intel

Documenting serverless architectures: Could we do it better?

Documenting serverless architectures: Could we do it better?

February 8, 2020

We don't yet have an adequate language for describing serverless architectures. Today, we use informal diagrams in which no precise meaning can be attached to the diagram as a whole or to any particular element. Asher Sterkin explains why this is a problem and what we can do about it.
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.
How to improve your architectural visualizations

How to improve your architectural visualizations

February 7, 2020

Communicating about architecture to non-IT and business stakeholders is a valuable skill for architects. After all, many architectural decisions are made by others, so they need to be informed with clear, honest, intelligible, and helpful information and advice. Jochem Schulenklopper shares theory and practical tips on eight facets of the visual communication of architecture.
Introducing serverless to your organization

Introducing serverless to your organization

February 7, 2020

Serverless computing offers the benefits of accelerated delivery and reduced operations costs. However it also brings tooling and architectural challenges. What are safe yet effective methods to introduce serverless to your organization? Mike Roberts discusses several options, drawn from his experience with teams that have faced this precise question.
Packet-Level Network Analytics without Compromises

Packet-Level Network Analytics without Compromises

February 7, 2020

Network analytics has been a key component of network management for decades. As we look to integrate more intelligence, whether for increased security or to bette …
360 Degree Vehicle Surround View in an Open Source Project: Libxcam

360 Degree Vehicle Surround View in an Open Source Project: Libxcam

February 6, 2020

Vehicle surround view is one of the key features in Advanced Driver Assistance Systems (ADAS). Several cameras are equipped around the car to capture the scenes outside. Conventional solution would bl …
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 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.
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.
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.
How machines learn to code: Machine learning on source code

How machines learn to code: Machine learning on source code

February 3, 2020

Thomas Endres and Samuel Hopstock demonstrate how to apply machine learning techniques on a program's source code, covering problems you may encounter, how to get enough relevant training data, how to encode the source code as a feature vector so that it can be processed mathematically, what machine learning algorithms to use, and more.
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.
Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

February 3, 2020

Even in the age of big data, labeled data is a scarce resource in many machine learning use cases. Florian Wilhelm evaluates generative adversarial networks (GANs) when used to extract information from vehicle registrations under a varying amount of labeled data, compares the performance with supervised learning techniques, and demonstrates a significant improvement when using unlabeled data.
Accelerating I/O in Big Data A Data Driven Approach and Case Studies

Accelerating I/O in Big Data A Data Driven Approach and Case Studies

February 1, 2020

The I/O infrastructure is key to Big Data ecosystem. New networking and storage hardware technologies are continuously being developed while software I/O stack remains relatively slow. In order to ens …
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.
Drone SITL Bringup with the IIO Framework

Drone SITL Bringup with the IIO Framework

January 27, 2020

This talk aims at an introduction to using the Industrial IO(IIO) framework to initialize sensors and acquire data to feed to a Software in the Loop (SITL) interface of drone software such as iNav/Cle …
Agile for data science teams

Agile for data science teams

January 26, 2020

Agile methodologies have been widely successful for software engineering teams but seem inappropriate for data science teams, because data science is part engineering, part research. Jennifer Prendki demonstrates how, with a minimum amount of tweaking, data science managers can adapt Agile techniques and establish best practices to make their teams more efficient.
AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think) (sponsored by Cisco)

AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think) (sponsored by Cisco)

January 26, 2020

DD Dasgupta explores the exciting development of the edge-cloud continuum, which is redefining business models and technology strategies while creating a vast array of new applications that will power the digital age. The continuum is also destroying what we know about the centralized data centers and cloud computing infrastructures that were so vital to the success of the previous computing eras.
Automating business processes with large-scale knowledge graphs

Automating business processes with large-scale knowledge graphs

January 26, 2020

Mike Tung offers an overview of available open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of them to provide intelligent experiences and enhanced business efficiency. Mike then discusses what's coming in the future.
Deep Learning Neural Network Acceleration at the Edge

Deep Learning Neural Network Acceleration at the Edge

January 26, 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 …
Deep learning on audio in Azure to detect sounds in real time

Deep learning on audio in Azure to detect sounds in real time

January 25, 2020

In this auditory world, the human brain processes and reacts effortlessly to a variety of sounds. While many of us take this for granted, there are over 360 million in this world who are deaf or hard of hearing. Swetha Machanavajhala and Xiaoyong Zhu explain how to make the auditory world inclusive and meet the great demand in other sectors by applying deep learning on audio in Azure.
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.