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BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets. Read more.

The TensorFlow library provides for the use of dataflow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Robert Schroll offers an overview of the TensorFlow graph using its Python API. Read more.

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. Read more.

Michael Li and Zachary Glassman offer a nontechnical overview of AI and data science. You'll learn basic concepts and vocabulary and develop a framework that will allow you to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making. Read more.

Carl Osipov leads an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you'll learn machine learning (ML) and TensorFlow concepts and develop skills in developing, evaluating, and productionizing ML models. Read more.

Max Katz and Eric Levin walk you through the fundamentals of deep learning, from training neural networks to using results to improve performance and capabilities. You'll then apply your newfound knowledge to digital content creation and game development, as you create digital assets with deep learning. No prior deep learning experience is required. Read more.

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.

Kubernetes—the container orchestration engine used by all of the top technology companies—was built from the ground up to run and manage highly distributed workloads on huge clusters. Thus, it provides a solid foundation for model development. Daniel Whitenack demonstrates how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes. Read more.

Carl Osipov walks you through creating increasingly sophisticated image classification models using TensorFlow. Read more.

In this day-long presentation, you'll walk through the process of building a complete machine learning pipeline, from ingest and exploration to training, evaluation, deployment, and prediction. Read more.

From open data marketplaces to privacy-preserving computation, explore how blockchain technologies are enabling a new arc of development in AI and machine learning. Read more.

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. Read more.

From social network photo filters to self-driving cars, computer vision has brought applied deep learning to the masses. Built by the pioneers of computer vision software, PyTorch enables developers to rapidly build computer vision models. Mo Patel and David Mueller offer an overview of computer vision fundamentals and walk you through using PyTorch to build computer vision applications. Read more.

High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsoft’s Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNN—both on single VMs and at scale on GPU clusters. Read more.

David Arpin offers an overview of the Amazon SageMaker machine learning platform, walking you through setup and using Amazon SageMaker Notebook (a hosted Jupyter Notebook server). You'll get hands-on experience with SageMaker's built-in deep learning algorithm as you dive into building your own neural network architecture using SageMaker's prebuilt TensorFlow containers. Read more.

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. Beth Partridge, assisted by Nick Paquin and Annie O'Connor, shares a breakthrough approach that bridges the gap between data science and business. Join in to gain a clear understanding of what AI can do for your business and how to go about implementing it. Read more.

Ray is a new distributed execution framework for reinforcement learning applications. Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art reinforcement learning algorithms. Read more.

Xiaoyong Zhu, Gheorghe Iordanescu, Wilson Lee, and Ivan Tarapov walk you through building a deep learning model and intelligent applications on edge devices running iOS, Android, and Windows, using a working example that helps clinicians in areas with less access to radiologists identify possible lung diseases. Read more.

In neuroscience, the wiring diagram of the brain is a connectome. Jana Eggers and Elsie Kenyon built a connectome of the AI/ML "brain" via arXiv papers. They share the results of how papers, topics, keywords, authors, institutions, publication dates, citations, and more are linked and with what strength, offering interesting insights on how the AI research world is connected. Read more.

Drawing on the McKinsey Global Institute's groundbreaking research, Mehdi Miremadi explores commonly asked questions relating to AI and its impact on work, including: How big is the AI opportunity? Which sectors and functions will capture the most value? As AI takes hold, will there be enough jobs for humans? What will they be, and what skills are needed? What are the implications for leaders? Read more.

Technology has opened up access to more information than ever before, but it’s still on humans to turn that data into knowledge. To solve this problem, organizations are turning to AI and natural language processing to augment human analysts. Sean Gourley explores how the world’s largest organizations use AI to summarize thousands of documents and scale human analysts. Read more.

TensorFlow is one of the world’s biggest open source projects, and it continues to grow in adoption and functionality. Laurence Moroney, Edd Wilder-James, and Sandeep Gupta share major recent developments and highlight some future directions. Join in to learn how you can become more involved in the TensorFlow community. Read more.

Transfer learning enables you to use pretrained deep neural networks and adapt them for various deep learning tasks (e.g., image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean to learn the secrets of transfer learning and discover how to customize these pretrained models for your own use cases. Read more.

