What Actually Is Artificial Intelligence?
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Talk for MK Geek Night, 23 Sep 2021 AI means more hype, more technology, more future - and more money! But what actually is it? In this talk, Doug will explain what people mean by artificial intelligence and machine learning, what sort of problems they can solve, and how they do it. We'll see a range of examples where they're being used, and look at how it goes well and how it goes wrong, from entertaining AI weirdness to serious algorithmic bias. You won't end up being able to implement techniques like Support Vector Machines or Generative Adversarial Networks (unless you already could) but you should end up with a better idea of what the people who can are up to.
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What Actually Is Artificial Intelligence?
- 1. What actually is Artificial Intelligence? Doug Clow MK Geek Night, 23 Sep 2021
- 2. Who I am Photo (CC)-BY-SA Bill Bertram https://commons.wikimedia.org/wiki/File:ZXSpectrum48k.jpg
- 3. Quick quiz: How much do you know about AI? A. Nothing special B. I’m interested C. I’ve applied it myself D. I have strong views about the use of gradient descent for hyperparameter optimisation in training deep convolutional neutral networks, or at least, I fully understood this sentence. Photo by Maximalfocus on Unsplash
- 4. 1. What is AI? 2. How does it work? 3. How does it go wrong? a) In entertaining ways b) In serious ways 4. How does it go well?
- 5. 1. What is AI? 2. How does it work? 3. How does it go wrong? a) In entertaining ways b) In serious ways 4.V How does it go well?
- 6. Can machines think? • Turing Test: Can you make a machine humans can’t tell is a machine? • Eliza (algorithm) • Chatbots (machine learning) • GPT-3 (neural network) Photo (CC) BY-SA Antoine Taveneaux https://en.wikipedia.org/wiki/Alan_Turing#/media/File:Turing-statue-Bletchley_14.jpg
- 7. (CC)-BY Four Bricks Tall https://flic.kr/p/2jBbHFo Can submarines swim?
- 8. Artificial Intelligence Algorithms Bubble sort Ofqual exam grading Gradient descent k-NN Machine learning SVMs Bayes nets Decision trees Neural networks Convolutional NNs Deep learning GANs
- 9. Photo by Timelab Pro on Unsplash • Games • Chess, Go, video games • Optimisation • Distribution, manufacturing, retail, finance • Complex control systems • Robots, drones, self-driving cars • Recommenders • Amazon, YouTube, Netflix • Speech recognition, natural language processing • Siri, Alexa, Hey Google
- 10. 1. What is AI? 2. How does it work? 3. How does it go wrong? a) In entertaining ways b) In serious ways 4. How does it go well?
- 11. Artificial Intelligence Algorithms Bubble sort Ofqual exam grading Gradient descent k-NN Machine learning SVMs Bayes nets Decision trees Neural networks Convolutional NNs Deep learning GANs
- 12. Photo by Anika Huizinga on Unsplash More like this (supervised learning) What have we here? (unsupervised learning) Get a high score (reinforcement learning)
- 14. 1. What is AI? 2. How does it work? 3. How does it go wrong? a) In entertaining ways b) In serious ways 4. How does it go well?
- 15. Runter End • Wimmer, Bourcestershire • Sniddington, County Durham • Randiskerton, North Yorkshire • Thornwithich, Pooestop • Runter End, Cambridgeshire Map data © 2019 Google
- 17. Bad optimisation Images from Lehman et al, 2019 https://arxiv.org/pdf/1803.03453.pdf Walk Jump
- 18. By Petty Officer Photographer Jay Allen - https://www.royalnavy.mod.uk/news-and-latest-activity/news/2021/may/20/210520-carriers-at-sea-and-strike-warrior, OGL 3, https://commons.wikimedia.org/w/index.php?curid=105562576 Fix bugs in sorting algorithms Delete the list to be sorted (no items = nothing out of order) Minimise difference between generated generated and target output Delete the target output Design an optimal lens It’s 20 m thick Land aircraft on carrier So fast the braking cable force overflows to zero Bad optimisation
- 19. 1. What is AI? 2. How does it work? 3. How does it go wrong? a)In entertaining ways b)In serious ways 4. How does it go well?
- 20. Bad optimisation Photo (CC)-BY-NC_SA Doug Letterman https://flic.kr/p/Ss2Kyv Time spent on site Clickthrough Engagement
- 21. Bias laundering Image by Biljana Jovanovic from Pixabay
- 22. BMJ 2020;369:m1328 7 April 2020 • 232 predictors • Poorly reported • High risk of bias • Two worth validating Nature Machine Intelligence 3, 199–217 (2021) • 2,212 studies • 415 after initial screening • 62 after quality screening • None of clinical use Photo by Fusion Medical Animation on Unsplash They used AI to help with Covid … … it wasn’t good.
- 23. MQ-0 Reaper over Afghanistan By Lt. Col. Leslie Pratt - commons file, Public Domain, https://commons.wikimedia.org/w/index.php?curid=68095681 New capabilities to do bad Increasing inequality Unemployment
- 24. Superintelligence (CC)-BY William Clifford https://flic.kr/p/37PkGN
- 25. Superintelligence (CC)-BY William Clifford https://flic.kr/p/37PkGN The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.
- 26. 1. What is AI? 2. How does it work? 3. How does it go wrong? a) In entertaining ways b) In serious ways 4. How does it go well?
