Human-like systematic generalization through a meta-learning neural network

Participants were allowed to take as much time as they needed to complete the ratings and could take short breaks if necessary. The artworks were displayed on a 19ʹʹ Iiyama ProLite B1906S monitor, with the longest dimension of each artwork fixed at a maximum of 500 pixels (1280 × 1024, 60 Hz resolution). One of the main stumbling blocks of symbolic AI, or GOFAI, was the difficulty of revising beliefs once they were encoded in a rules engine. Expert systems are monotonic; that is, the more rules you add, the more knowledge is encoded in the system, but additional rules can’t undo old knowledge. Monotonic basically means one direction; i.e. when one thing goes up, another thing goes up.

However surface-level permutations were not enough for MLC to solve the structural generalization tasks in the benchmarks. MLC fails to handle longer output sequences (SCAN length split) as well as novel and more complex sentence structures (three types in COGS), with error rates at 100%. Such tasks require handling ‘productivity’ (page 33 of ref. 1), in ways that are largely distinct from systematicity. In our view, neural-net machine learning approaches are suitable for situations where precise rules do not exist or cannot be identified (for example, translation between natural languages).

Performance of vertically-placed stiffened corrugated panels in steel plate shear walls: Shear elastic buckling analysis

Giving the considerations to the good performance of modified compression field theory (MCFT) in predicting the shear resistance of RC elements [34], which is introduced in this paper as the mechanical theory basis of the grey-box model. Both explicit mathematical equations and favorable prediction performance, the development of such a grey-box model plays an important role in evaluating the punching shear resistance of FRP-reinforced concrete slabs. Finally, as described in the Introduction, creativity is relevant in many areas1,2. Therefore, our study might be a promising approach for the future of creativity research in general1,2,3. Indeed, as Immanuel Kant proposed, visual art is subject to the subjective perception of each viewer while simultaneously encapsulating universal aspects of human experience.

Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents.

Machine Learning and Symbolic AI – the Best of Both Worlds

Finally, each epoch also included an additional 100,000 episodes as a unifying bridge between the two types of optimization. These bridge episodes revisit the same 100,000 few-shot instruction learning episodes, although with a smaller number of the study examples provided (sampled uniformly from 0 to 14). Thus, for episodes with a small number of study examples chosen (0 to 5, that is, the same range as in the open-ended trials), the model cannot definitively judge the episode type on the basis of the number of study examples. A,b, The participants produced responses (sequences of coloured circles) to the queries (linguistic strings) without seeing any study examples.

4.2 to the effort required for manual development of similar code generators. We also consider the skills and technical resources required for different code generator construction approaches. To learn the translation, we created 46 paired examples of DSL and SwiftUI, parsed these into ASTs using the respective Antlr parsers (MobileDSL.g4 and Swift5.g4), and applied the AgileUML toolset option ‘LTBE from ASTs’. The training time was 5.3 s, and the generated CSTL contained 81 LOC and scored 100% accuracy on an independent validation set. The accuracy of the generator produced for Assm is significantly lower than for the cases of 3GL targets, due to the wide syntactic and semantic distance between UML/OCL and assembly code. In particular, a pre-normalisation step on expressions is needed, and this cannot be learnt.

But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. The subsequent paragraphs delve into the interpretation of our findings from the perspectives of diverse research fields, including the humanities, philosophy, art history, and psychology, which have all focused on the concept of creativity. In addition to our discussion, we propose a model for future research that explores potential associations between art-attributes and creativity judgment behavior (see Fig. 3b). We also address the implications of our findings for future research, as well as the merits and limitations of our machine learning approach.

Code generation is a key technique for model-driven engineering (MDE) approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language. Specialised code generation languages and tools have been defined; however, the task of manually constructing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code generation language. In this paper, we apply novel symbolic machine learning techniques for learning tree-to-tree mappings of software syntax trees, to automate the development of code generators from source–target example pairs.

In light of these advances, we and other researchers have reformulated classic tests of systematicity and reevaluated Fodor and Pylyshyn’s arguments1. Notably, modern neural networks still struggle on tests of systematicity11,12,13,14,15,16,17,18—tests that even a minimally algebraic mind should pass2. We evaluate the approach by answering the research questions of “Research Questions”. For RQ1, we identify to what extent CGBE can learn the code generation idioms of “Code Generation Idioms”.

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. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. Thirty participants in the United States were recruited using Mechanical Turk and psiTurk. The participants produced output sequences for seven novel instructions consisting of five possible words.

Navigating the world of commercial open-source large language models

The measure \(1 – p\) also expresses the proportion of additional effort needed to correct the code generators. Another related AI-based approach is intelligent code completion, as supported by tools such as Github Copilot,Footnote 13 AlphaCodeFootnote 14 and Polycoder.Footnote 15 These use large datasets of programming solutions to identify completions of partially coded functions. The quality of the resulting code depends critically on the quality of code in the base dataset, and this can be of highly variable quality (e.g., code taken from Github repositories). In “Code Generation Technologies”, we survey the existing MDE code generation approaches and research, and “Code Generation Idioms” describes common idioms which arise in code generator processing. Symbolic AI is reasoning oriented field that relies on classical logic (usually monotonic) and assumes that logic makes machines intelligent. Regarding implementing symbolic AI, one of the oldest, yet still, the most popular, logic programming languages is Prolog comes in handy.

When the dataset is large, traditional machine learning methods can overfit and end up being inaccurate. Since symbolic regression models are simple and use the least possible amount of variables, they are typically more robust and may have lower chances of overfitting the data. Possible mappings between the set of sequences of length n over an alphabet of size K. However, only a subset of the possible mappings are relevant to code generation, or more generally, to software language translation.

Symbolic artificial intelligence

LISP provided the first read-eval-print loop to support rapid program development. 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. The word and action meanings are changing across the meta-training episodes (‘look’, ‘walk’, etc.) and must be inferred from the study examples.

• Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O.
• The applied Random Forests regressor models accounted for 30% of the variability in creativity judgments given our set of art-attributes.
• Importantly, although the broad classes are assumed and could plausibly arise through simple distributional learning68,69, the correspondence between input and output word types is unknown and not used.
• In addition to the range of MLC variants specified above, the following additional neural and symbolic models were evaluated.
• By feeding your data in .TXT or .CSV format into the program, you can immediately start searching for mathematical formulas that connect the variables.

Neural network-based ML approaches using training on bilingual datasets have achieved successful results for MTBE [3] and software language translation [4] tasks. These adapt established machine translation approaches for natural languages to software languages. In contrast to our approach, these techniques do not produce explicit transformation or translation rules, and they also require large training datasets of corresponding examples. Neural-net approaches encounter difficulties with novel inputs (not seen in the training sets) due to the dictionary problem [3], and tend to become less accurate as inputs become larger in size [4].

• These bridge episodes revisit the same 100,000 few-shot instruction learning episodes, although with a smaller number of the study examples provided (sampled uniformly from 0 to 14).
• As a representative of languages with implicit typing we also considered code generation of JavaScript.
• They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on.
• He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes.
• McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules.

Don’t get me wrong, machine learning is an amazing tool that enables us to unlock great potential and AI disciplines such as image recognition or voice recognition, but when it comes to firmly convinced that machine learning is not the best technology to be used. Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters. In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost. Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning.

However, it is a general-purpose learning system, whereas CGBE is restricted to learning code generation mappings. In contrast to our approach, ILP requires the user to manually provide counter-examples for invalid mappings. In experiments with ILP, we found that it was unable to discover complex tree mappings which our approach could recognise. As is pointed out by [11], successful cases of PBE usually require a strong restriction on the search space for candidate solutions.

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