2102 03406 Symbolic Behaviour in Artificial Intelligence

LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Compiled functions could be freely mixed with interpreted functions. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research.

If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge possessed by humans into an explicit form using symbols and rules for their manipulation. Artificial systems mimicking human expertise such as Expert Systems are emerging in a variety of fields that constitute narrow but deep knowledge domains. Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system. Probabilistic programming is an emerging field at the intersection of programming languages and artificial intelligence that aims to make AI systems much easier to develop, with early successes in computer vision, common-sense data cleaning, and automated data modeling.

Title:An Introduction to Symbolic Artificial Intelligence Applied to Multimedia

A perception module of neural networks crunches the pixels in each image and maps the objects. A language module, also made of neural nets, extracts a meaning from the words in each sentence and creates symbolic programs, or instructions, that tell the machine how to answer the question. A third reasoning module runs the symbolic programs on the scene and gives an answer, updating the model when it makes mistakes. Henry Kautz,[18] 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. Kahneman describes human thinking as having two components, System 1 and System 2.

Coupled neuro-symbolic systems are increasingly used to solve complex problems such as game playing or scene, word, sentence interpretation. In a different line of work, logic tensor networks in particular have been designed to capture logical background knowledge to improve image interpretation, and neural theorem provers can provide natural language reasoning by also taking knowledge bases into account. Coupling may be through different methods, including the calling of deep learning systems within a symbolic algorithm, or the acquisition of symbolic rules during training. Very tight coupling can be achieved for example by means of Markov logics. Using symbolic knowledge bases and expressive metadata to improve deep learning systems. Metadata that augments network input is increasingly being used to improve deep learning system performances, e.g. for conversational agents.

Disentangling visual attributes with neuro-vector-symbolic architectures, in-memory computing, and device noise

Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. Both statistical approaches and extensions to logic were tried. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists.

Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles. Chemical reaction databases that are automatically filled from the literature symbolic artificial intelligence have made the planning of chemical syntheses, whereby target molecules are broken down into smaller and smaller building blocks, vastly easier over the past few decades. However, humans must still search these databases manually to find the best way to make a molecule.

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Virtual assistants like Siri and Alexa are prime examples of how AI can support humans in a variety of ways – if only by making things more convenient. When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade. By 2015, his hostility toward all things symbols had fully crystallized. He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes.