HardikVala

Check out epoch.ai. You won't get a direct answer but it'll give you directional, and useful data points.

I think the frameworks became complex over time but were initially simpler because of the more managable set of AI workloads.

There might be but instead of thinking about it as a binary, think about it as a continuous conviction curve. Ask yourself, if this idea is true, what's the smallest testable prediction you can make? It can mean running a small experiment that doesn't give you statistical significance or would pass scientific scrutiny, but can be a gating function for a slightly more sophisticated experiment. And then you rinse and repeat until you prove/disprove the idea.

Not the first product category you think of when it comes to AI consumer hardware but how about smart glasses (eg. Meta Raybans). Extrapolating to the future, AR glasses are probably going to be heavily dependent on AI.

Great recommendation. Based on the ToC, its similar to AIMA.

Andrew Ng's course is great for the learning NN's from scratch, but not understanding how NN's fit in the broader discipline of AI.

True true. I should qualify my statement: Most founders who are able to find micro-PMF in a niche generally can expand that niche or grow threw composition. But this is based on my own anecdotal evidence, with a slant towards optimism.

I've been your position before and although I can't say I'm a successful entrepreneur, I can share lessons that helped me escape "analysis paralysis":

1. Fear. This is was a huge inhibitor of action. I was afraid of picking the wrong problem and then spending months-years having nothing to show for it.

2. To overcome the fear, I decided that instead of anchoring on the painpoint, I'll anchor on something else: The user. I chose ML engineers as the market I want to serve (its a terrible market, I advise you pick something else). It's hard to fathom a niche of users out there that don't have some pain they're willing to relieve by paying somebody else. You don't have to anchor on a user. You can anchor on something else, like a mission (eg. democratizing access to startup investing), or an industry (eg. semiconductor manufacturing). When you commit to a center point, now you have the freedom to iterate on ideas freely, knowing that even if an idea doesn't work out, you'll learn useful information you can use in the next iteration.

Does this guarantee that you'll company eventually grow into a unicorn? No, not really. You can end up picking a tiny niche, but in practice, most founders are able to expand the niche or find ways of expanding their market by combining niches.

This is more relevant to software businesses but hopefully some of it is still useful for other types of businesses.

No, but there is a PDF version, ahem, floating around on Reddit.

Was not aware of these resources. Thanks for sharing!

Will check out Discrete Mathematics, thanks!

AIMA is wide-ranging, a lot of which is not "must-have", but "nice-to-have" knowledge. But I do like its breadth-over-depth approach to get a full scope of the AI landscape.

3blue1brown's content on NN's is awesome -- The explanations are super intuitive. But I'm also looking to understand, as you say, the big picture and understand where NN's fit.

https://aima.cs.berkeley.edu/ has some good material on game AI. Its surface level but it does discuss SOTA methods like AlphaZero.

Exactly. Nailed it.

Thanks for the recommendations. What's your impression of these texts so far?

There are so many attributes that its impossible to list them all, just like there are countless attributes that can distinguish a person, with new ones being discovered everyday.

You mention "personality" and that's a good analogy for a company's cultures - It's the organization's personality. Just like personalities, most are neither good or bad inherently, they're just different. Some personalities are better suited for certain endeavors (eg. extroverts are generally better at sales) and attract certain type of people more than others.

So a "good" culture is one that aligns well with the business objectives and attracts the type of talent that are better aligned with those objectives.

Here's an example:

Apple has a design-led culture. Product designers have tremendous influence on what products get made and how they get made. One way this expresses itself is in how leaders make decisions: Through demos. Which makes perfect sense when your business is reliant on the tactile experience of a product and its look-feel.

Google, OTOH, has an engineering-led culture. A lot of product decisions aren't made via demo, but with data. Leaders may see a demo of an improvement to, say the search engine, but they'll rely on usage data to determine whether it should be rolled out or not.

These examples also demonstrate how one culture might not be the best for certain lines of business. Apple, relative to the other tech giants, is way behind on its implementation of AI, and I wouldn't be surprised if that's because its not data-driven at its very core.

Ya, the phrase "first principles" is vague...I meant starting from an axiomatic and actionable definition of AI and learning from there. The first chapter of AIMA does a swell job of enumerating different definitions of and then explicitly declaring which one is used and the foundational premises for the concepts and methods to follow. And it doesn't define AI then jump to neural networks, it gradually layers more atomic concepts, like agents (which I know, have been bastardized) and environments, until it gets to machine learning.

> The other big question is why you want to learn it.

Good question. I'm just looking for a wider context to understand contemporary AI. I don't know if this serves any practical purpose but I'm someone who likes to understand the "why" behind everything and starting from "first principles" helps uncover that.

Interesting idea. I like it.

+1, I'm a few chapters in and its highly instructive. Gives me a deeper appreciation for the modern deep learning regime. Also, as we enter the agent supercycle, I think many of the basic algorithms for search, planning, etc. will make comeback a in a huge way.

An interesting coincidence is that the first fabless semiconductor company, Chips and Technologies, was also founded in 1985, by Dado Banatao and Gordon Campbell. They didn't partner with TSMC but contracted with companies like Hitachi that had excess fab capacity to manufacture their semiconductors. Wild. They eventually sold the company to Intel, which obviously didn't appreciate the insight of de-verticalization in the semiconductor supply chain.

This lines up well with Google's definition of "quality" when it comes to search engine content, which is it must be non-trivial to reproduce. Content generated by prompting LLMs is likely easy to reproduce, and hence lower quality, and hence likely to get demoted in search results.

A curated list of opportunities to judge the work of other developers.

I served as a judge for many of the programs in the list in order to improve my odds of getting a US O-1A visa--a work visa for immigrant STEM workers/entrepreneurs that doesn’t require employer sponsorship. I received the visa earlier this year and have been advising people since.

I formed this list through my personal and coaching experiences (took some trial and error to separate the good ones from the bad). If you’re looking to qualify for the judging requirement of the O-1A visa, or the EB-1 green card, I can vouch for each selection in the list.

Happy Holidays!