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- 1. Jay Yagnik Vice President, Engineering Fellow Google AI A History Lesson on AI
- 2. Today we’ll talk about... ●Long term perspectives on AI ●Potential technology inflection points ○Predicted variables ○Quantum computing ●Potential use case inflection points ○Tackling societal challenges
- 3. AI vs. ML Artificial Intelligence The science of making things smart Machine Learning Making machines that learn to be smarter
- 4. Spam the old way: Write a computer program with explicit rules to follow if email contains sale then mark is-spam; if email contains … if email contains …
- 5. Spam the new way: Write a computer program to learn from examples try to classify some emails; change self to reduce errors; repeat;
- 6. Training
- 7. Machine Learning Arxiv papers per year ~50 new ML papers everyday
- 8. NIPS Registration Growth
- 9. A history lesson from photography
- 10. 1826 First photograph
- 11. 1826 1835 First (unintended) photo of a human being
- 12. 1826 1835 1839 The first selfie
- 13. 1826 1835 1839 1847 First news picture
- 14. 1826 1835 1839 1847 1860 First aerial photograph
- 15. 1826 18611835 1839 1847 1860 First color photograph
- 16. 1826 1861 18781835 1839 1847 1860 First moving picture
- 17. 1826 1861 1878 19571835 1839 1847 1860 First digital photograph
- 18. 1826 1861 1878 19571835 1839 1847 1860 Fast forward Today
- 19. 1826 Where ML is TODAY 1835 1839 1847 1860 1861 1878 1957 Today
- 20. Potential of ML across industries and use cases Personalize advertising Identify and navigate roads Personalize financial products Optimizing pricing and scheduling in real time Volume(Breadthandfrequencyofdata) Impact score Finance Trave l Automotiv e Teleco m Media Consumer Healthcare
- 21. Potential Inflection points ●Interface Innovation ○e.g. PVars ●Compute Substrate ○e.g. Quantum computing ●Optimization Framework ○e.g. Discrete Optimization
- 22. Potential technology inflection point: Predicted Variables
- 23. Growing adoption of ML ● Adoption of ML in applications has rapidly increased over the last 4 years ● Headroom is still very large ● ML systems are written from a ML engineer’s point of view ● Systems are built from the full stack SWE point of view leading to implicit mismatch → slow adoption ● Need a solution from full stack SWE perspective
- 24. Machine Learning today Goal: Predict a value - based on observations (from the past, maybe distant past) 1. Capture the observations in a feature vector 2. With a good feature vector, building good ML models is possible Feature Vector Multiple SWE Quarters ✔️ Prediction Experiments and Tuning System
- 25. Today’s World Chasm of Suffering Products ML C++ Java Python Javascript Model Tensorflow Hyperparameters Logging Feature Store Feature Computation Model Serving Operational Concerns Privacy Policy and Compliance
- 26. Mission ML as easy as if statements
- 27. Characteristics of a solution ● Feel native to programming and system building. ● Inversion of control, software developer holds control. ● Simplicity without sacrificing expressiveness, particularly with regard to types of problems it can solve.
- 28. Places to look ● Programs and systems are a mix of: ○ Variables / Data ○ Computation ○ Control Flow ● We can imagine overloading any of these for a new abstraction.
- 29. Which one to overload ● Programs and systems are a mix of: ○ Variables / Data → Object oriented view of prediction ○ Computation → Functional view of prediction ○ Control Flow → Decision view of prediction ● All are valid and can generate meaningful abstractions; we choose variables for the Object oriented abstraction and its flexibility in data visibility.
- 30. Predicted Variables ● Overload the notion of variables to give them “self assignment” ability. ● Allow them to observe state of the program. ● Blame after effects on them. ● They learn to evaluate themselves every time they are used.
