Lifeboat Foundation

Schmidt thinks that if the AI sector doesn’t create protections, politicians will have to step in.

Eric Schmidt, the former CEO of Google, has spoken out against the six-month ban on AI development that some tech celebrities and business executives demanded earlier.

“I’m not in favor of a six-month pause, because it will simply benefit China,” said Schmidt, Google’s first CEO.

Continue reading “Ex-Google CEO Eric Schmidt warns pausing ChatGPT-like AI will benefit China” »

The last few weeks have been abuzz with news and fears (well, largely fears) about the impact chatGPT and other generative technologies might have on the workplace. Goldman Sachs predicted 300 million jobs would be lost, while the likes of Steve Wozniak and Elon Musk asked for AI development to be paused (although pointedly not the development of autonomous driving).

Indeed, OpenAI chief Sam Altman recently declared that he was “a little bit scared”, with the sentiment shared by OpenAI’s chief scientist Ilya Sutskever, who recently said that “at some point it will be quite easy, if one wanted, to cause a great deal of harm”.

As fears mount about the jobs supposedly at risk from generative AI technologies like chatGPT, are these fears likely to prevent people from taking steps to adapt?

Continue reading “Doomsday Predictions Around ChatGPT Are Counter-Productive” »

Nowhere is the joy of existence so apparent as in music… Intelligent life-forms have created a multitude of sounds that express their exhilaration at being alive.

^c Department of Chemical Biology, Xiamen University, Xiamen, 361,005, China.

The concept of xeno-nucleic acids (XNAs) was first proposed in 2009 in a theoretical paper, referring to additional types of nucleic acids, whose sugar moieties would differ from those in DNA and RNA. However, with the rising popularity of XNAs, the definition of XNAs has been extended to unnatural nucleic acids with chemically modified sugar, nucleobase, or phosphate moieties that are distinct from those found in DNA and RNA. The discovery and engineering of both polymerases and reverse transcriptases to synthesize, replicate and evolve a diverse range of XNAs has attracted significant attention and has enabled the discovery of XNA ligands (aptamers) and XNA catalysts (XNAzymes) as well as the synthesis of XNA nanostructures with potential as novel therapeutics. The field of XNAs continues to grow rapidly towards realizing the potential of XNAs in biotechnology and molecular medicine. This themed issue unites a collection of articles attesting to the rapid progress in the field.

One of the key advantages of XNAs is their generally enhanced resistance to nuclease degradation. This biostability, the affinity and specificity towards a target, and the general lack of immunogenicity of modified nucleic acids are critical for their potential application as therapeutics. Modified sugar moieties such as 2′-modified analogs, conformationally locked analogs, and threose-replaced analogs in particular contribute to the increased biological stability of XNAs against enzymatic degradation. Replacing the phosphodiester linkages with charge-neutral backbones including peptide-like backbones and triazole-linked backbones offers further opportunities to tune the stability, conformation and physicochemical properties of XNAs and enhance the affinity to their targets.

A quantum computational solution for engineering materials. Researchers at Argonne explore the possibility of solving the electronic structures of complex molecules using a quantum computer. If you know the atoms that compose a particular molecule or solid material, the interactions between those atoms can be determined computationally, by solving quantum mechanical equations — at least, if the molecule is small and simple. However, solving these equations, critical for fields from materials engineering to drug design, requires a prohibitively long computational time for complex molecules and materials.

Poor, poor, Horizon Worlds. According to Facebook-turned-Meta CTO Andrew Bosworth, the company’s metaverse of dead-eyed avatars has been all but abandoned by Meta CEO Mark Zuckerberg — who, in an added blow, is instead said to be spending the bulk of his time chasing the investor-appeasing Silicon Valley squirrel that is generative AI.

“We’ve been investing in artificial intelligence for over a decade, and have one of the leading research institutes in the world,” Bosworth told Nikkei Asia in an interview on Wednesday. “We certainly have a large research organization, hundreds of people.”

Continue reading “Mark Zuckerberg Abandons Metaverse as Shiny New Toy Appears” »

I ve been quite impressed so far. And, if they can be improved over night i would love to see it.

With long-term memory, language models could be even more specific – or more personal. MemoryGPT gives a first impression.

Right now, interaction with language models refers to single instances, e.g. in ChatGPT to a single chat. Within that chat, the language model can to some extent take the context of the input into account for new texts and replies.

Continue reading “MemoryGPT is like ChatGPT with long-term memory” »

Fascinating proposal for methodology.

Models are scientific models, theories, hypotheses, formulas, equations, naïve models based on personal experiences, superstitions (!), and traditional computer programs. In a Reductionist paradigm, these Models are created by humans, ostensibly by scientists, and are then used, ostensibly by engineers, to solve real-world problems. Model creation and Model use both require that these humans Understand the problem domain, the problem at hand, the previously known shared Models available, and how to design and use Models. A Ph.D. degree could be seen as a formal license to create new Models^[2]. Mathematics can be seen as a discipline for Model manipulation.

Continue reading “The Red Pill of Machine Learning” »

In an advance they consider a breakthrough in computational chemistry research, University of Wisconsin–Madison chemical engineers have developed model of how catalytic reactions work at the atomic scale. This understanding could allow engineers and chemists to develop more efficient catalysts and tune industrial processes—potentially with enormous energy savings, given that 90% of the products we encounter in our lives are produced, at least partially, via catalysis.

Catalyst materials accelerate chemical reactions without undergoing changes themselves. They are critical for refining petroleum products and for manufacturing pharmaceuticals, plastics, food additives, fertilizers, green fuels, industrial chemicals and much more.

Scientists and engineers have spent decades fine-tuning catalytic reactions—yet because it’s currently impossible to directly observe those reactions at the extreme temperatures and pressures often involved in industrial-scale catalysis, they haven’t known exactly what is taking place on the nano and atomic scales. This new research helps unravel that mystery with potentially major ramifications for industry.