The video features Sat Ramphal, CEO and co-founder of Maya AI, a Tampa-based early-stage startup whose product is an analysis tool that derives insights from data and delivers those insights conversationally. Their target customers are businesses looking for suggestions and answers based on their company data, and they’ve already caught the attention of retail and pharmaceutical companies.
Maya AI’s co-founders, the Rampal brothers. From left to right: Sean, Sat, and Shivam.
ChatGPT is a language model developed by OpenAI, specifically trained to generate human-like text based on its received input. It is capable of answering questions, providing information, generating content, and performing various other language-related tasks.
On the other hand, Maya AI is a more general AI platform that can be used for various applications and industries. It can perform various tasks, such as image and voice recognition, data analysis, and decision-making.In summary, while ChatGPT is a broader focus on language processing, Maya AI is a specifically focused AI platform with a wider range of capabilities and potential use cases.
I appear in two segments of the piece, along with:
Local realtor Chris Logan, who’s been using ChatGPT to speed up the (presumably tedious) process of writing up descriptions of houses for sale
University of South Florida associate director of the School of Information Systems and Management Triparna de Vreede, who talked about its possible malicious uses and what might be possible when AI meets quantum computing.
IP lawyer Thomas Stanton, who talked about how AI could affect jobs.
Watching the segment, it occurred to me that I need to light my office the way that the news crew did for the segment — it’s got such a great “cinematic hacker” vibe!
Also: My thanks to Kristin Moore for inviting me to be part of the piece!
Sure, the name’s unwieldy, but it does explain exactly what the group is all about:
Tampa Bay: It’s based in Tampa Bay. Sometime we’ll convene in Tampa, sometimes we’ll convene in St. Pete. We’re trying to serve “The Other Bay Area!”
AI: It’s going to be about artificial intelligence in all its forms. The “classical” or “symbolic” AI from the 60s and 70s to neural networks and backpropagation from the 80s and 90s to deep learning and statistical modeling from the 2000s and 2010s to the present day.
Python: It’s also going to be about the Python programming language, which combines simplicity and power. We’ll mostly focus on AI, but we’ll also delve into using Python to solve problems and get things done.
Hands-On Coding: It’s nice to talk about AI and Python, but more fun (and educational) to do something with it. Each meetup will be a “code-along” exercise we’re you’ll actually write code to sharpen your Python skills or do something AI-related!
Group 2 be Renamed Later: The name’s clunky, but the point is to get the group launched. We’ll eventually come up with a better one. As for using “2” instead of “to”: that’s so the group’s name fits within Meetup’s limit for group name length.
What will happen at our meetups?
They’ll be coding workshops, mostly in Python, one of the preferred languages for implementing artificial intelligence / machine learning / data science-driven applications.
We’ll follow the model I use for the Tampa Bay Apple Coding Meetup, the “code along with me” exercise. We’ll start each meetup with some kind of goal in mind and write code towards achieving that goal. The idea is for you to leave each meetup with at least a partially working application, if not a complete one, and with the understanding gained from having worked on that application.
What will you need at our meetups?
The technical requirements for this meetup are relatively simple:
A laptop that was made sometime in the past 10 – 12 years and has 4GB or more of RAM. This meetup is all about being hands-on and coding in a group setting, so you’ll need a computer that you can bring with you that can also run Python 3.9 or later.
Python 3.9. The current version is Python 3.11.something, but 3.9 will do. At the time of writing, Python 3.9 is the version that comes with Anaconda, a Python distribution that comes with a lot of utlities and libraries. It’s a great choice if you’re new to Python.
Rasberry Pi 4 Model B. Typically under US$100.
Just for kicks, I might lead a session using my Raspberry Pi as my demo machine.
What will we build?
I’ve already got the first few sessions planned out. I thought we’d start with a bang and code ELIZA, the very first chatbot, developed between 1964 and 1966 at MIT by Dr. Joseph Weizenbaum. It simulates a Rogerian therapist, using pattern matching to reflect what the patient says back at them or gets the patient to talk about what they just said.
The first few sessions will cover “classical” AI applications, such as ELIZA, tic-tac-toe, and simple sentiment analysis. We’ll work our way up to data science-style applications such as the “recommended if you like” system that a lot of ecommerce sites use. And then, neural networks. The goal is to try to work our way up to a scaled-down version of ChatGPT.
Along the way, we’ll cover Python, and the useful algorithms, data structures, and libraries that you can use. We’ll also cover the onerous task of cleaning up data, which is important to AI, and a topic that needs more coverage.
Where will the meetups take place?
