AI Articles and Analysis about Research and development

We see evidence dating back to 2017 that Johnson & Johnson has been regularly publishing about their investments and initiatives related to artificial intelligence. At present, Johnson & Johnson does not seem to boast any mature, deployed applications with the firm itself, but its AI-related investment initiatives indicate their aspirations.

Large enterprises are eager to use artificial intelligence software, but many of them aren't aware of the hardware required to execute many AI capabilities. To get a better idea of these hardware considerations, Emerj spoke with Victoria Rege, Director of Alliances & Strategic Partnerships and Graphcore, for Kisaco Research's AI Hardware Summit in Europe, which takes place October 29 - 30 in Munich, Germany.

Artificial intelligence is transforming a variety of banking functions and allowing tech startups to compete with some of the largest banks for market share of key services, including lending and wealth management. Business news and media sites have been heralding the downfall of the banking industry as we know it because fintech companies are going to feel comfortable leveraging AI long before banks.

Generic pharmaceuticals require less research and development than their brand name counterparts. As a result, AI applications for research and development don’t seem to be the most prominent solutions for generic drug companies. That said, despite the lack of precedence, there may be many areas in which AI could help generic drug companies.

The facilitation of research and development (R&D) is perhaps the most common use case for AI applications in the pharmaceutical industry. There are numerous solutions for extracting and organizing research data from clinical trial notes and other medical documents. Additionally, there is software that can purportedly analyze data from images of drug compounds at the molecular level.

The focus of the National Aeronautics and Space Administration (NASA) is to provide information to civilian institutions to help them solve scientific problems at home and in space. This requires a continuous stream of raw data under a constantly shifting environment.  According to a 2017 interview with Kevin Murphy, Earth Science Data Systems Program Executive at NASA, the biggest challenge now is not going where no man has gone before, but managing the data.

McKinsey estimated that embarking on digital transformation to restructure value chains and drive R&D innovation across the pharmaceutical industry could be worth $50–150 billion of earnings before interest, taxes, depreciation, and amortization. In particular, machine learning is likely to continue finding a place in the pharmaceutical industry. Pharmaceutical companies have found applications for machine learning ranging from drug discovery to clinical trial retention.
The State of AI in the Asian Pharmaceutical Industry
AI seems to be making its way into the pharmaceutical space in Asia over the last two or three years, particularly in China and Japan. For the most part, the companies offering or using AI for drug discovery are just starting to acquire funding and talent. XtalPi seems to have the highest density of talent with a decent likelihood of being able to work with machine learning.

Here at Emerj we’re dedicated to cutting through the AI hype that’s permeating the current zeitgeist in the business world. Although we’re skeptical about many of the claims that AI vendors make on their websites about what they’re AI software can do, it seems unlikely that the AI hype is going to disappoint venture capitalists and governments enough to usher in a third AI winter.