This article is part of the Forum Network series on the Digitalisation and reflects on discussions at OECD Forum 2019 in Paris. But it doesn't stop there – wherever you are, become a member of the global Forum Network community to comment below and continue the conversation!
Deep learning currently dominates artificial intelligence research and its applications, and has generated considerable excitement – perhaps somewhat more than is actually warranted. Although deep learning has made considerable progress in areas such as speech recognition and game playing, it is far from a universal solvent, and by itself is unlikely to yield general intelligence.
- Principles in Practice: Establishing a governance framework for beneficial Autonomous and Intelligent Systems
To understand its scope and limits, it helps to understand what deep learning does; fundamentally, as it is most often used, it approximates complex relationships by learning to classify input examples into output examples, through a form of successive approximation that uses large quantities of training data. It then tries to extend the classifications it has learned to other sets of input “test” data pertaining to the same problem domains. However, unlike human reasoning, deep learning lacks a mechanism for learning abstractions through explicit, verbal definition. Current systems driven purely by deep learning face a number of limitations:
- Since deep learning requires large sets of training data, it works less well in problem areas where data are limited.
- Deep learning techniques can fail if test data differ significantly from training data, as often happens outside a controlled environment. Recent experiments show that deep learning performs poorly when confronted with scenarios that differ in minor ways from those on which the system was trained.
- Deep learning techniques do not perform well when dealing with data with complex hierarchical structures. Deep learning identifies correlations between sets of features that are themselves “flat” or non-hierarchical, as in a simple, unstructured list, but much human and linguistic knowledge is more structured.
- Current deep learning techniques cannot accurately draw open-ended inferences based on real-world knowledge. When applied to reading, for example, deep learning works well when the answer to a given question is explicitly contained within a text. It works less well in tasks requiring inference beyond what is explicit in a text.
- The lack of transparency of deep learning makes this technology a potential liability when applied to support decisions in areas such as medical diagnosis in which human users like to understand how a given system made a given decision. The millions or even billions of parameters used by deep learning to solve a problem do not easily allow its results to be reverse-engineered.
- Thus far, existing deep learning approaches have struggled to integrate prior knowledge, such as the laws of physics. Yet dealing with problems that have less to do with categorisation and more to do with scientific reasoning will require such integration.
- Relatively little work within the deep learning tradition has attempted to distinguish causation from correlation.
Deep learning should not be abandoned, but general intelligence will require complementary tools – possibly of an entirely different nature that is closer to classical symbolic artificial intelligence – to supplement current techniques.
This blogpost draws on: Marcus, G. (2018), “Deep Learning: A Critical Appraisal”, arxiv.org, Cornell University, Ithaca, NY
Continue the conversation and help us co-create the agenda