table of Content

1 Study Coverage1.1 Machine Learning Chips Product Introduction1.2 Key Market Segments in This Study1.3 Key Manufacturers Covered: Ranking of Global Top Machine Learning Chips Manufacturers by Revenue in 20191.4 Market by Type1.4.1 Global Machine Learning Chips Market Size Growth Rate by Type1.4.2 Neuromorphic Chip1.4.3 Graphics Processing Unit (GPU) Chip1.4.4 Flash Based Chip1.4.5 Field Programmable Gate Array (FPGA) Chip1.4.6 Other1.5 Market by Application1.5.1 Global Machine Learning Chips Market Size Growth Rate by Application1.5.2 Robotics Industry1.5.3 Consumer Electronics1.5.4 Automotive1.5.5 Healthcare1.5.6 Other1.6 Study Objectives1.7 Years Considered 2 Executive Summary2.1 Global Machine Learning Chips Market Size, Estimates and Forecasts2.1.1 Global Machine Learning Chips Revenue Estimates and Forecasts 2015-20262.1.2 Global Machine Learning Chips Production Capacity Estimates and Forecasts 2015-20262.1.3 Global Machine Learning Chips Production Estimates and Forecasts 2015-20262.2 Global Machine Learning Chips, Market Size by Producing Regions: 2015 VS 2020 VS 20262.3 Analysis of Competitive Landscape2.3.1 Manufacturers Market Concentration Ratio (CR5 and HHI)2.3.2 Global Machine Learning Chips Market Share by Company Type (Tier 1, Tier 2 and Tier 3)2.3.3 Global Machine Learning Chips Manufacturers Geographical Distribution2.4 Key Trends for Machine Learning Chips Markets & Products2.5 Primary Interviews with Key Machine Learning Chips Players (Opinion Leaders) 3 Market Size by Manufacturers3.1 Global Top Machine Learning Chips Manufacturers by Production Capacity3.1.1 Global Top Machine Learning Chips Manufacturers by Production Capacity (2015-2020)3.1.2 Global Top Machine Learning Chips Manufacturers by Production (2015-2020)3.1.3 Global Top Machine Learning Chips Manufacturers Market Share by Production3.2 Global Top Machine Learning Chips Manufacturers by Revenue3.2.1 Global Top Machine Learning Chips Manufacturers by Revenue (2015-2020)3.2.2 Global Top Machine Learning Chips Manufacturers Market Share by Revenue (2015-2020)3.2.3 Global Top 10 and Top 5 Companies by Machine Learning Chips Revenue in 20193.3 Global Machine Learning Chips Price by Manufacturers3.4 Mergers & Acquisitions, Expansion Plans 4 Machine Learning Chips Production by Regions4.1 Global Machine Learning Chips Historic Market Facts & Figures by Regions4.1.1 Global Top Machine Learning Chips Regions by Production (2015-2020)4.1.2 Global Top Machine Learning Chips Regions by Revenue (2015-2020)4.2 North America4.2.1 North America Machine Learning Chips Production (2015-2020)4.2.2 North America Machine Learning Chips Revenue (2015-2020)4.2.3 Key Players in North America4.2.4 North America Machine Learning Chips Import & Export (2015-2020)4.3 Europe4.3.1 Europe Machine Learning Chips Production (2015-2020)4.3.2 Europe Machine Learning Chips Revenue (2015-2020)4.3.3 Key Players in Europe4.3.4 Europe Machine Learning Chips Import & Export (2015-2020)4.4 China4.4.1 China Machine Learning Chips Production (2015-2020)4.4.2 China Machine Learning Chips Revenue (2015-2020)4.4.3 Key Players in China4.4.4 China Machine Learning Chips Import & Export (2015-2020)4.5 Japan4.5.1 Japan Machine Learning Chips Production (2015-2020)4.5.2 Japan Machine Learning Chips Revenue (2015-2020)4.5.3 Key Players in Japan4.5.4 Japan Machine Learning Chips Import & Export (2015-2020)4.6 South Korea4.6.1 South Korea Machine Learning Chips Production (2015-2020)4.6.2 South Korea Machine Learning Chips Revenue (2015-2020)4.6.3 Key Players in South Korea4.6.4 South Korea Machine Learning Chips Import & Export (2015-2020) 5 Machine Learning Chips Consumption by Region5.1 Global Top Machine Learning Chips Regions by Consumption5.1.1 Global Top Machine Learning Chips Regions by Consumption (2015-2020)5.1.2 Global Top Machine Learning Chips Regions Market Share by Consumption (2015-2020)5.2 North America5.2.1 North America Machine Learning Chips Consumption by Application5.2.2 North America Machine Learning Chips Consumption by Countries5.2.3 U.S.5.2.4 Canada5.3 Europe5.3.1 Europe Machine Learning Chips Consumption by Application5.3.2 Europe Machine Learning Chips Consumption by Countries5.3.3 Germany5.3.4 France5.3.5 U.K.5.3.6 Italy5.3.7 Russia5.4 Asia Pacific5.4.1 Asia Pacific Machine Learning Chips Consumption by Application5.4.2 Asia Pacific Machine Learning Chips Consumption by Regions5.4.3 China5.4.4 Japan5.4.5 South Korea5.4.6 India5.4.7 Australia5.4.8 Taiwan5.4.9 Indonesia5.4.10 Thailand5.4.11 Malaysia5.4.12 Philippines5.4.13 Vietnam5.5 Central & South America5.5.1 Central & South America Machine Learning Chips Consumption by Application5.5.2 Central & South America Machine Learning Chips Consumption by Country5.5.3 