This theorem provides insight into the structure of semi-simple rings, particularly those decomposed into matrix rings over division rings. It states that every semi-simple ring is isomorphic to a finite direct product of matrix rings over division rings.
A semi-simple ring is one with no radical - so, every module decomposes into simple modules. A division ring is one where each non-zero element has an inverse, forming a basis for constructing matrix rings in the decomposition. A matrix ring is a ring of matrices with entries from a division ring.
In practical terms, this theorem shows that any finite-dimensional algebra without a radical can be broken down into easy-to-understand building blocks, thereby applying linear algebraic methods to ring theory.
Developed by Kyle Kingsbury in 2013, the Jepsen Test framework has become the gold standard for verifying correctness of distributed databases under network partitions. Unlike traditional benchmarks (e.g., YCSB), Jepsen simulates real-world failures (e.g., node crashes, clock skew, network splits) to expose subtle bugs in systems like MongoDB, Cassandra, etc.
Jepsen’s methodology involves injecting chaos (e.g., random pauses, packet drops) while verifying linearizability or serializability. Its high-profile exposés (e.g., Redis losing data during failovers, PostgreSQL’s stale reads) forced vendors to harden their systems. While Jepsen isn’t a substitute for formal verification (e.g., TLA+), it bridges the gap between theory and production resilience.
eBPF (extended Berkeley Packet Filter) has evolved from a networking tool into a revolutionary framework for safe, efficient kernel-space programming. It allows runtime injection of sandboxed bytecode into the Linux kernel, enabling real-time monitoring, security enforcement, and performance tuning without rebooting or kernel modules.
Many Cloud providers already leverage eBPF for observability (e.g., Falco, Cilium). However, eBPF has limitations: it is restricted to a subset of kernel functionality, and writing correct BPF programs requires deep kernel expertise. While eBPF won’t replace traditional kernel modules entirely, it’s becoming the de facto standard for dynamic tracing and network security.
The Earth’s internal heat is a fundamental driver of planetary evolution. It powers mantle convection, sustains plate tectonics, contributes to volcanic activity, and helps maintain the geodynamo that generates the Earth’s magnetic field. Yet the origin and magnitude of this heat remain incompletely constrained. Geoneutrino detection offers a particle-physics method to estimate how much of…
Introduction The throughput of many modern computing systems is increasingly constrained not by arithmetic throughput alone, but by the cost of moving data through memory and I/O hierarchies. Zero-copy design addresses part of this problem by eliminating redundant payload replication wherever underlying ownership, protection, and lifetime semantics permit. Zero-copy is not a single mechanism, but…
Introduction Quantum optimization currently operates in two fundamentally distinct regimes. The near-term regime focuses on hybrid classical-quantum workflows, in which NISQ (Noisy Intermediate-Scale Quantum) processors act as heuristic co-processors. The long-term regime aims to execute fully fault-tolerant algorithms that deliver provable, asymptotic speedups on large-scale problems. The gap between these two regimes shapes current R&D…
Introduction Complex agentic systems are composed of autonomous reasoning units, planning pipelines, retrieval modules, and tool-execution components. They require execution environments that are fast, portable, predictable, and secure. Traditional approaches using heavyweight containers or scripting-runtime sandboxes often struggle with consistent isolation, deterministic behavior, cross-language interoperability, and lightweight deployment. WebAssembly (Wasm), along with the WebAssembly Component…
Introduction The rapid rise of Generative AI has often been driven by a straightforward principle: scale to make models become more capable. For much of the past decade, this formula of more parameters, more data, and more GPUs has held true, reinforcing the notion that performance is a nearly mechanical function of size. Scaling laws…
Introduction The competitive advantage in AI is shifting from model performance to systems infrastructure. As model capabilities converge and access to powerful foundation models becomes commoditized, the true moat is the agentic infrastructure built around them. This stack encompasses orchestration frameworks, memory systems, secure tooling, observability mechanisms, and evaluation loops, and transforms static models into…
Introduction Earthquake faults are not random cracks in the crust; they are the expression of deep-seated mechanical processes operating under the framework of plate tectonics. The lithosphere is segmented into rigid plates that move at rates of a few centimeters per year, generating long-term stress accumulation at their boundaries and interiors. When this stress exceeds…