I’m looking for a venue for the first meetup, and will announce it as soon as I’ve secured it. I want to hold meetups on both the Tampa and St. Pete sides of the bay, perhaps alternating. If you have suggestions, connections, or better still, a meetup venue, drop me a line!
Yesterday, I posted an article titled Would you be interested in a hands-on A.I. coding meetup?. It got over a thousand impressions on LinkedIn alone, so I’m already taking the first steps into making it real, with the help of ChatGPT…
I need names for a meetup group in Tampa Bay where the main topic of discussion is AI.
Here are a few name suggestions for your AI meetup group in Tampa Bay:
1. Tampa Bay AI Alliance 2. AI Minds of the Bay 3. The Bay Area AI Hub 4. The AI Network of Tampa 5. Tampa AI Innovators 6. The AI Roundtable of Tampa Bay 7. AI Futurists of Tampa 8.The AI Think Tank of Tampa Bay 9. The Bay Area AI Society 10. Tampa’s AI Connection.
I hope these names give you some inspiration for your group.
Can you suggest more?
Of course! Here are some more name suggestions for your AI meetup group in Tampa Bay:
11. The AI Nexus of Tampa Bay 12. The Tampa Bay AI Enthusiasts 13. AI Mavericks of the Bay 14. The Bay’s AI Pioneers 15. The Tampa AI Frontier 16. The AI Collective of Tampa Bay 17. The AI Vanguard of Tampa 18. The Tampa Bay AI Community 19. The AI Innovations of Tampa Bay 20. The AI Synergy of Tampa Bay.
These names aim to emphasize the cutting-edge and innovative nature of AI and the community aspect of your meetup group.
Can you suggest names that suggest that hands-on coding is involved?
Sure, here are some name suggestions that emphasize the hands-on coding aspect of your AI meetup group in Tampa Bay:
1. Code AI Tampa 2. The Bay Area AI Coders 3. AI Builders of Tampa 4. The Tampa AI Code Lab 5. The AI Developers of Tampa Bay 6. The Bay’s AI Code Sprint 7. Tampa AI Coders Collective 8. The AI Code Academy of Tampa Bay 9. The Tampa Bay AI Hackers 10. The AI Coding Crew of Tampa.
These names suggest a focus on hands-on coding and emphasize the practical and technical aspect of AI development.
How about names for hands-on coding meetups in Tampa that sometimes include AI?
Here are some name suggestions for hands-on coding meetups in Tampa that sometimes include AI topics:
1. Tampa Code & AI 2. The Bay’s Code & AI Mixer 3. The AI & Code Connection of Tampa 4. The Tampa Code & AI Summit 5. The Bay Area Code & AI Hub 6. The Code & AI Network of Tampa 7. The AI & Code Innovators of Tampa 8. The Tampa Code & AI Symposium 9. The Bay’s Code & AI Innovations 10. The Code & AI Minds of Tampa.
These names suggest a broader focus on hands-on coding, with a secondary emphasis on AI topics and how the two intersect.
I need more names.
Here are some additional name suggestions for hands-on coding meetups in Tampa that sometimes include AI topics:
1. Code to AI Tampa Bay 2. The Bay’s AI and Code Collaborative 3. Tampa Bay Coders and AI Enthusiasts 4. The Code & AI Exchange of Tampa Bay 5. The Tampa Bay AI-Coded Meetup 6. The Bay Area Code-AI Group 7. The Tampa AI & Coding Collective 8. The Code-AI Circuit of Tampa Bay 9. The Tampa Bay AI-Driven Coders 10. The Bay Area AI-Coded Community.
These names aim to convey the fusion of AI and coding, with a focus on practical skills and collaborative learning opportunities.
More names like that, but also take advantage that the Tampa Bay area is also known as “Suncoast”.
Sure! Here are some name suggestions that incorporate the “Suncoast” reference and emphasize the fusion of AI and coding:
1. Suncoast AI Coders 2. Tampa Bay Suncoast AI 3. The Suncoast AI & Code Society 4. The AI Suncoast Coders of Tampa Bay 5. The Suncoast AI-Coding Collective 6. The Tampa Bay Suncoast AI-Coders 7. The Suncoast AI & Code Connection 8. The AI-Coded Suncoast Community of Tampa Bay 9. Suncoast AI Hackers of Tampa 10. The Tampa Bay Suncoast AI Circuit.
These names play off the “Suncoast” moniker to emphasize the local nature of the group while highlighting the fusion of AI and coding.
Each era had its tech opportunity, and I have a meetup idea to capitalize on the opportunity we have right now.
