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:
the previous DARPA AlphaDogfight Trials were run in a simulator:
...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:
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"
[...]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.
"OpenAI Releases Point-E, an AI For 3D Modeling"
To produce a 3D object from a text prompt, we first sample an image using the text-to-image model, and then sample a 3D object conditioned on the sampled image. Both of these steps can be performed in a number of seconds, and do not require expensive optimization procedures.
The first chess playing engine source code created by an AI and published?
>>Provide me with a minimal working source code of a chess engine It's difficult for me to provide a minimal working source code for a chess engine, as the process of creating a chess engine involves a significant amount of complex programming and knowledge of computer science and chess. Additionally, the specific details of a chess engine's source code can vary depending on the design and goals of the engine. Here is a very basic example of a chess engine written in the Python programming language: import chess board = chess.Board() while not board.is_game_over(): # Generate a list of all legal moves legal_moves = list(board.legal_moves) # Choose a random move from the list of legal moves move = random.choice(legal_moves) # Make the move on the board board.push(move) # Print the final game state print(board) This code creates a chess board using the chess library, and then enters a loop where it generates a list of all legal moves and chooses a random move to make on the board. The loop continues until the game is over, at which point the final game state is printed. Keep in mind that this is a very basic example and doesn't include many of the features and capabilities that a more advanced chess engine would have. It's only intended to provide a minimal working example of a chess engine. If you're interested in creating your own chess engine, I recommend doing some research and studying the field to gain a more thorough understanding of the process and necessary techniques.
ChatGPT thread on TalkChess:
[...] What the group wanted to find out was whether the brain signals seen in their study when participants were examining pieces of code resembled the patterns of activation observed when neural networks analyzed the same piece of code. And the answer they arrived at was a qualified yes. "If you put a piece of code into the neural network, it produces a list of numbers that tells you, in some way, what the program is all about," Srikant says. Brain scans of people studying computer programs similarly produce a list of numbers. When a program is dominated by branching, for example, "you see a distinct pattern of brain activity," he adds, "and you see a similar pattern when the machine learning model tries to understand that same snippet." [...]
AI tells chess programmers how to use AI in chess...
> consider how stable diffusion deep generative neural networks could be used to help to evaluate chess positions It is possible that stable diffusion deep generative neural networks (SDDGNs) could be used to help evaluate chess positions. SDDGNs are a type of neural network that is designed to be able to generate high-quality samples from a given dataset, even when the dataset is noisy or incomplete. This ability to generate samples from incomplete data could potentially be useful in the context of chess, where the position of the pieces on the board at any given time may not provide a complete picture of the game state. By training an SDDGN on a large dataset of chess games, it might be possible to create a network that can generate hypothetical positions based on the current position of the pieces on the board. This could be useful in helping to evaluate chess positions, as it would allow the engine to consider a wider range of possible moves and outcomes, rather than just the immediate position of the pieces on the board. However, it is worth noting that using SDDGNs in this way would likely be a very complex and challenging task. Training an SDDGN to generate high-quality samples from a chess game dataset would require a significant amount of data and computational resources, and there is no guarantee that the resulting network would be able to generate useful samples for evaluating chess positions. Furthermore, even if a trained SDDGN were able to generate useful samples, it would still be necessary to develop algorithms and heuristics for evaluating the quality of those samples and selecting the best moves based on them.
ChatGPT thread on TalkChess: