Difference between revisions of "Team:SEU/Human Practices"

 
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              <h1 class="entry-title">Human Practices</h1>
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              <p>This project is based on one of our previously published article [1]. Artificial intelligence is one prevailing research field in recent years, but most of the implementations are on traditional silicon-based computers or chips. Is it possible to use biochemical materials to implement such systems? Our previous paper provides one possible method, but it is validated by only simulations. In this project, we aim to implement such a system in wet experiments. Also, to aid the design of such systems, we will develop a small software to automatically generate required DNA topological structures. </p>
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              <p>In our system, the concentrations of some input DNA species will be regarded as the input to the neural network. Some mathematical calculations are performed in solutions (weighted summation, activation, etc.) and the output of the neural network is the concentration of some certain DNA strands, similarly. </p>
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              <p>There are various possible applications of this technology. For example, as it utilizes only DNA, a type of bio-friendly material, with small modifications it may be integrated to other biosystems to create biochemistry robots.</p>
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                                        <h2>Overview</h2>
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                                        <p><font size="4">Molecular computing technology, a new cross-disciplinary field of the information science, gradually attracts researchers’ attention due to its low computational complexity and high parallelism. Some researchers have designed molecular computation systems to achieve functions which are originally realized by traditional silicon-based systems (e.g., clock design and logic gates), which inspires us to think that maybe we can use molecules to realize artificial intelligence systems which is a very hot topic in recent years. With this goal, we first learned about artificial intelligence and then consulted specialist in Purple Mountain Laboratory (PML) for more insights. After the discussion with the specialist, we understood the limitation of the implementation of artificial intelligence systems. We also knew that molecular computing is possible to solve these problems. </font></p>
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                                        <h2>Integrated Human Practices</h2>
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                                        <h4>Our visit in Purple Mountain Laboratory</h4>
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                                        <p><font size="4">At the beginning of our project, systems, we visited the Purple Mountain Laboratory (PML) in Nanjing to obtain better understanding of artificial intelligence. During our visit in PML, we discussed with specialist Dr. Tan about artificial intelligence.</font></p>
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                                        <p><font size="4">Dr. Tan told us that most artificial intelligence systems are implemented with silicon-based chips. When facing large systems, the area complexity and power cost of such chips are high. Many researchers are trying to reduce the complexity and cost of the silicon-based artificial intelligence systems. She also mentioned that it was meaningful for us to use bio-materials to implement artificial intelligence systems since molecular computation has higher computation parallelism and lower energy cost compared to silicon-based chips.</font></p>
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                                        <p><font size="4">During the discussion, we also learned that neural networks are basic parts in artificial intelligence systems. If we want to use molecules to realize artificial intelligence systems, we first need to build molecular computation models for artificial neurons, including all the related arithmetic operations. Besides, the training of neural networks should also be accomplished in molecular level. Meanwhile, Dr. Tan pointed out that our work may be efficient in the biological field. Our idea using DNA as the basis for construction may have good biological characteristics, and may be useful in intelligent recognition of biological information.</p>
  
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                                        <h4>Our contribution to synthetic biology</h4>
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                                        <p><font size="4">In order to make the DNA-based neural networks available to other researchers, we developed a software tool for users, which can generate DNA reaction models and relevant DNA sequences. With this tool, users can better understand and improve our work. </font></p>
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                                        <p><font size="4">Rome was not built in a day and every great advance is made up of many small achievements. In our work, we not only focus on case study, but also propose useful models and tools. We hope our project can provide convenience and help to other researchers.</font></p>
  
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              <p>DNA strands have been proved a powerful medium to perform computation. Previous researches [2], [3] have shown some interesting applicatoins of such materials, which implemented a "probabilistic switch" and a pattern recognition machine, respectively.</p>
 
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              <p>In this project, we plan to utilize a similar approach to conduct our experiment, implement a neural network using biochemical materials and validate our previous theory.</p>
 
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Latest revision as of 17:31, 21 October 2019









Overview

Molecular computing technology, a new cross-disciplinary field of the information science, gradually attracts researchers’ attention due to its low computational complexity and high parallelism. Some researchers have designed molecular computation systems to achieve functions which are originally realized by traditional silicon-based systems (e.g., clock design and logic gates), which inspires us to think that maybe we can use molecules to realize artificial intelligence systems which is a very hot topic in recent years. With this goal, we first learned about artificial intelligence and then consulted specialist in Purple Mountain Laboratory (PML) for more insights. After the discussion with the specialist, we understood the limitation of the implementation of artificial intelligence systems. We also knew that molecular computing is possible to solve these problems.

Integrated Human Practices

Our visit in Purple Mountain Laboratory

At the beginning of our project, systems, we visited the Purple Mountain Laboratory (PML) in Nanjing to obtain better understanding of artificial intelligence. During our visit in PML, we discussed with specialist Dr. Tan about artificial intelligence.

Dr. Tan told us that most artificial intelligence systems are implemented with silicon-based chips. When facing large systems, the area complexity and power cost of such chips are high. Many researchers are trying to reduce the complexity and cost of the silicon-based artificial intelligence systems. She also mentioned that it was meaningful for us to use bio-materials to implement artificial intelligence systems since molecular computation has higher computation parallelism and lower energy cost compared to silicon-based chips.

During the discussion, we also learned that neural networks are basic parts in artificial intelligence systems. If we want to use molecules to realize artificial intelligence systems, we first need to build molecular computation models for artificial neurons, including all the related arithmetic operations. Besides, the training of neural networks should also be accomplished in molecular level. Meanwhile, Dr. Tan pointed out that our work may be efficient in the biological field. Our idea using DNA as the basis for construction may have good biological characteristics, and may be useful in intelligent recognition of biological information.

Our contribution to synthetic biology

In order to make the DNA-based neural networks available to other researchers, we developed a software tool for users, which can generate DNA reaction models and relevant DNA sequences. With this tool, users can better understand and improve our work.

Rome was not built in a day and every great advance is made up of many small achievements. In our work, we not only focus on case study, but also propose useful models and tools. We hope our project can provide convenience and help to other researchers.