Cognitive Neurosystem Using Artificial Intelligence

arXiv (Cornell University), 2022

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities-inherited from over 500 million years of evolution-that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI. Over the coming decades, Artificial Intelligence (AI) will transform society and the world economy in ways that are as profound as the computer revolution of the last half century and likely at an even faster pace. This AI revolution presents tremendous opportunities to unleash human creativity and catalyze economic growth, relieving workers from performing the most dangerous and menial jobs. However, to reach this potential, we still require advances that will make AI more human-like in its capabilities. Historically, neuroscience has been a critical driver and source of inspiration for improvements in AI, particularly those that made AI more proficient in areas that humans and other animals excel at, such as vision, reward-based learning, interacting with the physical world, and language 1,2. It can still play this role. To accelerate progress in AI and realize its vast potential, we must invest in fundamental research in "NeuroAI." The seeds of the current AI revolution were planted decades ago, mainly by researchers attempting to understand how brains compute 3. Indeed, the earliest efforts to build an "artificial brain" led to the invention of the modern "von Neumann computer architecture," for which John von Neumann explicitly drew upon the very limited knowledge of the brain available to him in the 1940s 4,5. Later, the Nobel-prize winning work of David Hubel and Torsten Wiesel on visual processing circuits in the cat neocortex inspired the deep convolutional networks that have catalyzed the recent revolution in modern AI 6-8. Similarly, the development of reinforcement learning was directly inspired by insights into animal behavior and neural activity during learning 9-15. Now, decades later, applications of ANNs and RL are coming so quickly that many observers assume that the long-elusive goal of human-level intelligencesometimes referred to as "artificial general intelligence"-is within our grasp. However, in contrast to the optimism of those outside the field, many front-line AI researchers believe that major breakthroughs are needed before we can build artificial systems capable of doing all that a human, or even a much simpler animal like a mouse, can do. Although AI systems can easily defeat any human opponent in games such as chess 16 and Go 17 , they are not robust and often struggle when faced with novel situations. Moreover, we have yet to build effective systems that can walk to the shelf, take down the chess set, set up the pieces, and move them around during a game, although recent progress is encouraging 18. Similarly, no machine can build a nest, forage for berries, or care for young. Today's AI systems cannot compete with the sensorimotor capabilities of a four-year old child or even simple animals. Many basic capacities required to navigate new situationscapacities that animals have or acquire effortlessly-turn out to be deceptively challenging for AI, partly because AI systems lack even the basic abilities to interact with an unpredictable world. A growing number of AI researchers doubt that merely scaling up current approaches will overcome these limitations. Given the need to achieve more natural intelligence in AI, it is quite likely that new inspiration from naturally intelligent systems is needed 19. Historically, many key AI advances, such as convolutional ANNs and reinforcement learning, were inspired by neuroscience. Neuroscience continues to provide guidance-e.g., attention-based neural networks were loosely inspired by attention mechanisms in the brain 20-23-but this is often based on findings that are decades old. The fact that such cross-pollination between AI and neuroscience is far less common than in the past represents a missed opportunity. Over the last decades, through efforts such as the NIH BRAIN initiative and others, we have amassed an enormous amount of knowledge about the brain. The emerging field of NeuroAI, at the intersection of neuroscience and AI, is based on the premise that a better understanding of neural computation will reveal fundamental ingredients of intelligence and catalyze the next revolution in AI. This will eventually lead to artificial agents with capabilities that match those of humans. The NeuroAI program we advocate is driven by the recognition that AI historically owes much to neuroscience and the promise that AI will continue to learn from it-but only if there is a large enough community of researchers fluent in both domains. We believe the time is right for a large-scale effort to identify and understand the principles of biological intelligence and abstract those for application in computer and robotic systems.

