Schedule List ViewGrid View

Facebook's strength in AI innovation comes from its ability to quickly bring cutting-edge research into large-scale production using a multifaceted toolset. Joseph Spisak explains how PyTorch 1.0 helps to accelerate the path from research to production by making AI development more seamless and interoperable.

Clara’s human-in-the-loop scheduling service combines the precision of machine intelligence and the judgement of an expert team. Jason Laska explores the trade-offs between text annotations defined for fast data entry and those meant solely for training machine learning models, using the application of DateTime text as it pertains to meeting-attendee availability to guide the discussion.

Machine learning algorithms are being developed and improved at an incredible rate but are not necessarily accessible to the broader community. A. Besir Kurtulmus offers an overview of DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum that provides everyone access to high-quality, objectively measured machine learning models.

Sergey Levine shares techniques in reinforcement learning that allow you to tackle sequential decision-making problems that arise across a range of real-world deployments of artificial intelligence systems and explains how emerging technologies in meta-learning make it possible for deep learning systems to learn from even small amounts of data.

Noah Schwartz explores the most recent advances in cooperative learning systems, including distributed and federated learning systems for real-world, edge-based AI. He also considers the pros and cons of multi-agent systems and demonstrates how Quorum AI is working to bridge the gap with the Quorum AI Framework.

Lydia Liu discusses the results of research on how static fairness criteria interact with temporal indicators of well-being. These results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria and suggest a range of new challenges and trade-offs.

In recent years, there's been a quantum leap in the performance of AI, as deep learning made its mark in areas from speech recognition to machine translation and computer vision. However, as artificial intelligence becomes increasingly popular, data privacy issues also gain traction. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development.

Personalization is a common theme in social networks and ecommerce businesses. Personalization at Uber involves an understanding of how each driver and rider is expected to behave on the platform. Ankit Jain explains how Uber employs deep learning using LSTMs and its huge database to understand and predict the behavior of each and every user on the platform.

Online fraud is often orchestrated by organized crime rings, who use malicious user accounts to actively target modern online services for financial gain. Ting-Fang Yen shares a real-time, scalable fraud detection solution backed by deep learning and built on Spark and TensorFlow and demonstrates how the system outperforms traditional solutions such as blacklists and machine learning.

Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development.

Labhesh Patel explains how deep learning is informing Jumio's computer vision through smarter data extraction, fraud detection, and risk scoring and how Jumio is leveraging massive datasets and human review to dramatically improve the accuracy of its machine learning algorithms to detect bogus IDs and streamline the verification process of legitimate documents.

Estimating the performance of heavy earth-moving equipment on large construction projects is a complex task that can be riddled with uncertainty. Ramzi Roy Labban details how CCC uses machine learning, leveraging large datasets of actual performance of trucks on construction sites, to more accurately predict future performance and allow the company to make realistic performance assumptions.

Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities, and demonstrates emerging best practices for designing customer-centric and ethical products and services.

Executives are often asked to "innovate with AI," but barriers to successful adoption for most enterprises are organizational, not technical. Mariya Yao explains why effective AI requires not only technical talent but extended interdisciplinary coordination between teams, investments in retraining your workforces at all levels, and cultivation of an experimental, data-driven culture.

Leveraging structured knowledge will be a critical ingredient in the design of the next wave of intelligent applications. Mike Tung offers an overview of the current open source and commercial knowledge graphs and explains how consumer and business applications are already taking advantage of these to provide intelligent experiences and enhanced business efficiency.

AI-powered chatbots are increasingly becoming viable solutions for customer service use cases. Technology leaders must consider adopting a multichannel chatbot strategy to avoid siloed chatbot solutions. Sharad Gupta shares a framework to ensure long-term strategic investment in chatbots.

For AI to serve each individual customer, we need much more complex natural language understanding, ideas, and behaviors. Will compositional deep learning put us on a new curve?

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.

Deep learning (DL) has transformed much of AI and demonstrated how machine learning can make a difference in the real world. With DL, the massive expansion of available training data and compute gave neural networks a new instantiation that significantly increased their power. Evolutionary computation (EC) is on the verge of a similar breakthrough. Risto Miikkulainen explains why.

Much attention in enterprise AI today is focused on automation. Jake Saper explains why the more interesting applications focus on worker augmentation and offers an overview of coaching networks, which gather data from a distributed network of workers and identify the best techniques for getting things done.

Video games have used sophisticated AI techniques for decades to drive everything from area design to navigation to enemies to conversation and planning. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent offer an overview of the history of AI in video games and explain how the needs that drove AI advancement in the game development world map to almost-identical problems in the real world.

AI - everyone is talking about it but who is actually doing it (and generating business results). This session takes an industry by industry perspective on true AI adoption disambiguating the hype from the reality, the theoretical from the practical and the research labs from ROI.

