This week, Cherlynn and Devindra discuss the latest on Bing AI – Microsoft is loosening up some recent restrictions, following reports of its bad behavior – as well as the rise of ChatGPT stories on the Kindle store. Spotify is also launching its own AI DJ, starring the digitized voice of one of its current hosts. In other news, we discuss Microsoft's recent agreements with NVIDIA and Nintendo, which could warm regulators towards approving its Activision Blizzard acquisition. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments!

OpenAI declined to provide comment, but confirmed that none of the employees being harassed work directly on ChatGPT. Concerns about "politically biased" outputs from ChatGPT were valid, OpenAI wrote in a blog post last week. However, the company added, controlling the behavior of type of AI system is more like training a dog than coding software. ChatGPT learns behaviors from its training data and is "not programmed explicitly" by OpenAI, the blog post said.

New York attorney and writer Alexander Zubatov weighs in on how A.I. is rapidly changing society and says he's concerned about A.I. being used as a weapon against descent on'The Ingraham Angle.' The popular artificial intelligence bot ChatGPT was forced to admit that global warming has flattened in recent years after asserting there has been an increase in temperatures. Junk Science founder Steve Milloy published a lengthy exchange he had with ChatGPT beginning with the simple question, "Is CO2 warming a hoax?" ChatGPT was quick to say "no," telling Milloy, "It is widely accepted scientific fact" that human activity has fueled CO2 emission into Earth's atmosphere. "But why has there been no global warming since 2015 despite 500 billion tons of emissions?" OpenAI ChatGPT seen on mobile with AI Brain seen on screen.

Given that China already bans Google, Facebook, Twitter, and a host of foreign news websites (including time.com) In fact, ChatGPT parent company OpenAI's decision not to launch in China--Chinese and even Hong Kong phone numbers aren't permitted to sign up--appears to preempt that very fact, with the San Francisco-based firm telling Reuters that "conditions in certain countries make it difficult or impossible" to operate. Read More: Why China, Russia's Biggest Backer, Now Says It Wants to Broker Peace in Ukraine Nevertheless, canny Chinese netizens have found numerous workarounds to access the revolutionary service, such as using virtual private networks and an overseas friend's phone number; purchasing logins via online marketplace Taobao; or simply taking advantage of a variety of proxy bots embedded in ubiquitous messaging service WeChat. Chinese social media was so abuzz with ChatGPT content this month that one AI-generated fake government notice rescinding traffic regulations sparked bedlam and a police investigation in the eastern city of Hangzhou. Unsurprisingly, China's government has now stepped in with explicit bans on WeChat hosting proxy ChatGPT services, while a strident frontpage op-ed on the perils of investing in AI-related firms (and cited ChatGPT), which published earlier this month in the state-owed Securities Times newspaper, was linked to a fall in Chinese tech stocks.

Corresponding authors (denoted by) can be contacted at contact@bigcode-project.org The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack (Kocetkov et al., 2022) and evaluate them on the MultiPL-E text-to-code benchmark (Cassano et al., 2022). We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode. Over the last two years, we have witnessed tremendous progress in the development of code generating AI assistants (Chen et al., 2021; Chowdhery et al., 2022; Nijkamp et al., 2022; Fried et al., 2022; Li et al., 2022; Athiwaratkun et al., 2022). Machine learning models are now capable of assisting professional developers through the synthesis of novel code snippets, not only from surrounding code fragments, but also from natural language instructions. The models powering these code completion systems are usually referred to as Large Language Models for Code--or code LLMs--and are created by training large transformer neural networks (Vaswani et al., 2017) on big corpora of source code. However, with the exception of a few small-scale efforts (Xu et al., 2022b), there is generally a lack of transparency on the development of code LLMs, in part due to their commercial value and the legal uncertainty around distributing training data and models. Some groups have released model weights (Fried et al., 2022; Nijkamp et al., 2022) or provided access to the model through a paid API service (Chen et al., 2021; Athiwaratkun et al., 2022), but these works did not release the full training data or the preprocessing methods that were used.

