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This report characterizes the data center industry and its importance for AI development. Data centers are industrial facilities that efficiently provide compute at scale and thus constitute the engine rooms of today's digital economy. As large-scale AI training and inference become increasingly computationally expensive, they are dominantly executed from this designated infrastructure. Key features of data centers include large-scale compute clusters that require extensive cooling and consume large amounts of power, the need for fast connectivity both within the data center and to the internet, and an emphasis on security and reliability. The global industry is valued at approximately $250B and is expected to double over the next seven years. There are likely about 500 large (above 10 MW) data centers globally, with the US, Europe, and China constituting the most important markets. The report further covers important actors, business models, main inputs, and typical locations of data centers.

Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is always a key part for data driven models. In this paper, we propose ChartSumm: a large-scale benchmark dataset consisting of a total of 84,363 charts along with their metadata and descriptions covering a wide range of topics and chart types to generate short and long summaries. Extensive experiments with strong baseline models show that even though these models generate fluent and informative summaries by achieving decent scores in various automatic evaluation metrics, they often face issues like suffering from hallucination, missing out important data points, in addition to incorrect explanation of complex trends in the charts. We also investigated the potential of expanding ChartSumm to other languages using automated translation tools. These make our dataset a challenging benchmark for future research.

Visual language data such as plots, charts, and infographics are ubiquitous in the human world. However, state-of-the-art vision-language models do not perform well on these data. We propose MatCha (Math reasoning and Chart derendering pretraining) to enhance visual language models' capabilities in jointly modeling charts/plots and language data. Specifically, we propose several pretraining tasks that cover plot deconstruction and numerical reasoning which are the key capabilities in visual language modeling. We perform the MatCha pretraining starting from Pix2Struct, a recently proposed image-to-text visual language model. On standard benchmarks such as PlotQA and ChartQA, the MatCha model outperforms state-of-the-art methods by as much as nearly 20%. We also examine how well MatCha pretraining transfers to domains such as screenshots, textbook diagrams, and document figures and observe overall improvement, verifying the usefulness of MatCha pretraining on broader visual language tasks.

Advanced large language models like ChatGPT have gained considerable attention recently, including among students. However, while the debate on ChatGPT in academia is making waves, more understanding is needed among lecturers and teachers on how students use and perceive ChatGPT. To address this gap, we analyzed the content on ChatGPT available on TikTok in February 2023. TikTok is a rapidly growing social media platform popular among individuals under 30. Specifically, we analyzed the content of the 100 most popular videos in English tagged with #chatgpt, which collectively garnered over 250 million views. Most of the videos we studied promoted the use of ChatGPT for tasks like writing essays or code. In addition, many videos discussed AI detectors, with a focus on how other tools can help to transform ChatGPT output to fool these detectors. This also mirrors the discussion among educators on how to treat ChatGPT as lecturers and teachers in teaching and grading. What is, however, missing from the analyzed clips on TikTok are videos that discuss ChatGPT producing content that is nonsensical or unfaithful to the training data.

While OpenAI has really risen to fame with the release of ChatGPT in November 2022, the U.S.-based artificial intelligence research and deployment company is about much more than its popular AI-powered chatbot. In fact, as Statista's Felix Richter reports below, OpenAI's technology is already being used by hundreds of companies around the world. According to data published by the enterprise software platform Enterprise Apps Today, companies in the technology and education sectors are most likely to take advantage of OpenAI's solutions, while business services, manufacturing and finance are also high on the list of industries utilizing artificial intelligence in their business processes. Broadly defined as "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages" artificial intelligence (AI) can now be found in various applications, including for example web search, natural language translation, recommendation systems, voice recognition and autonomous driving. In healthcare, AI can help synthesize large volumes of clinical data to gain a holistic view of the patient, but it's also used in robotics for surgery, nursing, rehabilitation and orthopedics.

As the telecommunications space focuses most of its efforts on facilitating the transition from 4G to 5G, the R&D (research and development) world is already working on what's next--6G, the sixth generation of wireless technology. While we mustn't put the cart before the horse, research and analysis firms are already predicting 6G could be viable around 2030--and, in some predictions, even earlier. For instance, Statista's latest report on 6G suggests the 6G market in North America will be worth $364 million by 2028. Based on the R&D already underway in the U.S., Statista's report suggests the nation will be an early leader in 6G. ABI Research has also pinned 2028 and 2029 as early commercial deployment years for 6G.

In this post, we examine the state of 5G in 2020. And while the United States is seeing good growth with this technology, it lags in average download speeds. Last year, we presented a post asking and answering: Do YOU Know What 5G Is? In March 2020, we noted that a BI Intelligence study found that "39% of respondents to our survey saying they plan to support 5G in IoT products and services before 2021." All the things we hope will make our lives easier, safer, and healthier will require high-speed, always-on internet connections.

The graph represents a network of 1,188 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 02 October 2020 at 10:24 UTC. The requested start date was Friday, 02 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 0-hour, 2-minute period from Tuesday, 29 September 2020 at 23:37 UTC to Thursday, 01 October 2020 at 23:40 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.