In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by analyzing several challenges in this scenario, and then propose a multi-level contrastive learning framework to capture characters' global information in a fine-grained manner. To validate the proposed framework, we conduct extensive experiments on three character understanding sub-tasks by comparing with strong pre-trained language models, including SpanBERT, Longformer, BigBird and ChatGPT-3.5. Experimental results demonstrate that our method improves the performances by a considerable margin. Through further in-depth analysis, we show the effectiveness of our method in addressing the challenges and provide more hints on the scenario of character understanding. We will open-source our work on github at https://github.com/David-Li0406/Script-based-Character-Understanding.

Tel Aviv's Run.AI, a startup that is building a new virtualization and acceleration platform for deep learning, is coming out of stealth today. As a part of this announcement, the company also announced that it has now raised a total of $13 million. This includes a $3 million seed round from TLV Partners and a $10 million Series A round led by Haim Sadger's S Capital and TLV Partners. It's no secret that building deep learning models take a hefty amount of GPU power or access to specialized AI chips. Run.AI argues that the virtualization layers that worked so well for in the past don't quite cut it for training today's AI models.

Tel Aviv-based Run:ai, a startup that makes it easier for developers and operations teams to manage and optimize their AI infrastructure, today announced that it has raised a $75 million Series C funding round led by Tiger Global Management and Insight partners, which also led the company's $30 million Series B round in 2021. Previous investors TLV Partners and S Capital VC also participated in this round, which brings Run:ai's total funding to $118 million. Run:ai's Atlas platform helps its users virtualize and orchestrate their AI workloads with a focus on optimizing their GPU resources, no matter whether they are on-premises or in the cloud. It abstracts all of this hardware away, while developers can still interact with the pooled resources through standard tools like Jupyter notebooks and IT teams can get better insights into how these resources are being used. The new round comes at a time of fast growth for the company.

Did you miss a session at the Data Summit? While interest in AI remains high among enterprise organizations, particularly for its potential to improve decision-making and automate repetitive tasks, many of these businesses are struggling to deploy AI into production. In a February survey from IDC, only a third of companies claimed that their entire organization was benefitting from an enterprise-wide AI strategy. The same poll found that 69% of companies hadn't yet reached production with AI, and instead remained in the experimentation, evaluation, or prototyping phases. The challenges vary from organization to organization, but some common themes include infrastructure and data.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Run:AI today announced it has added support for both MLflow, an open source tool for managing the lifecycle of machine learning algorithms, and Kubeflow, an open source framework for machine learning operations (MLOps) deployed on Kubernetes clusters, to its namesake tool for graphical processor unit (GPU) resource optimization. The company also revealed that it has added support for Apache Airflow, open source software that can be employed to programmatically create, schedule, and monitor workflows. The overall goal is to enable GPU optimization, as well as training AI models from within an MLOps platform, Run:AI CEO Omri Geller told VentureBeat. "It can be managed more end-to-end," he said.

Run:AI this week announced the general availability of a namesake platform based on Kubernetes that enables IT teams to virtualize graphical processor unit (GPU) resources. Company CEO Omri Geller says the goal is to enable IT teams to maximize investments in expensive GPUs by leveraging a single line of code to plug in its platform on top of Kubernetes. That would enable IT teams to take advantage of container orchestration to schedule artificial intelligence (AI) workloads across multiple GPUs, and allows certain AI workloads to be prioritized over others, he says. Geller notes that GPUs don't lend themselves well to traditional virtual machines. Kubernetes provides an alternative approach to virtualizing bare-metal GPU resources, which are among the most expensive IT infrastructure resource any IT organization can invoke in the cloud or deploy in on-premises IT environments.

Five years ago, Paul Johnson wouldn't have thought predicting earthquakes would ever be possible. "I can't say we will, but I'm much more hopeful we're going to make a lot of progress within decades," the Los Alamos National Laboratory seismologist says. "I'm more hopeful now than I've ever been." The main reason for that new hope is a technology Johnson started looking into about four years ago: machine learning. Many of the sounds and small movements along tectonic fault lines where earthquakes occur have long been thought to be meaningless.

The discussion of sexual assault and harassment are all the more paramount in an era dominated by the #MeToo and Times Up movement, but now there is a mobile dating application that is working towards making such complaints a thing of the past. Enter "Yes to Sex," a dating app created in 2016 that requests for an explicit sexual consent agreement to be made between both parties. The app, which is available for download through Apple's App Store and Google Play, requires users to verbalize the words "yes" or "no" before engaging in sexual relations with another user. The entire process is expected to take no more than 25 seconds, Yes to Sex's website claimed. "The media talking about consent is the first step in the right direction of making it the norm to verbally ask a potential partner straight-up every time, if they are interested," Wendy Geller, the app's president, CEO and inventor, told International Business Times.