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Open Source models gaining on massive private models
Some enablers:
• Chincilla: 70B params over 4X data outperforms 280B params [1]
• LoRA: freeze pre-trained model, add trainable rank decomposition matrices to
each layer, greatly reducing training params for specialization [2]
• 4-bit quantization (replace every param with 4 bits) appears competitive [3]
• Training on small, specialized datasets offers better results (e.g., [4])
[1] Compute-optimal large language models, https://arxiv.org/abs/2203.15556
[2] LoRA: Low-Rank Adaptation of Large Language Models, https://arxiv.org/abs/2106.09685
[3] Quantization reduce accuracy in favor of memory size and inference latency https://arxiv.org/abs/2212.09720
[4] Koala: ~1M dialogue examples competitive with massive scrapes https://bair.berkeley.edu/blog/2023/04/03/koala/
March 3 March 13 March 19
More info: https://www.semianalysis.com/p/google-we-have-no-moat-and-neither
In the last few months…
Vicuna: 13B params,
trained on 8 x A100
GPUs in one day
Bard: 137B
params, thousands
of petaflop-days](https://image.slidesharecdn.com/hilda2023keynoteshort-230822182559-2f894498/75/HILDA-2023-Keynote-Bill-Howe-4-2048.jpg)
![Bill Howe, UW 11
an armchair in the
shape of an
avocado…
A [X] year old girl… ? ? ?](https://image.slidesharecdn.com/hilda2023keynoteshort-230822182559-2f894498/75/HILDA-2023-Keynote-Bill-Howe-11-2048.jpg)
![Conviction LP Letter - Dec 2023 [Redacted]](https://cdn.slidesharecdn.com/ss_thumbnails/convictionlpletter-dec2023sentredacted-250215033237-61bacefc-thumbnail.jpg?width=640&height=640&fit=bounds)
![ict_presentation_final_final_final[1].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/ictpresentationfinalfinalfinal1-251230145259-2b4839bd-thumbnail.jpg?width=640&height=640&fit=bounds)
HILDA 2023 Keynote Bill Howe
- 1. Curation as Programming:AI, Data Management, and Mediated Knowledge Interaction Bill Howe, Ph.D. Associate Professor, Information School Co-Director, Responsible AI Systems & Experiences Adjunct Associate Professor, Allen School of Computer Science & Engineering Adjunct Associate Professor, Electrical Engineering University of Washington 1
- 2. A view ofLLMs from 6 months ago… • Bigger is different • “Emergent” capabilities • A handful of “Foundation Models” can do any task; no need for specialized models • Re-training is too expensive anyway • No way to compete with OpenAI/Microsoft, and to a lesser extent Google Bill Howe, UW 2
- 3. 3 https://rollcall.com/2023/05/23/lawmakers-suggest-agency-to- supervise-artificial-intelligence/ May 23 2023 https://www.nytimes.com/2023/05/16/technology/opena i-altman-artificial-intelligence-regulation.html Whyare OpenAI and others lobbying for regulation in the US?
- 4. 4 Open Source modelsgaining on massive private models Some enablers: • Chincilla: 70B params over 4X data outperforms 280B params [1] • LoRA: freeze pre-trained model, add trainable rank decomposition matrices to each layer, greatly reducing training params for specialization [2] • 4-bit quantization (replace every param with 4 bits) appears competitive [3] • Training on small, specialized datasets offers better results (e.g., [4]) [1] Compute-optimal large language models, https://arxiv.org/abs/2203.15556 [2] LoRA: Low-Rank Adaptation of Large Language Models, https://arxiv.org/abs/2106.09685 [3] Quantization reduce accuracy in favor of memory size and inference latency https://arxiv.org/abs/2212.09720 [4] Koala: ~1M dialogue examples competitive with massive scrapes https://bair.berkeley.edu/blog/2023/04/03/koala/ March 3 March 13 March 19 More info: https://www.semianalysis.com/p/google-we-have-no-moat-and-neither In the last few months… Vicuna: 13B params, trained on 8 x A100 GPUs in one day Bard: 137B params, thousands of petaflop-days
- 5. Bill Howe, UW5 To create a regulatory barrier and maintain competitive advantage Prediction: Open source models will remain competitive and available. The curation and management of specialized, minimal, modular datasets for training and evaluation will drive AI progress. This is an opportunity for the data management community. So now: Why are OpenAI and others lobbying for regulation in the US?
