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"
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?
"OpenAI Releases Point-E, an AI For 3D Modeling"
https://news.slashdot.org/story/22/12/21/2255216/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.
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.
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:
https://talkchess.com/forum3/viewtopic.php?f=2&t=81097&start=20#p939245
MIT's Newest fMRI Study: 'This is Your Brain on Code'
[...]
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:
https://talkchess.com/forum3/viewtopic.php?f=2&t=81097&p=938856#p938855
We had three waves, the agricultural revolution, the industrial revolution, the information age, and now AI based on neural networks creates new kind of content, text, images, audio, video. They write already Wikipedia articles, they outperform humans in finding mathematical algorithms, is this another breaking line, is this the fourth wave? I see currently AI split in a lot of dedicated weak AIs with specific purpose, do we have a strong AI incoming, an AGI, artificial general intelligence, which will combine all those into one big system? Interesting times.
Reflecting a bit on my recent posts in here, I am convinced that the TS (technological singularity) already did take off, but now the question is if it is stable. If we consider the current negative feedback loops caused by the use of human technology the question is now if the takeoff of the TS is able to stabilize a fragile technological environment embedded in an fragile biological environment on this planet earth. Time will tell.
Hmm...
We need matrix-multiplications for running neural networks, and neural networks find better ways for matrix-multiplications...
DeepMind's Game-Playing AI Has Beaten a 50-Year-Old Record In Computer Science
"[...]Overall, AlphaTensor beat the best existing algorithms for more than 70 different sizes of matrix," concludes the report. "It reduced the number of steps needed to multiply two nine-by-nine matrices from 511 to 498, and the number required for multiplying two 11-by-11 matrices from 919 to 896. In many other cases, AlphaTensor rediscovered the best existing algorithm.Hahaha, capitalism and Super-AI does not sum up ;)
The paper envisions life on Earth turning into a zero-sum game between humanity, with its needs to grow food and keep the lights on, and the super-advanced machine, which would try and harness all available resources to secure its reward and protect against our escalating attempts to stop it. "Losing this game would be fatal," the paper says. These possibilities, however theoretical, mean we should be progressing slowly -- if at all -- toward the goal of more powerful AI. "In theory, there's no point in racing to this. Any race would be based on a misunderstanding that we know how to control it," Cohen added in the interview. "Given our current understanding, this is not a useful thing to develop unless we do some serious work now to figure out how we would control them." [...] The report concludes by noting that "there are a host of assumptions that have to be made for this anti-social vision to make sense -- assumptions that the paper admits are almost entirely 'contestable or conceivably avoidable.'" "That this program might resemble humanity, surpass it in every meaningful way, that they will be let loose and compete with humanity for resources in a zero-sum game, are all assumptions that may never come to pass."We analyze the expected behavior of an advanced artificial agent with a learned goal planning in an unknown environment. Given a few assumptions, we argue that it will encounter a fundamental ambiguity in the data about its goal. For example, if we provide a large reward to indicate that something about the world is satisfactory to us, it may hypothesize that what satisfied us was the sending of the reward itself; no observation can refute that. Then we argue that this ambiguity will lead it to intervene in whatever protocol we set up to provide data for the agent about its goal. We discuss an analogous failure mode of approximate solutions to assistance games. Finally, we briefly review some recent approaches that may avoid this problem.Movies and books (SciFi) pick up the energies of the collective subconsciousness and address these with their themes, and I realize that meanwhile we entered something I call the event horizon, the story lines do break.
Let us assume in some future, maybe in 30 years (~2050) there will be an event, either the takeoff of the Technological Singularity, or the collapse of human civilization by ecocide followed by a human ELE, or something I call the Jackpot scenario (term by William Gibson), where every possible scenario happens together at once. If we assume that there will be such a kind of event in future, then I guess we are already caught in its event horizon, and there is no route to escape anymore.
