If you’ve tried to go past the APIs like the ones OpenAI offers and learn how they work “under the hood” by trying to build your own neural network, you might find yourself hitting a wall when the material opens with equations like this:
How can you learn how neural networks — or more accurately, artificial neural networks — do what they do without a degree in math, computer science, or engineering?
There are a couple of ways:
Follow this blog. Over the next few months, I’ll cover this topic, complete with getting you up to speed on the required math. Of course, if you’re feeling impatient…
Read Tariq Rashid’s book, Make Your Own Neural Network. Written for people who aren’t math, computer science, or engineering experts, it first shows you the principles behind neural networks and then leaps from the theoretical to the practical by taking those principles and turning them into working Python code.
Along the way, both I (in this blog) and Tariq (in his book) will trick you into learning a little science, a little math, and a little Python programming. In the end, you’ll understand the diagram above!
One more thing: if you prefer your learning via video…
My third experiment for 2024 involves trying out the ideas from Noah Kagan’s new book, Million Dollar Weekend.
ℹ️ In case you’re wondering: my first experiment of 2024 was to turn my layoff experience into a series of articles; the second was to take a chance working with a pre-seed startup.
Why conduct such an experiment? For now, let’s just say that current circumstances make it necessary, and hey, if anyone can pull off this kind of thing, it would be me.
The general idea of Million Dollar Weekend is that you can start a lucrative business by doing the following:
Identify a problem that you can solve
Solve that problem in a way that is hard to resist and profitable
Test your solution at low (or no) cost by preselling it before you build it.
The prerequisite for the Million Dollar Weekend process is a certain amount of unmitigated gall. Time and again in the book, Kagan states that two things hold people back from starting businesses:
Fear of starting
Fear of asking
Kagan’s methodology is to start by trying out an idea, seeing if someone will pay for that idea, and then either refining that idea or coming up with a new one and repeating the cycle.
The methodology anticipates rejection, and in fact, it says that in selling your idea, you should aim for plenty of rejections. The idea is that if you’re getting rejected often, you’re asking often, and that’s what eventually leads to success.
I’ll write more as I continue with this experiment, but for now, if you’re curious, here are some resources I can point you to:
You might also find these interviews with Kagan interesting:
ℹ️ Also in case you were wondering: This is NOT a paid promo for the book — neither Noah Kagan nor his businesses have any idea who I am or how to deposit money into my bank account. I wish they did!
Masterminds Tampa Bay is holding their AI Superpowers Unlocked panel on Wednesday, May 15th with the following panelists:
Ken Pomella, CEO of RevStar, known for leveraging AI to enhance business growth and scalability.
Lenar Mukhamadiev, from IdelSoft, focusing on GenAI solutions for organizations and developing an AI-powered startup.
Sat Ramphal, CEO of Maya AI, a serial entrepreneur with deep expertise in AI applications in regulated industries.
Yours Truly, Joey de Villa, Supreme Developer Advocate for Unified.to, AI enthusiast, Python instructor, and general computational person about town.
Here’s Tampa Bay Masterminds’ description of the event:
Unlock the future of Artificial Intelligence at “AI Superpowers Unlocked: An Expert Panel,” an event meticulously crafted for entrepreneurs, tech enthusiasts, and forward-thinkers ready to explore AI’s transformative potential. Join us to gain practical insights on becoming a leader in AI application and connect with industry pioneers.
AGENDA
6:00 PM – 6:30 PM: Socializing Time
6:30 PM – 7:15 PM: Expert Panel Discussion
7:15 PM – 8:00 PM: Audience Q&A / Conclusion
Main Takeaways:
🧠 Understand the crucial role AI plays and why mastering it is essential.
🧠 Learn strategies to best leverage AI for 2024 and beyond.
🧠 Discover essential AI tools beyond ChatGPT.
🧠 Explore best practices, ethics, and more through interactive FAQs.
Why You Should Attend:
🚀 Tailored for Forward-Thinkers: Designed for those poised to disrupt markets and lead innovations, this panel will help you stay ahead in the AI curve.
🚀 Unparalleled Insights: Spend an hour with AI luminaries discussing strategies and visionary applications to outpace competitors and drive success.
🚀 Networking Opportunity: Connect with like-minded professionals and innovators, and perhaps discover your next great collaboration.
This is a paid event — attendance is $35 and supports Tampa Bay Masterminds’ mission of fostering innovation and education in technology, with all ticket sales considered donations.
