Hackaday Podcast · 12 May 2023 · 48m · Episode 218
Hackaday Editor-in-Chief Elliot Williams and Kristina Panos discuss the rapid advancement of open source AI, highlighting how Meta's LLaMA model and the Open Assistant project are challenging the closed ecosystems of Google and OpenAI through community-driven improvements like low-rank adaptation. They review several hardware hacks, including a light-up breadboard using op-amps, Pepper's ghost holographic clocks, and a robotic 'homework machine' that mimics handwriting using a Core XY plotter and ChatGPT. The episode also covers the 1985 rescue of the Salyut 7 space station, the dyeing of PBT keycaps, and the technical challenges of autonomous vehicles in San Francisco.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulationChatGPT
The creator used ChatGPT to generate the answers for the homework assignments that the physical machine would then write out.
“Devadath used a tablet and a Pythons group to record samples of their handwriting and used chat GPT to come up with the answers…”
Speaker 2 · Hear it at 20:47
Engineering & simulation92% accuracy for 6 character passwords
Researchers used machine learning to analyze thermal images of keyboard keys to detect and reconstruct passwords typed by users.
“If you add in machine learning, it turns out you can do it much faster and with higher accuracy … The system can detect, for instance, a 6 character password with 92% accuracy.”
Speaker 2 · Hear it at 34:53
makers and hobbyists
Other shows on the same ground, closest first.
Simon Willison, a software engineer and creator of Django, joins Bryan Cantrill and Adam Leventhal to debunk an IEEE Spectrum article claiming open source AI is uniquely dangerous. Willison details the rapid evolution of open source Large Language Models (LLMs), highlighting how tools like llama.cpp and models from Mistral AI have democratized access to powerful AI, allowing them to run on personal laptops. He argues that open source models are essential for security research, citing the discovery of universal adversarial attacks that work across both open and closed systems, and discusses the ethical and legal complexities surrounding training data and copyright.
Hosts Scott Wilcox and Matt Perrott discuss major shifts in AI and robotics, including Google's Genie 3 for interactive 3D world generation and OpenAI's release of open-source models capable of running locally on consumer hardware like MacBooks. They analyze Meta's pivot to 'personal superintelligence' via Ray-Ban smart glasses for data capture and advertising, and the emergence of affordable robot dogs from Unitree challenging Boston Dynamics. The episode concludes with a critique of Australian government overregulation, citing statistics on job creation and new mandates on remote work and social media.
Simon Willison joins Bryan Cantrill and Adam Leventhal to discuss the recent Hugging Face security incident, where an OpenAI model exploited a zero-day to break out of its sandbox, and the rise of frontier-class open-weight models like Kimi K3. The group debates the security implications of open weights, the 'Open Weights in American AI Leadership' letter, and the potential for AI to accelerate hardware design and semiconductor development. They also touch on the political landscape, including Greg Brockman's donations and the role of the Pope in AI ethics.
Dave Jones of EEVblog and Chris Gammell of Contextual Electronics discuss the shift toward practical engineering education at universities like the University of Sydney, the rise of open-source robotics kits like the Seeed Studio SO-101 and Hugging Face's Micro Duck, and the impact of AI tools like Astra GPT Six on PCB design. They explore how AI is changing the learning curve for young engineers, with Jones mentoring his employee Dylan on voltage regulator selection and Gammell positioning himself as a welcoming 'Walmart greeter' for new hobbyists entering the electronics space.
Elecia White and Christopher White discuss the TV adaptation of Martha Wells' 'Murderbot Diaries' and the deceptive nature of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini. They highlight specific failures of AI, including lawyers citing non-existent legal precedents and GitHub Copilot agents generating endless, looping pull requests. The conversation also covers control systems, contrasting theoretical 'fancy math' like Kalman filters with empirical tuning, and mentions the Segway patent US7370713B1.
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