This beautiful talk about Bayesian Thinking by Frank Harrell should be essential material for scientists who are trained in frequentist methods. The talk covers the shortcomings of frequentist approaches, but more importantly the paths out of those quagmires are also shown.
Frank discusses his journey to Bayesian stats in this blog post from 2017 which is also in the next section.
The most useful takeaway for me from this post is that even experienced statisticians had to steer towards Bayes fighting both agains norms and their education. The post has many many good references to whet your appetite if you are Bayes-curious. I particularly liked the following take:
slightly oversimplified equations to contrast frequentist and Bayesian inference.
Frequentist = subjectivity1 + subjectivity2 + objectivity + data + endless arguments about everything
Bayesian = subjectivity1 + subjectivity3 + objectivity + data + endless arguments about one thing (the prior)
where
subjectivity1 = choice of the data model
subjectivity2 = sample space and how repetitions of the experiment are envisioned, choice of the stopping rule, 1-tailed vs. 2-tailed tests, multiplicity adjustments, …
a work of maximalism succeeds when it triggers this property in the experiencer. You know that the whole is there, but you can’t see it all at once. You can only take it in sequentially. It is the awareness of an emergent form you, as a limited being with only a periscope for perception, cannot actually understand in full.
And this same reaction of “I don’t even know where to look, or where to begin” in the viewer (here, reader) is what I think the very best of blogging should strive for.
This was the exact reaction I had over 15 years ago when I came across my first real blog, ribbonfarm.com. Around this time blogging came into it’s heyday and many other blogs were found and devoured. The blogs all had this quality defined by the quantity, it was like making a friend without ever meeting the person. As AI slop (that’s the official term now) takes over and corrodes what was left finding good thing even thorough social media will be difficult. I agree with Erik, blogs being high ownership media will be where we will go to have fun.
Speaking of AI slop, Simon Willison, who coined the term, recently did this beautiful write up about how to post what’s perhaps the simplest kind of blog post, the link post. It is the same thing which you probably do with your friends in your private chats or your hundred channel discords post interesting things write a bit about them.
On the blog side, we can stand on the shoulders of these blogging giants and in the spirit of the internet copy and evolve their best practices. Simon has a longer list in the post that you should read, these bits about how to do it nicely stood out to me:
I always include the names of the people who created the content I am linking to, if I can figure that out. Credit is really important, and it’s also useful for myself because I can later search for someone’s name and find other interesting things they have created that I linked to in the past. […]
I try to add something extra. My goal with any link blog post is that if you read both my post and the source material you’ll have an enhanced experience over if you read just the source material itself.
Ideally I’d like you to take something useful away even if you don’t follow the link itself. This can be a slightly tricky balance: I don’t want to steal attention from the authors and plagiarize their message. Generally I’ll try to find some key idea that’s worth emphasizing. […]
I’m liberal with quotations. Finding and quoting a paragraph that captures the key theme of a post is a very quick and effective way to summarize it and help people decide if it’s worth reading the whole thing. […]
If the original author reads my post, I want them to feel good about it. I know from my own experience that often when you publish something online the silence can be deafening. Knowing that someone else read, appreciated, understood and then shared your work can be very pleasant.
A slightly self-involved concern I have is that I like to prove that I’ve read it. This is more for me than for anyone else: I don’t like to recommend something if I’ve not read that thing myself, and sticking in a detail that shows I read past the first paragraph helps keep me honest about that.
[full-text] As a practitioner in the field, I’ve always maintained that AI is a tool on the lines of spreadsheets. It gives superpowers no matter who you are but if you are an expert in your domain the power is multiplicative. Seems actual research, by Eric So and others, is coming to similar conclusions:
We hypothesize that the widening performance gap across investor groups stems from an inherent trade-off–making AI summaries accessible to less sophisticated investors sacrifices technical precision. For example, whereas a simple summary might note “a reduction in profits,” the advanced version specifies “operating margins declined due to higher input costs caused by supply chain disruptions”—creating a gap in the precision of the signals that participants receive. Consistent with this hypothesis, we find that the performance gap between more versus less sophisticated participants widens when more technical information is omitted from the simplistic summaries, such as discussion of R&D spending, share repurchases, and gaps between EBITDA vs. net profits. Thus, the efforts to make financial information more accessible via AI come at the cost of reduced precision, potentially limiting AI’s ability to fully democratize financial decision-making.
