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Comment Re:Depends on your goals, I guess. (Score 3, Interesting) 73

I looked at a waterfall project where the mayor ended up spending $3M to have an audit done on the current state of a project that was way behind on time and way over budget, only for them to come back and say that it'd be cheaper to burn all the effort to date and start fresh.

Comment Re:What could possibly go wrong? (Score 4, Interesting) 154

by Black Parrot (#66129968) Attached to: CO2 Levels In the Atmosphere Hit 'Depressing' New Record

You think migration is bad now, wait until "undesirable" people start mass migrating from unlivable areas.

It may go the opposite direction. If current water & agricultral "haves" become "have-nots" and vice versa due to changes in weather there will be endless wars as the "haves" try to maintain their position.

Submission + - The Audio Industry Is Grappling with the Rise of 'Podslop' (bloomberg.com)

Submitted by Anonymous Coward
An anonymous reader writes: Welcome to the modern era of podcasting in which thousands of new shows are released into the world every day with a sizable portion likely being AI-generated. Figuring out exactly which ones fall into that growing category is becoming more difficult just as the industry is starting to take this issue seriously. In only the past month or so, Amazon launched a feature that explains a product by generating a quasi-podcast, complete with co-hosts talking to each other and taking questions from users. Shout out to Business Insider reporter Katie Notopoulos for spotting this (and, naturally, demoing it with an adult diaper rash-cream). Not long ago, Nicholas Thompson, chief executive officer of the Atlantic, noted “podslop” dominated his Spotify search results when he typed in the word “Sora.” This was around the time that OpenAI shut down its user-generated, AI-content-only app.

[...] All of which raises some big, difficult questions. For one, what should the listening platforms do about this incursion? As of right now, Apple Podcasts requires creators who generated a “material portion” of their show using AI to disclose it. The platform also bans misleading or deceptive content. Spotify hasn’t published any specific guidelines around AI, though it maintains general rules around dangerous and misleading content. Where this conversation gets even trickier is when it comes to money. Many of these podcasts are hosted on at least one free service that allows programs to opt into their ad marketplace with zero barrier to entry, meaning these shows (and the hosting service) profit off every listen or download. Spreaker, a company owned by iHeartMedia, is the primary one to watch here. Though it tells users to disclose when they rely on AI, it still allows those shows to opt into its programmatic ad marketplace, which pays creators 60% of the revenue generated by the ads placed in their shows. It stands to reason that most of these thousands of shows don’t reach many people. But in the aggregate, the ears and dollars could add up. Are the advertisers on board with being next to AI-generated content, some of which might be deemed “slop?”

Submission + - Cisco releases open-source 'DNA test for AI models' (scworld.com)

Submitted by spatwei
spatwei writes: Cisco released an open-source tool to trace the origins of AI models and compare model similarities for great visibility into the AI supply chain.

The Model Provenance Kit, announced Thursday, is a Python toolkit and command-line interface (CLI) that looks at signals such as metadata and weights to create a “fingerprint” for AI models that can then be compared to other model fingerprints to determine potential shared origins.

“Think of Model Provenance Kit as a DNA test for AI models,” Cisco researchers wrote. “[] Much like a DNA test reveals biological origins, the Model Provenance Kit examines both metadata and the actual learned parameters of a model (like a unique genome that comprises a model), to assess whether models share a common origin and identify signs of modification.”

The tool aims to address gaps in visibility into the AI model supply chain. For example, many organizations utilize open-source models from repositories like HuggingFace, where models could potentially be uploaded with incomplete or deceptive documentation.

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