Iran War Provides a Large-Scale Test For AI-Assisted Warfare
An anonymous reader quotes a report from Bloomberg, written by Katrina Manson: The U.S. strikes on Iran ordered by President Donald Trump mark the arrival on a large scale of a new era of warfare assisted by artificial intelligence. Captain Timothy Hawkins, a Central Command spokesperson, told me last night that the AI tools the U.S. military is using in Iran operations don't make targeting decisions and don't replace humans. But they do help "make smarter decisions faster." That's been the driving ambition of the U.S. military, which has spent years looking at how to develop and deploy AI to the battlefield [...].
Critics, such as Stop Killer Robots, a coalition of 270 human-rights groups, argue that AI-enabled decision-support systems reduce the separation between recommending and executing a strike to a "dangerously thin" line. Hawkins said the military's use of AI assistance follows a rigorous process aligned with U.S. policy, military doctrine and the law. Artificial intelligence helps analysts whittle down what they need to focus on, generating so-called points of interest and helping personnel make "smart" decisions in the Iran operations, he told me. AI is also helping to pull data within systems and organize information to provide clarity.
Among the AI tech used in the Iran campaign is Maven Smart System, a digital mission control platform produced by Palantir [...]. That emerged from Project Maven, a project started in 2017 by the Pentagon to develop AI for the battlefield. Among the large language models installed on the system is Anthropic's Claude AI tool, according to the people, who said it has become central to U.S. operations against Iran and to accelerating Maven's development. Claude is also at the center of a row that pits Anthropic against the Department of Defense over limits on the software. Further reading: Hacked Tehran Traffic Cameras Fed Israeli Intelligence Before Strike On Khamenei
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Python 'Chardet' Package Replaced With LLM-Generated Clone, Re-Licensed
Ancient Slashdot reader ewhac writes: The maintainers of the Python package `chardet`, which attempts to automatically detect the character encoding of a string, announced the release of version 7 this week, claiming a speedup factor of 43x over version 6. In the release notes, the maintainers claim that version 7 is, "a ground-up, MIT-licensed rewrite of chardet." Problem: The putative "ground-up rewrite" is actually the result of running the existing copyrighted codebase and test suite through the Claude LLM. In so doing, the maintainers claim that v7 now represents a unique work of authorship, and therefore may be offered under a new license. Version 6 and earlier was licensed under the GNU Lesser General Public License (LGPL). Version 7 claims to be available under the MIT license.
The maintainers appear to be claiming that, under the Oracle v. Google decision, which found that cloning public APIs is fair use, their v7 is a fair use re-implementation of the `chardet` public API. However, there is no evidence to suggest their re-write was under "clean room" conditions, which traditionally has shielded cloners from infringement suits. Further, the copyrightability of LLM output has yet to be settled. Recent court decisions seem to favor the view that LLM output is not copyrightable, as the output is not primarily the result of human creative expression -- the endeavor copyright is intended to protect. Spirited discussion has ensued in issue #327 on `chardet`s GitHub repo, raising the question: Can copyrighted source code be laundered through an LLM and come out the other end as a fresh work of authorship, eligible for a new copyright, copyright holder, and license terms? If this is found to be so, it would allow malicious interests to completely strip-mine the Open Source commons, and then sell it back to the users without the community seeing a single dime.
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