A brief history of AI

0:00 I have a PhD in neuroscience and bioinformatics from McGill University in 2017. You can find some of my peer-reviewed article on PubMed, Google Scholar, bioRxiv and arXiv. I transitioned into industry about 5 years ago now and stayed within the data, AI, ML, and cloud ecosystem. I'm an AWS Hero and a Gold Jacket Ambassador. And on a more personal note, I'm a mother of two young girls. What I wanted to do today for this session is to give you an overview of generative AI and then move into the specifics of what agents are, when to use them and not to use them, how to design an agent, how to implement one, talk a little bit about architectural patterns, evaluations, and then we'll talk about how they might or might not affect careers.

1:11 So, artificial intelligence is not a new field. It is thought to have started in the 1940s with some of the earlier papers attributed to McCulloch and Pitts. In 1943, they wrote an article called A Logical Calculus of the ideas eminent in nervous activity, which proposed a mathematical model of biological neurons. In 1950, Alan Turing wrote his paper Computing Machinery and Intelligence, which proposed the Turing test, a benchmark for machine intelligence. In 1956, the Dartmouth Workshop established AI as a field of study and John McCarthy coined the term artificial intelligence.

1:58 We saw an AI winter between the 1950s and the 1980s. In the 1990s, we saw a machine learning renaissance with um Geoffrey Hinton being awarded his PhD in artificial intelligence from University of Edinburgh in 1978. I read some books that stated that when this young PhD candidate was deciding on his field of study, uh AI was thought to be a career suicide because it was a niche uh topic and a lot of people advised him against it.

2:20 Uh of course, fast forward 50 years today, uh Geoffrey Hinton is one of the godfathers of AI. He's gone to win the Turing Award and the Nobel Prize among many other awards. And he's gone to change the world, really. Um in the 1990s, we saw a lot of machine systems winning in gaming, uh in all sorts of games against human contenders. So, in 1997, IBM's Deep Blue defeated Garry Kasparov in chess and in 2011, IBM Watson won the TV game show Jeopardy.

2:45 Of course, that all um increases enthusiasm about the potential of the field. In 2010, we saw a deep learning boom. In 2012, AlexNet, uh which is a deep learning network that came from Geoffrey Hinton's lab, performed well in an image recognition competition. This, of course, revived the field and the excitement around all of the potential that uh neural nets can have. In 2012, Deep- Mind's uh AlphaGo defeated the reigning champion in the game of Go. Today, we're living through a generative AI boom.

3:33 I'm sure you noticed from the two letters AI being plastered on every product that you purchase. Um some notable events there is in 2017, the Transformer paper was released by Google researchers and researchers at the University of Toronto. This became the blueprint for a lot of the large language models we use today. And in November 2022, uh OpenAI released ChatGPT for the general public, which really uh got a lot of attention. And I remember the deep learning boom.

4:22 I was in the field back then, and I remember all the excitement that everybody in data or computer systems had around AI. Of course, it was still a niche um it was still very niche because it required very technical skill sets to be able to build these models yourself, train them, use them. But the generative AI boom is very different. It has really popularized AI in ways that made it percolate across all of society. And so, even my mother, who has never touched a computer, knows what ChatGPT is and and uses it on her phone um to discuss certain things.

4:46 I've heard uh young students brag on the streets on how they've used ChatGPT to um cheat on some exams, maybe, or people in various jobs tell me how they've used ChatGPT to help with resumes and ideas and just making more things more efficient. And so, unlike previous decades of machine learning and AI, the generative AI boom is really more uh popular and democratized. So, last year I gave a free uh CodeCamp lecture on machine learning fundamentals that went more into the technical detail of what machine learning is and all of the details.

5:35 If you're interested in that, you can find that also on YouTube. Um I don't want to go over a lot of the information that I went over in that course. So, if you find that there's missing background information that you need, you might very much find it there. So, what differentiates the generative AI boom that we're seeing today from traditional ML on a technical um plane? So, artificial intelligence sits on three pillars. So, it sits on um the pillar of algorithms.

6:23 So, these are the mathematical models that map input to output. Then you've got data, which is used to tune those algorithms for uh these models to learn from. And then you've got compute to be able to run these training