Boulder Future Salon

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"The origin of neuronal diversity." "Diversity" of neuron cells, that is. So in the brain there are cells that are considered "progenitor" cells that produce all the neurons, and also all the glial cells in the brain. And the question being researched here is whether cells that come from the same "progenitor" cell have the same cell type. There are over 100 different types of neurons in the brain, and the way they are classified here is by what genes are expressed in the neuron. They are using a technique called single-cell RNA sequencing (scRNA-seq) to determine what genes are being expressed in a cell -- what they call the "transcriptome" of that cell.

The "transcriptome", however, doesn't tell you lineage -- what cell came from which other cell. For that they used a "barcoding" system. For that, they have to actually modify the genes in the cell in a way they can detect later. The way they make the changes is actually by infecting the mouse -- oh, probably should mention, this research was done with mice, so no human brains or any other animal -- infecting the mouse with a virus called a lentivirus. Is it just me or is it kind of creepy that you can modify an animal's DNA with a virus -- shouldn't its immune system be able to stop the virus?

Anyway, the DNA is modified in such a way as to make the cell make a green fluorescent protein. This system is "enhanced" in such a way as to be able to make "barcodes" that encode information that identify the cell. The "barcoding" system is called "scRNA-seq-compatible tracer for identifying clonal relationships", or STICR. "Sticker", get it? It's just like putting stickers on cells. But then they divide and keep the stickers.

Anyway, they discovered two interesting things as a result of this process. The first is that the relationship between cell type and lineage doesn't hold like you'd expect. Cells that come from completely different progenitor cells turn into the same type of neuron in the end. At the same time, cells that come from the same progenitor turn into completely different types of neurons. Neurons, once produced from the progenitor cells, migrate all over the place -- forebrain, cortex, basal ganglia, hippocampus, olfactory bulb, amygdala, etc.

The second thing they discovered is this rule doesn't hold for inhibitory vs excitatory neurons. That is to say, a progenitor cell that produces inhibitory neurons will only produce inhibitory neurons, and a progenitor cell that produces excitatory neurons will only produce excitatory neurons. An "excitatory" neuron, when it sends a signal to a neighbor, will increase the chances that neuron will fire, while an "inhibitory" neuron, when it sends a signal to a neighbor, will decrease the chances that neuron will fire. The research started with GABAergic neurons. GABA (which stands for gamma-aminobutyric acid, if you care to know) is the most used inhibitory neurotransmitter in the brain.

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"Self-supervised learning from 100 million medical images." Ok, this is one of those things that I wouldn't think would work, and even knowing how it works, it still seems like it wouldn't work. So the idea is, you start with a boatload of medical images, and only a handful of them have "answer keys" -- the correct diagnosis made by a professional radiologist. And from this, the system can learn by looking at the boatload of images that don't have the diagnostic information and learn to diagnose better.

The way the system goes about this is: for each image, it creates two "augmented" images, then it runs each of these through the neural network, which outputs two "features", both of which are passed to a clustering system. You specify how many clusters you want, but not what the clusters are -- the system figures that out. Both the neural network parameters and the clustering algorithm's parameters are learned -- the paper spells out a "loss" function (that the system strives to minimize) that incorporates both.

The system even has a "multi-modality" variant, which means instead of having, say, only chest x-ray images, you have x-ray, computed tomography (CT), magnetic resonance (MRI), and ultrasound images. In this case the loss function sums up each modality, and the system is still able to learn.

To handle the "supervised learning" part where the system looks at examples with "correct answers" given for the diagnosis, the loss function is extended to have a term that adds to the loss based on the error in comparing the output with the known correct answer. Another neural network is introduced for this extra term, but all the parameters including this new neural network are trained together.

The "augmentation" step, which you were probably wondering about, randomly chooses between rescaling the image, rescaling the intensity of the pixels in the image, cropping the image, or doing something they call an "energy-based augmentation", where they "normalize" the "energy" of the image where "energy" is based on "energy bands" calculated by a "Gaussian filtering" algorithm.