- 31. Predicted Variables PVars is an end-to-end solution: go straight from code to predictions. Teams focus on data and product goals, not pipelines and infrastructure for ML. ✔️ Start using PVars Prediction PVars Predicted Variables in Programming
- 32. Example: Hello World pvar = PVar(dtype=bool) // create the variable v = pvar.value // read from the variable while not v: pvar.feedback(BAD) // provide feedback to the variable v = pvar.value // read from the variable pvar.feedback(GOOD) // provide feedback to the variable print("Hello World")
- 33. Caches using Predicted Variables Before PVars class LRUCache(object): def __init__(self, size): self.storage = CacheStorage(size) def get(self, key): if key not in self.storage: return None self._update_timestamp(key, now()) return self.storage[key] def store(self, key, value): if self.storage.full(): evict_key = self._get_key_to_evict() self.storage.evict(evict_key) self.storage.insert(key, value, now())
- 34. class PredictedCache(object): def __init__(self, size): self.storage = CacheStorage(size) self.pvar = PVar(dtype=float, observations = {'access': key_type, 'store': key_type}, initial_policy_fn=now) def get(self, key): if key not in self.storage: self.pvar.feedback(BAD) return None self.pvar.feedback(GOOD) self.pvar.observe('access', key) predicted_timestamp = pvar.value self._update_timestamp(key, predicted_timestamp) return self.storage[key] def store(self, key, value): self.pvar.observe('store', key) if self.storage.full(): evict_key = self._get_key_to_evict() self.storage.evict(evict_key) predicted_timestamp = pvar.value self.storage.insert(key, value, predicted_timestamp) Caches using Predicted Variables After PVars
- 35. Caches ●Predicting which slot to evict (1-out-C) ○Improvements over LRU policy on small cache sizes range ○Power law synthetic access pattern (5000 keys)
- 36. PVars can express a wide range of uses ● Constant value → Predicted Constants ● Single Invocation, ground truth feedback → Supervised ML ● Single Invocation, cost feedback → Bandits ● Multi Invocation, cost feedback → RL / blackbox / dynamical systems methods. ● All of above with stochastic or deterministic variables. ● Which evaluations should i get feedback on ? → Active learning ● Multi-level Data visibility → Privacy sensitive ML ● Device locality for variables → Federated computation ● …...and more
- 37. Binary Search
- 38. Binary Search
- 39. Predict: p * interpolation + (1-p) * binary Reward: |old| / |new| / 2 - (2 if not found) Predict: position Reward: |old| / |new| / 2 - (2 if not found) Simplify prediction Simplify reward +discount 0.75 (bandits RL) Predict: p * interpolation + (1-p) * binary Reward: -1 if not found, 10 when found Similar results on chi2, gamma, pareto, power distributions Simplify reward Simplify prediction Predict: position Reward: -1 if not found, 10 if found Works only on uniform and triangular distributions for now Binary Search (normal distribution)
- 40. We aspire to change programming Make it a natural thing to use ML for all developers in all programming languages ● Whenever you add a heuristic (or a constant or a flag) ● There's no reason not to use it whenever you put an "arbitrary" constant right now. Stretch Goals: ● C++ 202X, Python 4, and Java 10 will have predicted variables as a standard type
- 41. Potential technology inflection point: Quantum Computing
- 42. Space-time volume of a quantum computation
- 43. Task Produce samples {x1, ..., xm} from distribution pU(x). Recent result from complexity theory (Bouland, Fefferman, Nirkhe, Vazirani): It is #P-hard to compute pU(xi). Random circuits, the “hello world” program for quantum processors Formulate quantum circuit by randomly picking 1-qubit or 2-qubit gates from a universal gate set acting on the global superposition state.
- 44. Understanding the probability distribution involved in supremacy experiments “Speckles” in 2n = N dimensional Hilbert Space Porter Thomas Distribution P(p)=Ne-Np Sort bit strings by probability
- 45. {x1, . . . , xm} {x’1, . . . , x’m} Use cross entropy to measure quality of samples Formulate quantum circuit by randomly picking 1-qubit or 2-qubit gates from a universal gate set acting on the global superposition state. Experiment to demonstrate quantum supremacy
- 46. Google Quantum AI timeline 2018? 2028? 72 qubits 105 qubits Quantum supremacy Beyond classical computing capability demonstrated for a select computational problem NISQ processors Algorithms developed for ● Certifiable random numbers ● Simulation of quantum systems ● Quantum optimization ● Quantum neural networks Error corrected quantum computer Growing list of quantum algorithms for wide variety of applications with proven speedups ● Unstructured search ● Factoring ● Semidefinite programming ● Solving linear systems
- 47. Potential use case inflection point: Tackling Societal Challenges
- 48. Number of papers on Arxiv Healthcare and Life Sciences featuring Machine Learning 2010 2012 20132011 20152014 2016 2017
- 49. Predicting Medical Events Encounters Lab Medications Vital Signs Procedures Notes Diagnoses PatientTimeline
- 50. Predicting Medical Events Encounters Lab Medications Vital signs Procedures Notes Diagnoses PatientTimeline Primary care visit Urgent care visit Hospitalization Glucose 170 ml/dlCreatinine 4 mg/dlHemoglobin A!C: 9.0% Vancomycin 1.5 gmInsulin Glargine 10 units nightly Non-invasive blood pressure 90/65 mmHg Bone biopsy “49 year old man with difficult-to-control diabetes” Acute kidney injurySkin and soft tissue infectionType II Diabetes 76% chance of readmission
- 51. 415M people with diabetes 153.2M 29.6 M 14.2M 44.3M 78.3 M 44.3M 35.4M
- 52. Over the last year we’ve done research demonstrating how AI can help doctors screen for preventable blindness.
- 53. 500M+ people rely on cassava plants as their main source of nutrition
- 54. Flood warning system
- 55. 1. Be socially beneficial 2. Avoid creating or reinforcing unfair bias 3. Be built and tested for safety 4. Be accountable to people 5. Incorporate privacy design principles 6. Uphold high standards of scientific excellence 7. Be made available for uses that accord with these principles Google’s AI Principles

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