I have been fascinated by Chomsky's ideas about the nature of language and the debate it had started in the past. Based on his views on language and the nature vs. nurture debate, I am guessing Chomsky would have some criticisms of ChatGPT and other language models.

ChatGPT, a dating platform, recently started using AI software to test a new category of match questions. However, 47% of users were unsure whether they'd continue dating someone who admitted to using AI technology to first communicate. The online dating platform is experimenting with the AI technology to roll out a new category of match questions. The six questions ChatGPT yielded have already been answered by over 135,000 users.

In hindsight, ChatGPT may come to be seen as the greatest publicity stunt in AI history, an intoxicating glimpse at a future that may actually take years to realize--kind of like a 2012-vintage driverless car demo, but this time with a foretaste of an ethical guardrail that will take years to perfect. What ChatGPT delivered, in spades, that its predecessors like Microsoft Tay (released March 23, 2016, withdrawn March 24 for toxic behavior) and Meta's Galactica (released November 16, 2022, withdrawn November 18) could not, was an illusion--a sense that the problem of toxic spew was finally coming under control. ChatGPT rarely says anything overtly racist. Simple requests for anti-semitism and outright lies are often rebuffed. Indeed, at times it can seem so politically correct that the right wing has become enraged.

The arrival of OpenAI's ChatGPT and generative AI only a few years after the hype cycle over the metaverse has attracted both the AI bulls and bears as tech pursues its next big thing. The metaverse came with NFTs, an extension of cryptocurrencies and the blockchain, and for now, it's all looking like the hype cycle warning is a good thing to heed. One thing is certain: Silicon Valley needs a next big thing, as the industry is seeing a contraction unlike anything it has experienced over the past decade, with tech leading layoffs in the economy and cost-cutting now the norm for the one sector which has been accustomed to operating with a blank check from investors. At a CNBC Technology Executive Council virtual Town Hall on Thursday, we gathered technology executives at companies across the economy -- specifically, many at companies using AI but not creating it, for example, in retail, media, legal, agriculture and logistics. We gathered a roughly equal number of AI enthusiasts and skeptics, and broke them up into groups to discuss the sudden explosion of interest in ChatGPT, and to separate as best as they could the hype from the reality.

The debate around the use of GPT 3.5 has been a popular topic among academics since the release of ChatGPT. Whilst some have argued for the advantages of GPT 3.5 in enhancing academic writing, others have raised concerns such as plagiarism, the spread of false information, and ecological issues. The need for finding ways to use GPT 3.5 models transparently has been voiced, and suggestions have been made on social media as to how to use GPT 3.5 models in a smart way. Nevertheless, to date, there is a lack of literature which clearly outlines how to use GPT 3.5 models in academic writing, how effective they are, and how to use them transparently. To address this, I conducted a personal experience experiment with GPT 3.5, specifically by using OpenAI text davinci 003 model, for writing this article. I identified five ways of using GPT 3.5: Chunk Stylist, Bullet to Paragraph, Talk Textualizer, Research Buddy, and Polisher. I reflected on their efficacy, and commented on their potential impact on writing ethics. Additionally, I provided a comprehensive document which shows the prompts I used, results I got from GPT 3.5, the final edits and visually compares those by showing the differences in percentages.

With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems. The traditional techniques treat knowledge as part of the input sequence for the dialogue system, prepending a set of knowledge statements in front of dialogue history. However, such a mechanism forces knowledge sets to be concatenated in an ordered manner, making models implicitly pay imbalanced attention to the sets during training. In this paper, we first investigate how the order of the knowledge set can influence autoregressive dialogue systems' responses. We conduct experiments on two commonly used dialogue datasets with two types of transformer-based models and find that models view the input knowledge unequally. To this end, we propose a simple and novel technique to alleviate the order effect by modifying the position embeddings of knowledge input in these models. With the proposed position embedding method, the experimental results show that each knowledge statement is uniformly considered to generate responses.

Recent neural language models have taken a significant step forward in producing remarkably controllable, fluent, and grammatical text. Although studies have found that AI-generated text is not distinguishable from human-written text for crowd-sourcing workers, there still exist errors in AI-generated text which are even subtler and harder to spot. We primarily focus on the scenario in which scientific AI writing assistant is deeply involved. First, we construct a feature description framework to distinguish between AI-generated text and human-written text from syntax, semantics, and pragmatics based on the human evaluation. Then we utilize the features, i.e., writing style, coherence, consistency, and argument logistics, from the proposed framework to analyze two types of content. Finally, we adopt several publicly available methods to investigate the gap of between AI-generated scientific text and human-written scientific text by AI-generated scientific text detection models. The results suggest that while AI has the potential to generate scientific content that is as accurate as human-written content, there is still a gap in terms of depth and overall quality. The AI-generated scientific content is more likely to contain errors in factual issues. We find that there exists a "writing style" gap between AI-generated scientific text and human-written scientific text. Based on the analysis result, we summarize a series of model-agnostic and distribution-agnostic features for detection tasks in other domains. Findings in this paper contribute to guiding the optimization of AI models to produce high-quality content and addressing related ethical and security concerns.

Microsoft reportedly plans to introduce upgraded Office apps with AI features in the coming weeks. According to The Verge, the tech giant is preparing to show what its Prometheus AI technology and OpenAI's language AI can do for Word, PowerPoint, Outlook and other Microsoft 365 apps as soon as this March. Microsoft recently launched a reimagined Bing that can generate conversational responses to search queries, thanks to the Prometheus model, which was built with the help of OpenAI. Additionally, the company introduced a new Edge with a built-in "AI copilot" that's also powered by Prometheus. A button on the top-right corner gives users quick access to Bing's new chat feature, and as we mentioned in our hands-on, it's like having ChatGPT right in your browser.

It's great to see you on a Saturday. As you've likely seen, artificial intelligence has been the talk of the town. Nothing's been hotter than ChatGPT -- the bot's garnered 1 billion cumulative web hits since November, and users have used it to write articles, emails, and even dating-app messages. I caught up with Shark Tank star Kevin O'Leary to get his thoughts on the burgeoning tech trend and how he plans to play the market in 2023. If this was forwarded to you, sign up here. Kevin O'Leary is the chairman of O'Leary Ventures, a media personality, and veteran investor.

Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation. In this work, we present a new framework named ExHalder to address this challenge for headline hallucination detection. ExHalder adapts the knowledge from public natural language inference datasets into the news domain and learns to generate natural language sentences to explain the hallucination detection results. To evaluate the model performance, we carefully collect a dataset with more than six thousand labeled pairs. Extensive experiments on this dataset and another six public ones demonstrate that ExHalder can identify hallucinated headlines accurately and justifies its predictions with human-readable natural language explanations.