AI - history and recent breakthroughs
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An overview of AI and some recent breakthroughs
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AI - history and recent breakthroughs
- 1. Artificial Neural Networks recent breakthroughs and applications Armando Vieira
- 2. Summary ● What went wrong with traditional AI? ● Probabilistic machines - a new paradigm ● Why Deep Learning is different and why it matters ● Applications ○ NLP ○ Image ○ Drug discovery ○ Weather forecasting ● Large generative models ○ GTP3 ○ LAMP ○ GLAMP ● Is this intelligence?
- 3. Rule based programs Solve numerical equations Accurate Fast Pure logic Why “old” AI failed? Can’t deal with unstructured data Get stuck with exceptions to rules Not scalable All actions have to be programmed Rigid, can’t learn
- 4. The vodka was good but the meat was rotten Blind and insane English - Russian - English “The spirit was willing but the flesh was weak“ “Out of sight out of mind”
- 5. Summary
- 6. Symbolic thinking may be the supreme form of human intelligence but there are other ways to build “smart” machines
- 7. The associative paradigm Learning by associations No true or false but probabilities No symbols but distributions Pattern matching Trained by examples not hard coded No CPU or memory, just signals flowing through a mesh of connections
- 8. The Deep Learning Revolution
- 11. What made the difference?
- 12. Bigger is better
- 13. Train Deploy JOHN “JOHN” MARY “MARY” MARY “JULIE” MARY “MARY” PAUL “PAUL” MARY “MARY” How DL works?
- 14. How to make the right choice? POSITIVE MITOSES FALSE POSITIVE MITOSES TRUE POSITIVE MITOSES Janowczyk A, Madabhushi A
- 15. Accuracy in medical imagery
- 16. Applications
- 17. Alpha Fold
- 19. stateof.ai 2021 Deep learning models can learn drug-protein binding relationships from a small number of empirical experiments in order to help prioritise which areas of vast chemical spaces to virtually screen. Accelerating high-throughput virtual drug screening with model-guided search ● Structure-based drug discovery searches for drugs that bind a protein of interest whose 3D structure is available. This process, referred to as “docking”, can be run virtually using simulations. However, with databases of small molecule chemicals exploding past billions of records, virtually screening all combinations becomes computationally and commercially intractable. ● A solution is to train a model on a sample of drug-protein interactions with empirically determined docking scores. ● This model can be used to virtually score a library of interest, followed by docking the top scoring drug candidates. These results are used to update the model with active learning. With several iterations, model-guided search ultimately generates hits faster. #stateofai | 26 Introduction | Research | Talent | Industry | Politics | Predictions
- 22. stateof.ai 2021 DreamerV2 is the first model-based RL agent trained on a single GPU to surpass human level performance on 55 popular tasks of the Atari benchmark.The agent learns behaviors purely within the latent space of a world model trained from pixels, which makes these behaviors more generalisable to solving future tasks more efficiently. Superhuman world models for Atari, but on a budget ● DreamerV2 vastly outperforms other RL agents trained with the same computational budget, across all performance aggregation metrics. #stateofai | 29 Introduction | Research | Talent | Industry | Politics | Predictions
- 23. stateof.ai 2021 RL agents have shown impressive performance on challenging individual tasks. But can they generalize to tasks they never trained on? DeepMind trained RL agents on 3.4M tasks across a diverse set of 700k games in a 3D simulated environment, and show they can generalize to radically different games without additional training. Zero-shot generalisation in reinforcement learning ● The researchers created XLand, a vast controllable environment, which allows them to dynamically adapt both how the agents train and, crucially, the games on which they train. ● The distribution of games is learned using a hyperparameter optimization technique called Population Based Training. It allows them to find the games which have the right level of difficulty given the agents’ behaviour. This ensures the agents build evermore general capabilities. ● As training progresses, the agents exhibit heuristic behaviours such as experimenting, changing the state of the world, and cooperation, which are uncharacteristic of usual RL agents. These learned behaviours allow them to generalize to hand-designed held-out tasks, a first in RL research. Figure: Examples of XLand environments. Figure: Test metrics progress during training. #stateofai | 30 Introduction | Research | Talent | Industry | Politics | Predictions
- 25. Some BIG generative models GTP3 Language model Next word prediction DALLE 2 Image and text Diffusion model GLAMP / PALM NLP and text understanding OpenAI OpenAI Google
- 26. PALM
- 27. Humans vs machines Not everyone agrees. “Artificial intelligence programs lack consciousness and self-awareness,” researcher Gwern Branwen wrote in his article about GPT-3. “They will never be able to have a sense of humor. They will never be able to appreciate art, or beauty, or love. They will never feel lonely. They will never have empathy for other people, for animals, for the environment. They will never enjoy music or fall in love, or cry at the drop of a hat.”
- 28. Chain of thought
- 29. Explaining jokes
- 33. The rise of diffusion models
- 35. CLIP: Learning self-supervised representations of text and images
- 36. DALLE-2
- 37. A dinosaur dressing a suit is looking at the mirror
- 38. “Honeybees wearing welding helmets while welding a futuristic giant steel honeycomb, digital art.”
- 39. Medieval biblical scroll about Darwinian Evolution
- 40. Image processing What has been solved Identification of objects Automatic subtitles generation Image segmentation, Depth NLP Writing coherent text Explainability Generative models High quality synthetic data Conditional text to image models Reinforcement Learning “Almost zero shot” learning Learning by observing: replication Object manipulation Video Object tracking and Identification Pose estimation Science Drug discovery Weather forecasting Physics Informed Networks
- 41. IMAGE What’s still a challenge Zero shot learning Video Self driving cars Generative models Spatio-Temporal data Reinforcement learning Self exploration without goals NLP Keep coherence on long texts Understanding meaning Science Discover new laws Deductive thinking
- 42. Future
- 43. Beyond the present paradigma BEYOND GRADIENT DESCENDENT ● Gradient based algorithms are continuous but nature is discrete ● Learning can gradual but also through sharp transitions - paradigms ● Recursivity hard to model with GD FROM BLACK-BLOXES TO CONJECTURE MACHINES ● ANN are induction machines, but knownledge can also be deductive ● At the moment we are brute-forcing learning with big models and data ● Hard tp generalize with a single example