While specialization may be for insects, context is for kings (Star Trek) and jargon compresses the progress to circumscribe the exact matter at hand. Those who can compress better not only can judge better but can narrow down the scope of the LLM to get better analysis done. It seems this will also lead to a widening gap rather than a democratization:
Our analysis yields two central results. First, there is a significant improvement in both information processing ability and the quality of investment decisions following AI adoption. However, this effect holds (for both sophisticated and unsophisticated groups of users) as long as AI output is aligned with user sophistication. Second, AI widens the knowledge-driven disparities between sophisticated and unsophisticated participants.
numerical probability, I will argue — whether in a scientific paper, as part of weather forecasts, predicting the outcome of a sports competition or quantifying a health risk — is not an objective property of the world, but a construction based on personal or collective judgements and (often doubtful) assumptions. Furthermore, in most circumstances, it is not even estimating some underlying ‘true’ quantity. Probability, indeed, can only rarely be said to ‘exist’ at all. […] In the natural world, we can throw in the workings of large collections of gas molecules which, even if following Newtonian physics, obey the laws of statistical mechanics; and genetics, in which the huge complexity of chromosomal selection and recombination gives rise to stable rates of inheritance. It might be reasonable in these limited circumstances to assume a pseudo-objective probability — ‘the’ probability, rather than ‘a’ (subjective) probability.
In every other situation in which probabilities are used, however — from broad swathes of science to sports, economics, weather, climate, risk analysis, catastrophe models and so on — it does not make sense to think of our judgements as being estimates of ‘true’ probabilities. These are just situations in which we can attempt to express our personal or collective uncertainty in terms of probabilities, on the basis of our knowledge and judgement. […] we perhaps don’t have to decide whether objective ‘chances’ really exist in the everyday non-quantum world. We can instead take a pragmatic approach. Rather ironically, de Finetti himself provided the most persuasive argument for this approach in his 1931 work on ‘exchangeability’, which resulted in a famous theorem that bears his name. A sequence of events is judged to be exchangeable if our subjective probability for each sequence is unaffected by the order of our observations. De Finetti brilliantly proved that this assumption is mathematically equivalent to acting as if the events are independent, each with some true underlying ‘chance’ of occurring, and that our uncertainty about that unknown chance is expressed by a subjective, epistemic probability distribution. This is remarkable: it shows that, starting from a specific, but purely subjective, expression of convictions, we should act as if events were driven by objective chances.
Are we slowly entering the post data annotation world?
The act of annotating data for ML has always been a shortcut to access concepts not present in the training data. This new work, incorporates literature with data to achieve a fuller picture with complementary information.
Specifically the tech,
utilizes GPT-4 to induce reliable disease-specific human expert concepts from medical literature and incorporate them with a group of purely learnable concepts to extract complementary knowledge from training data.
The internet is big, actually
Over a decade ago I heard from researchers that they used twitter as a sort of internet nematode. Since then, I’ve repeated that the population on twitter does not represent the real world much the way works in nematode is far from works in humans.
Like radiation, social media’s algorithms and network effects are invisible scientific effluence that leaves us both more knowledgeable and more ignorant of the causes of our own afflictions than ever; in turn, this leads to deepening distrust in the experts who are blamed for producing them
And further articulates the actual effect of social media where the very online amplify a signal only they see:
Put simply: staring at the doomscroll isn’t good for anyone, but it’s especially dangerous for people with power and influence. Perhaps such a harm-reduction approach is more compassionate than an outright ban, weaning overly online journalists and celebrities off of the more dangerous stuff, steadily unplugging them from the Necronomicon of networks no mortal was meant to stare into.
As they used to be called, before they became users, the denizens of the internet, would very much benefit from a return to the old ways of the internet where you built a little corner of your own.
I’m going to try treating BlueSky as a temporaryplace to make connections or share ideas, but do my best to direct attention, deeper thoughts, “real” value to places that I have more control over.
We get 6 useful rules to consider and have the mindset of inviting friends over for tea than hanging out at the local global cafe:
Build your castle on land you own
Shamelessly use other kingdoms just like they’re using you
Always move people back to your kingdom, never to another kingdom
Operate like your castle can get shutdown tomorrow
Be suspicious of new kingdoms that give away easy visibility
Give good reasons to go back to the Castle in your Kingdom. And be persistent!
