Last week, Apple made a very visible and intentional move into the ultra-competitive AI race. This is not to say that the company had not previously signaled its investments in and prioritization of AI. However, at its WWDC event, Apple made it abundantly clear that AI is behind many of the features in both its forthcoming hardware and software.
This may not be generative AI, which is without a doubt the hottest subcategory of AI today. However, it seems to me that Apple’s intention was to make a comeback of sorts — to show that it is not to be underestimated after years of floundering machine learning projects, from the underwhelming Siri to the self-driving car in production hell. Projecting strength is not just a marketing ploy. Apple’s historical underperformance in AI has led to serious brain drain, reportedly.
The Information reported that talented machine learning scientists—including a team that had been working on the type of tech underlying OpenAI’s ChatGPT—left Apple for greener pastures. The report is 200 pages long, but it’s helpfully divided into digestible pieces. Elsewhere in the national lab ecosystem, Los Alamos researchers are hard at work on advancing the field of memristors, which combine data storage and processing in a way that’s similar to how our own neurons work.
This is a fundamentally different approach to computation, and it has yet to bear fruit outside the lab. However, it’s an exciting new development that has the potential to move the ball forward in the field of AI. Another AI health advance comes from Purdue, where researchers have developed software that approximates hyperspectral imagery with a smartphone camera.
This software has successfully tracked blood hemoglobin and other metrics. It is an interesting technique: by using the phone’s super-slow-mo mode, it gets a lot of information about every pixel in the image, giving the model enough data to extrapolate from. This could be a great way to get this kind of health information without special hardware.
I wouldn’t trust an autopilot to take evasive maneuvers just yet, but MIT is inching the tech closer with research that helps AI avoid obstacles while maintaining a desirable flight path. Any old algorithm can propose wild changes to direction in order to not crash, but doing so while maintaining stability and not pulping anything inside is harder. The team managed to get a simulated jet to perform some Top Gun-like maneuvers autonomously and without losing stability. It’s harder than it sounds.
