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.

SOURCE IEEE Computer Society

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

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

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.

We deliver the top business tech news stories about the companies, the people, and the products revolutionizing the planet. Delivered Daily

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

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

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...

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

Products featured here are selected by our partners at StackCommerce.If you buy something through links on our site, Mashable may earn an affiliate commission.All instructorscome from solid technical backgrounds.

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By StackCommerceMashable Shopping2020-06-05 19:43:23 UTC

TL;DR: Get involved with the world's most valuable resource data, of course with The Complete 2020 Big Data and Machine Learning Bundle for $39.90, a 96% savings as of June 5.

Big data has gotten sobigthat the adjective doesn't even do it justice any longer. If anything, it should be described as gargantuan data, given how the entire digital universe is expected to generate 44 zettabytes of data by the end of this year. WTF is zettabytes? It's equal to one sextillion (1021) or270 bytes. It's a lot.

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Look, we know all this sounds daunting, but trust that you'll be able to learn and synthesize everything, all thanks to the help of expert instructors who know their stuff.

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Pursue a future in big data and machine learning with these classes - Mashable

Machine Learning Market research Report is a valuable supply of perceptive information for business strategists. This Machine Learning Market study provides comprehensive data which enhances the understanding, scope and application of this report.

Summary of Report @ Machine Learning Market

A thorough study of the competitive landscape of the global Machine Learning Market has been given, presenting insights into the company profiles, financial status, recent developments, mergers and acquisitions, and the SWOT analysis. This research report will give a clear idea to readers about the overall market scenario to further decide on this market projects.

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Market Drivers and Restraints:

Emergence of new technologies in Enterprise Mobility

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Machine Learning Market 2020 Professional Survey Report; Industry Growth, Shares, Opportunities And Forecast To 2026 - Surfacing Magazine