A collection of stories set in George R. R. Martin's universe and a novel from author James S. A. Corey are among the science-fiction books we're looking forward to this month I am currently reading the science-fiction classic by Kim Stanley Robinson with the New Scientist Book Club (it's our April read). It's fantastic, so any other trips to the Red Planet are very welcome from my perspective, and I'm looking forward to Charlotte Robinson's thriller . Elsewhere in this month's science fiction, there's horror in space from S. A. Barnes, some resurrected Neanderthals from Douglas Preston and his daughter Aletheia Preston, and ghosts in AI-generated videos from Max Lury. Something for all tastes, I'd say. This near-future space-thriller follows a one-way mission to Mars, as well as the disappearance of a programmer in Hong Kong, who leaves nothing behind but a cryptic warning. As the Argo spaceship heads towards Mars, the crew realise they are being sabotaged.

A woman flying from Chicago to Marquette, Michigan was left baffled this week, after being mistaken for a baby. Patricia, 101, was boarding the flight with her daughter, Kris, when she was confronted by the cabin crew. Bizarrely, they had expected her to be aged one, due to an error with American Airlines' booking system. Patricia, who did not want her surname shared, was born in 1922, rather than 2022 - something the computer system could not seem to accept. Speaking to the BBC, who witnessed the mix-up, she said: 'It was funny that they thought I was only a little child and I'm an old lady!' A woman flying from Chicago to Marquette, Michigan was left baffled this week, after being mistaken for a baby.

Generative large language models (LLMs) have opened up numerous novel possibilities, but due to their significant computational requirements their ubiquitous use remains challenging. Some of the most useful applications require processing large numbers of samples at a time and using long contexts, both significantly increasing the memory communication load of the models. We introduce SparQ Attention, a technique for increasing the inference throughput of LLMs by reducing the memory bandwidth requirements within the attention blocks through selective fetching of the cached history. Our proposed technique can be applied directly to off-the-shelf LLMs during inference, without requiring any modification to the pre-training setup or additional fine-tuning. We show how SparQ Attention can decrease the attention memory bandwidth requirements up to eight times without any loss in accuracy by evaluating Llama 2 and Pythia models on a wide range of downstream tasks.

We propose a new single-winner voting system using ranked ballots: Stable Voting. The motivating principle of Stable Voting is that if a candidate A would win without another candidate B in the election, and A beats B in a head-to-head majority comparison, then A should still win in the election with B included (unless there is another candidate A' who has the same kind of claim to winning, in which case a tiebreaker may choose between such candidates). We call this principle Stability for Winners (with Tiebreaking). Stable Voting satisfies this principle while also having a remarkable ability to avoid tied outcomes in elections even with small numbers of voters.

DEERFIELD, Ill., June 07, 2021 (GLOBE NEWSWIRE) -- Surgalign Holdings, Inc. (NASDAQ: SRGA), a global medical technology company focused on elevating the standard of care through the evolution of digital surgery, and Inteneural Networks Inc., a developer of innovative artificial intelligence (AI) based applications focused on fully autonomous analytics of central nervous system imaging, today announced that they have entered into a strategic collaboration agreement. Under the agreement, Surgalign will gain access to Inteneural's proprietary technology for evaluation of future integration within the Surgalign digital surgery portfolio. Inteneural is the developer and owner of proprietary intellectual property that allows computers to autonomously segment and identify neural structures in medical images and rapidly deliver reference information using machine learning alogrithims. These algorithms have potential for future applications in cranial and neurosurgery for referencing of tumor, aneurysm, stroke, and neurovascular structures using existing magnetic resonance imaging and computed tomography technology. "While our initial focus is the application of digital surgery in spine procedures, we have a much more expansive vision for what we believe is possible with emerging technologies. New developments in the application of AI in neurosurgery and medical imaging make it an attractive space to further expand. Inteneural has developed machine learning-based analytics and fully autonomous brain anatomy segmentation capabilities that would be incredibly powerful when combined with neurosurgery," said Terry Rich, Surgalign Chief Executive Officer.