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Cognitive Computing

What happens when AI meets a pandemic?

This is what we can see clearly after some months of reading, watching, and listening to the pronouncements on the novel coronavirus crisis from around the globe: Content challenges continue to dog AI.

Cognitive computing and AI begin to grow together

How do we manage the hype and promise for new inventions while making sure that they represent a realistic opportunity? Can we invent self-driving cars or a Boeing 737 MAX without exposure to the risks these innovations can pose to our lives?

Talk a little, type a lot - Will conversational interfaces survive Siri and Alexa?

For the next generation of conversational computing, it is hard to avoid the conclusion that the only companies that have enough researchers, enough processing resources, enough motivation, and, above all, enough data to deliver the much- needed improvements are the consumer giants.

How robotic is your process ?

To break out of the structured process world, RPA will need to address the full range of cognitive computing capabilities.

Ethical issues in AI and cognitive computing

Many innovations from the past needed the insight of entrepreneurs as well as technologists to change the world. That's also the case with machine learning and AI.

Data and our future: too much of a good thing? Not enough? How will we know?

In today's AI-exploded world, analysts and business people loudly call for more data, complaining that cognitive computing and other AI applications need more raw material to build better models and more accurate predictions.

Usability testing for effective interactivity

Connecting the seeker to the information she seeks is not a new problem. Interaction design has been a stumbling block since the age of the card catalog.

Coming soon to your newsfeed —Ethics and AI

People need to be sensitive to the many ways ethical judgments are being baked into the fabric of their AI projects.

Rebooting the information refinery

In the field of knowledge management, of course, the idea of turning data into information into knowledge has been a foundation concept for knowledge managers. But frankly, the ability to achieve this alchemy of data to knowledge has not been broadly demonstrated in practice. A next generation information refinery is required to make something meaningful and valuable out of the raw data flying around the firm and throughout the internet economy.

AI: The issue is execution

By demonstrating on Jeopardy! that a machine could understand and analyze many fields of human knowledge and answer questions faster and more accurately than the reigning human experts, Watson's victory created an instant global brand.

Cognitive Computing: Another look at cognitive tasks

To build a practical framework for understanding what kinds of capabilities will be the key success factors for the intelligence economy, we need first to look hard at what kinds of cognitive tasks or capabilities are going to come into play to enable the innovations we will need as we partner more closely with machines. Can we delegate cognitive processes to silicon colleagues? How will we make judgments about what we need to retain as human responsibilities versus what we can partially or fully automate?

Cognitive Computing: Balancing the risks with the rewards from AI

The fact is that the effects of AI and cognitive computing will be even broader than current traditional computing systems. As we incorporate more and more data sources for better results, we also increase the likelihood of affecting more lives and more organizations.

Automating cognitive tasks: fact or fiction?

There is a long-standing debate in philosophical, psychological and educational circles about how to understand and measure intelligence. Is intelligence actually a singular thing that can be pointed to and measured, for example, by an IQ test? Or are there multiple kinds of intelligence whose existence and behaviors only come to light when individuals confront specific kinds of context in life?

My teammate the bot—really?

Mind the gap

The problems AI systems address are gnarly, multifaceted and require true innovation.

AI? Or cognitive computing?

Everyone is talking about AI. The past year has catapulted artificial intelligence into the public consciousness in dramatic fashion, utterly eclipsing the boomlet of inflated expectations that AI experienced in the 1980s.

Picked up from the podium

Two themes are top of mind at this stage of the new AI era: "Training data is the new ‘oil' for the AI economy," and "deep learning has left the labs and become mainstream.

What do we mean by a cognitive computing application?

But what is a cognitive computing application exactly? Would you know one if you saw one? And would you have a reasonably intelligent way to differentiate a cognitive application from the applications we are familiar with in this early cloud/big data period?

Framing the value for cognitive computing

We are articulating a framework designed to offer executives who are facing opportunities or challenges in the world of cognitive computing a systematic way to consider the key decision elements for a particular application goal.

Anticipate a bumpy ride

When we think about how to understand the emerging dynamics of the markets for cognitive computing and what it's going to look like as products begin to come to market, what better place to look for a model than to the field of autonomous vehicles?