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- 8. simulations internet databases generative models people Ex: mostLLMs massive, noisy data -> huge, general, yet unpredictable models GPT 3.5: 175B parameters (800GB) Bard: 137B parameters GPT-4: ???B billion parameters data AI rlhf scrapes Curation-on-read: scrape an uncurated convenience sample of the internet, then implement guardrails with reinforcement learning from human feedback, and/or by sanitizing inputs and outputs
- 9. Bill Howe, UW9 No more free data
- 10. Inputs Outputs an armchairin the shape of an avocado… DALL-E, OpenAI A teapot in the shape of a rubik’s cube… A shrimp with sunglasses riding a unicycle… https://openai.com/blog/dall-e/
- 11. Bill Howe, UW11 an armchair in the shape of an avocado… A [X] year old girl… ? ? ?
- 12. Robert Wolfe Prof AylinCaliskan FAccT 23 Yiwei Yang Training data polluted by objectified images of women
- 13. simulations internet databases generative models people data AI Curation-on-write:Carefully control the training and evaluation data, and produce correct, verifiable results curated, trusted data -> specialized, trusted models Ex: AlphaFold: Protein folding Ex: DWLP: Weather prediction
- 14. https://www.nature.com/articles/d41586-022-00997-5 https://www.nature.com/articles/nrm3461 AlphaFold covered 60% of thestructure as of October 2021, up from 30% from prior models nuclear pore complex: Largest molecular machine in human cells Before 2021: 100,000 structures determined experimentally and computationally over the last 50 years Since 2021 with AlphaFold: 992,316 structures and counting; expecting 130M within a year or two
- 15. 15 50+ years 100s ofmillions USD 130k structures https://www.nature.com/articles/s41586-021-03819-2/figures/1 AlphaFold uses 50 yrs of PDB
- 16. 16 Cubed sphere grid U-NetCNN (fairly standard) Dale Durran, UW Jonathon Weyn, UW Rich Caruana, Microsoft Deep Learning for Weather Prediction J Adv Model Earth Syst, Volume: 13, Issue: 7, First published: 25 June 2021, DOI: (10.1029/2021MS002502)
- 17. 17 RMSE at 500mbar isopotential vs. forecast day Full-physics model (lower res) (worse) Deep Learning DWLP (U-Net, CNNs) Full-physics model (comparable res) (better) Full-physics model (very high res) (best) Dale Durran, UW Jonathon Weyn, UW Rich Caruana, Microsoft Deep Learning for Weather Prediction 3 min for 1-mo. ensemble (+ 2-3 days to train) vs. 16 days for 1-mo. ensemble! Deep Learning DWLP (high res, ensemble) Full-physics model (very high res, ensemble) (better, but not by much!) JAMES 2020 JAMES 2021
- 18. simulations internet databases generative models people data AI Mygroup’s interests: AI for curation + curation for AI
- 19. AI for curation/ curation for AI • UrbanSynth: Curating City Data – Spatiotemporal imputation – Spatiotemporal disaggregation – Spatiotemporal integration – Information Extraction from Court Records • Curating Viz Interaction Data • Mitigating Bias w/ Curated Concept Sets • SynRD: Evaluating Synthetic Data • Learning from Curated Ontologies 19 SIGMOD 21, AAAI 20 ICML ws 23 VLDB 24 WWW22, NeurIPS 22 ArXiv 22 VLDB 24 subm.
