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.
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.
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.
When I worked at a startup, I didn't care about my stock option at all. My expectation is really low for any early stage companies. I understand why something like this could happen.
The author would have sold the shares before Nvidia stock skyrocketed even if he had received them. So, the actual loss might not be as large as a billion dollars in reality.
It casually apologies to me after I point out its hallucinations and sometimes scares me. I can't imagine fully trusting AI for making critical decisions about military operations without having a thorough review process. One twist might be that it could be better than human overall, considering we also make mistakes. The same can be said for other applications of AI like medical diagnosis.
I wish medicine were more like theoretical physics. I understand that it’d be difficult to come up with theories that could fully explain complex brain functions and diseases like Alzheimer’s. It’s also difficult to test those theories safely without crossing ethical boundaries. Being either overly cautious or overly optimistic could lead to bad outcomes. It seems like we need a reasonable balance.
As an ML engineer, I improve models through trial and error. Sometimes a change improves my metrics, and other times it makes them worse, and I just revert it and try something else.
It's scary to think about neurological surgery making progress in a similar way. We may not have a good theoretical understanding of why a procedure works, but we observe positive outcomes in a small number of patients and then start looking into expanding its use.
It reminds me of procedures where doctors sever connections between the two hemispheres of the brain to reduce seizures. I've heard they can reduce symptoms, but disrupting connections in the brain can also have unexpected consequences. I hope we can do better than essentially throwing spaghetti at the wall.
Ironically, developers may turn out to be easier to replace than taxi drivers. We've seen technological progress replaced workers or made certain roles obsolete, and this may become another example. I hope governments and public institutions take a more active role in supporting people going through the transition.
It sounds like a fun project. I worked on a vision based biometric system that used near infrared light as its light source. NIR was supposed to be more stable than natural light but we still experienced issues similar to yours. We found that certain problems appeared at different times of day, and the system also struggled to handle the diversity of people.
People use their laptops under various lighting conditions. I can imagine it would be difficult (or likely impossible) to bring this PoC to a solid production level technology. It looks like a fun project though.
I agree with you. About a decade ago, I got really tired of coding interview prep. I somehow ended up getting into Project Euler and worked through the first 100 problems. It was a great way to learn a bit of number theory and regain my interest in programming.
I found the news hilarious. Orcas used to wear salmon hats back in the 80s, then the trend disappeared, only to make a comeback in recent years. It really feels like they have their own version of human fashion trends or group behavior. The funniest part is that their fashion statement is a dead salmon instead of clothes.
I've heard that some philosophers like Schopenhauer argue that reading can become a passive process, where we simply follow another person's thoughts without engaging our own critical thinking. It's interesting to consider that it's not just LLMs but we too would become like stochastic parrots under certain circumstances.
I miss the old days when Facebook was simply a fun way to reconnect with friend and family who lived far away. Unfortunately, those days are gone. It feels like an over engineered attention-hogging system that collects a large amount of data and risks people's mental health along the way.
I recently used an LLM to translate an algorithm from Go to Python at my work. The translation was quite accurate, and it made me think tasks involving obvious one-to-one correspondence like code translation might be easier for LLMs compared to other tasks. I can see the potential for offloading such tasks to LLMs. But the main challenge I faced was trusting the output. I ended up writing my own version and compared them to verify the correctness of the translation.
I have a conundrum about this. If an LLM can learn our codebase and generate reasonable reviews, does this imply it could perform the work independently without us? Perhaps generating code and conducting code reviews are distinct tasks. Another related question is: for complex tasks that generative AI can't solve, could this service still provide somewhat meaningful reviews? Maybe it could be partially useful for certain subtasks like catching off-by-one errors.
So the progress of human proficiency in Go and our collective advancement over time is hindered by dogmatic rules introduced over time. These rules predispose players toward specific strategies and consequently limit the scope of our creative potential within the game. In contrast, AI algorithms operate without such biases offer a unique advantage in overcoming these limitations. They essentially inspire us to get out of established patterns (or local minima) of play and broaden the range of our strategic moves.
I started my journey in machine learning fifteen years ago. Ironically, at that time, my professor told me that neural networks were outdated and trying them wouldn't result in publishable research. SVMs were popular and emphasized in my coursework. I concur that SVMs don't hold as much practical significance today. But the progress in AI and ML is generally unpredictable, and no one knows what theory leads to the next leap in the field.