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The AI Entrepreneurship thread. Code AI? Interested in AI? Data Science? Machine Learning?

ChrisV

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Artificial Intelligence and Machine Learning are likely going to be the biggest boons of the 21st century.

Possibly one of the biggest technological boon the world has ever seen.

The Entrepreneurship opportunities are going to be endless.

I wanted to make a space for those pursuing AI or interested in AI to share resources and any type of AI related

So are you pursuing AI? What's your story? What interests you about AI? Have any resources to share?

Or feel free to just talk about anything AI or ML related.

@ZF Lee , @Strategery , @David Moyses

Feel free to tag anyone who you know that's involved in Data Science, Machine Learning or AI

So what interests you about the world of AI?
 
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ZF Lee

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Artificial Intelligence and Machine Learning are likely going to be the biggest boons of the 21st century.

Possibly one of the biggest technological boon the world has ever seen.

The Entrepreneurship opportunities are going to be endless.

I wanted to make a space for those pursuing AI or interested in AI to share resources and any type of AI related

So are you pursuing AI? What's your story? What interests you about AI? Have any resources to share?

Or feel free to just talk about anything AI or ML related.

@ZF Lee , @Strategery , @David Moyses

Feel free to tag anyone who you know that's involved in Data Science, Machine Learning or AI

So what interests you about the world of AI?
Haha I’m just taking some data analytics courses in university, and I would have never got into the field in the first place, if I didn’t have a great lecturer for my first year.

In high school, for elementary statistics, they just threw all the Greeks at you and you have to memorize everything without truly comprehending their roles.

But my lecturer was different.

He took real-life problems that spiraled out of control (eg newspaper reports that miscommunicated data and led to government F*ck-ups and societal backlash) and showed us how the concepts come into play.

So I’m in the field more for the problem-solving, and the background story, that gives data its meaning, rather than the numbers or lingo themselves.

Exploration is the key word.

For my current freelance copywriting jobs, I’m beginning to wonder if there were more effective methods of research, rather than just qualitative surveying and forum scrolling.

Or, if you will, I’m leaning towards a left-brained logical way of research, as sometimes leaving research to ‘wing it’ can result in some very deceptive interpretations.
 

Empires

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I am not currently in the AI space but have plans for it in the future. The sheer amount of opportunity in the space is incredible. Will surely change an insane amount of industries over time.
 
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ChrisV

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He took real-life problems that spiraled out of control (eg newspaper reports that miscommunicated data and led to government F*ck-ups and societal backlash) and showed us how the concepts come into play.

So I’m in the field more for the problem-solving, and the background story, that gives data its meaning, rather than the numbers or lingo themselves.
Yes! Statistics is boring in and of itself. It's what you can do with Statistics that's exciting.

The analogy I like to use is... Math is boring, right? Physics is boring, right?

But what if you could use your knowledge of Math and Physics to create the fastest Ferrari the world has ever seen. Now it's not so boring now is it? So the fields are always boring in a vacuum, but they become exciting when you see what they can help you accomplish.

Another great example comes from my favorite movie: Moneyball.

Stats is boring right? But in the early 2000s Harvard Statistician Paul DePodesta teamed up with Billy Beane and the Oakland A's to use statistics to create one of the most impressive baseball rosters of all time. They almost beat the Yankees in the World Series, but did it for one eighth of the price. Just by using Stats.

I made a quick 3 video Moneyball Playlist that I think illustrates everything nicely, even for people who have seen the movies


Human decision making is flawed because of a number of biases. Stats eliminates those.

In the Moneyball situation, certain players were overlooked becasue they were funny looking, had an ugly girlfriend, had a weird pitching stance. So most teams didn't realize how amazing they were as players because human decision making is biased.


 
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Strategery

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@ChrisV
I bought the course you recommended, Analytics Vidhya. Why do they use Jupyter? I've never used an IDE that needed to be accessed from the terminal, so I found it a little strange.
 
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ChrisV

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@ChrisV
I bought the course you recommended, Analytics Vidhya. Why do they use Jupyter? I've never used an IDE that needed to be accessed from the terminal, so I found it a little strange.
Hm, not all versions need to be accessed from the terminal. If you're more comfortable you can launch it from Anaconda, or your web browser.

Honestly though, the Terminal is sooooooo useful when coding, and you might find yourself preferring it. But whatever you prefer for now. But I don't believe that the terminal is a requirement for running Jupyter.
 

srodrigo

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So what interests you about the world of AI?
I love the endless applications it has. Literately, any thing can benefit from Machine Learning.

