The errors that quantum computers make are holding the technology back. Quantum computers won't be truly useful until they can correct their mistakes Quantum computers are already here, but they make far too many errors. This is arguably the biggest obstacle to the technology really becoming useful, but recent breakthroughs suggest a solution may be on the horizon. Errors creep into traditional computers too, but there are well-established techniques for correcting them. They rely on redundancy, where extra bits are used to detect when 0s incorrectly swap to 1s or vice versa.

Can LLMs provide support to creative writers by giving meaningful writing feedback? In this paper, we explore the challenges and limitations of model-generated writing feedback by defining a new task, dataset, and evaluation frameworks. To study model performance in a controlled manner, we present a novel test set of 1,300 stories that we corrupted to intentionally introduce writing issues. We study the performance of commonly used LLMs in this task with both automatic and human evaluation metrics. Our analysis shows that current models have strong out-of-the-box behavior in many respects -- providing specific and mostly accurate writing feedback. However, models often fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback.

Companies are also spending time and money improving their models by testing them with real people. A technique called reinforcement learning with human feedback, where human testers manually improve a bot's answers and then feed them back into the system to improve it, is widely credited with making ChatGPT so much better than chatbots that came before it. A popular approach is to connect chatbots up to databases of factual or more trustworthy information, such as Wikipedia, Google search or bespoke collections of academic articles or business documents.

At its most basic, AI refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as recognizing speech, making decisions, and solving problems. There are many different types of AI, including machine learning, deep learning, and natural language processing, all of which are used to create more advanced and sophisticated AI systems. One of the most well-known applications of AI is virtual personal assistants, such as Apple's Siri and Amazon's Alexa. These systems use natural language processing to understand and respond to voice commands, allowing users to control their smart homes, make phone calls, and access information with just their voice. Another key area of AI development is self-driving cars, which use a combination of computer vision, machine learning, and other technologies to navigate roads and avoid obstacles.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

In 2017, spurred by advocacy from civil society groups, the New York City Council created a task force to address the city's growing use of artificial intelligence. But the task force quickly ran aground attempting to come to a consensus on the scope of "automated decision systems." In one hearing, a city agency argued that the task force's definition was so expansive that it might include simple calculations such as formulas in spreadsheets. By the end of its eighteen-month term, the task force's ambitions had narrowed from addressing how the city uses automated decision systems to simply defining the types of systems that should be subject to oversight. As policymakers around the world have attempted to create guidance and regulation for AI's use in settings ranging from school admissions and home loan approvals to military weapon targeting systems, they all face the same problem: AI is really challenging to define.

On this week's Most Innovative Companies Podcast, Alexandr Wang, CEO and founder of Scale AI, explains how his company is using data to tackle problems from access to healthcare to supplies for the war in Ukraine.

"Google fires engineer who contended its AI technology was sentient." A new discovery (or debacle) is reported practically every week, sometimes exaggerated, sometimes not. Policymakers struggle to know what to make of AI and it's hard for the lay reader to sort through all the headlines, much less to know what to be believe. Here are four things every reader should know. First, AI is real and here to stay.

This week, we examine how DeepMind's AI system predicted the structure of virtually every known protein--and what the breakthrough means for both science and machine learning

DeepMind has predicted the structure of almost every protein so far catalogued by science, cracking one of the grand challenges of biology in just 18 months thanks to an artificial intelligence called AlphaFold. Researchers say that the work has already led to advances in combating malaria, antibiotic resistance and plastic waste, and could speed up the discovery of new drugs. Determining the crumpled shapes of proteins based on their sequences of constituent amino acids has been a persistent problem for decades in biology. Some of these amino acids are attracted to others, some are repelled by water, and the chains form intricate shapes that are hard to accurately determine. UK-based AI company DeepMind first announced it had developed a method to accurately predict the structure of folded proteins in late 2020, and by the middle of it 2021 it had revealed that it had mapped 98.5 per cent of the proteins used within the human body.