Deep Learning Models Phase 1

Early feedforward neural networks lacked a memory to retain information from previous processing states for a sequence of input data. As a result, they were not able to identify and keep track of long-distance relations among elements of these sequences. The main subject-verb dependency in 1, and the left-right bracket matching in 2, illustrate relations of this kind.

1. 1. The candidates interviewed for the position at the university where my friend teaches give a talk on their research.

2. 2. (₁ (₂ (₃ (₄)₄ (₅)₅)₃)₂ (₆ (₇ (₈)₈ (₉)₉)₇)₆)₁

Elman (Reference Elman1990) introduced Recurrent Neural Networks, which are sequential processing systems with a memory that retains information from a previous state and passes it forward to its successor. Figure 1 shows the architecture of a simple RNN.

Figure 1.

A simple Recurrent Neural Network (from Lappin Reference Lappin2021).

While simple RNNs are able to track certain long-distance relations, they do not filter or control the information that is passed forward to successive states. They are limited in their ability to recognize complex patterns in extended contexts.

Hochreiter and Schmidhuber (Reference Hochreiter and Schmidhuber1997) constructed a more powerful RNN, the Long Short Term Memory network (LSTM). Each of the processing units of an LSTM applies three functions to its input, as filtering gates, to determine which information is passed on to the next processing phase. The input data string is represented as a vector, which is a sequence of real numbers. Figure 2 displays the structure of an LSTM.

Figure 2.

An LSTM (from Olah Reference Olah2015, with permission).

Convolutional Neural Networks (LeCun et al. Reference LeCun, Kavukcuoglu and Farabet2010) identify a feature map from input data by recognizing subcomponents of these data. A pooling layer reduces the dimensions of the features to produce a compressed map, and to render it stable under small variations. Successive convolutional and pooling layers produce increasingly higher-level representations of the objects in the data. A softmax function generates a probability distribution over different possible classifications of the vector. CNNs have achieved good results for applications in visual image identification and speech recognition. Figure 3 illustrates the architecture of a CNN.

Figure 3.

A CNN (from Saha Reference Saha2015, with permission).

Neural networks learn to determine weights for the elements of their input data, which cause their processing units to ‘fire‘ (produce a specified value as output) when these weights are above a given threshold. Training a neural network involves assigning random values to their inputs, and then incrementally correcting these against the data to which it is exposed. The error rate of the network’s output is calculated as the gradient (angle) of a loss function. The learning cycles seek to minimize the values of the loss function. They proceed down the slope of the function by incremental correction, until a statistically estimated optimal point is reached. This learning procedure is known as stochastic gradient descent.

This procedure is generally applied through backpropagation (introduced by Rumelhart et al. Reference Rumelhart, Hinton and Williams1986), where one feeds the difference between the desired and the actual weights of the network backwards along the connections among the units, to calculate updates for the weights of the training inputs. Backpropagation can encounter one of two serious difficulties when used in training RNNs, CNNs, and feed-forward networks. If it is not constrained, the gradient of the loss function can become vanishingly small, or it can explode into unmanageably large values.

RNNs and CNNs were the primary vehicles of the first generation in the deep learning revolution. They greatly increased the accuracy and coverage of neural networks. They provided the first set of AI systems that offered the prospect of effective wide-coverage models that could be applied to a wide range of cognitively interesting AI tasks. These included, among others, machine translation, text understanding, text generation, information retrieval, image recognition, complex object classification, image description, medical diagnostics, and expert risk assessment.

Much of the power of Deep Neural Networks (DNNs) resides in the vector representations of the data on which they are trained. These representations, known as embeddings, express a variety of distributional relations among elements of these data. In the case of words, they provide a conceptual map of the co-occurrence patterns of the terms extracted from the training data. These patterns encode semantic and syntactic relations among the expressions of the corpora. Multimodal embeddings greatly expand the power and range of DNNs by permitting them to track relations between linguistic expressions and non-linguistic environments.

Adding attention (Bahdanau et al. Reference Bahdanau, Cho and Bengio2015) to DNNs significantly improved their ability to identify subtle patterns in data, and to recognize connections among the elements of their input. An attention layer produces a dynamic context vector that is updated with each new token of the input sequence. The vector effectively preserves the information of each hidden state of the part of the DNN that encodes input data. It assigns different weights to these states on the basis of their relations to the output that the decoder produces. The attentional context vector learns to align the weights of the encoded input with the elements of the decoded output. Figure 4 indicates the structure of a bidirectional LSTM with an attention layer.

Figure 4.

A bidirectional LSTM with an attention layer (from Bahdanau et al. Reference Bahdanau, Cho and Bengio2015, with permission).

Embeddings and attention provide key elements of transformers, the DNNs that have given rise to the current phase of the deep learning revolution.

