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  • Hello.

  • Hi. Welcome, everyone.

  • Thank you very much for venturing out on this cold, wintry December

  • evening to be with us tonight.

  • And if you're joining online, thank you for being here as well.

  • My name is Hari Sood, and that's ‘hurry’, like when you go somewhere quickly, you're in a hurry.

  • I am a research application manager at the Turing Institute, which means

  • I basically focus on finding real world use cases and users for the Turing's research outputs.

  • And I'm really excited to be hosting

  • this very special and, I’m told, sold out lecture for you all today.

  • It is the last in our series of 2023 of The Turing Lectures

  • and the first ever hybrid Turing Lecture / discourse.

  • And as we prepare and build up for the Christmas Lecture

  • of 2023 here at the Royal Institution.

  • Now, as has become

  • a bit of a tradition for the host of this year's Turing Lectures.

  • Quick show of Hands, who's been to a Turing Lecture before?

  • Some people.

  • Some people.

  • Who’s been to a lecture from this year's series before?

  • It looks like more hands than last time.

  • On the flip side of it,

  • who's coming to their first Turing Lecture today?

  • A lot of new faces.

  • Well, for all the new people here welcome! For the ones who've been here before,

  • welcome back.

  • Just as a reminder, the Turing Lectures are the Turing's flagship lecture series.

  • They've been running since 2016 and welcome world-leading experts

  • in the domain of data science and AI to come and talk

  • to you all.

  • The Alan Turing Institute itself -

  • we have had a quick video on it, which I was mesmerised by.

  • But just as a reminder, we are the national institute for data science,

  • and AI, we are named after Alan Turing,

  • who is one of the most prominent mathematicians from the 20th century in Britain.

  • He is very famous for I always normally saying 'most famous' -

  • but very famous for being part of the team that cracked the Enigma code,

  • that was used by Nazi Germany in World War Two, at Bletchley Park,

  • if you've heard of Bletchley Park, as well.

  • If you've seen The Imitation Game with Benedict

  • Cumberbatch, that's the right way to say isn't it? He is playing Alan Turing.

  • And our mission is to make great leaps in data science and AI

  • research to change the world for the better.

  • As I mentioned, today is not just the Turing Lecture -

  • It is also a discourse, which means two important things.

  • So, firstly, when I'm done with the intro, the lights will go down and it's going to go quiet

  • until exactly 7:30 on the dot when a bell is going to ring and a discourse will begin.

  • So, just to warn you guys, that will be happening,

  • the lights aren't broken, that is part of the programme for today.

  • But also it is a discourse,

  • and we want to get you guys involved.

  • There's a huge Q&A section at the end for about 30 minutes.

  • Please do think about what questions you'd like to ask our speaker today.

  • If you're in-person, we will have roaming mics that will be going round.

  • We can bring upstairs as well.

  • If you're online, you can ask a question in the Vimeo chat

  • and someone here will be checking the questions and we'll be able to chat from there as well.

  • If you'd like to share on social media

  • that you're here and having an amazing evening, please do tag us.

  • We are on Twitter / X,

  • whatever at

  • @TuringInst and we are on Instagram at @TheTuringInst.

  • And so please do tag us so we will be able to see what you're sharing and connect with you as well.

  • So, this year's lecture series has been answering the question

  • 'How AI broke the internet' with a focus on generative AI, and you guys

  • can basically think of generative AI as algorithms that are able to generate new content.

  • This can be text content like you see from ChatGPT.

  • It could be images that you can also get from ChatGPT, but also DALL-E as well, and can

  • be used for a wide range of things. Potentially professionally for blog posts or emails.

  • Your colleagues don't realise Were written by an algorithm and not by you.

  • If you've done that before?

  • If you're at school, maybe for some homework or at the university to write some essays.

  • And it can also be used for sort of when you have a creative,

  • When you hit a creative wall, when you can't get past it and you want some ideas and some prompts,

  • it can be a great way to like have some initial thoughts come through that you can build on

  • and it can be used for quite scary things,

  • as was mentioned by an audience member at the last Turing Lecture:

  • Of someone who submitted legal filings

  • for a court case using ChatGPT - which is terrifying,

  • but it can also be used for very everyday things as demonstrated.

