Today’s product builders and data scientists shape the way people see the world. The analysis, plots, and UI we create are places where others dwell. Not merely occupy but live and harness the mental space we give them access to. Martin Heiddeger wrote Building Dwelling Thinking(archive.org) in his 1971 book, Poetry Language Thought.
Man’s relation to locations, and through locations to spaces, inheres in his dwelling. The relationship between man and space is none other than dwelling, strictly thought and spoken
What Heidegger calls locations, I have thought of as places. Places are anything with order and purpose, as thought of by the user. Spaces are not so much the mathematical or physics concepts but more like domains ones. Such as the AI-space, or biotech-space. These spaces come into being as a result of thought extended from the boundaries of places to the explorations enabled by the affordances of said spaces.
A boundary is not that at which something stops but, as the Greeks recognized, the boundary is that from which something begins its presencing. […] The location admits the [space] and it installs the [space].
Heidegger addresses thinking only very briefly and from a distance. In the life of today’s knowledge worker thinking is everything. For the knowledge worker to be able to “dwell” they must be able to bring together the act of thinking and building. This is why good visualization, analysis that reveals rather than hides, and products that expand rather than limit the user’s ability are important.
Building and thinking are, each in its own way, inescapable for dwelling. The two, however, are also insufficient for dwelling so long as each busies itself with its own affairs in separation instead of listening to one another. They are able to listen if both building and thinking-belong to dwelling, if they remain within their limits and realize that the one as much as the other comes from the workshop of long experience and incessant practice.
To be able to free the user is critical. Everyone has their expertise and it us usually not in using your product. To make your place so convoluted that the user has to conform and constrict to be able to use it is not kind placemaking. At the beginning of the essay there is a definition of what it means “to free”
To free really means to spare. The sparing itself consists not only in the fact that we do not harm the one whom we spare. Real sparing is something positive and takes place when we leave something beforehand in its own nature, when we return it specifically to its being, when we “free” it in the real sense of the word into a preserve of peace. To dwell, to be set at peace, means to remain at peace within the free sphere that safeguards each thing in its nature. The fundamental character of dwelling is this sparing and preserving
My takeaway is that whenever a product/place is built it’s primary concern should be the freedom of the person expected to dwell there. The freedom you provide enables them to explore spaces they care about.
Image credit: Nagoya Castle (ca.1932) print in high resolution by Hiroaki Takahashi. Original from The Los Angeles County Museum of Art. Digitally enhanced by rawpixel.
Data Scientists check how well a statistical model fits observed data with numerical and graphical checks. Graphical checks have a huge range outside of the well knowns like Q-Q plots. Scientists are of course limited by their training and experience, and it’s not trivial to arrive at effective model checks. Both programmatic and visual plotting tools require significant effort to generate new plots increasing the friction to do proper checks. Work out of Jessican Hullman‘s lab has created the VMC package (github) is a tool to easily access these methods and determine the quality of your model quickly. VMC is
a high-level declarative grammar for generating model check visualizations. VMC categorizes design choices in model check visualizations via four components: sampling specification, data transformation, visual representation(s), and comparative layout. VMC improves the state-of-the-art in graphical model check specification intwo ways: (1) it allows users to explore a wide range of model checks through relatively small changes to a specification as opposed to more substantial code restructuring, and (2) it simplifies the specification of model checks by defining a small number of semantically-meaningful design components tailored to model checking.
The work comes from a thoughtful place aiming not just to help out statisticians but to properly address and solve design considerations of a good tool, extending the wonderful familiy of tools that is ggplot2 built on the Grammar of Graphics.
Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: 1. samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; 2. transformations on observed data to facilitate comparison; 3. visual representations of distributions; 4. layouts to facilitate comparing model samples and observed data.
The central vibe here is one of resonant contemplation. This guitar allows me to go deep. Deep to a place that I maybe have never quite gotten to before. This is a dusk-to-sunrise record, hard-core mellow.
I have often found myself as a listener searching for music to fill those hours, and honestly, I find it challenging to find the kinds of things I like to hear. As much “mellow” music as there is out there, a lot of it just doesn’t do the thing for me.
This record might offer something to the insomniacs and all-night folks looking for the same sounds, harmonies, spirits, and melodies that I was in pursuit of during the late nights and early mornings that this music was recorded.
The above is from Pat’s website. I discovered Pat Metheny relatively recently and have grown to like his music. Last year he released MoonDial which I picked up last week. It’s nice.
Check it out:
While I know nothing about musical instruments, the man is a proper geek:
Some years back, I had asked Linda Manzer, one of the best luthiers on the planet and one of my major collaborators, to build me yet another acoustic Baritone guitar, but this time one with nylon strings as opposed to the steel string version that I had used on the records One Quiet Night and What’s It All About.
My deep dive into the world of Baritone guitar began when I remembered that as a kid in Missouri, a neighbor had shown me a unique way of stringing where the middle two strings are tuned up an octave while the general tuning of the Baritone instrument remains down a 4th or a 5th. This opened up a dimension of harmony that had been previously unavailable to me on any conventional guitar.
There were never really issues with Linda’s guitar itself, but finding nylon strings that could manage that tuning without a) breaking or b) sounding like a banjo – was difficult.
Just before we hit the road, I ran across a company in Argentina (Magma) that specialized in making a new kind of nylon string with a tension that allowed precisely the sound I needed to make Linda’s Baritone guitar viable in my special tuning.
This article covers a pair of studies on bacteria and viruses in a lake.
researchers found that over the course of a year, most individual species of bacteria in Lake Mendota rapidly evolve, apparently in response to dramatically changing seasons.
Gene variants would rise and fall over generations, yet hundreds of separate species would return, almost fully, to near copies of what they had been genetically prior to a thousand or so generations of evolutionary pressures.
In the evolutionary arms race between viruses and their hosts, “kill-the-winner” and other forms of dynamics frequently occur, causing fluctuations in the abundance of various viral strains55. Despite these fluctuations, certain viral species persist over extended periods and demonstrate high occurrence over time, indicating their evolutionary success in adapting to changing environmental conditions. These high occurrence viral species may represent a ‘royal family’ viral species in the model used to explain the “kill-the-winner” dynamics, where certain sub-populations with enhanced viral fitness have descendants that become dominant in subsequent “kill-the-winner” cycles. It is probable that these high occurrence viral species maintain a stable presence at the coarse diversity level while undergoing continuous genomic and physiological changes at the microdiversity level. The dynamics at the level of viral and host interactions play a pivotal role in driving viral evolution and maintaining the dominance of ‘royal family’ viral species.
Image credit: Sitting cat, from behind (1812) drawing in high resolution by Jean Bernard. Original from the Rijksmuseum. Digitally enhanced by rawpixel.
Another one of Frank Harrell‘s posts. Given my day job, and the R&D background this one is quite close to home. As a team leader on the industry side one hopes to build a culture with the team that aligns scientific rigor with company goals. Any method, statistical or cultural (in this case both), that solves for this tension will get you the most bang for your buck.
Building a startup, doing research, or even just launching a moonshot project is dependent on emergent actions and decisions. Behind this madness, there is a kind of Science of “Muddling Through” and Bayesian methods are perhaps best equipped:
make all the assumptions you want, but allow for departures from those assumptions. If the model contains a parameter for everything we know we don’t know (e.g., a parameter for the ratio of variances in a two-sample t-test), the resulting posterior distribution for the parameter of interest will be flatter, credible intervals wider, and confidence intervals wider. This makes them more likely to lead to the correct interpretation, and makes the result more likely to be reproducible.
In an environment of limited resources (time, money, investor patience), being able to quantify your data and obtaining bankable evidence is critical.
only the Bayesian approach allows insertion of skepticism at precisely the right point in the logic flow, one can think of a full Bayesian solution (prior + model) as a way to “get the model right”, taking the design and context into account, to obtain reliable scientific evidence.
The post provides a much more in-depth view, including an 8-fold path to enhancing the scientific process.
Chris Arnade gives us a view into why he goes on insanely long (both distance and time) trips by foot and what he discovers there. I found this passage hilarious, in that money seems to converge on a version of living that is identical with different paint while the rich likely search for that unique way of living.
Every large global city has a few upscale neighborhoods that are effectively all the same. It is where the very rich have their apartments, the five and four star hotels are, the most famous museums, and a shopping district with the same stores you would find in Manhattan’s Upper East Side, or London’s Mayfair.
The only difference is the branding. So you get the Upper East Side with Turkish affectations, or a Peruvian themed Mayfair. The residents of these neighborhoods are also pretty comfortable in any global city. As long as it is the right neighborhood.
Having traveled a fair bit, the designation of tourist brings with it multiple horrors. Chris uses walking as a mini residing and side-steps much of it. :
Walking also changes how the city sees you, and consequently, how you see the city. As a pedestrian you are fully immersed in what is around you, literally one of the crowd. It allows for an anonymity, that if used right, breaks down barriers and expectations. It forces you to deal with and interact with things and people as a resident does. You’re another person going about your day, rather than a tourist looking to buy or be sold whatever stuff and image a place wants to sell you.
This particular experience reminded of the book Shantaram, a book about a foreigner being adsorbed to and absorbed by the local.
While interesting the research uses LLMs to play both patient and doctor. I like that there is research happening in this area and at least we are moving in the right direction with the testing of AI i.e. not just structured tests. I would perhaps not judge the “failure” too harshly as the source of the data as also an LLM and would suffer from deficiencies which an actual patient suffering from symptoms would not.
This paper introduces the Conversational Reasoning Assessment Framework for Testing in Medicine (CRAFT-MD) approach for evaluating clinical LLMs. Unlike traditional methods that rely on structured medical examinations, CRAFT-MD focuses on natural dialogues, using simulated artificial intelligence agents to interact with LLMs in a controlled environment.
While the paper has a negative result we do come away with a good set of recommendations for future evaluations.
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.
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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
Aneesh Sathe 