When Allied airmen heading for Germany had to make an emergency landing in the Netherlands, Visser ‘t Hooft sent Hepburn to the forest to meet a British paratrooper with code words and a secret message hidden in her sock. She made the meeting, but on the way out of the forest, she saw Dutch police approaching. She bent down to pick wildflowers, then flirtatiously presented them to the police. They were charmed and didn’t interrogate her further. After this, she often carried messages for the resistance.
“She believed very much that there is a struggle between good and evil and you have to take sides,” Dotti says.
🚨🔬🤖📜 New paper! Introducing #LiquidEngine and #NanoPyx – #AI for accelerating #microscopy that explores how to maximise performance 🚀. Brainchild of Bruno, @inesmcunha.bsky.social and António. Adventure with @guijacquemet.bsky.social et al. Faster #SRRF & #eSRRF!!📄: http://www.nature.com/articles/s41…
Good visualization tells the reader what the collected data is saying it creates a little place in an otherwise confusing space.
Truth and liberty are entwined in a self-reinforcing loop. When we design a good visualization we aren’t just conveying our best understanding of a truth; by sharing our contemplation of that truth, we’re also making ourselves and our readers freer.
Data science, statistics, machine learning is creative work its beauty lies in making visible what lies latent in the world.
Work harder, protect others, do good, and create beauty. Camus inspires those words. Your work matters; you matter. Doing ethical, beautiful work—visualization work, or any other creative work—and putting it out there for others to learn, enjoy, and inform themselves to live better lives, imbues a meaningless world with meaning. Any expression of beauty is a rebellion against darkness, a repudiation of ugliness, and an act of resistance against ignorance and malice.
Traversing through human history, even in the last two decades, we see a rapid increase in the accessibility of knowledge. The purpose of language, and of course all communication is to transfer a concept from one system to another. For humans this ability to transfer concepts has been driven by advancements in technology, communication, and social structures and norms.
This evolution has made knowledge increasingly composable, where individual pieces of information can be combined and recombined to create new understanding and innovation. Ten years ago I would have said being able to read a research paper and having the knowledge to repeat that experiment in my lab was strong evidence of this composability (reproducibility issues not withstanding).
Now, composability itself is getting an upgrade.
In the next essay I’ll be exploring the implications of the arrival of composable knowledge. This post is a light stroll to remind ourselves of how we got here.
In ancient times, knowledge was primarily transmitted orally. Stories, traditions, and teachings were passed down through generations by word of mouth. This method, while rich in cultural context, was limited in scope and permanence. The invention of writing systems around 3400 BCE in Mesopotamia marked a significant leap. Written records allowed for the preservation and dissemination of knowledge across time and space, enabling more complex compositions of ideas (Renn, 2018).
Shelves, Sheaves, and Smart Friends
The establishment of libraries, such as the Library of Alexandria in the 3rd century BCE, and scholarly communities in ancient Greece and Rome, further advanced the composability of knowledge. These institutions gathered diverse texts and fostered intellectual exchanges, allowing scholars to build upon existing works and integrate multiple sources of information into cohesive theories and philosophies (Elliott & Jacobson, 2002).
Scribes, Senpai, and Scholarship
During the Middle Ages, knowledge preservation and composition were largely the domain of monastic scribes who meticulously copied and studied manuscripts. The development of universities in the 12th century, such as those in Bologna and Paris, created centers for higher learning where scholars could debate and synthesize knowledge from various disciplines. This was probably when humans shifted perspective and started to view themselves as apart from nature (Grumbach & van der Leeuw, 2021).
Systems, Scripts and the Scientific Method
The invention of the printing press by Johannes Gutenberg in the 15th century revolutionized knowledge dissemination. Printed books became widely available, drastically reducing the cost and time required to share information. This democratization of knowledge fueled the Renaissance, a period marked by the synthesis of classical and contemporary ideas, and the Enlightenment, which emphasized empirical research and the scientific method as means to build, refine, share knowledge systematically (Ganguly, 2013).
Silicon, Servers, and Sharing
The 20th and 21st centuries have seen an exponential increase in the composability of knowledge due to digital technologies. The internet, open access journals, and digital libraries have made vast amounts of information accessible to anyone with an internet connection. Tools like online databases, search engines, and collaborative platforms enable individuals and organizations to gather, analyze, and integrate knowledge from a multitude of sources rapidly and efficiently. There have even been studies which allow, weirdly, future knowledge prediction (Liu et al., 2019).
Conclusion
From oral traditions to digital repositories, the composability of knowledge has continually evolved, breaking down barriers to information and enabling more sophisticated and collaborative forms of understanding. Today, the ease with which we can access, combine, and build upon knowledge drives innovation and fosters a more informed and connected global society.