Mexico5.5.3 Brazil5.5.3 Argentina5.6 Middle East and Africa5.6.1 Middle East and Africa Machine Learning Chips Consumption by Application5.6.2 Middle East and Africa Machine Learning Chips Consumption by Countries5.6.3 Turkey5.6.4 Saudi Arabia5.6.5 U.A.E 6 Market Size by Type (2015-2026)6.1 Global Machine Learning Chips Market Size by Type (2015-2020)6.1.1 Global Machine Learning Chips Production by Type (2015-2020)6.1.2 Global Machine Learning Chips Revenue by Type (2015-2020)6.1.3 Machine Learning Chips Price by Type (2015-2020)6.2 Global Machine Learning Chips Market Forecast by Type (2021-2026)6.2.1 Global Machine Learning Chips Production Forecast by Type (2021-2026)6.2.2 Global Machine Learning Chips Revenue Forecast by Type (2021-2026)6.2.3 Global Machine Learning Chips Price Forecast by Type (2021-2026)6.3 Global Machine Learning Chips Market Share by Price Tier (2015-2020): Low-End, Mid-Range and High-End 7 Market Size by Application (2015-2026)7.2.1 Global Machine Learning Chips Consumption Historic Breakdown by Application (2015-2020)7.2.2 Global Machine Learning Chips Consumption Forecast by Application (2021-2026) 8 Corporate Profiles8.1 Wave Computing8.1.1 Wave Computing Corporation Information8.1.2 Wave Computing Overview8.1.3 Wave Computing Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.1.4 Wave Computing Product Description8.1.5 Wave Computing Related Developments8.2 Graphcore8.2.1 Graphcore Corporation Information8.2.2 Graphcore Overview8.2.3 Graphcore Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.2.4 Graphcore Product Description8.2.5 Graphcore Related Developments8.3 Google Inc8.3.1 Google Inc Corporation Information8.3.2 Google Inc Overview8.3.3 Google Inc Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.3.4 Google Inc Product Description8.3.5 Google Inc Related Developments8.4 Intel Corporation8.4.1 Intel Corporation Corporation Information8.4.2 Intel Corporation Overview8.4.3 Intel Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.4.4 Intel Corporation Product Description8.4.5 Intel Corporation Related Developments8.5 IBM Corporation8.5.1 IBM Corporation Corporation Information8.5.2 IBM Corporation Overview8.5.3 IBM Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.5.4 IBM Corporation Product Description8.5.5 IBM Corporation Related Developments8.6 Nvidia Corporation8.6.1 Nvidia Corporation Corporation Information8.6.2 Nvidia Corporation Overview8.6.3 Nvidia Corporation Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.6.4 Nvidia Corporation Product Description8.6.5 Nvidia Corporation Related Developments8.7 Qualcomm8.7.1 Qualcomm Corporation Information8.7.2 Qualcomm Overview8.7.3 Qualcomm Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.7.4 Qualcomm Product Description8.7.5 Qualcomm Related Developments8.8 Taiwan Semiconductor Manufacturing8.8.1 Taiwan Semiconductor Manufacturing Corporation Information8.8.2 Taiwan Semiconductor Manufacturing Overview8.8.3 Taiwan Semiconductor Manufacturing Production Capacity and Supply, Price, Revenue and Gross Margin (2015-2020)8.8.4 Taiwan Semiconductor Manufacturing Product Description8.8.5 Taiwan Semiconductor Manufacturing Related Developments 9 Machine Learning Chips Production Forecast by Regions9.1 Global Top Machine Learning Chips Regions Forecast by Revenue (2021-2026)9.2 Global Top Machine Learning Chips Regions Forecast by Production (2021-2026)9.3 Key Machine Learning Chips Production Regions Forecast9.3.1 North America9.3.2 Europe9.3.3 China9.3.4 Japan9.3.5 South Korea 10 Machine Learning Chips Consumption Forecast by Region10.1 Global Machine Learning Chips Consumption Forecast by Region (2021-2026)10.2 North America Machine Learning Chips Consumption Forecast by Region (2021-2026)10.3 Europe Machine Learning Chips Consumption Forecast by Region (2021-2026)10.4 Asia Pacific Machine Learning Chips Consumption Forecast by Region (2021-2026)10.5 Latin America Machine Learning Chips Consumption Forecast by Region (2021-2026)10.6 Middle East and Africa Machine Learning Chips Consumption Forecast by Region (2021-2026) 11 Value Chain and Sales Channels Analysis11.1 Value Chain Analysis11.2 Sales Channels Analysis11.2.1 Machine Learning Chips Sales Channels11.2.2 Machine Learning Chips Distributors11.3 Machine Learning Chips Customers 12 Market Opportunities & Challenges, Risks and Influences Factors Analysis12.1 Machine Learning Chips Industry12.2 Market Trends12.3 Market Opportunities and Drivers12.4 Market Challenges12.5 Machine Learning Chips Market Risks/Restraints12.6 Porters Five Forces Analysis 13 Key Finding in The Global Machine Learning Chips Study 14 Appendix14.1 Research Methodology14.1.1 Methodology/Research Approach14.1.2 Data Source14.2 Author Details14.3 Disclaimer

