"In the development of this fast-moving program, Leonardo is using a digital twin for synthetic development, more than 40 composite components and the use of AI in its control software." Read here - https://bit.ly/3WgAjOI
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Another great new Thales use case! 📋 Cochl create #machinelistening technology for everyone. Their flagship product, Cochl.Sense, is AI-powered software that identifies acoustic sounds and labels them with a tag such as “baby crying” or “glass break" 👂 They use Thales Sentinel to protect their Python-based applications and #AI models, and implement customer-centric licensing and registrations 🛡️ Read the full case study below ⬇ https://lnkd.in/eBktkvZM
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In the early 1950s, American cryptoanalyst David H. Shepard, alongside his colleague Harvey Cook Jr., embarked on a groundbreaking journey to bridge the gap between man and machine. Their invention? A remarkable device named "Gismo." 📡🔠 Gismo wasn't just any machine—it had the extraordinary ability to read aloud, letter by letter, and interpret Morse code. Imagine a device that could decode and vocalize written text and cryptic signals, paving the way for future advancements in optical character recognition (OCR) and communication technology. Shepard and Cook’s creation was a beacon of innovation, demonstrating how machines could assist humans in deciphering complex information. This invention didn’t just stop at reading text; it showcased the potential of technology to revolutionize communication and data processing. Their pioneering work with Gismo set the stage for modern OCR technologies, which now play an integral role in various industries, from digitizing historical documents to automating data entry processes. #Innovation #OCR #TechnologyHistory #DavidHShepard #HarveyCook #TechPioneers #DigitalTransformation #DataRevival
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In the early 1950s, American cryptoanalyst David H. Shepard, alongside his colleague Harvey Cook Jr., embarked on a groundbreaking journey to bridge the gap between man and machine. Their invention? A remarkable device named "Gismo." 📡🔠 Gismo wasn't just any machine—it had the extraordinary ability to read aloud, letter by letter, and interpret Morse code. Imagine a device that could decode and vocalize written text and cryptic signals, paving the way for future advancements in optical character recognition (OCR) and communication technology. Shepard and Cook’s creation was a beacon of innovation, demonstrating how machines could assist humans in deciphering complex information. This invention didn’t just stop at reading text; it showcased the potential of technology to revolutionize communication and data processing. Their pioneering work with Gismo set the stage for modern OCR technologies, which now play an integral role in various industries, from digitizing historical documents to automating data entry processes. #Innovation #OCR #TechnologyHistory #DavidHShepard #HarveyCook #TechPioneers #DigitalTransformation #DataRevival
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Over the past two years, synthetic-aperture radar (SAR) data has been the subject of many conversations, and I’m finally excited to share Project Loch Ness by Ansys! This free synthetic dataset is designed to train and validate AI/ML models in ground-based target decision scenarios. Read more: https://shorturl.at/0K6It #AI #ML #Innovation #Ansys #Simulation #Defence #SAR #DigitalMissionEngineering #RadarTechnology #SyntheticData Ansys Digital Mission Engineering LEAP Australia #Australia
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IIM | BITS | OU || Associate Director at Techwave, AIML & Traceability, Program Management, Solution Architect, Agile Delivery!
CNN + Transformers + Attention is a perfect tool kit. Each component brings unique complementary strengths, creating a synergy that is reshaping the AI landscape. CNNs have long been the cornerstone of computer vision, efficiently extracting spatial hierarchies and local features through their layered structure. This capability makes them highly effective for tasks like object detection and image classification. However, CNNs often struggle with understanding global context. Enter Transformers, originally designed for sequential data like text, which leverage self-attention mechanisms to capture long-range dependencies and contextual information, effectively addressing the limitations of CNNs.When CNNs are fused with Transformers, the result is a powerful hybrid architecture. Attention mechanisms further enhance this blend, allowing models to dynamically focus on relevant features across both modalities. This combination excels in scenarios requiring detailed understanding, such as medical imaging, autonomous navigation, and video analysis. Models like ViT (Vision Transformer) and various hybrid architectures demonstrate state-of-the-art performance across benchmarks. VLM's are the latest innovation. #VLM #CNN #Transformer #Attention
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The unpredictable behavior of LLMs poses a significant challenge in developing autonomous & reliable AI solutions. An intriguing discussion paper, involving notable figures such as Ion Stoica (Databricks founder), explores leveraging concepts from diverse industries and software engineering to enhance the reliability of AI systems.
Today's AI landscape is reminiscent of the early automotive and aviation industries. Although we have seen remarkable demonstrations and early successes, the full transformative impact and proliferation of LLM systems are bottlenecked by *robustness* and *reliability* challenges. Building on the analogy, massive leaps were needed to progress from the Wright Brothers' initial Kitty Hawk breakthrough to the contemporary aviation industry, where over 2M humans fly daily. Notably, the gap from Kitty Hawk to what is considered the dawn of commercial aviation with Jannus was ~10 years. In this paper, Ion Stoica, along with collaborators Matei Zaharia, Joseph Gonzalez, Ken Goldberg, Hao Zhang, Anastasios Angelopoulos, Shishir Patil, Lingjiao Chen, Wei-Lin Chiang, and I, surveys the landscape and lays out a vision for advancing today’s LLM systems design into a mature engineering discipline with even broader deployed impact. This paper begins to address how we can reconcile the tensions arising from the value of these systems partially being their stochasticity and “creativity” (hallucination) and the engineering imperative to build robust, reliable 'compound AI' systems out of these noisy components. SPECIFICATIONS: THE MISSING LINK TO MAKING THE DEVELOPMENT OF LLM SYSTEMS AN ENGINEERING DISCIPLINE https://lnkd.in/gdvfCeu6
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Product Security | AI & ML Security and Governance | Cybersecurity Risk Management | WiCyS AI President | AI and Cybersecurity instructor
Practical and insightful paper on developing modular and reliable LLMs. " A key tool for building such reliable and modular systems is specification: the precise description of the expected behavior, inputs, and outputs of each component. However, the generality of LLMs and the inherent ambiguity of natural language make defining specifications for LLM-based components (e.g., agents) both a challenging and urgent problem. In this paper, we discuss the progress the field has made so far—through advances like structured outputs, process supervision, and test-time compute—and outline several future directions for research to enable the development of modular and reliable LLM-based systems through improved specifications." #softwareengineering #ModularLLM
Today's AI landscape is reminiscent of the early automotive and aviation industries. Although we have seen remarkable demonstrations and early successes, the full transformative impact and proliferation of LLM systems are bottlenecked by *robustness* and *reliability* challenges. Building on the analogy, massive leaps were needed to progress from the Wright Brothers' initial Kitty Hawk breakthrough to the contemporary aviation industry, where over 2M humans fly daily. Notably, the gap from Kitty Hawk to what is considered the dawn of commercial aviation with Jannus was ~10 years. In this paper, Ion Stoica, along with collaborators Matei Zaharia, Joseph Gonzalez, Ken Goldberg, Hao Zhang, Anastasios Angelopoulos, Shishir Patil, Lingjiao Chen, Wei-Lin Chiang, and I, surveys the landscape and lays out a vision for advancing today’s LLM systems design into a mature engineering discipline with even broader deployed impact. This paper begins to address how we can reconcile the tensions arising from the value of these systems partially being their stochasticity and “creativity” (hallucination) and the engineering imperative to build robust, reliable 'compound AI' systems out of these noisy components. SPECIFICATIONS: THE MISSING LINK TO MAKING THE DEVELOPMENT OF LLM SYSTEMS AN ENGINEERING DISCIPLINE https://lnkd.in/gdvfCeu6
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