sankha93

A few months back I was pessimistic about AI, and now I am the opposite. The perspective change happened when I realized giving it an entire problem and expecting it to solve that is unrealistic. The real value add is if you can use AI at the right steps of your workflow or larger system.

I did a PhD in program synthesis (programming languages techniques) and one the tricks there was to efficiently prune the space of programs. With LLMs it is more much more likely to start with an almost correct guess, the burden now shifts to lighter verification methods.

I still do not believe in the AGI hype. But I am genuinely excited. Computing has always been humans writing precise algorithms and getting correct answers. The current generation of LLMs are the opposite you can be imprecise, but the answers can be wrong. We have to figure out what interesting systems we can build with it.

Can you list the concrete problems a FastAPI approach will have, and what tools like Nvidia Triton do differently to get around it? I have no idea about running such models at scale.

The idea that symbolic AI lost is uninformed. Symbolic AI essentially boils down to different kinds of modeling and constraint solving systems, which are very much in use today: linear programming, SMT solvers, datalog, etc.

Here is here symbolic AI lost: any thing where you do not have a formal criteria of correctness (or goal) cannot be handled well by symbolic AI. For example perception problems like vision, audio, robot locomotion, or natural language. It is very hard to encode such problems in terms of formal language, which in turn means symbolic AI is bad at these kind of problems. In contrast, deep learning has won because it is good at exactly these set of things. Throw a symbolic problem at a deep neural network and it fails in unexpected ways (yes, I have read neural networks that solve SAT problems, and no, a percentage accuracy is not good enough in domains where correctness is paramount).

The saying goes, anything that becomes common enough is not considered AI anymore. Symbolic AI went through that phase and we use symbolic AI systems today without realizing we are using old school AI. Deep learning is the current hype because it solves a class of problems that we couldn't solve before (not all problems). Once deep learning is common, we will stop considering it AI and move on the to the next set of problems that require novel insights.

I have experienced something similar as well. I was visiting NYC, and the place we booked did not look anything like the online listing showed. Regardless, the hosts were nice and helpful, so I left them a 5 star rating on all parameters except cleanliness. This was the first time rated something on Airbnb that is not 5 stars, and I have travelled quite a bit before.

Few weeks after that I receive racist messages from the host on my personal cellphone. I am shocked Airbnb would share my personal details just like that. I reported it to Airbnb, but they were pretty clueless as this was happening outside their messaging system. I finally had to share screenshots of the text messages, and they said they will take down the hosts account. They did for sometime, but after a couple of months that host's account is still live and his place is still accepting reservations. My review has been taken down though.

Natural language is ambiguous, so I do not think any programming by example methods will work well here. I think if you are working with a limited vocabulary in the set of sentences you want to parse, it may be fine to roll out a handmade solution. For general english, there is work in intent identification in NLP that can give you structured intents from raw english text, which can then be lifted to make programs as a secondary step.

It is a hard task, for sure! But without any tests to convey your intent as a programmer, RbSyn also has no way of knowing what you intended a method's behavior to be.

The running thread here is if you would have written a method and some tests to check that you indeed wrote the method correctly, RbSyn will automate the task of writing the method for you. In other words, it is just like test driven development, where the writing the code part is automated.

By manual audit, I meant you can verify if you need more tests to capture your intended behavior or just take a shorter route and update the synthesized code directly without any new tests for the newly added behavior. One could argue the latter is bad programming practice.