- 20. Cities as complexsystems Cities as assemblies of independent subsystems Traffic speed prediction (Liao et al., 2018) Demand forecasting (Uber, 2018) Crowd flows (Zhang et al., 2018) Traffic accident prediction (Yuan et al., 2018) European Physical Journal 22
- 21. Curation is thebottleneck… • Sometimes we have expert-curated supervision – 50+ years of expert experimental curation via PDB enables AlphaFold – 70+ years of physics-based fluid dynamics models enables DL for weather • But city data is incomplete, aggregated, disintegrated • AI to curate city data Bill Howe, UW 21
- 22. AI-enabled curation ofcity data Reconstructed Missing Aggregated High-res Heterogeneous Learned weights 3D 2D 1D Bin Han An Yan SIGMOD 21 https://arxiv.org/abs/2301.04233 VLDB 24 (prep) J Transp Geography 19 AAAI 20
- 23. Wipe out manhattan Model recovers https://arxiv.org/abs/2301.04233 Bin Han Keyideas: 1) Borrow ideas from image in-painting 2) Bias the masking to follow population distribution 3) Use both space and time 4) Data repair by masking anomalies and reconstructing Reconstructing missing data in space and time
- 24. Urban image inpaintinghttps://arxiv.org/abs/2301.04233 Handle sparsity: Biased masking Bin Han
- 25. Bill Howe, UW28 Parade route anomaly – no traffic, but unrealistic 12 PM during parade 4 PM after parade Erase the anomaly and synthesize new data Low error in the prediction compared to a non-parade day https://arxiv.org/abs/2301.04233 Bin Han
- 26. Bill Howe, UW30 New York divided into regions at different scales Given aggregate data at this level Learn to disaggregate data at these levels (supervised by individual data or other variables) Bin Han VLDB 24 (prep)
- 27. 31 Idea: Spatially coherentarchitecture: Align the model with the aggregation levels; compute loss at each level Use prediction at block level to predict at tract level, and so on. Force spatial coherence; learn all aggregation levels simultaneously. Different loss strategies: full reconstruction, prior layer only, bottom-up only. Overall: * Baseline NN outperforms all classical methods * Coherent architectures outperforms baseline by a significant margin Bin Han VLDB 24 (prep)
- 28. Encapsulating urban dynamics: Learningreusable representations from multi-source data bikeshare demand + ML Model Prediction Representation Z An Yan SIGMOD 21
- 29. Bill Howe, UW34 An Yan SIGMOD 21
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- 31. Exogenous features matter Our “everything atonce” model approximates perfect variable selection Optimizing for fairness preserves accuracy Oracle-selected variables Single variable Equitensors (all variables)
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- 33. Bill Howe, UW38 VizDeck (SIGMOD 12, iConference 13): Generate lots of visualizations directly from data properties and design rules, try rules, try to learn from what users select. (no real ML, because we didn’t know how to do it + bad vis libraries) Viz Recommendation Trajectory… Voyager 1 (Vis 15), 2 (Vis 17): Generate alternate visualizations, in a principled way, from a user-created seed & report on how user behavior changes (no real ML, because I couldn’t convince Ham, Dom, and Jeff to do it :) DRACO (Vis 18, Best paper): Generate visualizations according to design rules in answer set programming, learn weights on rules from data. (a little ML, but we lacked good datasets for training) Lilly (NLViz workshop 19): Vision for AI-assisted non-expert analysis and storytelling. (We didn’t build it then, now it’s much easier, but we still lack data)
- 34. Bill Howe, UW40 Consistency warnings Zening Qu Multi-view consistency checks
- 35. Bill Howe, UW41 Zening Qu Multi-view consistency checks Afford direct navigation of the revision space. Hover for preview, click to set root node. Curated interaction log
- 36. Bill Howe, UW55 Surj: Ontological Learning for Fast, Accurate, and Robust Hierarchical Multi-label Classification Sean Yang Problem: Classify into an ontology Simple idea: Embed the ontology Embed the data Learn the mapping SoTA results, better robustness, much faster GraphLearning@WWW 22
- 37. Bill Howe, UW56 Sean Yang Bernease Herman Surj wins on almost all existing benchmarks, but we show the existing benchmarks aren’t measuring anything useful. Tool to generate better benchmarks NeurIPS 22 impossible trivial good
- 38. 57 src: wikipedia 2nd toLast slide As models become commoditized, AI reduces to data curation A hundred flowers will blossom – many specialized models, trained on many specialized datasets “Curation” means filtering, cleaning, integrating multi-modal data from a variety of sources: web, structured DBs, generative models, simulations, crowdsourcing. These are core strengths for the DB community: Queries are curation Databases are “training set management systems”
- 39. 58 src: wikipedia Last slide Examplesof Open Questions: • Querying for data near the decision boundary of a model • Querying generative models at scale: Create a diverse set of images with specific properties • Dataset minimization: smaller dataset, same model performance • Data-aware model minification • Experiment management systems • Synthetic data management systems • Bias mitigation in queries: Return query results with specific statistical properties