Q&A with Kai Fu Lee Read more.

Simon Crosby details an architecture for learning on time series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. You'll see that there are more than enough resources at “the edge” to cost-effectively analyze, learn from, and predict from streaming data on the fly. Read more.

Data is your most important asset, so you don't want to move it around too much just to cater to varying compute needs. Machine learning (ML) is no exception to this rule. Ujjwal Ratan shares an end-to-end workflow for building an ML pipeline. Read more.

Do you have constantly changing business environments across many business units and processes with multiple job schedulers and infrastructure platforms? Do you struggle with end-to-end visibility and a lot of alerts? Award-winning ignio can help. Jayanti Murty explains how and shares real-world examples of companies that have reduced operational risks and outages and technology and labor costs. Read more.

Neta Zmora offers an overview of Distiller, an open source Python package for neural network compression research. Neta discusses the motivation for compressing DNNs, outlines compression approaches, and explores Distiller's design and tools, supported algorithms, and code and documentation. Neta concludes with an example implementation of a compression research paper. Read more.

Ira Cohen shares a novel approach for building more reliable prediction models by integrating anomalies in them. Arun Kejariwal then walks you through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data. Read more.

Ashok Srivastava explains how to make your organization AI ready, determine the right AI applications for your business and products, and accelerate your AI efforts with speed and scale. Read more.

Danny Lange discusses the role of intelligence in biological evolution and learning and demonstrates why a game engine is the perfect virtual biodome for AI’s evolution. You'll discover how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning. Read more.

Laurence Moroney dives into machine learning, AI, deep learning, and more and explains where they fit in the programmers toolkit. Along the way, he walks you through what it's all about, cutting through the hype to show the opportunities that are available in machine learning. Read more.

Uber has implemented an ML and NLP system that suggests the most likely solutions to a ticket to its customer support representatives, making them faster and more accurate while providing a better user experience. Piero Molino, Huaixiu Zheng, and Yi-Chia Wang describe how Uber built the system with traditional and deep learning models and share the lessons learned along the way. Read more.

Magnus Hyttsten and Priya Gupta demonstrate how to perform distributed TensorFlow training using the Keras high-level APIs. They walk you through TensorFlow's distributed architecture, how to set up a distributed cluster using Kubeflow and Kubernetes, and how to distribute models created in Keras. Read more.

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially for computer vision. Anirudh Koul explains how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones. Read more.

Artificial intelligence requires a low-latency, high-throughput storage system to keep the compute layer fully saturated with data. Liran Zvibel demonstrates why NVMe-optimized, distributed filesystems are ideal storage solutions to support AI applications and introduces a next-gen massively parallel shared filesystem that's NAND flash and NVMe optimized, built to solve the I/O starvation problem. Read more.

As AI grows its reach throughout society and makes decisions that affect people, any business looking to capitalize on AI’s potential must also acknowledge its impact. Kishore Durg and Teresa Escrig explain why businesses must teach and test their AI systems to act as responsible representatives so that they reflect business and societal norms of responsibility, fairness, and transparency. Read more.

One of the biggest challenges in AI is how to translate advances in the lab into large-scale applications. Casimir Wierzynski reviews current trends in the field and shares case studies to illustrate why codesigning these components in concert will be critical for building the AI systems of the future. Read more.

KC Tung explains why LSTM provides great flexibility to model the consumer touchpoint sequence problem in a way that allows just-in-time insights about an advertising campaign's effectiveness across all touchpoints (channels), empowering advertisers to evaluate, adjust, or reallocate resources or investments in order to maximize campaign effectiveness. Read more.

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past six years. Read more.

After a slow start, the finance industry is quickly catching up with others in its adoption of AI. Joe Rothermich explains how Thomson Reuters Labs is using AI to perform research in building quantitative investment models and discusses the company's research in deep learning for credit risk, machine learning and NLP for unlocking alternative datasets, and financial graph-based analytics. Read more.

