I know someone who recently came across a scammer where a guy impersonated a sheriff's deputy and tried to get money out of her. She told me the guy sounded like a real sheriff and made her believe the guy until he asked her to pay some money in Bitcoin. I can imagine that this type of scam could go viral soon. Even worse, someone could record our voice and fine-tune a model like this so that it can produce our voice and then talk to our family members to deceive them. It'd be a real trouble.
Already had one call from a scammer AI voice that was pretending to be the official billing dept. of my phone company's larger carrier. Was pretty realistic and beleivable as a human voice, but the very moment i hesitated when asked to provide information.. "you should have that information" i started to say but the agent cut the call at the point between my first and second syllable. The caller ID was already suspect, and when I checked that number it is associated to a sex line. But the voice was polite and official sounding...it wasn't the voice that twigged my suspiciousness. Granny is going to have to start requiring everything via registered mail to not get scammed.
Totally agree. Those scam calls calling as IRS agent, or outsourced criminal shops that try to steal identity, now this makes it much easier. No more foreign accent. They can hyper optimize to sound like a person from that specific locality.
It's ironic that ML researchers don't fully understand why their models behave the way they do, yet continue to make progress using benchmarks to guide the efforts. Human understanding is important but whether and to what extent it's necessary seems to be a separate question, and the answer may depend on the nature of the field.
> Human understanding is important but whether and to what extent it's necessary seems to be a separate question
We are about to find out the answer soon enough, probably from the in vitro results of the mathematicians currently on the chopping block.
I submit that human understanding is overrated, and many attempts to elevate it in the wake of AI is mediated by protectionism masquerading as virtue and "deep".
What matters more is that what will happen to us when we find the answer. That's what Tao et al. are more worried about.
Traditional way of doing science means we (governments, NIH, NSF, and even private corporations) fund activities like asking seemingly unimportant questions, spending years running experiments on such hypothesis, publishing, reviewing, talking about results, reproducing results and such. We all agree that these are beneficial to us as a whole (Hacker news crowd might disagree). When we understand a process, we can apply it to a different problem and produce something useful. Euler developed a process to answer a whimsical question about walking in a town crossing 7 bridges only once. Now graph theory is applied everywhere.
We obviously have failed to stop OpenAI from dumping "solutions" to hundreds of problems. So going forward, instead of testing hypothesis and talking about results, mathematicians will be forced to read through AI slop and detect what's useful and what's wrong. Maybe it will improve our understanding, but someone has to fund that activity. Will NSF, NIH, or OpenAI for that matter, do that?
> What matters more is that what will happen to us when we find the answer.
I'd imagine that it'd be like the Hitchhiker's Guide to the Galaxy when the answer is 42. The answer itself is not sufficient, we need to understand to appreciate.
I was listening to another mathematician talk who gave a somewhat similar example. Suppose Neil Armstrong somehow teleported to the moon. That would be an achievement and it would make us very excited. But if nobody understood how it happened, none of the technological progresses that came out of moon landing program would've happen.
Even dumber, we train the AI on human understanding and explicitly make it produce text using tokenization. It's not like we prompted the AI to draw pixels and it simply knew how to draw the letters from the dataset.
What caaqil is basically saying that we should outsource all thinking to the few people that bother to understand things.
This news is exciting and sad at the same time. I've heard that AI chess programs sometimes have blind spots or quirks that human players don't. I've also heard that human players are learning from AI's playing styles (essentially human and AI evolving together). Maybe something similar will happen in mathematics.
Some publication companies like NYT prohibit the use of ai for both writing and illustration. I’m glad that they’re going this direction for the reason you mentioned.
> It could also create limitations for customers who expect a security camera to provide footage after a break-in, package theft, or other incident. How Apple’s wider security system would handle those situations remains unclear.
What would be the main advantage of this design? My understanding is that it captures image frames, retains them in RAM, and processes them with a CPU without saving them to a flash (or an SSD). Even if an attacker manages to compromise the camera, there wouldn't be historical data to retrieve. But couldn't an attacker with sufficient access read the frames currently in RAM and possibly capture footage while maintaining the access? Could hardware isolation prevent this even if some of the camera's software were compromised?
It feels like it's becoming harder to strike the right balance between privacy and the actual utility that the device is supposed to provide.
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