Business Intelligence Developer Task I.d. Ral-200369

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Business Intelligence Developer Task I.d. Ral-200369 – In the February 1989 issue of Popular Science, we dove deep into ‘brain-style’ computers and the newly emerging projects shaping their future over the next two decades.

Stories (both hits and misses) that helped explain scientific progress, understanding, and innovation—with additional hints of modern context. Explore the full.

Business Intelligence Developer Task I.d. Ral-200369

Social psychologist Frank Rosenblatt was so fascinated by brain mechanics that he built a computer model after the neural network of the human brain and trained it to recognize simple patterns. He called his IBM 704-based model the Perceptron. Oh

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Perceptrons are called “machines that learn.” At the time, Rosenblatt claimed that “it would be possible to construct brains that could reproduce themselves on an assembly line and that were aware of their existence.” The year was 1958.

Many have criticized Rosenblatt’s approach to artificial intelligence as computationally infeasible and hopelessly simplistic. A critical 1969 book by Turing Award winner Marvin Minsky marked the beginning of an era known as the AI ​​winter, when little funding was allocated to such research—despite the 1980s. A brief recovery early on.

In the piece, “Brain-Style Computers,” science and medical writer Naomi Freundlich was among the first journalists to predict the long winter thaw, which lasted into the ’90s. Even before Jeffrey Hinton, considered one of the founders of modern deep learning techniques, published his basic specification in 1992.

, Freundlich’s reporting offers one of the most comprehensive insights into what’s to come in AI over the next two decades.

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“The revival of more sophisticated neural networks was largely due to the availability of less expensive memory, more computer power, and more sophisticated learning rules,” Freundlich wrote. Of course, the missing ingredient in 1989 was data—the vast store of information, labeled and unlabeled, that today’s deep learning neural networks inhale to train themselves. It was the rapid expansion of the Internet, beginning in the late 1990s, that made big data possible and, in combination with other factors noted by Freundlich, nearly half a century since the debut of Rosenblatt’s Perceptron. Later, released the AI.

I walked into Columbia University’s semi-circular lecture hall and sought a seat in the crowded tiered gallery. An excited buzz followed some coughing and rustling of paper as a young man wearing circular wire-rimmed glasses walked up to the lectern with a portable stereo tape player under his arm. Dressed in a tweed jacket and corduroy, he looked like an Ivy League student about to play us his favorite rock tunes. But instead, when he pushed the “on” button, a string of baby talk, specifically, baby computer talk, flooded in. At first unintelligible, really just bursts of sounds, the children’s robot voice repeats the string over and over until it becomes ten distinct words.

“This is a recording of a computer that taught itself to pronounce English text overnight,” said Johns Hopkins University biophysicist Terrence Sajnowski. An enthusiastic crowd broke into lively applause. Sejnowski had just demonstrated a “learning” computer, one of a brand new type of artificial intelligence machine.

Called neural networks, these computers are loosely modeled after the interconnected web of neurons, or nerve cells, in the brain. These represent a dramatic shift in the way scientists think about artificial intelligence—a shift toward a more literal interpretation of how the brain works. The reason: Although some of today’s computers have extremely powerful processors that can crunch numbers at extraordinary speeds, they fail at tasks as simple as recognizing faces, learning to speak and walk, or printing. Reads the text. According to one expert, a human’s visual system can do more image processing than all the supercomputers in the world. Tasks of this type require a large number of rules and instructions that embody every possible variable. Neural networks do not require this kind of programming, but rather, like humans, seem to learn from experience.

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For the military, this means target recognition systems, self-navigating tanks, and even smart missiles that track targets. For the business world, neural networks hold promise for handwriting and facial recognition systems and computer loan officers and bond traders. And for the manufacturing sector, quality control vision systems and robot control are just two objectives.

