Code Generation by Example Using Symbolic Machine Learning SN Computer Science

In the latter case, vector components are interpretable as concepts named by Wikipedia articles. Each episode was scrambled (with probability 0.95) using a simple word type permutation procedure30,65, and otherwise was not scrambled (with probability 0.05), meaning that the original training corpus text was used instead. Occasionally skipping the permutations in this way helps to break symmetries that can slow optimization; that is, the association between the input and output primitives is no longer perfectly balanced. Otherwise, all model and optimizer hyperparameters were as described in the ‘Architecture and optimizer’ section. This probabilistic symbolic model assumes that people can infer the gold grammar from the study examples (Extended Data Fig. 2) and translate query instructions accordingly. Non-algebraic responses must be explained through the generic lapse model (see above), with a fit lapse parameter.

As in SCAN, the main tool used for meta-learning is a surface-level token permutation that induces changing word meaning across episodes. These permutations are applied within several lexical classes; for examples, 406 input word types categorized as common nouns (‘baby’, ‘backpack’ and so on) are remapped to the same set of 406 types. The other remapped lexical classes include proper nouns (103 input word types; ‘Abigail’, ‘Addison’ and so on), dative verbs (22 input word types; ‘given’, ‘lended’ and so on) and verbs in their infinitive form (21 input word types; such as ‘walk’, ‘run’). Surface-level word type permutations are also applied to the same classes of output word types. Other verbs, punctuation and logical symbols have stable meanings that can be stored in the model weights. 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.

Prediction model of the failure mode of beam-column joints using machine learning methods

Systematicity continues to challenge models11,12,13,14,15,16,17,18 and motivates new frameworks34,35,36,37,38,39,40,41. Preliminary experiments reported in Supplementary Information 3 suggest that systematicity is still a challenge, or at the very least an open question, even for recent large language models such as GPT-4. To resolve the debate, and to understand whether neural networks can capture human-like compositional skills, we must compare humans and machines side-by-side, as in this Article and other recent work7,42,43.

• Because symbolic reasoning encodes knowledge in symbols and strings of characters.
• Samples from the model (for example, as shown in Fig. 2 and reported in Extended Data Fig. 1) were based on an arbitrary random assignment that varied for each query instruction, with the number of samples scaled to 10× the number of human participants.
• Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with.
• The idea is to guide a neural network to represent unrelated objects with dissimilar high-dimensional vectors.

Each column shows a different word assignment and a different response, either from a different participant (a) or MLC sample (b). The leftmost pattern (in both a and b) was the most common output for both people and MLC, translating the queries in a one-to-one (1-to-1) and left-to-right manner consistent with iconic concatenation (IC). The rightmost patterns (in both a and b) are less clearly structured but still generate a unique meaning for each instruction (mutual exclusivity (ME)).

Art-attribute selection for rater assessments

Attributes such as complexity, abstraction, and valence held less significance in determining creativity. Figure 3a visually presents these findings in the form of a directed acyclic graph. We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analog to the human concept learning, given the parsed program, the perception module learns visual concepts based on the language description of the object being referred to.

Python includes a read-eval-print loop, functional elements such as higher-order functions, and object-oriented programming that includes metaclasses. Each study phase presented the participants with a set of example input–output mappings. For the first three stages, the study instructions always included the four primitives and two examples of the relevant function, presented together screen. For the last stage, the entire set of study instructions was provided together to probe composition.

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