Azure OpenAI Service is now available in preview, and ChatGPT is available in preview as well. With Azure OpenAI Service, over 1,000 customers are applying the most advanced AI models--including Dall-E 2, GPT-3.5, Codex, and other large language models backed by the unique supercomputing and enterprise capabilities of Azure--to innovate in new ways. Since ChatGPT was introduced late last year, we've seen a variety of scenarios it can be used for, such as summarizing content, generating suggested email copy, and even helping with software programming questions. Now with ChatGPT in preview in Azure OpenAI Service, developers can integrate custom AI-powered experiences directly into their own applications, including enhancing existing bots to handle unexpected questions, recapping call center conversations to enable faster customer support resolutions, creating new ad copy with personalized offers, automating claims processing, and more. Today, we are thrilled to announce that ChatGPT is available in preview in Azure OpenAI Service.

Artificial intelligence has become the tech industry's shiny new toy, with expectations it'll revolutionize trillion-dollar industries from retail to medicine. But the creation of every new chatbot and image generator requires a lot of electricity, which means the technology may be responsible for a massive and growing amount of planet-warming carbon emissions. Microsoft Corp., Alphabet Inc.'s Google and ChatGPT maker OpenAI use cloud computing that relies on thousands of chips inside servers in massive data centers across the globe to train AI algorithms called models, analyzing data to help them "learn" to perform tasks. The success of ChatGPT has other companies racing to release their own rival AI systems and chatbots or building products that use large AI models to deliver features to anyone from Instacart shoppers to Snap users to CFOs. AI uses more energy than other forms of computing, and training a single model can gobble up more electricity than 100 US homes use in an entire year.

Can you get excited about artificial intelligence (AI)? When I asked ChatGPT who my favourite artist was, it said I'd never publicly expressed a preference, because as an art historian I don't do "subjective opinions". Evidently, ChatGPT doesn't subscribe to The Art Newspaper. Even if it gave the correct answer--Van Dyck--I'd still not be excited. In a famous episode of the 1960s TV series The Prisoner, Patrick McGoohan's character is presented with an all-knowing computer which, he is told, will make man redundant.

For weeks now, Elon Musk has been preoccupied with worries about how many people are seeing his tweets. Last week, the Twitter CEO took his Twitter account private for a day to test whether that might boost the size of his audience. The move came after several prominent right-wing accounts that Musk interacts with complained that recent changes to Twitter had reduced their reach. On Tuesday, Musk gathered a group of engineers and advisors into a room at Twitter's headquarters looking for answers. Why are his engagement numbers tanking?

The advance of Artificial Intelligence has plenty of people mapping out doomsday scenarios where the machines become self-aware and take over the world. This exercise should assuage some fears. As part of a fantasy baseball preview for our draft kit, we included ChatGPT -- an AI language model/chatbot developed by OpenAI and launched in November 2022 -- alongside our four living and breathing experts in an advice roundtable. All the participants were given the same questions, and the answers would help fantasy players make informed decisions on who to draft, who to avoid -- the usual stuff. To be clear, this was meant as a fun, dumb experiment: Could you spot the bot among five answers to the same question? I've used ChatGPT for several things (episode summaries off podcast transcripts, for example) and I have friends who built shows around Q&A sessions with the open AI.

GPT-4 is coming next week: at an approximately one-hour hybrid information event entitled "AI in Focus - Digital Kickoff" on 9 March 2023, four Microsoft Germany employees presented Large Language Models (LLM) like GPT series as a disruptive force for companies and their Azure-OpenAI offering in detail. The kickoff event took place in the German language, news outlet Heise was present. Rather casually, Andreas Braun, CTO Microsoft Germany and Lead Data & AI STU, mentioned what he said was the imminent release of GPT-4. The fact that Microsoft is fine-tuning multimodality with OpenAI should no longer have been a secret since the release of Kosmos-1 at the beginning of March. "We will introduce GPT-4 next week, there we will have multimodal models that will offer completely different possibilities – for example videos," Braun said.

Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools. Human-centered evaluation of AI-based systems combines quantitative and qualitative analysis and human input. It has been explored to some depth in the explainable AI (XAI) and human-computer interaction (HCI) communities. Gaps remain, but the basic understanding that humans interact with AI and accompanying explanations, and that humans' needs -- complete with their cognitive biases and quirks -- should be held front and center, is accepted by the community. In this paper, we draw parallels between the relatively mature field of XAI and the rapidly evolving research boom around large language models (LLMs). Accepted evaluative metrics for LLMs are not human-centered. We argue that many of the same paths tread by the XAI community over the past decade will be retread when discussing LLMs. Specifically, we argue that humans' tendencies -- again, complete with their cognitive biases and quirks -- should rest front and center when evaluating deployed LLMs. We outline three developed focus areas of human-centered evaluation of XAI: mental models, use case utility, and cognitive engagement, and we highlight the importance of exploring each of these concepts for LLMs. Our goal is to jumpstart human-centered LLM evaluation.

This paper will define AI simply as "computer systems that perform tasks requiring cognition tasks autonomously". This is similar to earlier definitions (Russell, 2010). Emerging phenomena can often be overlooked in management research as they are poorly defined with unclear conceptual concepts and limited empirical data (Yadav, 2018). Large Language models like GPT, however, have growth drivers that suggest that they are worthy of researcher attention and specifically, their impact on academic knowledge production should be identified at this early stage of adoption. Previous academic research in business and management have identified the potential for machine learning analytics to change the nature of theorising in business and management reseach (Leavitt, Schabram, Hariharan & Barnes, 2021). Large Language models such as GPT, however, can go further to influence the nature of academic knowledge production itself in this domain.

Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal navigation requires the robot to search for a target object category in an unexplored environment, which may not be suitable for home-assistant robots that typically have some level of semantic knowledge of the environment, such as the location of static furniture. In our approach, we leverage this knowledge and the fact that a target object may be located close to its related objects for efficient navigation. To achieve this, we train a graph neural network using the Visual Genome dataset to learn the object co-occurrence relationships and formulate the searching process as iteratively predicting the possible areas where the target object may be located. This approach is entirely zero-shot, meaning it doesn't require new accurate object correlation in the test environment. We empirically show that our method outperforms prior correlational object search algorithms. As our ultimate goal is to build fully autonomous assistant robots for everyday use, we further integrate the task planner for parsing natural language and generating task-completing plans with object navigation to execute human instructions. We demonstrate the effectiveness of our proposed pipeline in both the AI2-THOR simulator and a Stretch robot in a real-world environment.

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to reduce computational time and cost. In this paper, we tried to omit the fine-tuning steps and investigate whether the Marginal Maximum Relevance (MMR)-based approach can help the pre-trained models to obtain query-focused summaries directly from a new data set that was not used to pre-train the models. First, we used topic modelling on Wikipedia Current Events Portal (WCEP) and Debatepedia datasets to generate queries for summarization tasks. Then, using MMR, we ranked the sentences of the documents according to the queries. Next, we passed the ranked sentences to seven transformer-based pre-trained models to perform the summarization tasks. Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary. As indicated by the experimental results, our MMR-based approach successfully ranked and selected the most relevant sentences as summaries and showed better performance than the individual pre-trained models.