Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Andrew Ng: Building Faster with AI [video]

youtube.com

Post reply on HN
Print view View original

71–80 of 104 posts

Re: Andrew Ng: Building Faster with AI [video]

#71

Post by dmoy »

yorwba wrote: post #53
janderson215 wrote: post #36

Earlier quoted context omitted.

Wu from Wu-Tang?

No, Wu-Tang ultimately derives from the Wudang Mountains, with the corresponding Cantonese being Moudong https://en.wiktionary.org/wiki/%E6%AD%A6%E7%95%B6%E5%B1%B1

Yea haha the chinese-to-english gets confusing, because it's not a 1:1, it's an N:1 thing, for the number different Chinese languages, different tones, and semi-malicious US immigration agents who botched the shit out of people's names in the late 19th and early 20th century.

Wu and Ng in Mandarin and Cantonese may be the same character. But Wu the common surname and Wu for some other thing (e.g. that mountain) may be different characters entirely.

It gets even more confusing when you throw a third Chinese language in, say Taishanese:

Wu = Ng (typically) for Mandarin and Cantonese et al. But if it's someone who went to America earlier, suddenly it's Woo. But even though they're both yue Chinese languages, Woo != Woo in Cantonese and Taishanese. For that name, it's Hu (Mandarin) = Wu / Wuh (Cantonese) = Woo (Taishanese, in America). Sometimes. Lol. Sometimes not.

Similarly, Mei = Mai = Moy

Re: Andrew Ng: Building Faster with AI [video]

#72

Post by nikolayasdf123 »

not a single word about overwhelming replacement of humans with AI. nothing about countless jobs lost. nothing about ever increasing competition and rat-race. (speaking of software, but applies to all industries). his rose-glasses view is somewhere in between optimism-in-denial to straight-up lunacy. if this is the leader(s) we have been following, this should be a wake up call.

Re: Andrew Ng: Building Faster with AI [video]

#74

Post by marcosdumay »

skipants wrote: post #64

I’m 20 minutes into the video and it does seem mostly basic and agreeable. Two arguments from Ng that really stuck out that is really tripping my skepticism alarm are: 1) He mentions how fast prototyping has begun because generating a simple app has become easier with AI. This, to me, has always been quick and never the bottleneck for any company I’ve been at, including startups. Validating an idea was simple enough…

On point 1, it's worse than that. Adding detail and veracity to a prototype is well known to bring negative value.

Prototypes must be exactly a sketchy as the ideas they represent, otherwise they mislead people into thinking the software is built and your ideas can't be changed.

Re: Andrew Ng: Building Faster with AI [video]

#75

Post by mehulashah »

This talk is deceptively simple. The most sage advice that founders routinely forget is what concrete idea are you going to implement and why do you think it will work? There has be a way to invalidate your idea and as a corollary you must have the focus to collect the data and properly invalidate it.

Re: Andrew Ng: Building Faster with AI [video]

#76

Post by mteoharov »

skipants wrote: post #64

I’m 20 minutes into the video and it does seem mostly basic and agreeable. Two arguments from Ng that really stuck out that is really tripping my skepticism alarm are: 1) He mentions how fast prototyping has begun because generating a simple app has become easier with AI. This, to me, has always been quick and never the bottleneck for any company I’ve been at, including startups. Validating an idea was simple enough…

At my company everybody codes, including PMs and business people. It can definitely be damaging done in the long run without any supervision from an actual programmer. This is why we assign an engineer to review every PR of a vibe coded project and they don’t really need all of the context to detect bs approaches that will surely fail.

About prototyping - its much faster and i dont know how anyone can argue this. PMs can get a full blown prototype for an MVP working in a day with AI assistance. Sure - they will be thrown in the trash after the demo, but they carry out their purpose of proving a concept. The code is janky but it works for its purpose.

Re: Andrew Ng: Building Faster with AI [video]

#77

Post by vlovich123 »

stego-tech wrote: post #65
vlovich123 wrote: post #41

Earlier quoted context omitted.

> The problem with the former (output) is that you cannot guarantee the output of an AI on a consistent basis Do you mean you cannot guarantee the result based on a task request with a random query? Or something else? I was under the impression that LLMs are very deterministic if you provide a fixed seed for the samplers, fixed model weights, and fixed context. In cloud providers you can't guarantee this because of h…

> I was under the impression that LLMs are very deterministic if you provide a fixed seed for the samplers, fixed model weights, and fixed context. That's all input-side, though. On the output side, you can essentially give an LLM anxiety by asking the exact same question in different ways, and the machine doesn't understand anymore that you're asking the exact same question . For instance, take one of these fancy "r…

Ok yes. I call that robustness of the model as opposed to determinism which to me implies different properties. And yes, I too have been frustrated by the lack of robustness of models to minor variations in input or even using a different seed for the same input.

Re: Andrew Ng: Building Faster with AI [video]

#79

Post by macNchz »

marcosdumay wrote: post #74
skipants wrote: post #64

I’m 20 minutes into the video and it does seem mostly basic and agreeable. Two arguments from Ng that really stuck out that is really tripping my skepticism alarm are: 1) He mentions how fast prototyping has begun because generating a simple app has become easier with AI. This, to me, has always been quick and never the bottleneck for any company I’ve been at, including startups. Validating an idea was simple enough…

On point 1, it's worse than that. Adding detail and veracity to a prototype is well known to bring negative value. Prototypes must be exactly a sketchy as the ideas they represent, otherwise they mislead people into thinking the software is built and your ideas can't be changed.

I’ve always said this as well, having done lots and lots of early stage building and prototyping, and suffering plenty of proto-duction foibles, however my view has shifted on this a lot in the last year or so.

With current models I’m able to throw together fully working web app prototypes so quickly and iterate often-sweeping UI and architectural changes so readily that I’m finding it has changed my whole workflow. The idea of trying to keep things low-fidelity at the start is predicated on the understanding that changes later in the process are much more difficult or expensive, which I think is increasingly no longer the case in many circumstances. Having a completely working prototype and then totally changing how it works in just a few sentences is really quite something.

The key to sustainability in this pattern, in my opinion, is not letting the AI dictate project structure or get too far ahead of your own understanding/oversight of the general architecture. That’s a balancing act to be sure, since purely vibe-coding is awfully tempting, but it’s still far too easy to wind up with a big ball of wax that neither human nor AI can further improve.

Re: Andrew Ng: Building Faster with AI [video]

#80

Post by contrast »

vlovich123 wrote: post #41
stego-tech wrote: post #13

Earlier quoted context omitted.

> So: what platforms are available? I couldn't tell you, but what I can contribute to that discussion is that orchestration of AI in its current form would focus on one of two approaches: consistent output despite the non-deterministic state of LLMs, or consistent inputs that leans into the non-deterministic state of LLMs. The problem with the former (output) is that you cannot guarantee the output of an AI on a cons…

> The problem with the former (output) is that you cannot guarantee the output of an AI on a consistent basis Do you mean you cannot guarantee the result based on a task request with a random query? Or something else? I was under the impression that LLMs are very deterministic if you provide a fixed seed for the samplers, fixed model weights, and fixed context. In cloud providers you can't guarantee this because of h…

Pointing out that LLMs are deterministic as long as you lock down everything, is like saying an extra bouncy ball doesn’t bounce if you leave it on flat surface, reduce the temperature to absolute zero, and make sure the surface and the ball are at rest before starting the experiment.

It’s true but irrelevant.

One of the GP’s main points was that even the simplest questions can lead to hundreds of different contexts; they probably already know that you could get different outcomes if you could instead have a fixed context.

Post reply on HN