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Chicago, June 15, 2022 (GLOBE NEWSWIRE) -- According to the new market research report AI in Drug Discovery Market by Offering (Software, Service), Technology (Machine Learning, Deep Learning), Application (Cardiovascular, Metabolic, Neurodegenerative), End User (Pharma, Biotech, CROs) - Global Forecasts to 2027, published by MarketsandMarkets, the global Artificial Intelligence in Drug Discovery Market is projected to reach USD 4.0 billion by 2027 from USD 0.6 billion in 2022, at a CAGR of 45.7% during the forecast period.

Browse in-depth TOC on Artificial Intelligence (AI) in Drug Discovery Market177 Tables 33 Figures 198 Pages

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The growth of this Artificial Intelligence in Drug Discovery Market is driven by the growing need to control drug discovery & development costs, and growing number of cross-industry collaborations and partnerships, On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market.

Services segment is expected to grow at the highest rate during the forecast period.

Based on offering, the AI in drug discovery market is segmented into software and services. In 2021, the services segment accounted for the largest market share of the global AI in drug discovery services market and also expected to grow at the highest CAGR during the forecast period. The benefits associated with AI services and the strong demand for AI services among end users are the key factors driving the growth of this market segment.

Machine learning technology segment accounted for the largest share of the global AI in drug discovery market.

Based on technology, the AI in drug discovery market is segmented into machine learning and other technologies. The machine learning segment accounted for the largest share of the global market in 2021 and expected to grow at the highest CAGR during the forecast period. The machine learning technology segment further segmented into deep learning, supervised learning. reinforcement learning, unsupervised learning, and other machine learning technologies. Deep learning segment accounted for the largest share of the market in 2021, and this segment also expected to grow at the highest CAGR during the forecast period.

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The immuno-oncology application segment accounted for the largest share of the AI in drug discovery market in 2021.

On the basis of application, the AI in drug discovery market is segmented into neurodegenerative diseases, immuno-oncology, cardiovascular diseases, metabolic diseases, and other applications. The immuno-oncology segment accounted for the largest share of the market in 2021, owing to the increasing demand for effective cancer drugs. The neurodegenerative diseases segment is estimated to register the highest CAGR during the forecast period. The role of AI in resolving existing complexities in neurological drug development and strategic collaborations between pharmaceutical companies & solution providers are the key factors responsible for the high growth rate of the neurodegenerative diseases segment.

Pharmaceutical & biotechnology companies segment accounted for the largest share of the global AI in drug discovery market.

On the basis of end user, the AI in drug discovery market is segmented into pharmaceutical & biotechnology companies, CROs, and research centers and academic & government institutes. The pharmaceutical & biotechnology companies segment accounted for the largest market share of AI in drug discovery market, in 2021, while the research centers and academic & government institutes segment is projected to register the highest CAGR during the forecast period. The strong demand for AI-based tools in making the entire drug discovery process more time and cost-efficient is driving the growth of this end-user segment.

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North America is expected to dominate the Artificial Intelligence in Drug Discovery Market in 2022.

North America accounted for the largest share of the global AI in drug discovery market in 2021 and also expected to grow at the highest CAGR during the forecast period. North America, which comprises the US, Canada, and Mexico, forms the largest market for AI in drug discovery. These countries have been early adopters of AI technology in drug discovery and development. Presence of key established players, well-established pharmaceutical and biotechnology industry, and high focus on R&D & substantial investment are some of the key factors responsible for the large share and high growth rate of this market

Top Key Players in Artificial Intelligence in Drug Discovery Market are:

Players in AI in Drug Discovery Market adopted organic as well as inorganic growth strategies such as product upgrades, collaborations, agreements, partnerships, and acquisitions to increase their offerings, cater to the unmet needs of customers, increase their profitability, and expand their presence in the global market.

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Artificial Intelligence in Drug Discovery Market worth $4.0 billion by 2027 Exclusive Report by MarketsandMarkets - GlobeNewswire

Accel Solutions and Veego today announced the integration of Veego's Artificial Intelligence and machine learning solution with Bezeq, Israel's largest Telecom. The integration will enhance Bezeq's ability to provide its subscribers with improved Internet quality of experience and mitigate network malfunctions. Bezeq will integrate Veego's solution in their consumer line of Be-Routers. The Veego solution provides Bezeq the ability to identify and mitigate connectivity malfunctions remotely, attain a broad overview of the network and improve the overall experience for its customers.

