CCF: Small: Paradox and Brain-inspired Computer Architecture

CCF：小型：悖论和受大脑启发的计算机架构

• 批准号: 2204780
• 负责人: JoAnn Paul
• 金额: $ 40.27万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204780&HistoricalAwards=false
• 关键词: CCF; Small; Paradox; Brain; inspired

In order to achieve true machine intelligence, the structural organization of the brain must be understood in the context of its potential impacts on future computer architecture. Current research in artificial intelligence and machine learning are focused on creating complex programs that requires large datasets for training and do not provide insight as to how the structure of the brain achieves intelligence, utilizes massive amounts of parallelism, trades off accuracy for real-time response, and creates new solutions from relatively few experiences. The brain utilizes large amounts of structural parallelism that current computer architecture cannot accommodate. It must also operate in real-time and provide the best possible answers based upon relatively few prior experiences. In order to utilize the vast structural parallelism in the brain, the best possible answer must be drawn from a set of independent possible answers, held simultaneously. But different answers result in conflict which may be the foundation of intelligence but which is inconsistent with conventional computer architecture. The brain is organized into a conscious and subconscious mind, with the subconscious mind utilizing the greatest amount of parallelism. Insight to how the brain achieves intelligence may lie in an examination of conflict in the subconscious – the production, accommodation and resolution of multiple, competing answers. Paradox is a logical result which produces a simultaneous true and false result – something computer circuitry cannot accommodate. While computer programs force such conflict resolution, the brain likely holds multiple results without resolving them, instead holding and developing multiple, conflicting models which continue to develop, over time, and are selected on an as needed basis. Instead of resolving the conflict, the brain picks and chooses, depending upon circumstances. Fundamentally, paradox produces multiple, conflicting answers to the same question. While computer programs operate on vast amounts of logic, they cannot accommodate paradox. Instead of holding the conflict, they force an answer. By focusing on paradox, the structural properties of the brain in the context computer architecture can be examined. Thus, the significance of the structural organization of the brain will be better understood, future computer architecture can utilize parallelism towards the order of the brain, and true machine intelligence can be studied in a new light. By focusing on a striking property of the brain, that of how the brain accommodates paradox, the brain likely functions as an MISD (Multiple Instruction Single Datastream) computer. Machine Learning results in algorithmic solutions, but does not provide insight into how the structure of the brain achieves true intelligence, including creativity. At the same time, conventional computer architecture has been unable to accommodate the vast amount of parallelism and complexity found in the human brain. MISD models of computing have been considered non-sensical because, like paradox, they result in multiple conflicting results. However, because they produce different results and do not need to converge, MISD computing can be the