Introduction Chaotic systems are governed by deterministic dynamics, but exhibit sensitive dependence on initial conditions.The Lyapunov time horizon \( T_L \) is the inverse of the largest Lyapunov exponent \( \lambda_{\max} \), and has generally been regarded as the limit of deterministic predictability for free-running trajectories. Beyond this horizon, exponential error growth destroys pointwise forecast…
Abstract High-Performance Computing (HPC) systems are engineered to solve large-scale, compute-intensive problems across diverse scientific and engineering fields – e.g., astrophysics, climate modeling, large-scale AI, molecular dynamics, nuclear simulations, particle physics, and quantum chemistry. Yet, real-world performance often lags significantly behind the theoretical capabilities that HPC systems promise. This gap primarily arises from systemic bottlenecks…
Introduction Quantum computers are powerful but inherently unstable. While they promise revolutionary breakthroughs in computational sciences, cryptography, optimization, machine learning, and other areas, they face a significant hurdle today in the form of Qubit Errors. Unlike classical bits, qubits are fragile and prone to decoherence, noise, and operational errors, which makes reliable computation difficult. Qubits…
The classical theory of generalization in statistical learning has largely revolved around complexity measures such as the Vapnik-Chervonenkis (VC) Dimension and Rademacher Complexity. While these tools have been instrumental in providing theoretical guarantees, they often rely on worst-case analyses and may not fully capture the intrinsic structure of real-world learning problems. In this paper, we…
Abstract Shor’s algorithm, developed by Peter Shor (1994), is a quantum algorithm that efficiently solves the integer factorization problem and the discrete logarithm problem. These tasks are computationally infeasible for classical computers when it comes to large numbers. Since the security of widely used public-key cryptosystems (e.g., RSA) relies on the hardness of these problems,…
Graph compilers are emerging as a core infrastructure in modern High-Performance Computing (HPC), particularly in accelerator-driven systems, deep learning, and scientific computing. This paper explores the rising importance of graph compilers, how they work, and what’s next in this fast-evolving space. Why Graph Compilers Matter in HPC Systems? High-Performance Computing (HPC) workloads increasingly involve complex…
Introduction The Earth’s inner core is understood to be a solid sphere that is composed primarily of iron and nickel. Located about 5,150 kilometers beneath the Earth’s surface, it is characterized by extreme pressure, and temperatures exceeding 5,000 degrees Celsius. Recent scientific advancements have revealed that this inner core is a dynamic entity undergoing significant…
Introduction: Over the past few years, Agentic AI has been generating significant excitement, and deservedly so. In tandem with Generative AI, it represents the new frontier in Artificial Intelligence. While agent-based systems have existed for decades, it is only now that their capabilities are capturing mainstream attention. Organizations, ranging from startups to tech giants, are…
The Krafla Magma Testbed (KMT) Project is an ambitious program to harness the potential of the Earth’s magma for extreme-scale geothermal energy production. Launched in 2014 and located at the Krafla volcanic system in northern Iceland, KMT is the world’s first field laboratory aimed at directly studying active magma reservoirs. The goal is to advance…
Java has been one of the cornerstones of enterprise software since 1995. It powers large-scale enterprise backends and a significant part of Android’s legacy code. While newer languages like Python, Go, and Rust have captured attention, particularly in AI, cloud-native, and systems programming, Java still dominates in mission-critical domains. The critical question is not whether…
Modern AI systems are advancing at an accelerated pace, expanding in complexity, scale, and scope. Python dominates the field of AI engineering, primarily due to its low learning curve, extensive community support, and rich libraries that simplify complex tasks, such as data manipulation and model development. However, as AI systems continue to evolve, Python’s inherent…
Introduction Recent advancements in artificial intelligence have fueled considerable excitement around what many call “AI Chips”, specialized hardware tailored to optimize and accelerate AI workloads. Amidst the noise, many companies appear to be branding nearly every processor enhancement or hardware upgrade as an AI chip, blurring the lines between genuine AI advancements and general performance…
