Which technologies get better faster?

ScienceDaily (May 17, 2011) — Some forms of technology -- think, for example, of computer chips -- are on a fast track to constant improvements, while others evolve much more slowly. Now, a new study by researchers at MIT and other institutions shows that it may be possible to predict which technologies are likeliest to advance rapidly, and therefore may be worth more investment in research and resources.

In a nutshell, the researchers found that the greater a technology's complexity, the more slowly it changes and improves over time. They devised a way of mathematically modeling complexity, breaking a system down into its individual components and then mapping all the interconnections between these components.

"It gives you a way to think about how the structure of the technology affects the rate of improvement," says Jessika Trancik, assistant professor of engineering systems at MIT. Trancik wrote the paper with James McNerney, a graduate student at Boston University (BU); Santa Fe Institute Professor Doyne Farmer; and BU physics professor Sid Redner. It appears online this week in the Proceedings of the National Academy of Sciences.

The team was inspired by the complexity of energy-related technologies ranging from tiny transistors to huge coal-fired powerplants. They have tracked how these technologies improve over time, either through reduced cost or better performance, and, in this paper, develop a model to compare that progress to the complexity of the design and the degree of connectivity among its different components.

The authors say the approach they devised for comparing technologies could, for example, help policymakers mitigate climate change: By predicting which low-carbon technologies are likeliest to improve rapidly, their strategy could help identify the most effective areas to concentrate research funding. The analysis makes it possible to pick technologies "not just so they will work well today, but ones that will be subject to rapid development in the future," Trancik says.

Besides the importance of overall design complexity in slowing the rate of improvement, the researchers also found that certain patterns of interconnection can create bottlenecks, causing the pace of improvements to come in fits and starts rather than at a steady rate.

"In this paper, we develop a theory that shows why we see the rates of improvement that we see," Trancik says. Now that they have developed the theory, she and her colleagues are moving on to do empirical analysis of many different technologies to gauge how effective the model is in practice. "We're doing a lot of work on analyzing large data sets" on different products and processes, she says.

For now, she suggests, the method is most useful for comparing two different technologies "whose components are similar, but whose design complexity is different." For example, the analysis could be used to compare different approaches to next-generation solar photovoltaic cells, she says. The method can also be applied to processes, such as improving the design of supply chains or infrastructure systems. "It can be applied at many different scales," she says.

Koen Frenken, professor of economics of innovation and technological change at Eindhoven University of Technology in the Netherlands, says this paper "provides a long-awaited theory" for the well-known phenomenon of learning curves. "It has remained a puzzle why the rates at which humans learn differ so markedly among technologies. This paper provides an explanation by looking at the complexity of technology, using a clever way to model design complexity."

Frenken adds, "The paper opens up new avenues for research. For example, one can verify their theory experimentally by having human subjects solve problems with different degrees of complexity." In addition, he says, "The implications for firms and policymakers [are] that R&D should not only be spent on invention of new technologies, but also on simplifying existing technologies so that humans will learn faster how to improve these technologies."

Ultimately, the kind of analysis developed in this paper could become part of the design process -- allowing engineers to "design for rapid innovation," Trancik says, by using these principles to determine "how you set up the architecture of your system."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Massachusetts Institute of Technology. The original article was written by David L. Chandler.

Journal Reference:

J. McNerney, J. D. Farmer, S. Redner, J. E. Trancik. Role of design complexity in technology improvement. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1017298108

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Which technologies get better faster?

ScienceDaily (May 17, 2011) — Some forms of technology -- think, for example, of computer chips -- are on a fast track to constant improvements, while others evolve much more slowly. Now, a new study by researchers at MIT and other institutions shows that it may be possible to predict which technologies are likeliest to advance rapidly, and therefore may be worth more investment in research and resources.

In a nutshell, the researchers found that the greater a technology's complexity, the more slowly it changes and improves over time. They devised a way of mathematically modeling complexity, breaking a system down into its individual components and then mapping all the interconnections between these components.

"It gives you a way to think about how the structure of the technology affects the rate of improvement," says Jessika Trancik, assistant professor of engineering systems at MIT. Trancik wrote the paper with James McNerney, a graduate student at Boston University (BU); Santa Fe Institute Professor Doyne Farmer; and BU physics professor Sid Redner. It appears online this week in the Proceedings of the National Academy of Sciences.

The team was inspired by the complexity of energy-related technologies ranging from tiny transistors to huge coal-fired powerplants. They have tracked how these technologies improve over time, either through reduced cost or better performance, and, in this paper, develop a model to compare that progress to the complexity of the design and the degree of connectivity among its different components.

The authors say the approach they devised for comparing technologies could, for example, help policymakers mitigate climate change: By predicting which low-carbon technologies are likeliest to improve rapidly, their strategy could help identify the most effective areas to concentrate research funding. The analysis makes it possible to pick technologies "not just so they will work well today, but ones that will be subject to rapid development in the future," Trancik says.

Besides the importance of overall design complexity in slowing the rate of improvement, the researchers also found that certain patterns of interconnection can create bottlenecks, causing the pace of improvements to come in fits and starts rather than at a steady rate.

"In this paper, we develop a theory that shows why we see the rates of improvement that we see," Trancik says. Now that they have developed the theory, she and her colleagues are moving on to do empirical analysis of many different technologies to gauge how effective the model is in practice. "We're doing a lot of work on analyzing large data sets" on different products and processes, she says.

For now, she suggests, the method is most useful for comparing two different technologies "whose components are similar, but whose design complexity is different." For example, the analysis could be used to compare different approaches to next-generation solar photovoltaic cells, she says. The method can also be applied to processes, such as improving the design of supply chains or infrastructure systems. "It can be applied at many different scales," she says.

Koen Frenken, professor of economics of innovation and technological change at Eindhoven University of Technology in the Netherlands, says this paper "provides a long-awaited theory" for the well-known phenomenon of learning curves. "It has remained a puzzle why the rates at which humans learn differ so markedly among technologies. This paper provides an explanation by looking at the complexity of technology, using a clever way to model design complexity."

Frenken adds, "The paper opens up new avenues for research. For example, one can verify their theory experimentally by having human subjects solve problems with different degrees of complexity." In addition, he says, "The implications for firms and policymakers [are] that R&D should not only be spent on invention of new technologies, but also on simplifying existing technologies so that humans will learn faster how to improve these technologies."

Ultimately, the kind of analysis developed in this paper could become part of the design process -- allowing engineers to "design for rapid innovation," Trancik says, by using these principles to determine "how you set up the architecture of your system."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Massachusetts Institute of Technology. The original article was written by David L. Chandler.

Journal Reference:

J. McNerney, J. D. Farmer, S. Redner, J. E. Trancik. Role of design complexity in technology improvement. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1017298108

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here