[TALK]@Telematika

Talks & Presentations Collection

December 7, 2019

Winning with data: How ThredUp, Twilio, and Warby Parker use data to build advantage

Daniel Mintz dives into case studies from three companiesThredUp, Twilio, and Warby Parkerthat use data to generate sustainable competitive advantages in their industries.

Yellow Pages (Canada): Our journey to speed of thought interactive analytics on top of Hadoop

December 7, 2019

The self-service YP Analytics application allows advertisers to understand their digital presence and ROI. Richard Langlois explains how Yellow Pages used this expertise for an internal use case that delivers real-time analytics with Tableau, using OLAP on Hadoop and enabled by its stack, which includes HDFS, Parquet, Hive, Impala, and AtScale, for fast, real-time analytics and data exploration.

Chainer: A flexible and intuitive framework for complex neural networks

December 6, 2019

Open source software frameworks are the key for applying deep learning technologies. Orion Wolfe and Shohei Hido introduce Chainer, a Python-based standalone framework that enables users to intuitively implement many kinds of other models, including recurrent neural networks, with a lot of flexibility and comparable performance to GPUs.

Combining statistics and expert human judgement for better recommendations

December 6, 2019

Jay Wang and Jasmine Nettiksimmons explore the business model of Stitch Fix, an emerging startup that uses artificial intelligence and human experts for a personalized shopping experience, and highlight the challenges encountered implementing Stitch Fix's recommendation algorithm and interacting AI with human stylists.

Deep learning at scale and use cases

December 6, 2019

Deep learning has made a major impact in the last three years. Imperfect interactions with machines, such as speech or image processing, have been made robust by deep learning that finds usable structure in large datasets. Naveen Rao outlines deep learning challenges and explores how changes to the organization of computation and communication can lead to advances in capabilities.

Deep learning: Modular in theory, inflexible in practice

December 6, 2019

The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. The reality isn't that simple, and the commonly used tools greatly limit what we are capable of doing. Diogo Almeida explains what we can do about it and offers a practical attempt at a deep learning library of the future.

Deep neural network model compression and an efficient inference engine

December 6, 2019

Neural networks are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. Song Han explains how deep compression addresses this limitation by reducing the storage requirement of neural networks without affecting their accuracy and proposes an energy-efficient inference engine (EIE) that works with this model.

Deep reinforcement learning for robotics

December 6, 2019

Pieter Abbeel explores deep reinforcement learning for robotics.

Deeply active learning: Approximating human learning with smaller datasets combined with human assistance

December 6, 2019

Natural-language assistants are the emergent killer app for AI. Getting from here to there with deep learning, however, can require enormous datasets. Christopher Nguyen and Binh Han explain how to shorten the time to effectiveness and the amount of training data that's required to achieve a given level of performance using human-in-the-loop active learning.

End-to-end learning for autonomous driving

December 6, 2019

Urs Muller presents the architecture and training methods used to build an autonomous road-following system. A key aspect of the approach is eliminating the need for hand-programmed rules and procedures such as finding lane markings, guardrails, or other cars, thereby avoiding the creation of a large number of if, then, else statements.

How AI is propelling driverless cars, the future of surface transport

December 6, 2019

Keynote by Shahin Farshchi

Humanizing AI development: Lessons from China and Xiaoice at Microsoft

December 6, 2019

Keynote by Lili Cheng

Interactive learning systems: Why now and how?

December 6, 2019

Alekh Agarwal explains why interactive learning systems that go beyond the routine train/test paradigm of supervised machine learning are essential to the development of AI agents. Along the way, Alekh outlines the novel challenges that arise at both the systems and learning side of things in designing and implementing such systems.

Lessons learned from deploying the top deep learning frameworks in production

December 6, 2019

By building a marketplace for algorithms, Algorithmia gained unique experience with building and deploying machine-learning models using a wide variety of frameworks. Kenny Daniel shares the lessons Algorithmia learned through trial and error, the pros and cons of different deep learning frameworks, and the challenges involved with deploying them in production systems.

Lessons on building data products at Google

December 5, 2019

Aparna Chennapragada explores building data products at Google.

Making AI a reality for the enterprise and the physical world

December 5, 2019

Aman Naimat and Mark Patel present an analysis of the current adoption of AI in industry based on a systematic study of the entire business Internet at over 500,000 companies. Drawing on this data, Aman and Mark offer a new economic framework to discover, measure, and motivate future use cases for AI.

Minds and brains and the route to smarter machines

December 5, 2019

Keynote by Gary Marcus

Obstacles to progress in AI

December 5, 2019

The essence of intelligence is the ability to predict. Prediction, perception, planning/reasoning, attention, and memory are the pillars of intelligence. Yann LeCun describes several projects at FAIR and NYU on unsupervised learning, question answering with a new type of memory-augmented network, and various applications for vision and natural language understanding.

Only humans need apply: Adding value to the work of very smart machines

December 5, 2019

The automation of decisions and actions now threatens even knowledge-worker jobs. Tom Davenport describes both the threat of automation and the promise of augmentationcombining smart machines with smart peopleand explores five roles that individuals can adopt to add value to AI, as well as what these roles mean for businesses.

Progress of delivering real AI workloads

December 5, 2019

Progress in enterprise AI workloads, particularly in deep learning, big data, and computing infrastructure, will profoundly impact productivity for users. XD Huang outlines enterprise AI examples to illustrate the collective efforts and exciting opportunities modern AI technologies are making possible.

Software engineering of systems that learn in uncertain domains

December 4, 2019

Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities).

TensorFlow for mobile poets

December 4, 2019

Pete Warden shows you how to train an object recognition model on your own images and then integrate it into a mobile application. Drawing on concrete examples, Pete demonstrates how to apply advanced machine learning to practical problems without the need for deep theoretical knowledge or even much coding.

The identities of bots: A learning architecture for conversational software

December 4, 2019

The recent explosion of bots on communication platforms has rekindled the hopes of conversational AI. However, building intelligent and customizable bots is not just bottlenecked by NLP and speech recognition. Our biggest limitation is the inability to modularize the goals of human bot interconnection. Suman Roy explains why we need a layered architecture for bots to learn about us from data.

The need for speed: Benchmarking deep learning workloads

December 4, 2019

Greg Diamos and Sharan Narang discuss the impact of AI on applications within Baidu, including autonomous driving and speech recognition, offering a brief introduction to the challenges in training deep learning algorithms as well as the different workloads that are used in various deep learning applications.

The new artificial intelligence frontier

December 4, 2019

Babak Hodjat discusses the progress in AI, diving into how AI can offer unique solutions in verticals such as investment, medical diagnosis, and ecommerce. Babak details how using massively scaled distributed evolutionary computation, mimicking biological evolution, allows an AI to learn, adapt, and react faster to provide customers with the answers and decisions they need.

Thors hammer

December 4, 2019

We are entering a new computing paradigman era where software will write software. This is the biggest and fastest transition since the advent of the Internet. Big data and analytics brought us information and insight; AI and deep learning turn that insight into superhuman knowledge and real-time action. Jim McHugh shares real-world examples of companies solving problems once thought unsolvable.

Unlocking AI: How to enable every human in the world to train and use AI

December 4, 2019

Fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is taught and make both teaching and using AI accessible to anyone in the world.

Why AI needs emotion

December 4, 2019

Highly connected, interactive artificial intelligence systems surround us daily, but as smart as these systems are, they lack the ability to truly empathize with us humans. Rana El Kaliouby explores why emotion AI is critical to accelerating adoption of AI systems, how emotion AI is being used today, and what the future will look like.