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ChatGPT sprung into the public consciousness back in November when it was released by the AI research firm OpenAI. The most widely known instance of generative AI is represented by an AI app named ChatGPT. I’ll be interweaving AI Ethics and AI Law related considerations into this discussion. It takes a village to keep AI and AI developers on a rightful path and deter the purposeful or accidental underhanded efforts that might undercut society. White House recently released to identify human rights in an age of AI, see the link here. One of the latest takes consists of a set of proposed AI Bill of Rights that the U.S. In a similar vein, new AI laws are being explored to try and keep AI on an even keel. For my coverage of the UN AI Ethics principles as devised and supported by nearly 200 countries via the efforts of UNESCO, see the link here. The development and promulgation of Ethical AI precepts are being pursued to hopefully prevent society from falling into a myriad of AI-inducing traps. For my ongoing and extensive coverage of AI Ethics and AI Law, see the link here and the link here, just to name a few. Likewise, there are proposed new AI laws that are being bandied around as potential solutions to keep AI endeavors from going amok on human rights and the like. A growing contingent of concerned and erstwhile AI ethicists are trying to ensure that efforts to devise and adopt AI takes into account a view of doing AI For Good and averting AI For Bad. Please be aware that there are ongoing efforts to imbue Ethical AI principles into the development and fielding of AI apps. Into all of this comes a slew of AI Ethics and AI Law considerations. Let’s first cover the fundamentals of generative AI and then we can take a close look at leveraging theory of mind constructs. Meanwhile, you might be wondering what in fact generative AI is.

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All I am pointing out is that if you can put your feet into the shoes of AI developers, plus if you are aware of some key AI-related programming and machine learning techniques, you can potentially puzzle out what generative AI is doing, akin to that stubborn toaster that I earlier mentioned. I abhor those going around making such false claims. To make this matter absolutely clear, I am not saying or implying that generative AI can think. In addition, I will explain how you can leverage the theory of mind constructs to try and indeed best make use of generative AI. I will be addressing this concern in today’s discussion.

Nonetheless, there is a tendency to ascribe thinking qualities to AI. Sure, they might realize that the AI isn’t human or an animal. When people use a generative AI program, they almost inevitably are lured and lulled into assuming that the AI can think. The recent brouhaha over a type of AI known as Generative AI has dramatically risen the visibility and anxious qualms about the longstanding theory of mind conundrum.

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We will also release our annotated data and code for future research.Now that I’ve gotten all the foregoing on the table, we are ready to talk about Artificial Intelligence (AI).

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We also introduce a novel method for learning to model hallucination detection, based on pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations.Įxperiments on machine translation and abstract text summarization demonstrate the effectiveness of our proposed approach - we obtain an average F1 of around 0.6 across all the benchmark datasets.įurthermore, we demonstrate how to use the token-level hallucination labels to define a fine-grained loss over the target sequence in the low-resource machine translation and achieve significant improvements over strong baseline methods. To better assess the faithfulness of the machine outputs, we propose a new task to predict whether each token in the output sequence is hallucinated conditioned on the source input, and collect new manually annotated evaluation sets for this task. Abstract: Neural sequence models can generate highly fluent sentences but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input, which can cause a lack of trust in the model.