There’s interesting research emerging comparing our ever-improving machine learning models to data generated from brains. Not surprisingly, we do best at replicating what the brain can do when the computer models begin to model the underlying neural machinery. Now the substrate is entirely different, but the predictive approaches appear to be similar. We used to think that the brain was creating representations of the world, with features being abstracted at each level of cortical processing.

The problem that everyone saw from the beginning with this concept is that there’s no little man in the theater of the mind to look at the representations. Instead, the brain is representing hypotheses and the these predictions are constantly updated by the stream of incoming sensation from the world.

Those hypotheses — and not the sensory inputs themselves — give rise to perceptions in our mind’s eye. The more ambiguous the input, the greater the reliance on prior knowledge.

And so too with language, the brain guesses what word comes next, providing a fixed interpretation when we hear unclear or ambiguous language. Of course, we often make wrong decisions, famously about song lyrics (e.g. “There’s a bathroom on the right”) known as Mondegreens.

I’m beginning to appreciate just how important this is as our ability to look at brain activity improves just as our computational ability to create these models begins to match it. We’re not recreating higher brain function from the bottom up by understanding circuits and connections, but instead from the top down. Perhaps not surprisingly, as this is how physical sciences like chemistry and physics have advanced. They create formulas and equations that are mathematical models of the world that have remarkable predictable powers. Once systems get too complex, these methods seem to fall apart and numerical simulation seems to be needed, but nevertheless, when those models start converging on the behavior of the real thing, they seem to tell us about what’s actually going on in that complex system being modeled. Truly a remarkable time for brain science.

I hadn’t really thought about how our social media environment might affect music and art criticism until I read this Eleanor Halls Interview

Where do you see music journalism headed?

I think we need to have honest conversations about the role of music journalism and whether much of it still has any value. I worry that music journalism—interviews and reviews—is becoming PR to some musicians. Most journalists are freelance and don’t have the support of editors or publishers, and reply on publicists for talent access so they can get work. It’s no wonder they often feel too intimidated by an artist and their team to write what they really think.

There’s always been a bargain between critics and artists regarding access and cooperation. It’s only natural that an artist would share insights with a sympathetic journalist and not one who has little enthusiasm for the style or approach of the artist. Personal relationships have always played a big role in what we read as criticism and commentary.

While some nasty letters from fans may have been the price for a critic to pay for publishing a negative take on something, I can see how the amplification of opinion in social media makes the pressure way more real. But without a publication behind the writer, freelance writers are much more dependent on these relationships for access to artists, creating a competition to curry favor with creators and their fans.

I think its true that the tone of discussion across the internet tends to be more promotional than print publications ever were. Editorial independence is lost. I don’t think its even a real bias, necessarily, but a function of writers choosing to write about what they like. It’s often just another symptom of our fragmentation. Sites team up with companies for synergy.

I like the idea of these personal blogs being islands of authenticity. I try to be positive in general, but that’s a personal bias. We’re all in this together, so my aim has to be to inform and teach a bit so we all do a bit better.

For a very long time I had the practice of putting an image at the top of each post here. As decoration for the most part as my images carry very little semantic content. This is another image from the San Francisco trip. I continue to think about the contradictions inherent in abstract photography, but I’ve concluded that the answers lie in making more images, not contemplation.

I’ve enjoyed Scott Young’s writing since he’s the kind of interested amateur who dips into all kinds of areas without committing to professional work. So it was interesting to read his impression of literature research; What if You Don’t Feel Smart Enough?

The expectation is that as you learn more and more, you’ll eventually hit a bedrock of irrefutable scientific fact. Except usually, the bottom of one’s investigation is muck. Some parts of the original idea get sharpened, others blur as more complications and nuance are introduced.

And it’s true that it’s not well appreciated how tentative scientific explanation is as new areas are explored. It’s been exciting for me to watch COVID-19 science develop in real time, so quickly. Yes, scary and polarized in ways that we generally don’t see in medicine, but a predictable back and forth on the properties of the virus, its propagation, and treatment.