In addition to outputting a category for the diagnosis from the clustering system, this system also somehow learns how to output a bounding box indicating where in the image the problem is, but I didn't get how the system learns to do that.

The system was compared with three others, called SimCLR, SwAV, and Supervised NI. The scores of these three on detection of lung lesions and pneumothorax in frontal chest radiographs were 0.90, 0.90, and 0.91, respectively, while the new system achieved 0.94. These aren't simple "accuracy" percentages, but a statistic known as ROC-AUC (receiver operating characteristic area under curve). It incorporates both false positives and false negatives.

The system also outperformed other systems on diagnosing brain metastases in 3D MRI scans and brain hemorrhage detection in CT scans (from people with head injury or stroke symptoms).

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Video about lithium production. And cobalt, another mineral needed by lithium-ion batteries. Demand for lithium-ion batteries is going to go up dramatically in the next decade -- some countries have already mandated electric vehicles by a certain date -- and the price of lithium has already increased. 92% of lithium comes from just 4 countries: Australia, Chile, Argentina, and China. In the US, the largest lithium deposits are at a place called Thacker Pass in Nevada, but major problems with lack of water needed for extraction and processing, proximity of endangered species and likely severe environmental impact on them, and negative impact on the native Americans in the area, have so far stopped its development. Mines in South America have already seen the aforementioned negative effects on indigenous populations and environmentally sensitive regions. Cobolt, in turn, has a human rights problem, as most comes from the Democratic Republic of the Congo, a place with no health protections for mine workers and child labor. Improvements in the batteries themselves are badly needed.

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Evaluation-driven machine learning. Analogous to test-driven development (TDD) for regular software. Define the evaluation and success criteria up front, decide on the evaluation data sets, create your data set splits for training, validation, and testing, decide what baseline models already developed you want to look at as a reference, implement your evaluation pipeline, and only then, after all that, build your model.

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"Real-time machine learning: challenges and solutions." "A year ago, I wrote a post on how machine learning is going real-time. The post must have captured many data scientists' pain points because, after the post, many companies reached out to me sharing their pain points and discussing how to move their pipelines real time. These conversations prompted me to start a company on real-time machine learning."

They divide the piece into "Towards online prediction" and "Towards continual learning".

In "Towards online prediction", we have "Stage 1. Batch prediction". "At this stage, all predictions are precomputed in batch, generated at a certain interval, e.g. every 4 hours or every day."

"Stage 2. Online prediction with batch features". "Instead of generating predictions before requests arrive, companies in this stage generate predictions after requests arrive."

"Stage 3. Online prediction with complex streaming + batch features". "Streaming features are features extracted from streaming data, often with stream processing."

Then there's an intermission discussion on "Online prediction for bandits and contextual bandits." The idea is that "contextual bandits" can server as a smarter alternative to A/B testing. "For those unfamiliar, bandit algorithms originated in gambling. A casino has multiple slot machines with different payouts. A slot machine is also known as a one-armed bandit, hence the name. You don't know which slot machine gives the highest payout. You can experiment over time to find out which slot machine is the best while maximizing your payout. Multi-armed bandits are algorithms that allow you to balance between exploitation (choosing the slot machine that has paid the most in the past) and exploration (choosing other slot machines that may pay off even more)."

Then the "Towards continual learning" section: "Stage 1. Manual, stateless retraining". In the beginning, your ML team focuses on developing ML models to solve as many business problems as possible."

"Stage 2. Automated retraining". "Instead of retraining your model manually in an ad-hoc manner, you have a script to automatically execute the retraining process. This is usually done in a batch process. Most companies with somewhat mature ML infrastructure are in this stage."

"Stage 3. Automated, stateful training". "Remember that stateful training is when you continue training your model on new data instead of retraining your model from scratch."

"Stage 4. Continual Learning". "What I'm working towards and what I hope many companies will eventually adopt is continual learning. Instead of updating your models based on a fixed schedule, continually update your model whenever data distributions shift and the model's performance plummets. The holy grail is when you combine continual learning with edge deployment. Imagine you can ship a base model with a new device -- a phone, a watch, a drone, etc. -- and the model on that device will continually update and adapt to its environment."