Niv Berkner, Head of Services and Product Innovation at Bezeq indicated "I have no doubt that the integration of the Veego solution in our Be-Routers will allow us to provide our customers an optimal quality of experience. The Veego solution will allow us to understand the interaction between all the devices connected in the home, and as such, provide Bezeq the ability to identify, anticipate and mitigate network related malfunctions remotely, without the customers involvement and/or being aware."

"Veego provides ISPs and CSPs the ability to leverage data and provide them with insights that alleviate churn, while constantly improving the customer experience." outlined Amir Kotler, Veego's CEO.

"The integration of Veego's AI and ML capabilities enable Telcos to actually experience what the customer experiences when he has an internet malfunction that disrupts a healthy Internet process," Kotler added "Veego's solution enables ISP to predict the issue, resolve it in autonomic ways and as a result, improves operational efficiency, increases revenues and reduces churn."

Ronen Shor, CEO of Accel Solutions stated, "The Bezeq - Veego agreement is another step in our continuous enhancement efforts on the products sold to Bezeq and a significant technological upgrade offered by Bezeq to its customers. Accel Solutions considers Veego as a strategic partner with tremendous potential in the international market.

Source:https://accelsolutionsllc.com/

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Veegos Artificial Intelligence and Machine Learning Solution Integrated with Israels Largest Telecom - AZoRobotics

The advisory and climate tech-led sustainability solution has opened a new London HQ after raising $5m in a pre-Series A funding round, to support growing demand from commercial and industrial UK real estate owners striving to meet net zero carbon targets

Ecolibriums Head of Commercial Real Estate Yash Kapila (left) and CEO Chintan Soni (right) will lead the business UK expansion from its new London HQ. Image credit: Max Lacome

UK expansion builds on considerable success in Asia Pacific, where Ecolibrium's technology has been deployed across 50 million sq ft by globally renowned brands including Amazon, Fiat, Honeywell, Thomson Reuters, Tata Power, and the Delhi Metro

The $5m pre-Series A funding round was co-led by Amit Bhatia's Swordfish Investments and Shravin Bharti Mittal's Unbound venture capital firm

Launches in the UK today having already signed its first commercial contract with Integral, real estate giant JLL's engineering and facilities service business

LONDON, June 13, 2022 /PRNewswire/ -- Machine learning-led decarbonisation platform Ecolibrium has today launched its revolutionary sustainability solution in the UK, as the race to reduce carbon emissions accelerates across the built environment.

Founded in 2008 by entrepreneur brothers Chintan and Harit Soni at IIM Ahmedabad's Centre for Innovation, Incubation and Entrepreneurship in India, Ecolibrium provides expert advisory as well as technology-driven sustainability solutions to enable businesses in commercial and industrial real estate to reduce energy consumption and ultimately achieve their net zero carbon ambitions.

Relocating its global headquarters to London, Ecolibrium has raised $5m in a pre-Series A funding round as it looks to expand its international footprint to the UK. The round was co-led by Amit Bhatia's Swordfish Investments and Shravin Bharti Mittal's Unbound venture capital firm, alongside several strategic investors.

Ecolibrium launches in the UK today having already signed its first commercial contract with Integral, JLL's UK engineering and facilities service business.

The fundraising and UK expansion builds on Ecolibrium's considerable success in Asia Pacific, where its technology is being used across 50 million sq ft by more than 150 companies including Amazon, Fiat, Honeywell, Thomson Reuters, Tata Power, and the Delhi Metro. An annual reduction of 5-15% in carbon footprint has been achieved to date by companies which have deployed Ecolibrium's technology.

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Ecolibrium has also strengthened its senior UK management team, as it prepares to roll-out its green platform across the UK, by hiring facilities and asset management veteran Yash Kapila as its new head of commercial real estate. Kapila previously held senior leadership positions with JLL across APAC and EMEA regions.

Introducing SmartSense

At the heart of Ecolibrium's offer is its sustainability-led technology product SmartSense, which assimilates thousands of internet of things (IoT) data points from across a facility's entire energy infrastructure.

This information is then channelled through Ecolibrium's proprietary machine learning algorithms, which have been developed over 10 years by their in-house subject matter experts. Customers can visualise the data through a bespoke user interface that provides actionable insights and a blueprint for achieving operational excellence, sustainability targets, and healthy buildings.

