Good luck! You should check out Math Academy, it's more effective/efficient/cheaper but also a good supplement since it's accredited.
I recently turned 40 myself and I'm working through their Foundations courses (made to help adults catch up) before tackling the Machine Learning and other uni courses.
I'll tell you my experience as someone who's been using Math Academy for past 6 months.
Math Academy does what every good application or service does. Make things convenient. That's it. No juggling heavy books or multiple tabs of PDFs. Each problem comes with detailed solution so getting them wrong doesn't mean looking around on the internet for a hint about your mistake (this is pre ChatGPT era of course, where not getting something correct meant putting down MathJax on stackexchange).
> better than just prompting ChatGPT/Claude/etc
The convenience means you are doing the most important part of learning maths with most ease: problem solving and practice. That is something an LLM will not be able to help you with. For me, solving problems is pretty much the only way to mostly wrap my head around the topic.
I say mostly because LLMs are amazing at complementing Math Academy. Any time I hit a conceptual snag, I run off to ChatGPT to get more clarity. And it works great.
So in my opinion, Math Academy alone is pretty good. Even great for school level maths I'd say. Coupled with ChatGPT the package becomes a pretty solid teaching medium.
Yes, much better. ChatGPT/Claude/etc. are useful the times I want extra explanation to help connect the dots, but Math Academy incorporates spaced repetition, interleaving, etc. the way a dedicated tutor would, but in a better structured environment/UI.
Their marketing website leaves a lot to be desired (a perk since they are all math nerds focused on the product), but here are two references on their site that explain their approach:
They also did a really good interview last week that goes in depth about their process with Dr. Alex Smith (Director of Curriculum) and Justin Skycak (Director of Analytics) from Math Academy: https://chalkandtalkpodcast.podbean.com/e/math-academy-optim...
The second link really impressed me, I'm tentatively sold on (and excited for) their approach. Does anyone know of any other accredited programs similar to Math Academy, but for other subjects?
Anything in the soft sciences, or biology/organic chemistry, or comp sci. I know there are a lot of courses for the latter especially, but I'm looking for accredited ones.
I used an early e-learning platform not because I wanted to but because I was one of its developers. I didn't develop the course-content just the technical implementation.
What I didn't like about the content is I often had questions about it but there was no-one to ask the questions from. Whoever wrote that material was no longer around. It's a frustrating feeling when you can't really trust what you're studying is factually correct, or is misleading.
I assume AI will have a huge improvement in this respect.
Not OP, but I have found MathAcademy to be infinitely better. I really liked the assessment portion which levels you and gives you an idea of where you are are at the present. As someone who graduated with an engineering degree a while ago, there were things I realized I didn’t know as well as I thought I did and I probably would not have prompted an LLM to review.
Math is something that should be taught in an opinionated way with an eye toward pedagogy. Self study with GPT is an excellent tool in math, but only for those who have enough perspective to know which directions to set out on. I don’t think anybody who doesn’t know linear algebra should be guiding their studies themselves.
Given my ChatGPT and friends experience has been one of overwhelming frustration due to incorrect information, I would say Math Academy is in an entirely different galaxy. ChatGPT is great if you want to learn that pi is equal to 4.
b-b-b-but the next supercalifragilistic ChatGPT version will be able to tell you that pi is between 3.1 and 3.2. that will be a Quantum improvement, asymptotically close to AGI.
at least, i think i heard alt samman say so.
you plebs and proles better shell out the $50 a month, increasing by $10 per day, to keep dis honest billionaires able to keep on buying deir multi-million dollar yachts and personal jets.
be grateful for the valuable crumbs we toss to you, serfs.
Not yet, but now I will, just out of curiosity.
There's a problem with mathematicians teaching the subject. After all, the youtube lectures were also given by mathematicians. In attempt to make things "accessible", they de-emphasize the algebraic part of the subject and replace it with... I don't know what. The common theme is to consider only R^n. That's not what it's about.
