Over the last five years the interest of pharmaceutical professionals towards machine learning (ML) and artificial intelligence (AI) has measurably increased -- while only one “AI-related” research collaboration involving “big pharma” appeared in the news in 2013, the number of such events increased up to 21 in 2017 alone, involving some of the top pharma players like GSK, Sanofi, Abbvie, Genentech, etc.
Topic: ‘Artificial Intelligence’
Section: Biopharma Insights View all sections
(Last updated 08.10.2018)
The type of artificial intelligence (AI) which scares some of the greatest minds, like Elon Musk and Stephen Hawking, is called “general artificial intelligence” -- the one which can “think” pretty much like humans do, and which can quickly evolve into a dangerous “superintelligence”. There is a notion that it might be invented in the nearest decades, but today we are definitely not there yet. The AI which is making headlines these days is a “narrow artificial intelligence”, a limited type of machine “intelligence” able to solve only a specific task or a group of tasks. It can’t go anywhere beyond specifics of the problem for which it is designed, so apparently, it will not hurt anyone in the nearest time. But already now it can provide meaningful practical results on those narrow tasks, like natural language processing, image recognition, controlling self-driving cars, and helping develop new drugs more efficiently. With the ability to find hidden and unintuitive patterns in vast amounts of data in ways that no human can do, AI represents a considerable promise to transform many industries, including pharma and biotech.
What is a super-platform?
A “super-platform” is a term which describes a relatively new phenomenon in a modern technological world -- an online-to-offline (O2O) type of digital infrastructure, which spans across multiple sectors of economic activity providing a way for users (both businesses and consumers) to operate with multiple resources, products and services within a uniform, standardized, and highly interconnected way.
Imagine, you want to be able to search for information, shop online, pay for products and services, communicate with someone by email, or chat, create and manage text and spreadsheet documents, translate them into any language on the go, store and organize data like photos and videos, find local restaurants and get driving directions, or just entertain yourself by playing games -- and you prefer to have all of that in one place without needing to search for numerous websites and resources? You can do just that in your single Google account. This is what a super-platform does: it provides a way to conveniently engage in totally different types of activity across different sectors.
Today Basel is crowded with some of the top business and research leaders representing a young and rapidly growing industry of artificial intelligence (AI) in healthcare and pharmaceutical research. They come together to announce mission and launch activities of a global Alliance for Artificial Intelligence in Healthcare (AAIH), which is to become a leading international organization for advancing artificial intelligence innovations in Drug Discovery, Clinical Research, Diagnostics, Precision Medicine and other key areas of pharmaceutical research and healthcare. The newly formed alliance will be a voice of the industry in matters of education, lobbying for policies and regulations, facilitating investment, and promoting AI-innovations among top drug makers and healthcare institutions.
(Edited version of this post originally appeared in Forbes)
“It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change” -- Leon C. Megginson
Last year brought about new hope and even more hype around the idea of applying artificial intelligence (AI) for “revolutionizing” drug discovery research -- via machines being able to “learn” chemistry and biology from vast amounts of experimental data to propose potent drug candidates, accurately predict their properties and possible toxicity risks. It is supposed to dramatically minimize failures in clinical trials -- saving R&D budgets, time, and most importantly, lives of patients.