In 2023, Noam Chomsky, considered the founder of modern linguistics, wrote that LLMs "learn humanly possible and humanly impossible languages with equal facility." However, in the Mission: Impossible Language Models paper that received a Best Paper award at the 2024 Association of Computational Linguistics (ACL) conference, researchers shared the results of their testing of Chomsky's theory, having discovered that language models actually struggle with learning languages with non-standard characters. Rogers Jeffrey Leo John, CTO of DataChat Inc., a company that he cofounded while working at the University of Wisconsin as a data science researcher, said the Mission: Impossible paper challenged the idea that LLMs can learn impossible languages as effectively as natural ones. "The models [studied for the paper] exhibited clear difficulties in acquiring and processing languages that deviate significantly from natural linguistic structures," said John. "Further, the researchers' findings support the idea that certain linguistic structures are universally preferred or more learnable both by humans and machines, highlighting the importance of natural language patterns in model training. This finding could also explain why LLMs, and even humans, can grasp certain languages easily and not others."

Jongboom is an embedded engineer and machine learning advocate, always looking for ways to gather more intelligence from the real world.

The reconstruction of geographical data over a century, allows to figuring out the evolution of the French railway landscape, and how it has been impacted by major events (eg.: WWII), or longer time span processes : industry outsourcing, metropolization, public transport policies or absence of them. This work is resulting from the fusion of several public geographical data (SNCF, IGN), enriched with the computer-assisted addition of multiple data gathered on the Internet (Wikipedia, volunteer geographic information). The dataset compounds almost every rail stations (even simple stops) and railway branch nodes, whose link to their respective rail lines allows to build the underlying consistent graph of the network. Every rail line has a "valid to" date (or approx) so that time evolution can be displayed. The present progress of that reconstruction sums up to roughly 90% of what is expected (exact total unknown). This allows to consider temporal demographic analysis (how many cities and towns served by the railway since 1925 up on today), and environmental simulations as well (CO2 cost by given destination ).

The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. Two systems have been constructed and deployed to provide defense analysts with the tools required to advise and assist the Australian Defense Force in the conduct of maritime surveillance and patrol. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

AI Magazine Volume 18 Number 2 (1997) ( AAAI) Date: 4/1/2002 WASA -- World Aeronautics & Space Administration Executive Summary of Committee Report on Disaster Investigation, Incident # 362 Analysis of records downloaded from the 2001 Jupiter Orbital Black Parallelopiped Investigation Mission indicates that the basic source of failure was excessive emotional stress in the HAL computer, leading to a previously unknown condition now called Computational Paranoia. This in turn was an unforeseen side-effect of the design of the HAL-9000 series. HAL was given a genuine personality, enabling it to act as an onboard psychiatric advisor, colleague, and confidante to the human crew members. As a consequence, much of HAL's perceptual software was devoted to reading subtleties of facial expression, unconscious intonation stresses, and other emotional signals. Its performance at empathy and emotional insight was at least two orders of magnitude (as measured by the Kraft-Ebbing-Rachmaninoff method) better than that of the rest of the crew.

Automated Problem Solving Group Jet Propulsion Laboratory 4800 Oak Grove Dr. Pasadena, California 91109 AI research at JPL started in 1972 when design and construction of an experimental "Mars Rover" began. Early in that effort, it was recognized that rover planning capabilities were inadequate. Research in planning was begun in 1975, and work on a succession of AI expert systems of steadily increasing power has continued to the present. Within the group, we have concentrated our efforts on expert systems, although work on vision and robotics has continued in a separate organization, with which we have maintained informal contacts. The thrust of our work has been to build expert systems that can be applied in a real-world environment, and to actually put our systems into such environments, taking a consultative responsibility for meeting user requirements.

The National Aeronautics and Space Administration (NASA) is being challenged to perform more frequent and intensive space-exploration missions at greatly reduced cost. Nowhere is this challenge more acute than among robotic planetary exploration missions that the Jet Propulsion Laboratory (JPL) conducts for NASA. This article describes recent and ongoing work on spacecraft autonomy and ground systems that builds on a legacy of existing success at JPL applying AI techniques to challenging computational problems in planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation. I research and technology development reached critical mass at the Jet Propulsion Laboratory (JPL) about five years ago. In the last three years, the effort has begun to bear fruit in the form of numerous JPL and National Aeronautics and Space Administration (NASA) applications of AI technology in the areas of planning and scheduling, real-time monitoring and control, scientific data analysis, and design automation.

The Modified Antarctic Mapping Mission (MAMM) was conducted from September to November 2000 onboard RADARSAT. The mission plan consisted of more than 2400 synthetic aperture radar data acquisitions of Antarctica that achieved the scientific objectives and obeyed RADARSAT's resource and operational constraints. Mission planning is a time-and knowledge-intensive effort. It required over a workyear to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This article describes the design and use of the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks and enabled rapid generation of what-if scenarios for evaluating alternative mission designs.

This article describes requirements for synthetic adversaries for urban combat training and a prototype application, MOUTBots. MOUTBots use a commercial computer game to define, implement, and test basic behavior representation requirements and the Soar architecture as the engine for knowledge representation and execution. The article describes how these components aided the development of the prototype and presents an initial evaluation against competence, taskability, fidelity, variability, transparency, and efficiency requirements. Urban combat is characterized by building-to-building, room-to-room fighting. Frequent training is an essential element in reducing casualties.

The MAPGEN system represents a successful mission infusion of mixed-initiative planning technology. MAPGEN was deployed as a mission-critical component of the ground operations system for the Mars Exploration Rover mission. Each day, the ground-planning personnel employ MAPGEN to collaboratively plan the activities of the Spirit and Opportunity rovers, with the objective of achieving as much science as possible while ensuring rover safety and keeping within the limitations of the rovers' resources. The Mars Exploration Rover mission has now been operating for more than two years, and MAPGEN continues to be employed for activity plan generation for the Spirit and Opportunity rovers. During the multiyear deployment effort and subsequent mission operations experience, we have learned valuable lessons regarding application of mixed-initiative planning technology to mission operations.