In Newton’s era it was rare to say things like “if I have seen further, it is by standing on the shoulders of giants” and actually mean it. Now it’s trivial. With education, training, and experience, professionals always stand “on shoulders of giants” (OSOG). Experts readily solve complex problems but the truly difficult ones aren’t solved through training. Instead, a combination of muddling through and the dancer style of curiosity is deployed, more on this later. We have industries like semiconductors, solar, and gene sequencing with such high learning rates that the whole field seems to ascend OSOG levels daily.
These fast moving industries follow Wright’s Law. Most industries don’t follow Wright’s law due to friction against discovering and distributing efficiencies. In healthcare regulatory barriers, high upfront research costs, and resistance to change keeps learning rates low. Of course, individuals have expert level proficiencies, many with private hacks to make life easier. Unfortunately, the broader field does not benefit from individual gains and progress is made only when knowledge trickles down to the level of education, training, and regulation.
This makes me rather unhappy, and I wonder if even highly recalcitrant fields like healthcare could be nudged into the Wright’s law regime.
No surprise that I view AI being central, but it’s a specific cocktail of intelligence that has my attention. Even before silicon, scaling computation has advanced intelligence. However, we will soon run into limits of scaling compute and the next stage of intelligence will need a mixed (or massed, as proposed by Venkatesh Rao). Expertise + AI Agents + Knowledge Graphs will be the composite material that will enable us not just to see further, but to bus entire domains across what I think of as the Giant’s Causeway of Intelligence.
Lets explore the properties of this composite material a little deeper, starting with expertise and it’s effects.
An individual’s motivation and drive are touted as being the reason behind high levels of expertise and achievement. At best, motivation is an emergent phenomenon, a layer that people add to understand their own behavior and subjective experience (ref,ref). Meanwhile, curiosity is a fundamental force. Building knowledge networks, compressing them, and then applying them in flexible ways is a core drive. Everyday, all of us (not just the “motivated”) cluster similar concepts under an identity then use that identity in highly composable ways (ref).
There are a few architectural styles of curiosity that are deployed. ‘Architecture’ is the network structure of concepts and connections uncovered during exploration. STEM fields have a “hunter” style of curiosity, tight clusters and goal directed. While great for answers, the hunter style has difficulty making novel connections. Echoing Feyerabend’s ‘anything goes’ philosophy, novel connections require what is formally termed as high forward flow. An exploration mode where there is significant distance between previous thoughts and new thoughts (ref). Experts don’t make random wild connections when at the edge of their field but control risk by picking between options likely to succeed, what has been termed as ‘muddling through’.
Stepping back, if you consider that even experts are muddling at the edges then the only difference between low and high expertise is their knowledge network. The book Accelerated Expertise, summarized here, explores methods of rapidly extracting and transmitting expertise in the context of the US military. Through the process of Cognitive Task Analysis expertise can be extracted and used in simulations to induce the same knowledge networks in the minds of trainees. From this exercise we can take away that expertise can be accelerated by giving people with base training access to new networks of knowledge.
Another way to build a great knowledge network is through process repetition, you know… experience. These experience/learning curves predict success in industries that follow Wright’s Law. Wright’s Law is the observation that every time output doubles the cost of production falls by a certain percentage. This rate of cost reduction is termed as the learning rate. As a reference point, solar energy drops in price by 20% every time the installed solar capacity doubles. While most industries benefit from things like economies of scale they can’t compete with these steady efficiency gains. Wright’s Law isn’t flipped on through some single lever but emerges through the culture right from the factory floor all the way up to strategy.
Labor efficiency – where workers are more confident, learn shortcuts and design improvements.
Methods improvement, specialization, and standardization – through repeated use the tools and protocols of work improve.
Technology-driven learning – better ways of accessing information and automated production increases rates of production.
Better use of equipment – machinery is used as full capacity as experience grows
Network effects – a shared culture of work allows people to work across companies with little training
Shared experience effects – two or more products following a similar design philosophy means little retraining is needed.
Each of these is essentially a creation, compression, and application of knowledge networks. In fields like healthcare efficiency gain is difficult because skill and knowledge diffusion is slow.
