YouSaid · the spoken record

Karan Singhal

lines on the record: 41
first: 2023-05-18
most recent: 2023-05-18
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I think that's going to be really important for moving this forward. I think also solid research, thinking about better ways to improve the ways we are taking in human feedback. I think the jury is still out with respect to how to best collect human feedback even. I think people are still debating things like whether or not pairwise comparison versus rewrites are the best things to do. And that's a valuable thing to think about. I think another thing to think about is how to actually use that human feedback in the most valuable way, especially given all the scalable oversight concerns that you guys mentioned. I think that's a significant limitation of MedPom as it is today. And I think there's a lot of exciting things to do. And I think a lot of these questions are like foundational questions for AI more broadly, but become more acute and more relevant in the setting.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. I mean, I think for real world uptake of these models, there are a few large language model capabilities, in some cases that already exist, but we need to figure out the right way to do them. And I think a few of them are just multimodality, which is something that we're working on. We kind of previewed last week at I.O. and grounding and authoritative sources, I think, is important as well, thinking about how these models can use tool format-like approaches to, for example, query authoritative medical information like a human would, but potentially better. I think that's also one way of getting around the risk averseness that you see in this area with respect to health information. If you're able to attribute information to an authoritative source, I think that has been something that has progressed this area in big companies before. And so where, for example, Google is doing that with health information is largely because it can attribute things to the Myoclinic and other organizations.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“A longer term thing than five years, but I think there's also short term things that we can do as well. So thinking about looking at correlations across modalities and existing data to find novel Mahayan markers for existing diseases that we know about or kind of using large language models as research assistants. So I think there's already a lot of work on the idea of literature search and augmenting literature search with large language models. I think there's a lot of opportunity there. And that goes a little bit beyond what MetPom is likely going to do, but I think that's something that I think is going to be really promising with respect to the future of AI. Because I think in the long term, when things go really well with AI, it's going to be because we've solved a lot of the most pressing scientific problems of today. And I think that's going to be because it augmented scientists. It helps scientists. It helped us figure out what are the things that we're missing. And I think there's a lot of potential there. So I'm also really excited about that in the long term.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I guess I think about this in two broad buckets. I think there are two broad types of things that we can do for large language models in the medical field. I think the first is increasing the standard of care very broadly. And so that looks a lot like increasing access to health information, providing assistance to physicians. So the radiology example I gave earlier, potentially clinical decision support, like double checking a doctor's decision or quality assurance for a radiologist report. So if a radiologist is dictating a report, they say no plural effusion scene, but then it's written down as plural effusion scene, then maybe an AI double checks that and just makes sure that that's what was intended. I think augmenting telemedicine, I think, is kind of a short-term opportunity. I think in the next five years is very achievable. I think the other big bucket of things that is very much achievable is augmenting scientific workflows. And I think this could be.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Important problems we need to solve versus like getting caught up in the hype wave and forgetting to solve the most important problems as well.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“But there's also a lot of justified concern about the potential limitations of these models and how we can get over them. Personally, I mean, from what I've seen from giving talks to different groups and chatting with different folks and different stakeholders, I think there's like a widely held optimism about this technology and about the potential. But I think there's also kind of a little bit of fear that I think people have seen in other domains like I think programmers often feel a little bit of fear when they see GPT-4, for example. And I think it's not necessarily a fear that like jobs will be replaced in the short term or things like that, but it's more of a fear of, look how fast things are moving. This is not like think about just the improvement from MedPom 1, GPD4, MedPom 2 in three months. It's absolutely crazy. And I think we, you know, it's definitely an inflection point for AI, as you guys know. And I think it's definitely a good time to think about what are the most important.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. That's a really great question. I think when we started this brain moonshot, which we call it within Google, that was actually our motivation. It was really to think about the fact that these models had already kind of already exist. And there was this opportunity to catalyze the medical AI community to really think about them carefully and think about the promise there and to catalyze the AI community to think about how we can resolve any remaining limitations that would prevent real world uptake. And so this was really our goal. And I think when we started this, there was much less conversation about the potential for large language models and foundation models for healthcare. And I think partly because of, I think largely also because of other work that's gone on, you know, with GPT-4 and excitement around that, I think there's much, much more conversation about how these models can be used in the setting in a productive way. I think that's really, really exciting. And I think there's a lot of optimism I see.