Unbalanced composition with gazebo, trees, and other things

Michale Levin on bioelectricity, regeneration, cancer treatment

How bioelectricity could regrow limbs and organs, with Michael Levin (Ep. 112), Big Brains Podcast, UChicago News, April 27, 2023.

"Software" for the cells:

Paul Rand: It’s kind of mind-blowing in its own way. How do the cells have these memories, if that’s the right word?

Michael Levin: Well, I think it’s the right word. I think many people probably don’t think it’s the right word, but I think it’s exactly the right word. I think you’re right. It is mind-blowing because. look, each of us makes this journey from an unfertilized [inaudible 00:07:46], which is a little blob of chemicals. You look at that little blob of chemicals and you would say, “well, this is just physics. This is just chemistry. This thing doesn’t have any goals, any intelligence, you know, you name it.” And then eventually that little blob of chemistry turns into, nine months and some years later, it turns into a being that absolutely has an inner perspective, it has goals, it has preferences, it has behavioral [inaudible 00:08:13], and it will go on to say things like, “Well, I’m not a machine I’m a human being.” Okay, great.

What’s really important to realize is that this process of development, very robust, meaning consistent, so there’s this amazing ability for life to get to the correct outcome, meaning the correct target morphology for that species, despite all kinds of crazy things, multiple copy numbers of the DNA, more cells, less cells, bigger cells, they still figure out how to get it done.

So then it makes really a lot of sense to ask, “Okay, if you’re solving this problem, if you’re going to get to the same goal despite various things that could happen to you, what are you using to remember what that goal is? You’re navigating these spaces trying to get to the correct final outcome, but how do you know what that outcome is?”

Paul Rand: Levin thinks bioelectricity is the architect building the blueprint, so to speak.

Michael Levin: These pattern memories are encoded in the electrical network of the body of the early embryo and subsequent exactly in the way that we think of as memories about navigating three-dimensional space are encoded in the brain. Now, I should point out that we, of course, we don’t know exactly how memories are encoded in the brain. We still don’t know, and there are many mysteries about that in the body as well, but I think we should get really comfortable with the idea that electrical networks store memories, they store goal states and they facilitate these complex beings to navigate space to get to those goals.

Paul Rand: As you talk about this, it’s almost like the cells are like a hardware and the electrical patterns are almost the software. Is that a fair analogy?

Michael Levin: I think that’s a very fair analogy. A lot of people don’t like that analogy because they’re visualizing hardware and software the way that they think about their laptops. But what is really powerful about that notion and what makes that analogy work really well is the idea of reprogrammability. So what’s powerful about computers is that the exact same piece of hardware can do multiple things without rewiring. And so when I give talks about this, I ask people, “Why is it that on their computer when they want to switch from Photoshop to PowerPoint, they don’t get out their soldering iron and start rewiring?” Isn’t it amazing.

Regeneration, cancer treatment:

The bigger picture here is that currently the medical model that we currently have, one of the problems with it is that it’s fundamentally unsustainable for any, no matter how many resources we have, because every advance that we make to prolong the life of a patient ends up giving you a sicker patient, that’s the baseline for the next intervention. So the better you are at extending the last stages of the lifespan, the more expensive and more heroic the next measures have to be. Inevitably, the logic of it is inescapable. And so that’s a spiral. That’s a constant spiral that is fundamentally unavoidable and unsustainable for any society unless we figure out how to crank up the regenerative process very early on so that you never get to the stage of that sinking ship that you need to keep propping up. It means that you are not just chasing symptoms, you are fundamentally, and we can talk about what that is, but we need a completely different approach to medicine that leverages literally the intelligence of the body so that the regenerative process is happening all the time.

In addition to leg regeneration, two kind of flagship applications in our group have been, first of all, the repair of birth defects. And so we were able to show that a wide range of birth defects of the brain, heart, face, and gut induced either by genetic mutations or by chemicals, can be prevented by an appropriate bioelectrical treatment that was designed by a computational model. So there’s a computational model that tells you which ion channels you would need to turn on and off to make specific patterns. And so we’ve used that to repair birth defects in the frog model. The other side is the cancer side, and we started in frogs showing that if we understand cancer to be the breakdown of the electrical signaling that normally harnesses cells towards this common anatomical purpose, so when that breaks down, they simply roll back to their amoeba-like ancient lifestyle where they just al their goals are little tiny cell level goals, which means go wherever life is good, reproduce as much as you can. Then that’s metastasis. And so we were able to show that despite really nasty human oncogenes, we could suppress to or prevent correct tumor genesis by forcing the appropriate bioelectrical states. And we started this in frog, and we are now in human glioblastoma. So we’re working to try the same thing in glioblastoma.

There's more at the link.

H/t 3QD.

Japanese maple

New Method for Science: Use a neural network to model scientific data, then interpret the resulting model

Miles Cranmer - The Next Great Scientific Theory is Hiding Inside a Neural Network (April 3, 2024)

From the Simons Foundation:

Machine learning methods such as neural networks are quickly finding uses in everything from text generation to construction cranes. Excitingly, those same tools also promise a new paradigm for scientific discovery.

In this Presidential Lecture, Miles Cranmer will outline an innovative approach that leverages neural networks in the scientific process. Rather than directly modeling data, the approach interprets neural networks trained using the data. Through training, the neural networks can capture the physics underlying the system being studied. By extracting what the neural networks have learned, scientists can improve their theories. He will also discuss the Polymathic AI initiative, a collaboration between researchers at the Flatiron Institute and scientists around the world. Polymathic AI is designed to spur scientific discovery using similar technology to that powering ChatGPT. Using Polymathic AI, scientists will be able to model a broad range of physical systems across different scales.

About the Speaker

Cranmer is an assistant professor in data intensive science at the University of Cambridge with joint appointments in the Department of Applied Mathematics and Theoretical Physics and the Institute of Astronomy. He completed his Ph.D. at Princeton University. His research focuses on accelerating scientific discovery by developing and applying novel methods at the intersection of machine learning and physics. Cranmer has created a suite of standard software libraries for ‘symbolic regression’ that have been utilized in numerous scientific discoveries. His work covers various areas of deep learning, including physics-motivated architectures such as Lagrangian neural networks.

Miles Cramer at Google Scholar

Roughly, it goes like this: 1) train a neural network on scientific data. Then, 2) interpret the model using the mathematical language of science.

Hmmmm... I wonder. If we had a mathematical model of language, could we use that model to interpret LLMs?

A physics that explains 'everything' while evading the temptation of being a Grand Unified Theory?

The (Simple) Theory That Explains Everything | Neil Turok

56,115 views Apr 23, 2024 Theories of Everything with Curt Jaimungal

Physicist Neil Turok, recipient of the James Clerk Maxwell Medal and Prize, and the John Torrence Tate Award for International Leadership in Physics, joins Curt Jaimungal and Theories of Everything to discuss his new hypothesis regarding the origins of the universe. Building on Stephen Hawking's geometrical model, Turok proposes a theoretical approach that avoids the singularity at the Big Bang by suggesting a minimal, mirror universe scenario without requiring inflation.

Consider signing up for TOEmail at https://www.curtjaimungal.org

Timestamps:
00:00 - The Big Bang Is A Mirror
15:40 - Minimalism In Physics
28:28 - Neil’s Theory “Minimalism SM LCDM”
31:20 - Fields Vs. Particles
49:15 - The Arrow Of Time (Bolztmann)
55:44 - Black Hole Singularity Vs. Big Bang Singularity
01:09:21 - Numerology And The Number 36
01:19:26 - Neil’s Theory Solves EVERYTHING
01:23:32 - What Do Other Scientists Think?
01:36:28 - The Dual Universe
01:44:14 - Predictions From Neil’s Theory
01:48:28 - What Motivates Neil?
01:52:20 - Wave Function Of The Universe
01:57:20 - Support TOE

Guerilla libraries in Hoboken, NJ

On Monday my friend Leanne Ogasawara had a post at 3 Quarks Daily that was, in part, about Little Free Libraries. According to Wikipedia:

Little Free Library is a 501©(3) nonprofit organization that promotes neighborhood book exchanges, usually in the form of a public bookcase. More than 150,000 public book exchanges are registered with the organization and branded as Little Free Libraries. Through Little Free Libraries, present in 115 countries, millions of books are exchanged each year, with the aim of increasing access to books for readers of all ages and backgrounds. The Little Free Library nonprofit organization is based in St. Paul, Minnesota, United States.

The first one was built in 2009 by Todd Bol as a tribute to his mother. Then this that and the other and his idea became a movement. His original goal was to create 2150 libraries, thereby surpassing the number of libraries endowed by the industrialist Andrew Carnegie.

I can remember going to “the Carnegie library” when I was a kid. To me that label was just the name of the library. It was only somewhat later that I learned who Carnegie was. I particularly remember taking out books about American Indians, which, by the way, is what I wanted to be when I grew up. That's also why I wasn’t fond of my curly blond hair. Whoever heard of a blond Indian?

I digress.

Anyone, at the very end of her article, the last line, Leanne had a link to a map of Little Free Libraries. So I went looking, and found one in Hoboken only a few blocks from me. Here it is, beneath the 14th Street viaduct:

And here’s the official tag, with its charter number: 137333.

According to the map, there are seven Little Free Libraries in Hoboken. The red tear drops mark the libraries.

The one in the photos is the one near the upper left of the map. The two at the right are even closer to my place. For some reason I didn’t spot them the first time I looked at the map.

Anyhow, a couple of years ago I discovered something very like those free libraries a bit closer to me than that Little Free Library. The box is a bit larger and is decorated:

It seems to have been adopted by a Girl Scout troop. I’m not sure what’s going on with the spelling of “Hoboken.”

Here’s what it looked like back in 2020 when I first spotted it:

In this last photo you can see how I stuffed it with copies of a little book I’d edited: We Need a Department of Peace: Everybody’s Business, Nobody’s Job.

More later.

Lawfare analysts on Trump's immunity case before the Supreme Court

Matt Gluck, Hyemin Han, Quinta Jurecic, Natalie K. Orpett, Roger Parloff, Alan Z. Rozenshtein, “For the Ages”: The Supreme Court Hears the Presidential Immunity Defense, April 26, 2024.

Introductory remarks:

On April 25, the U.S. Supreme Court heard oral arguments in Trump v. United States, the case arising from the Special Counsel’s Office’s decision to charge former president Donald Trump for his effort to overturn the results of the 2020 election. Trump has argued that he is absolutely immune from the charges brought by the Justice Department because, he asserts, they target his official presidential conduct. The U.S. District Court for the District of Columbia rejected Trump’s argument that he enjoys absolute criminal immunity for his official acts, and the U.S. Court of Appeals for the D.C. Circuit affirmed that ruling.

Despite the nearly three hours of oral argument, only a portion of that time was spent on the particulars of the Jan. 6 case or its procedural posture. That’s because the justices were, as Justice Gorsuch put it, writing a ruling “for the ages.” The Court grappled with the distinction between private acts and official acts—everyone seemed to agree that private acts could be prosecuted—and then wrestled with which subset of official acts, if any, could be prosecuted. Several justices further focused on which criminal statutes can apply to the president without conflicting with his Article II powers. There did not appear to be much consensus on these questions, and the justices seem poised to issue a splintered decision rejecting Trump’s maximalist arguments, while establishing at least some presidential criminal immunity for at least some types of official acts.

