The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved in each crash is essential to understanding crash dynamics and system accountability. However, existing approaches often overlook automation-specific factors and lack model sophistication to capture distinctions between different SAE levels. To address this gap, this study evaluates the performance of three advanced tabular deep learning models MambaAttention, TabPFN, and TabTransformer for classifying SAE automation levels using structured crash data from Texas (2024), covering 4,649 cases categorized as Assisted Driving (SAE Level 1), Partial Automation (SAE Level 2), and Advanced Automation (SAE Levels 3-5 combined). Following class balancing using SMOTEENN, the models were trained and evaluated on a unified dataset of 7,300 records. MambaAttention demonstrated the highest overall performance (F1-scores: 88% for SAE 1, 97% for SAE 2, and 99% for SAE 3-5), while TabPFN excelled in zero-shot inference with high robustness for rare crash categories. In contrast, TabTransformer underperformed, particularly in detecting Partial Automation crashes (F1-score: 55%), suggesting challenges in modeling shared human-system control dynamics. These results highlight the capability of deep learning models tailored for tabular data to enhance the accuracy and efficiency of automation-level classification. Integrating such models into crash analysis frameworks can support policy development, AV safety evaluation, and regulatory decisions, especially in distinguishing high-risk conditions for mid- and high-level automation technologies.

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E-commerce has long represented both challenges and opportunities for e-tailers and omnichannel retailers. Now, a spotlight has been shown on both aspects of e-commerce by the acceleration of online shopping during the pandemic. Consumer expectations are rising along with e-commerce volumes, and success is being defined from the consumer's perspective. In the process, e-fulfilment and returns management have become complex undertakings that increasingly require the use of advanced automation. This is one of the foremost trends in logistics today -- and, as logistics operations become more reliant on automation for satisfactory results, the combination of humans and machines that is driving efficiency can help improve health and safety as well.

The Science Research Council has been receiving an increasing number of applications for research support in the rather broad field with mathematical, engineering and biological aspects which often goes under the general description Artificial Intelligence (AI). The research support applied for is sufficient in volume, and in variety of discipline involved, to demand that a general view of the field be taken by the Council itself. In forming such a view the Council has available to it a great deal of specialist information through its structure of Boards and Committees; particularly from the Engineering Board and its Computing Science Committee and from the Science Board and its Biological Sciences Committee. These include specialised reports on the contribution of AI to practical aims on the one hand and to basic neurobiology on the other, as well as a large volume of detailed recommendations on grant applications. To supplement the important mass of specialist and detailed information available to the Science Research Council, its Chairman decided to commission an independent report by someone outside the AI field but with substantial general experience of research work in multidisciplinary fields including fields with mathematical, engineering and biological aspects. I undertook to make such an independent report, on the understanding that it would simply describe how AI appears to a lay person after two months spent looking through the literature of the subject and discussing it orally and by letter with a variety of workers in the field and in closely related areas of research. Such a personal view of the subject might be helpful to other lay persons such as Council members in the process of preparing to study specialist reports and recommendations and working towards detailed policy formation and decision taking. The report which follows must certainly not be viewed as more than such a highly personal view of the AI field. The author is grateful for the large amount of help and advice readily given in reply to his many requests. He must emphasize, however, that none but himself is responsible for the opinions expressed in this report. They represent mere!y the broad overall view of the subject which he reached after such limited studies as he was able to make in the course of two months. Readers might possibly have expected that the report would include a summary, but the author decided against this partly because considerable material is summarised already in almost every paragraph.

The basic fact is that technology eliminates jobs, not work. It is the continuous obligation of economic policy to match increases in productive potential with increases in purchasing power and demand. Otherwise the potential created by technical progress runs to waste in idle capacity, unemployment, and deprivation. The fear that machines will replace human labor is a durable one in the public mind, from the time of the Luddites in the early 19th century. Yet most economists have viewed "the end of humans in jobs" as a groundless fear, inconsistent with the evidence.

Many of the concepts and technologies considered in this study for possible use in future space missions are elements of a diverse field of research known as "artificial intelligence" or simply AI. The term has no universally accepted definition or list of component subdisciplines, but is commonly understood to refer to the study of thinking and perceiving as general information processing functions - the science of intelligence. Although, in the words of one researcher, "It is completely unimportant to the theory of AI who is doing the thinking, man or computer" (Nilsson, 1974), the historical development of the field has followed largely an empirical and engineering approach. In the past few decades, computer systems have been programmed to prove theorems, diagnose diseases, assemble mechanical equipment using a robot hand, play games such as chess and backgammon, solve differential equations, analyze the structure of complex organic molecules from mass-spectrogram data, pilot vehicles across terrain of limited complexity, analyze electronic circuits, understand simple human speech and natural language text, and even write computer programs according to formal specifications - all of which are analogous to human mental activities usually said to require some measure of "intelligence." If a general theory of intelligence eventually emerges from the AI field, it could help guide the design of intelligent machines as well as illuminate various aspects of rational behavior as it occurs in humans and other animals.