ai

Practical Verification for Edge AI use and Effort for Functional Improvement

Practical Verification for Edge AI use and Effort for Functional Improvement

February 12, 2020

Theme presentation regarding practical verification result of study data updating model at edge AI use and functional improvement (OpenCL application to AGL). Through above, we are willing to pick up …
Building the Global Open Software Platform for Fully Autonomous Vehicles

Building the Global Open Software Platform for Fully Autonomous Vehicles

February 7, 2020

Rapidly advancing SoC technologies are delivering unprecedented performance for fully automated cars and vehicles. Open Software platforms enable global collaboration and acceleration of innovation. A …
Enterprise architecture for artificial intelligence

Enterprise architecture for artificial intelligence

February 7, 2020

The future of software is being driven by intelligent applications. By the year 2020, more than 85% of customer interactions will be carried out without humans. The road to enterprise intelligence starts with the humans behind the curtain. Kishau Rogers explains how to reduce the friction of AI adoption in the enterprise using systems thinking and people-centered workflows.
A day in the life of a data scientist in an AI company

A day in the life of a data scientist in an AI company

February 5, 2020

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
AI and machine learning at Amazon (sponsored by Amazon Web Services)

AI and machine learning at Amazon (sponsored by Amazon Web Services)

February 5, 2020

Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and explains how you can use easily accessible services from AWS to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
AI for a better world

AI for a better world

February 5, 2020

Industry buzz sometimes focuses on an AI future with dire unintended consequences for humanity. Ashok Srivastava draws on his cross-industry experience to paint an encouraging picture of how AI can solve big problems with people, data, and technology to benefit society.
AI in production: The droids youre looking for

AI in production: The droids youre looking for

February 5, 2020

Artificial intelligence in the future, at least represented in science fiction, can learn, interpret, and take action based on data analysis. AI in production is the present, a present that feels decidedly futuristic. Jonathan Ballon explains why Intels leading portfolio of AI and computer vision edge technology will drive advances that improve how we work and live.
Architecting AI applications

Architecting AI applications

February 5, 2020

Mikio Braun looks back on the past 20 years of machine learning research to explore aspects of artificial intelligence. He then turns to current examples like autonomous cars and chatbots, putting together a mental model for a reference architecture for artificial intelligence systems.
Deep Learning Neural Network Acceleration at the Edge

Deep Learning Neural Network Acceleration at the Edge

February 5, 2020

The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that …
Bringing AI into the enterprise: A functional approach to the technologies of intelligence

Bringing AI into the enterprise: A functional approach to the technologies of intelligence

February 4, 2020

Kristian Hammond walks you through an approach to bring AI into the enterprise, based on the functional, business aspects of AI technologies. Kristian maps out simple rules, useful metrics, and where AI should live in the org chart, laying out the route you should follow to make good on the promise of the technologies of intelligence.
Deep learning at scale: A field manual

Deep learning at scale: A field manual

February 4, 2020

Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems from a practitioner's point of view. Along the way, Jason dives deep into available tools, resources, and venues for getting started without having to go it alone.
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

February 4, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI

Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI

February 4, 2020

We're experiencing an AI gold rush. Tech giants, corporations, startups, and governments are investing billions, and headlines about AI have reached fever pitch. It's dizzying to keep track of the latest AI developments and claims. Join Simon Greenman to learn who can and who will make money in this gold rushand who will become economic casualties along the way.
How artificial intelligence is changing advertising in China: A conversation with Bessie Lee and Ching Law

How artificial intelligence is changing advertising in China: A conversation with Bessie Lee and Ching Law

February 3, 2020

Advertising in China is on the frontline of AI adoption and innovation. Join Bessie Lee and Ching Law for a conversation on how AI is changing advertising. You'll hear how China's white-hot AI advertising applications can serve as roadmaps and spark ideas in other industries and how companies like Tencent are improving performance by leveraging AI technology.
Lessons learned implementing AI for the IoT globally at Panasonic

Lessons learned implementing AI for the IoT globally at Panasonic

February 3, 2020

Christopher Nguyen shares lessons learned implementing multiple AI commercial projects at Panasonic. Along the way, Christopher discusses a number of use cases at various stages of implementation maturity and explains what AI really means today in enterprise products, where the key opportunities are, their impact, and key success factors in the adoption of AI across the enterprise.
Notes from the frontier: Making AI work

Notes from the frontier: Making AI work

February 3, 2020

Drawing on the McKinsey Global Institute's groundbreaking research, Michael Chui explores commonly asked questions relating to AI and its impact on work. Michael also previews new research showing that despite the rapid pace of AI adoption, much foundational work in enterprises remains to be done to capture value at scale.
PyTorch 1.0: Bringing research and production together

PyTorch 1.0: Bringing research and production together

February 3, 2020

Dmytro Dzhulgakov explores PyTorch 1.0, from its start as a popular deep learning framework for flexible research to its evolution into an end-to-end platform for building and deploying AI models at production scale.
Rethinking software engineering in the AI era

Rethinking software engineering in the AI era

February 2, 2020

Yangqing Jia shares a series of examples to illustrate the uniqueness of AI software and its connections to conventional computer science wisdom. Yangqing then discusses future software engineering principles for AI compute.
The last mile on democratizing AI

The last mile on democratizing AI

February 2, 2020

Zhipeng Huang explains how resource representation (RR) works with various intermediate representation (IR) technologies to help achieve the democratization of AI.
The use of recommender systems in the chief investment office: A case study

The use of recommender systems in the chief investment office: A case study

February 2, 2020

Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today.
Trust and transparency of AI for the enterprise (sponsored by IBM Watson)

Trust and transparency of AI for the enterprise (sponsored by IBM Watson)

February 2, 2020

TBC
A day in the life of a data scientist: How do we train our teams to get started with AI?

A day in the life of a data scientist: How do we train our teams to get started with AI?

January 27, 2020

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
A high-performance system for deep learning inference and visual inspection

A high-performance system for deep learning inference and visual inspection

January 27, 2020

Moty Fania and Sergei Kom share their experience and lessons learned implementing an AI inference platform to enable internal visual inspection use cases. The platform is based on open source technologies and was designed for real-time, streaming, and online actuation.
Machine Learning Made Easy on Kubernetes. DevOps for Data Scientists

Machine Learning Made Easy on Kubernetes. DevOps for Data Scientists

January 27, 2020

Though machine learning and AI are immensely powerful, these solutions are by no means easy. In many cases, there are many diverse components that are not designed to work together. Additionally, thes …
Deep learning on YARN: Running distributed TensorFlow, MXNet, Caffe, and XGBoost on Hadoop clusters

Deep learning on YARN: Running distributed TensorFlow, MXNet, Caffe, and XGBoost on Hadoop clusters

January 24, 2020

In order to train deep learning and machine learning models, you must leverage applications such as TensorFlow, MXNet, Caffe, and XGBoost. Wangda Tan discusses new features in Apache Hadoop 3.x to better support deep learning workloads and demonstrates how to run these applications on YARN.
Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)

Deep learning: Assessing analytics project feasibility and requirements (sponsored by NVIDIA)

January 24, 2020

Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects.
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

January 24, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Current and Future Use Cases of OpenDaylight

Current and Future Use Cases of OpenDaylight

January 22, 2020

OpenDaylight is the leading open source SDN controller and an Integrated Framework for NFV Management. It is a modular open platform for customizing and automating networks of any size and scale and p …