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QY Research established in 2007, focus on custom research, management consulting, IPO consulting, industry chain research, data base and seminar services. The company owned a large basic data base (such as National Bureau of statistics database, Customs import and export database, Industry Association Database etc), experts resources (included energy automotive chemical medical ICT consumer goods etc.

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Machine Learning Chips Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - 3rd Watch News

Skoltech scientists and their industry colleagues have found a way to use machine learning to accurately predict rock thermal conductivity. Credit: Pavel Odinev / Skoltech

Skoltech scientists and their industry colleagues have found a way to use machine learning to accurately predict rock thermal conductivity, a crucial parameter for enhanced oil recovery. The research, supported by Lukoil-Engineering LLC, was published in the Geophysical Journal International.

Rock thermal conductivity, or its ability to conduct heat, is key to both modeling a petroleum basin and designing enhanced oil recovery (EOR) methods, the so-called tertiary recovery that allows an oil field operator to extract significantly more crude oil than using basic methods. A common EOR method is thermal injection, where oil in the formation is heated by various means such as steam, and this method requires extensive knowledge of heat transfer processes within a reservoir.

For this, one would need to measure rock thermal conductivity directly in situ, but this has turned out to be a daunting task that has not yet produced satisfactory results usable in practice. So scientists and practitioners turned to indirect methods, which infer rock thermal conductivity from well-logging data that provides a high-resolution picture of vertical variations in rock physical properties.

Today, three core problems rule out any chance of measuring thermal conductivity directly within non-coring intervals. It is, firstly, the time required for measurements: petroleum engineers cannot let you put the well on hold for a long time, as it is economically unreasonable. Secondly, induced convection of drilling fluid drastically affects the results of measurements. And finally, there is the unstable shape of boreholes, which has to do with some technical aspects of measurements, Skoltech Ph.D. student and the papers first author Yury Meshalkin says.

Known well-log based methods can use regression equations or theoretical modeling, and both have their drawbacks having to do with data availability and nonlinearity in rock properties. Meshalkin and his colleagues pitted seven machine learning algorithms against each other in the race to reconstruct thermal conductivity from well-logging data as accurately as possible. They also chose a Lichtenecker-Asaads theoretical model as a benchmark for this comparison.

Using real well-log data from a heavy oil field located in the Timan-Pechora Basin in northern Russia, researchers found that, among the seven machine-learning algorithms and basic multiple linear regression, Random Forest provided the most accurate well-log based predictions of rock thermal conductivity, even beating the theoretical model.

If we look at todays practical needs and existing solutions, I would say that our best machine learning-based result is very accurate. It is difficult to give some qualitative assessment as the situation can vary and is constrained to certain oil fields. But I believe that oil producers can use such indirect predictions of rock thermal conductivity in their EOR design, Meshalkin notes.

Scientists believe that machine-learning algorithms are a promising framework for fast and effective predictions of rock thermal conductivity. These methods are more straightforward and robust and require no extra parameters outside common well-log data. Thus, they can radically enhance the results of geothermal investigations, basin and petroleum system modelling and optimization of thermal EOR methods, the paper concludes.

Reference: Robust well-log based determination of rock thermal conductivity through machine learning by Yury Meshalkin, Anuar Shakirov, Evgeniy Popov, Dmitry Koroteev and Irina Gurbatova, 5 May 2020, Geophysical Journal International.DOI: 10.1093/gji/ggaa209

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Using Machine Learning to Accurately Predict Rock Thermal Conductivity for Enhanced Oil Production - SciTechDaily

OpenAI, the machine learning nonprofit co-founded by Elon Musk, has released its first commercial product: a rentable version of a text generation tool the organisation once deemed too dangerous to release.

Dubbed simply the API, the new service lets businesses directly access the most powerful version of GPT-3, OpenAIs general purpose text generation AI.