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"China last year ranked 9th in robot density -- measured by the number of robot units per 10,000 employees -- up from 25th five years earlier. With a robot density of 246 per 10,000 employees, China still lagged behind South Korea, which has a current density of 932 and has ranked first since 2010. Still, China's level was well above the global average of 126, and close to the United States' 255."

"Under a five-year plan jointly published by several government agencies, including the Ministry of Industry and Information Technology, China aims to achieve a minimum annual growth of 20 per cent in robotics sales, and develop a group of industry champions to double the 'robot density' of the world's most populous country."

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"Formant is solving the robotic Tower of Babel with a unified platform." "The insight was that the robotics hardware was incredible, with folks like Boston Dynamics, really pushing the envelope. Before that, we had 20-25 years of industrial robots that are incredibly accurate, powerful and build every car that you see on the road. That said, the software was in the stone age. There's no unified operating system. Everybody is building every part of the stack, for every application. If you were starting a company, you had to build your perception and your autonomy, of course. But on top of that, you'd also have to build your data management and everything else."

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In January 2017 (our first future salon meeting after Donald Trump was elected), I proffered the prediction that the US was headed towards civil war. It was predicated on a very simple assumption: if tension between the two sides (liberal and conservative) continues to increase, at some point it will be high enough that some sort of physical violence breaks out. At that time, I had no idea what might be the underlying causal driver, and so no way of knowing whether my assumption would hold and tensions would indeed continue to increase. Since then my investigations led to the future salon discussion based on Peter Turchin's Age of Discord, where he presents a mathematical model of society (based on 44 variables that feed back on one another as represented by differential equations), which is the most plausible explanation of a cause-and-effect explanation that I've come across so far (although we will talk about more in an upcoming future salon where we expand the scope of the discussion to geopolitics and political conflict outside the border as well as inside).

In this video, UCSD political scientist Barbara F. Walter discusses her examination of civil wars in other countries in recent years and what parallels those have with the current US. If you thought this type of discussion is limited to the conservative media, this video is on the MSNBC YouTube channel.

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"Artificial intelligence can now craft original jokes." "Earlier this year, at the International Conference on Computational Creativity, Joe Toplyn presented a research paper outlining Witscript, a joke-generation system trained on a data set of TV-monologue jokes that detects keywords in entered text and creates a relevant punch line. Unlike other forms of robot comedy, the system -- which Toplyn has patented -- can generate contextually relevant jokes on the spot in response to a user's text."

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Learning Monopoly with an AI algorithm called NEAT, which stands for neuro-evolution of augmenting topologies, and combines neural networks and genetic algorithms.

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Tom Scott reviews his 2012 predictions for 2022. All the political guesses were completely wrong. Right about 2022 iPhone and phone network speed. Lifelogging -- recording everything you do 24-hours a day, "the quantified self" -- was wrong. People don't want to remember everything and store that data with corporations. People don't want to wear a body cam and record audio of every conversation with everybody. Schools don't have alumni forums -- everything is a Facebook group, or a WhatsApp thread, or a Slack or Discord server -- some centralized service. Failed to predict "mobile first". Blogging? Declined. Replaced by Twitter. Another miss.

What about 10 years from now? Short-form video. Like the transition from blogs to Twitter.

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Pictures of the Hierarchical Temporal Memory of the Thousand Brains Theory. The book by Jeff Hawkins didn't have any pictures. It described the gist of the ideas but referred the reader to research papers for the details. Here we have a picture of real neurons, neurons in traditional deep learning, neurons in the HTM model, layers and mini-columns, and the wiring of the proximal dendrite and basal dendrites.

If you're wondering what proximal and basal dendrites are, it's all about where the dendrite connects to the neuron cell body and where its information comes from, relative to layers in the neocortex. "Proximal dendrites carry input information from outside of the layer of cells, and if they trigger, they cause the entire neuron to fire. Basal dendrites are connected to other cells in the same layer, and if they trigger, they predispose it to firing, causing it to fire faster than it normally would, thus preventing other neurons in the same region from also firing. (Apical dendrites are not modeled this this paper.)"

