From Richard Attenborough in'Miracle on 34th Street' to Kurt Russell in'The Christmas Chronicles' a number of famous actors have taken on the role of Santa Claus in blockbuster hits through the years. But who would take on the leading role if Hollywood cast a new movie featuring Father Christmas? To answer this burning question, MailOnline turned to ChatGPT. While the AI bot says that casting for a dream Santa would depend on the tone and style of the film, it suggests five actors who could take on the role. So, do you agree with its star-studded suggestions?

Let us be thankful for the AI industry. Its leaders may be nudging humans closer to extinction, but this year, they provided us with a gloriously messy spectacle of progress. When I say "year," I mean the long year that began late last November, when OpenAI released ChatGPT and, in doing so, launched generative AI into the cultural mainstream. In the months that followed, politicians, teachers, Hollywood screenwriters, and just about everyone else tried to understand what this means for their future. Cash fire-hosed into AI companies, and their executives, now glowed up into international celebrities, fell into Succession-style infighting.

Do not preach doom to Yann LeCun. A pioneer of modern AI and Meta's chief AI scientist, LeCun is one of the technology's most vocal defenders. He scoffs at his peers' dystopian scenarios of supercharged misinformation and even, eventually, human extinction. He's known to fire off a vicious tweet (or whatever they're called in the land of X) to call out the fearmongers. When his former collaborators Geoffrey Hinton and Yoshua Bengio put their names at the top of a statement calling AI a "societal-scale risk," LeCun stayed away.

Natural Language Understanding (NLU) is a subfield of artificial intelligence that requires computer software to comprehend input in the form of sentences. Representative NLU tasks include natural language inference (NLI), question answering (QA), and reading comprehension, among others. Furthermore, NLU has several real-world applications, including Alexa, Siri, and Google Assistant. The major characteristic of NLU tasks is they are difficult and typically require world knowledge and commonsense reasoning. Recently, large language models (LLMs), such as BERT,8 RoBERTa,19 T5,29 GPT-3,4 have been reported to achieve state-of-the-art performance in a series of high-level NLU tasks.

Now that communications and storage are encrypted by default, confidential computing (CC) is the next big step in making the cloud more secure: By terminating network connections to a confidential service within a hardware-isolated trusted execution environment (TEE) whose memory is encrypted with keys accessible only to the processor, it is possible to protect data in use and to treat the cloud hosting infrastructure as part of the adversary, much as networking and storage infrastructures are treated today. Consider, for example, an AI cloud service that uses a large language model to chat with users about a range of sensitive topics such as health, finances, and politics. Many users worry that these services may store their conversations and use them for malicious purposes. Can CC be leveraged to offer strong technical guarantees the conversations will remain private?

Andrew Perlman, Dean of the Suffolk University School of Law in Boston, is no stranger to examining innovative legal technology, but his recent experiment with generative artificial intelligence (AI)--Open AI's ChatGPT, to be precise--led him to think the technology may create bigger changes to the way law is practiced than the Internet itself. Perlman published one of the legal community's first evaluations of ChatGPT's capabilities in creating convincing arguments and answers to typical questions, in "The Implications of ChatGPT For Legal Services and Society" (https://bit.ly/3NuhFxG),

In recent years, the integration of vision and language understanding has led to significant advancements in artificial intelligence, particularly through Vision-Language Models (VLMs). However, existing VLMs face challenges in handling real-world applications with complex scenes and multiple objects, as well as aligning their focus with the diverse attention patterns of human users. In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications. First, we collect hundreds of minutes of gaze data to demonstrate that we can mimic human gaze modalities using localized narratives. We then design an automatic data annotation pipeline utilizing GPT-4 to generate the VOILA-COCO dataset. Additionally, we innovate the Voila Perceiver modules to integrate gaze information into VLMs while preserving their pretrained knowledge. We evaluate Voila-A using a hold-out validation set and a newly collected VOILA-GAZE Testset, which features real-life scenarios captured with a gaze-tracking device. Our experimental results demonstrate that Voila-A significantly outperforms several baseline models. By aligning model attention with human gaze patterns, Voila-A paves the way for more intuitive, user-centric VLMs and fosters engaging human-AI interaction across a wide range of applications.

This study explores the impact of Large Language Models (LLMs) in higher education, focusing on an automated oral examination simulation using a prototype. The design considerations of the prototype are described, and the system is evaluated with a select group of educators and students. Technical and pedagogical observations are discussed. The prototype proved to be effective in simulating oral exams, providing personalized feedback, and streamlining educators' workloads. The promising results of the prototype show the potential for LLMs in democratizing education, inclusion of diverse student populations, and improvement of teaching quality and efficiency.

Cognitive maps are a proposed concept on how the brain efficiently organizes memories and retrieves context out of them. The entorhinal-hippocampal complex is heavily involved in episodic and relational memory processing, as well as spatial navigation and is thought to built cognitive maps via place and grid cells. To make use of the promising properties of cognitive maps, we set up a multi-modal neural network using successor representations which is able to model place cell dynamics and cognitive map representations. Here, we use multi-modal inputs consisting of images and word embeddings. The network learns the similarities between novel inputs and the training database and therefore the representation of the cognitive map successfully. Subsequently, the prediction of the network can be used to infer from one modality to another with over $90\%$ accuracy. The proposed method could therefore be a building block to improve current AI systems for better understanding of the environment and the different modalities in which objects appear. The association of specific modalities with certain encounters can therefore lead to context awareness in novel situations when similar encounters with less information occur and additional information can be inferred from the learned cognitive map. Cognitive maps, as represented by the entorhinal-hippocampal complex in the brain, organize and retrieve context from memories, suggesting that large language models (LLMs) like ChatGPT could harness similar architectures to function as a high-level processing center, akin to how the hippocampus operates within the cortex hierarchy. Finally, by utilizing multi-modal inputs, LLMs can potentially bridge the gap between different forms of data (like images and words), paving the way for context-awareness and grounding of abstract concepts through learned associations, addressing the grounding problem in AI.

Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture. The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. To address this, an innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios. While promising, further research is required into optimizing episodic memory encoding, storage, prioritization, retrieval, and security. Overall, this paper provides a strategic blueprint for developing LLM agents with more sophisticated, human-like memory capabilities, highlighting memory mechanisms as a vital frontier in artificial general intelligence.