This connected infrastructure generates a granular view of an asset's carbon footprint, unlocking inefficiencies and empowering smart decision-making, while driving a programme of continuous improvement to deliver empirical and tangible sustainability and productivity gains.

Preparing for future regulation

Quality environmental data and proof points are also providing a distinct business advantage at this time of increasing regulatory requirements that require corporates to disclose ESG and sustainability performance. Ecolibrium will work closely with customers to lead the way in shaping their ESG governance.

According to Deloitte, with a minimum Grade B Energy Performance Certification (EPC) requirement anticipated by 2030, 80% of London office stock will need to be upgraded an equivalent of 15 million sq ft per annum.

Research from the World Economic Forumhas found that the built environment is responsible for 40% of global energy consumption and 33% of greenhouse gas emissions, with one-fifth of the world's largest 2,000 companies adopting net zero strategies by 2050 or earlier. Technology holds the key to meeting this challenge, with Ecolibrium and other sustainability-focused changemakers leading the decarbonisation drive.

Chintan Soni, Chief Executive Officer at Ecolibrium, said:"Our mission is to create a balance between people, planet and profit and our technology addresses each of these objectives, leading businesses to sustainable prosperity. There is no doubt the world is facing a climate emergency, and we must act now to decarbonise and protect our planet for future generations.

"By using our proprietary machine learning-led technology and deep in-house expertise, Ecolibrium can help commercial and industrial real estate owners to deliver against ESG objectives, as companies awaken to the fact that urgent action must be taken to reduce emissions and achieve net zero carbon targets in the built environment.

"Our goal is to partner with companies and coach them to work smarter, make critical decisions more quickly and consume less. And, by doing this at scale, Ecolibrium will make a significant impact on the carbon footprint of commercial and industrial assets, globally."

The UK expansion has been supported by the Department for International Trade's Global Entrepreneur Programme. The programme has provided invaluable assistance in setting up Ecolibrium's London headquarters and scaling in the UK market.

In turn, Ecolibrium is supporting the growth of UK innovation, promoting green job creation, and providing tangible economic benefits, as part of the country's wider transition to a more sustainable future.

Minister for Investment Lord Grimstone said: "Tackling climate change is crucial in our quest for a cleaner and green future, something investment will play an important part in.

"That's why I'm pleased to see Ecolibrium's expansion to the UK. Not only will the investment provide a revolutionary sustainability solution to reduce carbon emissions across various sectors, it is a continued sign of the UK as a leading inward investment destination, with innovation and expertise in our arsenal".

About Ecolibrium

Ecolibrium is a machine learning-led decarbonisation platform balancing people, planet and profit to deliver sustainable prosperity for businesses.

Founded in 2008 by entrepreneur brothers Chintan and Harit Soni, Ecolibrium provides expert advisory as well as technology-driven sustainability solutions to enable commercial and industrial real estate owners to reduce energy consumption and ultimately achieve their net zero carbon ambitions.

Ecolibrium's flagship technology product SmartSense is currently being used across 50 million sq ft by more than 150 companies including JLL, Amazon, Fiat, Honeywell, Thomson Reuters, Tata Power, and the Delhi Metro. SmartSense collects real-time information on assets, operational data and critical metrics using internet of things (IoT) technology. This intelligence is then channelled through Ecolibrium's proprietary machine learning algorithms to visualise data and provide actionable insights to help companies make transformative changes to their sustainability goals.

For more information, visit: http://www.ecolibrium.io

For press enquiries, contact: FTI Consulting: ecolibrium@fticonsulting.com, +44 (0) 2037271000

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Machine learning-led decarbonisation platform Ecolibrium launches in the UK - Yahoo Finance

The COVID-19 pandemic forced a shift to remote learning overnight for most higher-education students, starting in the spring of 2020. To complement video lectures and engage students in the virtual classroom, educators adopted technologies that enabled more interactivity and hybrid models of online and in-person activities. These tools changed learning, teaching, and assessment in ways that may persist after the pandemic. Investors have taken note. Edtech start-ups raised record amounts of venture capital in 2020 and 2021, and market valuations for bigger players soared.

A study conducted by McKinsey in 2021 found that to engage most effectively with students, higher-education institutions can focus on eight dimensionsof the learning experience. In this article, we describe the findings of a study of the learning technologies that can enable aspects of several of those eight dimensions (see sidebar Eight dimensions of the online learning experience).