Maybe, there could be an app for that…
Knowledge graphs (KGs) are databases but instead of a table they create a network graph, capturing relationships between entities where both the entities and the relationship have metadata. Much like the mental knowledge networks built during curious exploration, knowledge graphs don’t just capture information like Keanu → Matrix but more like Keanu -star of→ Matrix. And all three, Keanu, star of, and Matrix have associated properties. In a way KGs are crystalized expertise and have congruent advantages. They don’t hallucinate and are easy to audit, fix, and update. Data in KGs can be linked to real world evidence enabling them to serve as a source of truth and even causality, a critical feature for medicine (ref).
Medicine pulls from a wide array of domains to manage diseases. It’s impossible for all the information to be present in one mind, but knowledge graphs can visualise relationships across domains and help uncover novel solutions. Recently projects like PrimeKG have combined several knowledge graphs to integrate multimodal clinical knowledge. KGs have already shown great promise in fields like drug discovery and leading hospitals, like Mayo Clinic, think that they are the path to the future. The one drawback is poor interactivity.
LLMs meanwhile are easy to interact with and have wonderful expressivity. Due to their generative structure LLMs have zero explainability and completely lack credibility. LLMs are a powerful, their shortcomings make them risky in applications like disease diagnosis. The right research paper and textbooks trump generativity. Further, the way that AI is built today can’t fix these problems. Methods like fine-tuning and retraining exists, but they require massive compute which is difficult to access and quality isn’t guaranteed. The current ways of building AI, throwing in mountains of data into hot cauldrons of compute and stirred with the network of choice (mandatory xkcd), ignores the very accessible stores of expertise like KGs.
LLMs (and really LxMs) are the perfect complement to KGs. LLM can access and operate KGs in agentic ways making understanding network relationships easy through natural language. As a major benefit, retrieving an accurate answer from KGs is 50x cheaper than generating one. KGs make AI explainable “by structuring information, extracting features and relations, and performing reasoning” (ref). With easy update and audit abilities KGs can easily disseminate know-how. When combined with a formal process like expertise extraction, KGs could serve as a powerful store of knowledge for institutions and even whole domains. We will no longer have to wait a generation to apply breakthroughs.
Experts+LxMs+KGs are the composite material to accelerate innovation and lower costs of building the next generation of intelligence. We have seen how experts are always trying to have a more complete knowledge network with high compression and flexibility allowing better composability. The combination of knowledge graphs and LLMs provide the medium to stimulate dancer like exploration of options. This framework will allow high-proficiency but not-yet-experts to cross the barrier of experience with ease. Instead of climbing up a giant, one simply walks The Giant’s Causeway. Using a combination of modern tools and updated practices for expertise extraction we can accelerate proficiency even in domains which are resistant to Wright’s Law unlocking rapid progress.
****
Appendix
Diving a little deeper into my area of expertise, healthcare, a few ways where agents and KGs can help:
Application
Role of Intelligence
Outcomes
Efficiency in Data Management
KGs organize data in a way that reflects how entities are interconnected, which can significantly enhance data accessibility and usability
faster and more accurate diagnoses, streamlined patient care processes, and more personalized treatment plans
Predictive Analytics
AI can analyze vast amounts of healthcare data to predict disease outbreaks, patient admissions, and other important metrics
allows healthcare facilities to optimize their resource allocation and reduce wastage, potentially lowering the cost per unit of care provided
Automation of Routine Tasks
AI agents can automate administrative tasks such as scheduling, billing, and compliance tracking using institution specific KGs.
With widespread use, the cumulative cost savings could be in a similar range as Wright’s law
Improvement in Treatment Protocols
Refine treatment protocols using the knowledge graph of patient cases.
More effective treatments being identified faster, reducing the cost and duration of care.
Scalability of Telehealth Services
Agentic platforms rooted in strong Knowledge Graphs can handle more patients simultaneously, offering services like initial consultations, follow-up appointments, and routine check-ups with minimal human intervention.
Drive down costs of service delivery at high patient volumes
Enhanced Research and Development
Already in play, AI and KGs accelerate medical research by better utilizing existing data for new insights.
Decreases time and cost of developing new treatments
Customized Patient Care
AI can analyze multimodal KGs of individual patients integrating history, tests, and symptoms for highly customized care plans.
When aggregated across the patient population healthcare systems can benefit from economies of scale and new efficiencies
While exploring the application of AI agents in healthcare we see that standard Retrieval-Augmented Generation (RAG) and fine-tuning methods often fall short in the interconnected realms of healthcare and research. These traditional methods struggle to leverage the structured knowledge available, such as knowledge graphs. Data approaches like Fast Healthcare Interoperability Resources (FHIR) used alongside advanced knowledge graphs can significantly enhance AI agents, providing more effective and context-aware solutions.