TensorFlow.js is the recently released JavaScript version of TensorFlow that runs in the browser and Node.js. Nick Kreeger and Ping Yu offer an overview of the TensorFlow.js ML framework and demonstrate how to perform the complete machine learning workflow, including training, client-side deployment, and transfer learning. Read more.

Deep learning is often called a black box, so how do you diagnose and fix problems in a deep neural network (DNN)? Avesh Singh and Kevin Wu explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and DNN unit tests. Read more.

In spite of the enormous excitement about the potential of deep learning, several key challenges—from prohibitive hardware requirements to immature software offerings—are impeding its widespread enterprise adoption. Evan Sparks and Ameet Talwalkar detail fundamental challenges facing organizations looking to adopt deep learning and present novel solutions to overcome several of them. Read more.

Shaoshan Liu explains how PerceptIn built a reliable autonomous vehicle with a total cost under $10,000. Read more.

The time to gain market advantage through AI is now. By 2021, Gartner projects that 40% of new enterprise applications will include AI, but the majority of AI-enhanced use cases have not been developed. Ranjeeta Singh and Nick Switanek shed light on pain points and use cases where AI can drive business value for companies that make the right investments in people, process, and technology. Read more.

What if you could QA everything and make your best employees 10–100x more efficient? Ben Taylor shares real use cases of business transformation and realized value in production using deep learning and discusses some of the executive conversations and behaviors Dell EMC is seeing in the market. Read more.

Here at the dawn of the cognitive era, we must focus our design talents upon a new type relationship with machines—machines that can hold a conversation, interpret nonverbal cues, and draw from vast stores of human knowledge. But what should these relationships look like? Adam Cutler explains why designing for AI requires new considerations and new rules. Read more.

Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards. Read more.

Now more than ever, success in AI requires expertise in multiple disciplines, including big data, efficient software, and novel models and algorithms. Forrest Iandola shares an approach for developing a full stack AI team that can use all of these diverse skills to execute on industrial-scale AI problems. Read more.

Given the revolution in data and healthcare, Shelley Zhuang predicts how certain sectors may unfold and shares opportunities for innovation. Along the way, Shelley discusses innovations that advance precision medicine by bringing together interdisciplinary fields across biology, engineering, data science, and clinical care. Read more.

Richard Wei and Andrew Selle discuss Swift for TensorFlow and TensorFlow Lite, covering the current status of development and the latest developments. They then teach you how to prepare your model for mobile and how to write code that executes it on a variety of different platforms. Read more.

Modern radiological lung cancer screening is an entirely manual process, leading to high costs and inter-reader variability. Daniel Golden offers an overview of a deep learning-based system that automatically detects and segments lung nodules in lung CT exams and explains how it was tested for safety and efficacy. The system is FDA cleared and segments nodules as accurately as a clinician. Read more.

Woj Zaremba discusses deep reinforcement learning for robotics. Read more.

Alex Wong discusses some of the operational challenges associated with scalability and explainability in deep learning for real-world operational scenarios and explains how these are being tackled to enable more seamless, accessible, deployable, and transparent deep learning design and development through advancements made in the respective areas. Read more.

Ben Vigoda discusses idea learning, a new approach to deep learning. Idea learning can turn the customer journey into a personalized natural language conversation using deep, probabilistic, Turing-complete models that learn more from business people providing ideas and much less from supervised (labeled) data. Read more.

Carlos Escapa explains how to identify use cases for ML, shares best practices for framing problems in a way that key stakeholders and senior management can understand and support, and helps you set the stage for delivering successful ML-based solutions to your business. Read more.

We now live in the age of assistive and AI companions, where everything is coming alive. Danielle Krettek demonstrates how to design for the "invisible" emotional layer of experience that marks this new wave of technology. Read more.

Danny Goodman discusses reinforcement learning and the future of software. Read more.

Data-driven companies making intelligent products must design for security and privacy to be competitive globally. Amanda Casari details the high-level changes that EU General Data Protection Regulation (GDPR)-compliant businesses face and how this translates to teams designing products driven by machine learning and artificial intelligence. Read more.

