40 AI Founders Discuss Current Artificial Intelligence Technology

what is the most maybe unexpected way that you use AI in your regular life yeah I don't know if I should say this but uh writing speeches for weddings yeah we both set our answering machines to our our boy spot hey how's it going

oh it's the strong Padawan nice this one's uh I think programmed to talk about before now we have it open every day to help us code helps us code it's great at coding

it's just making me code infinitely faster you can describe to the AI what change you want to make to your UI like build dark mode and it'll edit all the code to implement that feature like these tools will make humans more and

more like uh like narrators like like people who are describing what they want then the model will actually create something that's even better than what humans would do if they're doing it themselves and I think

the fundamental like problem solving skills are always going to be like really needed in understanding the technology and its constraints and how to leverage it is going to be so important

what are current ly I'm good at yeah General sense yeah really really good questions I think one of the things is really counter-intuitive about generative AI tools is that they're really good at

what we thought they would be really bad at which is sort of creative um storytelling work AI for creativity the goal is to make software so anyone can make South Park from their bedroom

example you can make a photo of me Eric and Rihanna playing volleyball on the beach I like this one that one that one's my favorite War I like this I like this one we are building truly

human-like AI voices that's like very conversational like humans without AI the voices sounded horrible now

it's it's just indistinguishable from uh from a human voice for us personally what it's amazing at is semantic search

that's something that didn't really work before just like taking a random piece of text and finding relevant things llms have great ability to read they're

pretty good at being able to take like arbitrary data and be able to answer questions about it because there's so much new type of data like it's really hard to adapt and so we are currently

fine-tuning all of our models on these new types of data to make it as accurate as possible for fashion for very specifically there are new terms popping up all the time you have to like keep

updating these models to know oh like for this month the trend is mermaid court but for next month maybe it's ballet Court yeah tools are pretty good in giving you around like an 85 to 90

solution uh but there's a lot more fine-tuning or a lot more hacks that you need to put in place on top of them to ensure that you can deliver genuine value you can use a bunch of simple

operations to actually do something really complicated you need to really like give them structure about like how it should look like and give them like one particular task

to do and then they do it very well if you're able to think through the process that you go through then you can actually engineer a prompt or engineer like a sequence of steps so that you can have that entire process be even more

reliable than you would be it's important to just be very iterative in your process and just debug and tune and iterate on your prompts as you as you go if you think you have a solution it may

not be the same solution over time your data can change the actual underlying model quality can change with that and so the biggest difference is just there's this

sort of iteration required I think the hardest part is you're trying to marry deterministic kind of software with probabilistic models and we sit right at the middle of

that it is like a quite an exciting thing to work with because in the past with programming the computer really just followed your instructions to a T

and you can expect the same results given the same inputs now you put in the same inputs you might get some variants if we can actually introduce some

Randomness into our outputs then we can explore our space a bit better and our models will get better from learning from all of these other choices that we can make it's not reliable in the way

that you expect it to be reliable which is great for us because for us we're doing entertainment so it's like as long as it's funny it doesn't matter but I guess if you're operating a car that

seems more complicated so it is a double-edged sword sometimes it can hallucinate and make up something that was intended I would Define a

hallucination as AI generating something that doesn't exist but looks like it might or should exist they're still really bad at distinguishing fact from fiction so they could create a storytellers but they're surprisingly

bad at knowing the difference between what's true and false if you're a doctor uh finding out what GPT decided uh was the diagnosis for this patient probably

takes a ton of time to verify and if there's any mistake then you're in a lot of trouble at what point do you trust the AI over the doctor there's been a lot of effort in the industry recently to sort of prevent these hallucinations

but that's created this opposite problem which is now it it will often think things aren't real or pretend that it doesn't know things that it really should do right it will tell you it's like never heard of that article even

though it's definitely in the training set right like it's got to be there a bit like a human um you know when you read things and you take something away from that and internalize it you can't necessarily exactly remember where you read it and

so when you're using these models with real world data uh it's actually even harder to disambiguate what's what's hallucination versus something that was a nuanced a piece of data you can't ask it for citations consistently

um that's that's still a challenge and so the trustworthiness there has has some way to go it's not enough just to say like hey the accuracy metrics are better you have to understand that um

there's more at play especially for you know human trust and and that is a key component if you're going to develop a technology that people are going to use at the end of the day there's still like

a lot of nuance where we have to like steer them and that's why you'll hear a lot about this human loop it's really important to still have a human in the loop having humans in the

loop to initially assess whether the corrections that were needed to be made were accurate or not uh there needs to be someone supervising now but making sure that there are no hallucinations so there are a lot of pros and cons it's

really about like figuring out the right ways to steer it and that's the challenge that I think like all the YC companies uh working on the AI are facing were we're given this new tool to

work with and we're all really just trying to figure it out I never want to lose sight of the fact that ultimately this is technology and service of humans and we get to keep human say thus so

what ideally it's actually like deepening human connection where it's a lot more about you know interacting with people and and figuring out what's what's actually valuable to them foreign [Music]