Abstract Mathematical optimization drives complex decision-making across a diverse range of problems – e.g., energy management, inventory planning, network design, pricing & revenue management, production planning & scheduling, supplier selection, and transportation planning. This field has significantly evolved over decades, from its formative years around World War II to the modern age. While conventional optimization…
Data security has become one of the paramount concerns for businesses, governments, and individuals in an increasingly digital world. As data proliferates across cloud infrastructures and shared environments, its privacy and security pose a strategic challenge. To address this, organizations generally depend on encryption at rest (e.g., with an AES 256 key), and encryption in…
Quantization can mathematically be defined as a process of mapping a continuous set of values (X ⊆ R) to a discrete set (Q ⊂ R) with a finite number of values |Q|. It can be expressed as the following function: Q(x) = qi, for x ∈ Si ⊂ X, where each subset Si (called a…
Many modern systems (e.g., autonomous systems, cloud infrastructure, gaming devices, machine learning applications, and scientific computing systems) demand unprecedented levels of computing power, speed and efficiency. Unlike traditional software, which often relies on sequential processing, these systems are driven by the need for massively parallel processing. As systems become increasingly complex, addressing their specific computing…
The lower mantle of the Earth, beneath the Pacific and Indian Oceans, is dominated by two vast, continent-sized regions exhibiting low shear velocities and increased density. These massive blob structures, known as Large Low-shear Velocity Provinces (LLSVPs) or Superplumes, are hundreds of kilometers in height and thousands of kilometers in width. The first one (called…
A recent paper jointly published by DeepMind, LG, and KAIST researchers introduced a new innovation for Transformer models: the global-to-local Block Architecture. The paper highlights up to 20x improvement in inference throughput vis-à-vis conventional transformer models. While this claim needs to be empirically validated by early adopters, the ideas posited in the paper do seem…
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts This paper combines State Space Modeling (SSM) with the Mixture of Experts (MoE) approach, and introduces the MoE-Mamba model in which every other Mamba layer is replaced with a MoE feed-forward layer based on the Switch transformer. MoE-Mamba is shown to not only outperform both…
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models This paper highlights 32 techniques to mitigate hallucination in LLMs, including a well-defined taxonomy to categorize these methods. AlphaGeometry: An Olympiad-level AI system for geometry DeepMind introduced its AI system for solving Olympiad geometry problems. Trained exclusively on synthetic data, AlphaGeometry uses a…
Deep Learning-based architectures have had a significant impact on computer vision, natural language processing, and other machine learning areas. However, the scenario is not so straightforward when it comes to Forecasting, an area where statistical and traditional machine learning models have generally outperformed other types of models. In recent years, Transformer architectures (e.g., Google’s Temporal…
Volatility in financial markets, major fluctuations in weather conditions, the outbreak of infectious diseases, the unpredictability of interactions among different animal species, and intermittent failures of power grids are phenomena from diverse fields but have two things in common. Firstly, they are manifestations of high-to-extreme uncertainty, and secondly, the environments in which they operate are…
Dask is a Python framework for distributed and parallel computing. It offers low-level task APIs that extend the functionality of popular Python libraries and high-level data-centric APIs that serve modern workloads such as machine learning. In particular, Dask addresses the key limitations of two widely used Python libraries: NumPy (arrays) and Pandas (Data Frames). Dask:…
Amazon Prime Video’s March 2023 announcement of migrating from distributed microservices to a monolith architecture surprised many in the global developer community, and reignited the Monolith versus Microservices debate. A varied range of analyses surfaced after that – some supported the move, some inferred that Amazon had a sub-optimal architecture in the first place, and…
The Sun’s magnetic field goes through an approximately 11-year cycle during which it completely changes its polarity (i.e., the Sun’s South and North Poles trade places.) This phenomenon, called the Solar Cycle (or Schwabe cycle), leads to significant fluctuations in the frequency and intensity of coronal mass ejections, sunspots, solar flares, etc. These disruptions are…