The tool is already a more than capable writer. Feeding an earlier version of the opening line of George Orwells Nineteen Eighty-Four It was a bright cold day in April, and the clocks were striking thirteen the system recognises the vaguely futuristic tone and the novelistic style, and continues with: I was in my car on my way to a new job in Seattle. I put the gas in, put the key in, and then I let it run. I just imagined what the day would be like. A hundred years from now. In 2045, I was a teacher in some school in a poor part of rural China. I started with Chinese history and history of science.

Now, OpenAI wants to put the same power to more commercial uses such as coding and data entry. For instance, if, rather than Orwell, the prompt is a list of the names of six companies and the stock tickers and foundation dates of two of them, the system will finish it by filling in the missing details for the other companies.

It will mark the first commercial uses of a technology which stunned the industry in February 2019 when OpenAI first revealed its progress in teaching a computer to read and write. The group was so impressed by the capability of its new creation that it was initially wary of publishing the full version, warning that it could be misused for ends the nonprofit had not foreseen.

We need to perform experimentation to find out what they can and cant do, said Jack Clark, the groups head of policy, at the time. If you cant anticipate all the abilities of a model, you have to prod it to see what it can do. There are many more people than us who are better at thinking what it can do maliciously.

Now, that fear has lessened somewhat, with almost a year of GPT-2 being available to the public. Still, the company says: The fields pace of progress means that there are frequently surprising new applications of AI, both positive and negative.

We will terminate API access for obviously harmful use-cases, such as harassment, spam, radicalisation, or astroturfing [masking who is behind a message]. But we also know we cant anticipate all of the possible consequences of this technology, so we are launching today in a private beta [test version] rather than general availability.

OpenAI was founded with a $1bn (0.8bn) endowment in 2015, backed by Musk and others, to advance digital intelligence in the way that is most likely to benefit humanity. Musk has since left the board, but remains as a donor.

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Elon Musk-backed OpenAI to release text tool it called dangerous - The Guardian

Machine Learning in Communication Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Machine Learning in Communication Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market.

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Top Key Players Profiled in This Report:

Amazon, IBM, Microsoft, Google, Nextiva, Nexmo, Twilio, Dialpad, Cisco, RingCentral

The key questions answered in this report:

Various factors are responsible for the markets growth trajectory, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the global Machine Learning in Communication market. It also gauges the bargaining power of suppliers and buyers, threat from new entrants and product substitute, and the degree of competition prevailing in the market. The influence of the latest government guidelines is also analyzed in detail in the report. It studies the Machine Learning in Communication markets trajectory between forecast periods.

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Table of Contents:

Global Machine Learning in Communication Market Research Report

Chapter 1 Machine Learning in Communication Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Machine Learning in Communication Market Forecast

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Massive Growth in Machine Learning in Communication Market 2020 | Trends, Growth Demand, Opportunities & Forecast To 2026 | Amazon, IBM,...

On May 13, 2020, The Zucchetti Group - the leading Italian software development company, with clients in 130+ countries - officially acquired Lybra.Tech - developer of advanced Artificial Intelligence-based, demand centric revenue management software for hotels. After the acquisition, Zucchetti is the majority shareholder, owning 51% of the company.

Lybra.Tech's Intelligent Revenue Assistant (RMS) will integrate The Zucchetti Group's global booking data (collected from the company's hospitality subsidiaries around the world), giving hoteliers access to an unparalleled amount of market and demand data and maximizing the accuracy of the RMS' room rate suggestions, in real-time - no matter how market demand changes - for all types and sizes of hotels, worldwide.

"We recognize the significant value that Lybra.Tech's RMS offers to hotels, making this acquisition an important one for Zucchetti's thriving hospitality technology division," said Angelo Guaragni, Director of Zucchetti Hospitality. "We're excited for the opportunity to nurture and support Lybra.Tech's future growth and, through integration of our data into their RMS, contribute even more to the industry's return to profitability."

"The Zucchetti Group is an important player in the European and international hospitality technology market; in fact, it is one of the top suppliers of hospitality operational software in Europe and worldwide," said Fulvio Giannetti, CEO of Lybra.Tech. "The company is also a hub for innovation, devoting more than 25% of the company's workforce to developing new and game-changing technologies - a priority which we also value highly at Lybra.Tech. We are proud to join this industry-leading company and look forward to collaborating with the company's innovative hospitality industry subsidiaries and partners to, jointly, give hoteliers everything that they need to become - and remain - profitable."

To learn more about the acquisition, about Lybra.Tech and the company's Intelligent Revenue Assistant RMS, or for expert hospitality industry commentary for an upcoming article, please contact Jennifer Nagy at any time: [emailprotected] or +1.786.420.1160.

With more than 6.000 employees, a nationwide distribution network exceeding 1.650 Partners in Italy and 350 in over 50 countries in the world, and more than 600.000 customers, Zucchetti Group is one of the most important Italian companies in the IT sector in Europe. Zucchetti offers a range of products that is unmatched in Italy and Europe, allowing customers to gain major competitive advantages and to rely on a single partner for all their IT needs. Zucchetti designs Software and Hardware solutions and innovative services designed and developed to meet the specific needs of small, medium and large sized companies. For more information about The Zucchetti Group, please visit http://www.zucchetti.com.