Rachael Rekart offers an overview of Autodesk Virtual Agent (AVA), which has revolutionized the way Autodesk approaches customer service. Customers chat with AVA as they would a human, in natural language, and AVA processes transactions quickly, returns accurate answers, or gathers information to pass to a human counterpart to resolve the query. Read more.

As machine learning evolves from experimentation to serving production workloads, so does the need to effectively manage the end-to-end training and production workflow including model management, versioning, and serving. Clemens Mewald offers an overview of TensorFlow Extended (TFX), the end-to-end machine learning platform for TensorFlow that powers products across all of Alphabet. Read more.

Complex diseases like Alzheimer’s cannot be cured by pharmaceutical or genetic sciences alone, and current treatments and therapies lead to mixed successes. Ayin Vala explains how to use the power of big data and AI to treat challenging diseases with personalized medicine, which takes into account individual variability in medicine intake, lifestyle, and genetic factors for each patient. Read more.

Earlier this year, Amazon, Facebook, and Microsoft partnered to create the Open Neural Network Exchange (ONNX)—an open format to represent deep learning models. Sarah Bird explains in detail how the ONNX framework can help you take AI from research to reality as quickly as possible. Read more.

David Martinez discusses an AI canonical architecture suitable for a number of different classes of applications and shares examples focused on cybersecurity to illustrate an application area that benefits from an end-to-end AI architecture. Read more.

The public dialogue around artificial intelligence takes place at extremes, with some focusing on how robots will take everyone’s job and others speaking of a utopia where AI will do everything from cure cancer to solve world hunger. Irmak Sirer offers an alternative to these extreme views of AI: instead of artificial intelligence, we instead must focus on augmenting people’s intelligence. Read more.

What does it mean to explain a machine learning model, and why is it important? Armen Donigian addresses those questions while discussing several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques offers a different perspective, and their clever application can reveal new insights and solve business requirements. Read more.

Allison Duettmann offers an overview of AI philosophy and explains why traditional approaches need updating, because they pave the way for a singleton AI that will hardly be benevolent. Allison then discusses potential alternative AI safety strategies and their shortcomings and shares a brief survey of interesting problems in AI safety and what we can hope for if we get it right. Read more.

The future isn’t about AI or machine learning; it’s about human-machine teaming. Candace Worley explains why, as long as there are human adversaries behind cybercrime and cyberwarfare, there will always be a critical need for the human beings teamed with machines in cybersecurity. Read more.

Michelle Casbon offers an overview of Kubeflow. By providing a platform that reduces variability between services and environments, Kubeflow enables applications that are more robust and resilient, resulting in less downtime, quality issues, and customer impact. It also supports the use of specialized hardware such as GPUs, which can reduce operational costs and improve model performance. Read more.

Captricity has deployed a machine learning pipeline that can read handwriting at human-level accuracy. Ramesh Sridharan discusses the big ideas the company learned building and deploying this system, using data to identify specific problems to solve using AI and to evaluate and validate the algorithm itself and the overall system once deployed. Read more.

Join Ari Kaplan, Erin LeDell, Chris Coad, and David Kearns to see where AI meets business intelligence, as they explore the latest ML technologies and concepts powering today's decisions, including Hortonworks, Aginity Amp, H2O.ai, IBM Data Science Experience, and more—using real-life baseball data to illustrate the concepts. Read more.

Using novel techniques in model-free deep reinforcement learning and control theory, Cathy Wu explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, such as congestion on a variety of important traffic contexts. Read more.

Dmitry Rizshkov offers an overview of OpenVINO and explores real customer case studies that exceeded the most challenging inference requirements. Read more.

There are immense opportunities to apply machine learning to investment management if you know where to look. Unlike many Silicon Valley challenges, it's not simply a matter of throwing capital and PhDs at the financial markets. Michael Weinberg explains why you must exploit domain expertise to achieve disruptive success. Read more.

Join in for a panel discussion on how AI and data are transforming the enterprise as we know it. Panelists will discuss data, talent, and technology challenges, enterprise adoption barriers, areas of disruption, and talent concerns (enterprises' willingness to pay) as AI turns the enterprise world as we know inside and out. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

Session by Andrew Feldman Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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? Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read 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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.

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. Read more.