This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.

Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides goal-based scenarios where college students are exposed to challenging life-inspired assignments in a competitive programming environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly college courses in AI. According to the results, students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity, and mastery in robust AI. Moreover, the leaderboard, our key gamification element in Maestro, has effectively contributed to students' engagement and learning. Results also indicate that Maestro can be effectively adapted to any course length and depth without losing its educational quality.

Professors Mireille Hildebrandt (VUB, Brussels) and Catherine Régis (Université de Montréal – Mila, Canada) will present some of the major current questions around Law and Artificial Intelligence. How to bring AI applications under the rule of law, and what fundamental rights assessments must be put in place? Does the GDPR set the right tone and how can AI development be aligned with individual rights and freedoms, including rights to non-discrimination, privacy, due process and the presumption of innocence? Mireille Hildebrandt will focus on the concepts of robust AI (in terms of reliability and resilience) and robust law (in terms of the rule of law), and discuss how robust AI could support the rule of law and vice versa. She will also give an overview of future developments in the legal regulation of AI, and explore the role of ethical guidelines and charters, through their formalization process and their potential for legal developments.

Rodney Brooks knows a fair bit about robots. Besides being a pioneer of academic robotics research, he has founded companies that have given the world the robot vacuum cleaner, the bomb disposal bot, and a factory robot anyone can program. Now Brooks wants to introduce another revolutionary type of robot helper--a mobile warehouse robot with the ability to read human body language to tell what workers around it are doing. Robots are increasingly working in close proximity to humans, and finding ways to maximize human-machine teamwork could help companies boost productivity and perhaps lead to new kinds of jobs rather than robots replacing people. But giving robots the ability to read human cues is far from easy.

Artificial Intelligence is at the core of the Metaverse, but how trustworthy is it? Although there have been many previous studies into AI's trustworthiness in Web 2.0, these cannot be extended to the metaverse, which requires more complicated metrics to assess system performance and user experience. A recent study from a multinational team of researchers suggests that as we currently lack a set of tested trustworthiness metrics, we should not put our trust in AI to run the metaverse [1]. Today's large scale AI integration means that AI has access to vast amounts of user data; AI can leverage the data to uncover sensitive user behavior like visits to certain websites or personal buying habits. To address these concerns, many agencies, corporations, and government bodies have studied Trustworthy AI (TAI), including the European Commission, United States Department of Defense, and FAANG companies (Meta (Facebook), Amazon, Netflix; and Alphabet (Google)).

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation.Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made.Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

Running a robotics startup is no easy task. Yet, we are always amazed by the number of robotics startups working on innovative technologies. Here, in alphabetical order, are 10 robotics startups The Robot Report will be watching in 2020. The companies are working on a variety of products, including autonomous vehicles, mobile robots for construction, toy robots, and software to give robots common sense and make them easier to use. It's hard to narrow this list down to just 10 robotics startups, so please share in the comments some robotics startups you will be watching in 2020. Make sure to also check out our must-watch robotics startups from 2019.

Artificial Intelligence (AI) has been one of the most discussed topics in recent times and efforts are being put every day to make it more human. However, the future of AI is uncertain since it is hard to determine the direction AI is heading. CEO and Cofounder of Robust.AI, Gary Marcus an expert in AI has recently a published a new paper by the name'The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence', which draws attention to a crucial fact about artificial intelligence, i.e., AI is not aware of its own operations and is only functioning as per certain commands within a controlled environment. The paper consists of 55 pages. It is an expansion of Marcus' argument against Yoshua Bengio during the 2019 AI debate.

The media world is filled with breathless accounts of how artificial intelligence soon will transform almost all fields, take away countless human jobs, and keep improving until, by some accounts, AI ends up running the planet as our overlords. Gary Marcus is here to reset the conversation with a more realistic assessment of the actual capability of AI today, in the next few years, and in the decades to come too. He's the co-author of the brand new book Rebooting AI: Building Artificial Intelligence We Can Trust, and our featured guest at the September What's Now: San Francisco.

Following written evidence from PHG Foundation submitted to a House of Lords inquiry on Artificial Intelligence, PHG Foundation was invited to give oral evidence on the implications of AI for healthcare to the Select Committee on 21 November. In evidence to the House of Lords Select Committee on Artificial Intelligence (AI), PHG Foundation's Head of Science, Dr Sobia Raza highlighted the potential value of an NHS wide strategy on using health data for algorithm development to realise the potential of AI for patients and to ensure data sets are sufficiently representative of the UK population. The committee is considering the economic, ethical and social implications of advances in artificial intelligence. Responding to a question on how to ensure the robust evaluation of machine learning tools before they are used on patients, Sobia used the analogy of driverless cars, which may have been trained and then successfully tested on broad open highways in California but are faced with a very different set of decisions on narrow British country lanes. She said'It's the same in healthcare – the AI algorithms have to be tested under the conditions and the population in which they will be used.'