Is It Still Artificial Intelligence? Knowm Rolls Out Adaptive Machine Learning Stack

(Excerpt from original post on the Taneja Group News Blog)

When we want to start computing at biological scales and speeds – evolving today’s practical machine learning forward towards long-deferred visions of a more “artificial intelligence” – we’ll need to take advantage of new forms of hardware that transcend the strictly digital.

Digital computing infrastructure, based on switching digital bits and separating the functions of persisting data from processing, is now facing some big hurdles with Moore’s law. Even if there are a couple of magnitudes of improvement yet to be squeezed out of the traditional digital design paradigm, it has inherent limits in power consumption, scale, and speed. For example, there simply isn’t enough power available to meet the desires of those wishing to reach biological scale and density with traditional computing infrastructure, whether evolving artificial intelligence or more practically scaling machine learning to ever larger big data sets.

Knowm Inc. is pioneering a brilliant new form of computing that leverages the adaptive “learning” properties of memristive technology to not only persist data in fast memory (as others in the industry like HP are researching), but to inherently – and in one operation – calculate serious compute functions that would otherwise require the stored data to be offloaded into CPU’s, processed, and written back (taking more time and power).

The Knowm synapse, their circuit-level integrated unit of calculation and data persistence, was inspired by biological and natural world precedent. At a philosophical level this takes some deep thinking to fully appreciate the implications and opportunities, but this is no longer just a theory. Today, Knowm is announcing their “neuromemristive” solution to market supported with a full stack of  technologies – discrete chips, scalable simulators, defined low-level API’s and higher-level machine learning libraries, and a service that can help layer large quantities of Knowm synapses directly onto existing CMOS (Back End of Line or BEOL) designs.

Knowm is aiming squarely at the machine learning market, but here at Taneja Group we think the opportunity is much larger. This approach that intelligently harnesses analog hardware functions for extremely fast, cheap, dense and memory-inherent computing could represent a truly significant change and turning point for the whole computing industry.

I look forward to finding out who will take advantage of this solution first, and potentially cause a massively disruptive shift in not just machine learning, but in how all computing is done.
 

…(read the full post)

IT pros get a handle on machine learning and big data

An IT industry analyst article published by SearchDataCenter.


Machine learning is the force behind many big data initiatives. But things can go wrong when implementing it, with significant effects on IT operations.

Unfortunately, predictive modeling can be fraught with peril if you don’t have a firm grasp of the quality and veracity of the input data, the actual business goal and the real world limits of prediction (e.g., you can’t avoid black swans).

It’s also easy for machine learning and big data beginners to either make ineffectively complex models or “overtrain” on the given data (learning too many details of the specific training data that don’t apply generally). In fact, it’s quite hard to really know when you have achieved the smartest yet still “generalized” model to take into production.

Another challenge is that the metrics of success vary widely depending on the use case. There are dozens of metrics used to describe the quality and accuracy of the model output on test data. Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed).

…(read the complete as-published article there)