The reborn Commodore 64 is an astonishing remake--but daunting if you weren't there the first time around. "Digital detox" approach is compelling. It's hard to overstate just how seismic an impact the Commodore 64 had on home computing. Launched in 1982, the 8-bit machine--iconic in its beige plastic shell with integrated keyboard--went on to become the best-selling personal computer of all time . Despite the success, manufacturer Commodore International folded in 1994, with rights to the name floating around for years.

Earth's turbulent atmosphere makes it hard to detect new planets from the ground. Astronomer Rebecca Jensen-Clem is working out how to find them anyway. The pendant on Rebecca Jensen-Clem's necklace is only about an inch wide, composed of 36 silver hexagons entwined in a honeycomb mosaic. At the Keck Observatory, in Hawaii, just as many segments make up a mirror that spans 33 feet, reflecting images of uncharted worlds for her to study. Jensen-Clem, an astronomer at the University of California, Santa Cruz, works with the Keck Observatory to figure out how to detect new planets without leaving our own. Typically, this pursuit faces an array of obstacles: Wind, fluctuations in atmospheric density and temperature, or even a misaligned telescope mirror can create a glare from a star's light that obscures the view of what's around it, rendering any planets orbiting the star effectively invisible.

It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances (Entezari et al., 2022). This means that a linear path can connect two independent solutions with low loss, given the weights of one of the models are appropriately permuted. However, current methods to test this theory often require very wide networks to succeed. In this work, we conjecture that more generally, the SGD solution set is a "star domain" that contains a "star model" that is linearly connected to all the other solutions via paths with low loss values, modulo permutations. We propose the Starlight algorithm that finds a star model of a given learning task. We validate our claim by showing that this star model is linearly connected with other independently found solutions. As an additional benefit of our study, we demonstrate better uncertainty estimates on the Bayesian Model Averaging over the obtained star domain. Further, we demonstrate star models as potential substitutes for model ensembles. Our code is available at https://github.com/aktsonthalia/starlight.

Our test sets were too small and too haphazard to support statistically valid conclusions, but they were suggestive of a number of conclusions. We summarize these here, and discuss them at greater length in section 7. Over the kinds of problems tested, GPT-4 with either plug-in is significantly stronger than GPT-4 by itself, or, almost certainly, than any AI that existed a year ago. However it is still far from reliable; it often outputs a wrong answer or fails to output any answer. In terms of overall score, we would judge that these systems performs on the level of a middling undergraduate student. However, their capacities and weaknesses do not align with a human student; the systems solve some problems that even capable students would find challenging, whereas they fail on some problems that even middling high school students would find easy.

Machine learning, advanced heuristics, or artificial intelligence: the language is shifting but the idea that computers are taking a greater role in automating security functions is moving straight ahead. Two new product announcements demonstrate that direction in very different ways. Aella Data and Senzing each brings a product based on AI technology to market, and in some ways the products could not be more different in purpose, intended audience, or business model. But both share a critical similarity: Each uses AI to correlate data from many different sources to present information that assists humans in doing their jobs. Aella Data came out of stealth mode just before this year's RSA Conference.

Although Einstein himself made many advances in physics, from special and general relativity to the photoelectric effect and statistical mechanics, there were many problems he couldn't solve during his life. How much better could AI have done? At the dawn of the 20th century, there were a number of crises in physics. Radiating objects like stars emitted a finite, well-defined amount of energy at every wavelength, defying the best predictions of the day. Newton's laws of motion broke down and failed when objects approached the speed of light.

WIRE)--Aella Data, a leading innovator in AI-driven cybersecurity solutions, officially exits stealth mode today with the launch of Starlight Pervasive Breach Detection System (PBDS), the industry's first AI-driven pervasively deployable solution. Founded by security industry veterans, Aella Data's Starlight PBDS is the only system that can be deployed pervasively across all network infrastructures. It unifies collected data and conducts advanced analysis to deliver high-fidelity, actionable alerts that reduce detection time from months to mere minutes, and alert volume from thousands to the critical few. The company formally launches at RSA Conference 2018 with a growing list of key customers and partners. Aella Data's launch and its disruptive technology is timely in a world gone awry with increasingly costly and far-reaching cyberattacks.

How far will AI take us in areas such as healthcare? Will we see medical pods like this one featured in science fiction film Prometheus to treat future patients? CIO veteran and industry consultant, Geoff Wenborn, sees enormous opportunity for artificial intelligence (AI) in healthcare - identifying 35 use cases alone for robotics and AI at places like UnitingCare Queensland. Wenborn, who provides IT transformation consulting to several organisations including UnitingCare Queensland and Starlight Children's Foundation Australia (and built some proof of concepts), said the sky's the limit in terms of use case possibilities. At UnitingCare, he was the interim CIO charged with doing transformation, and is currently the chair of the Starlight IT Advisory Board.

How far will AI take us in areas such as healthcare? Will we see medical pods like this one featured in science fiction film Prometheus to treat future patients? CIO veteran and industry consultant, Geoff Wenborn, sees enormous opportunity for artificial intelligence (AI) in healthcare - identifying 35 use cases alone for robotics and AI at places like UnitingCare Queensland. Wenborn, who provides IT transformation consulting to several organisations including UnitingCare Queensland and Starlight Children's Foundation Australia (and built some proof of concepts), said the sky's the limit in terms of use case possibilities. At UnitingCare, he was the interim CIO charged with doing transformation, and is currently the chair of the Starlight IT Advisory Board.

It's not that James Williams, a doctoral candidate at the Oxford Internet Institute's Digital Ethics Lab (motto: "Every Bit as Good"), had a "God, what I have I done?" moment during his time at Google. But it did occur to him that something had gone awry. Williams joined Google's Seattle office when it opened in 2006 and went on to win the company's highest honor, the Founder's Award, for his work developing advertising products and tools. Then, in 2012, he realized that these tools were actually making things harder for him. Modern technology platforms, he explained to me, were "reimposing these pre-Internet notions of advertising, where it's all about getting as much of people's time and attention as you can." By 2011, he had followed his literary and politico-philosophical bent (he is a fan of George Orwell's 1984 and Aldous Huxley's Brave New World) to Oxford, while still working at Google's London office.