O'Reilly Artificial Intelligence Conference 2019 - San Jose
2019-09-09..2019-09-12
San Jose, United States
[O'Reilly Artificial Intelligence Conference 2017 - New York, New York, O'Reilly Artificial Intelligence Conference 2019 - New York, New York, O'Reilly Artificial Intelligence Conference 2019 - San Jose]
| title | author | length |
|---|---|---|
| Getting from A to AI | Eric Gardner | 04m |
Businesses recognize the transformational potential for advanced analytics, machine, and deep learning but often get lost on their path to AI. Eric Gardner… | ||
| Unlocking the value of your data | Dinesh Nirmal | 10m |
Claims that only 19 percent understand the data required for AI and that 1 in 20 models reach production set the stakes, then a Wimbledon use case with IBM… | ||
| Developing AI responsibly | Sarah Bird | 03m |
Microsoft-style set of six ethical principles—fairness, reliability and safety, privacy and security, inclusiveness, plus transparency and accountability—is… | ||
| Enabling AI’s potential through Wafer-scale integration | Andrew Feldman | 02m |
A wafer scale chip called Wafer Scale Engine (WSE) replaces conventional GPUs by scaling a die to ~46,225 mm² and packing 1.2 trillion transistors, 400,000+… | ||
| AI for Ophthalmology | Daniel Russakoff, Ph.D. | 05m |
A retrospective two-year cohort of dry AMD patients is used: baseline OCT images are fed to convolutional neural nets with a proprietary normalization step… | ||
| Going beyond supervised learning | Srinivas Narayanan | 03m |
Srinivas Narayanan takes a deeper look into the next change you’re seeing in AI—going beyond fully supervised learning techniques.
Subscribe to O'Reilly on… | ||
| On gradient-based methods for finding game-theoretic equilibria | Michael Jordan | 04m |
Matches a learning multi-armed bandit problem to a matching market where choices are exclusive so only one agent wins each arm, models agents exploring unknown… | ||
| Accelerate with purpose | Ananth Sankaranarayanan | 03m |
Three shifts drive the recommendations: models with billions of hyperparameters, massive multi-node clusters, and exploding data volumes that push latency… | ||
| Practical insights into deep reinforcement learning | Sahika Genc | 05m |
Reinforcement learning (RL) is an advanced machine learning (ML) technique that makes short-term decisions while optimizing for a longer-term goal through… | ||
| Open-endedness: A new grand challenge for AI | Kenneth Stanley | 03m |
Argues for creating open-ended algorithms that continually invent novel, increasingly complex tasks rather than optimizing a single objective; contrasts Deep… | ||
| Better Together Diversity Lunch | Tracy Stone, Amy Chou | 43m |
Intuit reports higher-than-industry representation of women in technical roles and 32% of leaders are female and runs a four-month returnship to rehire… | ||
| Scaling the future of Machine Learning in enterprises | Kurt Muehmel | 12m |
Predicts enterprises reach millions of models deployed within roughly five to ten years and argues the bottleneck shifts from model creation to systems that… | ||
| Creating a fair and trusted AI enterprise | Dinesh Nirmal | 12m |
Enterprise AI starts with a concrete use case and a clean training dataset, not headlines about deep nets. Deployment and data prep take more time than model… | ||
| Overcoming challenges when implementing AI | Eric Gardner | 05m |
Claim: biggest barrier is scale not theory — too many interested customers and a talent gap in deep learning that makes time management and enablement the core… | ||
| Considerations for maturing AI initiatives from PoC to Production | Saikumar Devulapalli | 10m |
Distinguishes ML vs deep learning by real numbers: ML uses tens to hundreds of terabytes up to a petabyte and tens-to-thousands of servers for feature models… | ||
| nothing matches | ||