In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, discusses how AI is revolutionizing the manufacturing industry. This revolution promises to enhance efficiency, reduce costs, and improve overall product quality. We explore the multifaceted impact of AI on manufacturing and how it is reshaping the industry’s landscape.
Algorithmiq Demonstrates Path to Quantum Utility with IBM
Algorithmiq, a scaleup developing quantum algorithms to solve the most complex problems in life sciences, has successfully run one of the largest scale error mitigation experiments to date on IBM’s hardware. This achievement positions them, with IBM, as front runners to reach quantum utility for real world use cases. The experiment was run with Algorithmiq’s proprietary error mitigation algorithms on the IBM Nazca, the 127 qubit Eagle processor, using 50 active qubits x 98 layers of CNOTS and thus a total of 2402 CNOTS gates. This significant milestone for the field is the result of a collaboration between the two teams, who joined forces back in 2022 to pave the way towards achieving first useful quantum advantage for chemistry.
IBM Launches $500 Million Enterprise AI Venture Fund
IBM (NYSE: IBM) today announced that it is launching a $500 million venture fund to invest in a range of AI companies – from early-stage to hyper-growth startups – focused on accelerating generative AI technology and research for the enterprise.
IBM’s Groundbreaking Analog AI Chip: Ushers New Era of Efficiency and Accuracy
In this contributed article blogger Justin Varghise discusses the ground breaking advancement for AI that IBM has unveiled – a cutting edge analog AI chip that promises and has potential to redefine the landscape of deep neural networks (DNNs). This chip is 100 times more energy-efficient and up to 10 times faster than traditional digital AI chips for performing deep neural network (DNN) computations.
The insideAI News IMPACT 50 List for Q3 2023
The team here at insideAI News is deeply entrenched in keeping the pulse of the big data ecosystem of companies from around the globe. We’re in close contact with the movers and shakers making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List.
Video Highlights: Modernize your IBM Mainframe & Netezza With Databricks Lakehouse
In the video presentation below, learn from experts how to architect modern data pipelines to consolidate data from multiple IBM data sources into Databricks Lakehouse, using the state-of-the-art replication technique—Change Data Capture (CDC).
AI for Legalese
Have you ever signed a lengthy legal contract you didn’t fully read? Or have you every read a contract you didn’t fully understand? Contract review is a time-consuming and labor-intensive process for everyone concerned — including contract attorneys. Help is on the way. IBM researchers are exploring ways for AI to make tedious tasks like contract review easier, faster, and more accurate.
Accelerating Training for AI Deep Learning Networks with “Chunking”
At the International Conference on Learning Representations on May 6, IBM Research will share a deeper look around how chunk-based accumulation can speed the training for deep learning networks used for artificial intelligence (AI).
Movies, Neural Networks Boost AI Language Skills
When we discuss about artificial intelligence (AI), how are machines learning? What kinds of projects feed into greater understanding? For our friends over at IBM, one surprising answer is movies. To build smarter AI systems, IBM researchers are using movie plots and neural networks to explore new ways of enhancing the language understanding capabilities of AI models.
Advancements in Dynamic and Efficient Deep Learning Systems
We’re seeing much hype in the marketplace about the potential of AI, especially with respect to computer vision systems and its ability accelerate the development of everything from self-driving cars to autonomous robots. To create more dynamic and efficient deep learning systems, that don’t compromise accuracy, IBM Research is exploring new and novel computer vision techniques from both a hardware and software angle.