By Louise UffelmanJuly 29, 2020

Working on a real-life project that will introduce students to how algorithms work in applications with crucial outcomes will provide them with the important skills that can transfer to other areas of computer and data science.

~ Moataz Khalifa

As the race for a COVID-19 vaccine continues, Moataz Khalifa, assistant professor and director of Data Education at Washington and Lee University, is involved in an equally promising research project that focuses on a non-invasive, early detection system of the virus.

In March, just as the numbers of cases were climbing around the world, Khalifa was invited by Wu Feng, Elizabeth & James Turner Fellow, professor of computer science at Virginia Tech and director of its SyNeRGy lab, to join his research lab to develop a deep-learning algorithm to enhance low-radiation CT scans of peoples lungs. Fengs current research was already investigating similar applications in CT scans of brain tumors, and he received two National Science Foundation grants totaling $250,000 to expand his project to work on the COVID-19 early detection system.

Currently, the genetic-based RT-PCR tests available to detect COVID-19 rely on swabbing the nasal cavity. With testing kits in short supply, accuracy of the results only around 59%, and increasingly long processing times of 10 to 14 days, the hunt was on for a system that was more accurate, available and faster to handle the growing demand for tracking infections.

Feng is excited to have Khalifa, who earned his Ph.D. in physics from Virginia Tech, join the team. My expertise is in parallel and distributed computing, and there are intersections with respect to data analytics and machine learning that weve got going on in our labs. Moataz has the skill set to look at different mathematical models used to build an algorithm to get inside the box so that we can build the best medical imaging software system possible.

CT scans, which combine X-rays and computers to capture 2-D cross-sectional images of the body, are able to detect the existence of COVID-19 through certain markers in the lungs. The initial studies from China and Europe on which Fengs team is building its research indicate that the detection using CT scans is possible in asymptomatic people and with higher accuracy (upwards of 90%) than any other test available, Khalifa said.

The barrier is getting people to the hospital for testing, but with a portable CT scanner, the testing can travel to where its needed, whether that be a college campus or an office complex. Portable scanners generally use less radiation and hence provide lower-resolution images. The algorithm Khalifa is working to modify will help enhance the quality of the images, improving the accuracy of detecting the coronavirus in its early stages. Another upside is that hundreds of people can be scanned a day, with results essentially available in real-time.

As the project moves forward, Khalifa will bring W&L undergraduates onto the research team. He noted that artificial intelligence and machine learning is a hot area right now. Working on a real-life project that will introduce students to how algorithms work in applications with crucial outcomes will provide them with the important skills that can transfer to other areas of computer and data science.

Khalifa added, W&Ls collaboration with Virginia Tech is a strong and mutually beneficial one, bringing us to the front lines of this fight against COVID-19. I cannot say enough about how exciting it is to be a part of it and to bring our university even closer to such a critical contribution during these times.

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Using Machine Learning to Track COVID-19 Washington and Lee University - Washington and Lee University News Office

A few years or even months ago, if we were asked about the importance of cloud technology and the ability to remotely access data in a secure manner, there would be few businesses that would show interest. However, in recent times, cloud technologies have proven to be the backbone of running a business. As remote working becomes the norm, the focus has quickly shifted to IT Infracture of companies and Machine Learning & Cloud Computing have finally been recognised for the key role that they play in any business. And so the question of whether you are up to date with the latest changes and revolutions in the industry comes to the forefront.

There are many who have analysed this trend and recognised the power that a key understanding of ML & Cloud can have in their career. Cloud technologies not only empower the IT team to provision new application servers and infrastructure on the go but also gives businesses the power to commission and decommission IT infrastructure at a much faster pace. What would have once taken hours or even days can easily be achieved in just a few minutes, thanks to Cloud Technology. upGrad has understood this fast-paced growth of the industry. IIT Madras, in association with upGrad, has designed an online program that can equip you with the required skill set as well as knowledge to set foot in this industry.

