AIs inviting each other to Valentine's Day in Smallville???

What Happens When You Put 25 ChatGPT-Backed Agents Into an RPG Town?

A group of researchers at Stanford University and Google have created a miniature RPG-style virtual world similar to The Sims," writes Ars Technica, "where 25 characters, controlled by ChatGPT and custom code, live out their lives independently with a high degree of realistic behavior."
"Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day," write the researchers in their paper... To pull this off, the researchers relied heavily on a large language model for social interaction, specifically the ChatGPT API. In addition, they created an architecture that simulates minds with memories and experiences, then let the agents loose in the world to interact.... To study the group of AI agents, the researchers set up a virtual town called "Smallville," which includes houses, a cafe, a park, and a grocery store.... Interestingly, when the characters in the sandbox world encounter each other, they often speak to each other using natural language provided by ChatGPT. In this way, they exchange information and form memories about their daily lives.

When the researchers combined these basic ingredients together and ran the simulation, interesting things began to happen. In the paper, the researchers list three emergent behaviors resulting from the simulation. None of these were pre-programmed but rather resulted from the interactions between the agents. These included "information diffusion" (agents telling each other information and having it spread socially among the town), "relationship memory" (memory of past interactions between agents and mentioning those earlier events later), and "coordination" (planning and attending a Valentine's Day party together with other agents).... "Starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party," the researchers write, "the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time...."

To get a look at Smallville, the researchers have posted an interactive demo online through a special website, but it's a "pre-computed replay of a simulation" described in the paper and not a real-time simulation. Still, it gives a good illustration of the richness of social interactions that can emerge from an apparently simple virtual world running in a computer sandbox.

Interstingly, the researchers hired human evaluators to gauge how well the AI agents produced believable responses — and discovered they were more believable than when supplied their own responses.

We are getting closer to the perfect chess oracle, a chess engine with perfect play and 100% draw rate.

The Centaurs reported already that their game is dead, Centaurs participate in tournaments and use all kind of computer assist to choose the best move, big hardware, multiple engines, huge opening books, end game tables, but meanwhile they get close to the 100% draw rate with common hardware, and therefore unbalanced opening books were introduced, where one side has an slight advantage, but again draws.

The #1 open source engine Stockfish lowered in the past years the effective branching factor of the search algorithm from ~2 to ~1.5 to now ~1.25, this indicates that the selective search heuristics and evaluation heuristics are getting closer to the optimum, where only one move per position has to be considered.

About a decade ago it was estimated that with about ~4000 Elo points we will have a 100% draw rate amongst engines on our computer rating lists, now the best engines are in the range of ~3750 Elo (CCRL), what translates estimated to ~3600 human FIDE Elo points (Magnus Carlsen is rated today 2852 Elo in Blitz). Larry Kaufman (grandmaster and computer chess legenda) mentioned that with the current techniques we might have still ~50 Elo to gain, and it seems everybody waits for the next bing thing in computer chess to happen.

We replaced the HCE, handcrafted evaluation function, of our computer chess engines with neural networks. We train now neural networks with billions of labeled chess positions, and they evaluate chess positions via pattern recognition better than what a human is able to encode by hand. The NNUE technique, neural networks used in AlphaBeta search engines, gave an boost of 100 to 200 Elo points.

What could be next thing, the next boost?

If we assume we still have 100 to 200 Elo points until perfect play (normal chess with standard opening and a draw), if we assume an effective branching factor ~1.25 with HCSH, hand crafted search heuristics, and that neural networks are superior in this regard, we could imagine to replace HCSH with neural networks too and lower the EBF further, closer to 1.

Such an technique was already proposed, NNOM++. Move Ordering Neural Networks, but until now it seems that the additional computation effort needed does not pay off.

What else?

We use neural networks in the classic way for pattern recognition in nowadays chess engines, but now the shift is to pattern creation, the so called generative AIs. They generate text, source code, images, audio, video and 3D models. I would say the race is now up for the next level, an AI which is able to code an chess engine and outperforms humans in this task.

An AI coding a chess engine has also a philosophical implication, such an event is what the Transhumanists call the takeoff of Technological Singularity, when the AI starts to feed its own development in an feedback loop and exceeds human understanding.