2020

Computational Intelligence and Artificial Intelligence are both aiming at building machines and softwares capable of intelligent behavior. They are consequently prone to interactions, even if the latter is not necessarily interested in understanding how cognition emerges from the brain substrate. In this chapter, we enumerate, describe and discuss the most important fields of interactions. Some are methodological and are concerned with information representation, processing and learning. At the functional level, the focus is set on major cognitive functions like perception, navigation, decision making and language. Among the salient characteristics of the critical contributions of Computational Neuroscience to the development of intelligent systems, its systemic view of the cerebral functioning is particularly precious to model highly multimodal cognitive functions like decision making and language and to design cognitive architectures for the autonomous behavior of robots.

Artificial Intelligence (AI) and the latest advancements in Information Technology (IT) have drastically altered our view of the world. With the ability to process and analyze vast amounts of data in real-time, AI has revolutionized several industries, including healthcare, finance, and manufacturing. For instance, AI-powered medical devices can assist doctors in making diagnoses and predicting patient outcomes more accurately, while also helping them identify early warning signs of diseases. Furthermore, the rise of Machine Learning (ML) has enabled organizations to leverage predictive analytics to gain valuable insights into consumer behavior and market trends. For example, online retailers such as Amazon use AI algorithms to personalize product recommendations based on a customer's browsing and purchase history. In this way, AI-powered systems are able to offer customized experiences and improve overall customer satisfaction. A key challenge on that is the potential impact of AI on human consciousness and perception. As AI systems become more advanced and more integrated into our daily lives, there is a risk that they may alter our perception of the world and our understanding of reality. This raises important questions about the ethical implications of AI and the need to ensure that AI systems are designed and used in a responsible and ethical manner. A shift in a new revolutionary science such as AI and applications such as chatGPT is telling us again that man is not at the center of the universe as an intelligent being. The ideal model of the "Cartesian Man" embodied the basic tenets of Renaissance humanism, which considered man the centre of the universe, limitless in his capacities for development, led to the notion that men should try to embrace all knowledge and develop their own capacities as fully as possible, unfortunately it seems that AI is replacing those limitless capacities! My question is whether AI as a potentially transformative technology that has the potential to enhance our understanding of the world and ourselves.

The human brain is the best example of intelligence known, with unsurpassed ability for complex, real-time interaction with a dynamic world. AI researchers trying to imitate its remarkable functionality will benefit by learning more about neuroscience, and the differences between Natural and Artificial Intelligence. Steps that will allow AI researchers to pursue a more braininspired approach to AI are presented. A new approach that bridges AI and neuroscience is described, Embodied Cultured Networks. Hybrids of living neural tissue and robots, called hybrots, allow detailed investigation of neural network mechanisms that may inform future AI. The field of neuroscience will also benefit tremendously from advances in AI, to deal with their massive knowledge bases and help understand Natural Intelligence.

How the Brain Works -Insights for AI Systems Analysts

Given the author’s decades of publications in neurophysiology, this text proposes a brief conceptual synthesis intended to be useful for educational purposes. The following article is not a research paper; rather, it serves as a commentary on the current state of knowledge. The article offers a concise synthesis of fundamental neurophysiological concepts, aiming to bridge neuroscience and artificial intelligence for educational use. Instead of presenting original research, it provides an intuitive overview of how the human brain performs perception, mental imagery, problem-solving, and self-awareness — presented in a manner accessible to both medical professionals and computer scientists. Key models are highlighted, including neurons as “integrate-and-fire” units; hierarchical neural circuits underlying perception and memory; dual memory loops (hippocampal-cortical and limbic); and the distributed function of the brain’s speech areas. The author also reviews theories positing the brain’s endogenous electromagnetic field and the controversial hypothesis of quantum processing in neuronal microtubules (Orch OR) as possible substrates of consciousness. The paper draws explicit parallels between human cognitive processes and contemporary AI systems. For example, certain AI models can now generate images from language in ways that functionally resemble the brain’s translation of words into mental imagery—though, crucially, AI lacks consciousness and embodiment. The author proposes that comparisons between brain function and AI systems should focus on four priority processes: language and speech, problem-solving mechanisms, the formation of mental images, and AI-driven image generation from linguistic prompts. This synthesis provides a practical, conceptual framework for understanding and comparing cognition in brains and machines, advocating for interdisciplinary clarity and intuitive understanding over excessive technical detail.