Minimum Spanning Tree (MST) offers powerful applications in a wide range of domains, including circuit design, computer science, electrical grids, financial markets, and telecom networks. They are also indirectly leveraged (e.g., as algorithm subroutines) for solving other critical problems, such as the Traveling Salesman or Minimum Cut problems. Broadly speaking, MST aims to connect a set…
Google introduced Spotlight, a foundational model for mobile UI modeling, particularly for tasks like command grounding, screen summarization, tappability prediction, and widget captioning. Traditional mobile UI design often uses the concept of view hierarchy information, but these view hierarchies are sometimes either not available, or corrupted. Spotlight not only bypasses the need for view hierarchies,…
Microsoft introduced VALL-E, its neural codec language model for zero-shot Text-to-Speech Synthesis (TTS) that generates high-quality audio/speech with only a 3-second acoustic prompt (i.e., voice recording.) Unlike conventional models that consider TTS a continuous signal regression task, VALL-E approaches this as a conditional language modeling problem. Trained on LibriLight’s 60K+ audio hours, VALL-E was shown…
Large Language Models (LLMs) are state-of-the-art today, and generally perform well for simple and low-interaction tasks, such as single-turn conversations, and command-and-response systems. However, their direct use is generally limited in the case of applications with complex and high-interaction tasks, such as multi-turn dialogue systems, and enterprise-grade chatbots. Most real-world applications are complex, and are…
Generative AI technologies have garnered much attention over the last 24 to 30 months. Innovations like GPT-3 and 3.5 (ChatGPT), DALL-E2, and Stable Diffusion have occupied a large part of the public discourse. The developers of these technologies, and the entire ecosystems around them are naturally creating a lot of hype. Others are adding to…
Built on top of Apache Lucene and first released in 2010, Elasticsearch has become the de-facto search technology for most enterprise use cases today. In conjunction with Beats, Kibana & Logstash (cumulatively, the Elastic Stack), it offers a tremendous value proposition for storing, searching, and analyzing large volumes of data. It has been consistently rated…
The materialization of systemic risk in any system leads to high instability and turbulence. This disruption is even greater in the case of complex systems. Power grid failures, financial market collapses, natural disasters, and global pandemics are some examples. This paper attempts to explain why overreacting and taking extreme measures at the early stages of…
[siteorigin_widget class=”thinkup_builder_divider”][/siteorigin_widget] NeurIPS 2022 declared 13 submissions as outstanding papers from its main track. Is Out-of-distribution Detection Learnable? Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Elucidating the Design Space of Diffusion-Based Generative Models ProcTHOR: Large-Scale Embodied AI Using Procedural Generation Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines A…
To understand the Hubble Tension, we must first understand the Hubble Constant. The Hubble constant (H0) is a value that measures the rate of expansion of the universe. As we know, the universe is constantly expanding. However, what it is expanding into is still an open question that, perhaps, may never be convincingly answered by…
Researchers at the University of Oxford have achieved the quantum entanglement of two strontium-based optical clocks. While previous entanglement demonstrations were limited to microscopic distances, this one appears to be the first reported case of macroscopic entanglement. Simply put, quantum entanglement is a phenomenon that enables two physically-distant entities (generally, subatomic particles) to be linked,…
3D Vision with Transformers: A Survey: https://arxiv.org/pdf/2208.04309v1.pdf Unifying Visual Perception by Dispersible Points Learning: https://arxiv.org/pdf/2208.08630v1.pdf ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild: https://arxiv.org/pdf/2208.11547v1.pdf ROLAND: Graph Learning Framework for Dynamic Graphs: https://arxiv.org/pdf/2208.07239v1.pdf Investigating Efficiently Extending Transformers for Long Input Summarization: https://arxiv.org/pdf/2208.04347v1.pdf Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion: https://arxiv.org/pdf/2207.14172v1.pdf TransNorm:…
In a recent Nature Neuroscience paper, scientists from Boston University postulated that a specific type of brain stimulation called Transcranial Alternating Current Stimulation (tACS) could enable a path toward achieving long-lasting memory enhancement. They proposed that brain rhythms can be neuro-moderated through repetitive tACS to improve cognitive functions (e.g., auditory–verbal memory) in older adults. The…
High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs: https://arxiv.org/pdf/2207.00257.pdf Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding: https://arxiv.org/pdf/2207.02971v1.pdf More ConvNets in the 2020s: Scaling up Kernels Beyond 51 × 51 using Sparsity: https://arxiv.org/pdf/2207.03620v1.pdf Softmax-free Linear Transformers: https://arxiv.org/pdf/2207.03341v1.pdf Learning Quality-aware Dynamic Memory for Video Object Segmentation: https://arxiv.org/pdf/2207.07922v1.pdf 3D…
