A New Vision for Artificial Intelligence

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A new vision for artificial intelligence is using smaller more relevant data sets for dynamic learning generating more effective outcomes and better predictions. This model uses cognitive architecture, learns, transfers learning, and retains knowledge – enabling more valuable and compelling artificial intelligence applications. This approach is more closely related to the brain’s actual structures and much more effective than “neural networks,” which is a catchy name but the similarity to the brain’s actual functioning is in name only. Real advancement in artificial intelligence must live in reality, not theoretical marketing. The current state of artificial intelligence shows the shortcomings of big data and trial-and-error approaches. A new AI vision can be a more effective solution. Smaller data sets, more relevant information, dynamic data, and algorithms will lead to more appropriate outcomes, better tools, and more effective applications.

A New Vision for Artificial Intelligence

A New Vision for Artificial Intelligence

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A new vision for artificial intelligence is using smaller more relevant data sets for dynamic learning generating more effective outcomes and better predictions.

This model uses cognitive architecture, learns, transfers learning, and retains knowledge – enabling more valuable and compelling artificial intelligence applications. This approach is more closely related to the brain’s actual structures and much more effective than “neural networks,” which is a catchy name but the similarity to the brain’s actual functioning is in name only. Real advancement in artificial intelligence must live in reality, not theoretical marketing.

Deepmind

Artificial Intelligence and Transformation

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Artificial intelligence, while generating powerful tools for analysis, is only the beginning of a more ambitious phase making AI systems more accurate, less biased, and effective prediction tools. Gathering more and more raw data does not create value. One cannot simply push a button and have valuable output generated. Data needs to be collected, processed, stored, managed, analyzed, and visualized – only then can we begin to interpret the results. Each step is challenging, and every step in this cycle requires massive amounts of work and value-added tools. It’s not just the software and hardware artifacts we produce that will be physically present everywhere and touch our lives all the time, it will be the computational concepts we use to approach and solve problems, manage our daily lives, and communicate and interact with other people. It will be a reality when it is so integral to our lives it disappears. The problems and solutions we address are limited only by our own curiosity and creativity.

First Principles – Disruption’s Source

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“Assume no knowledge” (Socrates) No successful company can create or sustain its competitive strength without constantly examining its First Principles. It means defining a problem effectively, understanding the actions needed, and then implementing those plans. This requires a unique combination of perspective, talent, drive, and organizational flexibility. It is rare, but when discovered, it is…

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AI Does Not Live Up to the Hype

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The history of AI shows that attempts to build human understanding into computers rarely work. Instead, most of the field’s progress has come from the combination of ever-increasing computer power and exponential growth in available data. Essentially, the ability to bring ever more brute computational force to bear on a problem-focused on larger data sets…

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pandemic time

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Instead of “internet time” we now have “pandemic time.” The need for advanced systems to keep society functioning, manufacturing moving, and give consumers some sense of safety is immediate. Driving innovations – whether those innovations are in health care, technology or other areas of production and manufacturing – is essential to not only offset the…

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Distributed Machine Learning Can Bring Healthcare Breakthroughs

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Over the last decade, the dramatic rise of deep learning has led to stunning transformations in dozens of industries. It has powered our pursuit of self-driving cars, fundamentally changed the way we interact with our devices, and reinvented our approach to cybersecurity.

In health care, however, despite many studies showing its promise for detecting and diagnosing diseases, progress in using deep learning to help real patients has been agonizingly slow. All this could change with distributed learning.