Aman Bhandari

The conclusion that @sayashk and I have reached over and over: direct harms from gen AI (that you can regulate) pale in comparison to the cost of societal adaptation and of the extractive business model. Without structural reform, those will continue. aisnakeoil.com/p/ai-safety-is…

Look out for the ChatGPT wrapper SPAC masquerading as a TechBio company :)

@PearlF The speed of this (GenAI —> job replacement) seems awfully fast, seems like a cover to make org changes.

20% of pharma & life sciences CEOs who participated in PwC's survey (below Jan story) said they expect to reduce jobs by 5% or more in '24 "as a result of generative AI." I don't know if this is related to the survey, but Novartis told Reuters they're eliminating nearly 700 development org jobs with plans to hire more data scientists. Genentech announced 3% reduction of workforce today

Why the rush to use emerging, experimental AI technology in high-stakes settings? As Emily Tavoulareas writes, people and organizations are integrating LLMs and chatbots into their lives and work as if the technology is reliable — but it is clearly not. techpolicy.press/sure-no-one-kn…

This is true, and why AI doing the writing for you is intrinsically limited. The act of writing is the act of *thinking*. If you're not doing the writing yourself, the end product will be superficial and banal because the prompts were underthought

“You don’t imagine something first and then write it down. It’s through the act of writing that ideas make themselves known.” nautil.us/the-magic-of-t…

Impressive timeline as @DanaFarber governance addressed needs and constraints to allow for rapid deployment of generative AI at their institution. @NEJM_AI ai.nejm.org/doi/full/10.10… Example for other research institutions and academic centers. #cancer #healthcare #biomedicalResearch

This is a useful thread of recent papers showing that LLMs are biased when judging people in HR contexts. The papers offer some potential solutions, but it is important to recognize that current systems carry subtle biases that can be hard to recognize without extensive testing.

I have talked to a lot of executives buying Microsoft Copilot for their firms. Not one seems to have considered what it means to suddenly automate the vast majority of management writing without training or reconsidering the meaning of the work. From my book, Co-Intelligence:

Scope is the enemy of proactivity on data teams. Proactivity is all about seeing what's happening and understanding what's coming next so you can be ready for it. The more things your team is responsible for, the more difficult this becomes.

I work on health-inspired AI, and often find it difficult to be taken seriously in the ML community (e.g., "you should submit to a more specialized venue"). This paper sums up my experience perfectly + highlights how beneficial application-driven ML can be to the field!

Noticing an interesting trend in the world of techbio, where people with no research background say ‘when I worked in lab’ and they mean their one semester in a bio class. Never in my wildest dreams did i think the daily slog I did for 10+ yrs would become a desirable trait 😂

More from @LifeSciVC on AI: I think true generative AI—coming up with brand new molecules never before seen by man, I think that remains elusive. A lot of the discovery-focused AI shops are mining patent literature and adding methyl groups here and there to existing things—and that has value. But separating that from the sort of hype of, hey, we're going to turn a four year discovery process into four days & a computer and it's going to be great and we're going to be cranking out tons of drugs—I think that's just not borne out by history. Right? What was it the cover of Fortune magazine in 1981 was Merck’s new computer that was going to design drugs? And here we are, four decades later, and you know, we're still learning how to apply all this powerful computation to making new medicines. I'm a big fan of it. We put money where our mouth is on that. Starting Nimbus back in 2009 as a computer-focused drug company this, and every one of our companies has some version of that in its wheelhouse around AI and machine learning—big compute, Big Data, computer processing. I’m enthusiastic about it, you can't dismiss it. But I think a level of cautious optimism is required.

How do healthcare systems considering AI avoid the pitfalls articulated by Participant 7 below? @NEJM_AI ai.nejm.org/doi/full/10.10… #practicalAIGovernance

How can we build an economy enabling all people to have what they need to experience wellbeing? Excited to announce a new Health & Political Economy Project to tackle such questions. @DarrickHamilton @victorroy Join us for our 4/2 launch event in NYC! bit.ly/newagendahealth

📢 #CallforPapers! JMIR Medical Informatics seeks contributions for a new section on "AI Language Models in Health Care." Explore real-world impacts, challenges, & strategies. Learn More: jmirpublications.com/announcements/…… #aiinhealthcare #medicalinformatics

I’m obsessed with great leadership. But when I was young, I wasted years and opportunities not knowing what great leadership looked like. Learn from my mistakes. 21 clear signals you have a great leader:

Two things can be true at the same time: 1. Pretty much no one anticipated what could be done in 2023 with next-token prediction models. 2. The notion that current LLMs possess human-level language understanding is delusional.

Accenture is doing more Gen AI revenue than all VC backed startups combined