Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Schedule: Algorithms sessions

9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Nob Hill 2 & 3
Bruno Goncalves (Data For Science)
Average rating: ****.
(4.50, 2 ratings)
Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture. Read more.
9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Yosemite BC
Marcos Campos (Bonsai)
Average rating: **...
(2.33, 9 ratings)
Marcos Campos offers an overview of reinforcement learning, walking you through the various classes of reinforcement learning algorithms, the types of problems that can be solved with this technique, and how to build and train AI models using reinforcement learning and reward functions. Read more.
1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Imperial A
Ion Stoica (University of California, Berkeley), Robert Nishihara (University of California, Berkeley), Philipp Moritz (University of California, Berkeley)
Average rating: ****.
(4.57, 7 ratings)
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
11:05am–11:45am Tuesday, September 19, 2017
Implementing AI
Location: Franciscan AB
Susan Etlinger (Altimeter Group)
Average rating: ***..
(3.60, 5 ratings)
Drawing on her report The Conversational Business: How Chatbots Will Reshape Digital Experiences, Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots. Read more.
11:55am–12:35pm Tuesday, September 19, 2017
Verticals and applications
Location: Franciscan AB
Andy Steinbach (NVIDIA)
Average rating: ***..
(3.43, 7 ratings)
Andy Steinbach shares case studies and applications in artificial intelligence that are having an impact on financial markets. Read more.
2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Grand Ballroom
Kenny Daniel (Algorithmia)
Average rating: ****.
(4.00, 1 rating)
Kenny Daniel explains why AI and machine learning are a natural fit for serverless computing and shares a general architecture for scalable and serverless machine learning in production. Along the way, Kenny discusses the issues Algorithmia ran into when implementing its on-demand scaling over GPU clusters and outlines one possible vision for the future of cloud-based machine learning. Read more.
4:00pm–4:40pm Tuesday, September 19, 2017
Implementing AI
Location: Grand Ballroom
Jeremy Howard ( fast.ai | USF | doc.ai and platform.ai)
Average rating: ****.
(4.00, 1 rating)
Although most devs are aware of the benefits of GPU acceleration, many assume that the technique is only applicable to specialist areas like deep learning and that learning to program a GPU takes complex specialist knowledge. Jeremy Howard explains how to easily harness the power of a GPU using a standard clustering algorithm with PyTorch and demonstrates how to get a 20x speedup over NumPy. Read more.
4:00pm–4:40pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite BC
Art Popp (ServiceNow)
Average rating: *....
(1.00, 1 rating)
Art Popp walks you through a “from scratch" implementation of two algorithms to demonstrate the tools available for original algorithm development, using both SIMD and SIMT designs, the leading hardware architectures of which are Xeon Phi and NVIDIA Cuda. Along the way, Art explores the performance per watt, performance per dollar (initial cost), and performance per dollar (TCO) of each. Read more.
11:05am–11:45am Wednesday, September 20, 2017
Implementing AI
Location: Imperial B
Ben Vigoda (Gamalon)
Average rating: ****.
(4.20, 5 ratings)
Ben Vigoda demonstrates new advances in AI technology that enable companies to accurately read millions of complex customer messages and take action. Read more.
11:55am–12:35pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B
Kenneth Stanley (Uber AI Labs | University of Central Florida)
Average rating: ****.
(4.62, 8 ratings)
Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas. Read more.
2:35pm–3:15pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC
Mark Hammond (Microsoft)
Average rating: ****.
(4.67, 3 ratings)
Mark Hammond explores how enterprises can move beyond games and leverage deep reinforcement learning and simulation-based training to build programmable, adaptive, and trusted AI models for their real-world applications. Read more.
4:00pm–4:40pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC
Melanie Warrick (Google)
Average rating: ****.
(4.00, 4 ratings)
Reinforcement learning is a popular subfield in machine learning because of its success in beating humans at complex games like Go and Atari. The field’s value is in utilizing an award system to develop models and find more optimal ways to solve complex, real-world problems. This approach allows software to adapt to its environment without full knowledge of what the results should look like. Read more.
4:00pm–4:40pm Wednesday, September 20, 2017
Verticals and applications
Location: Franciscan AB
Gang Wang (Intuit)
Average rating: ****.
(4.75, 4 ratings)
Taxes are one of consumers' most complex financial transactions, thanks to a tax code that is 80,000 pages long. Gang Wang explains how Intuit built the industry’s only Tax Knowledge Engine, a constraint-based engine that encodes changing financial regulations and provides the foundation for a host of artificial intelligence technologies that save customers time and money. Read more.