Scaling Vision Transformers: https://arxiv.org/pdf/2106.04560.pdf Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation: https://arxiv.org/pdf/2107.01378.pdf Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control: https://arxiv.org/pdf/2109.08947.pdf LightSeq2: Accelerated Training for Transformer-based Models on GPUs: https://arxiv.org/pdf/2110.05722.pdf Conditionally Elicitable Dynamic Risk Measures For Deep Reinforcement Learning: https://arxiv.org/pdf/2206.14666.pdf Cooperative Retriever and Ranker in Deep Recommenders:…
Computational Storytelling And Emotions: A Survey: https://arxiv.org/pdf/2205.10967.pdf Are Large Pre-Trained Language Models Leaking Your Personal Information?: https://arxiv.org/pdf/2205.12628.pdf FreDo: Frequency Domain-based Long-Term Time Series Forecasting: https://arxiv.org/pdf/2205.12301.pdf A Survey on Long-tailed Visual Recognition: https://arxiv.org/pdf/2205.13775.pdf Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors: https://arxiv.org/pdf/2205.12854.pdf On the Robustness of Safe Reinforcement Learning under Observational Perturbations: https://arxiv.org/pdf/2205.14691.pdf Nesterov’s…
The AI that your company buys (or builds) may not be as secure as you think! As companies across the globe accelerate AI adoption, the corresponding security threats also continue to increase rapidly. More importantly, the potential consequences of security threats are becoming more severe as AI gets applied in critical business functions, especially for…
Multiview Transformers for Video Recognition: https://arxiv.org/pdf/2201.04288.pdf ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer: https://arxiv.org/pdf/2202.07305.pdf Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer: https://arxiv.org/pdf/2204.00843.pdf A Tour of Visualization Techniques for Computer Vision Datasets: https://arxiv.org/pdf/2204.08601.pdf Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings: https://arxiv.org/pdf/2204.04063.pdf Data Distributional Properties Drive Emergent Few-Shot Learning in…
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism: https://arxiv.org/pdf/2203.05804.pdf Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces: https://arxiv.org/pdf/2203.03254.pdf A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization: https://arxiv.org/pdf/2203.16615.pdf Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits: https://arxiv.org/pdf/2203.16707.pdf…
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review: https://arxiv.org/pdf/2202.12205.pdf NeuralFusion: Neural Volumetric Rendering under Human-object Interactions: https://arxiv.org/pdf/2202.12825.pdf Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection: https://arxiv.org/pdf/2202.07586.pdf Deep Recurrent Modelling of Granger Causality with Latent Confounding: https://arxiv.org/pdf/2202.11286.pdf Generalizable Information Theoretic Causal Representation: https://arxiv.org/pdf/2202.08388.pdf Artificial Intelligence for the…
LaMDA: Language Models for Dialog Applications: https://arxiv.org/pdf/2201.08239v3.pdf Data-Driven Offline Optimization For Architecting Hardware Accelerators: https://arxiv.org/pdf/2110.11346v3.pdf Don’t Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis: https://arxiv.org/pdf/2202.07728v1.pdf Block-NeRF: Scalable Large Scene Neural View Synthesis: https://arxiv.org/pdf/2202.05263v1.pdf Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?: https://arxiv.org/pdf/2202.01034v1.pdf Transformers Can Do Bayesian Inference:…
A Systematic Exploration Of Reservoir Computing For Forecasting Complex Spatiotemporal Dynamics: https://arxiv.org/pdf/2201.08910.pdf FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting: https://arxiv.org/pdf/2201.12740.pdf Quantifying Epistemic Uncertainty in Deep Learning: https://arxiv.org/pdf/2110.12122.pdf What’s Wrong With Deep Learning In Tree Search For Combinatorial Optimization: https://arxiv.org/pdf/2201.10494.pdf A Leap among Quantum Computing and Quantum Neural Networks: A Survey: https://arxiv.org/pdf/2107.03313.pdf Causality And…
Renewable energy has historically been costlier than fossilized energy. This is changing now. Lazard’s latest annual report on Levelized Cost of Energy Analysis highlighted that certain renewable energy technologies are becoming cost-competitive vis-à-vis conventional energy technologies. For example, see the chart below. Source: Levelized Cost Of Energy, Levelized Cost Of Storage, and Levelized Cost Of…
In 2017, Mozilla and others in the World Wide Web Consortium (W3C) released a new browser-based technology to enable high-performance applications on web pages. Initially based on Mozilla’s asm.js, this technology has evolved into the WebAssembly (WASM) that we know today. Not only has it been adopted by all the major browser systems, it is…