It’s been five years and a couple of jobs since I’ve had business cards, but as Unified.to’sSupreme Developer Advocate, I’ve been issued a set, and they arrived in the mail earlier this week.
They came in nice packaging…
…and rather than the traditional rectangular design, ours are square. On the front is Unified.to’s octopus logo and mascot…
…and on the back in Unified.to’s QR code and URL:
And, damn, do I like that box. It reminds me of this tweet about boxes and being a grown-up:
I don’t know much about Jukebox, the company that made these cards, but it seems that they’ve matched MOO (whose cards I’m familiar with from a handful of previous companies) and beaten them at the unboxing experience.
By popular demand, here are the “slides” from my presentation this morning at Civo Navigate Local Tampa, Make Smarter AI Apps with RAG!
Retrieval-Augmented Generation, also known as RAG for short, is an AI technique that combines…
A machine learning model with
A mechnanism for retrieving additional information that the model doesn’t have
…to enhance or improve the responses generated by the model.
At this point, you’re probably thinking this:
This talk runs from 11:15 to 11:30 a.m., which is just before lunch, and I’m not at my cognitive best. Can you explain RAG in an easy-to-digest way, possibly using Star Wars characters?
I’m only too happy to oblige!
Consider the case where you ask an LLM a question that it doesn’t “know” the answer for. The exchange ends up something like this:
Tap to view at full size.
With retrieval-augmented generation, you improve the response by augmenting the prompt you send to the LLM with data or computation from an external source:
Tap to view at full size.
Because RAG provides additional information to the LLM, it solves two key problems:
Tap to view at full size.
Here’s a lower-level view of RAG — it starts with the cleaning and conversion of the supplementary data:
Tap to view at full size.
Once that supplemetary data has been cleaned and converted, the next step is to convert it into small chunks of equal size:
Tap to view at full size.
Those chunks are then converted into vectors. If you’re not really into math but into programming, think of vectors as arrays of numbers. Each of the numbers in the vector is a value between 0.0 and 1.0, and each vector typically has hundreds of elements. In a diagram below, I’ve greatly simplified the vectors so that they’re made up of only three elements:
Tap to view at full size.
The whole process of cleaning/converting, then chunking, then embedding is called indexing:
Tap to view at full size.
Now that you know what’s happening “under the hood,” let revisit the RAG diagram, but with more detail:
Tap to view at full size.
Here’s what’s happening:
Luke asks the question: “Who built you, Threepio?” That’s the query.
The query is converted into vectors.
The “vectorized” query is compared against the vectors that make up the supplementary information — the vectorstore — and the system retrieves a small set of the vectors that are most similar to the query vector.
The query vector and the supplmentary vectors from the vectorstore are combined into a prompt.
The prompt is then sent to the LLM.
The LLM responds to the prompt.
That was the “hand-wavey” part of my lightning talk. The rest of the talk was demonstrating a simple RAG system written in Python and running in a Jupyter Notebook. If you’re really curious and want to see the code, you can download the Jupyter Notebook here.
My session was part of Austin Forum on Technology and Society’s third annual AI April, a month of presentations, events, and podcasts dedicated to AI capabilities, applications, future impacts, challenges, and more.
Here are links to the video and supplementary material for the presentation:
Notes from my new work notebook. Tap to view at full size.
To me, it always felt that I learned better and retained more if I took notes by hand rather than typing them in, and there’s research that backs up my hunch!
More notes from my new notebook. Tap to view at full size.
The gist of the Scientific American article:
Taking notes by hand versus typing results in more electrical activity across many interconnecting brain regions that handle things like:
movement
vision
sensory processing
memory
It’s slower than typing, which means you have to pick and choose what you record, forcing you to:
prioritize the information you’re presented with
figure out what the main points are
relate what you’re writing notes about to what you’ve learned before
…and that means you’re more likely to stay engaged and grasp new concepts better
Writing by hand means that your motor and memory systems have to work together, which improves your memory of what you’re writing notes about
My new notebook and many colored pens. Tap to view at full size.
As Unified API’s newest member and Supreme Developer Advocate, I have a lot of work ahead of me — and a lot of note-taking. So I customized a notebook from a Scrum software vendor (I can’t even remember when I got it) with Unified’s octopus logo (see the photo above) and have been taking furious notes. As a result, I’m retaining what I’m learning, which is very, very important at this very early stage in the game.
If you’re learning something new, trying breaking away from the computer as a note-taking device, get a paper notebook, and try writing notes by hand! You may be pleasantly surprised.
