A machine learning conference debating the use of machine learning? While that might seem so meta, in its call for paper submissions on Monday, the International Conference on Machine Learning did, indeed, note that “papers that include text generated from a large-scale language model (LLM) such as ChatGPT are prohibited unless the produced text is presented as a part of the paper’s experimental analysis.”

It didn’t take long for a brisk social media debate to brew, in what may be a perfect example of what businesses, organizations and institutions of all shapes and sizes, across verticals, will have to grapple with going forward: How will humans deal with the rise of large language models that can help communicate — or borrow, or expand on, or plagiarize, depending on your point of view — ideas? Read More

A spokesperson for OpenAI, which developed ChatGPT, said it is “already developing mitigations to help anyone identify text generated by that system.”

New York City’s Department of Education announced a ban on the wildly popular chatbot ChatGPT — which some have warned could inspire more student cheating — from its schools’ devices and networks.

Jenna Lyle, a spokesperson for the department, said the decision to ban ChatGPT, which is able to generate conversational responses to text prompts, stemmed from concerns about the “negative impacts on student learning.”

“While the tool may be able to provide quick and easy answers to questions, it does not build critical-thinking and problem-solving skills, which are essential for academic and lifelong success,” Lyle said in a email statement. Read More

The lab trained a chatbot to learn from human feedback and search the internet for information to support its claims.

The trick to making a good AI-powered chatbot might be to have humans tell it how to behave—and force the model to back up its claims using the internet, according to a new paper by Alphabet-owned AI lab DeepMind. 

In a new non-peer-reviewed paper out today, the team unveils Sparrow, an AI chatbot that is trained on DeepMind’s large language model Chinchilla.

Sparrow is designed to talk with humans and answer questions, using a live Google search to inform those answers. Based on how useful people find those answers, it’s then trained using a reinforcement learning algorithm, which learns by trial and error to achieve a specific objective. This system is intended to be a step forward in developing AIs that can talk to humans without dangerous consequences, such as encouraging people to harm themselves or others. Read More

The largest store of continually updating knowledge on our planet can be accessed via internet search. In this work we study giving access to this information to conversational agents. Large language models, even though they store an impressive amount of knowledge within their weights, are known to hallucinate facts when generating dialogue (Shuster et al., 2021); moreover, those facts are frozen in time at the point of model training. In contrast, we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information. We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledge driven discussions in order to ground their responses. We find that search-query based ac cess of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020) Read More

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