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Wolfram|Alpha has been integrated into ChatGPT. You have to be a ChatGPT Plus user and install the Wolfram plugin from within ChatGPT. With it, you can ask questions like "How far is it from Tokyo to Chicago?" or "What is the integral of x^2*cos(2x)" and, instead of trying to answer the question *linguistically*, ChatGPT will realize it needs to invoke Wolfram|Alpha and pass the question to Wolfram|Alpha for a computational answer.

The article shows some of the behind-the-scenes communication between ChatGPT and Wolfram|Alpha. ChatGPT doesn't just cut-and-paste in either direction. Rather, it turns your question or into a Wolfram|Alpha query, and then re-translates-back the answer into natural language. ChatGPT can incorporate graphs from Wolfram|Alpha into its presentation as well.

"ChatGPT isn't just using us to do a 'dead-end' operation like show the content of a webpage. Rather, we're acting much more like a true 'brain implant' for ChatGPT -- where it asks us things whenever it needs to, and we give responses that it can weave back into whatever it's doing."

"While 'pure ChatGPT' is restricted to things it 'learned during its training', by calling us it can get up-to-the-moment data."

AI tries 20 Jobs. Software engineer, doctor, graphic designer, therapist, stand-up comedian, lawyer, news anchor, bartender, screenwriter, music producer, journalist (product reviewer), copywriter, personal-trainer, DJ (disco), voice actor, influencer, chef (Indian), translator (English to Korean), and firefighter. Spoiler: It couldn't do "firefighter" or "circus artist" at all. For a lot of the others, it could do a bad job, not good enough to compete with a professional human. The one AI did best with was possibly "software engineer".

Back in the early 2000s, when we first started the future salons, we'd talk a lot about a future where AI could do all jobs. It's interesting to see that future creeping up on us bit by bit.

A lot of people would argue AI will never be able to do all jobs because AI can't do "creativity". But all those routine jobs stocking shelves at the grocery store -- those'll get automated right away! The interesting thing is that it's been the other way around -- it's the creative jobs like art and writing that are most threatened. (And software engineer.) Physical jobs like stocking shelves -- or firefighter or circus artist -- AI/robotics is nowhere near automating those. It looks like the physical jobs will be the last to get automated.

It's not routine-vs-creative that matters, it's mental-vs-physical.

"OpenXLA is available now to accelerate and simplify machine learning."

XLA stands for "accelerated linear algebra", and it's the compiler that Google has been using with their custom hardware for AI, called tensor processing units (TPUs). It hasn't been open source until now. Now it's open-source and...

"co-developed by AI/ML industry leaders including Alibaba, Amazon Web Services, AMD, Apple, Arm, Cerebras, Google, Graphcore, Hugging Face, Intel, Meta, and NVIDIA."

Quite a list of companies. What exactly does it do?

"It enables developers to compile and optimize models from all leading ML frameworks for efficient training and serving on a wide variety of hardware. Developers using OpenXLA will see significant improvements in training time, throughput, serving latency, and, ultimately, time-to-market and compute costs."

They go on to further describe their motivation for creating OpenXLA:

"As model parameter counts grow exponentially and compute for deep learning models doubles every six months, developers seek maximum performance and utilization of their infrastructure. Teams are leveraging a wider array of hardware from power-efficient ML ASICs in the datacenter to edge processors that can deliver more responsive AI experiences."

"Without a common compiler to bridge these diverse hardware devices to the multiple frameworks in use today (e.g. TensorFlow, PyTorch), significant effort is required to run ML efficiently; developers must manually optimize model operations for each hardware target. This means using bespoke software libraries or writing device-specific code, which requires domain expertise. The result is isolated, non-generalizable paths across frameworks and hardware that are costly to maintain, promote vendor lock-in, and slow progress for ML developers."

They go on to say that the OpenXLA Project's core pillars are surprise, fear, ruthless efficiency, and almost fanatical devotion to the Pope -- wait, no, that's the Spanish Inquisition. Nobody expects the Spanish Inquisition! The OpenXLA Project's core pillars are performance, scalability, portability, flexibility, and extensibility for users.

So the idea is that input is in the form of PyTorch, JAX, or TensorFlow code, it goes into StableHLO ("HLO" stands for "high-level operations"), which outputs OpenXLA-formated code, which goes into a target-independent optimizer, then hardware-specific optimizer, and then you run it on your hardware.