Lybra.Tech is an Italian SaaS company, offering an innovative, machine learning revenue management system (RMS) for the global hospitality industry. Lybra.Tech's Intelligent Revenue Assistant RMS was designed to improve the quality of hoteliers' lives, by simplifying and automating daily operations to skyrocket their property's bookings and revenue - even in times of decreased demand.

In May 2020, Lybra.Tech was acquired by The Zucchetti Group, a leading international technology company offering software, hardware and ITC services to many global sectors, including hospitality, education, transport and logistics, manufacturing, among others. As part of The Zucchetti Group, Lybra.Tech is even more well-positioned to offer hotel clients the most accurate pricing suggestions because of the wealth of international market and demand data - compiled by the global hospitality technology companies that are owned by The Zucchetti Group - that is now integrated into the company's Intelligent Revenue Assistant. To learn more about Lybra.Tech, visit lybra.tech.

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Lybra.Tech's Machine Learning, Demand-Centric RMS Obtained in Acquisition by The Zucchetti Group - Hospitality Net

LOS ALAMITOS, Calif., June 12, 2020 /PRNewswire/ --The Computer Vision and Pattern Recognition (CVPR) Conference, one of the largest events exploring artificial intelligence, machine learning, computer vision, deep learning, and more, will take place 14-19 June as a fully virtual event. Over the course of six days, the event will feature 45 sessions delivered by 1467 leading authors, academics, and experts to more than 6500 attendees, who have already registered for the event.

"The excitement, enthusiasm, and support for CVPR from the global community has never been more apparent," said Professor of Computer Science at Cornell University and Co-Chair of the CVPR 2020 Committee Ramin Zabih. "With large attendance, state of the art research, and insights delivered by some of the leading authorities in computer vision, AI, and machine learning, our first-ever fully virtual event is shaping up to be an exciting experience for everyone involved."

As a fully virtual event, attendees will have access to all CVPR program components, including fireside chats, workshops, tutorials, and oral and poster presentations via a robust, fully searchable, password-protected portal. Credentials to access the portal are provided to attendees shortly upon registration.

CVPR fireside chats, workshops, and tutorials will be conducted via live video with live Q&A between presenters and participants. Oral and poster presentations, which will be repeated, will include a pre-recorded video from the presenter(s), followed by a live Q&A session. Attendees will also be able to access presentations/papers and the pre-recorded videos at their convenience to help ensure maximum access given the diverse time zones in which conference participants live. Additionally, CVPR participants can leverage complementary video chat features and threaded question and answer commenting associated with each session and each sponsor to support further knowledge sharing and understanding. Multiple online networking events with video and text chat elements are also included.

"The CVPR Committee has gone to great lengths to deliver a first-in-class virtual conference experience that all attendees can enjoy," said IEEE Computer Society Executive Director Melissa Russell, co-sponsor of the event. "We are thrilled to be part of this endeavor and are excited to deliver and witness in the coming days the 'what's next' in AI, computer vision and machine learning."

Details on the full virtual CVPR 2020 schedule can be found on the conference website at http://cvpr2020.thecvf.com/program. All times are Pacific Daylight Time (Seattle Time).

Interested individuals can still register for CVPR at http://cvpr2020.thecvf.com/attend/registration. Accredited members of the media can register for the CVPR virtual conference by emailing [emailprotected].

About CVPR 2020CVPR is the premier annual computer vision and pattern recognition conference. With first-in-class technical content, a main program, tutorials, workshops, a leading-edge expo, and attended by more than 9,000 people annually, CVPR creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation. CVPR 2020, originally scheduled to take place 14-19 June 2020 at the Washington State Convention Center in Seattle Washington, will now be a fully virtual event. Authors and presenters will virtually deliver presentations and engage in live Q&A with attendees. For more information about CVPR 2020, the program, and how to participate virtually, visit http://cvpr2020.thecvf.com/.

About the Computer Vision FoundationThe Computer Vision Foundation is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV).

About the IEEE Computer SocietyThe IEEE Computer Society is the world's home for computer science, engineering, and technology. A global leader in providing access to computer science research, analysis, and information, the IEEE Computer Society offers a comprehensive array of unmatched products, services, and opportunities for individuals at all stages of their professional career. Known as the premier organization that empowers the people who drive technology, the IEEE Computer Society offers international conferences, peer-reviewed publications, a unique digital library, and training programs. Visit http://www.computer.org for more information.

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http://www.computer.org

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CVPR 2020 Convenes Thousands from the Global AI, Machine Learning and Computer Vision Community in Virtual Event Beginning Sunday - PRNewswire

Applications are now open for the nanodegree program, which will help Udacity train developers on the Microsoft Azure cloud infrastructure.

