05 Sep ML-based reconfigurable symbol decoder: An alternative for next-generation communication systems

Explanation-Based Learning: A survey SpringerLink

In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada King, Countess of Lovelace, invented the first design for a programmable machine. Policymakers in the U.S. have yet to issue AI legislation, but that could change soon. A “Blueprint for an AI Bill of Rights” published in October 2022 by the White House Office of Science and Technology Policy (OSTP) guides businesses on how to implement ethical AI systems. The U.S. Chamber of Commerce also called for AI regulations in a report released in March 2023. In addition to AI’s fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient.

• This is the processing of human language by a computer program.
• The debate between symbol-based learning using propositional (think boolean logic)/higher-order logic and connectionist learning (neural networks) dates back several decades.
• Its creation demonstrated that some of these Turing machines could perform any mathematical computation if it were representable by an algorithm.
• However, the relational program input interpretations can no longer be thought of as independent values over a fixed (finite) number of propositions, but an unbound set of related facts that are true in the given world (a “least Herbrand model”).
• As some AI scientists point out, symbolic AI systems don’t scale.

The two presented their groundbreaking Logic Theorist, a computer program capable of proving certain mathematical theorems and referred to as the first AI program. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice.

RedHat Expert Session on Kubernetes and OpenShift

However, using this uncompressed representation can lead to an exponential increase in the size of the symbolic state space and a corresponding increase in the sample complexity of learning the symbolic models. Figures 3a, ​,3b,3b, and ​and3c3c show the percentage of time that the agent spends on exploring asteroids 1, 3, and 4, respectively. The random and greedy policies have difficulty escaping asteroid 1, and are rarely able to reach asteroid 4. On the other hand, our algorithm allocates its time much more proportionally. Learning macro-operators—i.e., searching for useful macro-operators to be learned from sequences of basic problem-solving actions. Good macro-operators simplify problem-solving by allowing problems to be solved at a more abstract level.

The superiority of the proposed approach over the previous works is due to the importance of class balancing to avoid biasing classifiers toward the majority classes. In addition, the unigram method is better for extracting features from cuneiform texts because it converts the text into the smallest possible units (symbols). These single symbols can be used in operations such as identifying symbol frequency in text and extracting information about the text. Also, it allows reducing data size and improving processing speed, which makes it very useful in machine learning and classification techniques.

An introduction to reconfigurable systems

They could not possibly have anticipated the enmity that soon emerged. If we investigate the concepts in the incremental learning experiment, we find that the relevant attributes have obtained a high certainty score already after the first phase of the experiment (see Figure 11). Consequently, these remain stable over the various phases, while other attributes never achieve high certainty scores. Additionally, we note that the resulting same high-scoring attributes as those obtained in the baseline experiment, independent of the phase in which they were introduced (see Figure 12).

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