Trump launches'Genesis Mission' to harness AI for scientific breakthroughs United States President Donald Trump has unveiled a national initiative to mobilise artificial intelligence (AI) for accelerating scientific breakthroughs. Trump signed an executive order on Monday to establish "The Genesis Mission", the latest iteration of his administration's aggressive strategy for spurring AI development through deregulation, infrastructure investment and public-private collaboration. Under the initiative, US supercomputers and data resources will be integrated to create a "closed-loop AI experimentation platform", according to the order. The White House, which likened the initiative to the Apollo programme that put the first man on the moon, said priority areas of focus would include the "greatest scientific challenges of our time," such as nuclear fusion, semiconductors, critical materials and space exploration. Michael Kratsios, the White House's top science adviser, said the initiative took a "revolutionary approach" to scientific research.

Could an AI scientist help researchers come up with breakthroughs by analysing data and searching the existing scientific literature? That's the claim of the inventors of Kosmos, but not everyone is convinced Artificial intelligence can process large amounts of data, but can it do science? An AI scientist can work independently for hours while doing research that would take humans months to complete, and has made several "novel contributions" to science, its creators claim - but others are more doubtful. The system, called Kosmos, is actually a collection of AI agents that are specialised in analysing data and searching through the existing scientific literature, in an effort to make new scientific breakthroughs. "We've been working on building an AI scientist for about two years now," says Sam Rodriques at Edison Scientific, the US-based firm behind Kosmos.

Novelty evaluation is vital for the promotion and management of innovation. With the advancement of information techniques and the open data movement, some progress has been made in novelty measurements. Tracking and reviewing novelty measures provides a data-driven way to assess contributions, progress, and emerging directions in the science field. As academic papers serve as the primary medium for the dissemination, validation, and discussion of scientific knowledge, this review aims to offer a systematic analysis of novelty measurements for scientific papers. We began by comparing the differences between scientific novelty and four similar concepts, including originality, scientific innovation, creativity, and scientific breakthrough. Next, we reviewed the types of scientific novelty. Then, we classified existing novelty measures according to data types and reviewed the measures for each type. Subsequently, we surveyed the approaches employed in validating novelty measures and examined the current tools and datasets associated with these measures. Finally, we proposed several open issues for future studies.

The US department for energy has announced that it has made a'major scientific breakthrough' in the race to recreate nuclear fusion. At a press conference on Tuesday US energy secretary, Jennifer Granholm, said scientists at the Lawrence Livermore National Laboratory in California'achieved fusion ignition', which is'creating more energy from fusion reactions than the energy used to start the process.' Describing the experiments results as a'BFD' [Big Fucking Deal], she added that'this milestone moves us one significant step closer to the possibility of zero carbon abundant fusion energy powering our society'

The possibility of super-AIs taking over the world has been intensively studied by numerous scholars. This paper focuses on the multi-AI competition scenario under the premise of super-AIs in power. Firstly, the article points out the defects of existing arguments supporting single-AI domination and presents arguments in favour of multi-AI competition. Then the article concludes that the multi-AI competition situation is a non-negligible possibility. Attention then turns to whether multi-AI competition is better for the overall good of humanity than a situation where a single AI is in power. After analysing the best, worst, and intermediate scenarios, the article concludes that multi-AI competition is better for humanity. Finally, considering the factors related to the formation of the best-case scenario of multiple AIs, the article gives some suggestions for current initiatives in AI development.

Overview: This paper explores the emergence of AI as a new industry in Montreal, Canada.

Today, Avenga Labs is here with the latest news about the cloud, and AI for NLP and developers, but at the end I'll address the event that is predicted to be the most important scientific breakthrough of our lifetime. EC2 is one of the oldest services available in the AWS cloud. There was a lot of noise generated by this announcement. In fact, it's a very old service from more than 15 years ago and it hasn't been recommended for use (required explicit permission) since 2013. The actual usage was minimal according to many sources.

If the past is any guide, the thrilling future of neuroscience has already arrived, but most of us just haven't noticed it yet. With previous scientific breakthroughs that elevated the human condition--such as the discovery that bacteria cause infectious disease (leading to antiseptics and antibiotics) and the discovery that silicon integrated circuits could be made inexpensively (fueling the digital revolution)--key discoveries emerged decades before anyone, let alone leading scientists, grasped their full importance. Ignaz Semmelweis discovered that "cadaverous particles" (bacteria) caused disease in 1848, over 20 years before antiseptic techniques to combat infection were adopted. The integrated circuit and Complimentary Metal on Silicon (CMOS) developments in 1958 and 1963, respectively, occurred long before these discoveries made possible Moore's Law (digital circuit performance doubles every 18 months), personal computers, mobile phones, and the World Wide Web. I believe that developments comparable to previous seminal scientific breakthroughs have already occurred in neuroscience, but most of the world hasn't realized it yet for a number of reasons, chief among them that some of these earthshaking advances aren't actually in neuroscience at all, but in fields such as Computational Mathematics and Artificial Intelligence (AI). Before describing the "non-neuroscience" advances that are propelling neuroscience into an exciting future, let me focus on recent key breakthroughs that are in the field of neuroscience.

Utopians believe that once AI far surpasses human intelligence, it will provide us with near-magical tools for alleviating suffering and realizing human potential. In this vision, super-intelligent AI systems will so deeply understand the universe that they will act as omnipotent oracles, answering humanity's most vexing questions and conjuring brilliant solutions to problems such as disease and climate change. But not everyone is so optimistic. The best-known member of the dystopian camp is the technology entrepreneur Elon Musk, who has called super-intelligent AI systems "the biggest risk we face as a civilization," comparing their creation to "summoning the demon." This group warns that when humans create self-improving AI programs whose intellect dwarfs our own, we will lose the ability to understand or control them.

As you may probably know, DeepMind has recently published a paper on AlphaZero [1], a system that learns by itself and is able to master games like chess or Shogi. Before getting into details, let me introduce myself. I am a researcher in the broad field of Artificial Intelligence (AI), specialized in Natural Language Processing. I am also a chess International Master, currently the top player in South Korea although practically inactive for the last few years due to my full-time research position. Given my background I have tried to build a reasoned opinion on the subject as constructive as I could.