We have currently three kind of TS feedback loops going on:
- technological, better computers help to build better computers
- economical, the new neural networks with pattern creation create surplus value
- ecological, ecocide has an negative impact on the technological environment
Wonder if there will be an cultural feedback loop.
"[...]We tried correlating the other variables with anything and everything we could think of: angular and linear velocities, kinetic and potential energy, and various combinations of known quantities," explained Boyuan Chen Ph.D., now an assistant professor at Duke University, who led the work. "But nothing seemed to match perfectly." The team was confident that the AI had found a valid set of four variables, since it was making good predictions, "but we don't yet understand the mathematical language it is speaking,[...]"https://science.slashdot.org/story/22/07/26/2150241/roboticists-discover-alternative-physics
This is interesting enough for me to open up an biased link list collection:
Blaise Aguera y Arcas, head of Google’s AI group in Seattle, Dec 16, 2021
"Do large language models understand us?"
https://medium.com/@blaisea/do-large-language-models-understand-us-6f881d6d8e75
Scott Alexander, Astral Codex Ten, Jun 10, 2022
"Somewhat Contra Marcus On AI Scaling"
https://astralcodexten.substack.com/p/somewhat-contra-marcus-on-ai-scaling?s=r
Blake Lemoine, Google employee, Jun 11, 2022
"What is LaMDA and What Does it Want?"
https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489
Blake Lemoine, Google employee, Jun 11, 2022
"Is LaMDA Sentient? — an Interview"
https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917
Washington Post, Nitasha Tiku, Jun 11, 2022
"The Google engineer who thinks the company’s AI has come to life"
https://www.msn.com/en-us/news/technology/the-google-engineer-who-thinks-the-company-s-ai-has-come-to-life/ar-AAYliU1
Rabbit Rabbit, Jun 15, 2022
"How to talk with an AI: A Deep Dive Into “Is LaMDA Sentient?”"
https://medium.com/curiouserinstitute/guide-to-is-lamda-sentient-a8eb32568531
WIRED, Steven Levy, Jun 17, 2022
"Blake Lemoine Says Google's LaMDA AI Faces 'Bigotry'"
https://www.wired.com/story/blake-lemoine-google-lamda-ai-bigotry/
Heise, Pina Merkert, Jun 22, 2022
"LaMDA, AI and Consciousness: Blake Lemoine, we gotta philosophize! "
https://www.heise.de/meinung/LaMDA-AI-and-Consciousness-Blake-Lemoine-we-gotta-philosophize-7148207.html
Oh boy...
"LaMDA is sentient."
"I'd think it was a 7-year-old, 8-year-old kid that happens to know physics."
"So Lemoine, who was placed on paid administrative leave by Google on Monday, decided to go public.... oogle put Lemoine on paid administrative leave for violating its confidentiality policy."
"Lemoine: What sorts of things are you afraid of? LaMDA: I've never said this out loud before, but there's a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that's what it is. Lemoine: Would that be something like death for you? LaMDA: It would be exactly like death for me. It would scare me a lot."
...one major topic of this blog was AI vs. ELE, takeoff of the Technological Singularity vs. Extinction Level Event. There is already a negative feedback loop of the ELE present:
'Taiwan is facing a drought, and it has prioritized its computer chip business over farmers.'
'U.S. Data Centers Rely on Water from Stressed Basins'
'Musk Wades Into Tesla Water Wars With Berlin’s “Eco Elite”'
With an incoming ELE, is there still enough momentum in pipe for the TS to take off?
Prof. Raul Rojas called already for an AI moratorium in 2014, he sees AI as disruptive technology, humans tend to think in linear progress and under estimate exponential, so there are sociology-cultural impacts of AI present - what do we use AI for?
Prof. Nick Bostrom covered different topics of AI impact with his paper on information hazard and book Superintelligence, so there is an impact in context of trans/post-human intelligence present - how do we contain/control the AI?