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of standard for verification is a bit lower here. And so there's that ability for humans to be able to judge a response that potentially they wouldn't be able to judge otherwise via things like debate. And so that's another thing. I mean, another thing which is people are working on as well is thinking about how we can take AIs that are less capable and use that to kind of supervise other AIs that are more capable. And so this is kind of the motivation. I mean, this is partly the motivation of RLHF as well, even though it's about human feedback. It's about training a reward model that takes into account human feedback. And then at that point, it's AI feedback from then on. And then you use your RL algorithm, and then you get rewards from your reward model. AIF or constitutional AI kind of builds on that idea. But there's also limitations to that approach as well. I mean, I think if you ask researchers across all these organizations, have we solved this problem? Do we know what we're supposed to do?”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“This is really, really interesting question. I don't think I have all the answers, but I think there are approaches that people at Google and other organizations have been looking at. I think a couple ideas here that I think are interesting and useful. One is the idea of kind of self-refinement or self-critique of these models. And so this is the idea that these models can take their own responses, give critiques, often guided with human feedback. And so that's where the place where human feedback comes in. But some of these techniques, there is no human feedback. And in that case, I'm not sure that's as valuable. Give critiques guided by human feedback and then use that to produce better answers. That's one line of approaches. I think a second line of approaches is around debate. And so the idea here is that it's easier for a human to judge a debate between two different answers than to judge itself. And so the”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Because AI capabilities are starting to reach human level. And so when we start to get to that point, like things like RLHF start to fail and starts to become unclear what to do. And so I actually think the medical setting is a scenario in which this is already more obvious. So you're already in a setting in which you need experts to be able to evaluate answers. And one thing we're seeing with MedPalm2 is we get closer to physician level performance on medical question answering is that It's hard to tell the difference anymore. It's hard to tell the difference between different models, hard to tell the difference between models and physicians. And when you're at that point where it's uninformed.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Or maybe more broadly, I think ends up being a better scenario to study concerns about technical safety and to mitigate concerns like misalignment with human values or hallucinations or things like that. And so I think this comes down to things like making sure the incentives are aligned with respect to releasing products. So for example, I think if any organization wants to release products in the space, it actually needs to work on these problems more so than I think ChatGPT. I think it also comes down to kind of the stakes of the setting. I think everybody feels like the stakes of the setting are high enough that everybody feels like these issues are especially important and there's no debate about that. And I think there's also more subtle technical points. Like I think one issue that alignment researchers are now working on is the idea of scalable oversight, which means how do you give human feedback to a model when human feedback might not be super well informed or it might be unreliable?”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. I mean, this is a lot of what got me thinking about the setting, especially coming into the setting as somebody who didn't have much of a medical background in terms of expertise. I was really thinking about the big things that I could do to help shape the trajectory of AI or nudge it in a more beneficial direction. And thinking about AI safety seriously in terms of both short-term and longer-term risks, I think was important to me. And so, you know, one thing I've become more convinced of about over time is this idea that many organizations right now, Google, DeepMind, Anthropic OpenAI are right now looking at the idea of a general chat assistant. And kind of instead of doing alignment research in a vacuum are looking at that setting as a way in which we can think about kind of better refining these models and better aligning them to human values. I think there's a good chance that this setting, this medical setting, for example, medical question answering.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“From sallow any patient health information in the future potentially, or any other data that's quite sensitive from engineers or other folks at big companies or small companies.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Much data you have and how relevant you have, how relevant that data is. The ideal thing would be to have access to all the data, but in a privacy-preserving way, in a way that people are in control of their data, are able to revoke access to that data and are able to kind of benefit from that shared understanding of their data. And so that's kind of the ideal world. But I think there are real world obstacles to doing federated learning on health data, which actually kind of increase the activation energy to the point where in the next few years, I doubt that like the biggest advances are going to come from using federated learning approaches. But I think there are kind of intermediate solutions, which people often sometimes refer to as federated, but maybe are not technically federated, which are things like trusted execution environments or other environments in which models are running, but don't have the folks at Google don't have access to the data or the direct access to the models. And so there is this ability to kind of silo that.