Microsoft and Udacity are teaming together to invest $4 million in a machine learning (ML) training collaboration, which begins with the Machine Learning Scholarship Program for Microsoft Azure which starts today.

The program focuses on artificial intelligence, which is continuing to grow at a face pace. AI engineers are in high demand, particularly as enterprises build new cloud applications and move old ones to the cloud. The average AI salary in the US is $114,121 a year based on data from Glassdoor.

"AI is driving transformation across organizations and there is increased demand for data science skills," said Julia White, corporate vice president, Azure Marketing, Microsoft, in a Microsoft blog post. "Through our collaboration with Udacity to offer low-code and advanced courses on Azure Machine Learning, we hope to expand data science expertise as experienced professionals will truly be invaluable resources to solving business problems."

SEE: Building the bionic brain (free PDF) (TechRepublic)

The interactive scholarship courses begin with a two-month long course, "Introduction to machine learning on Azure with a low-code experience."

Students will work with live Azure environments directly within the Udacity classroom and build on these foundations with advanced techniques such as ensemble learning and deep learning.

To earn a spot in th foundations course, students will need to submit an application. According to the blog post, "Successful applicants will ideally have basic programming knowledge in any language, preferably Python, and be comfortable writing scripts and performing loop operations."

Udacity's nanodegrees have been growing in popularity. Monthly enrollment in Udacity's nanodegrees has increased by a factor of four since the beginning of the coronavirus lockdown. Among Udacity's consumer customers, in the three weeks starting March 9 the company saw a 56% jump in weekly active users and a 102% increase in new enrollments, and they've stayed at or just below those new levels since then, according to a Udacity spokesperson.

After students complete the foundations course, Udacity will select top performers to receive a scholarship to the new machine learning nanodegree program with Microsoft Azure.

This typically four-month nanodegree program will include:

Students who aren't selected for the scholarship will still be able to enroll in the nanodegree program when it is available to the general public.

Anyone interested in becoming an Azure Machine Learning engineer and learning from experts at the forefront of the field can apply for the scholarshiphere.Applications will be open from June 10 to June 30.

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Microsoft and Udacity partner in new $4 million machine-learning scholarship program for Microsoft Azure - TechRepublic

WALTHAM, Mass.--(BUSINESS WIRE)--X-Chem, Inc., the leader in DNA-encoded small molecule library screening, and ZebiAI Therapeutics, a drug discovery company unlocking new disease targets, today announced the publication of a large prospective study to evaluate the power of machine learning (ML) to accelerate and improve the drug discovery process. The study, published in the Journal of Medicinal Chemistry, titled Machine Learning on DNA-encoded Libraries: A New Paradigm for Hit-finding, was conducted in collaboration with Google Accelerated Science (GAS), who developed the highly predictive ML algorithms.

The paper describes an effective machine learning platform to accelerate drug discovery based on DNA-encoded small molecule library (DEL) selection data and demonstrates the efficacy of the platform to predict highly potent small molecule inhibitors within a virtual library of compounds across three diverse protein targets. It details the identification of active compounds outside of the DEL library which are structurally different from the molecules used in training. These results indicate that, at least for certain targets, ML applied to DEL data enables access to unlimited chemical space in a time- and cost-effective manner.

Utilizing this methodology as its foundational technology, ZebiAI and GAS have initiated a program, coined the Chemome Initiative, to collaborate with academic researchers to utilize the platform to further characterize the function of understudied proteins and validate novel therapeutic targets. Thousands of proteins remain understudied with limited or complete lack of understanding about their function and/or relevance to disease pathophysiology. As a result, there is untapped potential for major scientific advances within the unexplored proteome. ZebiAI and GAS will develop chemical probe molecules for the academic community across thousands of novel targets, driving deeper understanding of the biology of intractable diseases.

This exciting paper demonstrates that combining X-Chems industry-leading DEL screening data with machine learning can significantly accelerate the discovery of potent small molecules against a diverse set of targets. With our validation against nearly 2,000 molecules and 3 targets, this is the largest published prospective study of virtual screening, commented Patrick Riley, senior researcher of Google. This is a major step forward in the quest to utilize machine learning to accelerate the drug discovery process.

The quality of our DEL screening data, driven by expert selection protocols, vast compound libraries developed over 10+ years, and sophisticated informatics and data formatting, enabled these exciting results, commented Matt Clark, CEO of X-Chem. We look forward to continuing to provide our industry-leading data to ZebiAI to drive powerful ML models.

Rick Wagner, Founder and Director of ZebiAI said, The Chemome Initiative will apply the techniques we have developed to efficiently deliver new chemical probes to the research community for thousands of human proteins of interest. We will ultimately apply the algorithms we develop and results of the research using chemical probes to further our understanding of disease pathways. This breakthrough will enable significant new biological discoveries and ultimately accelerate discovery of new therapeutics to treat intractable diseases.

