I'm biased, but I think the answer is semi structured problems.<p>Automating a tractor that has to till or seed is much easier than a self driving car, and alleviates some very low margins high labor intensive activities.<p>(I work at a place that is automating logistic yard operations, which is fixed cost, well structured, and predictable problem/environment)<p>I think "everywhere all the time" AI for chat, driving, coding, or medical image analysis etc is a pipe dream absolutely. And what we will lose when it all pops is the pragmatic solutions to solveable problems that sound more like "algorithms for assistance occasionally or part time".
Even if the subsidies that drive increasingly sophisticated models evaporate, we'll still have the existing models we can run on commodity hardware. Some of those models will be good enough for the risk-tolerant low-value applications. Low value doesn't mean no value.<p>I also think that machine vision for agriculture can be a little more risk tolerant than for cars (doesn't matter if your weed burner occasionally torches a stalk of corn the way that it does if your robotaxi occasionally runs over a pedestrian).<p>Compared to what the institutional investors are chasing, that will not have been worth it. But it'll be nonzero.
I don’t think the autonomous vehicle example and the radiology example are really analogous. I’ve agreed for a long time with the premise that an “almost self-driving” car isn’t much good - certainly at the consumer level the whole value proposition is that you don’t have to pay attention. But that’s a result of it being a real-time application constrained by physics. Medical diagnosis is a more deliberative process that already incorporates inputs from multiple tools.
Various good arguments along the way, but I don't buy the overarching premise.<p>It can't both be useful and valuable enough to become pervasive and then suddenly disappear because it isn't valuable enough.<p>Sure some of the free VC money startup will go bust & there is hype but that doesn't make the entire thing a bubble that implodes into nothing.<p>Also - the fact that I derive personal value and am willing to pay to me indicates that this is less bubbly than say crypto or dot com.
For some reason the author thinks that skills you learn developing in PyTorch will go away if Facebook does. Wouldn’t the open source community just fork the source or develop a new framework? It’s not like backprop will stop working.
There is already a lot of real value in scientific domain-specific models. One such example is weather forecasting. We can suddenly create forecasts with astonishing accuracy on commodity hardware in a matter of seconds, versus several hours with traditional numeric models. This is life-saving.<p>And it's open source, e.g.: <a href="https://github.com/NVlabs/FourCastNet">https://github.com/NVlabs/FourCastNet</a>
What type of AI? AGI? Textual based AI? Computer vision AI? What kind of bubble - the kind that has happened before in the history of AI…a slow evolution of progress just like now with fits and bumps but always progressing over time. It will never fit a business or persons agenda but it will always get better.
I would like to have a reading assistant that highlights persuasive techniques like rhetorical questions and - probably even better - neutralizes them for me if needed.
Yet another catastrophist haphazardly connecting dots that shouldn't be connected. The article "lost" me here:<p>> More than $100b has been incinerated chasing self-driving cars, and cars are nowhere near driving themselves<p>Erhm, ..what ? An over-eager Cruise rushing to market does not negate 20 years of Google-quality research. I have driven Waymo cars multiple times in SF and by user testimonials, they have already avoided accidents (possibly even saved lives). I am finding myself thinking WWWD (what-would-Waymo-do) when I'm driving.
It's as if every second person on earth became a Scientologist spouting correct sounding but flawed ideas and conjectures ultimately aiming for your pocket book. But every once in a long time it discovers a useful and important new protein so you can't discount it entirely.
The term "bubble" in AI refers to inflated investments and valuations based on speculative rather than intrinsic values. While the AI sector experiences significant hype, its broad applicability and real-world utility across various industries offer a stabilizing factor against bubble dynamics. Continuous technological advancements in AI contribute to its tangible value creation, countering speculative pressures. The future of AI's market stability will hinge on aligning expectations with actual capabilities, amidst navigating regulatory and ethical landscapes.<p>(edited for brevity)