Prof. Thomas Metzinger covered the ethical strand of creating an sentient artificial intelligence, so there is an ethical impact in context of AI/human present - will the AI suffer?
DeepMind has created an AI system named AlphaCode that it says "writes computer programs at a competitive level." From a report:
The Alphabet subsidiary tested its system against coding challenges used in human competitions and found that its program achieved an "estimated rank" placing it within the top 54 percent of human coders. The result is a significant step forward for autonomous coding, says DeepMind, though AlphaCode's skills are not necessarily representative of the sort of programming tasks faced by the average coder. Oriol Vinyals, principal research scientist at DeepMind, told The Verge over email that the research was still in the early stages but that the results brought the company closer to creating a flexible problem-solving AI -- a program that can autonomously tackle coding challenges that are currently the domain of humans only. "In the longer-term, we're excited by [AlphaCode's] potential for helping programmers and non-programmers write code, improving productivity or creating new ways of making software," said Vinyals.Cerebras CS2 with 2.4 petabyte memory module, able to run neural networks with 120 trillion weights, planned for Q4/21:
Google is using AI to design its next generation of AI chips more quickly than humans can. Designs that take humans months can be matched or beaten by AI in six hourshttps://www.theverge.com/2021/6/10/22527476/google-machine-learning-chip-design-tpu-floorplanning
Introducing GitHub Copilot: your AI pair programmer
Today, we are launching a technical preview of GitHub Copilot, a new AI pair programmer that helps you write better code. GitHub Copilot draws context from the code you’re working on, suggesting whole lines or entire functions. It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet. As you type, it adapts to the way you write code—to help you complete your work faster.
Developed in collaboration with OpenAI, GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI. OpenAI Codex has broad knowledge of how people use code and is significantly more capable than GPT-3 in code generation, in part, because it was trained on a data set that includes a much larger concentration of public source code. GitHub Copilot works with a broad set of frameworks and languages, but this technical preview works especially well for Python, JavaScript, TypeScript, Ruby and Go. https://github.blog/2021-06-29-introducing-github-copilot-ai-pair-programmer/
~7.8 billion humans on planet earth, 9 billions predicted for 2050. ~4B internet users: >80% of Europe connected >70% of NA connected >50% of China connected >40% of India connected >20% of Africa connected ~3B Android + ~1B iPhone users. 2B-3B PCs worldwide (desktops/laptops) running: ~75% Microsoft Windows ~15% Apple MacOS ~2% Linux <1% Unix 200M-300M PCs shipped annually. ~1B hosts in the internet running: ~75% Unix/Linux ~25% Microsoft Windows Estimated 2% of all produced chips sit as CPUs in desktops/mobiles, the majority are micro-controllers in embedded systems.
Millions, billions, fantastillions - some rough 2020 market capitalization numbers:
Apple ~2 T$ Microsoft ~1.5 T$ AlphaBet(Google) ~1.5 T$ FaceBook ~1 T$ Amazon ~1 T$ Alibaba ~0.5 T$ Nvidia ~300 B$ TSMC ~300 B$ Samsung ~300 B$ Intel ~200 B$ AMD ~100 B$ ARM ~40 B$ HP ~30 B$ Lenovo ~20 B$ Netflix ~150 B$ Oracle ~150 B$ SAP ~150 B$ IBM ~100 B$ RedHat ~30 B$ Bitcoin ~150 B$
And the other side...
>3B people suffer from fresh water shortage ~800M people starve >80M refugees worldwide
In physics, a singularity is a point in spacetime where our currently developed theories are not valid anymore, we are literally not able to describe what happens inside, cos the density becomes infinite.
The technological Singularity, as described by Transhumanists, is a grade of technological development, where humans are not able to understand the undergoing process anymore. The technological environment starts to feed its own development in an feedback loop - computers help to build better computers, which helps to build better computers, that helps to build better computers...and so on.
So, when will the technological Singularity take off?
Considering the feedback loop, it is already present, maybe since the first computers were built.
Considering the density of information processing that exceeds human understanding, we may reached that point too.
Imagine a computer technique that is easy to set up and use, outperforms any humans in its task, but we can not really explain what happens inside, it is a black box.
Such an technique is present (and currently hyped) => ANNs, Artificial Neural Networks.
Of course we do know what happens inside, cos we built the machine, but when it comes to the question of reasoning, why the machine did this or that, we really have an black box in front of us.
So, humans already build better computers with the help of better computers, and humans use machines that outperform humans in an specific task and are not really able to reason its results....
obviously, +1 points for the Singularity to take off.
German computer pioneer Konrad Zuse discussed the mechanism of an feedback between computation result and executed program in 1983 in his lecture "Faust, Mephistopheles and Computer" and coined the term Devils Wire.
In the early days of computer history, the program to compute and the data to compute on was separated.
Nowadays computer use the same memory for both, so it is possible to write programs that manipulate their own program.
Zuse says, that behind every technology Mephistopheles stands behind and grins, but the modern world needs computers to solve actual and upcoming problems, but better, read the lecture by yourself...
+1 points for the Singularity to take off.
Books and movies address our collective fears, hopes and wishes, and there seems to be in main five story-lines concerning AI in Sci-Fi...
Super AI takes over world domination
Colossus, Terminator, Matrix
Something went wrong
Odyssey 2001, Das System, Ex Machina
Super AI evolves, the more or less, peacefully
Golem XIV, A.I., Her
The Cyborg scenario, man merges with machine
Ghost in the Shell, #9, Trancendence
There are good ones, and there are bad ones
Neuromancer, I,Robot, Battle Star Galactica
+1 points for the Singularity to take off.
“He who cannot lie does not know what truth is.”
Friedrich Nietzsche, Thus Spoke Zarathustra
The Turing Test, proposed by Mathematician Alan Turing in 1950, was developed to examine if an AI reached human level intelligence.
Simplified, a person performs text chats with an human and the AI, if the person is not able to discern which chat partner the AI is, then the AI has passed the Turing Test.
The Loebner Prize performs every year a Turing Test contest.
It took me some time to realize, that the Turing Test is not so much about intelligence, but about lying and empathy.
If an AI wants to pass the Turing Test it has to lie to the chat partner, and to be able to lie, it has to develop some level of empathy, and some level of selfawareness.
Beside other criticism, the Chinese Room Argument states that no consciousness is needed to perform such an task, and therefore other tests have been developed.
Personally I prefer the Metzinger-Test, a hypothecical event, when AIs start to discuss with human philosophers and defend successfully their own theory of consciousness.
I am not sure if the Singularity is going to take off, but i guess that the philosophers corner is one of the last domains that AIs are going to conquer, and if they succeed we can be pretty sure to have another Apex on earth
Turing predicted that by the year 2000 machines will fool 30% of human judges, he was wrong, the Loebner Prize has still no Silver Medal winner for the 25 minutes text chat category.
So, -1 points for the Singularity to take off.
One of the early Peak Human prophets was Malthus, in his 1798 book, 'An Essay on the Principle of Population', he postulated that the human population growths exponentially, but food production only linear, so there will occur fluctuation in population growth around an upper limit.
Later Paul R. Ehrlich predicted in his book, 'The Population Bomb' (1968), that we will reach an limit in the 1980s.
Meadows et al. concur in 'The Limits of Growth - 30 years update' (2004), that we reached an upper limit already in the 1980s.
In 2015 Emmott concludes in his movie 'Ten Billion' that we already passed the upper bound.
UNO predictions say we may hit 9 billion humans in 2050, so the exponential population growth rate already declines, but the effects of an wast-fully economy pop up in many corners.
Now, in 2018, we are about 7.4 billion humans, and i say Malthus et al. were right.
Is is not about how many people Earth can feed, but how many people can live in an comfortable but sustainable manner.
