The advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information.

Causal graphs [e.g., Pearl, 2009, Spirtes et al., 2000] allow us to reason about this in a principled way when the domains correspond to different external interventions on the system, or

The advancement of machine learning (ML) techniques relies on large amounts of training data. However, data collection also poses a significant risk of exposing private information.

Years before everyone was being impressed with the human-like text output of ChatGPT and other generative AI systems, IBM's Watson was blowing our minds on Jeopardy. IBM's cognitive computing project famously dominated its human opponents, but the company had much larger long-term goals, such as using Watson's ability to simulate a human thought process to help doctors diagnose patients and recommend treatments. Now, IBM is pivoting its supercomputer platform into Watsonx, an AI development studio packed with foundation and open-source models companies can use to train their own AI platforms. If that sounds familiar, it may be because NVIDIA recently announced a similar service with its AI Foundations program. Both platforms are designed to give enterprises a way to build, train, scale and deploy an AI platform.

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IBM Watson NLP (Natural Language Understanding) and Watson Speech containers can be run locally, on-premises or Kubernetes and OpenShift clusters. Via REST and gRCP APIs AI can easily be embedded in applications. This post describes how to run Watson NLP locally in Minikube. To set some context, check out the landing page IBM Watson NLP Library for Embed. The Watson NLP containers can be run on different container platforms, they provide REST and gRCP interfaces, they can be extended with custom models and they can easily be embedded in solutions.

When we include the unprecedented computing power offered by the cloud, it's clear we are living in an exciting era for building applications. When IBM Watson defeated the two Jeopardy champions back in 2011, it opened a new era in the practical application of Artificial Intelligence technology and contributed to the growing research and interest in this field. IBM Watson has evolved from being a game show winning question & answering computer system to a set of enterprise-grade artificial intelligence (AI) application program interfaces (API) available on IBM Cloud. These Watson APIs can ingest, understand & analyze all forms of data, allow for natural forms of interactions with people, learn, reason – all at a scale that allows for business processes and applications to be reimagined. This course is intended for business and technical users who want to learn more about the cognitive capabilities of IBM Watson Discovery service.

IBM yesterday announced it would be providing the AI brain for a robot being built by Airbus to accompany astronauts aboard the International Space Station (ISS). When only the best of the best will do, it looks like Watson has the right stuff. The robot, which looks like a flying volleyball with a low-resolution face, is being deployed with German astronaut Alexander Gerst in June for a six month mission. It's called CIMON, an acronym for Crew Interactive Mobile Companion, and it's headed to space to do science stuff. It'll help crew members conduct medical experiments, study crystals, and play with a Rubix cube.

The connector leverages eGain's unique BYOB (Bring Your Own Bot) architecture, allowing business users to easily plug in the Watson Assistant into the eGain platform with no coding. Per Gartner, less than 10% of customer service journeys are fulfilled using self-service, which is why it is critical to integrate chatbots with human-assisted service channels such as live chat. The eGain Connector for Watson Assistant improves customer, agent, and business experiences at once. When customers escalate from Watson to human-assisted chat, their context is passed to the contact center agent so that they do not need to repeat information to the agent. Agents get to see interactions that customers have already had with Watson before they start their conversation with the customer.

IBM Watson is a brilliant idea that failed in the marketplace. After being acquired and changing its name to Merative, will it win at the AI and IoT health game going forward?