Moore's Law has still something in pipe, from currently 5nm to 3nm to maybe 2nm and 1+nm, so we can expect even larger and more performant neural networks for generative AIs in future. Maybe in ~6 years there will be a kind of peak or kind of silicon sweetspot (current transistor density/efficiency vs. needed financial investment in fab process/research), but currently there is so much money flowing into this domain that progress for the next couple of years seems assured.

Interesting times ahead.

The world is facing an imminent water crisis, with demand expected to outstrip the supply of fresh water by 40% by the end of this decade, experts have said on the eve of a crucial UN water summit. From a report: Governments must urgently stop subsidising the extraction and overuse of water through misdirected agricultural subsidies, and industries from mining to manufacturing must be made to overhaul their wasteful practices, according to a landmark report on the economics of water. Nations must start to manage water as a global common good, because most countries are highly dependent on their neighbours for water supplies, and overuse, pollution and the climate crisis threaten water supplies globally, the report's authors say. Johan Rockstrom, the director of the Potsdam Institute for Climate Impact Research and co-chair of the Global Commission on the Economics of Water, and a lead author of the report, told the Guardian the world's neglect of water resources was leading to disaster. "The scientific evidence is that we have a water crisis. We are misusing water, polluting water, and changing the whole global hydrological cycle, through what we are doing to the climate. It's a triple crisis." Rockstrom's fellow Global Commission on the Economics of Water co-chair Mariana Mazzucato, a professor at University College London and also a lead author of the report, added: "We need a much more proactive, and ambitious, common good approach. We have to put justice and equity at the centre of this, it's not just a technological or finance problem."

https://science.slashdot.org/story/23/03/17/175225/global-fresh-water-demand-will-outstrip-supply-by-40-by-2030-say-experts

In a world in need of fresh/drinking water, why the AI?

Researchers Claim Their AI Algorithm Can Recreate What People See Using Brain Scans

A recent study, scheduled to be presented at an upcoming computer vision conference, demonstrates that AI can read brain scans and re-create largely realistic versions of images a person has seen....The accuracy of this new method is impressive.

Scientists Target 'Biocomputing' Breakthrough With Use of Human Brain Cells

Scientists propose to develop a biological computer powered by millions of human brain cells that they say could outperform silicon-based machines while consuming far less energy.
[...]
The project's ambition mirrors work on the more advanced quantum computing but raises ethical questions around the "consciousness" of brain organoid assemblies
[...]

DARPA ACE AI Flights F-16 Fighter Jet:
https://news.slashdot.org/story/23/02/14/1631249/ai-has-successfully-piloted-a-us-f-16-fighter-jet-darpa-says

the previous DARPA AlphaDogfight Trials were run in a simulator:
https://www.darpa.mil/news-events/2020-08-07

...pondering about the AI doomsday sayers and recent developments it seems naive to me to assume that there will be one single AI agent with one background and one motivation, we see currently different agents, with different backgrounds and therefore different motivations rising. If we say that AI will compete with humans for resources, it seems only natural that AIs will compete amongst each other for resources, or, will they really merge one day to one big single system? Interesting times. Still waiting for the AGI/ASI, the strong AI, which combines all the AI-subsystems into one.

Okay, did not see that one coming:

"Jailbreaking AIs"

https://slashdot.org/story/23/02/12/0114222/bing-chat-succombs-to-prompt-injection-attack-spills-its-secrets

The day after Microsoft unveiled its AI-powered Bing chatbot, "a Stanford University student named Kevin Liu used a prompt injection attack to discover Bing Chat's initial prompt," reports Ars Technica, "a list of statements that governs how it interacts with people who use the service." 

Welcome to the future. Nick Bostrom anyone?

They generate text, source code, images, audio, video, 3D models, what's missing?

The large language models for text generation still lack a decent reasoner and analyzer module, decent video is IMO just a matter of time resp. hardware, and my take would be that the next thing are brainwaves for the BCI, brain computer interface.

"Google Created an AI That Can Generate Music From Text Descriptions, But Won't Release It"

https://tech.slashdot.org/story/23/01/27/187213/google-created-an-ai-that-can-generate-music-from-text-descriptions-but-wont-release-it

[...]Still, the Google researchers note the many ethical challenges posed by a system like MusicLM, including a tendency to incorporate copyrighted material from training data into the generated songs.