foundation of nearly perfect parallelism, thus may hold the secret to how the brain accommodates vast degrees of parallelism as well as accommodate conflict. The brain must also operate in real-time, and thus likely holds different solutions to the same set of inputs with some accommodating a quick answer, but not the best overall, but one which is necessary to survival. This work will focus on algorithm/processor pairs such as sort and speech recognition for which different algorithms process the same set of inputs, producing different results. The best result is then chosen as a tuple of time and quality, where quality is sacrificed in the interest of time. For example, the spoken word might be processed imprecisely at first, but in a better than nothing mode. However, more thinking might result in a different answer. These two modes of processing would process the same set of inputs, but be held in different parts of the brain that operate in nearly perfect independence. In this way, novel brain-inspired computer architecture can result in which the subconscious mind contains vast amounts of MISD parallel algorithms that operate in nearly perfect independence, and the conscious mind is viewed as a selector of the many possible answers. Since creativity also requires conflict, this research may also reveal how the brain’s structure results in creativity, which is required for true machine intelligence. This project will conduct experiments to demonstrate problems that have multiple possible algorithmic solutions with varying degree of accuracy dependent upon time and resources. This will form the basis of a high-level foundation for a new architecture that accommodates high degrees of parallelism, accommodates time and quality trade-offs, resolves conflict resolution and leads to an initial investigation of how the brain creates. Experiments will be conducted to illustrate our approach and generalization of the architecture and approach will be developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了实现真正的机器智能，必须在其对未来计算机架构的潜在影响的背景下理解大脑的结构组织。目前人工智能和机器学习的研究主要集中在创建复杂的程序，这些程序需要大量的数据集进行训练，并且不能提供关于大脑结构如何实现智能的见解，利用大量的并行性，权衡实时响应的准确性，并从相对较少的经验中创建新的解决方案。大脑利用了大量的结构并行性，这是当前计算机体系结构无法容纳的。它还必须实时运行，并根据相对较少的先前经验提供最佳答案。 为了利用大脑中巨大的结构并行性，必须从同时保持的一组独立的可能答案中得出最佳答案。但不同的答案会导致冲突，这可能是智能的基础，但与传统的计算机体系结构不一致。大脑被组织成意识和潜意识，潜意识利用最大量的并行性。对大脑如何获得智力的洞察力可能在于对潜意识中冲突的研究--多重竞争答案的产生、适应和解决。Parabolic是一种逻辑结果，它同时产生真和假的结果-这是计算机电路无法容纳的。虽然计算机程序强制这种冲突解决，但大脑可能持有多个结果而不解决它们，而是持有和开发多个冲突模型，这些模型随着时间的推移继续发展，并根据需要进行选择。大脑不是解决冲突，而是根据环境进行挑选。从根本上说，悖论会对同一个问题产生多个相互矛盾的答案。虽然计算机程序运行在大量的逻辑上，但它们不能容纳悖论。他们不是坚持冲突，而是强迫对方回答。通过关注悖论，可以在计算机架构的背景下检查大脑的结构特性。 因此，大脑结构组织的重要性将得到更好的理解，未来的计算机体系结构可以利用并行处理大脑的顺序，真正的机器智能可以在一个新的角度进行研究。通过关注大脑的一个惊人特性，即大脑如何适应悖论，大脑可能像一台MISD（多指令单数据流）计算机一样工作。机器学习产生了算法解决方案，但并没有深入了解大脑结构如何实现真正的智能，包括创造力。与此同时，传统的计算机体系结构已经无法适应人类大脑中发现的大量并行性和复杂性。MISD计算模型一直被认为是无意义的，因为像悖论一样，它们会导致多个相互冲突的结果。然而，由于它们产生不同的结果并且不需要收敛，MISD计算可以成为近乎完美的并行性的基础，因此可能掌握大脑如何适应巨大程度的并行性以及适应冲突的秘密。大脑还必须实时操作，因此可能会对同一组输入有不同的解决方案，其中一些可以快速解决，但不是最好的整体，而是生存所必需的。这项工作将集中在算法/处理器对，如排序和语音识别，不同的算法处理相同的输入集，产生不同的结果。然后选择最佳结果作为时间和质量的元组，其中为了时间的利益而牺牲质量。例如，一开始可能会不精确地处理所说的单词，但这是一种聊胜于无的模式。然而，更多的思考可能会导致不同的答案。这两种处理模式将处理相同的输入，但在大脑的不同部分进行处理，这些部分几乎完全独立地运作。通过这种方式，新的大脑启发的计算机架构可以导致潜意识包含大量的MISD并行算法，这些算法几乎完全独立地运行，而有意识的头脑被视为许多可能答案的选择器。由于创造力也需要冲突，这项研究也可能揭示大脑的结构如何导致创造力，这是真正的机器智能所必需的。 这个项目将进行实验，以证明问题有多种可能的算法解决方案，不同程度的准确性取决于时间和资源。这将形成一个新架构的高层次基础的基础，该架构可容纳高度并行性，可容纳时间和质量权衡，解决冲突解决方案，并导致对大脑如何创建的初步调查。将进行实验来说明我们的方法，并开发架构和方法的概括。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。