Symbolic AI vs Machine Learning with Walt Mayo & Paulo Nunes

This chapter outlines the technologies driving the recent rise in AI. It describes the promises of AI in science, illustrating its current uses across a range of scientific disciplines. Later sections raise the question of explainability of AI and the implications for science, highlighting gaps in education and training programmes that slow down the rollout of AI in science.

They produce vectors, like arrays of numbers, which form the inner representation of the model (embeddings). On the other hand, in efficient algorithmic computations, we have hard (discreet) symbols, which are very different. Although they offer impressive performance, many AI approaches provide little in the way of transparency regarding their function.

Symbolic AI

A more flexible kind of problem-solving occurs when reasoning about what to do next occurs, rather than simply choosing one of the available actions. This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance. Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds.

And it displays it all to the evaluators, and the evaluators point out even more problems when they get this input. And so the idea would be eventually to have very minimal input, and the machine would be improving of through increasingly automated self-critiquing. These neural networks take some floating point numbers, and they do some matrix operations, maybe a couple of extra operations on top of that.

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As an example, back in 2015 Google’s DeepMind released a paper showing how it had trained an A.I. To play classic video games, with no instruction other than the on-screen score and the approximately 30,000 pixels that made up each frame. Told to maximize its score, reinforcement learning meant that the software agent gradually learned to play the game through trial and error.

Researchers at the University of Tokyo have taken this same idea and applied it to robots. In doing so, they’ve figured out a way to take everyday natural objects like pieces of wood and get deep reinforcement learning algorithms to figure out how to make them move. Using just a few basic servos, they’ve opened up a whole new way of building robots — and it’s pretty darn awesome. This decade, artificial neural networks have benefited from the arrival of deep learning, in which different layers of the network extract different features until it can recognize what it is looking for.

The most frequent input function is a dot product of the vector of incoming activations. Next, the transfer function computes a transformation on the combined incoming signals to compute the activation state of a neuron. The learning rule is a rule for determining how weights of the network should change in response to new data. Lastly, the model environment is how training data, usually input and output pairs, are encoded. Machine learning is an application of AI where statistical models perform specific tasks without using explicit instructions, relying instead on patterns and inference.

• With such levels of abstraction in our physical world, some knowledge is bound to be left out of the knowledge base.
• I am myself also a supporter of a hybrid approach, trying to combine the strength of deep learning with symbolic algorithmic methods, but I would not frame the debate on the symbol/non-symbol axis.
• Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods.
• Identifying the inconsistencies is a symbolic process in which deduction is applied to the observed data and a contradiction identified.
• While efficient for tasks with clear rules, it often struggles in areas requiring adaptability and learning from vast data.

Can we find some set of problems on which the GPT completely fails while humans do great? You don’t want to have hundreds of same tasks, that’s not interesting. For this reason Francois Chollet developed a dataset ARC on which GPT-3 got zero score. GPT-4 is not evaluated yet, but they are developing a new version of the dataset. This is a super intuitive idea of solving complicated coding problems by using libraries. In the first iteration, it solves simpler coding problems, stores the programs it found, and analyzes them using algorithms.

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We hope that by now you’re convinced that symbolic AI is a must when it comes to NLP applied to chatbots. Machine learning can be applied to lots of disciplines, and one of those is Natural Language Processing, which is used in AI-powered conversational chatbots.

In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in incorporating learning and knowledge. Henry Kautz,[17] Francesca Rossi,[80] and Bart Selman[81] have also argued for a synthesis. Their arguments are based on a need to address the two kinds of thinking discussed in Daniel Kahneman’s book, Thinking, Fast and Slow.

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With such levels of abstraction in our physical world, some knowledge is bound to be left out of the knowledge base. Thomas Hobbes, a British philosopher, famously said that thinking is nothing more than symbol manipulation, and our ability to reason is essentially our mind computing that symbol manipulation. René Descartes also compared our thought process to symbolic representations. Our thinking process essentially becomes a mathematical algebraic manipulation of symbols.

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