Teaching ML and AI to Coders

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Often it's thought that to be able to succeed with Machine Learning and Deep Learning, as an onramp to Artificial Intelligence, that you need a deep background in mathematics and calculus, as well as some form of PhD. But you don't. With modern APIs like TensorFlow, much of the complexity is abstracted away in pre-built libraries, so you can focus on learning. In this session, Laurence Moroney, from Google, will explain how he has used this to create courses with hundreds of thousands of students, and from there, how a certificate program was created.

This talk has been presented at ML conf EU 2020, check out the latest edition of this Tech Conference.

FAQ

The Gartner's life cycle curve is a model that describes the progression of any technology from its introduction to its peak of inflated expectations, followed by a trough of disillusionment, and finally reaching a plateau of productivity. AI is currently believed to be in the peak of inflated expectations stage.

According to a survey, there are only about 300,000 AI practitioners globally compared to approximately 30 million software developers. This disparity contributes to the global skill shortage in AI.

Google aims to train 10% of the world's software developers in machine learning and AI, which would increase the number of AI and ML developers to around 3 million.

The mission of making AI easy involves creating resources and strategies to simplify AI and machine learning for developers. This includes producing courses, hands-on labs, and various training initiatives to make AI more accessible.

Google employs several strategies for AI training, including massively online open courses (MOOCs), direct at scale training through platforms like YouTube, and high-touch managed efforts such as boot camps and academies.

The three specializations are TensorFlow in Practice, TensorFlow Data and Deployment, and the upcoming TensorFlow from Basics to Mastery. These courses cover various aspects of AI and machine learning, including computer vision, natural language processing, and model deployment.

The TensorFlow YouTube channel provides short, quick lessons on how to use TensorFlow, along with updates on new releases. It aims to be a go-to resource for both beginner and advanced TensorFlow users.

The employment-oriented developer's certificate is a rigorous exam that tests skills in natural language processing, computer vision, and sequence modeling. It aims to validate the capabilities of AI practitioners for potential employers.

Google assists universities by providing curriculum, financial support, and other resources to develop AI courses. This includes offering the same curriculum used in MOOCs and other training programs.

AI and machine learning are considered high-growth job sectors due to the increasing demand for these skills. Reports from organizations like the World Economic Forum and Forbes predict significant growth in AI-related job opportunities and market revenue in the coming years.

Laurence Moroney
Laurence Moroney
34 min
02 Jul, 2021

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Video Summary and Transcription

The video discusses the importance of teaching AI and ML to developers, emphasizing the need for practical skills in computer vision, NLP, and sequence modeling. The certificate program launched in 2020 aims to bridge the gap between employers and qualified AI professionals, making AI more accessible. MOOCs have garnered over 600,000 learners, recognized by the World Economic Forum as essential skills. Collaboration with universities like Imperial College and online platforms like Coursera has expanded the reach of AI education. The talk also highlights the benefits of TensorFlow for deploying models across various platforms and the importance of hands-on experience through projects and code labs. The video mentions the vision of training 10% of the world's developers in AI and ML, along with the creation of resources like the TensorFlow YouTube channel and specialized training initiatives like the Google Developers Machine Learning Boot Camp in Korea. Laurence Moroney, the author of "AI and Machine Learning for Coders", aims to demystify AI and prepare developers for the future job market.

1. Introduction to AI and its Current State

Short description:

I'm excited to talk about my job educating the world about AI and making it a better place through AI. I'm also the author of the book AI and Machine Learning for Coders, a recent bestseller. AI is currently in the inflated expectations phase, and my role is to help people understand its true capabilities. The number of AI practitioners is 300,000, compared to 30 million.

Thank you. So I'm really excited to be here today to talk about my job to educate the world around AI and to try and make the world a better place through AI. A little bit about me is I'm also the author of this book AI and Machine Learning for Coders. It was just released so it's quite a new release and it was actually the number one bestseller in a number of AI categories.

So first of all I want to talk a little bit about AI and where AI is at. And this curve I like to use and this is the Gartner's life cycle curve. And the life cycle curve of any technology usually begins with the technology being introduced and then it reaches this peak of inflated expectations. And the peak of inflated expectations is the kind of thing that when you see massive hype around the technology, but that hype isn't really based around anything real on the technology. It's based around speculation and the technology itself. And then often the lifecycle curve drops us into the trough of disillusionment. And despite the negative sounding name it's actually a very positive thing because that's when we blow through the inflated expectations. We blow through the hype and we understand what the product and what the technology is really all about. And then once we reach that point from there on we can reach productivity. Unfortunately AI right now is probably somewhere about here on the curve. There's still many inflated expectations. And just to call this out sometimes so inflated expectations can be positive where everybody's thinking about like the amazing things that can be done with the technology. And sometimes they can be negative where people are terrified and they're afraid of the technology. But their expectations about its capabilities or what are inflated because of this hype cycle curve. But my job is generally I'm trying to get people down here into the trough of disillusionment. And I sometimes joke that I'm a professional disillusioner but really with the idea of having the world understand what AI really is all about what you can do with AI how you can build with AI and from there then you can grow up into the productivity. So like I said we're here right now.

And my question then becomes why do you think we're here right now. What is the number of reasons behind this. The first one I will show is this number. And this number is 300.000 and 300.000 is the number of AI practitioners in the world according to a survey done by a company in China. And so they wanted to just take a look at why is there such a global skill shortage around AI. This was about two and a half years ago. Why was there such a global skill shortage around them. And they wanted to say well how many qualified people are out there. And it was 300.000 AI practitioners. Now I like to compare this with this number which is 30 million.

2. Challenges and Mission at Google

Short description:

And they vary wildly. I've seen some around 22 million. I've seen some around 35 million. So for example at WWDC this year Tim Cook mentioned that there are 28 million developers in the Apple ecosystem alone. We made it our vision at Google to train 10 percent of the world's developers to be effective in machine learning and in artificial intelligence. We started this journey about 18 months ago, and today I just want to share the strategies we use and the results we've gotten. When working with software developers, I received feedback on the difficulties they face, including unfamiliar terminology and complex concepts. This presented a challenge, but it became our mission at Google to overcome these obstacles.

And they vary wildly. I've seen some around 22 million. I've seen some around 35 million. I'm going to like just picking a number roughly in the middle of that and it was 30 million. And I could argue actually that the number is far greater than this.

So for example at WWDC this year Tim Cook mentioned that there are 28 million developers in the Apple ecosystem alone. So if I did a rule of thumb that half of the developers in the world are in the Apple ecosystem we could be actually closer to 60 million developers globally. Well let's let's work with this number of 30 million.

Now remember there were 300.000 AI practitioners according to the survey. There are 30 million software developers according to my estimate. So I made it our vision at Google is that what if we could train 10 percent of the world's developers to be effective in machine learning and in artificial intelligence. And if we did that would have three million A.I. and M.L. developers which is 10 times this number. So we made that our goal. Can we increase the number of practitioners globally by 10 by a factor of 10. Not by a number of 10. So you know we said we'd set that would make this our goal. We started this journey about 18 months ago a little over 18 months ago. And today I just want to share like the strategies that we use and the results that we've gotten.

But first of all when working with software developers and when I talk with them and when I would look at how they were being trained. I got a lot of feedback around why they thought I was difficult and why I was something that was while it was something that was of interest to them. It was something that it was going to be too difficult for them to kind of give up a lot of their time and a lot of their study time to be able to pick it up. And I started seeing like a lot of terminology like I've put in this chart here people saying it was difficult. There was a lot of math. There were a lot of terms that they weren't familiar with like unsupervised learning or supervised learning. They really like me they hadn't done things like calculus and probability in 25 years. And as a result the number of concepts that were being thrown at them just to get started made it like there was a massive roadbump that you'd have to get across to be able to get started to be able to transform your career and transform your skill set to be a machine learning or an AI developer.

So I saw that as a challenge. And one of the things that we do with Google is that we give ourselves a mission and our mission is defined by three words.

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