The Shortcomings of Standard RAG in Healthcare
Traditional RAG models, designed to pull information from external databases or texts, often disappoint in healthcare—a domain marked by complex, interlinked data. These models typically fail to utilize the nuanced relationships and detailed data essential for accurate medical insights (GitHub) (ar5iv).
Leveraging FHIR and Knowledge Graphs
FHIR offers a robust framework for electronic health records (EHR), enhancing data accessibility and interoperability. Integrated with knowledge graphs, FHIR transforms healthcare data into a format ideal for AI applications, enriching the AI’s ability to predict complex medical conditions through a dynamic use of real-time and historical data (ar5iv) (Mayo Clinic Platform).
Enhancing AI with Advanced RAG Techniques
Advanced RAG techniques utilize detailed knowledge graphs covering diseases, treatments, and patient histories. These graphs underpin AI models, enabling more accurate and relevant information retrieval and generation. This capability allows healthcare providers to offer personalized care based on a comprehensive understanding of patient health (Ethical AI Authority) (Microsoft Cloud).
Implementing AI Agents in Healthcare
AI agents enhanced with RAG and knowledge graphs can revolutionize diagnosis accuracy, patient outcome predictions, and treatment optimizations. These agents offer actionable insights derived from a deep understanding of individual and aggregated medical data (SpringerOpen).
A Novel Approach: RAG + FHIR Knowledge Graphs
Integrating RAG with FHIR-knowledge graphs to significantly enhance AI capabilities in healthcare. This method maps FHIR resources to a knowledge graph, augmenting the RAG model’s access to structured medical data, thus enriching AI responses with verified medical knowledge and patient-specific information. View the complete notebook in my AI Studio.
Challenges and Future Directions
While promising, integrating FHIR, knowledge graphs, and advanced RAG with AI agents in healthcare faces challenges such as data privacy, computational demands, and knowledge graph maintenance. These issues must be addressed to ensure ethical implementation and stakeholder consideration (MDPI).
Conclusion
Integrating FHIR, knowledge graphs, and advanced RAG techniques into AI agents represents a significant advancement in healthcare AI applications. These technologies enable a precision and understanding previously unattainable, promising to dramatically improve care delivery and management as they evolve.
If you’re in the field or exploring applying AI, do get in touch!
In this video, Stephen Fry narrates Nick Cave’s letter, which argues that using ChatGPT as a shortcut to creativity is detrimental. Surprisingly, I found myself in agreement. Having been involved in the AI industry for over a decade, I’ve always viewed AI positively. As an entrepreneur who pitches AI to investors and customers, I liken AI to technologies like spreadsheets: they eliminate tedious tasks, but you still need to understand what you’re doing.
I use various contemporary AI tools daily for tasks ranging from creating ISO template documents to drafting reference letters. However, when I’ve tried using AI as a thinking partner or advisor, it has fallen short, primarily due to its inability to discern or have taste.
Expertise involves having taste – the ability to distinguish good from bad, one decision from another. We depend on experts and advisors not just for their knowledge, but for their ability to guide us optimally, sometimes even questioning our intended goals. They speak confidently amid uncertainty, drawing on their experience.
ChatGPT/LLMs and their generative counterparts exhibit confidence but, by design, lack real-world experience.
My agreement with the video’s sentiment stems not from an inherent issue with computational tools, but from the understanding that taste develops through experience, however imperfect. Just as we learn arithmetic before using calculators, and calculators before spreadsheets, we need to cultivate a new culture around these emerging tools.
In mission-critical fields like healthcare, balancing exploration and regulation is crucial. Over-regulation can hinder society from benefiting from innovative uses and discoveries, while a lack of regulation places undue risk on vulnerable populations, as seen in historical clinical trials
I envision a path where doctors integrate AI into their workflows with enthusiasm yet maintain high standards. Unlike drug development and other biotech fields, AI and its software can be corrected relatively easily. Creating a selective environment will drive quality.
Users who recognize what is good will elevate the collective ability to achieve excellence.
3. First self-amplifying mRNA vaccine was approved in Japan. Apparently a much lower (1/6th) has the same effect as a normal dose. I wonder if the side effects are better:
4. Norah Jones will have a new album out on March 8. The woman is a machine! With the very active podcast I had thought she might be taking a break, but nope here she is with Visions:
Aneesh Sathe 