Chain-of-thought prompting (CoT) advances the reasoning abilities of large language models (LLMs) and achieves superior performance in arithmetic, commonsense, and symbolic reasoning tasks. However, most CoT studies rely on carefully designed human-annotated rational chains to prompt the language model, which poses challenges for real-world applications where labeled training data is available without human-annotated rational chains. This creates barriers to applications of CoT prompting to these general tasks. This paper proposes a new strategy, Automate-CoT (Automatic Prompt Augmentation and Selection with Chain-of-Thought), that can bypass human engineering of CoTs by automatically augmenting rational chains from a small labeled dataset, and then pruning low-quality chains to construct a candidate pool of machine-generated rationale chains based on the labels. Finally, it selects the optimal combination of several rationale chains from the pool for CoT prompting by employing a variance-reduced policy gradient strategy to estimate the significance of each example in a black-box language model. Automate-CoT enables a quick adaptation of the CoT technique to different tasks. Experimental results demonstrate the effectiveness of our method, where state-of-the-art results are achieved on arithmetic reasoning (+2.7\%), commonsense reasoning (+3.4\%), symbolic reasoning (+3.2\%), and non-reasoning tasks (+2.5\%). Our code will be available at https://github.com/shizhediao/automate-cot.

Programming skills are rapidly becoming essential for many educational paths and career opportunities. Yet, for many international students, the traditional approach to teaching introductory programming courses can be a significant challenge due to the complexities of the language, the lack of prior programming knowledge, and the language and cultural barriers. This study explores how large language models and gamification can scaffold coding learning and increase Chinese students sense of belonging in introductory programming courses. In this project, a gamification intelligent tutoring system was developed to adapt to Chinese international students learning needs and provides scaffolding to support their success in introductory computer programming courses.

Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is time-consuming for robot end-users, thus there is a need for investigating automatic behavior-tree-based task generation. Prior behavior-tree-based task generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree-based task generation approach that utilizes state-of-the-art large language models. We propose a Phase-Step prompt design that enables a hierarchical-structured robot task generation and further integrate it with behavior-tree-embedding-based search to set up the appropriate prompt. In this way, we enable an automatic and cross-domain behavior-tree task generation. Our behavior-tree-based task generation approach does not require a set of pre-defined primitive tasks. End-users only need to describe an abstract desired task and our proposed approach can swiftly generate the corresponding behavior tree. A full-process case study is provided to demonstrate our proposed approach. An ablation study is conducted to evaluate the effectiveness of our Phase-Step prompts. Assessment on Phase-Step prompts and the limitation of large language models are presented and discussed.

Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. Leveraging methods such as masked autoencoding and contrastive learning, these representations exhibit strong transfer to policy learning for visuomotor control. But, robot learning encompasses a diverse set of problems beyond control including grasp affordance prediction, language-conditioned imitation learning, and intent scoring for human-robot collaboration, amongst others. First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite. We then introduce Voltron, a framework for language-driven representation learning from human videos and associated captions. Voltron trades off language-conditioned visual reconstruction to learn low-level visual patterns, and visually-grounded language generation to encode high-level semantics. We also construct a new evaluation suite spanning five distinct robot learning problems $\unicode{x2013}$ a unified platform for holistically evaluating visual representations for robotics. Through comprehensive, controlled experiments across all five problems, we find that Voltron's language-driven representations outperform the prior state-of-the-art, especially on targeted problems requiring higher-level features.

One side effect of unlimited content-creation machines--generative AI--is unlimited content. On Monday, the editor of the renowned sci-fi publication Clarkesworld Magazine announced that he had temporarily closed story submissions due to a massive increase in machine-generated stories sent to the publication. In a graph shared on Twitter, Clarkesworld editor Neil Clarke tallied the number of banned writers submitting plagiarized or machine-generated stories. The numbers totaled 500 in February, up from just over 100 in January and a low baseline of around 25 in October 2022. The rise in banned submissions roughly coincides with the release of ChatGPT on November 30, 2022. Large language models (LLM) such as ChatGPT have been trained on millions of books and websites and can author original stories quickly.