In November 2021, McKinsey surveyed 600 faculty members and 800 students from public and private nonprofit colleges and universities in the United States, including minority-serving institutions, about the use and impact of eight different classroom learning technologies (Exhibit 1). (For more on the learning technologies analyzed in this research, see sidebar Descriptions of the eight learning technologies.) To supplement the survey, we interviewed industry experts and higher-education professionals who make decisions about classroom technology use. We discovered which learning tools and approaches have seen the highest uptake, how students and educators view them, the barriers to higher adoption, how institutions have successfully adopted innovative technologies, and the notable impacts on learning (for details about our methodology, see sidebar About the research).

Exhibit 1

Survey respondents reported a 19 percent average increase in overall use of these learning technologies since the start of the COVID-19 pandemic. Technologies that enable connectivity and community building, such as social mediainspired discussion platforms and virtual study groups, saw the biggest uptick in use49 percentfollowed by group work tools, which grew by 29 percent (Exhibit 2). These technologies likely fill the void left by the lack of in-person experiences more effectively than individual-focused learning tools such as augmented reality and virtual reality (AR/VR). Classroom interaction technologies such as real-time chatting, polling, and breakout room discussions were the most widely used tools before the pandemic and remain so; 67 percent of survey respondents said they currently use these tools in the classroom.

Exhibit 2

The shift to more interactive and diverse learning models will likely continue. One industry expert told us, The pandemic pushed the need for a new learning experience online. It recentered institutions to think about how theyll teach moving forward and has brought synchronous and hybrid learning into focus. Consequently, many US colleges and universities are actively investing to scale up their online and hybrid program offerings.

Some technologies lag behind in adoption. Tools enabling student progress monitoring, AR/VR, machine learningpowered teaching assistants (TAs), AI adaptive course delivery, and classroom exercises are currently used by less than half of survey respondents. Anecdotal evidence suggests that technologies such as AR/VR require a substantial investment in equipment and may be difficult to use at scale in classes with high enrollment. Our survey also revealed utilization disparities based on size. Small public institutions use machine learningpowered TAs, AR/VR, and technologies for monitoring student progress at double or more the rates of medium and large public institutions, perhaps because smaller, specialized schools can make more targeted and cost-effective investments. We also found that medium and large public institutions made greater use of connectivity and community-building tools than small public institutions (57 to 59 percent compared with 45 percent, respectively). Although the uptake of AI-powered tools was slower, higher-education experts we interviewed predict their use will increase; they allow faculty to tailor courses to each students progress, reduce their workload, and improve student engagement at scale (see sidebar Differences in adoption by type of institution observed in the research).

While many colleges and universities are interested in using more technologies to support student learning, the top three barriers indicated are lack of awareness, inadequate deployment capabilities, and cost (Exhibit 3).

Exhibit 3

More than 60 percent of students said that all the classroom learning technologies theyve used since COVID-19 began had improved their learning and grades (Exhibit 4). However, two technologies earned higher marks than the rest for boosting academic performance: 80 percent of students cited classroom exercises, and 71 percent cited machine learningpowered teaching assistants.

Exhibit 4

Although AR/VR is not yet widely used, 37 percent of students said they are most excited about its potential in the classroom. While 88 percent of students believe AR/VR will make learning more entertaining, just 5 percent said they think it will improve their ability to learn or master content (Exhibit 5). Industry experts confirmed that while there is significant enthusiasm for AR/VR, its ability to improve learning outcomes is uncertain. Some data look promising. For example, in a recent pilot study, students who used a VR tool to complete coursework for an introductory biology class improved their subject mastery by an average of two letter grades.

Exhibit 5

Faculty gave learning tools even higher marks than students did, for ease of use, engagement, access to course resources, and instructor connectivity. They also expressed greater excitement than students did for the future use of technologies. For example, while more than 30 percent of students expressed excitement for AR/VR and classroom interactions, more than 60 percent of faculty were excited about those, as well as machine learningpowered teaching assistants and AI adaptive technology.

Eighty-one percent or more of faculty said they feel the eight learning technology tools are a good investment of time and effort relative to the value they provide (Exhibit 6). Expert interviews suggest that employing learning technologies can be a strain on faculty members, but those we surveyed said this strain is worthwhile.