I was into ML for a while (before I got distracted with games programming) and loved it. But it felt like climbing the Everest. There's so much to learn that's one of the fields that really require specific education to get somewhere.

I played around with ML for finance and betting, it was fun. I have some real projects in mind, but they aren't my current priority. I specially like ML for real estate (although getting good data seems tricky).
 

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I'm interested in seeing what developments AI makes, and maybe getting into a vertical aligned with it. Currently run 2 domains meant to generate b2b AI SaaS leads, will see how that goes :p. Big problem I've found is predicting buyer intent with b2b products, especially AI. Perhaps product reviews or specific white paper detailing a specific problem are the way to go.
 
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srodrigo

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I'm actually thinking about some fields that could be packed with opportunities for ML and are in my areas of interest:
  • Marketing & Sales: this is only going to be more necessary over time. Reaching more customers, increasing discoverability, or making the most out of each customer, are things that will always need to be improved.
  • Health: staying alive is always going to be a high priority need.
  • Real Estate: I have my doubts here. Having a roof is definitely a high priority need, so it should be a good market. However, in some developed countries with very old population, I think the market can eventually blow up when there are too many abandoned houses (see Japan giving houses away). I might be completely wrong, but some countries could see a population decrease and have more houses than needed.
 

ZF Lee

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Perhaps product reviews or specific white paper detailing a specific problem are the way to go.
I write white papers myself, and I often read up other competing white papers.

Some writers just cannibalize each other for the same shitty stats, or just throw in data from irrelevant articles or websites that purport themselves to be 'summaries of complicated government and scientific papers'.

Just to illustrate the situation...

A client asked me to change a certain market growth statistic, since it was similar to that from a competing research firm's white paper. I went to look for a different estimate from another source, and along the way, I found lots of other articles and papers on the similar topic with the SAME STATISTIC BY THE COMPETITOR FIRM, even in one paper from the Australian international trade council website. :inpain:

So...if you want a truly great white paper, you still need the firm to do its own research and surveys.
Expensive, but you'll cover the gaps.
 

ChrisV

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My story: I run a Data Science company. I work with a number of researchers and various corporations to help them solve problems accurately through data. I'm hoping to expand my skills to encompass more Machine Learning techniques into my research. I got into Data Science becasue I just love Science. Not so much chemistry, or physics or anything else when you think of "science," but just the scientific method. The philosophy of science. The idea that rather that rely on intuition or faith, we actually test our ideas to make sure they're true.

Science vs Faith.png

I've found that with good data you can accurately answer pretty much any question you can think of.

Data Science gave me the opportunity to basically run quasi-experiments finding the answer to almost any question I needed to. Traditionally with science, it was a lot of work testing a hypothesis. You'd have to set up experiments, conduct long questionnaires, etc. Not so with Data Science.

From a Medium post I wrote a while back:

The world is changing. All sorts of new technology is on the horizon. Ever wonder how Amazon knows exactly what books you might like? How Pinterest figures out which ‘pins’ might interest you. How Apply Music or Spotify finds new songs that you absolutely fall in love with. How do these machines all this out? How are our devices becoming so intelligent? The answer is Data Science.

We now have the ability to track every click, swipe, ‘like,’ and repost that we want.

Within this data is enormous insights about consumer behavior and even general psychology. Companies have enormous amounts of Data and can do all sorts of incredible things with it. Data Scientist: The Sexiest Job of the 21st Century. Data Science can call presidential races, can tell you which movie is best for you based on your tastes and moods. Netflix upgraded itself from a simple DVD Mailing service and made itself a household name by applying algorithms that figure out which movies you’re most likely to enjoy.

Data Science can show you new music that you would like based on your tastes and even mood. Mercedes-Benz for instance is experimenting with heart-rate sensors to determine your mood so it can decide what music to play for you. It can tell if you’re in a relaxed mood and just want some relaxing music, or if you’re in an upbeat mood and want something more energetic.


And here's the full Medium post for anyone interested.

 
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srodrigo

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My story: I run a Data Science company. I work with a number of researchers and various corporations to help them solve problems accurately through data. I'm hoping to expand my skills to encompass more Machine Learning techniques into my research. I got into Data Science becasue I just love Science. Not so much chemistry, or physics or anything else when you think of "science," but just the scientific method. The philosophy of science. The idea that rather that rely on intuition or faith, we actually test our ideas to make sure they're true.

View attachment 26717

I've found that with good data you can accurately answer pretty much any question you can think of.

Data Science gave me the opportunity to basically run quasi-experiments finding the answer to almost any question I needed to. Traditionally with science, it was a lot of work testing a hypothesis. You'd have to set up experiments, conduct long questionnaires, etc. Not so with Data Science.

From a Medium post I wrote a while back:

The world is changing. All sorts of new technology is on the horizon. Ever wonder how Amazon knows exactly what books you might like? How Pinterest figures out which ‘pins’ might interest you. How Apply Music or Spotify finds new songs that you absolutely fall in love with. How do these machines all this out? How are our devices becoming so intelligent? The answer is Data Science.

We now have the ability to track every click, swipe, ‘like,’ and repost that we want.

Within this data is enormous insights about consumer behavior and even general psychology. Companies have enormous amounts of Data and can do all sorts of incredible things with it. Data Scientist: The Sexiest Job of the 21st Century. Data Science can call presidential races, can tell you which movie is best for you based on your tastes and moods. Netflix upgraded itself from a simple DVD Mailing service and made itself a household name by applying algorithms that figure out which movies you’re most likely to enjoy.

Data Science can show you new music that you would like based on your tastes and even mood. Mercedes-Benz for instance is experimenting with heart-rate sensors to determine your mood so it can decide what music to play for you. It can tell if you’re in a relaxed mood and just want some relaxing music, or if you’re in an upbeat mood and want something more energetic.


And here's the full Medium post for anyone interested.

How long did it take you to learn enough to start your company?
 

ChrisV

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How long did it take you to learn enough to start your company?
That's a good question. It's actually pretty fast if you get the right system. Much of the time that it took was due to mistakes, ie learning a bunch of unimportant crap.

I code in R. I spent a lot of time learning the fundamentals of base R, only to realize that nobody really uses base R. So I had to go back and learn the Tidyverse. I learned a lot of python just to realize that python didn't suit my needs.

I think the best way to learn Data Science is to just do Data Science. Find some practice problems and competitions that are similar to what you want to accomplish and use StackOverflow and Google to fill in the details.

If you find the right course, you can learn it fast. But so many of these places waste time teaching "the fundamentals." F--- the fundamentals. You'll waste so much time learning crap you'll never use.

The only site I found who had really good courses was this:


I do think it's important to understand the fundamentals of scientific philosophy though.

I mean i guess it depends on what you want to do though.

I wrote a post on learning R fast, but you may be able to gleam lessons from other programming languages.

The big thing is to grasp the Pareto principle... 80% of the important stuff will come from 20% of the knowledge available. The trick is to just figure out what that 20% is in your field.

 

ChrisV

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Attorney General Anne Milgram uses "Moneyball"-esque techniques to reform the criminal justice system, with great success.

Anne Milgram: Why smart statistics are the key to fighting crime


When she became the attorney general of New Jersey in 2007, Anne Milgram quickly discovered a few startling facts: not only did her team not really know who they were putting in jail, but they had no way of understanding if their decisions were actually making the public safer. And so began her ongoing, inspirational quest to bring data analytics and statistical analysis to the US criminal justice system.
 

ChrisV

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else
 

ZF Lee

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else

Would be interesting, although that means I’ll have to make a run for it on DataCamp ARIMA.

I have no idea where to get football datasets for forecasts though. Haven’t heard about one yet.

It’ll be rad to break down all the aspects that could lead to the football scores, such as training regimes, timing of player slots, and even overtimes, if any.

Depending on where you guys come from in your approaches of stats and data, you either will come from a time series POV (which heavily involves data visualization) or econometrics causal studies (basically all about looking at errors and patching them up).

(And yup, Chris, I need to send the notes and slides for that Rob Hyndman talk soon. He explained some stuff on the difference between econometrics, machine learning and statistics.

And Rob talked a bit on how he went up against the Chief Data Scientist from Uber in a stats competition.

Rob basically came up with a package that uses most classical forecasting tools, while the Uber fella combined data science and classical stats by ‘putting together neural network and ETS’, if I am not wrong. Not sure what is ETS though.

Rob got like 4th place or 7th, while the Uber guy won.)
 
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Strategery

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else
I'm a very novice coder getting my bearings back after about a year hiatus. I know very little about football and don't follow it at all. With that being said, sure, let's do it!
 