The article goes on to describe this system's learning rule. The author experiments with constant input, sparse input, multiple simultaneous predictions, and finally, sequence learning.

"Overall, hierarchical temporal memory is a neat way to train models to recognize sequences. Personally, I am worried about how much space the model takes up, but aside from that the model is fairly clean and I was able to get up and running very quickly."

"The paper did not compare against any other substantial sequence learners (merely a dumb baseline). I'd love for a good comparison to something like a recurrent neural network to be done."

"I noticed that the HTM only trains dendrites that become predictive (or chooses dendrites in the case of a burst activation). [...] If a certain column is never activated, the dendrites for that neuron will not be trained."

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A mechanism causing nerve destruction in the motor neuron disease ALS has been discovered. ALS (which stands for amyotrophic lateral sclerosis, if you care to know) is the disease that Stephen Hawking got. "The team discovered that an abnormal buildup of a protein called TDP-43 in neuromuscular junctions, which translate brain signals into physical movements, leads to the degeneration and death of nerve cells (motor neurons). They found that this hinders the activity of mitochondria, which are critical for cells to function."

"The researchers found that this process occurs during the early stages of ALS, initiating damage to motor neurons before patients develop serious symptoms. Eventually, the deterioration of nerve cells in the brain and spinal cord causes ALS patients to gradually lose voluntary muscle ability, leading to complete paralysis including the inability to breathe independently."

"Using an experimental molecule (originally developed to enhance neural regeneration after injury), the team demonstrated its success in dismantling the toxic protein buildup found in ALS patients. Additionally, in lab models, the researchers showed that this approach actives the process of nerve regeneration, leading to almost complete rehabilitation from the disease."

What the press release doesn't mention but is described in the paper is that what the researchers did was reproduce the problem in transgenic mice. So this research was done primarily on mice neurons, but enough was also done with human neurons to believe that their solution will work with humans.

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Rodney Brooks' Predictions Scorecard, 2022 January 01. "On January 1st, 2018, I made predictions about self driving cars, Artificial Intelligence, machine learning, and robotics, and about progress in the space industry. Those predictions had dates attached to them for 32 years up through January 1st, 2050."

"I made my predictions because at the time I saw an immense amount of hype about these three topics, and the general press and public drawing conclusions about all sorts of things they feared (e.g., truck driving jobs about to disappear, all manual labor of humans about to disappear) or desired (e.g., safe roads about to come into existence, a safe haven for humans on Mars about to start developing) being imminent. My predictions, with dates attached to them, were meant to slow down those expectations, and inject some reality into what I saw as irrational exuberance."

"I was accused of being a pessimist, but I viewed what I was saying as being a realist. Today, I am starting to think that I too, reacted to all the hype, and was overly optimistic in some of my predictions. My current belief is that things will go, overall, even slower than I thought four years ago today."

Self-driving cars: "Very little movement in deployment of actual, for real, self driving cars. Way back four years ago when I made my predictions about 'self driving cars' that term meant that the cars drove themselves, and that there was no one in the loop at a company office, or following in a chase car, or sitting in the drive or passenger seat ready to take over or punch a big red button."

Robotics, AI, and machine learning: "With respect to my predictions for AI and ML there are only three that come close to being in play this year, either in terms of work that was done that impacts my predictions, or the date is close to when I said that something would or would not happen. I have annotated the table below in those three places; the next big thing, change in perspective of how to measure AI success, and robots that can really get around in our houses in a general purpose way."

"Back in 2018 I predicted that 'the next big thing', to replace Deep Learning, as the go to hot topic in AI would arrive somewhere between 2023 and 2027. I was convinced of this as there has always been a next big thing in AI. Neural networks have been the next big thing three times already. But others have had their shot at that title too, including (in no particular order) Bayesian inference, reinforcement learning, the primal sketch, shape from shading, frames, constraint programming, heuristic search, etc."