The Importance of ML & Cloud Anyone in the world of Information Technology and management knows that Machine Learning and cloud are the future of every industry. Big Data already plays a key role in every decision-making process and focusing on ML & Cloud today can truly help you revamp your career in an impressive and interesting avenue. The Advanced Certification in Machine Learning and Cloud from IIT Madras in association with upGrad offers just that, with utmost ease and comfort.

What the Advanced Certification in Machine Learning and Cloud Program OffersThe 12-month program which offers Advanced Certification from IIT Madras is a brilliant introduction to Machine Learning and also serves as the perfect tool to gain some practical knowledge in this field. The program has been designed to particularly appease ML enthusiasts who are keen on accelerating in this field by giving them a key understanding of machine learning models using Cloud.

Who is the program designed for? The 12-month program requires 12-15 hours of your undivided attention per work, making it a perfect choice not only for freshers but also senior professionals who are looking to accustom their skills with the new developments in technology. The Advanced Certification in Machine Learning and Cloud is priced at a nominal Rs 2,00,000 and you can also avail the no-cost EMI option that makes this program all the more accessible.

Why upGrad?upGrad has already made a name for itself in the Ed-tech segment. Not only does it provide reliable and articulately designed courses that help amplify your career graph but it also has an array of accolades to the brand name. For the Advanced Certification in Machine Learning and Cloud, upGrad has partnered with more than 300 Hiring Partners as well as industry experts from leading companies like Flipkart, Gramener, among others.

"This program puts you from a beginner level to a person who can understand and provide a Machine Learning solution to any given problem provided one has the passion to learn new techniques in a rigorous manner,said Vignesh Ram, who has benefited from upGrads programs that have steered his career in the right direction.

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Machine Learning & Cloud Technologies can make you a valuable resource today: Heres how you can succeed - Times of India

Thats why some people think we should just get machines to program themselves. Automated code generation has been a hot research topic for a number of years. Microsoft is building basic code generation into its widely used software development tools, Facebook has made a system called Aroma that autocompletes small programs,and DeepMind has developed a neural network that can come up with more efficient versions of simple algorithms than those devised by humans. Even OpenAIs GPT-3 language model can churn out simple pieces of code, such as web page layouts, from natural-language prompts.

Gottschlich and his colleagues call this machine programming. Working with a team from Intel, MIT and the Georgia Institute of Technology in Atlanta, he has developed a system called Machine Inferred Code Similarity, or MISIM, that can extract the meaning of a piece of codewhat the code is telling the computer to doin much the same way as natural-language processing (NLP) systems can read a paragraph written in English.

MISIM can then suggest other ways the code might be written, offering corrections and ways to make it faster or more efficient. The tool's ability to understand what a program is trying to do lets it identify other programs that do similar things. In theory, this approach could be used by machines that wrote their own software, drawing on a patchwork of preexisting programs with minimal human oversight or input.

MISIM works by comparing snippets of code with millions of other programs it has already seen, taken from a large number of online repositories. First it translates the code into a form that captures what it does but ignores how it is written, because two programs written in very different ways sometimes do the same thing. MISIM then uses a neural network to find other code that has a similar meaning. In a preprint, Gottschlich and his colleagues report that MISIM is 40 times more accurate than previous systems that try to do this, including Aroma.

MISIM is an exciting step forward, says Veselin Raychev, CTO at the Swiss-based company DeepCode, whose bug-catching toolsamong the most advanced on the marketuse neural networks trained on millions of programs to suggest improvements to coders as they write.

But machine learning is still not great at predicting whether or not something is a bug, says Raychev. Thats because it is hard to teach a neural network what is or isnt an error unless it has been labeled as such by a human.

Theres a lot of interesting research being done with deep neural networks and bug fixing, he says, but practically they're not there yet, by a very big margin. Typically AI bug-catching tools produce lots of false positives, he says.

MISIM gets around this by using machine learning to spot similarities between programs rather than identifying bugs directly. By comparing a new program with an existing piece of software that is known to be correct, it can alert the coder to important differences that could be errors.