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“My sense is that I think one hesitation I have there is that I think a lot of the most impactful work that's going to happen in this setting is going to happen with the largest and most capable models, at least for the next few years, it seems like. And I think that one thing that we're seeing is that even without any patient health information put into these models, like for example, MedPom and MedPom2 are trained without any patient health information. They're just kind of taking all the knowledge of POM and POM2 and then just kind of aligning them and making them behave in a certain way. I think in the short term, there is this kind of thing that we see where models like GPT-4 and MEPMOM and MEPMOM2 are able to do surprisingly well without any patient health information. And so it seems like we can get fairly far with that. I mean, in the longer run, I do think that coming back to that question of data and how do you think about how to train a model depending on how”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that's a great question. So, I mean, as I mentioned before, the first couple of years of my career, we're really thinking more about privacy-preserving machine learning and federated learning and scaling that up and coming up with new algorithms that can learn new things without sending all the data to a centralized place. And so in a lot of ways, that has a very, very natural fit with the setting. And part of my motivation when I first started working on the setting was bringing in a lot of that expertise and bringing it into that setting.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“I think in the longer run, I think a lot of these concerns, I think, are actually unclear in terms of how things will work out. I think there is kind of a bigger question about software of unknown provenance and how that will be used and regulated in the future. There could be some kind of situation in which these things actually end up being very hard to scale up and apply in the real world for high stake settings. But I think we'll probably end up with a scenario where it'll become obvious that we need to and that we must and that doing so will improve patient outcomes. And so then I think it'll be time to have a serious conversation about what regulating these models and making sure privacy concerns are mitigated looks like. And I think we have yet to have that discussion.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it's a great question. I mean, I think this is something that people are just trying right now and just seeing what happens. And it's kind of interesting. People are just putting in patient information into GBD4. Sometimes they're redacting information and all these kinds of things. I mean, I think the ideal way to do this obviously is more privacy forward, I think, in terms of building trust with the relevant stakeholders in all these kinds of things. A starting point is just models that are able to automatically redact very sensitive information from being sent further down a pipeline. I think that's something that's like a very low hanging fruit that many people can do. There's also potential for HIPAA compliance within organizations. So I know some organizations working in the space are partially HIPAA compliant or are kind of trying to make that claim. And I think that's something that's useful. And I think that's something that we should work towards as well.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of help contextualize a patient's medical record or any previous scans or different angles of scans that a patient has had to help a radiologist write a more accurate report. I think that's something that's the kind of thing which I think is in the sweet spot of both feasible today, leverages the benefits of AI in terms of taking in additional context and potential multimodality and all these kinds of things. And it's also potentially in a sweet spot with respect to regulation as well. And so I think that's something that could happen in the shorter, medium to short term.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think we're already starting to see it in some clinical workflows when it comes to documentation and building. I think there are a lot of companies and people thinking about taking models like GPT-4 and applying them in that setting. And I think that is definitely going to be something. I think that is also going to be something where players like Epic are going to be able to partner with existing models. And I think potentially deliver real value there. And I think that's very exciting. I think that's something that also general domain models will be potentially quite good at as well. I think where there might be more of a need for specialized models is when it comes down to kind of higher stakes workflows. And I think that might look in the short term more like a physician's assistant. And so imagine, for example, an agent that can work with a radiologist, help them interpret a scan, and leverage the benefits of AI to”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Access to health information or provide access to health information are also thinking not maybe super counterfactually about the positive benefits of things and they're thinking more about the risks. And so, you know, I think that is also a concern that's been slowing folks down, both in terms of big companies and smaller companies. And I think there is an opportunity to kind of think more about that and what that could look like. And I think the company that gets that right or the set of companies that get that right. I think we'll also have a seat at the conversation when it comes to policy and regulation and things like that. And so they have the chance to shape what this looks like for the future. And so I think that's going to be potentially quite impactful.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“These are great questions. I think on the drug discovery front, there's a bit of a playbook here which any new company here looking for some revenue in the short term can follow. And that could be a safe option. There are, for example, existing AI augmented pipelines for doing things like given small molecule chemistry predicting things like absorption or toxicity. And it's kind of relatively easy to see that some of the more modern models, if placed into these pipelines, could perform better. And so there's like a relatively safe bet there. And so I think that probably accounts for a lot of the popularity of that as a use case. I totally agree that there is a kind of a chance to go for the jugular here in terms of health information, for example. And so I think this is something that is going to be crucial, but I think it is also something where a lot of the big players are more risk averse. And so the people who”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think that is crucial as well. And I think one thing that has been missing from our work so far is really grounded evaluations in a specific use case, in a workflow to show that there is a benefit, both in terms of safety in the short term and in terms of kind of long-term patient outcomes as well. And so I think that could be a health informational use case. It could be other clinical workflows. But I think that's one thing that we have to really make sure we do. And are careful about before any kind of real world use case here?”