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AI isn’t a dual-use technology, it is inherently violent

When the Pentagon branded Anthropic CEO Dario Amodei “a liar with a god complex” over fears that his company’s AI could be used for weapons and surveillance, it exposed a deeper truth: the boundary between civilian and military technology no longer exists. The same systems that power translation, logistics, and digital assistants can just as easily identify targets or manipulate populations. Thomas Christian Bächle and Jascha Bareis argue that today’s AI is not simply “dual use” — it is inherently violent in design. Adaptive, autonomous, and globally networked, these machines fuse daily life with geopolitics, making peace itself a fading abstraction.

Drones have become an uncanny threat—not least in the wake of the cost of human life and the degrees of suffering and destruction they have inflicted in Russia’s war on Ukraine. In many European countries they have been sighted near critical infrastructure or military sites, either used for reconnaissance or sabotage, at times causing major disruptions in civilian air travel. Drones unsettle a population that is fearful and weary of the brutality of war at their doorstep. They have become a major element to what is labelled hybrid warfare, fought beyond the conventional ways of violence.

But this is not the whole picture. For years, drones have also been envisioned as a technology that bears the potential of bringing about major changes for the better: more efficient disaster relief, medical supply chains reaching even the remotest areas, optimized logistics or transportation. Drones also introduced a new visual – bird’s-eye-aesthetic of how to see the world.

Singularity Summer 2026: 8 Live Lectures on AI and Story

The technological singularity is usually sold to you as a prediction. A date. A curve. A moment when the machines wake up, and everything changes.

I have conducted more than 300 interviews since 2009, listening to the people who tell that story. The futurists. The engineers. The believers.

Here is what I have come to believe.

The singularity is not a forecast. It is a story. And whoever gets to write that story gets to shape what it means to be human.

This July and August, I am joining machine learning expert Thomas Hamelryck at Philosophy Portal for Singularity Summer. Eight live lectures across two months. Thomas takes the first month to open up the machine. What machine learning actually is, what it is not, and where it breaks.

Then I take August for the part that the engineering never answers. The human story inside the #AI machine, and how we might write a better one.

This is not a course about #MachineLearning as destiny. It is about #technology as the How, never the Why or the What. It is about who holds the pen writing our story.

Designing a Generational Ship (Proxima Centauri b)

To build a Proxima Centauri b generational ship; designed to protect the life aboard during the 100 year journey from Earth; designed to build the first structures of the settlement on the surface of the new planet.

✧ Find me on Substack where I write sci-fi story essays: J.Barry / Between Worlds.
https://jbarrybetweenworlds.substack… The fourth volume of ‘The Encyclopedia of the Future’ is now available on my Patreon. / shop ✧ The Garden Telescope (and Other Short Stories) Ebook is also available to Patreon members, or as a single purchase download Visit my Patreon here: / venturecity Created by: J. Barry — Book recommendations on artificial intelligence, future technology and innovations, and sci-fi stories (affiliate links): • Superintelligence: Paths, Dangers, Strategies https://amzn.to/3j28WkP • Life 3.0: Being Human in the Age of Artificial Intelligence https://amzn.to/3790bU1 • The Expanse: https://amzn.to/3Q0mG61 • The Hitchhikers Guide to the Galaxy: https://amzn.to/3kNFSyW — Other videos to watch: 1. The 100 Year Journey to Proxima Centauri b • The 100 Year Journey to Proxima Centauri B… 2. The First 10,000 Days on Proxima Centauri b • The First 10,000 Days on Proxima Centauri… 3. TIMELAPSE of Future Space Stations • TIMELAPSE of Future Space Stations.
✧ The fourth volume of ‘The Encyclopedia of the Future’ is now available on my Patreon.
/ shop.
✧ The Garden Telescope (and Other Short Stories) Ebook is also available to Patreon members, or as a single purchase download.

Visit my Patreon here: / venturecity.

Created by: J. Barry.

Book recommendations on artificial intelligence, future technology and innovations, and sci-fi stories (affiliate links):
• Superintelligence: Paths, Dangers, Strategies https://amzn.to/3j28WkP
• Life 3.0: Being Human in the Age of Artificial Intelligence https://amzn.to/3790bU1
• The Expanse: https://amzn.to/3Q0mG61
• The Hitchhikers Guide to the Galaxy: https://amzn.to/3kNFSyW

Other videos to watch:

They Just Shrunk AI Data Centers by 10,000x

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

My Podcast on Apple: https://podcasts.apple.com/at/podcast
My Podcast on Spotify: https://open.spotify.com/show/3drr7A8… Let’s connect on LinkedIn: / anastasiintech Newsletter: https://anastasiintech.substack.com Instagram: / anastasi.in.tech Patreon: / anastasiintech.

Let’s connect on LinkedIn: / anastasiintech.
Newsletter: https://anastasiintech.substack.com.
Instagram: / anastasi.in.tech.
Patreon: / anastasiintech.

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.

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