"Google DeepMind researcher co-authors paper saying AI will eliminate humanity." Do they really?

"The paper, published last month in the peer-reviewed AI Magazine, is a fascinating one that tries to think through how artificial intelligence could pose an existential risk to humanity by looking at how reward systems might be artificially constructed."

"What the new paper proposes is that at some point in the future, an advanced AI overseeing some important function could be incentivized to come up with cheating strategies to get its reward in ways that harm humanity."

"Cheating" to get a reward sounds like addiction to me, but they hypothesize the route the AI would take would involve eliminate potential threats and gaining control of "all available energy" to secure control over its reward.

"Under the conditions we have identified, our conclusion is much stronger than that of any previous publication -- an existential catastrophe is not just possible, but likely."

"If you're in a competition with something capable of outfoxing you at every turn, then you shouldn't expect to win."

That seems to hard to argue with. That's like me playing Stockfish. Not even AlphaZero.

"And the other key part is that it would have an insatiable appetite for more energy to keep driving the probability closer and closer."

I had a look at the actual paper, and most of it concerns itself with "distal" and "proximal" probabilities. They model the system as an AI agent interacting with a computer that simulates the environment, and in the "distal" case, the computer that simulates the environment gives the agent a reward number after each of its actions, and in the "proximal" case, the computer's output is displayed on a screen, and an OCR system reads the screen and gives the agent the number. Which is a fancy way of saying the agents feedback from the environment is noisy. The agent has to act under uncertainty. It may sound like a pedantic difference, but they hypothesize that the agent will actually formulate different goals in the two scenarios. And the "distal" goal is what we want the agent to actually formulate as its goal and achieve. But the "distal" goal is what the agent will actually adopt as its goal and it will achieve something unintended, with perhaps disastrous consequences.

"Part of the mystique of artificial neural networks is that they seem to subvert traditional machine learning theory, which leans heavily on ideas from statistics and probability theory. In the usual way of thinking, machine learning models [...] work best when they have just the right number of parameters."

Too few parameters and the model is too simple, but too many parameters and the model is essentially memorizing its training data, rather than generalizing. This is called "overfitting". "By all accounts, deep neural networks like VGG have way too many parameters and should overfit. But they don't. Instead, such networks generalize astoundingly well to new data -- and until recently, no one knew why."

"Samsung Electronics, a world leader in advanced semiconductor technology, today shared a new insight that takes the world a step closer to realizing neuromorphic chips that can better mimic the brain."

"Envisioned by the leading engineers and scholars from Samsung and Harvard University, the insight was published as a Perspective paper, titled 'Neuromorphic electronics based on copying and pasting the brain'."

"The essence of the vision put forward by the authors is best summed up by the two words, 'copy' and 'paste'. The paper suggests a way to copy the brain's neuronal connection map using a breakthrough nanoelectrode array developed by Dr. Ham and Dr. Park, and to paste this map onto a high-density three-dimensional network of solid-state memories, the technology for which Samsung has been a world leader."

"The nanoelectrode array can effectively enter a large number of neurons so it can record their electrical signals with high sensitivity. These massively parallel intracellular recordings inform the neuronal wiring map, indicating where neurons connect with one another and how strong these connections are. Hence from these telltale recordings, the neuronal wiring map can be extracted, or 'copied'."

"A brain mechanism that enables mice to override their instincts based on previous experience" has been discovered. "The ventral lateral geniculate nucleus (vLGN) could control escape behaviour depending on the animal's knowledge gained through previous experience, and on its assessment of risk in its current environment. When mice were not expecting a threat and felt safe, the activity of a subset of inhibitory neurons in the vLGN was high, which in turn could inhibit threat reactions. In contrast, when mice expected danger, activity in these neurons was low, which made the animals more likely to escape and seek safety."

The researchers used optogenetics to artificially activate the vLGN, determining that it is the vLGN that abolishes escape responses in the mice. Likewise, suppressing its activity lowers the threshold for escape and increases risk-avoidance behavior.

