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ryeguy_24 commented on Fallout from the AWS outage: Smart mattresses go rogue quasa.io/media/the-strang... · Posted by u/jerlam
I feel like we need some sort of "Offline-First" or "Offline-Compatible" certification process for "smart" devices. It would require some threshold of usability and total safety without network connection, which would vary depending on the device category. Companies in compliance could put a badge on their products so wary consumers know who to trust.
This is a very smart idea. I couldn't turn my Ring Alarm off and I was on the same Wifi connection as the system. In retrospect, it would be quite smart to switch over to local network.
ryeguy_24 commented on 95% of Companies See 'Zero Return' on $30B Generative AI Spend thedailyadda.com/95-of-co... · Posted by u/speckx
Does anyone have this mystical report?
ryeguy_24 commented on Ask HN: Why haven't we seen AI enhanced AirPods microphone yet? · Posted by u/ryeguy_24
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Isn’t there a whole bunch of dependency here related to prompting and methodology that would significantly impact overall performance? My gut instinct is that there are many many ways to architect this around the LLMs and each might yield different levels of accuracy. What do others think?
Edit: In reading more, I guess this is meant to be a dumb benchmark to monitor through time. Maybe that’s the aim here instead of viability as an auto close tool.
I appreciate the video and generally agree with Fei-Fei but I think it almost understates how different the problem of reasoning about the physical world actually is.
Most dynamics of the physical world are sparse, non-linear systems at every level of resolution. Most ways of constructing accurate models mathematically don’t actually work. LLMs, for better or worse, are pretty classic (in an algorithmic information theory sense) sequential induction problems. We’ve known for well over a decade that you cannot cram real-world spatial dynamics into those models. It is a clear impedance mismatch.
There are a bunch of fundamental computer science problems that stand in the way, which I was schooled on in 2006 from the brightest minds in the field. For example, how do you represent arbitrary spatial relationships on computers in a general and scalable way? There are no solutions in the public data structures and algorithms literature. We know that universal solutions can’t exist and that all practical solutions require exotic high-dimensionality computational constructs that human brains will struggle to reason about. This has been the status quo since the 1980s. This particular set of problems is hard for a reason.
I vigorously agree that the ability to reason about spatiotemporal dynamics is critical to general AI. But the computer science required is so different from classical AI research that I don’t expect any pure AI researcher to bridge that gap. The other aspect is that this area of research became highly developed over two decades but is not in the public literature.
One of the big questions I have had since they announced the company, is who on their team is an expert in the dark state-of-the-art computer science with respect to working around these particular problems? They risk running straight into the same deep, layered theory walls that almost everyone else has run into. I can’t identify anyone on the team that is an expert in a relevant area of computer science theory, which makes me skeptical to some extent. It is a nice idea but I don’t get the sense they understand the true nature of the problem.
Nonetheless, I agree that it is important!
Agree. Also, with respect to training, what is the goal that we are maximizing? LLMs are easy, predicting the next word and we have lots of training data. But what are we training for in real world? Modeling the next spatial photograph to predict things that will happen next? It’s not intuitive to me what that objective function would be in spatial intelligence.
Mercenaries over missionaries.
Many employers want employees to act like cult members. But then when going gets tough, those are often the first laid off, and the least prepared for it.
Employers, you can't have it both ways. As an employee don't get fooled.
100% agree. There is no reason for employees to be loyal to a company. LLM building is not some religious work. It’s machine learning on big data. Always do what is best for you because companies don’t act like loyal humans, they act like large organizations that aren’t always fair or rationale or logical in their decisions.
ACM started this open access effort back in 2020, I don't think that LLM generated papers were on their mind when they started it.