ChatGPT has been around for a little less than a quarter (remember, it came out at the end of November), and in that time, a lot of people are already adapting to the change. Even the stand-up comic puppet “Chad the Bird” has already created an epic routine about it:
One way to look at the opportunity it presents is through the lens of paradigm shifts over the past couple of decades:
It may be 2023, but it’s also 1981, 1994, the early 2000s, 2008, and the 2010s all over again. What would you need to know to get ahead in this new age of AI?
Here’s a better question: What if there was a meetup where you could get hands-on practice on building AI?
Backpropgation (a key feature of neural networks) diagram from one of my notebooks. We could cover this at my proposed meetup! Tap to view at full size.
There’s no way you could build ChatGPT in a couple of hours at a meetup. But what if you worked up to it, a little at a time?
This meetup would start at the very beginning — and by “beginning,” I mean the original chatbot from the 1960s, ELIZA…
Unlike ChatGPT, it is possible to build ELIZA in a couple of hours. It’s also a great way to sharpen your skills in Python (which is very popular in AI circles) at the same time!
Eliza was created by computer scientist Joseph Weizenbaum at MIT’s Artificial Intelligence Lab over a two-year period from 1964 to 1966:
It simulated a psychotherapist that reflects what the patient says back at them or gets the patient to talk about what they just said. Although it was written for the IBM 7094, a room-sized computer whose operator console is pictured below…
…it later became a popular program on home computers in the 1980s under the name “Eliza” or “Doctor”:
We’d start by building Eliza — in Python. I’d give you a “starter” project, and you’d code along with me until you had a working Eliza version that you could tweak into your own chatbot.
You wouldn’t need the latest and greatest computer to do it, either! A laptop from 2010 (and remember, that’s 13 years ago now!) or later would be all you’d need.
We’d then play around a little with old-school AI and work our way towards the APIs, algorithms and data structures that make up what we consider to be AI today. The goal is to eventually gain the skills necessary to write AI applications or use AI-powered APIs to give our applications human-like smarts.
If the recent popularity of ChatGPT and other recent AI advancements has got you interested in neural networks, how they work, and how you might implement one, these three video series should help you get started.
Note that while you might get some basic insights into neural networks by simply watching the videos, the real benefit comes from doing the exercises shown in the video and experimenting with the math and programming they feature.
Neural networks from a general point of view
If you’re new to neural networks, you’ll want to start with the Neural Networks video series from Grant Sanderson, whose YouTube channel, 3Blue1Brown, is a great place to learn math and algorithms.
Number of videos in the playlist: 4
Total length: 1 hour, 4 minutes
Published: October – November 2017
Math difficulty: 1 out of 5 in the first video, up to about 3 out of 5 in the last two. You can’t do neural networks without linear algebra and differential calculus, but this series works hard at making them as easy to understand as possible.
Programming difficulty: 0 out of 5. This is all about the general principles behind neural networks and doesn’t cover any programming at all.
Neural networks from a math point of view
Ever wish you had a math teacher who can make complex topics easier to understand with fun explanations and ukulele music breaks? You do now — Josh Starmer, host of the StatQuest YouTube channel, is that math teacher, and his Neural Networks / Deep Learning video series looks deep into the math behind neural networks.
Number of videos in the playlist: 20
Total length: 5 hours, 28 minutes
Published: August 2020 – January 2023
Math difficulty: 0 out of 5 in the intro video, working its way up to 3 out of 5 as the series progresses. Once again, linear algebra and differential calculus are involved, but like 3Blue1Brown’s Grant Sanderson, Josh Starmer does a lot to make math concepts easier to understand.
Programming difficulty: 0 out of 5, until the last three videos, which cover using Python and the PyTorch library. If you’re comfortable with OOP, you’ll do fine.
Neural networks from a Python programmer’s point of view
Are you a Python programmer? Would you like to learn neural networks from the co-founder of OpenAI and the former director of artificial intelligence for Tesla? Then Andrej Karpathy’s Neural Networks: Zero to Hero video series is for you. He says that all you need to understand his video is a “basic knowledge of Python and a vague recollection of calculus from high school,” but you should keep in mind that this is someone who eats, sleeps, and breathes neural networks and Python. I’m currently working my way through these videos, and if I can follow them, chances are that you can too.
Number of videos in the playlist: 20
Total length: 12 hours, 23 minutes
Published: August 2022 – January 2023
Math difficulty: 3 out of 5, as there’s what Karpathy calls “high school calculus.” If you took first-year calculus in university and remember the basics of differential calculus — including the bit where dy/dx doesn’t mean you can simply cancel out the d’s — you’ll be fine.
Programming difficulty: 3.5 out of 5. You’ve got to be comfortable with OOP, lambda functions, and recursion for most of the videos, and in the final video, Let’s Build GPT, you should be familiar with PyTorch. You should also have Jupyter Notebook or one of its variants set up.