Deep Learning Models Phase 2

Vaswani et al. (Reference Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser and Polosukhin2017) introduce transformers. These are encoder–decoder DNNs that consist entirely of blocks of multi-head attention units, connected by normalization and feedforward layers. An attention block computes self-attention weights for the token of an input sequence, where self-attention is the relative importance of each token in relation to the others. Each block of multi-head self-attention units in a transformer can be dedicated to a different dimension of information concerning its input. In this way, transformers can identify patterns and connections among the elements of the data on which it is pre-trained, across a wide range of features. As a result, transformers are generally more successful than RNNs and CNNs at recognizing long distance relations among elements in input. Figure 5 shows the architecture of a transformer.

Figure 5.

The architecture of a transformer (from Lappin Reference Lappin2021, based on Vaswani et al. Reference Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser and Polosukhin2017).

Transformers are pretrained on large data sets from which they extract embeddings. They can then be fine-tuned for specific applications by adding new dedicated layers of attention heads, or by retraining some of its pre-trained layers. This is a procedure for customizing the model to a particular domain and a set of applications. Transformers can be efficiently trained because the blocks of attention heads can be trained independently of each other, in parallel. Due to their normalization layers and the fact that they retain information for input throughout all phases of processing, they avoid the vanishing and the exploding gradient problems that beset the earlier generation of DNNs.

Transformers can be trained on multi-modal data without revising their architecture. Their attention heads can recognize connections among text, visual images and sound in the same way as they do among the terms of purely linguistic input. Transformers constitute a significant breakthrough in deep learning. They are driving the large data models that have yielded the impressive results currently on display in many areas of AI.

Bender et al. (Reference Bender, Gebru, McMillan-Major and Shmitchell2021) and Chomsky et al. (Reference Chomsky, Roberts and Watumull2023) claim that the LLMs that transformers sustain are little more than ‘stochastic parrots’ returning the data on which they are pretrained. In fact, this assertion is incorrect. They identify abnormalities in X-rays and predict new molecular structures for proteins, within the medical domain. They learn complex new verbal commands in virtual environments. They identify scenes in graphic images, and they produce accurate descriptions for these scenes. They generate new computer code to solve difficult programming problems, and they create novel proofs of mathematical theorems. The inferences that sustain these achievements cannot be reduced to simple variations of previously encountered regularities in data.

Figure 6 exhibits ChatGPT-4’s ability to interpret a sequence of photographic images and to identify the source of the humour in the main photograph in the left panel.

Figure 6.

ChatGPT-4 interprets a sequence of images (from OpenAI 2023, arXiv.org non-exclusive license to distribute).

Problems with LLMs

It is important to recognize that transformers are designed to generate the next token in a sequence on the basis of the tokens encountered up to that point, or from the elements of the token’s left and right context. They do not attend to the factual content of their input, and they have no way of doing so. LLMs frequently produce fluent, plausible text, which is entirely fictional. LLM ‘hallucination’ can create serious difficulties. In one instance, a lawyer in the US who relied on ChatGPT-3 to find legal precedents unknowingly went to court with a set of fabricated cases to support his argument (Weiser Reference Weiser2023). Devising effective procedures for verifying LLM output remains one of the major challenges posed by contemporary AI systems (see Lappin Reference Lappin2024, Reference Lappin2025, on LLM hallucination and the problem of verification).

LLMs have created, or intensified, a variety of significant problems, which pose important issues of public policy (Lappin Reference Lappin2025). As a partial list, consider the following. Pre-training LLMs requires very large amounts of energy and water. Chip production is also energy-intensive, and it produces toxic chemical waste. The servers used to run the online systems that use LLMs, and to store the data needed for these applications, occupy substantial tracts of land. The demands for energy and open land are causing serious environmental problems. These sorts of difficulties are not often associated with the AI revolution in public discourse.

It is primarily large tech companies that have the funds and computing resources to develop and train LLMs. They have achieved quasi-monopoly status in controlling the design and development of AI systems. Universities, public scientific research institutes and small start-ups are becoming clients of the tech companies, in fine-tuning and applying their deep learning models. Consequently, many problems in basic science and engineering that motivated earlier work in AI are being marginalized. In particular, some of the questions about the nature of learning and cognition are no longer in the focus of AI research. Most tech companies are not prioritizing the development of smaller, less computationally intensive models, which can identify significant patterns with much less training data. This sort of research demands long-term investment, and so it does not yield rapidly marketable results.

The capacity of LLMs to produce deep fakes in text, visual and audio modalities facilitates disinformation, identity theft, and a variety of other sorts of harm. AI-powered bots are pumping out false content on social media in the service of extremists, trolls and malign government actors. This stream of digital deception is undermining trust in foundational institutions, such as fair elections and public-health measures. It is also used to propagate racism, misogyny, and other forms of hate speech.

Assessment systems powered by AI models incorporate a variety of ethnic, religious, class, and gender biases in credit and hiring decisions, as well as recommendations for medical treatment. These models can cause serious discrimination against people from the populations against which the bias is encoded.

The prospect of large-scale automation through expert AI systems, and AI-supported robotics, across a wide spectrum of skilled and unskilled professions, raises the possibility of large-scale unemployment. In previous technological revolutions, job loss due to transformation in one industry was offset by the emergence of new areas of production and service. If AI-based models automate a large enough range of industries, a balancing demand for labour in emerging industries might not be available. This could generate major economic and social dislocation.