Summary of Panel on Representational Paradigms for Cognitive AI

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00:00:00 - 01:00:00

The panelists in this video discuss various ways of representing information for cognitive AI systems. They argue that deep learning is not reliable or trustworthy, and discuss the idea of adaptive resonance theory. They also discuss the importance of memory and representation, and how these concepts can be used to build neural architectures that can control movement and decision-making.

00:00:00 The first speaker of the panel, Mark Pickard, will discuss cognition and truth value. He believes that traditional representations, such as pictures and symbols, do not adequately represent the way the mind works. His approach, based on the idea of "unified representations," is that the mind has a model of the entire universe that can be related everything to. These representations are real-time and connected to the environment.
00:05:00 The panel discusses various paradigms for representing cognitive AI, with the most popular being correspondences between facts and mental representations. However, this model has a number of problems, including the impossibility of verifying the truth of representations and the impossibility of guiding behavior based on error detection.
00:10:00 Steven Grossberg is a professor emeritus at Boston University and is responsible for the adaptive resonance theory, which is one of the first big cognitive architectures to be developed. He will discuss how neural activity is able to produce representation and cognition, and how it is connected and embedded in the environment.
00:15:00 The panel discussed how information is represented in the brain, and how different modes of cognition (perception, recognition, cognition, and analytic thought) are implemented. They also discussed the relationship between perception and reasoning.
00:20:00 The panel discusses the various cognitive paradigms that exist for artificial intelligence, and how these paradigms can be used to design efficient general representations for learning. They also discuss the idea of "adaptive resonance theory" which posits that resonances between different brain regions play a role in triggering conscious states.
00:25:00 The panel discusses various representational paradigms for cognitive AI, deep learning being one example. They argue that deep learning is unreliable and untrustworthy because it's not explainable and it experiences catastrophic forgetting. They go on to say that orders are not just the feed forward adaptive filter, it's a self-organizing explainable production system that carries out hypothesis testing. They also discuss qualia, saying that multiple processing stages are needed to convert incomplete and noisy sensory data into sufficiently complete representations. They conclude that consciousness occurs when visual cortical area of the forehand and posterior parietal cortex resonate together.
00:30:00 The speaker will discuss the importance of memory and representation, and how they are related. The speaker will also discuss the cost of achieving reliability in computers without noise.
00:35:00 The panel discusses the various ways in which cognitive AI systems need to represent information. The most important factor is the system's ability to remember and anticipate what is happening in the environment. Memory is also necessary for the system to be able to plan future movements and sequences. Memory is also necessary for the system to learn from experience.
00:40:00 This video discusses the concepts of representational paradigms for cognitive AI, and describes how to create stable attractor states that can sustain memory representations over time. The video also discusses the concept of intentional structure, and explains how it can be used to build neural architectures that can control movement and decision-making.
00:45:00 The video discusses the various representational paradigms for cognitive AI, and discusses how a system of elementary behaviors could be augmented with another node, "condition of failure", to allow for the detection of states in which the system currently finds itself versus could be in a state that sees an object but doesn't recognize it. The video also discusses how a system of elementary behaviors could be used to plan sequences of actions to achieve a goal.
00:50:00 Jerome Busemeyer's talk focused on the use of quantum theory in the study of human behavior, specifically in the area of judgment and decision making. He discussed the principle of complementarity, which states that a measurement can change the psychological process it is measuring. He also discussed non-communicativity of measurements and context effects.
00:55:00 The panel discusses representational paradigms for cognitive AI, including complementarity, which was suggested to Nobel Prize-winning physicist Neils Bohr by fellow physicist Edgar Rubin. Steve discussed complementary tunes, his neural models, and quantum theory, which provides an optimal way to compute probabilities in vector spaces. Vine von Neumann and Birkhoff developed a new generalised program of logic based upon quantum theory. The key difference between classical and quantum probability theory is that in quantum theory, the state is a vector sitting in a unit length vector, while in classical probability theory, the state is a boolean algebra sitting in a set. Conditional probabilities are different in quantum theory, where the property of b given a is commutative.

01:00:00 - 01:55:00

The panelists discuss different representational paradigms for cognitive AI. They suggest that consciousness is only possible with the use of certain representational paradigms, and that humans are losing control over the knowledge that is being created by machines. The panelists also discuss the need for new representations for cognitive AI, and present three perspectives on this issue.

01:00:00 Quantum theory is a theory that explains violations of laws of probability, such as the law of total probability. In this toy model, two events, "yes" and "no" to feminism, are incompatible, and the probability of one event is greater than the probability of the other.
01:05:00 The speaker discusses the principles of representations, qualia, and how they guide the development of AI. He shares a story of how the principles of representations help to guide the development of AI.
01:10:00 The panelists discuss the idea that cognitive AI should aim to replicate the experience of consciousness, which is an "illusory" experience. They also discuss the idea that cognitive AI should be designed to answer the question "why is it like that?"
01:15:00 The panel discusses the different representational paradigms that cognitive AI can take, with the focus on consciousness. They suggest that consciousness is only possible with the use of certain representational paradigms, and that humans are losing control over the knowledge that is being created by machines.
01:20:00 The presenters discuss the need for new representations for cognitive AI, and present three perspectives on this issue: the neural circuit, image-net representations, and the universality phenomenon. They suggest that features are the fundamental units of neural networks, and that the universality phenomenon is a testament to the generality of features and circuits.
01:25:00 The three paradigms for cognitive AI discussed in the video are state machines, neural networks, and dynamical systems. Each has its own advantages and disadvantages, and it is still unclear which is the best approach for building AI. Jerome's work is an example of a cognitive representation that uses quantum probability to describe how the brain generates percepts. All three paradigms have their own strengths and weaknesses, and it will be important to develop a unified model that can account for all of cognitive processing.
01:30:00 The panel discuss how cognitive AI must represent the world in order to make patterns intelligible. These representations are hidden states which are variables inside of models, and feature variables are related to each other with computable functions. These relationships reduce the uncertainty and the next set of observables, and bias the model towards convergence. Developmental psychology can provide insights into how a cognitive AI system adapts to the environment.
01:35:00 In this panel, various perspectives on cognitive AI were discussed. One perspective is that cognitive AI is based on neural circuitry, while another is that it is based on a dynamical system. The last perspective discussed is that of content, which is implicit rather than explicit. This implicit content is based on a notion of dynamic implicit definition. It is claimed that content is an implicit presupposition of potential interactions, and that it differs from pointer-based encoding in that there is no explicit representation of what it is about those interactions that is going to be supported.
01:40:00 Markus Uhr and Steve Uh discuss thought experiments that explore how cognitive systems can autonomously correct predictive errors. They also discuss cognitive emotional models, which explain how emotion and motivation play a role in constraining our predictions. Finally, they talk about a thought experiment that leads to cognitive emotional models, which explain a variety of behaviors.
01:45:00 The speaker discusses how cognitive AI research has progressed from thought experiments to actual experiments in order to better understand how the mind works. He also points out that such research has been ongoing for over 40 years and that mobile robots are a recent example of this trend.
01:50:00 This panel discussed the various ways in which cognitive AI can be represented, from mathematical predictions to models of art. They also touched on the challenges of funding cognitive AI research, and how to ensure its sustainability. Finally, they mentioned the potential for qualia in cognitive AI, and how to achieve it.
01:55:00 The panel discusses the different representational paradigms for cognitive AI, and how each will have an impact on the development of the technology.