The Court could send the case down several different paths to resolve and eventually move past the immunity issue, but none is likely to lead to a quick resumption of the trial in Judge Tanya Chutkan’s courtroom.

Then we have analysis of remarks by the advocates and by each justice.

Concluding remarks:

The justices seem certain to send the case back to either the court of appeals or, more likely, the district court for further proceedings. Precisely what those proceedings will look like, what they will decide, and whether the findings reached therein would, themselves, be subject to a second interlocutory appeal, all remain very live questions.

Even Sauer acknowledged that certain accusations of the indictment concerned purely private acts, and that a former president could at least theoretically be charged with crimes based solely upon those. But much of the indictment also alleges that Trump used the trappings of his office for personal gain. And the justices appeared deeply split over whether these sorts of acts were protected by some sort of immunity and, if so, whether it was absolute or qualified.

Likewise unclear—and decisive in terms of whether this case can yet conceivably be tried before the election—is what sort of procedures the Court will require the lower court to engage in to resolve whatever questions the Court wants resolved. If the case returns to Judge Chutkan, one possibility is that she could proceed with the current indictment, as is, and simply instruct the jurors that certain accusations can only be used as evidence of Trump’s intent—not as a basis for finding him criminally culpable. Another is that she would have to “expunge” certain accusations and that even evidence of that conduct would be precluded from being introduced at trial. Still another is that Judge Chutkan would need to hold some sort of evidentiary hearing. Finally, in any of these scenarios, the crowning question will be whether Trump will be entitled to make an interlocutory appeal on whatever findings Judge Chutkan makes—ensuring that no trial could take place for many months to come.

It looks increasingly unlikely that this case will be tried before the election. And if Trump wins that election, the case will likely never be tried at all.

White flower with infolded petals

To which values should an A.I. be aligned?

Nathan Gardels, The Babelian Tower Of AI Alignment, Noema, April 26, 2024:

As generative AI models become ever more powerful on their way to surpassing human intelligence, there has been much discussion about how they must align with human values so they end up serving our species instead of becoming our new masters. But what are those values?

The problem is that there is no universal agreement on one conception of the good life, nor the values and rights that flow from that incommensurate diversity, which suits all times, all places and all peoples. From the ancient Tower of Babel to the latest large language models, human nature stubbornly resists the rationalization of the many into the one.

Despite the surface appearance of technological convergence, a deep ontological plurality — profoundly different beliefs about the nature of being — still informs the active values of variegated societies.

Silicon Valley Vs. China

This is most readily evident in the politico-cultural clash of the leading AI powers, Silicon Valley and China. At the risk of reductive essentialism for the purpose of brevity, the values of the former are aligned with the libertarian worldview of the sovereign individual long cultivated in the Judeo-Christian West. The values of the latter are aligned with the concept of the collectively embedded person rooted in Confucian, Buddhist and Daoist beliefs of social interdependence.

An early mission statement by OpenAI, which developed GPT, reflects the deep well from which its innovations have sprung: “We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible.”

By contrast, after Alibaba released its latest version of generative AI in 2023, the Cyberspace Administration of China quickly laid down the law: “Content generated by generative artificial intelligence should embody core socialist values and must not contain any content that subverts state power, advocates the overthrow of the socialist system, incites splitting the country or undermines national unity.”

And of course there is diversity in the West, not to mention the rest of the world. India? The Middle East? Africa? The Pacific nations? 

There's more at the link.

Forlorn tree on a submerged pier

Collective intelligence: A unifying concept for integrating biology across scales and substrates

Patrick McMillen & Michael Levin, Collective intelligence: A unifying concept for integrating biology across scales and substrates, Communications Biology, (2024) 7:378, https://doi.org/10.1038/s42003-024-06037-4

Abstract: A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

How is an LLM like a mechanical clock?

Let's think about LLMs a little more here, but first let's think about mechanical clocks.

A clock movement is an arrangement of gears and springs and levers that rotates 2 or more shafts are very specific speeds. That's it. An LLM is a lot like a mechanical clock movement.

