To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes.
Another benefit of symbolic ai is its ability to handle uncertainty and incomplete information. This is achieved by representing knowledge in a probabilistic way, which allows the machine to make decisions based on the likelihood of certain outcomes. Neural-Symbolic models have been shown to be more explainable, robust to noisy data, capable of generalizing beyond the training data, as well as learning from less data and using less processing power. However, it falls short in applications likely to encounter variations.
This course presents a cross-section of fundamental AI techniques and technologies. Lectures will cover core concepts, theories, mechanisms and results, while practicals and tutorials will allow students to implement and use these techniques. Example topics covered in this course include approaches to searching in AI; automated planning and scheduling; AI techniques used in robotics; knowledge representation and natural language understanding. Trusted autonomous systems (TAS) rely on AI components that perform critical tasks for stakeholders that have to rely on the services provided by the system, e.g., self-driving cars or intelligent robotic systems. Two techniques that help the designers automatically construct symbolic AI systems for trusted autonomous systems from data and specification are model learning and reactive synthesis. Model learning relies on data and observations to derive a model of the AI component for transparency, analysis, and quality assurance tasks.
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He is also collaborating with a start-up based in Cambridge that is working on ‘explainable AI’. It’s called Tenyks (the inverse of Skynet from the Terminator films!) so far funded by two PhD researchers from his department. Researchers at Tenyks are concentrating on improving robustness and reliability within AI networks. Born in Turin in Italy, Pietro studied a BSc in Computer Science and Mathematics for his Masters, because it offered the opportunity to study the foundations of machine learning. In May 2022, Google’s subsidiary, DeepMind, published a paper describing a generalist agent called Gato. It is capable of performing various tasks with the same underlying trained model.
(3) Symbolic logic is useful for simplifying complicated electrical circuits. The techniques of symbolic logic are used to create a simpler circuit that works the same as a more complicated and more expensive circuit. (4) Symbolic logic is useful for analyzing the theoretical limits of ideal digital computers.