Category

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

Feb 10
The Explanation

Explainable AI – Is Everybody Reading the Same Articles?

By Artificial Intelligence and Machine Learning No Comments

Everybody is Reading the Same Articles on Artificial Intelligence and Machine Learning

In a Scientific American article titled Ethics in the Age of Artificial Intelligence, the author posits the following question: “If we don’t know how AIs make decisions, how can we trust what they decide?”

The author claims “AI also took away the transparency, explainability, predictability, teachability and auditability of the human move, replacing it with opacity.” And makers of certain classes of AI systems are quick to agree. Tesla, for instance, once claimed it had ‘no way of knowing’ if its autopilot self-driving system was used in a fatal crash.

I, too, expressed concerns about biases and algorithmic opacity in deep learning algorithms and suggested that industry must engage proactively in promoting algorithmic transparency and taking responsibility for the outcomes their software produces.

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Jul 02
Death of Socrates

Will the Internet of Things Accelerate the Development of Artificial Intelligence?

By Artificial Intelligence and Machine Learning, Internet of Things (IoT) No Comments

Artificial Intelligence and Machine Learning

Like the Internet of Things that shed its drab machine-to-machine image of a decade ago to become the cornerstone of the digital transformation movement, artificial intelligence technology is sprouting a new life from its 50-plus years old roots. (Yes, we’ve been doing artificial intelligence, admittedly with limited success, since the late 50s.)

There’s no doubt that AI has achieved impressive success over the last decade, promising a growing field of practical use of AI technology.

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May 28
Philosopher Illuminated by the Light of the Moon and the Setting Sun (Dali, 1939)

Designing Self-Driving Cars: Asimov’s Three Laws of Robotics Are Not Helpful

By Artificial Intelligence and Machine Learning, Autonomous, Connected, Electric, Shared Vehicles No Comments

Artificial intelligence (AI) technology continues its aggressive foray into nearly every aspect of our lives, from the seemingly human-like (most of the time) interactions with Alexa to fully autonomous cars that in the not so distant future will be able to handle complex navigation and steering scenarios better than most human drivers.

Conversations about robot cars that make split-second life-and-death decisions involving car occupants and pedestrians inevitably invoke Isaac Asimov’s famous Three Laws of Robotics that first appeared in his 1942 short story Runaround:

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Apr 13
Five Hammers (Wayne Thiebaud, 1972 )

The Problem With This Big Hammer Called Artificial Intelligence

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Everything is Artificial Intelligence

If your only tool you have is a hammer, then every problem looks like a nail.  This overly used cliché seems very apropos when thinking about the rush to suggest artificial intelligence technology for practically every product and flaunt machine learning as a silver bullet for whatever problem the new product or service is purported to crack.

Indeed, AI technology can be very powerful.  Recent advances in machine learning, aided by ubiquitous connectivity and distributed cloud computing, demonstrate how potent this technology is and promise much more to come.  But AI-based systems can also be difficult to build and even more so to scale and deploy.

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Nov 18
Wine is a Mocker (Jan Steen, 1663–64)

The Industrial Internet of Things: When the Party is Over

By Artificial Intelligence and Machine Learning, Augmented / Virtual Reality, Internet of Things (IoT), Manufacturing, Mergers & Acquisitions No Comments

IoT Industry Snapshot and Predictions

The industrial Internet of Things community is finally beginning to sober up from the bacchanalia of counting connected IoT devices and terabytes of cloud data storage that has dominated the IoT narrative for too long.

IoT platform vendors and consultants are shifting their focus from the lower rung of the IoT technology stack that focuses on device connectivity to the other end of the stack, to technologies that provide meaningful business value: multidisciplinary data aggregation, complex data analytics and higher capacity for optimal decision-making.

Robust articulation of the business value of industrial IoT has been absent from much of the narrative, in the vein of “if you build it, they will come.” Many IoT platform vendors provide tools to draw snazzy dashboards, plot complex data graphs and display virtual gauges. But their data analytics tools are not as robust and trending and predictive capabilities are over optimistic. And the recent rush to add statistical analysis tools (often linear regression tools masqueraded as artificial intelligence and machine learning) will face real-world challenges of data biases, inconsistency and scale. Read More