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“That's an amazing, great question. I think, as you said, there's two competing forces here, right? Obviously, the stakes are high in the medical setting and counterfactually, you want to make sure that the information you provide versus the information they would have otherwise gotten is actually high quality. And so that's very, very careful as you think about any informational use case for these models. At the same time, I think it's useful to”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Us, that was like a significant motivator for the MedPOM work being relatively evaluation forward and thinking carefully about human evaluation with both physicians and lay people.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“In some cases, we felt like these benchmarks were not high quality. And so that was one thing that we saw. I mean, another thing that we saw, which was more acute, I think, was kind of a lack of detailed human evaluation across many of these works. And so there was some steps in this direction that we were able to build on. But I think for the most part, a lot of these models that have already existed didn't have kind of detailed human evaluation given a use case like medical question answering. And so I think that to us was a significant limitation as we think about the real world potential of these models, because when it comes down to it, we have to make sure that it actually serves humans and is beneficial to humans. And so”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. And this is not the first work to explore the potential of a large language model in science or biomedicine. And so I think it's important to acknowledge all the work that's come before us. What we saw when we first came into this work and tried to understand what other models existed, what other evaluation has been done, was that, one, there was a few exciting works from other teams like ALACA or BioGPT and so on that we thought we could learn from and benefit from. And so that was a really exciting thing to be able to see. And the second thing we saw was that there was a bit of a shortage of kind of a systematic way of doing evaluation of these models. And so it didn't feel like there was a systematic way to think about automated evaluation of the clinical knowledge of these models. So for example, via multiple choice benchmarks, there were a few popular benchmarks like the MedQA benchmark, but it varied across paper what benchmarks they were studying.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“All the way to full fine tuning, I think it largely comes down to, so given an existing pre-trained model, which is, I think, a big hurdle for most teams and most people to train a large scale pre-trained model, then the question is, do you prompt it? Do you prompt tune it? Do you full fine-tune it? I think that largely comes down to data if you have three to five examples, let's say, then I would prompt it. If you have maybe 10 or 50 examples, it would either be prompt tuning or fine-tuning. I think generally in that realm, prompt tuning and fine-tuning perform similarly. And I would prefer prompt tuning if you're at all sensitive to things like compute or cost. If you care about the best performance and you have more than 100 examples, then probably fine-tuning is your best bet. And it's not as expensive as full pre-training if you're doing it with a model that's been pre-trained, of course.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, this is a great question. I think it really comes down to the data that's available both in quantity and relevance to a particular topic. I think if you have an infant supply of data that's relevant for the specific problem that you're trying to solve, then probably the best thing to do is pre-train everything from scratch and do everything end to end. If you don't mind compute and money as well, if you are working on a task in which general pre-training data in the web confers general advantages to that task. And so that could be domain knowledge. It could be general abilities like reasoning, you know, which is very applicable across many tasks, which I think is the case for medical reasoning as well. Then I think it makes a lot of sense to build on top of an existing model, especially if you're sensitive to things like cost or compute, which most people are these days. And so, you know, I think on that spectrum between things like prompting and prompting.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“To add in more data, we could do so, we could train a better model and a more compute efficient way. So this model also did that. So that's an important improvement as well. The third thing was kind of improvements in the data that were used to train the model. And so this especially focused on multilingual data, including more multilingual data and more code data in a bunch of different coding languages as well.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, this is the work of many folks other than myself. So just preface it with that. I mean, I think a few things that have been important have been one is better objectives for pre-training and using something like a mixture of objectives training objective. And so that's been something that's been crucial. And so this is work that started with UL2, a paper that was released also last year. And then two other things that ended up being super important. One is following the optimal scaling laws that were empirically evaluated again in this work. And there's been a few works that have tried to do this from OpenAI and DeepMind. And again, this work tried to understand in this context what are the optimal scaling laws with respect to data and compute and how do you trade those things off. And so this paper, again, found something similar to the Cinchilla paper, which was that the total amount of data being used for these models was a relatively low compared to the number of parameters. And that if we wanted”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Paper, which is kind of a data efficient technique and a technique that doesn't require too much data to work because getting labels from doctors is expensive, which took a bunch of expert demonstrations of good behavior from doctors and then used that to tune the parameters of the model and do that in a way that's a little bit more learned than prompting, but also less expensive than full fine-tuning.