intelligence

Using big data to unlock the delivery of personalized, multilingual real-time chat services for global financial service organizations

January 17, 2020

Financial service clients demand increased data-driven personalization, faster insight-based decisions, and multichannel real-time access. Tim Walpole details how organizations can deliver real-time, vendor-agnostic, personalized chat services and explores issues around security, privacy, legal sign-off, data compliance, and how the internet of things can be used as a delivery platform.

Using machine learning to drive intelligence at the edge

January 16, 2020

The focus on the IoT is turning increasingly to the edge, and the way to make the edge more intelligent is by building machine learning models in the cloud and pushing them back out to the edge. Dave Shuman and Bryan Dean explain how Cloudera and Red Hat executed this architecture at one of Europe's leading manufacturers, along with a demo highlighting this architecture.

Connected arms (sponsored by Microsoft)

January 14, 2020

Will artificial intelligence revolutionize prosthetic care and assistance? Join Microsofts 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.

Edge intelligence: Machine learning at the enterprise edge

January 13, 2020

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.

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

January 12, 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: When privacy scalesIntelligent product design under global data privacy regulation

January 12, 2020

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.

Hit a home run making baseball decisions using artificial intelligence and machine learning

January 12, 2020

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 moreusing real-life baseball data to illustrate the concepts.

How Autodesk is humanizing customer support with AI: Meet AVA

January 11, 2020

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.

Neural Network Distiller: A PyTorch environment for neural network compression

January 11, 2020

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.

Citizen data science: An enterprise use case from inside the US intelligence community

January 9, 2020

Dave Stuart explains how Jupyter was used inside the US Department of Defense and the greater intelligence community to empower thousands of "citizen data scientists" to build and share analytics in order to meet the communitys dynamic challenges.

Scaling AI Inference Workloads with GPUs and Kubernetes

January 7, 2020

Deep Learning (DL) is a computational intense form of machine learning that has revolutionize many fields including computer vision, automated speech recognition, natural language processing and artif …

The future of data-driven discovery in the cloud

January 5, 2020

Drawing on his experience with the Pangeo project, Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud. The cloud offers a way to make the largest datasets instantly accessible to the most sophisticated computational techniques. A global scientific data commons could usher in a golden age of data-driven discovery.

Meet Hedley, an AI, Linux, and smart sensor robotic skull

December 28, 2019

Rob Reilly explains how he brought Hedley, his robotic skull, to life. Hedley uses a JeVois smart machine vision sensor and artificial intelligence algorithms (developed by Laurent Itti) to track subjects as they move around in the skull's field of view. Come meet Hedley and learn about the latest developments in open source sensors, AI algorithms, and Linux-based physical computing.

Recognizing cultural bias in AI

December 25, 2019

Camille Eddy walks you through how the services we all use everyday have adapted machine learning to become more inclusive. Camille also explains what we can do to create culturally sensitive computer intelligence and why that is important for the future of AI.

Artificial intelligence versus actionable intelligence (sponsored by PagerDuty)

December 23, 2019

Artificial intelligence has been almost here for 50 years, but we don't need to wait for it to escape the laboratory. Adding a manageable dose of actionable intelligence to your operations management workflow can save you time and aggravation. PagerDuty will talk about how AI's limitations and how it can decrease your noise and suggest possible courses of action.

Deep learning for recommender systems

December 11, 2019

In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.

Designing ethical artificial intelligence

December 10, 2019

Artificial intelligence systems are powerful agents of change in our society, but as this technology becomes increasingly prevalenttransforming our understanding of ourselves and our societyissues around ethics and regulation will arise. Jivan Virdee and Hollie Lubbock explore how to address fairness, accountability, and the long-term effects on our society when designing with data.

Executive Briefing: The ROI of data-driven digital transformation

December 10, 2019

Financial and consumer ROI demands that business leaders understand the drivers and dynamics of digital transformation and big data. Kevin Sigliano explains why disrupting value propositions and continuous innovation are critical if you wish to dramatically improve the way your company engages customers and creates value and maximize financial results.

Learning how to design automatically updating AI with Apache Kafka and Deeplearning4j

December 7, 2019

Jason Bell offers an overview of a self-learning knowledge system that uses Apache Kafka and Deeplearning4j to accept data, apply training to a neural network, and output predictions. Jason covers the system design and the rationale behind it and the implications of using a streaming data with deep learning and artificial intelligence.

Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench

December 6, 2019

Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud.

Modernizing the healthcare industry with AI

December 3, 2019

Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines.

Smart diagnosis in healthcare with deep learning

December 3, 2019

Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis.

December 1, 2019

AIHIAIHIAIHI

: Artificial intelligence in autonomous vehicle systems

December 1, 2019

AI"Apollo", AIApolloAIApollo, ApolloAI

Automating business insights through artificial intelligence

November 29, 2019

Decision makers are busy. Businesses can hire people to analyze data for them, but most companies are resource constrained and cant hire a small army to look through all their data. Wayde Fleener explains how General Mills implemented automation to enable decision makers to quickly focus on the metrics that matter and cut through everything else that does not.

Privacy in the age of machine learning

November 21, 2019

Ben Lorica shares emerging security best practices for business intelligence, machine learning, and mobile computing products and explores new tools, methods, and products that can help ease the way for companies interested in deploying secure and privacy-preserving analytics.

Progressive data governance for emerging technologies

November 21, 2019

Emerging technologies such as the IoT, AI, and ML present businesses with enormous opportunities for innovation, but to maximize the potential of these technologies, businesses must radically shift their approach to governance. Anne Buff explains what it takes to shift the focus of governance from standards, conformity, and control to accountability, extensibility, and enablement.

The mathematical corporation: A new leadership mindset for the machine intelligence era

November 20, 2019

How can you most effectively use machine intelligence to drive strategy? By merging it in the right way with the human ingenuity of leaders throughout your organization. Stephanie Beben shares insights from her work with pioneering companies, government agencies, and nonprofits that are successfully navigating this partnership by becoming mathematical corporations.