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Human Component in Machine Learning


With automation becoming increasingly popular in the field of machine learning, one may wonder if the role of humans in machine learning will become non-essential at some point. When building a machine learning model, it's important to remember that the model must produce meaningful and interpretable results in real-life situations. This is where the human experience comes in. A human (qualified data science professional) has to examine the results produced by algorithms and computers to ensure that the results are consistent with real-world situations before recommending a model for deployment. With automation in machine learning, humans are still indispensable to make the connection between data, algorithms, and the real world.

AI and pharma


The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions. This experience has transformed mindsets; leaders are more likely to lean into a future that lies in digital investments, data, and AI because of this experience. At present, the life sciences industry has only begun to scratch the surface of AI's potential, primarily applying it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products. We will highlight a number of these use cases in this report.

Spectroscopy and Chemometrics News Weekly #47, 2020


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Future of Smart Healthcare


The field of healthcare and medicine and specially the digital healthcare will get a great boost with the advancement and wide scale use of Quantum Computing and Artificial Intelligence. In fact, these technologies have already started transforming different areas of Healthcare and Medicine in a big way. Before even quantum computers were there, scientists at the University of Virginia School of Medicine long back anticipated the potential of quantum computers to better understand genetics and different diseases. The envision been realized and a team at the University's center for quantum computing & biology is now harnessing the power of quantum computing to gain better insights into genetic diseases with the help of machine learning algorithms. Researchers expecting that these efforts will benefit not only health care and medicine but also many other streams of science and technology.

The Upsurge of Artificial Intelligence Scientist


Artificial Intelligence (AI) is already underway. Perhaps, not the way you may have been led to think. Though AI has been a recurring topic since the 1950s, it is only now that the field started gaining traction due to the advancement in technology and algorithms. Most companies are excited to join the new fray of the AI trend. With modern AI, deep learning techniques, and natural language processing (NLP), organizations are ready to embark on the AI journey.

Bristol Myers' Richard Hargreaves pays $70M to launch a neurodegeneration alliance with a star player in the machine learning world


Bris tol My ers Squibb is turn ing to one of the star up starts in the ma chine learn ing world to go back to the draw ing board and come up with the dis ease mod els need ed to find drugs that can work against two of the tough est tar gets in the neu ro world. Daphne Koller's well-fund ed in sitro is get ting $70 mil lion in cash and near-term mile stones to use their ma chine learn ing plat form to cre ate in duced pluripo tent stem cell-de rived dis ease mod els for ALS and fron totem po ral de men tia. Then they'll use those in sights to start build ing new drugs for those two ail ments; a com plex, ground-up ap proach that has al ready won a close al liance with Gilead. Suc cess would trig ger up to $2 bil lion in mile stones, run ning a gamut of re search and com mer cial goals. "We be lieve that ma chine learn ing and da ta gen er at ed by nov el ex per i men tal plat forms of fer the op por tu ni ty to re think how we dis cov er and de sign nov el med i cines," said Richard Har g reaves, the chief of the neu ro group at Bris tol My ers, who made the leap from Cel gene.

How Machine Learning is Transforming Drug Discovery


In a world where a drug takes years and billions of dollars to develop, just one in 20 candidates makes it to market. Daphne Koller is betting artificial intelligence can change that dynamic. Twenty years ago, when she first started using artificial intelligence to venture into medicine and biology, Koller was stymied by a lack of data. There wasn't enough of it and what there was, was often not well suited to the problems she wanted to solve. Fast-forward 20 years, however, and both the quantity and quality of data, and the tools for studying biology, have advanced so dramatically that the adjunct professor of computer science at Stanford founded a company, insitro, that uses machine learning (a subspecialty of artificial intelligence) to explore the causes and potential treatments for some very serious diseases.

AI as a tool for invention


We at Prosus have an approach to AI that builds on four pillars: (1) AI everywhere, (2) at scale, (3) by design, and (4) ethical and responsible. As we continue developing capabilities across the group and increase the number of models in production, we also dedicate significant resources to understanding how AI can be used above and beyond mainstream applications. One of these is "AI as a tool for invention". AI is common in many areas, from object detection, to language processing or task automation. AI as a tool for invention is different.

Why does deep learning work so well?


Modern machine learning research has demonstrated remarkable achievements. Today, we can train machines to detect objects in images, extract meaning from text, stop spam emails, drive cars, discover new drug candidates, and beat top players in Chess, Go, and countless other games. A lot of these advancements are powered by deep learning, in particular deep neural networks. Yet, the theory behind deep neural networks remains poorly understood. Sure, we understand the math of what individual neurons are doing, but we're lacking a mathematical theory of the emergent behavior of entire network.

Building a chemical blueprint for human blood


Our blood transports many chemicals besides oxygen and carbon dioxide. Some of these molecules provide useful indicators of the state of our health. Indeed, measuring such biomarkers is a common feature of clinical blood tests. Other molecules present, such as hormones and drugs, directly affect health by modulating processes such as metabolism and immune responses. Writing in Nature, Bar et al.1 shed light on the factors that affect the recipe for human blood's chemical brew.