Donald Trump's return to the presidency shifts AI policy toward deregulation and defense applications. This episode dissects the implications: accelerated AI in military, surveillance, policing; diminished ethical oversight; risks like privacy breaches, biased algorithms, AI arms race with China; lack of support for workers displaced by AI automation. Explore how ethics, regulation, and worker protections may be neglected in favor of profit and control, and what this means for the future of AI.

Read more and stay up to date on handyai.substack.com

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Model collapse guts AI's intelligence as it feeds on its own output. Training on AI-generated junk reduces models to echo chambers, detached from reality. Culprits are statistical approximation errors, functional expressivity limits, and flawed learning procedures. We can fix this mess with rigorous data curation, model distillation, and human oversight.

Read more on the Handy AI Substack newsletter.

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In this episode, we explore the voracious energy consumption of large language models (LLMs). These AI systems consume massive amounts of electricity during training and inference. A single training run for a model like GPT-3 uses around 1,287 MWh of electricity—equivalent to the carbon emissions from 550 round-trip flights between New York and San Francisco. Inference amplifies the problem, with ChatGPT's monthly energy usage ranging from 1 to 23 million kWh.

The energy appetite of LLMs mirrors the cryptocurrency mining crisis, consuming enormous power with questionable societal benefits. Closed-source models like GPT-4o and Gemini hide their energy usage, hindering regulation and public accountability. The unchecked expansion of LLMs threatens global efforts to reduce energy consumption and combat climate change. It's time to confront the dangerous appetite of AI.

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AI bias lurks beneath the surface of our increasingly automated world. As algorithms continue to make decisions that impact human lives, the potential for discrimination grows and grows. It’s important to understand why this is happening and how companies (and individuals) can continue efforts to mitigate.

Otherwise we risk a world where AI’s negative effects on society hit on a deeper level that is less obvious — but more harmful.

Read more about AI bias (and other fun things) at handyai.substack.com.

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Last month, OpenAI announced o1, a new AI model series designed to "reason" through complex problems before responding. This release marks a shift in OpenAI's approach, moving beyond simply scaling up model size to fundamentally changing how AI systems process information.

As o1 and similar models mature, it will be essential for researchers, policymakers, and the public to grapple with these questions. The era of "reasoning" AI is upon us.

Read more about o1 over at handyai.substack.com.

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The quick rise of AI tools for music creation has sparked numerous legal battles between these emerging tech companies and the established music industry. Three major lawsuits filed this year (or soon to be filed) could reshape the landscape for both AI development and music copyright law. The outcomes of these lawsuits, prosecutions, and future legal battles will likely shape the future of both the music and AI industries for years to come.

Read a more in-depth analysis over at handyai.substack.com.

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Hosted on Acast. See acast.com/privacy for more information.

Hosted on Acast. See acast.com/privacy for more information.