; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…Continue
Many people worry that "AI" will usher in a new Industrial revolution where machines replace humans. My take is that it will be more like the Printing press revolution that launched the Age of Enlightenment! The effect will be less of soaring productivity but more of better decision-making leading to a SMARTER society.
Part of the problem is the misnomer, "AI or artificial intelligence"…Continue
Added by PG Madhavan on November 2, 2016 at 12:00pm — No Comments
What exactly is “IoT”? Internet of Things, yes; but what does that mean?
Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?
Being able to connect things together is “table stakes” at the intelligence augmentation game. What…Continue
Added by PG Madhavan on October 16, 2016 at 9:30am — No Comments
In an earlier blog, “Need for DYNAMICAL Machine Learning: Bayesian exact recursive estimation”, I introduced the need for Dynamical ML as we now enter the “Walk” stage of “Crawl-Walk-Run” evolution of machine learning. First, I defined Static ML as follows: Given a set of inputs and outputs, find a static map between the two…Continue
Added by PG Madhavan on October 6, 2016 at 10:04am — No Comments
In this year of Rudolf Kalman’s demise, this article is dedicated to his memory.
We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same…Continue
Added by PG Madhavan on September 18, 2016 at 8:00am — No Comments
As a new sub-discipline of Data Science, I notice that SYSTEMS Analytics is starting to get some traction! There are a couple of Analytics graduate level programs with *Systems* in its title (Stevens Institute of Technology and University of North Carolina are the only ones I know). Web search brings up NO books on *Systems* Analytics. With the publication of my book with *Systems* in the title, that gap has been filled now! “…Continue
Reading some recent blogs, I sense a level of angst among Data Science practitioners about the nature of their field. What exactly IS Data Science - a question that seems to lurk just below the surface . . .
As a young field of study and work, it will naturally take time for a definition of Data Science to crystallize. In the meantime, see if this works for you . . .…Continue
In my recent blog, Marrying Kalman Filtering & Machine Learning, we saw the merger of Bayesian exact recursive estimation (algorithm for which is Kalman Filter/Smoother in the linear, Gaussian case) and Machine Learning. We developed a solution called Kernel Projection Kalman Filter for business applications that…Continue
When you Google “Kalman Filter AND Machine Learning”, very few interesting references pop up! Perhaps my search terms are not the best, perhaps Fintech guys keep such algorithms close to their vests, perhaps there is not much of work done in bringing these two incredibly powerful tools together...
In any case, Part II of my new book, “Systems Analytics: Adaptive Machine Learning workbook” focuses exactly on this merger.
I am happy to report that pre-publication copy…Continue
Added by PG Madhavan on July 18, 2016 at 7:24am — No Comments
Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”. Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an…Continue
Added by PG Madhavan on May 20, 2016 at 5:30am — No Comments
Instead of seeing each Machine Learning (ML) method as a “shiny new object”, here is an attempt to create a unified picture. There is no consensus when it comes to an ontology for ML methods; organizational principles are simply ways to get our arms around knowledge so that we are not swamped by too many unconnected notions.
A powerful organization of the concepts or Ontology of ML is based on conditional expectation.
Added by PG Madhavan on April 20, 2016 at 6:11am — No Comments
ISVs are commonplace in software and technology business. Per Wikipedia, an independent software vendor (ISV) is an organization specializing in making software, designed for mass or niche markets. This is in contrast to software developed for in-house use only within an…Continue
Added by PG Madhavan on March 8, 2016 at 11:30am — No Comments
An insightful person once said, “Prediction is like driving your car forward by looking only at the rearview mirror!”. If the road is dead-straight, you are good . . . UNLESS there is a stalled vehicle ahead in the middle of the road.
We should consider short-term and long-term prediction separately. Long-term prediction is nearly a lost cause. In the 80’s and 90’s, chaos and complexity theorists showed us that things can spin out of control even when we have perfect…Continue
Added by PG Madhavan on January 26, 2016 at 2:08pm — No Comments
#1: What really is BIG Data?
Today, we can store and process so much data that we have nearly captured reality; no more sampling biases/ errors or related issues - this is my definition of Big Data; not tera or peta bytes! If you have measured the entire population (or close to it) and not sample just a small fraction, resulting data is BIG Data!
#2: Analytics - what is in a name?