Toggle light / dark theme

Artificial Intelligence Is Teaching Us New, Surprising Things About the Human Mind

The world has been learning an awful lot about artificial intelligence lately, thanks to the arrival of eerily human-like chatbots.

Less noticed, but just as important: Researchers are learning a great deal about us – with the help of AI.

AI is helping scientists decode how neurons in our brains communicate, and explore the nature of cognition. This new research could one day lead to humans connecting with computers merely by thinking–as opposed to typing or voice commands. But there is a long way to go before such visions become reality.

Has GPT-4 really passed the startling threshold of human-level artificial intelligence? Well, it depends

Recent public interest in tools like ChatGPT has raised an old question in the artificial intelligence community: is artificial general intelligence (in this case, AI that performs at human level) achievable? An online preprint this week has added to the hype, suggesting the latest advanced large language model, GPT-4, is at the early stages of artificial general intelligence (AGI) as it’s exhibiting “sparks of intelligence”.

Using artificial intelligence to design innovative materials

Advanced materials are urgently needed for everyday life, be it in high technology, mobility, infrastructure, green energy or medicine. However, traditional ways of discovering and exploring new materials encounter limits due to the complexity of chemical compositions, structures and targeted properties. Moreover, new materials should not only enable novel applications, but also include sustainable ways of producing, using and recycling them.

Researchers from the Max-Planck-Institut für Eisenforschung (MPIE) review the status of physics-based modelling and discuss how combining these approaches with artificial intelligence can open so far untapped spaces for the design of complex materials.

They published their perspective in the journal Nature Computational Science (“Accelerating the design of compositionally complex materials via physics-informed artificial intelligence”).

THE FIRST 2 YEARS ON MARS (Prequel) Timelapse

10 SpaceX Starships are carrying 120 robots to Mars. They are the first to colonize the Red Planet. Building robot habitats to protect themselves, and then landing pads, structures, and the life support systems for the humans who will soon arrive.

This Mars colonization mini documentary also covers they type of robots that will be building on Mars, the solar fields, how Elon Musk and Tesla could have a battery bank station at the Mars colony, and how the Martian colony expands during the 2 years when the robots are building. Known as the Robotic Age of Mars.

Additional footage from: SpaceX, NASA/JPL/University of Arizona, ICON, HASSEL, Tesla, Lockhead Martin.

A building on Mars sci-fi documentary, and a timelapse look into the future.
See more of Venture City at my website: https://vx-c.com.

_______
Books.

• The Martian book showcases the science, math, and physics of living on the red planet — told through the story of someone who has to survive there.

GPT-4 poses too many risks and releases should be halted, AI group tells FTC

Anti AI / AI ethics clowns now pushing.gov for some criminalization, on cue.


A nonprofit AI research group wants the Federal Trade Commission to investigate OpenAI, Inc. and halt releases of GPT-4.

OpenAI “has released a product GPT-4 for the consumer market that is biased, deceptive, and a risk to privacy and public safety. The outputs cannot be proven or replicated. No independent assessment was undertaken prior to deployment,” said a complaint to the FTC submitted today by the Center for Artificial Intelligence and Digital Policy (CAIDP).

Calling for “independent oversight and evaluation of commercial AI products offered in the United States,” CAIDP asked the FTC to “open an investigation into OpenAI, enjoin further commercial releases of GPT-4, and ensure the establishment of necessary guardrails to protect consumers, businesses, and the commercial marketplace.”

Why It’s Difficult To Predict Where GPT And Other Generative AI Might Take Us

Derek Thompson published an essay in the Atlantic last week that pondered an intriguing question: “When we’re looking at generative AI, what are we actually looking at?” The essay was framed like this: “Narrowly speaking, GPT-4 is a large language model that produces human-inspired content by using transformer technology to predict text. Narrowly speaking, it is an overconfident, and often hallucinatory, auto-complete robot. This is an okay way of describing the technology, if you’re content with a dictionary definition.


He closes his essay with one last analogy, one that really makes you think about the-as-of-yet unforeseen consequences of generative AI technologies — good or bad: Scientists don’t know exactly how or when humans first wrangled fire as a technology, roughly 1 million years ago. But we have a good idea of how fire invented modern humanity … fire softened meat and vegetables, allowing humans to accelerate their calorie consumption. Meanwhile, by scaring off predators, controlled fire allowed humans to sleep on the ground for longer periods of time. The combination of more calories and more REM over the millennia allowed us to grow big, unusually energy-greedy brains with sharpened capacities for memory and prediction. Narrowly, fire made stuff hotter. But it also quite literally expanded our minds … Our ancestors knew that open flame was a feral power, which deserved reverence and even fear. The same technology that made civilization possible also flattened cities.

Thompson concisely passes judgment about what he thinks generative AI will do to us in his final sentence: I think this technology will expand our minds. And I think it will burn us.

Thompson’s essay inadvertently but quite poetically illustrates why it’s so difficult to predict events and consequences too far into the future. Scientists and philosophers have studied the process of how knowledge is expanded from a current state to novel directions of thought and knowledge.

Nvidia Rides The Generative AI Wave At GTC

This year’s NVIDIA GPU Technology Conference (GTC) could not have come at a more auspicious time for the company. The hottest topic in technology today is the Artificial Intelligence (AI) behind ChatGPT, other related Large Language Models (LLMs), and their applications for generative AI applications. Underlying all this new AI technology are NVIDIA GPUs. NVIDIA’s CEO Jensen Huang doubled down on support for LLMs and the future of generative AI based on it. He’s calling it “the iPhone moment for AI.” Using LLMs, AI computers can learn the languages of people, programs, images, or chemistry. Using the large knowledge base and based on a query, they can create new, unique works: this is generative AI.

Jumbo sized LLM’s are taking this capability to new levels, specifically the latest GPT 4.0, which was introduced just prior to GTC. Training these complex models takes thousands of GPUs, and then applying these models to specific problems require more GPUs as well for inference. Nvidia’s latest Hopper GPU, the H100, is known for training, but the GPU can also be divided into multiple instances (up to 7), which Nvidia calls MIG (Multi-Instance GPU), to allow multiple inference models to be run on the GPU. It’s in this inference mode that the GPU transforms queries into new outputs, using trained LLMs.

Nvidia is using its leadership position to build new business opportunities by being a full-stack supplier of AI, including chips, software, accelerator cards, systems, and even services. The company is opening up its services business in areas such as biology, for example. The company’s pricing might be based on use time, or it could be based on the value of the end product built with its services.

/* */