Microsoft's AI for Earth team helps NGOs apply AI to challenges in conservation biology and environmental science. Jennifer Marsman outlines Microsoft’s objectives for AI for Earth and highlights recent successes in applying AI to agriculture, poacher detection, animal identification in camera trap and citizen scientist photography, and more.

We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. Varun Arora covers some of those important techniques, along with real-world examples.

Martin Görner explores the newest developments in image recognition and convolutional neural network architectures and shares tips, engineering best practices, and pointers to apply these techniques in your projects. No PhD required.

Cloud AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google’s state-of-the-art transfer learning and neural architecture search technology. Torry Yang explores the AutoML Vision, Translate, and Natural Language services and APIs and demonstrates how powerful and easy they are to use.

Building machine learning models is a multistage process. TensorFlow's high-level APIs make this process smooth and easy, whether you're starting small or going big. Karmel Allison walks you through a practical example of building, training, and debugging a model and then exporting it for serving using these APIs.

Alexandre Passos and Frank Chen offer an overview of TensorFlow AutoGraph, which automatically converts plain Python code into the TensorFlow equivalent, using source code transformation. They then lead a technical deep dive into Google's Cloud TPU accelerators and show you how to program them.

Join in for two talks on TensorFlow in space and mathematics. Josh Dillon discusses TensorFlow Probablity (TFP), and Wahid Bhimji discusses deep learning for fundamental sciences using high-performance computing.

Lucasz Kaiser offers an overview of Tensor2Tensor, a library of deep learning models and datasets that facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning, and more, enabling the exploration of various ideas much faster than previously possible.

Vikram Saletore and Luke Wilson discuss a collaboration between SURFSara and Intel to advance the state of large-scale neural network training on Intel Xeon CPU-based servers, highlighting improved time to solution on extended training of pretrained models and exploring how various storage and interconnect options lead to more efficient scaling.

In recent years, there has been lots of work done on low-precision inference that shows that by training for quantization, large gains in energy efficiency can be achieved. Cormac Brick offers a look at industry challenges and progress needed to close the portability performance gap.

Jake Porway sheds light on AI’s true potential to impact the world in a positive way. Drawing on his experience as the head of DataKind, an organization applying AI for social good, Jake shares best practices, discusses the importance of using human-centered design principles, and addresses ethical concerns and challenges encountered when using AI to tackle complex humanitarian issues.

AI possesses an incredible potential to help address the challenges of our planet. Drawing on her experience as the head of AI foundations and codirector of Science for Social Good at IBM Research, Aleksandra Mojsilovic shares innovative examples of applying AI to humanitarian problems and discusses gaps that challenge us from making larger impact with our work.

Machine learning is eating software. As decisions are automated, model interpretability must become an integral part of the ML pipeline rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. Mayukh Bhaowal discusses the steps Salesforce Einstein is taking to make machine learning more transparent and less of a black box.

Human trafficking is a form of modern-day slavery. Online sex advertisement activity on portals like Backpage provide important clues that, if harnessed and analyzed at scale, can help resource-strapped law enforcement crack down on trafficking activity. Mayank Kejriwal details an AI architecture called DIG that law enforcement have used (and are using) to combat sex trafficking.

With the help of better software, cloud infrastructure, and pretrained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. Brian Dalessandro and Chris Smith share their experience and lessons learned while building a computer vision and OCR app for reading and classifying insurance cards.

Building complex, real-world reinforcement learning systems requires leveraging techniques such as curriculum learning, hierarchical RL, and reward shaping. Mark Hammond explores many of these techniques and illustrates how they can be effectively combined into a comprehensive machine teaching program.

Deep learning (DL) creates impactful advances following a virtuous recipe: a model architecture search, creating large training datasets, and scaling computation. Joel Hestness discusses research done by Baidu Research's Silicon Valley AI Lab on new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing.

Abhishek Tayal offers insight into how Twitter's ML platform team, Cortex, is developing models, related tooling, and infrastructure with the objective of making entity embeddings a first-class citizen within Twitter's ML platform. Abhishek also shares success stories on how developing such an ecosystem increases efficiency and productivity and leads to better outcomes across product ML teams.

Over 400M people worldwide have some sort of speech or hearing disorder that prevents them from participating in the job market. Goodman Gu offers an overview of Stride4All, an initiative using AI to open work up for disabled people and empower them for teamwork, and showcases a prototype that uses deep learning and computer vision technologies for gesture recognition of American Sign Language.

Predicting driver intention and behavior is of great importance for the planning and decision-making processes of autonomous driving vehicles. Zhou Xing shares a methodology that can be used to build and train a predictive driver system, helping to learn on-road drivers' intentions, behaviors, associated risks, etc.

As prices drop, new markets open up. AI inference will likely follow the same trends as general purpose compute or storage, and the market for AI hardware and software stacks could approach $100B in the next 10 years. Michael Henry dives into AI innovation at a hardware and software level.

