- A newly engineered cranial-implant material—8 mol % yttria-stabilized zirconia (8YSZ) ceramic—was introduced for its 81 % ultrasound transmission at 1 MHz, creating a non-invasive acoustic window that bypasses the near-total absorption of natural skull bone and could enable implant-free brain therapy.

- Participants explored the photoacoustic neuromodulation concept: using near-infrared light (700–900 nm) to excite endogenous hemoglobin and generate ultrasound directly inside brain tissue, potentially eliminating the need for implanted transducers.

- A curated set of recent machine-learning advances was shared, including sub-second 512×512 style-transfer networks, sketch-to-anime and sketch-to-photorealistic image synthesis, video frame prediction, and ultra-compact CNNs such as SqueezeNet that match AlexNet accuracy while occupying <0.5 MB.

- Drew Endy’s 2017 presentation reusing crowdsourced iGEM project summaries (slides 35 & 40), a CRISPR-based bacterial contraception scenario from crispr.kitchen, and a patent citing the channel’s own recombinant-virus work were circulated as open-access resources.

- Antarctic “ice gun” orbital launch concepts—accelerating momentum-exchange bolts between orbiting platforms—were critiqued for atmospheric drag, line-of-sight clearance, and finite bolt supply, highlighting practical limits to the idea.

- New microscopy techniques now capture real-time motion of individual molecular motors, while silicon-photonics manufacturing remains bottlenecked by fabrication challenges.

- Neural-network compression via pruning, trained quantization, and Huffman coding was demonstrated to shrink a 240 MB CNN model to 6 MB without significant accuracy loss, pointing toward deployable edge AI.

- A surprising experimental result showed CNNs initialized with random weights achieving performance comparable to fully-trained networks, prompting debate on whether extensive training is always necessary.

- A lecture note introducing “rectified-correlations on a sphere (RECOS)” layers was referenced, suggesting a new geometric interpretation or layer type for convolutional networks.

- To counter “cargo-cult” ML practices, the paper “Comprehensibility in Machine Learning” was circulated, advocating for interpretable models and linking to http://diyhpl.us/~bryan/papers2/ai/machine-learning/comprehensibility/.