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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.

https://web.archive.org/web/20251001135117/https://www.natio...

His name could be interpreted as "aspiring to be a scholar". I guess he's done an exceptional job living up to it.

To add: Reading is also thinking (ideally).

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.

It's sad to see the interviewer wasting the opportunity to interview Penrose. I found Lex Fridman does a much better job: https://www.youtube.com/watch?v=hXgqik6HXc0

We should thank Meta for laying off a talented engineer.

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.

I’m the same way. I bought 12 and 13 minis and stopped upgrading my phone since then. I’d love to have a mini version with a good optical zoom camera.

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.

This is a relevant thread: https://news.ycombinator.com/item?id=28191408

I thought neither mathematics nor engineering is strictly science. Engineering utilizes scientific knowledge to solve complex problems, but its purpose isn't to make scientific discoveries through hypothesis testing. It's interesting, though, that new artificial scientific disciplines (e.g., theory of how quantum computing works) occasionally spin out of engineering projects.

Taiwan has important semiconductor companies like TSMC. It’s also one of geopolitically unstable regions. I guess this is part of mitigating the risk of chip shortages.

debugging a machine learning algorithm is already hard without this approximation method. I can see adding another layer could make it extremely harder.

http://philsci-archive.pitt.edu/8833/4/dome_100711.pdf

It looks there's a paper related to the topic.

I'm not familiar with ketamine at all, but it just feels unsafe to decide by ourselves. Do psychiatrists have to monitor patients closely and determine the amount and frequency of doses? I wouldn't mind taking medicine for a cold or other common diseases. but could this be a lot risker?

I will use tor browser and duckduckgo for the rest of my life

Is this because they are afraid of the model misused, like used for generating fake reviews? It is frustrating that I've been hearing great news on NLP but am able to try none of them myself.