Exhibit 6

While faculty surveyed were enthusiastic about new technologies, experts we interviewed stressed some underlying challenges. For example, digital-literacy gaps have been more pronounced since the pandemic because it forced the near-universal adoption of some technology solutions, deepening a divide that was unnoticed when adoption was sporadic. More tech-savvy instructors are comfortable with interaction-engagement-focused solutions, while staff who are less familiar with these tools prefer content display and delivery-focused technologies.

According to experts we interviewed, learning new tools and features can bring on general fatigue. An associate vice president of e-learning at one university told us that faculty there found designing and executing a pilot study of VR for a computer science class difficult. Its a completely new way of instruction. . . . I imagine that the faculty using it now will not use it again in the spring. Technical support and training help. A chief academic officer of e-learning who oversaw the introduction of virtual simulations for nursing and radiography students said that faculty holdouts were permitted to opt out but not to delay the program. We structured it in a were doing this together way. People who didnt want to do it left, but we got a lot of support from vendors and training, which made it easy to implement simulations.

Despite the growing pains of digitizing the classroom learning experience, faculty and students believe there is a lot more they can gain. Faculty members are optimistic about the benefits, and students expect learning to stay entertaining and efficient. While adoption levels saw double-digit growth during the pandemic, many classrooms have yet to experience all the technologies. For institutions considering the investment, or those that have already started, there are several takeaways to keep in mind.

In an earlier article, we looked at the broader changes in higher education that have been prompted by the pandemic. But perhaps none has advanced as quickly as the adoption of digital learning tools. Faculty and students see substantial benefits, and adoption rates are a long way from saturation, so we can expect uptake to continue. Institutions that want to know how they stand in learning tech adoption can measure their rates and benchmark them against the averages in this article and use those comparisons to help them decide where they want to catch up or get ahead.

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Technology is shaping learning in higher education - McKinsey

PALO ALTO, Calif. and TORONTO, June 14, 2022 (GLOBE NEWSWIRE) -- Today, Cohere For AI, a non-profit research lab and community, announced its official launch. Dedicated to contributing open source, fundamental machine learning research, the lab will focus on solving some of the most complex challenges in the field of machine learning.

Sara Hooker will serve as Head of Cohere For AI, bringing a wealth of knowledge across AI and machine learning, with a specialty in deep learning. Prior to Cohere For AI, Sara was a Research Scientist at Google Brain where she focused on training models that go beyond top-line metrics to effectively demonstrate ability to be interpretable, compact, fair, and robust. She also founded Delta Analytics, a non-profit that brings together researchers, data scientists, and software engineers to volunteer their skills for non-profits around the world.

In order to realise the potential of machine learning, we need to make sure were working across a diverse set of people, disciplines, backgrounds, and geographies, said Aidan Gomez, CEO and Cofounder at Cohere. Im so excited to have Sara at the helm of Cohere For AI and cant wait to build the community together.

Cohere For AI aims to create open collaboration with the broader machine learning community. The lab is committed to supporting fundamental research on machine learning topics, while also prioritizing good stewardship of open source scientific practices.

This is the lab I wish had existed when I entered the field, said Hooker, Head of Cohere For AI. Depending on where youre located, theres often a lack of opportunities in machine learning. Cohere For AI aims to reimagine how, where, and by whom research is done. Im inspired by the opportunity to make an impact in ways that dont just advance progress on machine learning research, but also broadens access to the field.

In addition to contributions to fundamental research, Cohere For AI will support a machine learning community where members can connect with each other, discover new colleagues, and spur open discussion and collaboration. The lab and community will work to create new points of entry to machine learning research and will, ultimately, reflect the diversity of its members experiences and interests.

To get involved, browse our open research positions at jobs.lever.co/cohere, and stay in the loop on new programs and lab developments by signing up here.

About Cohere For AICohere For AI is a non-profit research lab and community dedicated to contributing fundamental research in machine learning, working to solve some of the field's most challenging problems. It supports responsible research across machine learning, while also prioritizing good stewardship of open source scientific practices. As a borderless research lab, Cohere For AI is community-driven and motivated by the opportunity to establish an inclusive, distributed community made up of brilliant research and engineering talent from across the globe.

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Cohere For AI Announces Non-Profit Lab Dedicated to Open Source Fundamental Research - GlobeNewswire