This paper introduces an Artificial Intelligence (AI) foundation model for time series in engineering applications, where causal operations are required for real-time monitoring and control. Since engineering time series are governed by physical, rather than linguistic, laws, large-language-model-based AI foundation models may be ineffective or inefficient. Building on the classical innovations representation theory of Wiener, Kallianpur, and Rosenblatt, we propose Time Series GPT (TS-GPT) -- an innovations-representation-based Generative Pre-trained Transformer for engineering monitoring and control. As an example of foundation model adaptation, we consider Probabilistic Generative Forecasting, which produces future time series samples from conditional probability distributions given past realizations. We demonstrate the effectiveness of TS-GPT in forecasting real-time locational marginal prices using historical data from U.S. independent system operators.

In 2017, soon after Google researchers invented a new kind of neural network called a transformer, a young OpenAI engineer named Alec Radford began experimenting with it. What made the transformer architecture different from that of existing A.I. systems was that it could ingest and make connections among larger volumes of text, and Radford decided to train his model on a database of seven thousand unpublished English-language books--romance, adventure, speculative tales, the full range of human fantasy and invention. Then, instead of asking the network to translate text, as Google's researchers had done, he prompted it to predict the most probable next word in a sentence. The machine responded: one word, then another, and another--each new term inferred from the patterns buried in those seven thousand books. Radford hadn't given it rules of grammar or a copy of Strunk and White.

We present empirical evidence that neural networks with ReLU and Absolute Value activations learn distance-based representations. We independently manipulate both distance and intensity properties of internal activations in trained models, finding that both architectures are highly sensitive to small distance-based perturbations while maintaining robust performance under large intensity-based perturbations. These findings challenge the prevailing intensity-based interpretation of neural network activations and offer new insights into their learning and decision-making processes.

Data valuation seeks to answer the important question, "How much is this data worth?" Existing data valuation methods have largely focused on discriminative models, primarily examining data value through the lens of its utility in training. However, with the push for ever-larger language models, relying on valuation methods that require training becomes increasingly expensive and dependent on specific techniques. We propose an alternative perspective on the data value problem for language models, centering around the plausibility of the data. We posit that data holds lesser value if it can be plausibly generated by the model itself. Starting from some intuitive criteria that align with our notions of valuable data, we develop a novel value function that is computationally tractable and derived from first principles with provable properties. We conduct a theoretical analysis of our value function and evaluate it across multiple scenarios and datasets.

The backpropagation algorithm, or backprop, is a widely utilized optimization technique in deep learning. While there's growing evidence suggesting that models trained with backprop can accurately explain neuronal data, no backprop-like method has yet been discovered in the biological brain for learning. Moreover, employing a naive implementation of backprop in the brain has several drawbacks. In 2022, Geoffrey Hinton proposed a biologically plausible learning method known as the Forward-Forward (FF) algorithm. Shortly after this paper, a modified version called FFCL was introduced. However, FFCL had limitations, notably being a three-stage learning system where the final stage still relied on regular backpropagation. In our approach, we address these drawbacks by eliminating the last two stages of FFCL and completely removing regular backpropagation. Instead, we rely solely on local updates, offering a more biologically plausible alternative.

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

In the winter of 1958, a 30-year-old psychologist named Frank Rosenblatt was en route from Cornell University to the Office of Naval Research in Washington DC when he stopped for coffee with a journalist. Rosenblatt had unveiled a remarkable invention that, in the nascent days of computing, created quite a stir. It was, he declared, "the first machine which is capable of having an original idea". Rosenblatt's brainchild was the Perceptron, a program inspired by human neurons that ran on a state-of-the-art computer: a five-tonne IBM mainframe the size of a wall. Feed the Perceptron a pile of punch cards and it could learn to distinguish those marked on the left from those marked on the right.

Geometric Deep Learning is a term for approaches considering ML problems from the perspectives of symmetry and invariance. It provides a common blueprint for CNNs, GNNs, and Transformers. Here, we study the history of GDL from ancient Greek geometry to Graph Neural Networks.

In this article, we will look at the development of AI and the field of deep learning. Deep learning originated in the era of vacuum tube computers. In 1958, Frank Rosenblatt of Cornell University designed the first artificial neural network. This was later named "deep learning". Rosenblatt knew that this technology surpassed the computing power at that time.

In this article, we will look at the development of AI and the field of deep learning. Deep learning originated in the era of vacuum tube computers. In 1958, Frank Rosenblatt of Cornell University designed the first artificial neural network. This was later named "deep learning". Rosenblatt knew that this technology surpassed the computing power at that time.