December 17, 2019

151 words 1 min read

The future of AI is now (sponsored by IBM)

The future of AI is now (sponsored by IBM)

Damion Heredia explores how augmented intelligence is helping companies disrupt industries and enabling them to make better decisions.
Talk Title The future of AI is now (sponsored by IBM)
Speakers Damion Heredia (IBM Watson and Cloud Platform )
Conference O’Reilly Artificial Intelligence Conference
Conf Tag Put AI to Work
Location New York, New York
Date June 27-29, 2017
URL Talk Page
Slides
Video Talk Video

IBM is offering an alternative definition of AI: augmented intelligence. Augmented intelligence is enabling a new level of engagement and quickly becoming the differentiator to transform business, revolutionizing the partnership between man and machine, extending our capabilities, bridging understanding, and improving decision making. No longer a nascent technology, Watson APIs and services are integrated into enterprises across industry and across practices. Damion Heredia explores how augmented intelligence is helping these companies disrupt industries and enabling them to make better decisions. This keynote is sponsored by IBM.

api intelligence intel ibm ai
comments powered by Disqus
The AI-powered newsroom

The AI-powered newsroom

December 17, 2019

The promise of AI in the newsroom is contradictory: NLG revolutionizes news writing, but robot journalists threaten jobs; NLP improves fact-checking but requires investments that slimmed-down newsrooms cannot afford. Drawing on Norwegian AI startup Orbits experience, Codruta Gamulea explains how AI can help solve the industry resource constraints and improve the quality of journalism.
Accelerate analytics and AI innovations with Intel (sponsored by Intel)

Accelerate analytics and AI innovations with Intel (sponsored by Intel)

December 5, 2019

Ziya Ma outlines the challenges for applying machine learning and deep learning at scale and shares solutions that Intel has enabled for customers and partners.
Deep learning: Assessing analytics project feasibility and computational requirements

Deep learning: Assessing analytics project feasibility and computational requirements

December 4, 2019

Adam Grzywaczewski offers an overview of the types of analytical problems that can be solved using AI and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Adam then covers the computational requirements for the deep learning training process, leaving you with the key tools you need to initiate an analytical AI project.
Deploy Spark ML TensorFlow AI models from notebooks to hybrid clouds (including GPUs)

Deploy Spark ML TensorFlow AI models from notebooks to hybrid clouds (including GPUs)

December 4, 2019

Chris Fregly explores an often-overlooked area of machine learning and artificial intelligencethe real-time, end-user-facing "serving layer in hybrid-cloud and on-premises deployment environmentsand shares a production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly scalable and highly available robustness.
Real-time intelligence gives Uber the edge

Real-time intelligence gives Uber the edge

December 1, 2019

M. C. Srivas covers the technologies underpinning the big data architecture at Uber and explores some of the real-time problems Uber needs to solve to make ride sharing as smooth and ubiquitous as running water, explaining how they are related to real-time big data analytics.
Open source AI at AWS and Apache MXNet

Open source AI at AWS and Apache MXNet

November 22, 2019

A wide variety of open source frameworks and tools support artificial intelligence and deep learning. Adrian Cockcroft explains how AWS has packaged a number of themincluding deep learning frameworks such as Caffe, CNTK, Keras, MXNet, TensorFlow, Theano, and Torch and supporting tools like Jupyter and Anacondainto an Amazon Machine Image with optimized GPU support.