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. I mean, when we tried evaluating POM in the medical setting, we noticed it was out of box on multiple choice questions performing pretty well. And when we took a variation of POM, the flan POM model, which was, again, worked from Jason Way and Team, this is an instruction to model a model that's been trying to follow instructions better. Again, it was able to perform quite well out of the box. And this was the first model that was able to perform above the pass mark on the MedQA set of USMA style questions. But then what we noticed is that when we evaluated it on long-form medical question answering, like actually getting the model to generate response, there was a lot of limitations. And we compared that to clinician performance. It actually didn't do super well. And so really that was the motivation for that medal specific alignment. And so what we did there was really thinking about instruction prompt tuning, which was this technique, which we explored in that med palm.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“What do we need to do to kind of better align that model with this domain? And so really, MedPalm was an attempt to do that.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe a few months after the palm work itself, people were able to show that on STEM benchmarks there was this kind of zero to 100 or 0 to 60 at least effect where you went from random chance to solid performance across a bunch of benchmarks. And that laid the foundation for a lot of the work that Jason Wei and others have had on thinking about immersion abilities of Mars language models. And so for us, that was part of the motivation for looking at multiple choice benchmarks as well for MedPom. And so for MedPom in particular, what we did was we took MedPom, this kind of general large language model trained on web scale data and then kind of further aligned it to the medical domain. We evaluated it base, but also thought about like given its limitations in long-form medical question answering, thinking about things like safety, factuality, low likelihood of outputting an answer with bias.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. I mean, so the original MedPom work built on this model called POM, which stands for Pathways Language Model. And so this is really an infrastructure that Google has built to be able to scale up large language model training that is kind of Google-wide. And so the first POM model was released in 2022, which was kind of this 540B decoder-only transformer model at the time, the largest densely activated model. And it kind of realized this breakthrough achievements in code and multilingual capabilities, in reasoning. And so I think a lot of the work with respect to kind of improving benchmarks specifically that we're seeing with like POM, MedPOM, GPT-4 recently, I think all comes down to a lot of the improvements that were made during POM, during the training of POM. And so, you know, shortly after POM, there was this Minerva work where”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Team, I kind of got to the point where I was thinking, I've upskilled in a lot of ways. I've gotten to the point where I can mentor many other researchers in a lot of ways. And now it's a great time to be thinking about my next thing and going for something ambitious in terms of shaping the trajectory of AI. And so about a year and a half ago, a few of us had the idea to think about this medical setting as kind of a setting in which these concerns are especially important and that there was a ripe opportunity to think about this paradigm of foundation models in medical AI. And so within Google, we had the opportunity to pitch what's called a brain moonshot, which is kind of like an internal incubator program for ambitious research projects. And this is a lot of cool research projects that you've heard of from Google have eventually come out of this program. As we pitched that, we got it accepted and funded. We got the ability to kind of get a bunch of compute to bring other folks on board with the sponsorship of a bunch of leaders. And our first thing together was really MedPom.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I started working out in representation learning and federated learning. So this is kind of the technology representation learning in particular is kind of the technology underlying a lot of the deep neural networks of today, including GPT-3, GPT-4, and so on. And so this is largely about learning representations of text, of images of other modalities such that you can efficiently encode them, you can learn from them in the future, you can generalize new texts and images and so on. So the work for this really started back in the beginnings of the deep learning era, like in 2013 with convolutional neural networks and scaling those up and work to VEC around 2015 and Glove and all these things. And I think since then, we've been working on technologies around self-supervised learning, around doing that in a privacy-preserving way. And so after a couple years of working on that at Google, I had the opportunity to kind of quickly grow and start to lead it.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“No, I mean, for me, this is just something I've gotten to the last year and a half. So I've been new to it, I've been learning from an excellent team, and it's been an amazing journey so far.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Point where really going for the juggler in terms of thinking about how to train these models make them better in the setting. And so very excited about that kind of work.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that was one of my first AI projects. I really got into AI thinking about how it could be used in socially responsible ways. And for me, I was thinking around the time of the 2016 election that maybe a little bit naively that we could AI-based solutions could be a bit of help for things like misinformation and detecting that. I think in the longer run, I mean, I've thought of that as kind of a more naive project. And I think in the longer run, I've been thinking more about how it can help shape the trajectory of AI to be more beneficial more broadly. And I think for me, thinking about the medical setting has been motivated largely by thinking about the fact that it's a great place to think about concerns around safety, reducing hallucination and misinformation as well here, thinking about how we can produce medical question answers that are less likely to be harmful and all these kinds of things. And that motivation, I think, has driven us to this.”
2023-05-18 · No Priors · The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal · IDENTIFIED FROM THE TRANSCRIPT · source