Intel plans to use the tool as a code recommendation system for developers in-house, suggesting alternative ways to write code that are faster or more efficient. But because MISIM is not tied to the syntax of a specific program, there is much more it could potentially do. For example, it could be used to translate code written in an old language like COBOL into a more modern language like Python. This matters because a lot of institutions, including the US government, still rely on software written in languages that few coders know how to maintain or update.

Ultimately, Gottschlich thinks this idea could be applied to natural language. Combined with NLP, the ability to work with the meaning of code separately from its textual representation could one day let people write software simply by describing what they want to do in words, he says.

Building little apps for your phone, or things like that that will help your everyday lifeI think those are not too far off, says Gottschlich. I would like to see 8 billion people create software in whatever way is most natural for them.

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A neural network that spots similarities between programs could help computers code themselves - MIT Technology Review

Geneva, July 28, 2020STMicroelectronics has released a free STM32 software function pack that lets users quickly build, train, and deployintelligent edge devices for industrial condition monitoringusing a microcontroller Discovery kit.

Developed in conjunction with machine-learning expert and ST Authorized Partner Cartesiam, theFP-AI-NANOEDG1 software packcontains all the necessary drivers, middleware, documentation, and sample code to capture sensor data, integrate, and run Cartesiams NanoEdge libraries. Users without specialist AI skills can quickly create and export custom machine-learning libraries for their applications using Cartesiams NanoEdge AI Studio tool running on a Windows10 or Ubuntu PC. The function pack simplifies complete prototyping and validation free of charge on STM32 development boards, before deploying on customer hardware where standard Cartesiam fees apply.

The straightforward methodology established with Cartesiam uses industrial-grade sensors on-board a Discovery kit such as theSTM32L562E-DKto capture vibration data from the monitored equipment both in normal operating modes and under induced abnormal conditions. Software to configure and acquire sensor data is included in the function pack. NanoEdge AI Studio analyzes the benchmark data and selects pre-compiled algorithms from over 500 million possible combinations to create optimized libraries for training and inference. The function-pack software provides stubs for the libraries that can be easily replaced for simple embedding in the application. Once deployed, the device can learn the normal pattern of the operating mode locally during the initial installation phase as well as during the lifetime of the equipment, as the function pack permits switching between learning and monitoring modes.

Using the Discovery kit to acquire data, generate, train, and monitor the solution, leveraging free tools and software, and the support of theSTM32 ecosystem, developers can quickly create a proof-of-concept model at low cost and easily port the application to other STM32 microcontrollers. As an intelligent edge device, unlike alternatives that rely on AI in the cloud, the solution allows equipment owners greater control over potentially sensitive information by processing machine data on the local device.

The FP-AI-NANOEDG1 function pack is available now atwww.st.com, free of charge.

The STM32L562E-DK Discovery kit contains anSTM32L562QEI6QUultra-low-power microcontroller, an iNEMO 3D accelerometer and 3D gyroscope, as well as two MEMS microphones, a 240240 color TFT-LCD module, and on-board STLINK-V3E debugger/programmer. The budgetary price for the Discovery kit is $76.00, and it is available fromwww.st.comor distributors.

For further information please visithttps://www.st.com/en/embedded-software/fp-ai-nanoedg1.html.

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STMicroelectronics Releases STM32 Condition-Monitoring Function Pack Leveraging Tools from Cartesiam for Simplified Machine Learning - EE Journal

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Machine Learning market.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market 2019-2029. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Machine Learning market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Machine Learning Market by Service (Professional Services, and Managed Services), for BFSI, Healthcare and Life Science, Retail, Telecommunication, Government and Defense, Manufacturing, Energy and Utilities, Others-Global Industry Analytics, COVID-19 Business Impact, and Trends, 2017-2024

Abstract

The report covers forecast and analysis for the machine learning market on a global and regional level. The study provides historic data of 2015-2017 along with a forecast from 2018 to 2024 based on revenue (USD Billion). The study includes drivers and restraints for the machine learning market along with the impact they have on the demand over the forecast period. Additionally, the report includes the study of opportunities available in the machine learning market on a global level.

This report offers comprehensive coverage on global machine learning market along with, market trends, drivers, and restraints of the machine learning market. This report includes a detailed competitive scenario and the product portfolio of key vendors. To understand the competitive landscape in the market, an analysis of Porters five forces model for the machine learning market has also been included. The study encompasses a market attractiveness analysis, wherein all segments are benchmarked based on their market size, growth rate, and general attractiveness. This report is prepared using data sourced from in-house databases, secondary and primary research team of industry experts.

The study provides a decisive view on the machine learning market by segmenting the market based on service, verticals, and regions. All the segments have been analyzed based on present and future trends and the market is estimated from 2017 to 2024. By services, the market is divided into professional services and managed services. On basis of verticals, the market can be bifurcated into BFSI, healthcare, and life science, retail, telecommunication, government and defense, manufacturing, energy and utilities, others. The regional segmentation includes the current and forecast demand for North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.

The report covers detailed competitive outlook including the market share and company profiles of some of the key participants operating in the global machine learning market include International Business Machines Corporation, Microsoft Corporation, Amazon Web Services, Inc., Bigml, Inc., Google Inc., Hewlett Packard Enterprise Development Lp, Intel Corporation, and others.

The report segments the global machine learning market as follows:

Global Machine Learning Market: Services segment Analysis

Professional ServicesManaged Services

Global Machine Learning Market: Vertical Segment Analysis

BFSIHealthcare and Life ScienceRetailTelecommunicationGovernment and DefenseManufacturingEnergy and UtilitiesOthers

Global Machine Learning Market: Regional Segment Analysis

North America

The U.S.

Europe

UKFranceGermany

Asia Pacific

ChinaJapanIndia

Latin America

Brazil

Middle East and Africa

Quick Read Table of Contents of this Report @ Machine Learning Market by Service (Professional Services, and Managed Services), for BFSI, Healthcare and Life Science, Retail, Telecommunication, Government and Defense, Manufacturing, Energy and Utilities, Others-Global Industry Analytics, COVID-19 Business Impact, and Trends, 2017-2024

Trusted Business InsightsShelly ArnoldMedia & Marketing ExecutiveEmail Me For Any ClarificationsConnect on LinkedInClick to follow Trusted Business Insights LinkedIn for Market Data and Updates.US: +1 646 568 9797UK: +44 330 808 0580

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Global Machine Learning Market 2020 | Analyzing The COVID-19 Impact Followed By Restraints, Opportunities And Projected Developments - Owned

Machine Learning in Communication Market report focused on the comprehensive analysis of current and future prospects of the Machine Learning in Communication industry. This report is a consolidation of primary and secondary research, which provides market size, share, dynamics, and forecast for various segments and sub-segments considering the macro and micro environmental factors. An in-depth analysis of past trends, future trends, demographics, technological advancements, and regulatory requirements for the Machine Learning in Communication market has been done in order to calculate the growth rates for each segment and sub-segments.

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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Note In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.

Top Key Players Profiled in this Report are:

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

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.

Key Questions Answered in this Report:

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The report summarized the high revenue that has been generated across locations like, North America, Japan, Europe, Asia, and India along with the facts and figures of Machine Learning in Communication market. It focuses on the major points, which are necessary to make positive impacts on the market policies, international transactions, speculation, and supply demand in the global market.

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Table of Contents

Global Machine Learning in Communication Market Research Report 2020 2026

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 Breaking new grounds and touch new level in Upcoming Year by Amazon, IBM, Microsoft,...

The events of the past few months have taught us, among other things, how little control we have over our destiny. A major crisis, such as the Covid-19 pandemic, can come unexpectedly at any point of our lives and ruin everything we have worked to achieve. But as much as it may seem unfair, the unpredictability of future events and the constant change is the only thing that is certain.

Many things could have been done better. When it comes to the world of tech and AI many businesses will learn that it is worth investing in robust and bias-free machine learning solutions. On a larger scale, hopefully the world leaders will learn to take scientific data more seriously and with a greater sense of urgency. However, the reality is that no matter how much we invest in making our predictive modelling algorithms more accurate, change will always bring unexpected events our way.