Chemical probes are small molecules that selectively inhibit or promote the function of specific protein targets, enabling the study of disease systems and pathways. It is common practice to use chemical probes to study the function of specific protein targets. Currently, there are not enough small molecule probes available, with only an estimated four percent of the human proteome having a usable probe. Most screening methods are limited by the scope of chemical space to which they provide access. However, DNA-encoded libraries (DELs) combined with ML present a new solution.

DELs are libraries with millions or billions of distinct molecules that are generated by iterative combinatorial synthesis of small molecules tethered to DNA tags that record the synthetic history of the small molecule. Every small molecule in the library has a unique DNA barcode attached to it, allowing the molecules to be easily catalogued. The library is used to find which small molecules bind to proteins of interest, by mixing the DEL molecules and proteins and washing away what doesnt stick. DNA sequencing methods are then used to determine the DNA barcode of the molecules that are bound to the protein target, therefore identifying the molecules.

Data on the thousands of molecules that bind to a protein target in a DEL screen provide a chemical imprint of the target. This makes it possible to derive a ML model that can predict active compounds from virtual libraries to the protein of interest, opening up unlimited chemical space. Broader and deeper study of the biology of intractable diseases using this approach will accelerate the discovery of novel therapeutics, ultimately improving human health.

About X-Chem, Inc.

X-Chem, Inc. is a privately-owned biotechnology company based in Waltham, Massachusetts. The companys mission is to apply its powerful product engine to the discovery of small molecule leads against high-value therapeutic targets. X-Chem has established partnerships with AbbVie, Alexion, Almirall, Bristol-Myers Squibb, AstraZeneca, Bayer, Department of Defense/Harvard, Gilead, Janssen, Maruho, MD Anderson Cancer Center, Ono, Otsuka, Pfizer, Roche, Sanofi, Taiho Pharma, Vertex, and several other leading pharmaceutical companies, biotechnology organizations, and academic centers. For further information on X-Chem, please visit: http://www.x-chemrx.com/.

About ZebiAI Therapeutics.

ZebiAI Therapeutics is focused on improving human health by powering machine learning to map the chemistry of the genome and discover new therapeutics. The companys core technology applies ML algorithms to vast amounts of high quality protein-small molecule interaction data. ZebiAI was launched in 2019 and has partnerships with Google and X-Chem, the leader in DNA encoded library (DEL) small molecule discovery. Anterra Capital, a Fidelity-backed venture group led a seed round of financing for the company. For more information, please visit: http://www.zebiai.com.

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Study Published in the Journal of Medicinal Chemistry Demonstrates the Power of Machine Learning to Unlock New Chemistry and Biology to Treat Disease...

From data quality issues to architecting and optimizing models and data pipelines, there are many considerations to keep in mind with regard to machine learning.

At Data Summit Connect, a free 3-day series of data-focused webinars, a session, titled "Unlocking the Power of Machine Learning," provided a close look at the challenges involved in using machine learning, as well as the enabling technologies, techniques, and applications required to achieve your goals.

As part of the session, Rashmi Gupta,director data architecture,KPMG LLC, explained how to use tools for orchestration and version control to streamline datasets in a presentation, titled "Operationalizing of Machine Learning Data." She also discussed how to secure data to ensure that production control access is streamlined for testing. A challenge of machine learning is operationalizing the data volume, performance, and maintenance.

Challenges today in realizing the potential benefits of machine learning in the enterprise include data access issues (agility and security), data quality issues (disaggregated data with errors), lack of governance for validating certifying model accuracy, and lack of collaboration between business and IT. If the underlying data is not accurate, then the organization will not be able to reach its goals with machine learning, said Gupta. What is needed is a centralized framework with governance that operates and integrates various capabilities to support multiple domain solutions. Gupta highlighted market leaders for machine learning platforms as well as the advantages of various tool choices.

Outlining the best practices for machine learning success, Gupta said, organizations should:

Adding to the discussion, Andy Thurai,thought leader, blogger, and chief strategist at the Field CTO (thefieldcto.com), shared how infusing AI into operations can lead to improvements with his presentation, "AIOps the Savior for Digital Business Unplanned Outages."

Citing MarketsandMarkets research that the AIOps market is set to be worth $11 billion by 2023, Thurai said that after starting with automating the IT operations tasks, now AIOps has moved beyond the rudimentary RPA, event consolidation, noise reduction use cases into mainstream use cases such as root causes analysis, service ticket analytics, anomaly detection, demand forecasting, and capacity planning.

According to Thurai, a 2019 ITIC survey of 1,000 business executives found that, according to 86% of respondents, the cost of an outage was estimated to be $300,000 per hour, and according to 33%, the cost of an outage was as high as $1 million an hour. The research also found that the average unplanned service outage lasts 4 hours and the average number of outages per year is two.