Maybe Math Academy course is different though.
That's not a "mathematician" thing, it's a US thing. US universities, for some reason, insist on teaching mathematics twice, once with lots of handwaving and then at some point you get to do a "proof-based course".
In Europe (at least in certain countries, can't speak to all of them), maths lectures will typically be abstract and proof-based from day 1 - at least for maths majors (but frequently for CS and physics students too). Other majors, such as economics and maybe engineering, may get their own lectures that tend to be more hand-wavey because they don't necessarily need the axioms of real numbers to take a derivative here and there.
My linear algebra course was algebra and proof based to the extent that maybe a little bit more geometric intuition would have helped.
The hard thing is that it's both a bubble and not.
It's a bubble in the respect that the hype around integrating into existing companies/software is likely often falling flat.
It may not be a bubble in that all of the best/useful/valuable use cases of AI are in new software, which have yet to prove themselves in the enterprise. This makes sense because you can't just bolt it onto existing software/organizations and expect it to work because they're built around the way things used to be, similar how when factories first tried to integrate electricity.
For example, I'm sure Palantir is doing some good stuff, but I just have doubts about how useful AI can be in the context of existing companies. And their valuation seems insane, which screams bubble, especially since they're an older companies and less 'AI-native' than the newer ones, like the clunky ways Salesforce and Microsoft implement AI.
But do I expect startups to continue to emerge that approach problems in AI-native ways that help companies reorganize? Yes, it's just a question about how long it takes these companies to work their way into the enterprise and earn enough credibility to drive organizational change and restructuring.
The 'bubble' question is really about whether this latent/potential productivity will be enough to inflate the bubble before it bursts.
My money is on yes, but rather than picking a winner at the app layer and trying to win the lottery I'm heavily invested in the boring stuff like chips (NVDA) and those building data centers with low P/Es (back when I bought them), thus a lot of room to grow even conservatively.
Comparing AI to crypto doesn't really work due to the utility of AI. If you believe that there haven't been meaningful use cases from the recent generative AI surge, then you might be out of touch.
On the investment side, it's hard to say that since ROIC is still generally up and to the right. As long as that continues, so will investment.
Then biggest gap I see is expected if you look at past trends like mobile and the internet: In the first wave of new tech there's a lot of trying to do the old things in the new way, which often fails or gives incremental improvements at best.
This is why the 'new' companies seem to be doing the best. I've been shocked at so many new AI startups generating millions in revenue so quickly (billions with OpenAI, but that's a special case). It's because they're not shackled to past products, business models, etc.
However, there are plenty of enterprise companies trying to integrate AI into existing workflows and failing miserably. Just like when they tried to retrofit factories with electricity. It's not just plug and play in most cases, you need new workflows, etc. That will take years and there will be plenty more failures.
The level of investment is staggering though, and might we see a crash at some point? Maybe, but likely not for a while since there's still so much white space. The hardest thing with new technologies like this is not to confuse the limits of our imagination with the limits of reality (and that goes both ways).
Awesome improvements. How does this compare to Braintrust? I've played with it a bit and we're gearing up to implement a solution in during the Christmas lull.
We use various LLMs as a core part of our app but I'm looking for ways to more quickly iterate on our prompts, test different LLM outputs against each other, etc. ideally while minimizing deploys. Would Langfuse serve that purpose?
Real estate can be speculative as well. A better way to state that might be "things outside of productive assets and services" are increasingly speculative.
The bright side of this that has me excited is what seems to be a growing sense of optimism about the future, driven by AI entering the public conscious, Space X's "rocket catch" etc. There seems to be a growing belief that the future can and will be better, more so than a decade ago.
+1 for Math Academy. I’ve been using it daily for over a year now (started October 2023). I summarized my experiences after 100 days here in case it helps anyone: https://gmays.com/math
I recently turned 40 myself and I'm working through their Foundations courses (made to help adults catch up) before tackling the Machine Learning and other uni courses.
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