- 40. 59 Sean Yang Bernease Herman BinHan Yiwei Yang Maxim Grechkin Poshen Lee Binbing Wen graduated Alumni Year 1-3 Jackson Brown Annie Yan Shrainik Jain
- 41. Where we are… •Sometimes we have highly curated data for supervision – 50+ years of expert experimental curation via PDB enables AlphaFold – plus lots of domain expertise in the model itself • Other times data is incomplete or disintegrated – So: Use AI to replace missing data, disaggregate, and integrate to complete the data record – Also: Synthesize scenarios that don’t appear in the data record • Improve robustness to unseen / OOD events • Disasters, anomalies, hypothetical situations • Other times the bias is more subtle – Next: Unlearning bias in vision-language models Bill Howe, UW 60
- 42. 61 Curation as programmingin vision models Vision models trained on convenience samples learn societal biases Yiwei Yang Goal: Penalize this bias during training, without human labeling
- 43. 62 A man sittingat a desk with a laptop computer. A woman sitting in front of a laptop computer. A man holding a tennis racquet on a tennis court. A man holding a tennis racquet on a tennis court. Right answer, wrong reasons Wrong answer, wrong reasons Proposed solution is specific to gender Two new loss terms: 1) reduce confidence when gender information is absent 2) increase confidence when it is present Women also Snowboard: Overcoming Bias in Captioning Models Kaylee Burns, Lisa Anne Hendricks, Kate Saenko, Trevor Darrell, Anna Rohrbach, CVPR 2018
- 44. Bill Howe, UW63 nurse doctor doctor Hypothetical ”good” visual explanations Yiwei Yang
- 45. Bill Howe, UW64 nurse doctor nurse Hypothetical bad visual explanations reflecting societal biases Yiwei Yang
- 46. Bill Howe, UW65 Key Idea: Penalize the model for using the wrong explanations How? 1. Curate a concept set of images representing a particular bias (e.g., gender) (ok to use generative models, web search, or anything else you have.) 2. Penalize the model’s sensitivity to this concept set during training with a new loss term Benefits: No need for explicit human annotations, concept sets reusable across models, concept sets can be application-specific – anything that can be represented by images. Yiwei Yang
- 47. 66 Interpretability Beyond FeatureAttribution: Quantitative Testing with Concept Activation Vectors (TCAV) Kim et al. ICML 2018 https://arxiv.org/abs/1711.11279
- 48. 67 Concept: Stripes Zebra images Amodel split at layer l into an encoder and a decoder Concept Activation Vector Sensitivity of prediction of zebra to the concept of stripes at layer l (Sensitivity of class k at layer l to a concept set C ) Given an image, if we perturb its representation at layer l in the direction of the concept set “stripes,” how much would the model’s prediction of zebra change? Kim et al. ICML 2018 https://arxiv.org/abs/1711.11279
- 49. Bill Howe, UW68 For each x in batch, For each class logit, Gradient of the decoder Vector pointing towards “hairstyle” Loss term to penalize inappropriate explanations e.g., “hairstyle should not be used to determine profession” Centroid of images representing hairstyle Centroid of random images Yiwei Yang
- 50. 69 Test: Spurious correlationby construction Mismatched foreground and background Matched foreground and background Training set encourages model to attend to the background, not the bird
- 51. Bill Howe, UW70 Dataset planted with a spurious correlation: Mismatched foreground and background Matched foreground and background Red = high attention Blue = low attention Model ignores the bird, and misclassifies it
- 52. 71 Yiwei Yang A simpleexperiment Adapted from Colored MNIST dataset, Arjovsky et al, Invariant Risk Minimization https://arxiv.org/abs/1907.02893 Number analogous to profession Color analogous to race, gender, or any other bias we want to unlearn No labels available Curate concept sets representing… redness blueness greenness
- 53. 72 Original model istotally fooled Loss term significantly improves Yiwei Yang 0 1 2 1 2 0 Prediction Ground truth 1 2 0 0 1 2 Ground truth Prediction Goal: Predict the correct digit based on shape, but ignore the bias of color Initial results: We can mitigate bias by providing lots of images representing colors and penalize representations that use them Concept set: Google image search for “red”, “green”, “blue” Open question: How much does the quality of the concept set matter? (e.g., “Women” vs. “Woman Doctor”)
Editor's Notes
- #13 High-expertise means balancing multiple competing quality metrics – LLMs are making this difficult.
- #18 Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere, Jonathan A. Weyn, Dale R. Durran, Rich Caruana, Journal of Advances in Modeling Earth Systems, volume 12, issue 9, August 2020 https://doi.org/10.1029/2020MS002109 Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models, Jonathan A. Weyn,Dale R. Durran,Rich Caruana,Nathaniel Cresswell-Clay, Journal of Advances in Modeling Earth Systems, volume 13, issue 7, July 2021 https://doi.org/10.1029/2021MS002502
- #34 Why
- #43 eviction lawsuit court records Download OCR Assess accuracy 75% ML: OCR to get tokens, train a model to recognize addresses Predict race from location and surname Predict sex from firstname and cross-reference with social security database (SSA) and census microdata (IPUMS)