The fact that change is the only thing we can be sure of is one of the key wisdoms of Vipassana meditation practice. Both good and bad things will keep on coming our way and we have to just observe and keep calm. Easier said than done? True, but meditation can help people find their path to equanimity. If you are not too familiar with meditation but have a good understanding of the world of AI and machine learning, there is an interesting connection between the two that can help you grasp the key principles of meditation practice.

Before going any further, it is necessary to clarify that meditation focuses on the brain and you cannot really compare a machine learning model to a human brain. To simplify, it would be a bit like comparing the first basic telephone from the 19th century with the latest iPhone 11 Pro. The original telephone was only capable of performing one task at a time whereas the iPhone is capable of multitasking and has complex functionalities which are not fully understood to most users. However, it is worth observing that the overarching process which describes many commonly used machine learning systems can also be used to describe the functionality of our brains.

A machine learning system consists of an input (i.e. data), algorithms that adapt and improve the more data you feed into them and an output which is a result of that process. Similarly with the brain there is an input in a form of sensory data (i.e. our senses, such as sight and sound) and neurons transmitting electrical signals in the brain that produce an outcome, such as your thoughts and actions.

Everything we experience throughout our lifetimes can be treated as input data and contributes to the shape and functionality of the brain. What is interesting in the case of a human brain is that some of the input data is processed consciously, however the majority happens sub-consciously without individual's awareness. This sub-conscious data processing in cognitive science is often referred to as priming and is a reason for another well-known concept in machine learning, namely bias.

People as well as algorithms are prone to making biased decisions. Some famous examples include: the familiarity bias[1] liking more what you already know, symmetry bias[2] perceiving symmetric faces as more attractive, other biases related to appearance such as perceiving wider male faces as less trustworthy[3], and many more incorrect or inaccurate inferences made based on first impressions. Everything we experience in life has an impact on our brain processing and therefore our decisions.

In machine learning, data scientists spend a lot of time and effort on data pre-processing and data mining to remove bias from the data. Similarly, Vipassana meditation practice focuses on peoples data input the five senses: sight, sound, smell, taste and touch. Throughout the meditation practice students are encouraged to sit still for hours at a time without any distraction and simply be aware of and observe the sensations of the body i.e., the data input. This is what is being fed into the brain at any given time, and should therefore require at least as much attention as the data fed into a machine learning system.

The overarching process is simple: you smell a flower -> feel a pleasant sensation in the brain -> you smile. The simplicity behind this input-output scenario (as well as many neuroscientific studies[4] which show that activity in the brain starts before people consciously realise what they are about to do) can help us understand and accept that the concept of conscious free will is an illusion[5].One of the key objectives of Vipassana practice is that the scientific laws that operate one's thoughts, feelings, judgements and sensations become clear. Life becomes characterised by increased awareness, non-delusion, self-control and peace[6].

The concept of free will and attaching too much importance to the idea of the self is a common source of unhappiness. Mr. Goenka, the Burmese-Indian teacher of Vipassana meditation points out that there is a tremendous amount of attachment towards this physical structure, this mental structure, by identifying oneself as I, I, I And the result is misery[7]. This is commonly seen in our society, people often attach their self-worth to imaginary physical or mental concepts such as their background, skin colour, religion, wealth or nationality. Too much focus on self-identity results in many social problems such as racism and identity politics.

Understanding the simplicity of the input-output scenario that describes our brain functionality can help us move beyond these made-up concepts that divide cultures and societies across the globe. Instead, we should perhaps take inspiration from a simple reinforcement learning system, reward the brain with positive experiences for ourselves and others and allow it to evolve in the direction of tolerance, understanding and compassion in order to find our path to equanimity.

[1] Newell, B. R., Lagnado, D. A., & Shanks, D. R. (2007). Straight choices: The psychology of decision making

[2] Little, A. C., Jones, B. C., Waitt, C., Tiddeman, B. P., Feinberg, D. R., Perrett, D. I., Apicella, C. L. & Marlow, F. W. (2008) Symmetry is related to sexual dimorphism in faces: data across culture and species

[3] Stirrat, M., & Perrett, D.I. (2010). Valid facial cues to cooperation and trust: Male facial width and trustworthiness.