Lagrange Points (or L-points) recently came into the limelight during the launch of the James Webb Space Telescope (JWST). Named after the famous mathematician Joseph-Louis Lagrange, these are special points in space where the net gravitational forces of the earth and the sun (or to generalize, those of any two bodies with large masses) are…
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning: https://arxiv.org/pdf/2201.05151.pdf A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models: https://arxiv.org/pdf/2201.02262v1.pdf MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs: https://arxiv.org/pdf/2201.02534v1.pdf Applications of Signature Methods to Market Anomaly Detection: https://arxiv.org/pdf/2201.02441v1.pdf Does entity abstraction help generative Transformers reason?: https://arxiv.org/pdf/2201.01787v1.pdf Debiased Learning…
Traditional processor architectures have inherent limitations in fulfilling the high-performance needs of modern applications. This necessitated the development of domain-specific architectures and hardware accelerators. After a decade of development, RISC-V is now evolving as the preferred instruction-set-architecture for a wide range of modern processing workloads, especially in embedded, mobile and machine learning systems. And yet,…
Exponential technologies like Artificial Intelligence, Blockchain and the Internet of Things (IoT) are creating significant breakthrough today. Lots of hype are woven around these technologies, huge investments are flowing into them, and new use cases are getting created on a regular basis. However, most organizations tend to study and assess the impact of these technologies…
A Globally Convergent Distributed Jacobi Scheme for Block-Structured Non-convex Constrained Optimization Problems: https://arxiv.org/pdf/2112.09027.pdf A Robust Optimization Approach to Deep Learning: https://arxiv.org/pdf/2112.09279.pdf A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation: https://arxiv.org/pdf/2112.09747.pdf A Survey of Natural Language Generation: https://arxiv.org/pdf/2112.11739.pdf Are Large-scale Datasets Necessary for Self-Supervised Pre-training?: https://arxiv.org/pdf/2112.10740.pdf A Survey on Gender Bias in Natural…
Simulation Intelligence: Towards A New Generation Of Scientific Methods: https://arxiv.org/pdf/2112.03235v1.pdf Information is Power: Intrinsic Control via Information Capture: https://arxiv.org/pdf/2112.03899v1.pdf GLaM: Efficient Scaling of Language Models with Mixture-of-Experts: https://arxiv.org/pdf/2112.06905v1.pdf Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning: https://arxiv.org/pdf/2112.03763v1.pdf Efficient Geometry-aware 3D Generative Adversarial Networks: https://arxiv.org/pdf/2112.07945v1.pdf GAN-Supervised Dense Visual Alignment: https://arxiv.org/pdf/2112.05143v1.pdf BEVT: BERT Pretraining of Video…
Covariate Shift in High-Dimensional Random Feature Regression: https://arxiv.org/pdf/2111.08234v1.pdf Improving Transferability of Representations via Augmentation-Aware Self-Supervision: https://arxiv.org/pdf/2111.09613v1.pdf GFlowNet Foundations: https://arxiv.org/pdf/2111.09266v1.pdf Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability: https://arxiv.org/pdf/2111.10752v1.pdf Benchmarking Detection Transfer Learning with Vision Transformers: https://arxiv.org/pdf/2111.11429v1.pdf Improved Knowledge Distillation via Adversarial Collaboration: https://arxiv.org/pdf/2111.14356v1.pdf End-to-End Referring Video Object Segmentation with Multimodal Transformers: https://arxiv.org/pdf/2111.14821v1.pdf…
In the 1980s, space scientists and astronomers started talking about the existence of a new planet (called Planet X) to explain certain phenomena, such as the unexplained residuals in the orbit of Neptune. As new discoveries emerged over time, the idea gradually evolved and this hypothetical entity came to be known as Planet Nine –…
Gradients are Not All You Need: https://arxiv.org/pdf/2111.05803v1.pdf RAVE: A variational autoencoder for fast and high-quality neural audio synthesis: https://arxiv.org/pdf/2111.05011v1.pdf NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework: https://arxiv.org/pdf/2111.04130v1.pdf A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis: https://arxiv.org/pdf/2110.15678v2.pdf On Representation Knowledge Distillation for Graph Neural Networks: https://arxiv.org/pdf/2111.04964v1.pdf Meta-Learning to Improve Pre-Training:…
Shaking the foundations: delusions in sequence models for interaction and control: https://arxiv.org/pdf/2110.10819v1.pdf Understanding Dimensional Collapse in Contrastive Self-supervised Learning: https://arxiv.org/pdf/2110.09348v1.pdf SOFT: Softmax-free Transformer with Linear Complexity: https://arxiv.org/pdf/2110.11945v2.pdf Understanding How Encoder-Decoder Architectures Attend: https://arxiv.org/pdf/2110.15253v1.pdf Parameter Prediction for Unseen Deep Architectures: https://arxiv.org/pdf/2110.13100v1.pdf From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence: https://arxiv.org/pdf/2110.15245v1.pdf Implicit MLE: Backpropagating Through…
