O'Reilly Artificial Intelligence Conference 2019 - New York, New York
2019-04-15..2019-04-18
New York, 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 |
|---|---|---|
| Checking in on AI tools | Roger Chen, Ben Lorica | 08m |
Survey of over 1300 respondents shows AI moves from research to implementation: patent filings in AI are rising with 24% growth since 2011 in computer vision… | ||
| 4 real-world AI deployments at enterprise scale | Gadi Singer | 04m |
Over the last three to four years deployment replaces exploration: deep learning frameworks produce optimized libraries and graph compilers that connect down… | ||
| Is AI human-ready? | Aleksander Madry | 04m |
State-of-the-art classifiers flip from “pig” to “airliner” with 99% confidence after a small, carefully chosen noise pattern, and MIT students print a turtle… | ||
| Automated ML: A journey from CRISPR.ML to Azure ML | Danielle Dean | 11m |
A meta‑learning system turns CRISPR.ML’s months of manual tuning into a recommender over a discrete space of preprocessing, model families and hyperparameters… | ||
| Toward Ethical AI | Kurt Muehmel | 05m |
AI systems repeatedly produce harms — denied loans, misdiagnosed patients, wrongful arrests — because bias emerges from the interaction of code, training data… | ||
| Machine Learning for Personalization | Tony Jebara | 03m |
Netflix treats each account as 140 million personalized products, using machine learning to rank catalog items, arrange page layout, pick billboard promotions… | ||
| How AI Adaptive Technology can Upgrade Education Industry | Joleen Liang | 06m |
Squirrel AI Learning runs a personalized platform plus 1,800 physical centers in 300+ Chinese cities and reports $150M revenue. The system gives students 70%… | ||
| AI and the Robotics Revolution | Martial Hebert | 04m |
Overview of specific robotics results and gaps: built a drone inspection stack, a manipulation pipeline and an autonomous navigation prototype using deep… | ||
| Data Fueling AI of the Future | Thomas Henson | 03m |
Claims that modern AI architectures in weeks or months replace decades of manual setup, reports work across medical research and finance, and points to a… | ||
| Automation of AI: Accelerating the AI Revolution | Ruchir Puri | 11m |
Describes concrete AutoAI tools: AI designing AI via "new nets" in Watson OpenScale that synthesizes neural networks from scratch for a given training set and… | ||
| Making real-world distributed deep learning easy with Nauta | Carlos Humberto Morales | 03m |
Nauta is an open-source, multiuser, multi-node platform for distributed deep learning training (not inference), released in January and built kubernetes-native… | ||
| Decoding the Human Genome with Deep Learning | Olga Troyanskaya | 05m |
Machine learning builds a predictive regulatory map across the 3 billion letters of the human genome so single‑letter variants get biochemical consequences… | ||
| Applied machine learning at Facebook | Kim Hazelwood | 03m |
Describes Facebook’s end to end ML stack with concrete builds and open releases: hardware servers for each pipeline stage including two storage platforms… | ||
| Joleen Liang (Squirrel AI Learning) interviewed at the O'Reilly Artificial Intelligence Conference | Joleen Liang | 15m |
Adaptive platform built 2014–2017 combines about 20 algorithms with proprietary MKC nano-scale knowledge points and a route-tracing system to 4th–6th grade… | ||
| Kurt Muehmel (Dataiku) interviewed at the O'Reilly Artificial Intelligence Conference NY 2019 | Kurt Muehmel | 17m |
Argues that ROI needs concrete, small measurements not grand totals and recommends quantify a few impacts (time saved, process improvements, revenue or cost… | ||
| Danielle Dean (Microsoft) interviewed at the O'Reilly Artificial Intelligence Conference NY 2019 | Danielle Dean | 06m |
Describes concrete barriers to production AI: difficulty to translate business problems into data science problems, need for end-to-end flows that capture… | ||
| Successful AI adoption | Rajendra Prasad | 11m |
Argues against piecemeal, siloed pilots and insists on a holistic AI platform that uses microservices + MLOps, defines sprints for data curation versus… | ||
| Gadi Singer (Intel) interviewed at the O'Reilly Artificial Intelligence Conference NY 2019 | Gadi Singer | 11m |
Intel describes VNNI augmenting AVX vector instructions to give some deep‑learning ops up to 14x boost on CPUs, targeting inference-heavy loads where current… | ||
| Better Together Diversity Networking Lunch | Reshama Shaikh | 19m |
Community builds and runs Women and Machine Learning and Data Science with 63 chapters in 27 countries, ~30,000 members and 130 organizers, a free job board… | ||
| Sponsor Perspectives | 02m | |
Attendees already understand basic AI and instead ask detailed questions about knowledge mining, cloud ML and what appears as Bot services; conversations focus… | ||
| Increasing value in client collaborations with AI | Prince Harfouche | 21m |
Deloitte claims placement at the top of Gartner’s quadrant for execution and vision and focuses on turning backlog dark data — scanned contracts, low‑quality… | ||
| Artificial Intelligence - the “refinery” for Data | Nick Curcuru | 06m |
Mastercard frames itself as a technology company, not a credit card issuer, running AI across payments scale: 2.5 billion customers, 23,000 banks, 50 million… | ||
| nothing matches | ||