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Open letter calls for prohibition on superintelligent AI, highlighting growing mainstream concern

An open letter released Wednesday has called for a ban on the development of artificial intelligence systems considered to be “superintelligent” until there is broad scientific consensus that such technologies can be created both safely and in a manner the public supports.

The statement, issued by the nonprofit Future of Life Institute, has been signed by more than 700 individuals, including Nobel laureates, technology industry veterans, policymakers, artists, and public figures such as Prince Harry and Meghan Markle, the Duke and Duchess of Sussex.

The letter reflects deep and accelerating concerns over projects undertaken by technology giants like Google, OpenAI, and Meta Platforms that are seeking to build artificial intelligence capable of outperforming humans on virtually every cognitive task. According to the letter, such ambitions have raised fears about unemployment due to automation, loss of human control and dignity, national security risks, and the possibility of far-reaching social or existential harms.

The Neuroscience Behind Writing: Handwriting vs. Typing—Who Wins the Battle?

Writing is a complex phenomenon that requires diverse skills: perceiving the pen and paper, moving the writing instrument, and directing the movement through thought. Using a pen involves paying attention to motor aspects such as drawing letters legibly, controlling the pressure of the tip on the paper, following lines and spaces on the page, and coordinating thought, action, and vision. This multisensory integration underlies memory abilities. Moreover, handwriting involves a wide variety of supporting materials, including pens, pencils, or chalk on a blackboard, all of which offer different experiences and create new neural activations and skills.

Despite sharing similar central goals and processes, handwriting and typing differ significantly in terms of the tools used, spatiotemporal dimensions, motor programming, and fine motor development. Compared with handwriting, which requires more time and attention to learn, typing can be considered simpler and faster, as it enables the production of a more easily readable and homogeneous product in less time. However, focused attention and a longer processing time improve memory retention, and once automatic control of the graphic gesture is achieved, minimal cognitive effort is required. Moreover, the specific movements memorized when learning to write contribute to the visual recognition of graphic shapes and letters and secondarily also improve reading ability. Indeed, since the ability to recognize letters is widely recognized in the literature as the first phase of reading, improving it through writing may effectively influence how children read.

The comparison between handwriting and typing reveals important differences in their neural and cognitive impacts. Handwriting activates a broader network of brain regions involved in motor, sensory, and cognitive processing, contributing to deeper learning, enhanced memory retention, and more effective engagement with written material. Typing, while more efficient and automated, engages fewer neural circuits, resulting in more passive cognitive engagement. These findings suggest that despite the advantages of typing in terms of speed and convenience, handwriting remains an important tool for learning and memory retention, particularly in educational contexts.

AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference

On April 17, 2025, the MIT Shaping the Future of Work Initiative and the MIT Schwarzman College of Computing welcomed Arvind Narayanan, Professor of Computer Science at Princeton University, to discuss his latest book, \.

AI streamlines search for catalysts to clear hydrogen production hurdles

To increase energy efficiency and reduce the carbon footprint of hydrogen fuel production, Fanglin Che, associate professor in the Department of Chemical Engineering at Worcester Polytechnic Institute, is leveraging the power and potential of machine learning and computational modeling. The multi-university team she leads has completed a study that was just published in Nature Chemical Engineering. The study utilized artificial intelligence to identify catalysts with the potential to facilitate cleaner and more efficient hydrogen production.

AI tools fall short in predicting suicide, study finds

The accuracy of machine learning algorithms for predicting suicidal behavior is too low to be useful for screening or for prioritizing high-risk individuals for interventions, according to a new study published September 11 in the open-access journal PLOS Medicine by Matthew Spittal of the University of Melbourne, Australia, and colleagues.

Numerous risk assessment scales have been developed over the past 50 years to identify patients at high risk of suicide or self-harm. In general, these scales have had poor predictive accuracy, but the availability of modern machine learning methods combined with electronic health record data has re-focused attention on developing to predict suicide and self-harm.

In the new study, researchers undertook a systemic review and meta-analysis of 53 previous studies that used machine learning algorithms to predict suicide, self-harm and a combined suicide/self-harm outcome. In all, the studies involved more than 35 million and nearly 250,000 cases of suicide or hospital-treated self-harm.

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