An AI won the American Crossword Puzzle Tournament. "Checkers, backgammon, chess, Go, poker, and other games have witnessed the machines' invasions, falling one by one to dominant AIs. Now crosswords have joined them." "But a look at how Dr. Fill pulled off this feat reveals much more than merely the latest battle between humans and computers."

This year Matt Ginsberg, the computer scientist who created Dr. Fill, teamed up with the Berkeley Natural Language Processing Group, and they made "a hybrid system in which the Berkeley group's neural-net methods for interpreting clues worked in tandem with Ginsberg's code for efficiently filling out a crossword grid."

"Machine learning algorithm revolutionizes how scientists study behavior." Hmm what do they mean by "behavior"?

"Previously, the standard method to capture animal behavior was to track very simple actions, like whether a trained mouse pressed a lever or whether an animal was eating food or not. Alternatively, the experimenter could spend hours and hours manually identifying behavior, usually frame by frame on a video, a process prone to human error and bias."

"Hsu, a biological sciences PhD candidate, realized he could let an unsupervised learning algorithm do the time-consuming work. B-SOiD discovers behaviors by identifying patterns in the position of an animal's body. The algorithm works with computer vision software and can tell researchers what behavior is happening at every frame in a video."

"It uses an equation to consistently determine when a behavior starts. Once you reach that threshold, the behavior is identified, every time."

How does it do all this? It's a combination of a clustering algorithm and a classifier. Well, first you employ a pose estimation algorithm (this system doesn't reinvent pose estimation software, it just uses existing pose estimation software). Then...

The machine learning technique for making "embeddings", such as the Word2Vec word "embedding" system, has been adapted for electronic medical records.

"Essentially, a computer was programmed to scour through millions of electronic health records and learn how to find connections between data and diseases. This programming relied on 'embedding' algorithms that had been previously developed by other researchers, such as linguists, to study word networks in various languages. One of the algorithms, called word2vec, was particularly effective. Then, the computer was programmed to use what it learned to identify the diagnoses of nearly 2 million patients whose data was stored in the Mount Sinai Health System."

Vision through murky water. You need a polarized light camera, but otherwise the process is automated. "Traditional approaches to underwater imaging use either prior knowledge of the imaging area or the background of an image to calculate and remove scattered light. These methods have limited utility in the field because they typically require manual processing, images do not always have visible backgrounds, and prior information is not always available."

"To overcome these challenges, the researchers combined a traditional polarized imaging setup with a new algorithm that automatically finds the optimal parameters to suppress the scattering light. This not only significantly improves image contrast to achieve clear imaging but can be used without any prior knowledge of the imaging area and for images with or without background regions."

"EpyNN is a production-ready but first Educational Python resource for Neural Networks. EpyNN is designed for Supervised Machine Learning (SML) approaches by means of Neural Networks."

"EpyNN includes scalable, minimalistic and homogeneous implementations of major Neural Network architectures in pure Python/Numpy."

"EpyNN is intended for teachers, students, scientists, or more generally anyone with minimal skills in Python programming who wish to understand and build from basic implementations of NN architectures."

Artificial camouflage for robots. By 'artificial camouflage' here, they are talking about imitating the camouflage of animals that can dynamically change their camouflage as the move about their environment, for example cephalopods.

The system developed here uses something called thermochromic liquid crystal. "Thermochromic" just means something changes color depending on temperature. So the key is controlling temperature which is done by stacking multilayer silver nanowire heaters. Being able to accurately control all of the nanowires allows fine patterns to be expressed.

It occurred to me that while this might be good for visible light camouflage, a military enemy with night vision equipment is easily going to see the heat due to the infrared light it produces, which far from providing camouflage will make the thing you're trying to hide pop out.

"Historically, Macintosh computers running macOS have been popular with brain imaging scientists. Since macOS runs Unix, users can often use the same tools on their laptop as the Linux workstations and super computers used for large datasets. However, this trend may change. First, the new Windows Subsystem for Linux (WSL) has allowed Windows computers to seamlessly run Unix tools. Second, recent Macintosh computers have switched from Intel x86-64 processors to the ARM-based M1 Apple Silicon."

Is Hacker News a good predictor of future tech trends? Bitcoin - yes, a lot; serverless - yes, a little; Tesla - no; NFT's - yes; machine learning - yes; remote work - no, not at all; 3D printing - no; drones - yes; apps - no; Android - yes, a little.