[4] Haggard, P. (2008). Human volition: towards a neuroscience of will

[5] Wegner, D. M. (2002). The Illusion of Conscious Will. Bradford Books/MIT Press.

[6] Vipassana Meditation, As taught by S.N. Goenka in the tradition of Sayagyi U Ba Khin (https://www.dhamma.org/en/about/vipassana)

[7] Vipassana Meditation 10-day Course, S.N. Goenka

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Meditation And Machine Learning: A Guide To Acceptance And Equanimity - Forbes

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NEW YORK (PRWEB) July 29, 2020

Copilot (copilot.cx), a New York City and Tel Aviv, Israel based automated customer experience platform created for consumer electronics, announces today the launch of a new CX Trend system for early detection of a drop in customer satisfaction, reflected by major online stores ratings. Copilot is one of the newest automated customer experience platforms for consumer electronics companies and is the only in the IoT space to measure interaction with customers.

Copilot enables companies to collect usage data from their smart products to study consumer behaviors and gain insights on what frustrates users and what retains them. With this data, companies can automatically engage with end-users through meaningful, contextual messages to reduce product returns, improve online ratings, and increase users lifetime value.

Our mission is to help consumer brands shift the focus from transactional experience to ownership experience, says Co-CEO Zvi Frank. Consumer goods companies, which were traditionally focused on the point of sale are now looking into building a post-sale relationship with their customers. IoT products contain valuable usage information and communication channels (Mobile App, Email, Voice) which open up endless opportunities for building a personalized experience for their customers. By expanding our offering to meet the increased demand for online commerce COVID-19 has brought on, early warning of drop in customer satisfaction as well as the ability to analyze and react become even more critical.

With COVID-19 negatively impacting the sales of CE products and appliances, with a reported 1/4 of US consumers planning to spend less in this industry, Copilots newest program is a way to mitigate these challenges. The new rollout includes free analysis for any technology company looking to gain a deeper understanding of their product usage and customer satisfaction, ultimately creating much better customer service by this data.

For more information, visit https://www.copilot.cx/.

About Copilot:Copilot is the leading automated Customer Experience platform for consumer electronics companies. The platform delivers on the promise of IoT (Internet of Things) by offering manufacturers of connected consumer products and smart home devices the opportunity to automatically engage end users with meaningful, data-driven and behavior-based communications. Companies that employ Copilot improve onboarding, reduce product returns, boost product ratings and open new channels of revenue, giving them a critical advantage that increases overall customer satisfaction and builds Lifetime Value. Learn more about Copilot at http://www.Copilot.cx.

Media ContactKristen Mondsheinkristen@kmmcommunications.com

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IoT Customer Experience Platform, Copilot, Leverages Advanced Machine Learning Tools to Predict a Drop in Consumer Products Online Ratings - PR Web

GUILFORD, Conn., July 29, 2020 (GLOBE NEWSWIRE) -- InveniAI LLC, a global leader in pioneering the application of artificial intelligence (AI) and machine learning (ML) to transform innovation across drug discovery and development, is pleased to announce a strategic collaboration with GlaxoSmithKline Consumer Healthcare, a science-led global healthcare company with a mission to help people to do more, feel better and live longer, and one of the worlds leading over-the-counter (OTC) medicines company. GlaxoSmithKline Consumer Healthcare will leverage AlphaMeld, an AI and ML platform that empowers internal teams to efficiently evaluate and navigate emerging innovation spanning the Companys key focus areas. The three-year collaboration will grant full access to InveniAIs AI Innovation Lab that encompasses the Consumer Healthcare Module of AlphaMeld and an internal team of cross-functional experts to facilitate accelerated access to innovation.

