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(Image by Andreas Lischka from Pixabay )
Quantum Computing aims to apply the science of very small things - the behaviour of subatomic particles - to solving very big problems. These are the questions that classical computers are either unable to answer or would take years to compute in the yes/no world of binary processors.
One of the biggest problem areas is reasoning. Namely, can computers reason in a way that is similar to human thought processes, which often veer into the complex, intuitive, and experiential?
For example, people are generally able to infer that something has happened from partial information. You or I could look out of a window and see a sopping wet lawn, a clear sky, dry pavements, and a sprinkler, and instantly conclude that someone has watered the grass. Computers find that tough.
Is fairness in AI an (im)possibility? Not necessarily, but it requires a proper definition of fairness, in the context of systems design. Here's a fresh look into the decision-making constructs of AI fairness.
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For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here s a practical guide to how Bayes Nets can solve enterprise problems.
In part one of this series, we covered some basic probability theory principles - and compared Machine Learning approaches to Bayesian Belief Nets (Can Bayesian Networks provide answers when Machine Learning comes up short?). In this article, we ll dig a little deeper into Bayesian Belief Networks and how they can be applied to complex decisions.
Understanding Bayesian Inference
In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, very few have any experience implementing Judea Pearl s Bayesian Belief Networks:
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Manufacturers are obsessed with quality - but have they reached a wall with the tools at hand? Fero Labs is seeking to change that, via a practical application of explainable ML and IoT.
Over the past four decades, complex, large-scale manufacturing processes have been defined, measured, analyzed, refined, improved upon, Six-Sigma-ed, into efficient producers of products that are safe, reliable and profitable.
But, there are signs that manufacturers are reaching the limits of quality improvement using only the old methods that reshaped American manufacturing in the 1980s.
That s where Fero Labs, a New York-based startup that makes actionable machine learning software for improving processes and increasing quality of manufacturing facilities comes in. In a telephone interview, CEO Berk Birand explained: