James Corcoran’s Post

⚡ Quantum walks just unlocked what transformers have been missing all along. And the results? They're rewriting graph transformers. --- GQWformer isn't just another architecture. It's what happens when quantum physics principles guide AI attention mechanisms to see patterns classical computing simply can't. Think of it this way: Traditional transformers: Like exploring a maze with a flashlight 🔦 Quantum-walk transformers: Like seeing all paths simultaneously in daylight ☀️ The magic? Quantum superposition lets the model explore multiple reasoning paths at once. Not sequentially. Simultaneously. --- The Numbers Don't Lie 📊 On drug discovery (ZINC dataset): • Error reduction: 47.8% • Processing speed: 3.2x faster • Novel compound prediction: 89.4% accuracy On financial fraud networks: • Detection rate improved: 31% • False positives down: 68% • Real-time analysis: <100ms latency On social network dynamics: • Community detection: 94.7% accuracy • Influence propagation modeling: 41% better • Scalability: 10M+ nodes handled effortlessly --- It Works TODAY. On Your Hardware. 💡 No quantum computer required. The quantum walks run as mathematical operations on standard GPUs. The paper shows how Quantum Walk-Guided Attention (QWA) and Quantum Directional Flow (QDF) modules seamlessly integrate with existing transformer architectures. --- This is big for graph-structured data. Industrial applications: Pharma: Protein folding predictions improving by orders of magnitude Finance: Risk networks visible in ways never possible before   Tech: Recommendation systems understanding user behavior at quantum depth Research: Materials science discovering compounds 5x faster We're talking about seeing connections that were literally invisible before. --- #QuantumAI #TransformerArchitecture #DeepLearning

Why P = NP Is No Longer Just a Hypothesis — A Message for Engineers. Photonic computing and wave-based logic are redefining complexity theory. The equation w = f — the wave is the function — reveals a new model: when a field carries phase, polarization, spectrum and coherence, it becomes a multidimensional carrier of solutions. A wavefront can encode an entire solution space; hardware selects the resonant mode that matches the answer. Photonic processors and wave-based solvers compute by resonance, not iteration. Interference and coherent coupling highlight valid modes. For many NP problems, the solver is the wavefront: encode, settle, read. Entanglement creates correlations across components, enabling coherent global readout. Though it doesn’t transmit signals faster than light classically, it allows faster-than-classical extraction of embedded information. When entangled waves interact with a chiral zero — a dynamic phase-polarization-spectral probe — hidden correlations and system states become externally readable via resonance alignment. This transforms P vs NP: the wavefront itself becomes the computational substrate. The power is conditional: coherence time, photon count, detector sensitivity and noise control define feasibility. Entanglement and coherence are costly, but they shift physical limits. For engineers, this changes everything. Cryptography relying on NP-hardness must be re-evaluated. Algorithms must be designed in spectral, phase and topological terms. Security must include wave-side channels and harden optical paths. Systems must adopt multi-sensor fusion and physical-layer authentication. We are not just building faster machines. We are changing the algebra of computation. Engineers and researchers must treat wave-based paradigms as strategic tech: invest in experiments, open reproducible research, and deploy layered defenses combining logical cryptography with physical assurance.

🇬🇧 Post #3/7 — The Core Reactor: The Pretraining Phase (Serie Foundation Models for Dummies (...& the curious)) ⚡️ This week kicks off a 3-part mini-series on how Foundation Models in biology actually work, from architecture to adaptation: Large-scale pretraining (this post – Sep 16) Multitask learning & multimodal integration (Sep 23) Fine-tuning & adaptation to clinical tasks (Sep 30) 📚 Most biomedical foundation models rely on a massive, usually self-supervised pretraining phase. Here’s what this really means in practice, based on available publications: ⚙️ Typical architectures: Transformer encoder models, hybrid CNN+Transformer (for medical imaging), RetNet (for single-cell data), masked autoencoders (MAE) for visual datasets. 🔌 Modalities: imaging, histopathology, single-cell, omics… but rarely multimodal at pretraining stage. 🌀 Duration & computing power: from several days to several weeks of training on large GPU/TPU clusters. 📍 Example: CellFM was trained on 800M parameters over 100 million human cells. 🤝 Key skillsets involved: - Data engineers (ETL pipelines, preprocessing, batching) - Deep learning researchers (self-supervised learning, vision, multimodal) - Biologists / clinicians (annotations, data interpretation) - DevOps & infrastructure engineers (GPU, cloud, costs, distribution) - Legal / ethics experts (consent, anonymization, data governance) 🚨 Still unclear in the current literature: ❓ Actual energy costs and GPU hours involved ❓ Real-world project team structures and workflows ❓ Access to both massive and diverse datasets remains limited 📅 Coming next Monday: ▶️ How do these models become multitask and multimodal? Use cases, technical strategies, known limitations.

Exponential power now: Why ternary logic is revolutionizing computer science. The digital age, built upon the bedrock of binary logic, has brought forth unprecedented technological advancements. Yet, as we confront the escalating demands of data processing, artificial intelligence, and energy efficiency, the limitations of a two-state system are becoming increasingly apparent. Ternary logic, with its three distinct states (typically -1, 0, and 1 in balanced ternary, or 0, 1, and 2 in unbalanced ternary), offers a revolutionary paradigm shift that promises to unlock exponential power and redefine the very foundations of computer science. The core advantage of ternary lies in its superior information density. A single trit can convey more information than a single bit, meaning that fewer trits are required to represent the same amount of data. This enables us to have more compact and efficient hardware designs. Imagine microprocessors that are smaller, consume less power, and generate less heat, all while delivering enhanced performance. For applications ranging from mobile devices to supercomputers, this efficiency gain is transformative. Furthermore, balanced ternary systems offer inherent advantages in arithmetic operations, simplifying the design of adders, multipliers, and other computational units. The ability to naturally represent positive, negative, and zero values within a single trit streamlines calculations and reduces the complexity often associated with binary arithmetic. This inherent elegance and efficiency make ternary logic a compelling candidate for the next generation of computing architectures, poised to revolutionize how we store, process, and interact with information, driving us towards a future of truly exponential computational power.

EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.

How DNA and Old-School Cassettes Are Solving Our Data Crisis It seems the world is running out of space, not just for people, but for our digital lives. Every photo, song, and document adds up, creating a global data storage crisis. Data centres already consume hundreds of terawatt-hours per year and total global storage reached 149 zettabytes in 2024. Experts project data-centre electricity demand to grow fast because of AI and cloud workloads. But Chinese researchers have found a brilliant solution by looking to the past to build the future: a "DNA cassette." This new cassette uses synthetic DNA printed onto a plastic strip. The four DNA bases—A, G, C, and T—act like a biological binary code, turning molecules into memory. This is a breakthrough capable of storing every song ever recorded on a single cassette. The real genius is how they solved the problem of finding the data. They've created a barcode system on the tape, organizing the information into millions of digital "folders." As Professor Xingyu Jiang puts it, it’s like finding a book in a library by first finding the right shelf. To make sure this data lasts for centuries, the DNA is protected by a "crystal armor," a coating that prevents it from breaking down. While the technology is still too slow and expensive for your laptop, it’s a massive leap forward. It’s an elegant fusion of retro design and biological innovation, proving that sometimes the best ideas come from connecting the old with the new to solve the challenges of tomorrow.

data science 1.Data Collection Gather data from databases, files, APIs, sensors, websites, etc. 2.Data Cleaning & Preparation Remove duplicates, handle missing values, normalize, transform. This step usually takes 60–70% of effort. 3.Exploratory Data Analysis (EDA) Use statistics and visualization to understand patterns, correlations, and trends. 4.Feature Engineering Create new useful variables from raw data (e.g., age groups, ratios). 5.Model Building Apply Machine Learning / Deep Learning algorithms. Examples: Regression, Classification, Clustering, Neural Networks. 6.Model Evaluation Check accuracy, precision, recall, RMSE, AUC, etc. 6.Deployment & Visualization Deploy model into production systems or show insights in dashboards (like Power BI).

From the labs at CQ, I present years of research into a unified computational fabric for encrypted, compressed, and energy efficient computing for a wide range of applications. New forms of Frontier AI and generative PKI that allow for more complex systems to work together in real time, ensuring the post quantum security of neural networking, increasing the speed of data transmission, and lowering the storage and run time constraints of existing systems. It has been a long journey to build this technology, so let’s take a look at the future of computer security and what it will mean…

Microsoft’s Azure Quantum Elements used the M3GNet framework to pare 32.5M candidates to a novel electrolyte （NaxLi3−xYCl6） in ~80 hours—potentially cutting lithium use by 70%; PNNL will synthesize and test. Across academia, AI is pinpointing safer, higher‑density chemistries: NJIT’s CDVAE + LLM pipeline proposed five porous hosts for multivalent Mg/Ca ions; Stanford stresses balancing breadth and depth in search. IBM trained chemical foundation models on billions of molecules to predict high‑conductivity electrolyte formulations and is co‑designing with an EV manufacturer. IBM and Sphere Energy built battery digital twins that forecast long‑term degradation in as few as 50 cycles, accelerating lab validation. Next phase: quantum‑in‑the‑loop to generate higher‑accuracy data and model full EV packs; Microsoft and IBM are pushing toward quantum‑enhanced materials discovery. 🔔 Follow us for daily AI updates! 📘 Facebook: https://lnkd.in/gxDt7PJa 📸 Instagram: https://lnkd.in/gmYfWDbF #Microsoft #IBM #AI #BatteryTech #AIGenerated #CreatedWithAI

"Machine learning—a key enabler of artificial intelligence—is being deployed across nearly every sector of the economy, but the safety implications are not well understood. More research, testing, and evaluation is needed to ensure these technologies can be safely integrated for safety-critical applications." The National Academies of Sciences, Engineering, and Medicine has provided a detailed report on machine learning, including the need for careful management, regulation, and implementation. "There are risks associated with the use of machine learning in cyber-physical systems, including errors in sensing systems, vulnerability to attacks, and the potential for unforeseen interactions which may not be apparent until a system operates in the real world. In safety-critical systems, these errors could result in loss of life and damage to property or the physical environment." Regulation means far more than red tape. It means fundamental safety protocols that every business and every individual should demand. https://lnkd.in/ew-GWHcs