Knowledge Tracing (KT) is vital in educational data mining, enabling personalized learning by tracking learners' knowledge states and forecasting their academic outcomes. This study introduces the LOKT (Large Language Model Option-weighted Knowledge Tracing) model to address the cold start problem where limited historical data available using large language models (LLMs). While traditional KT models have incorporated option weights, our research extends this by integrating these weights into an LLM-based KT framework. Moving beyond the binary classification of correct and incorrect responses, we emphasize that different types of incorrect answers offer valuable insights into a learner's knowledge state. By converting these responses into text-based ordinal categories, we enable LLMs to assess learner understanding with greater clarity, although our approach focuses on the final knowledge state rather than the progression of learning over time. Using five public datasets, we demonstrate that the LOKT model sustains high predictive accuracy even with limited data, effectively addressing both "learner cold-start" and "system cold-start" scenarios. These findings showcase LOKT's potential to enhance LLM-based learning tools and support early-stage personalization.

A large literature suggests that people are intuitive Dualists--they consider the mind ethereal, distinct from the body. Past research also shows that Dualism emerges, in part, via learning (e.g., Barlev & Shtulman, 2021). But whether learning is sufficient to give rise to Dualism is unknown.The evidence from human learners does address this question because humans are endowed not only with general learning capacities but also with core knowledge capacities. And recent results suggest that core knowledge begets Dualism (Berent, Theodore & Valencia, 2021; Berent, 2023). To evaluate the role of learning, here, we probe for a mind-body divide in Davinci--a large language model (LLM) that is devoid of any innate core knowledge. We show that Davinci still leans towards Dualism, and that this bias increases systematically with the learner's inductive potential. Thus, davinci (a GPT-3 model) exhibits mild Dualist tendencies, whereas its descendent, text-davinci-003 (a GPT-3.5 model), shows a full-blown bias. It selectively considers thoughts (epistemic states) as disembodied--as unlikely to show up in the body (in the brain), but not in its absence (after death). While Davinci's performance is constrained by its syntactic limitations, and it differs from humans, its Dualist bias is robust. These results demonstrate that the mind-body divide is partly learnable from experience.They also show how, as LLM's are exposed to human narratives, they induce not only human knowledge but also human biases.

As we do every year, last December we published a list of 10 predictions about the world of artificial intelligence in 2022. To keep ourselves honest, with 2022 now coming to a close, let's revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today. Boy, did this come true. The breathtaking progress in language AI has been the defining theme in the world of artificial intelligence in 2022.

We predicted that tensions would flare between the U.S. and China over AI in 2022. This proved all ... [ ] too true. As we do every year, last December we published a list of 10 predictions about the world of artificial intelligence in 2022. To keep ourselves honest, with 2022 now coming to a close, let's revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today.

We each make decisions based on certain values and principles. But whose do we choose as the template for ethical AI?

Do we need artificial intelligence to tell us what's right and wrong? The idea might strike you as repulsive. Many regard their morals, whatever the source, as central to who they are. But everyone faces morally uncertain situations, and on occasion, we seek the input of others. We might turn to someone we think of as a moral authority, or imagine what they might do in a similar situation.

In December 2020, we published a list of 10 predictions about the world of artificial intelligence in the year 2021. With 2021 now coming to a close, let's revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today. As of the beginning of this year, no autonomous vehicle company had ever gone public. TuSimple, Embark and Aurora have all debuted on public markets this year.

Monday was met with a swift 9:30am start, made easier to digest with a talk on AI and Ethics, why all the fuss? This talk, and subsequent discussion, covered the thought-provoking topic of fairness within AI. The main lesson considered whether we actually need new ethical principles to govern AI, or whether we can take inspiration from well-established areas, such as medicine. Medicine works by four key principles: Beneficence, non-maleficence, autonomy and justice and AI brings some new challenges to this framework. The new challenges include autonomy, decision making and culpability.

Gazing at the horizon, there's no shortage of excitement in technology: the promise of a more interconnected world, greater opportunities, and the sheer wonder of what's going to be possible next. Of course Automated Speech Recognition (ASR) will be a key player, but this application fits into a greater narrative that includes everything from augmented reality (AR) to quantum computing to the nearly endless uses of artificial intelligence (AI). Using technology to create a better world, however, is harder than just developing the tech. There's a lot that can go wrong; and in truth, there's a lot that's already gone wrong. While most tech companies would rather pull the wool over their customers' eyes--your eyes--we're facing these issues head-on because we want to live in a world where tech helps instead of harms. Tech, especially AI, is a powerful tool.

Can artificial intelligence learn the moral values of human societies? Can an AI system make decisions in situations where it must weigh and balance between damage and benefits to different people or groups of people? Can AI develop a sense of right and wrong? In short, will artificial intelligence have a conscience? This question might sound irrelevant when considering today's AI systems, which are only capable of accomplishing very narrow tasks.