Interest in neural networks has grown rapidly. A recent meeting in San Diego drew 2,000 people. More than 100 companies are working on neural networks, including several small startups that have begun marketing neural network software and peripherals. Some computer companies, such as IBM, AT&T, Texas Instruments, Nippon Electric Co., and Fujitsu, are also going full steam ahead with research. And the Defense Advanced Research Projects Agency (or DARPA) released a study last year recommending $400 million in neural network funding over eight years. It will be one of the largest programs ever undertaken by the agency.

Since the early days of computer science, the brain has been a model for emerging machines. But compared to the brain, today’s computers are little more than glorified calculators. Reason: A computer has a single processor that works on programmed instructions. Each task is broken down into many small steps that are executed one at a time, quickly. This pipeline approach makes computers vulnerable to a situation commonly found on California freeways: A stalled car—an intractable step—can back up traffic indefinitely. The brain, by contrast, is made up of billions of neurons, or nerve cells, each connected to thousands of others. A specific task records the activity of entire fields of neurons. Communication channels between them lead to solutions.

Enthusiasm over neural networks is not new, and neither are “brain teasers.” Warren S. McCulloch, a psychologist at the Universities of Illinois and Chicago, and his student Walter H. Pitts began studying neurons as logical devices in the early 1940s. He wrote an article explaining how neurons communicate with each other electrochemically: A neuron receives input from surrounding cells. If the sum of the inputs is positive and above a certain preset threshold, the neuron will fire. Suppose, for example, that a neuron has threshold two and has two connections, A and B. The neuron will turn on only if both A and B are on. This is called the logical “AND” operation. Another logical operation called “include-or” is achieved by setting the threshold to one: if either A or B is on, then the neuron is on. If both A and B are on, the neuron is also on.

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In 1958 Cornell University psychologist Frank Rosenblatt used hundreds of these artificial “neurons” to develop a two-layer pattern-learning network called a perceptron. The key to Rosenblatt’s system was that he learned. In the brain, learning occurs primarily through modification of connections between neurons. Simply put, if two neurons are active together and they are connected, the synapses between them will become stronger. This learning principle is called Hebb’s rule and was the basis of learning in the Perceptron. Using Hebb’s principle, the network seems to “learn by experience” because frequently used connections are reinforced. The electronic analog of a synapse is a resistor, and in a perceptron resistors control the amount of current flowing between transistor circuits.

Other simple networks were also created at this time. Bernard Widrow, an electrical engineer at Stanford University, has developed a machine called Adalin (for adaptive linear neurons) that can translate speech, play blackjack and predict the weather in the San Francisco area better than any weatherman. can say The neural network field was an active one until 1969.

This year Marvin Minsky and Seymour Papert of the Massachusetts Institute of Technology, major forces in the principles-based AI field, wrote a book called Perceptrons that criticized perceptron design as being “too simple to be serious”. attacked as The main problem: the perceptron was a two-layer system—taking input directly into output—and learning was limited. “What Rosenblatt and others essentially wanted to do was to solve difficult problems with knee jerk reflexes,” Sejnowski says.

A second problem was that perceptrons were limited in the logical operations they could perform, and therefore could only solve clearly defined problems – deciding between L and T, for example. Reason: Perceptrons could not handle the third logical operation called “exclusive or”. This operation requires that the logic unit be on if either A or B is on, but not both.

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According to Tom Schwartz, a neural network consultant in Mountain View, Calif., technology constraints limited the perceptron’s success. “The idea of ​​a multilayer perceptron was proposed by Rosenblatt, but without a good multilayer learning law you were limited in what you could do with neural nets.” Minsky’s book, combined with the Perceptron’s failure to meet developers’ expectations, undermines the speed of neural networks. Computer scientists charged ahead with traditional artificial intelligence, such as expert systems.

And the recent resurgence of neural networks, some die-hard “connectionists”—adherents of neural networks—prevailed. One of them was the physicist John J. Hopfield, who divided his time between the California Institute of Technology and AT&T Bell Laboratories. A paper he wrote in 1982 described mathematically how neurons can work collectively to process and store information, the physics of problem solving in a neural network. I compare with achieving the lowest energy state.

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