- 27. AlphaZero: self play • Reinforcement learning • Learns from playing against itself • Rediscovered trad ideas • Discovered new play Image © DeepMind 2018 https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-
- 28. GANs generator vs discriminator Cats from http://thesecatsdonotexist.com/ https://devopstar.com/2019/02/25/generating-cats-with-stylegan-on-aws-sagemaker
- 29. AlphaFold Image © DeepMind https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery predicting protein shapes from DNA sequences
- 30. Ethical AI • Fairness • Accountability • Sustainability • Safety • Transparency Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute. https://doi.org/10.5281/zenodo.3240529 Photo by Hendo Wang on Unsplash
- 31. (CC)-BY-SA Christian Bortes https://commons.wikimedia.org/wiki/File:Spiderman.JPG
- 35. Photo (CC)-BY David Goehring https://flic.kr/p/3XfwU
- 36. Photo by Rick J. Brown on Unsplash
- 37. 1770 Mechanical Turk vs Crowned Heads of Europe
- 38. Alan Turing by Elliott & Fry, bromide print, 29 March 1951 NPG x27078 © National Portrait Gallery, London Claude Shannon & Theseus (CC)-BY-NC-SA tericee https://flic.kr/p/GFJvWG 1947–1953 Turing vs Shannon
- 39. © Computer History Museum https://www.computerhistory.org/chess/stl-431614f6482e6/ 1958 704 Mainframe vs Alex Bernstein
- 40. 1980s Fidelity Electronics vs Clow & Clegg
- 41. Deep Blue photo (CC)-BY Jim Gardner https://flic.kr/p/UmoL7 Photo © 2007, S.M.S.I., Inc. - Owen Williams, The Kasparov Agency. http://www.kasparovagent.com/photo_gallery.php, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=4507359 1990s Deep Blue vs Kasparov
- 42. 2017 AlphaZero vs the world Magnus Carlsen won't play computer chess because “he just loses all the time and there's nothing more depressing than losing without even being in the game” Image © DeepMind 2018 https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-
- 43. How to use a neural network 1. Gather data 2. Train your network 3. Use it on new data 4. Profit Photo by Joshua Lanzarini on Unsplash
- 44. • It isn’t magic • It depends on the data • It has got way better • It can go wrong • in entertaining ways • and in really bad ways. • It will get better
Editor's Notes
- So pleased to be here giving a talk. Love giving talks, first in nearly 3y This little guy here was put in by AI
- Data scientist, digital transformation leader, researcher, teacher Interested in human & machine learning for decades
- Turing Test Aka The Imitation Game, starring Benedict Cumberbatch
- PowerPoint suggested these icons. Woah.
- What coes AI do well now?
- PowerPoint suggested these icons. Woah.
- More like this – known data, cats/dogs: prediction, generation, pattern recognition, anomaly/novelty What have we here – unknown data, find patterns, group this data, classify, categorise our customers High score – a way of keeping score, explore/exploit: maximise revenue, minimise inventory, improve completion rate
- Cells like in Excel Numbers in Input are your data, photo pixels, Output, cat or dog Adjust the numbers until the inputs give you the right output Many more cells, more layers, arrows
- I trained a NN on English placenames It generated some fun ones
- Janelle Shane Inktober, creative prompt, draw every day for October NN-generated prompts
- Learn a locomotion strategy, fast. Makes a tall thing that falls over. Jump as high as you can. No not rolling over. Highest point your lowest bit reaches. It falls over & pole vaults, not jumping.
- YouTube, what sucks in your attention, has lots of vortexes that suck people in, tries them out on you. Ends up with the hard stuff on conspiracy theories, radicalization, etc Facebook, socially meaningful engagement = most controversial posts, from friends & family, stuff you can’t help but engage on, and pull in reinforcements. I left.
- Job applications, hard work, biased. Train NN, prev applicants, who appointed. Racist! Delete ethnicity. Names. Career paths, univs. AI won’t stop racism, the people doing it have to work hard at stopping.
- Sometimes it’s just not very good. https://www.bmj.com/content/369/bmj.m1328 https://www.nature.com/articles/s42256-021-00307-0
- gig economy needs AI
- How could really good AI be bad? Artificial general intelligence, not narrow Self-improvement. Paperclip maximiser- optimise manuf process, get smarter Social intelligence, better deals with suppliers, sales
- Far fetched, but so are the AIs below Problem this week? No. Worth looking at? Yes. And people are.
- Chess used to mean intelligent! DeepBlue vs Kasparov 1990s AlphaZero knew almost nothing, learned it from the game. Chess, Shogi, Go Huge compute effort to train, much less to play.
- Generative adversarial network Images, then cue generator in with prompt images Deepfakes Art!
- Proteins DO everything, nanobots Shape is everything DNA, amino acids, protein Lots of DNA seqs, fewer shapes Transformatory
- Fairness in data, design, outcome, implementation Accountability before and after Sustainability – economic, environmental, social (license to operate) Safety – accuracy, reliability, security, robustness Transparency – explainable AI, openness
- There is, as the man used to say, one more thing. Example of a GAN applied to the video domain with both a motion prompt and a separate photo prompt Combine with audio track to produce composite video
- There is, as the man used to say, one more thing
- If you can play chess, you’re intelligent
- Mid40s, Turing theorized how computers could play chess. 1949, Claude Shannon at Bell Labs published paper w description of how do it. 1950, Turing made an actual algorithm. No suitable machine, so manually calculated, 30 min per move. Algorithm lost, history made, paper in 1953. https://www.pcworld.com/article/2036854/a-brief-history-of-computer-chess.html#slide3
- 1958 IBM programmer Alex Bernstein playing his chess program at the console of the 704 mainframe. Bernstein told the computer what move to make by flipping the switches on the front panel. The program took about eight minutes to calculate each move.
- algorithms
- Machine learning, custom algorithms
- Neural network, more later
- These are my key takeaways