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Category: Artificial Intelligence

A “First Principles” Approach to General AI

A “First Principles” Approach to General AI

What We Need to Take the Next Tiny, Incremental Little Step: The “next big thing” is likely to be the next small thing – a tiny step, an incremental shift in perspective. However, a perspective shift is all that we need in order to make some real advances towards general artificial intelligence (GAI). In the second chapter of the ongoing book , I share the following figure (and sorry, the chapter itself is not released yet): Now, we’ve actually been…

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A “Hidden Layer” Guiding Principle – What We Minimally Need

A “Hidden Layer” Guiding Principle – What We Minimally Need

Putting It Into Practice: If we’re going to move our neural network-type architectures into a new, more powerful realm of AI capability, we need to bust out of the “sausage-making” mentality that has governed them thus far, as we discussed last week. To do this, we need to give our hidden layer(s) something to do besides respond to input stimulus. It’s very realistic that this “something” should be free energy minimization, because that’s one of the strongest principles in the…

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How Getting to a Free Energy Bottom Helps Us Get to the Top

How Getting to a Free Energy Bottom Helps Us Get to the Top

Free Energy Minimization Gives an AI Engine Something Useful to Do:   Cutting to the chase: we need free energy minimization in a computational engine, or AI system, because it gives the system something to do besides being a sausage-making machine, as I described in yesterday’s blog on What’s Next for AI. Right now, deep learning systems are constrained to be simple input/output devices. We force-feed them with stimulus at one end, and they poop out (excuse me, “pop out”)…

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What’s Next for AI (Beyond Deep Learning)

What’s Next for AI (Beyond Deep Learning)

The Next Big Step:   We know there’s got to be something. Right now, deep learning systems are like sausage-making machines. You put raw materials in at one end, turn the crank, and at the other end, you get output – nicely wrapped-up sausages. Wherever you are in your studies of machine learning / deep learning / neural networks / AI, you know there’s got to be more. If we’re going to make anything like general artificial intelligence (GAI), we…

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Statistical Mechanics, the Future of AI, and Personal Stories

Statistical Mechanics, the Future of AI, and Personal Stories

Statistical Mechanics and Personal Stories (On the Same Page!)   Yikes! It’s Thursday morning already. I haven’t written to you for three weeks. That’s long enough that I have to pause and search my memory for my username to get into the website. Thanksgiving was lovely. The Thursday after that was grading, all day – and for several days before and after. By now, I (and most of you) have had a few days of recovery, from what has been…

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Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Third Stage Boost – Part 2: Implications of Neuromorphic Computing

Neuromorphic Computing: Statistical Mechanics & Criticality   Last week, I suggested that we were on the verge of something new, and referenced an article by von Bubnoff: A brain built from atomic switches [that] can learn, together with the follow-on article Brain Built on Switches. The key innovation described in this article was a silver mesh, as shown in the following figure. This mesh is a “network of microscopically thin intersecting silver wires,” grown via a combination of electrochemical and…

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Machine Learning: Multistage Boost Process

Machine Learning: Multistage Boost Process

Three Stages to Orbital Altitude in Machine Learning Several years ago, Regina Dugan (then Director of DARPA) gave a talk in which she showed a clip of epic NASA launch fails. Not just one, but many fails. The theme was that we had to risk failure in order to succeed with innovation. This YouTube vid of rocket launch failures isn’t the exact clip that she showed (the “action” doesn’t kick in for about a minute), but it’s pretty close. For…

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Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text

Making Sense: Extracting Meaning from Text by Matching Terms and Entities to Ontologies and Concepts Text analytics is the means by which computer algorithms can extract meaning and useful insights from raw text sources. This can have enormous impact in realms such as marketing, business intelligence, and political campaigns. However, text analytics is one of the toughest challenges in predictive analytics. The reason why this is so hard? It’s because – when done right – text analytics must effectively connect…

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Novelty Detection in Text Corpora

Novelty Detection in Text Corpora

Detecting Novelty Using Text Analytics Detecting novel events – new words, meaning new events – is one of the most important text analytics tasks, and is an important step towards predictive analytics using text mining. On July 24, 2015, The New York Times (and many other news sources) published an article identifying potential inclusion of classified information in the emails which Hillary Clinton had sent via private email and stored on her private email server. How would we use text…

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Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Data, Big Graphs, and Graph Theory: Tools and Methods

Big Graphs Need Specialized Data Storage and Computational Methods {A Working Blogpost – Notes for research & study} Processing large-scale graph data: A guide to current technology, by Sherif Sakr (ssakr@cse.unsw.edu.au), IBM Developer Works (10 June 2013). Note: Dr. Sherif Sakr is a senior research scientist in the Software Systems Group at National ICT Australia (NICTA), Sydney, Australia. He is also a conjoint senior lecturer in the School of Computer Science and Engineering at University of New South Wales. He…

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