1/

— amolitor.dolt (@amolitor99) April 25, 2024

The rest of the tweet stream:

A clock movement has no concept of "time" if you take the gears and levers apart, nowhere in there will you find any notion of what "time" is, nor, especially, the *correct* time of day. It just turns shafts, and even that function is not obvious to the non-expert.

If you attach some hands to the shafts, and position a "face" with numerals on it behind, and so forth, you can cause the combined machine to "tell the time" in the sense of making an assertion about what the time of day is.

The position of the hands over the face has a *meaning* for a suitably trained reader, in the same way that a sentence has *meaning*. If you speak the language, a sentence, as a 'signifier,' points to a 'signified,' the meaning of the sentence.

A book does not "know" anything, it does not "mean" anything by itself. If you, a language reader, read it, the meaning arises in that interaction.

In the same way, "it is 2:17" is a meaning which arises when you look at a clock face.

If the clock has not been wound, or if the clock has been set to the wrong time, the statement "it is 2:17" will still have meaning, but that meaning will likely be false. It is 10:17, not 2:17.

The clock is not *lying* in any reasonable sense. It itself does not even carry the meaning of the signs it displays. The meaning arises in the interaction of you with the hands and face attached to the clock.

The gears and levers don't even have "2:17" encoded in them.

In the same way, an LLM has no meaning encoded in it, although it produces signs which in their interaction with you often have a great deal of meaning.

LLMs do not "lie" or "hallucinate" any more than a clock does. It's simply a very very complicated set of gears and lever which, if you wind it up, and if you set it to the correct time, can produce true statements like "it is 10:20" some of the time.

a set of signs on the screen are incapable of being true or false, hallucinations or reality. They're just arrangements of letters.

Only meanings are true or false, and there are no meanings in the LLM. 

It is true that you will find echoes of meaning inside the LLM. Objects arise in the data which resemble an Othello board in interesting ways, when you train an LLM to play Othello.

This is like the position of the shaft of a clock movement.

You can see artifacts in the clock's machinery which, if you know what's going on, you can map to the output statement about what time it is. At the same time, it's foolish to argue that the clock "knows" what time it is, and then to argue that "it's lying."

The clock just turns shafts. This is not changed by the fact that you can imagine the hands and face without actually putting them on, and thereby imaginatively replicate the ability of the clock to make temporal assertions.

Now, an electronic clock can extract the actual correct time from a number of over-the-air oracles (GPS etc) and in fact if you take one of these apart you *will* find a place in there which contains the actual time. Allegedly the "correct" time.

A sequence of ones and zeros in a memory location encodes the "correct" time in some sense, or at least is supposed to.

There is a meaning inside the thing, kinda! It can be wrong, now, and it can arguably lie! If the clock is damaged, say.

When you look at the display you see that it reads "2:17" but the correct time is now 10:27. The clock is making some mistake between the oracle it consults and the display is shows you. It is "lying" or "hallucinating" in some meaningful way.

There is a model of the world attached to the clock, which the clock consults. That model can be interpreted to "mean" that "the time is 10:27" and the display on the clock can be interpreted to "mean" that "the time is 2:17" and that mismatch is a falsehood.

LLMs have no world model. They are clock movements, just clock movements. Attach them to a screen and a keyboard, and you've given the clock hands and a face. You can now read meaning if you like, but to propose that the "meaning" is inside the LLM is simply false.

fin/

Then there's this:

Friday Fotos: Let's play in the park!

Take your kids to work day – to Mt. Kenya [Don't punish women scientists because they have kids]

Toby Kiers, A Simple Act of Defiance Can Improve Science for Women, NYTimes, April 26, 2024.

sci

They don’t tell you beforehand that it will be a choice between having a career in science or starting a family. But that’s the message I heard loud and clear 17 years ago, in my first job after completing my Ph.D. in evolutionary biology. During a routine departmental meeting, a senior academic announced that pregnant women were a financial drain on the department. I was sitting visibly pregnant in the front row. No one said anything.

She has another child. There are incidents: "It felt like an impossible choice: to be a bad scientist or a bad mother."

What to do:

Through an act of academic defiance: I bring my kids with me on my scientific expeditions. It’s a form of rebellion that is available to mothers not just in the sciences but also in other disciplines that require site visits and field work, such as architecture and journalism. [...]

It started for me as a simple necessity. When my son was just under 2 and my daughter not yet 4, I took them on an expedition to the base of Mount Kenya in Africa, to study how fungi help trees defend themselves against the elephants and giraffes who feed on them. My son was still nursing, and I didn’t want to stop working. My husband, a poet, came along to stay with them at base camp.

As time went on, I began to embrace the decision to bring my kids with me on my expeditions, not as an exigency of parenting but as a kind of feminist act.

It has worked out well:

I started tasting soils in the field — a technique I now use to notice subtle differences across ecosystems — only after seeing my kids eat dirt. Children have an uncanny ability to make local friends quickly; many of those new friends have led me to obscure terrain and hidden fungal oases that I otherwise would never have come across. And my kids’ naïve minds routinely force me to rethink old assumptions by asking questions that are simultaneously absurd and profound. Can you taste clouds? Do fungi dream? How loud are our footsteps underground?

And so it goes. More examples of how things worked out.

At its core, feminism is about having the power to choose. For female scientists, this means having the ability to bring children into the field — or the full support to leave them at home. The pressure is acute because, as research shows, women on scientific teams are significantly less likely than men to be credited with authorship. So for me, it is crucial to keep collecting data with my own hands.

There's much more at the link.

Lilacs

The Industrial Revolution started earlier than conventional wisdom would have us believe

Fred Lewsey, ‘Nation of makers’: Britain industrialised over a century earlier than history books claim, University of Cambridge, 5 April, 2024.

Britain was well on its way to an industrialised economy under the reign of the Stuarts in the 17th century – over 100 years before textbooks mark the start of the Industrial Revolution – according to the most detailed occupational history of a nation ever created.

Built from more than 160 million records and spanning over three centuries, the University of Cambridge’s Economies Past website uses census data, parish registers, probate records and more to track changes to the British labour force from the Elizabethan era to the eve of World War One.

The research shows that 17th century Britain saw a steep decline in agricultural peasantry, and a surge in people who manufactured goods: from local artisans like blacksmiths, shoemakers and wheelwrights, to an explosion in networks of home-based weavers producing cloth for wholesale.

Historians say the data suggests that Britain was emerging as the world’s first industrial powerhouse several generations before the mills and steam engines of the late 18th century – long credited as the birth of global industry and economic growth.

Distributed manufacture:

Yet in the England of 1700, half of all manufacturing employment was in the countryside. “In addition to village artisans, there were networks of weavers in rural areas who would work for merchants that supplied wool and sold the finished articles,” said Shaw-Taylor.

Industries of textiles, or metalworkers making nails and scythes, were shaped like “factories without machines spread out over hundreds of households” according to Shaw-Taylor – and increasingly produced goods for international markets.

In Gloucestershire, for example, expansions in textiles, footwear and metals saw the share of the male workforce in industry grew from a third (33%) to almost half (48%) over the 17th century.comm

While in Lancashire, the share of men in manufacturing work grew from 42% in 1660 to 61% in 1750, driven by a doubling of textile workers (from 15% to 30%). This all occurred prior to the Industrial Revolution.

Some networks evolved into workshops, and eventually the mills of Blake’s visions as industries migrated to the North of England, where coal was abundant and crops were harder to grow.

This meant that by the mid-18th century – considered the start of the Industrial Revolution – much of England’s South and East had actually lost its long-established industries, and even returned to farm labour, according to the research.

There's more at the link.

H/t Tyler Cowen.