Thurai noted that AIOps, a term coined by Gartner, refers to the use of big data, modern machine learning, and other advanced analytics technologies to directly and indirectly enhance IT operations (including monitoring, automation, and service desk processes) functions with proactive, personal, and dynamic insight. AIOps, he noted, allows concurrent use of data sources, data collection, analytics technologies, and presentation technologies.

Thurai offered three common use cases where AIOps can offer benefit: event consolidation to help reduce "noise" and alleviate alert fatigue; anomaly detection; and root cause analysis since it has been found that a large percentage of outages are due to problems related to changes, and if those problematic changes can be identified earlier, outages can be shortened. Additional advanced use cases include service ticketing and help desk scheduling, demand forecasting, capacity planning, botnet detection and traffic isolation, ticket enhancements, and proactive support.

Webcast replays of Data Summit Connect, a free 3-day webinar series held Tuesday, June 9 through Thursday, June 11, will be made available on the DBTA website.

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Unlocking the Power of Machine Learning at Data Summit Connect 2020 - Database Trends and Applications

VANCOUVER, British Columbia--(BUSINESS WIRE)--Few would expect the science behind stopping the next wave of COVID-19 to come from an AgTech company on a mission to reduce global pesticide loads but Terrameras computational chemistry, machine learning and artificial intelligence (ML/AI) innovations are being called upon for just that purpose. Today, Terramera announced its leadership of a collaborative project within Canadas Digital Technology Supercluster (Supercluster) to predict COVID-19 virus variations before they emerge.

Terrameras leadership in computational biochemistry and ML/AI led the federal government to back this unique research collaborative with $1.8M in initial funding. Working with world-class partners including Microsoft and the University of British Columbia, Terramera will deliver computational models to identify and combat future mutants of SARS-CoV-2, the virus that causes COVID-19. Other project partners include Menten AI, ProMIS Neurosciences and D-Wave.

We love to take on so-called impossible challenges that no one expects and succeed, said Terramera CEO and Founder, Karn Manhas. This is an enormous opportunity to harness complementary areas of cutting-edge science across industry and academia. Working together, we can help solve some of the worlds biggest problems, from sustainable food production to treatments for COVID, with novel predictive technologies.

Terrameras predictive technology forecasts successful combinations of the companys revolutionary Actigate technology with active ingredients to lower the worlds synthetic pesticide use 80% by 2030. Its powerful artificial intelligence core will play a central role in the project. All AgTech operations and research will continue as usual while Terramera leads this project.

Viruses are always changing, and SARS-CoV-2 is no exception, said Dr. Steven Slater, Terrameras lead scientist on the project and VP of Strategic Initiatives. Instead of playing catch-up again as another wave wraps around the world, well predict likely new strains with our machine learning models, and then well pre-design medicines and therapies to stop future pandemics.

Manhas and Slater expressed their gratitude to the Honourable Navdeep Bains, Canadian Minister of Innovation, Science and Industry; the Department of Innovation, Science and Economic Development (ISED), and the Supercluster for their global leadership and support in tackling COVID-19 forecasting.

About Terramera

Terramera is a global AgTech leader fusing science, nature and artificial intelligence to transform how food is grown and the economics of agriculture in the next decade. With its revolutionary Actigate technology platform, which was recognized by Fast Company as a 2020 World Changing Idea, Terramera is committed to reducing the global synthetic pesticide load 80% by 2030 to protect plant and human health and ensure an earth that thrives and provides for everyone. The privately-held, venture-backed company was founded in 2010 and has grown to include a world-class bench of engineers, scientists, advisors and investors. Terramera is headquartered in Vancouver, British Columbia, with integrated operations in Canada, the US and India that include research labs, a greenhouse and farm, and more than 200 patents in its global IP portfolio. For more information, please visit terramera.com

About Digital Technology Supercluster

The Digital Technology Supercluster solves some of industrys and societys biggest problems through Canadian-made technologies. We bring together private and public sector organizations of all sizes to address challenges facing Canadas economic sectors including healthcare, natural resources, manufacturing and transportation. Through this collaborative innovation the Supercluster helps to drive solutions better than any single organization could on its own. The Digital Technology Supercluster is led by industry leaders such as D-Wave, Finger Food Advanced Technology Group, LifeLabs, LlamaZOO, Lululemon, MDA, Microsoft, Mosaic Forest Management, Sanctuary AI, Teck Resources Limited, TELUS, Terramera, and 1Qbit. Together, we work to position Canada as a global hub for digital innovation. A full list of Members can be found here.

About the COVID-19 Program:

The COVID-19 Program aims to improve the health and safety of Canadians and support Canadas ability to address issues created by the COVID-19 outbreak. In addition, the program will build expertise and capacity to anticipate and address issues that may arise in future health crises, from healthcare to a return to work and community. More information can be found here.

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Terramera Leads Collaborative Effort to Predict New COVID-19 Virus Strains With Machine Learning and AI - Business Wire