Originally posted by eggbert
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What Is Machine Intelligence?
Because today’s computers are programmed, they can only do exactly as they are told. In stark contrast, intelligent machines continuously and automatically learn patterns in their environment without being programmed, enabling them to tackle problems in entirely new ways. Intelligent machines that learn and act will have an enormous beneficial impact in the coming decades.
Because today’s computers are programmed, they can only do exactly as they are told. In stark contrast, intelligent machines continuously and automatically learn patterns in their environment without being programmed, enabling them to tackle problems in entirely new ways. Intelligent machines that learn and act will have an enormous beneficial impact in the coming decades.
Secondly, "Machine intelligence" does exactly what it is told to do as well. It IS running software, not flinging pixie dust around. And, contrary to popular opinion, it is not rewriting that software as it goes. Consider "learns patterns in their environment". How does it "know" what a pattern is? By comparing it to patterns it has been programmed to recognize as it built up its stock of neural network pattern solutions. These systems can get very good at recognizing patterns of numbers in hand written scribbles, for example, or in recognizing packet patterns or electrical fluctuation in servers, but that isn't "thinking" or even intelligence. That the Numata's first product, Grok, can handle computer server performance but could not transition by itself to facial recognition or guiding a vehicle is proof of its limitations.
The Numata builds on work by O'Reilly and Rudy in papers written in 2000 and 2001:
Our framework accommodates this finding by establishing a principled division of labor between the cortex and hippocampus, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities, while the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework shows that nonlinear discrimination problems are not good tests of hippocampal function, and suggests that tasks involving rapid, incidental conjunctive learning are better. We implement this framework in a computational neural network model, and show that it can account for a wide range of data in animal learning
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Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion.
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Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion.
I've been paid to write neural network models of the commodity market and studied them extensively, including the calculus behind them. When I read about the claims of AI it is easy to see all the mental hand waving proponents do to support their contentions, relying on the neophyte or unlearned to assume in their mental evaluation "facts" that are not in evidence.
Claiming that they are modeling cortical learning techniques Numata introduced a programming language called NuPIC, which impliments a "Cortical Learning Algorithm" (CLA). They even supply a NuPIC Studio to provides an HTM construction toolkit and 3D visualizations. For those not knowledgable about programming the term "studio" is used to describe programming tools which encompass a complete method for writing specific software. Using their software development GUI you can simulate their approach. Notice that while they change the naming terminology to neurological terms the approach is essential the old multi layered approach with weighted feedbacks and feed forwards with non-linear detection methods to determine boundary conditions. As you use their tool and change your camera position, identical to changing the camera position in a POV model of a house, you can see where the intelligence lies: in the brain of the programmer running the software and setting up the conditions and properties of the network.
Stanford is teaching a class in writing software using the Neocortex model and gives many examples:
http://web.stanford.edu/class/cs379c/readings.html
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