"We are starting to get close to my window for the next big thing. Are there any candidates? I must admit that so far they all seem to be derivatives of deep learning in one way or another."

We got transformers, foundation models, and unsupervised learning.

"Roombas could never be Rosie the Robot from the cartoon series The Jestsons." "One of my predictions is that A robot that can navigate around just about any US home, with its steps, its clutter, its narrow pathways between furniture, etc. won't even be a lab demo until 2026, and not be deployed for real until 2030 and at low cost in 2035."

"Amazon just made a good and necessary step towards this capability with release of the Astro home robot." "The impressive step that Amazon has made is in getting a really reliable SLAM (Simultaneous Localization And Mapping) system that can quickly build a very good map of a house without any help from humans."

Space: "There are three big stories in the space industry from 2021, and they all impact one or more predictions that I made. The first is that space tourism notched up significantly from where it had been, both for sub-orbital and orbital trips. The second is that Boeing had another serious setback on launching people into space; I had said by 2022, now I think that is less likely to happen. And third, a lot of visibility into progress on SpaceX's Starship second stage progress in the first half of the year, and much less than people had expected for the first stage, with no launch yet."

Really admire Rodney Brooks for taking predictions seriously. I always tell people, any time you make a prediction and it turns out to be wrong, it proves the world does not work the way you think it did and you need to "go back to the drawing board", as the old expression goes, and figure it out all over again. And if you actually make predictions and write them down, you will be astonished by how often you are wrong, and that implies you don't understand the world very well. Most people simply never write down any predictions and check them, allowing them to be fooled by the "I knew it all along" fallacy that our brains trick us into by rewriting our memories once we know what actually happened. (This is called the narrative fallacy in psychology). I pulled out a few quotes but Brooks' post is worth reading in full.

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"The researchers successfully extracted DNA from a collection of blocks of sediment prepared as long as 40 years ago, from sites in Africa, Asia, Europe and North America." "The fact that these blocks are an excellent source of ancient DNA -- including that originating from hominins - despite often decades of storage in plastic, provides access to a vast untapped repository of genetic information."

"The scientists used blocks of sediment from Denisova Cave, a site located in the Altai Mountains in South Central Siberia where ancient DNA from Neanderthals, Denisovans and modern humans has been retrieved, and showed that small organic particles yielded more DNA than sediment sampled randomly."

"Diyendo Massilani, the lead author of the study, was able to recover substantial amounts of Neanderthal DNA from only a few milligrams of sediment. He could identify the sex of the individuals who left their DNA behind, and showed that they belonged to a population related to a Neanderthal whose genome was previously reconstructed from a bone fragment discovered in the cave." "The Neanderthal DNA in these small samples of plastic-embedded sediment was far more concentrated than what we typically find in loose material."

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"Two dozen study participants were shown 70 short, unique video clips." "The following day, they were slid into the MRI tube to watch the videos again, but this time, half of the video clips were interrupted suddenly and without warning at the critical moment of the narrative, such as when the baseball batter was swinging at the pitch."

"On the third day, participants were interviewed in great detail to try to recall as much of the videos as they could." "A couple of people were incredibly detailed and super accurate, but a couple of people had an insane number of false memories. It was hard to keep a straight face."

"What the researchers saw in the MRI images is that surprise changed the role of the hippocampus, a brain region important for creating, retrieving, and editing memories. After unsurprising videos, the hippocampus seemed to be in 'preserving mode,' strengthening memories. But after surprising videos, the hippocampus switched into 'updating mode,' getting ready to edit memories. Surprise disrupted the stability of patterns in the hippocampus, showing this mode switch. More pattern disruption led to more false memories."

Going into a bit more detail of the study, what they found is that when there was greater activation in the hippocampus, if the ending was expected that meant greater memory preservation, but if the ending was a surprise ending, that meant more false memory. That's the primary factor, but forebrain activation, which they say indicates how well the person is paying attention, is also a secondary factor. They think in general, your brain uses your memory to predict what it expects to experience, and when a "prediction error" occurs, your hippocampus switches to "memory editing" mode.