Strategery

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(And yup, Chris, I need to send the notes and slides for that Rob Hyndman talk soon. He explained some stuff on the difference between econometrics, machine learning and statistics.
I'm interested
 

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Great idea starting this thread! I don’t have any passion for sports, but I did want to chime in as an AI developer.

Rather than the data analytics side, I’m focused on Computer Vision and Reinforcement Learning using the Unity game engine. So far, I’ve created a cigarette butt detector and a weed detector (for my lawn), plus a number of projects where video game characters learn to navigate their environment purely by being rewarded for good behavior. I created a Udemy course on the technique I used to create the cig and weed datasets and I have a bunch of YouTube videos about the game stuff. If you’re curious, I post about it on my website, www.immersivelimit.com.

I’m particularly interested in the use of simulators combined with real world data to train robots and computer vision systems. I think there’s a lot of business potential there, especially since it’s so difficult. Definitely fits the Entry commandment of CENTS.
 

srodrigo

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else
Hey Chris,

That sounds great. Unfortunately, I committed just yesterday to do a small project after weeks and weeks of back-and-forth and indecision, so I wouldn't like to break my commitment so soon :D I'll keep an eye on the thread, if I change my mind last minute I'll join.
 

PedroG

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else

Sounds like a fun project, although I wouldn't be able to participate. I'm swamped as it is with work, working on my side business, and trying to finish the Coursera specialization.
 
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ChrisV

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I'm sorry, but I don't know how cool I am with this:



Hey Chris,

That sounds great. Unfortunately, I committed just yesterday to do a small project after weeks and weeks of back-and-forth and indecision, so I wouldn't like to break my commitment so soon :D I'll keep an eye on the thread, if I change my mind last minute I'll join.
Cool beans
 

akTwelve

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I'm sorry, but I don't know how cool I am with this
Just one of the many terrifying things AI will be used for. I see a lot of potential for good too, but I think we will see a radical shift in our lives as the tech matures and spreads. Sentient AI is hardly a requirement either.
 

ChrisV

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Just one of the many terrifying things AI will be used for. I see a lot of potential for good too, but I think we will see a radical shift in our lives as the tech matures and spreads. Sentient AI is hardly a requirement either.
It's good in that it will be helpful in catching dangerous criminals, but it's bad in that we have to now trust the government to use it right.

Extreme example but: what if technology gets so good that they in the future they can scan somebody's face, get an estimate for their DNA, see if that DNA predisposes them to marijuana use, and then search their bookbag because their face said they might have genes more likely to be marijuana smokers.

Maybe an extreme example, but definitely plausible. I think with all this technology we're going to have to really go back and reassess if our laws are fair.
 
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ChrisV

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AI is certainly the buzzword these days. I invested in a startup AI company early this year -- six months later, they were able to recapitalize at 25x the valuation with institutional money and had VCs fighting to get in the raise (luckily I got my money in before they realized they didn't need it :).

I have a feeling that we're going to be talking about a bubble in the AI space in a couple years...
It depends on how you use it. It's almost definitely going to be both a boon as well as a bubble.

It's going to be like the dot com era. There are going to be legitimate uses for it, and there's going to be the crackpots who just want to create MeToo crap.

We're already seeing tons of legitimate uses for it. Netflix Recommendations, Google searches. Self Driving cars. Every major tech company uses some amount of AI.

It's how it always goes with these tech advances. There's the wheat, and the chaff.
 

ChrisV

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I think you're misinterpreting what I mean by bubble. I wasn't implying that the industry isn't viable.
Oh gotcha. I thought you were leaning toward meaning a speculative bubble.

But yes I agree with the oval sentiment. There will be a legitimate bubble, and a speculative bubble.

There will probably be Tesla, and then companies who want to put AI technology in your shoes (actually maybe that isn't a bad idea.. it would make a cool pedometer, but you get my point.)

We'll see the same thing in AI (in my opinion) -- valuations will be overblown, and in a few years, after a lot of buyouts, consolidations and IPOs, we'll see a correction in market cap. And then we'll see more rationale growth for the subsequent decades.
Yep. Everyone's gonna want to get on board, whether their idea is legitimate or not. The legit ones will stick, and the nutty ones will burst. And I think AI will act as a catalyst for other innovations (medical, scientific, agriculture.)

Definitely going to be interesting to watch.
 

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