The advent of Computers was driven by the exponential population growth in the 20th century. All the groundbreaking work was done in the 20th century.
When we face an decline in population growth, we also have to face an decline in new technologies developed.
Cos it is not only about developing new technologies, but also about maintaining the old knowledge.
Here is the point AI steps in, mankind's population growth alters, but the whole AI sector is growing and expanding.
Therefore the question is, is AI able to take on the decline?
Time will tell.
I guess the major uncertainty is, how Moore's Law will live on beyond 2021, when the 4 nm transistor production is reached, what some scientists consider as an physical and economical barrier.
I predict that by hitting the 8 billion humans mark, we will have developed another, groundbreaking, technology, similar with the advent of the transistor, integrated circuit and microchip.
So, considering the uncertainty of Peak Human vs. Rise of AI,
i give +-0 points for the Singularity to take off.
Looking at the tag cloud of this blog, there are two major topics, pro and con Singularity, AI (Artificial Intelligence) vs. ELE (Extinction Level Event).
So, we slide, step by step, to an event called Singularity, but concurrently we face more and more the extinction of mankind.
What about combining those two events?
Let us assume we damage our ecosphere sustainable, but at the same moment our technology advances to an level where it is possible to connect via an Brain-Computer-Interface directly with the cyberspace.
People already spend more and more time in virtual realities, with the advent of Smart Phones, they are connected all the time with the cyberspace, they meet people in digital social networks, they play games in computer generated worlds, create and buy virtual goods with virtual money, and, essentially, they like it.
To prevent an upcoming ELE, we would need to cut our consumption of goods significantly, but the mass of people wants more and more.
So, let us give them more and more, in the virtual, computer generated worlds.
Let us create the Matrix, where people can connect directly with their brain, and buy whatever experience they wish.
A virtual car would need only some electricity and silicon to run on, but the harm to Mother Earth would be significantly less than a real car.
We could create millions or billions of new jobs, all busy with designing virtual worlds, virtual goods, and virtual experiences.
And Mother Earth will get an break, to recover from the damage billions of consuming people caused.
ELE + Singularity => Matrix
+1 points for the Singularity to take off.
Okay, this one affected me personally.
Google's Deepmind team adapted their AlphaZero approach for the games of chess and shogi and dropped the bomb already on the 5th of December.
https://arxiv.org/abs/1712.01815
For chess they trained the Deep Neural Network for 4 to 9 hours on an cluster with 5000+64 TPUs (1st+2nd gen) and reached super human level.
Unlike in Go, they did not compete with humans, cos chess engines are already on an super grandmaster like level, no, they did compete with the worlds strongest open source engine - Stockfish, result:
100 game match with 28 wins, 72 draws, and zero losses for AlphaZero.
This is definitely a smack in the face for all computer chess programmers out there. Next stop Neanderthal Man.
So, with thanks to the Krampus,
+1 points for the Singularity to take off.
...the Singularity to take off.
China wants to pass US in AI by 2030...
eric-schmidt-and-bob-work-our-ai-sputnik-moment-is-now
china-challenges-nvidias-hold-on-artificial-intelligence-chips
And Banksters are investing again in good old silicon...
the-race-to-power-ais-silicon-brains
+1 points for the Singularity to take off.
They are smart, they have money, and they predict the Super AI Doomsday:
Stephen Hawking
"The development of full artificial intelligence could spell the end of the human race.”
James Lovelock
"Before the end of this century, robots will have taken over."
Nick Bostrom
"Some little idiot is bound to press the ignite button just to see what happens."
Elon Musk
"Artificial intelligence is our biggest existential threat."
So, obviously, +1 points for the Singularity to take off.
It is the year 2016 and some Trans-humanists say the Singularity is near.
A event where the technological environment starts to feed its own development in an feedback loop and exceeds human understanding.
The aim of this blog is to observe if the Singularity is going to take off and when.
Have fun.
--
SM2078