We generally know what we know

Scott misses the important point that there is a bedrock of knowledge, the literature just doesn’t bother to discuss it. In neuroscience, the basic physical architecture and cellular makeup of the brain was established with great clarity over the last 100 years or so. As techniques have been introduced, new areas opened up and took a while to get settled into bedrock, but much of that is done now. In fact, my first published paper in 1983 was part of a major chapter in that story when labs used retrograde tracing techniques to map brain connections. My paper established the identity of all of the areas that sent connections to the motor trigeminal nucleus in the rat. That’s the collection of motor neurons that innervate the jaw closing muscles.

We’re in an in interesting era where cognitive science is successfully exploring its underlying neuronal circuitry. As is typical, the process is messy but the picture is getting filled out, even in some very tricky areas like working memory and perception.

It’s of little importance to my day job in drug development at this point, but these are the kinds of questions that sparked my interest in brain science at the beginning. So while I look on as a spectator, I’m spending time reading papers and developing at least a superficial understanding of the techniques and progress.

Building models to explore the unknown

Neuroscience Twitter is a great resource to keep up with trends across cognitive science. Case in point: I’m reading through Bayesian models of perception and action which is a draft of a book by Wei Ji Ma, Konrad Kording, and Daniel Goldreich, to be published by MIT press. I’ve been dipping into papers published by the three authors to get a feel for the deeper applications of the approach. I learned about it on Twitter

I think this is an important area to watch. I’ve talked about the idea that the brain, in order to control behavior, has to contain a model of the system. One approach is create computer models of circuitry based on observed connectivity and activity in animals when these systems are active. If some models can reproduce the brain activity, then they are candidates for hypothesized mechanisms and be used to make predictions about how the real neural circuits behave. Think about it like a physicist using equations to model physical laws and then testing the predictions from those equations against new observations. Except for the brain we don’t have any such equations, so we can use the immense computer power we have at our disposal to do the same kind of abstraction as the physicist.

Just like the equations of physics describe reality, but aren’t reality, these neural models describe little bits of the brain, they aren’t thinking. But interestingly, some of these brain inspired models can be put to work for real life tasks like image or speech recognition because the escape simple algorithmic approaches to analysis and classification.

It dawned on me yesterday, just like the developing “Creator Economy” we’re seeing the creation of the “Knowledge Worker Industry”.

A recent episode of Mac Power Users was full of the usual interesting workflow ideas and productivity hacks. But then it got real serious for me real fast.

You see, Sean McCabe for years has been using automation and workflow tools to improve his own productivity. Now he’s using it to gleefully industrialize knowledge work. His current company takes podcasts and videos and hacks them up to nice promo bites for social media. It’s the kind of work that takes some editing skill, editorial ability, and thought. If you can get that down to a process, an assembly line you have knowledge workers sitting in front of a screen doing industrial line work. Get an assignment, process it, send it down the line and get the next job on the line. It sounds so nerdy and innocent: Mac Power Users #613: The Future of Work, with Sean McCabe

David and Stephen talk with Sean McCabe about how he runs his businesses from what can only be described as a Mac battle station while stitching together macOS apps and several cloud services to be more productive.

But I can see how the productivity hack industry can be used to maximize the productivity of knowledge workers. And it brought to mind Cal Newport’s vision of a world without email. Rather than summarizing Cal’s latest book, here’s an interview where he makes the point: Cal Newport on an industrial revolution for office work

On Cal’s account, those opportunities are staring us in the face. Modern factories operated by top firms are structured with painstaking care and two centuries of accumulated experience to ensure staff can get the greatest amount possible done.

Our productivity grew 50x in the 20th century. Why? Because, the early 20th century is when we got really serious about process engineering. Like hey, wait a second. If we use an assembly line, we can build cars better. We really started to get serious about building things as a process we could get better and better at. And as he underscored, he’s like, 50x growth is almost inconceivably large.

I’m a highly paid knowledge worker because of my 40 plus year career in science, medicine and drug development. I work in a company that Cal thinks is a “hyperactive hive mind” when what it is a organization of shifting expert teams loosely tied together around the world by a combination of asynchronous (email, document sharing) and synchronous (Teams, Zoom, teleconferences) in which I leverage my knowledge by contributing in dozens of different ways every day.

This is how teams work. I understand that Cal, as an academic, wants to be left alone and leave the operational stuff to a theoretical knowledge industry. But I see that as a digital salt mine. Assembly line knowledge work like Sean’s company. Where creativity and improvisational problem solving go to die.