Session by Andrew Feldman

Would you believe that AI inferencing can be done on chips that cost less than a dollar? uTensor, a custom TensorFlow runtime for microcontrollers (MCUs), lets you do just that. Neil Tan offers an overview of uTensor, the first framework to streamline model deployments on MCUs, allowing you to push AI to the edge rather than sending everything to the cloud.

Google's AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan walks you through setting up and building the kits and demonstrates how to use the kits' Python SDK for machine learning both in the cloud and locally on a Raspberry Pi.

For developers building a bot or virtual agent, the critical question is which bot to build and why? Today, most can’t answer it without a manual intent discovery process, largely based on guesswork, that uncovers only a percentage of possible opportunities. Ofer Ronen demonstrates techniques, based on machine learning, for faster, more efficient intent discovery.

Vas Chellappa explains how to keep your GPUs fed with data as you train the next generation of deep learning architectures and shares a new benchmark suite for evaluating and tuning input pipelines.

To help enterprises formulate their strategies for actionable and effective use of AI, HfS and IBM have jointly developed a practical guide to starting your AI journey, leveraging insights from IBM’s Institute for Business Value (IBV) and recent HfS research, as well as real-world experiences, gleaned from interviews with clients and field practitioners.

Google extends the "AI-first" DNA to all enterprises via the cloud. Anand Iyer walks you through the Google Cloud AI platform, from managed services to specialized ML accelerators, and shares examples of how businesses have leveraged the platform to overcome challenges in delivering customer value.

Businesses can use AI techniques to make accurate predictions but still not act effectively on this knowledge. Since business value derives from actions rather than knowledge, it’s essential to identify a clear path from model predictions to effective actions. Nachum Shacham outlines a path that leverages model interpretability and Teradata Analytics Platform functions to guide effective actions.

Mark Hamilton shares a novel deep learning approach for creating a robust object detection network for use in an infrared, UAV-based poacher recognition system.

Beginning in 2010, EPAM started developing a platform to manage and drive its complex IT services business with greater AI analytics. By processing project, team, and individual data, the platform helps IT teams make more-informed decisions faster. Jitin Agarwal shares lessons learned from creating this AI-driven TechOps platform to improve IT performance.

AI is an area of fast-paced innovation and a tool that's more accessible than ever for the enterprise, but integrating AI into businesses is not without its challenges. Alex Ge outlines a practical framework for working with AI, from dealing with the data to building and interpreting models to deploying and operationalizing—all while keeping collaboration front and center within the enterprise.

Lukas Biewald offers an overview of real-world deployments of deep learning models at companies like Home Depot, P&G, Coca-Cola, and Uber, covering practical issues that come up training and iterating on models and problems that can arise post deployment. Lukas also discusses recent research that is directly relevant to industry such as active learning, multitask learning, and transfer learning.

Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Thursday and Friday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun.

Join fellow executives, business leaders, and strategists for a networking lunch on Friday for AI Business Summit attendees and speakers.

Program chairs Ben Lorica and Roger Chen open the second day of keynotes.

AWS puts machine learning in reach of every developer and data scientist. Matt Wood explores key trends in machine learning, the importance of designing models for scale, and the impact that machine learning innovation has had on startups and enterprises alike.

Levent Besik explains how can enterprises stay ahead of the game with customized ML in our ever-changing world of AI capabilities and limited data science resources.

Huma Abidi discusses the importance of optimization to deep learning frameworks and shares Xeon performance results and work that Intel is doing with its framework partners, such TensorFlow.

Dawn Song details challenges and exciting new opportunities at the intersection of AI and security and explains how AI and deep learning can enable better security and how security can enable better AI. You'll learn about secure deep learning and approaches to ensure the integrity of decisions made by deep learning.

AI is real and has immense value potential for enterprises. However, there is a lot of hype and confusion around AI, creating a critical need for every business to be thoughtful about developing the right strategy and vision for AI within the organization. Join Manish Goyal to explore four success factors for an AI journey and learn how you can best unlock the value of enterprise AI.

In 2011, we saw a sudden increase in the abilities of computer vision systems brought about by academic researchers in deep learning. Today, Peter Norvig explains, we see continued progress in those fields, but the most exciting aspect is the diversity of applications in fields far astray from the original breakthrough areas, as well as the diversity of the people making these applications.

Will artificial intelligence revolutionize prosthetic care and assistance? Join Microsoft’s Joseph Sirosh for an intriguing story on AI-infused prosthetics that are able to see, grip, and feel and discover how this is enabling affordable and functional prosthetic care.

High-level, domain-specific languages and architectures and freeing architects from the chains of proprietary instruction sets will usher in a new golden age. David Patterson explains why, despite the end of Moore’s law, he expects an outpouring of codesigned ML-specific chips and supercomputers that will improve even faster than Moore’s original 1965 prediction.

Program chairs Ben Lorica and Roger Chen offer closing remarks.