  • I'm not sure if you guys saw the thread by Garrett Scott, who gave ChatGPT

  • an image of a goose and said: "Can you make this goose sillier?"

  • And then asked ChatGPT to progressively make the goose sillier and sillier.

  • Until ChatGPT gave him the image of a crazy

  • silly goose and said this is the silliest goose in the history of the universe.

  • I do not think it is possible to get any more silly a goose.

  • So, obviously a wide range of applications from the technology.

  • If you guys want to

  • look at that Twitter thread, the geese to come out of it are absolutely mesmerising.

  • And that's been the focus of this year's series.

  • We started with Professor Mirella Lapata in September this year

  • asking the question what is generative AI and having an introduction to it.

  • We then had a lecture from Dr Mhairi Aitken

  • in October on the risks of this technology,

  • which basically leaves one final big question unanswered, which is: we

  • are here now, but what is the future of generative AI?

  • And that is the focus for this evening.

  • So, that is pretty much it for the intro, unless I've forgotten anything,

  • which I don't think I have. Cool. So, just a reminder,

  • the lights are now going to go down and it will be quiet until exactly 7:30pm,

  • when a soft bell will ding and we will start the discourse.

  • I hope you enjoy the evening.

  • Artificial intelligence as a scientific discipline

  • has been with us since just after the Second World War.

  • It began, roughly speaking, with the advent of the first digital computers.

  • But I have to tell you that, for most of the time, until recently,

  • progress in artificial intelligence was glacially slow.

  • That started to change this century.

  • Artificial intelligence is a very broad discipline,

  • which encompasses a very wide range of different techniques.

  • But it was one class of AI techniques in particular

  • that began to work this century and, in particular,

  • began to work around about 2005.

  • And the class of techniques which started to work at problems that were interesting enough

  • to be really practical- practically useful in a wide

  • range of settings were machine learning.

  • Now, like so many other names in the field of artificial intelligence,

  • the name "machine learning" is really, really unhelpful.

  • It suggests that a computer, for example, locks itself away in a room

  • with a textbook and trains itself how to read French or something like that.

  • That's not what's going on.

  • So, we're going to begin by understanding a little bit more

  • about what machine learning is and how machine learning works.

  • So, to start us off:

  • Who is this? Anybody recognise this face?

  • Do you recognise this face?

  • It's the face of Alan Turing.

  • Well done. Alan Turing.

  • the late, great Alan Turing.

  • We all know a little bit about Alan Turing from his codebreaking work in the Second World War.

  • We should also- we should also know a lot more

  • about this individual's amazing life.

  • So, what we're going to do is we're going to use Alan Turing to help us understand machine

  • learning.

  • So, a classic application of artificial

  • intelligence is to do facial recognition.

  • And the idea in facial recognition is that we want to show the computer

  • a picture of a human face and for the computer to tell us whose face that is.

  • So, in this case, for example, we show a picture of Alan Turing,

  • and, ideally, it would tell us that it's Alan Turing.

  • So, how does it actually work?

  • How does it actually work?

  • Well, the simplest way of getting machine learning to be able to do

  • something is what's called supervised learning. And supervised learning,

  • like all of machine learning, requires what we call training data.

  • So, in this case, the training data is on the right-hand side of the slide.

  • It's a set of input-output pairs - what we call the training dataset -

  • and each input-output pair consists of an input

  • ("if I gave you this") and an output ("I would want you to produce this").

  • So, in this case, we've got a bunch of pictures, again, of Alan Turing,

  • the picture of Alan Turing and the text that we would want

  • the computer to create if we showed it that picture.

  • And this is "supervised learning" because we are showing the computer what we want it to do.

  • We're helping it, in a sense:

  • We're saying "this is a picture of Alan Turing.

  • If I showed you this picture, this is what I would want you to print out".

  • So, that could be a picture of me.

  • And the picture of me would be labelled with the text.

  • "Michael Wooldridge". ("If I showed you this picture,

  • then this is what I would want you to print out").

  • So, we've just learned an important lesson about artificial intelligence

  • and machine learning, in particular.

  • And that lesson is that AI.

  • requires training data and, in this case the pictures -

  • pictures of Alan Turing labelled with

  • the text - that we would want a computer to produce.

  • "If I showed you this picture, I would want you to produce the text,