This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

Both DataStax and Kaskada have a track record of contributing to open source communities. Datastax will open source the core Kaskada technology initially, and it plans to offer a new machine learning cloud service later this year. Most machine learning initiatives don't deliver the results that businesses need because the process is manual, complex and frustrating. Compounding this problem, many models underperform because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax's portfolio of cloud services--which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra and event streaming with Astra Streaming-- will give organizations a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders such as Netflix and Uber.

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. Voicemod is releasing its proprietary, world-first voice conversion product powered by artificial intelligence. This technology is powering a whole collection of voices within the Voicemod voice changer software. The Spanish-based company sees the AI-driven voices as a giant leap in innovation in speech-to-speech voice transformation capabilities. Voicemod is a piece of audio software that offers voice modulation, custom sound effects and soundboards.

IBM (NYSE: IBM) today unveiled IBM z16, IBM's next-generation system with an integrated on-chip AI accelerator -- delivering latency-optimized inferencing. This innovation is designed to enable clients to analyze real-time transactions, at scale -- for mission-critical workloads such as credit card, healthcare and financial transactions. Building on IBM's history of security leadership, IBM z16 also is specifically designed to help protect against near-future threats that might be used to crack today's encryption technologies. IBM innovations, including the IBM z16, have formed the technology backbone of the global economy for decades. Today's modern IBM mainframe is central to hybrid cloud environments, valued by two-thirds of the Fortune 100, 45 of the world's top 50 banks, 8 of the top 10 insurers, 7 of the top 10 global retailers and 8 out of the top 10 telcos as a highly secured platform for running their most mission-critical workloads.

An organization's ability to quickly detect and respond to anomalies is critical to success in a digitally transforming culture. Google Cloud customers can strengthen this ability by using rich artificial intelligence and machine learning (AI/ML) capabilities in conjunction with an enterprise-class streaming analytics platform. We refer to this combination of fast data and advanced analytics as real-time AI. There are many applications for real-time AI across businesses, including anomaly detection, video analysis, and forecasting. In this post, we walk through a real-time AI pattern for detecting anomalies in log files.

Validata Sense.ai is the industry's only unified platform that delivers intelligent automation powered by real-time AI, cognitive robotic process automation (CRPA) and deep analytics, enabling Temenos clients to test, monitor, analyse and predict on the quality and usability of core banking processes across different interfaces, platforms, payment hubs and mobile. With automation at the core, Validata Sense.ai is uniquely positioned to simplify DevTestOps processes and accelerate DevOps at scale. Its AI-powered automation capabilities empower organisations to master the speed and complexity of cloud operations, supporting'shift left' testing so "only good builds" reach production and shift-right self-healing, to minimize risk for business and operations.

Processed tissue slide viewing and assessment is crucial in determining the diagnosis and cancer staging. This protocol is thus instrumental in deciding the treatment therapy for a patient. Applications of deep learning and AI in the medical fields such as dermatology, radiology, ophthalmology, and pathology have shown great potential in providing great accuracy in diagnosis. Although AI promises to provide quality healthcare, the cost of slide digitization and lack of infrastructure for AI deployments remain as barriers for widespread adoption of digital pathology in clinical settings. Google recently published a paper in Nature demonstrating the prediction of metastatic breast cancer in lymph nodes using convolutional neural networks at an accuracy comparable to pathologists.

Artificial Intelligence (AI) is right here, right now--and it's changing our lives. The need for business optimization, combined with explosive growth in data and recent advances in applied statistics and cloud computing, has created a perfect storm of innovation. TIBCO brings real-time AI to business challenges with the TIBCO Connected Intelligence Cloud. In this webinar, we show real-time AI in action using Amazon SageMaker, TIBCO Connected Intelligence Cloud, and open source--with at-scale, in-database compute, visual composition and notebooks, Slack-style collaboration among users, and model lifecycle deployment via low-code tooling such as TIBCO Cloud Live Apps software. We include case studies in equipment surveillance, dynamic pricing, risk management, route optimization, and customer engagement.

What do you think will drive more disruption and the use of AI? Satish Maripuri: Disruption comes from bringing organizations together across industries with a unique combination of capabilities to drive change. In the case of AI, we look for partnerships inside and outside of healthcare that can drive innovation effectively and more quickly create value. Right now, we are focused on some unique AI-related partnerships that allow radiologists to be the technology trailblazers they always have been. Radiologists began trailblazing technology with the introduction of picture archiving and communication systems (PACS) more than 20 years ago. Today, the latest advancements in radiology are highly receptive to the power of AI to improve productivity and accuracy while reducing the repetitive tasks that lead to burnout.