Beyond the Screen co-founder Frances Haugen discusses the emergence of ChatGPT and the ethical trap of advanced artificial intelligence on'The Claman Countdown.' Billionaire Mark Cuban is telling people to be careful when using artificial intelligence (AI) tools like ChatGPT and DaVinci, cautioning that there are very few guardrails in place to help determine fact from fiction. Cuban joined "The Problem with Jon Stewart," an Apple TV podcast, warning that technology's next "big battle" won't be over who's running operations at Twitter. "It's who controls the AI models and the information that goes in them," Cuban told Stewart in December. "Once these things start taking on a life of their own, and that's the foundation of a ChatGPT, a DaVinci 3.5 taking on a life of its own, so the machine itself will have an influence, and it'll be difficult for us to define why and how the machine makes the decisions that it makes and who controls the machine."

What if every student had a personal tutor and study tool that could provide tailored answers to their questions at any time? ChatGPT can serve that purpose. Created by Open AI, ChatGPT is an artificial intelligence chatbot that can accurately answer a broad range of questions and generate long, humanlike conversations, among other requested tasks. Critics regard ChatGPT as a threat to classroom integrity, but they fail to realize the educational potential that the free service offers. ChatGPT can be used like a search engine, but it remembers questions in previous conversation threads and can generate personal replies, unlike Google and other engines that only provide direct answers to questions.

ChatGPT and similar programs are, by design, unlimited in what they can "learn" (which is to say, memorize); they are incapable of distinguishing the possible from the impossible. Unlike humans, for example, who are endowed with a universal grammar that limits the languages we can learn to those with a certain kind of almost mathematical elegance, these programs learn humanly possible and humanly impossible languages with equal facility. Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time. The most prominent strain of A.I. encodes a flawed conception of language and knowledge.

Artificial-intelligence systems that can churn out fluent text, such as OpenAI's ChatGPT, are the newest darlings of the technology industry. But when faced with mathematical queries that require reasoning to answer, these large language models (LLMs) often stumble. A line parallel to y 4x 6 passes through (5, 10). What is the y-coordinate of the point where this line crosses the y-axis? Although LLMs can sometimes answer these types of question correctly, they more often get them wrong. In one early test of its reasoning abilities, ChatGPT scored just 26% when faced with a sample of questions from the'MATH' data set of secondary-school-level mathematical problems1. This is to be expected: given input text, an LLM simply generates new text in accordance with statistical regularities in the words, symbols and sentences that make up the model's training data.

Disclaimer: The text below is a press release that is not part of Cryptonews.com editorial content. Artificial Intelligence has become an integral part of our lives, and its use in various sectors is becoming increasingly widespread. In recent weeks, we've heard most about chatGPT and Google Bard, these "generative" AI tools, which as the name suggests, are AIs capable of generating content. However, the world of AI is also being explored in other sectors such as agriculture and agri-food, where it can be used to solve problems related to supply, storage, and food waste. One example of AI's potential in a different sector is machine learning where algorithms are used to analyze vast amounts of data to deduce conclusions or pose new problems.

Machine Learning's Large Language Models (LLMs) -- like ChatGPT, GPT3 and BERT -- have recently captured the attention of the world. Put simply, LLMs are artificial intelligence (AI) tools that read, summarize, translate and generate text. They're able to predict which words would come next in a sentence with high confidence, which allows them to generate language similar to how humans speak and write. These models are so advanced, in fact, that some have even questioned their ability to achieve sentience. But, while it's no secret that LLMs have become an important foundation for conversational AI systems, many people incorrectly assume that LLMs will eventually be the silver bullet that will solve all conversational AI problems -- and that's just not the case.

Even if you haven't tried artificial intelligence tools that can writing essays and poems or conjure new images on command, chances are the companies that make your household products are already starting to do so. Mattel has put the AI image generator DALL-E to work by having it come up with ideas for new Hot Wheels toy cars. Used vehicle seller CarMax is summarizing thousands of customer reviews with the same "generative" AI technology that powers the popular chatbot, ChatGPT. Meanwhile, Snapchat is bringing a chatbot to its messaging service. And the grocery delivery company Instacart is integrating ChatGPT to answer customers' food questions.

We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments, often involving short snippets of code, from introductory and intermediate programming courses at the postsecondary level. This emerging technology stirs countless discussions of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming education (e.g., cheating). However, the capabilities of GPT models and their limitations to reason about and/or analyze code in educational settings have been under-explored. We evaluated several OpenAI's GPT models on formative and summative MCQ assessments from three Python courses (530 questions). We found that MCQs containing code snippets are not answered as successfully as those that only contain natural language. While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e.g., what is true/false about the snippet, or what is its output) appear to be the most challenging. These findings can be leveraged by educators to adapt their instructional practices and assessments in programming courses, so that GPT becomes a valuable assistant for a learner as opposed to a source of confusion and/or potential hindrance in the learning process.

Following the hype around OpenAI's ChatGPT conversational agent, the last straw in the recent development of Large Language Models (LLMs) that demonstrate emergent unprecedented zero-shot capabilities, we audit the latest OpenAI's GPT-3.5 model, `gpt-3.5-turbo', the first available ChatGPT model, in the LexGLUE benchmark in a zero-shot fashion providing examples in a templated instruction-following format. The results indicate that ChatGPT achieves an average micro-F1 score of 47.6% across LexGLUE tasks, surpassing the baseline guessing rates. Notably, the model performs exceptionally well in some datasets, achieving micro-F1 scores of 62.8% and 70.2% in the ECtHR B and LEDGAR datasets, respectively. The code base and model predictions are available for review on https://github.com/coastalcph/zeroshot_lexglue.

Large language models (LLMs) are used to generate content for a wide range of tasks, and are set to reach a growing audience in coming years due to integration in product interfaces like ChatGPT or search engines like Bing. This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs. While alignment techniques like reinforcement learning with human feedback (RLHF) and red-teaming can mitigate some safety concerns and improve model capabilities, it is unlikely that an aggregate fine-tuning process can adequately represent the full range of users' preferences and values. Different people may legitimately disagree on their preferences for language and conversational norms, as well as on values or ideologies which guide their communication. Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user. However, there are several normative challenges in defining the bounds of a societally-acceptable and safe degree of personalisation. In this paper, we ask how, and in what ways, LLMs should be personalised. First, we review literature on current paradigms for aligning LLMs with human feedback, and identify issues including (i) a lack of clarity regarding what alignment means; (ii) a tendency of technology providers to prescribe definitions of inherently subjective preferences and values; and (iii) a 'tyranny of the crowdworker', exacerbated by a lack of documentation in who we are really aligning to. Second, we present a taxonomy of benefits and risks associated with personalised LLMs, for individuals and society at large. Finally, we propose a three-tiered policy framework that allows users to experience the benefits of personalised alignment, while restraining unsafe and undesirable LLM-behaviours within (supra-)national and organisational bounds.