Large language models (LLMs) have generated much hype in recent months (see Figure 1). The demand has led to the ongoing development of websites and solutions that leverage language models. ChatGPT set the record for the fastest-growing user base in January 2023, proving that language models are here to stay. This is also shown by the fact that Bard, Google's answer to ChatGPT, was introduced in February 2023. Yet, large language models are a new development in computer science.

Spotify has bigger plans for the technology behind its new AI DJ feature after seeing positive consumer reaction to the new feature. Launched just ahead of the company's Stream On event in L.A. last week, the AI DJ curates a personalized selection of music combined with spoken commentary delivered in a realistic-sounding, AI-generated voice. But under the hood, the feature leverages the latest in AI technologies and large language models, as well as generative voice -- all of which are layered on top of Spotify's existing investments in personalization and machine learning. These new tools don't necessarily have to be limited to a single feature, Spotify believes, which is why it's now experimenting with other applications of the technology. Though the highlight from Spotify's Stream On event was the mobile app's revamp, which now focuses on TikTok-like discovery feeds for music, podcasts, and audiobooks, the AI DJ is now a prominent part of the streaming service's new experience.

On February 8, 2023, Marc Benioff and Salesforce made a Twitter announcement about the teaser, giving a few cryptic hints about what to expect. Now that the official announcement has been made, the mystery surrounding Einstein GPT is slowly being cleared up. The first generative AI for CRM, Einstein GPT, was announced by Salesforce, the top provider of CRM software worldwide. This ground-breaking technology aids businesses of all sizes in automating procedures, boosting productivity, enhancing customer service and much more. By fusing NLP and machine learning, Einstein GPT can assist businesses in better understanding their clients, increasing sales, and improving marketing.

A Microsoft executive has revealed OpenAI is planning on releasing its upgraded model that powers AI tools such as ChatGPT and Microsoft's Bing Chat. The news comes from Microsoft Germany's Chief Technology Officer (CTO) Andreas Braun, who appeared on stage at the AI in Focus - Digital Kickoff event on March 9 alongside other Microsoft Germany employees. All of the Microsoft officials discussed the upcoming ventures with artificial intelligence language models and the involvement of OpenAI's GPT series. Reports now indicate that OpenAI, in which Microsoft has heavily invested with more than $10 billion already pledged, is nearing the end of development for GPT-4, the next upgrade in the underlying technology powering the now immensely popular AI tools. According to Baum, OpenAI will be rolling out GPT-4 sometime "next week", which means we can expect some kind of announcement from OpenAI this week since Baum made those comments on March 9. "We will introduce GPT-4 next week, there we will have multimodal models that will offer completely different possibilities - for example videos," Braun said.

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This article was originally published on the author's blog and re-published to TOPBOTS with permission from the author. There's a quote about how in polite society, you should never talk about three things: politics, religion, and money. In this article, I break polite conventions to determine how an AI would respond to all three of those topics. As AI tools become more and more integrated into our lives (such as writing news articles or being used in mental health chatbots), it's important (and curious) to know if these tools generate outputs that reflect certain political opinions. In this article, I probe OpenAI's GPT-3 model on contentious political, economic, and social topics by having it take the Political Compass, a popular test for measuring one's political leaning.

次世代大規模言語モデルGPT-4は今週にもリリースされるか？画像、音、テキスト、動画に対応するマルチモーダル。

The COVID-19 pandemic was a shock to higher education systems everywhere. But while some changes, like moving lectures online, were relatively easy to make, assessment posed a much bigger challenge. Assessment can take many forms, from essays to exams to experiments and more. They increased their use of programs like Turnitin to check for matched wording in students' assignments. And for closed-book, timed tests they used tools such as Proctorio, which monitor a student's computer or phone while they write exams.

Large Language Models (LLMs) have demonstrated impressive text generation capabilities, prompting us to reconsider the future of human-AI co-creation and how humans interact with LLMs. In this paper, we present a spectrum of content generation tasks and their corresponding human-AI interaction patterns. These tasks include: 1) fixed-scope content curation tasks with minimal human-AI interactions, 2) independent creative tasks with precise human-AI interactions, and 3) complex and interdependent creative tasks with iterative human-AI interactions. We encourage the generative AI and HCI research communities to focus on the more complex and interdependent tasks, which require greater levels of human involvement.

ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions. In this report, we aim to evaluate ChatGPT on the Grammatical Error Correction(GEC) task, and compare it with commercial GEC product (e.g., Grammarly) and state-of-the-art models (e.g., GECToR). By testing on the CoNLL2014 benchmark dataset, we find that ChatGPT performs not as well as those baselines in terms of the automatic evaluation metrics (e.g., $F_{0.5}$ score), particularly on long sentences. We inspect the outputs and find that ChatGPT goes beyond one-by-one corrections. Specifically, it prefers to change the surface expression of certain phrases or sentence structure while maintaining grammatical correctness. Human evaluation quantitatively confirms this and suggests that ChatGPT produces less under-correction or mis-correction issues but more over-corrections. These results demonstrate that ChatGPT is severely under-estimated by the automatic evaluation metrics and could be a promising tool for GEC.