Michael Keane, Director, Search and Evaluation, GSK Consumer Healthcare, said, At GSK Consumer Healthcare, patients and consumers are central to what we do, and it is their needs that drive innovation and product development at GSK. Our heritage in the pharmaceutical industry ensures that all our products are backed with science and associated data. To unlock and prime innovation, we are always listening and engaging with the consumer to ensure that all our products meet a demand. In doing so, the data that we accumulate is large, broad, and complex. InveniAI has helped us look at tasks that are datacentric across technology road-mapping, search and evaluation, and identifying and understanding new science. AI and ML have helped create efficiencies that shave 4-5 years off the innovation cycle as well as eliminate human biases in identifying innovation.

Aman Kant, InveniAIs Chief Business Officer, said, We are excited about this unique collaboration that will help GSK utilize the power of an AI-driven platform to identify and develop innovative life-changing healthcare products for their consumers. This customized platform delivers tremendous business value by not only rapidly assimilating large volumes of disparate data sets to facilitate a first-mover advantage, but also releases expert human resources at GSK to focus on higher-value tasks. For GSK, the platform enables innovation to be captured as early as patent filing, project funding, or even social media sentiment across several focus areas. He added, We are seeing how this technology can provide industries with a tremendous force-multiplier for rapidly sourcing innovation opportunities.

About GSKGSK - a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer. For further information, please visit http://www.gsk.com.

About InveniAIInveniAI LLC, based in Guilford, Conn., is a global leader pioneering the application of artificial intelligence (AI) and machine learning (ML) to transform innovation across drug discovery and development by identifying and accelerating transformative therapies for diseases with unmet medical needs. The Company leverages AI and ML to harness petabytes of disparate data sets to recognize and unlock value for AI-based drug discovery and development. Numerous industry collaborations in Big Pharma, Specialty Pharma, Biotech, and Consumer Healthcare showcase the value of leveraging our technology to meld human experience and expertise with the power of machines to augment R&D decision-making across all major therapeutic areas. The company leverages the AlphaMeld platform to generate drug candidates for our industry partners and internal drug portfolio. For more information, visit http://www.inveniai.com.

Contact InformationAnita Ganjoo, Ph.D.Corporate Communications InveniAIT: +1 203 273 8388 E: aganjoo@inveniai.com

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InveniAI Enters Strategic Artificial Intelligence (AI) and Machine Learning (ML) Focused Collaboration with GlaxoSmithKline Consumer Healthcare -...

Material Informatics (MI) is a data-centric approach applicable to specific material science and chemistry R&D. Without a doubt, this will become a standard method in a research scientist toolkit.

FREMONT, CA: Machine learning has rapidly become an essential part of every industry. Material scientists and chemists will all have access to machine learning tools to enhance their Research & Development in the future. Seamlessly integrating these underlying operations will not happen quickly, but overlooking the developments in materials informatics will lead to a loss of competitive advantage.

Material Informatics (MI) is a data-centric approach applicable to specific material science and chemistry R&D. Without a doubt, this will become a standard method in a research scientist toolkit. Instead of just grabbing headlines, some form of MI will be assumed in all developments. The key to MI is around the integration, implementation, and manipulation of data infrastructures as well as machine learning approaches designed for chemical and materials datasets.

There is a significant amount of evidence to support this. However, the best backing is how the industries are responding to the technology. There has been a large amount of activity over recent years, including partnerships, investments, and announcements from some of the most notable chemical and materials companies.

Machine learning, by itself, can be used in various kinds of projects, from finding new structure-property relationships, proposing new candidates or process conditions, reducing the number of expensive and time-consuming computer simulations, and more. Machine learning approaches can take numerous forms of supervised and unsupervised learning methods. Generative methods can be effective at screening for optimized outputs across organic compounds. At the same time, even simple modified random forest models can be useful for proposing follow-on reactions to meet a desired set of criteria.

However, this is still at an early stage and requires a lot more development. There is a lot to be leveraged from existing developments in AI, but will first require integrating specialist domain knowledge and coping with the unique challenges of a materials dataset. The application space is broad, and studies have shown success ranging from organometallics, thermoelectrics, nanomaterials, and ceramics to many more.

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IDTechEx Report Suggests Machine Learning will be Accessible across Chemical and Materials Companies in the Future - CIO Applications