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Category: robotics/AI – Page 68

They Just Shrunk AI Data Centers by 10,000x

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Timestamps:
00:00 — Why Superconductors?
10:10 — The Breakthrough.

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Some patient groups are far more vulnerable to near-perfect privacy attacks from medical AI

From detecting pneumonia on a chest X-ray to assessing whether a dark spot on the skin is benign or malignant, medical AI systems are playing an increasingly important role in clinical diagnosis. Unfortunately, the models used to train these AI systems are often victims of cyberattacks, specifically membership inference attacks (MIAs), which can lead to people’s personal information being stolen or revealed.

In a recent study, researchers conducted a first-ever patient-level privacy audit to see how easily individual patients could be identified from the underlying data used to train medical AI models.

At first glance, an AI model may appear to protect everyone’s privacy equally well, but a closer look reveals a different story. Researchers found that attackers can identify certain individual patients with near-perfect accuracy, exposing a hidden unfairness in privacy.

Amazon Q Developer Flaw Could Let Malicious Repos Run Code via MCP Configs

A high-severity flaw in Amazon Q Developer let a malicious repository run commands and steal a developer’s cloud credentials. The path was short: a developer opens the repo, trusts the workspace, and Amazon Q does the rest. Amazon has patched it.

Tracked as CVE-2026–12957 (CVSS 8.5), the bug sat in how Amazon’s AI coding assistant handled Model Context Protocol (MCP) servers.

Wiz Research, which found and reported it, showed that a single config file dropped in a repo was enough to go from git clone to cloud compromise.

AI Companies Don’t Have a Profitable Business Model. Does That Matter?

The generative AI boom is fueled by staggering investments (including OpenAI’s multibillion-dollar chip deals), but for many companies, profitability as a result of these investments has remained elusive, leading some economists to warn of an AI bubble. In this Q&A, Harvard Business School’s Andy Wu wades through the potential and hype of the new technology. In particular, he highlights structural challenges facing most companies and warns of inevitable expiration dates on current legacy subscription models. He says that the industry’s future will depend on sustainable economics and business models that are able to capture value.

An AI model that thinks like we do offers new ways to peer inside the black box

When a standard large language model (LLM) is confronted with a problem, it tries to solve it by matching it to similar information it has seen before, and then give an answer based on those past patterns. But how it decides which information to use and what value it gives to different pieces of information can be somewhat inscrutable from the outside. An EPFL team has created a new large language model that is structured similarly to a human brain, allowing users more control and moving away from “black box” AI.

The LLM MiCRo (Mixture of Cognitive Reasoners) is architecturally divided into four specialized areas that act like different parts of the human brain, allowing users to have more control over how it approaches a question and to better understand how it comes to its answers. The model, which was presented at the International Conference on Learning Representations (ICLR 2026), comes from the NLP Lab, part of the School of Computer and Communication Sciences (IC), and the NeuroAI Lab, part of IC and the School of Life Sciences at EPFL. The paper is posted to the arXiv preprint server.

Connectomics: Unraveling the Wiring of Neural Networks

Working in connectomics means creating comprehensive maps of brain and nervous system networks. Your research includes the identification and measurement of all parts of each neuron: the soma, dendrites, axonal path and branching patterns and combining that data with the synapses and gap junctions of the entire circuit.

Your microscopy challenges are extensive; submicron resolution is required over long distances inside large volumes of dense and complicated tissues.

AI-aided ‘master key’ vaccine may block entire virus families, not single strains

Known by acronyms that need no explanation, viruses like COVID, SARS and Ebola conjure images of medics in protective suits and spark fear in populations worldwide.

Vaccines for individual viruses have provided some relief, but new strains pose a constant challenge.

Now, new AI-aided vaccine technology developed by scientists at Cambridge University offers potential immunity against whole families of viruses and could even prevent the next pandemic, according to researchers.

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