Radical Reads

Radical Talks Podcast: Artificial Intelligence and the Future of Healthcare with Dr. Eric Topol  (Radical Talks Podcast)

Dr. Eric Topol joins Radical Ventures Managing Partner Jordan Jacobs for a discussion about the latest challenges in combating COVID-19, and how Artificial Intelligence is the future of healthcare — the key to better health and more compassionate care.

Dr. Topol is the founder and director of the Scripps Research Translational Institute, professor of molecular medicine, and executive vice president of Scripps Research. His latest book is Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.

Radical Scale: Managing the Sales Pipeline in a Pandemic  (Radical Scale)

Over the past several months, the Radical Ventures Impact Team has hosted expert roundtables on go-to-market and talent strategies with our portfolio companies. We are now sharing the key strategic and tactical insights from those sessions via a new series we are calling Radical Scale.

Given the extraordinary balance sheet challenges facing many startups in the wake of the pandemic, Radical Scale kicks off with insights from sales leaders from Snowflake, Lars Nilsson and Travis Henry, on best practices for managing the sales pipeline during Covid-19.

AI in Healthcare: How AI-controlled sensors could save lives in ‘smart’ hospitals and homes  (Science Daily)

“As many as 400,000 Americans die each year because of medical errors, but many of these deaths could be prevented by using electronic sensors and artificial intelligence to help medical professionals monitor and treat vulnerable patients in ways that improve outcomes while respecting privacy.”

Radical Commentary: Radical’s healthcare thesis touched on how remote assistants and sensors will achieve significant adoption across clinical settings driven by an increased trust in these solutions, as well as more sophisticated digital infrastructure. In the referenced report, two Stanford professors, computer vision pioneer Fei Fei Li and Dr. Arnold Milstein discuss how clinical decision support tools, when combined with the increasing sophistication of machine learning algorithms, will soon surpass human capabilities. For example, AI-powered systems can give real-time feedback on patients and those caring for them: a patient getting out of bed could be quickly evaluated, as could the steadiness of a surgeon’s hand with a scalpel.

Dr. Milstein is also a co-founder of DawnLight, a Radical portfolio company which uses ambient intelligence to support caregivers in delivering the highest quality care. For those with chronic conditions, DawnLight’s technology will assist with detecting early signs of deterioration and preventing unnecessary hospitalizations. In hospitals, DawnLight can help track vitals continuously during a patient’s stay, reducing the workload on healthcare professionals while improving the quality of care.

AI M&A: NVIDIA to Acquire Arm for $40 Billion   (NVIDIA)

“NVIDIA and SoftBank Group Corp. (SBG) today announced a definitive agreement under which NVIDIA will acquire Arm Limited …in a transaction valued at $40 billion…

The combination brings together NVIDIA’s leading AI computing platform with Arm’s vast ecosystem to create the premier computing company for the age of artificial intelligence, accelerating innovation while expanding into large, high-growth markets.”

Radical Commentary: NVIDIA’s acquisition of ARM is notable not only because it is the largest semiconductor acquisition in history, but also because it lays the foundation for a future where machine intelligence operates on edge devices everywhere. As NVIDIA CEO Jensen Huang puts it: “In the years ahead, trillions of computers running AI will create a new internet-of-things that is thousands of times larger than today’s internet-of-people.” Achieving this vision will require integrated GPU and CPU data centers, edge compute data centers, and a software layer that connects edge devices powered by NVIDIA and ARM hardware.

Untether.AI, a Radical portfolio company, has developed a world-leading AI inference chip, which is clearly becoming an extraordinarily valuable part of the future of the semiconductor and edge computing space.

AI Safety: AI researchers devise failure detection method for safety-critical machine learning   (VentureBeat)

“Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence. Safety-critical machine learning systems make decisions for automated technology like self-driving cars, robotic surgery, pacemakers, and autonomous flight systems for helicopters and planes. Unlike AI that helps you write an email or recommends a song, safety-critical system failures can result in serious injury or death. Problems with such machine learning systems can also cause financially costly events like SpaceX missing its landing pad.”

Radical Commentary: The pandemic has accelerated the digitizing of processes across many industries. A notable tailwind has been in the transportation industry which includes automobiles, trucks, drones, and, most recently, plans for autonomous seafaring vessels. However, for AI deployments in safety-critical industries such as autonomous vehicles, a great deal of trust must be established between the operator, regulators, and the public. Transparency can be challenging when a company’s value depends on protecting the IP of their raw models. Neural bridge sampling may offer regulators, academics, and industry experts a solution, whereby the public can ensure that a system has been rigorously tested and an organization can protect their proprietary information. This approach may offer a step toward a safer, statistic-driven, future that enables technological innovation.

***
Editor’s Note: We will continue to use this platform to share without commentary articles focused on data and the use of it to illustrate and illuminate racial injustice. Because you cannot fix problems you cannot see or understand.

Racial and Ethnic Differences in Self-Reported Telehealth Use during the COVID-19 Pandemic: A Secondary Analysis of a U.S. Survey of Internet Users from Late March  (Journal of the American Medical Informatics Association)

“Approximately 17% of respondents reported using telehealth because of the pandemic, with significantly higher unadjusted odds among Blacks, Latinos, and those identified with other race compared to White respondents. The multivariable logistic regressions and sensitivity analyses show Black respondents were more likely than Whites to report using telehealth because of the pandemic, particularly when perceiving the pandemic as a minor threat to their own health.

The systemic racism creating health and health care disparities has likely raised the need for telehealth among Black patients during the pandemic. Findings suggest opportunities to leverage a broadly defined set of telehealth tools to reduce health care disparities post-pandemic.”