Multitask prompted training enables zero-shot task generalization: https://arxiv.org/pdf/2110.08207v1.pdf DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries: https://arxiv.org/pdf/2110.06922v1.pdf Object DGCNN: 3D Object Detection using Dynamic Graphs: https://arxiv.org/pdf/2110.06923v1.pdf Symbolic Knowledge Distillation: from General Language Models to Common sense Models: https://arxiv.org/pdf/2110.07178v1.pdf Graph Neural Networks with Learnable Structural and Positional Representations: https://arxiv.org/pdf/2110.07875.pdf Human-Robot Collaboration and Machine Learning:…
Messaging systems form the backbone of many critical applications today. An important question for software architects and engineers is the selection of the right messaging system that can address multiple requirements like cross-platform communication, fault-tolerance, latency needs, and scalability. Several open-source and commercial messaging systems are available today, and careful consideration is needed to select…
The Astronomy and Astrophysics Decadal Survey is an exhaustive report on astronomy and astrophysics that the United States’ National Academy of Sciences publishes at the end of every decade. This report explains the current state of play in astronomy and astrophysics, and recommends the research priorities of the upcoming decade to the government. The last…
It is common knowledge that efficient architecture design is a key aspect of building any product/solution, including AI applications. However, in reality, it is often observed that companies pay limited attention to developing robust end-to-end architectures before initiating AI application development. This leads to several problems like schedule/cost overruns, automation job failures, interoperability & scalability…
The 12-Factor App Manifesto, released by Heroku in 2011, gradually became a gold standard in modern software development. While there are debates in some quarters today (and justifiably so) whether this manifesto represents the needs of modern SaaS applications, the fundamental philosophy of this approach is not challenged. The focus is on building software applications…
Modern software development is becoming more complex. The scope and scale of applications are becoming bigger, both in terms of functional and non-functional requirements. For instance, application teams want to release new innovations to their users at an accelerating pace, highly feature-rich user interfaces have become a norm in many industries, customers expect peak application…
It has been over ninety years since the fundamental concept of Schwarzschild Wormholes (also called Einstein–Rosen Bridges) was first proposed. Over the years, scientists have been trying to determine if wormholes are actually a physical reality, and if they do exist, whether they are traversable in nature. Two challenges exist for the traversable wormhole hypothesis:…
The speed of light is often considered to have a specific upper limit (c when light travels in a vacuum) but this limitation can be overcome under certain physical conditions. Pulses of light passing through specific materials can achieve group velocities that are higher or lower than c, depending on how the shapes of these…
Human perception is multimodal. We make sense of objects and events through multiple modalities (sensory organs), and that is why we excel in our understanding of the world around us. Similarly, in many real-world problems, Artificial Intelligence systems become more efficient when they process inputs (signals) from multiple modalities, and then generate the outputs (prediction.)…
Researchers from MIT & IBM recently introduced CRL, a new self-supervised framework that learns task-agnostic visual representations in embodied environments. This approach is able to construct representations not only from unlabeled datasets, but also from environments. The CRL framework jointly learns a reinforcement learning policy, and visual representation model. The policy tries to maximize the…
Low-code engineering is not always the first option when companies decide to build new applications. Many engineering leaders are only partly aware of the capabilities of these platforms, or like to stick to the technologies that they know best. However, unless the goal is to build complex systems (such as Artificial Intelligence applications, or those…
The European Union has just released its first legal framework for Artificial Intelligence. It covers a wide range of areas, including:▪︎ Defining the fundamental notion of an AI system.▪︎ Laying down the requirements for high risk AI systems, and obligations of their operators.▪︎ Prohibiting certain AI practices, e.g., attempts to distort human behavior; real-time remote…
The Beijing Academy of Artificial Intelligence (BAAI) released China’s first super-scale intelligence model system: WuDao 1.0. This is a combination of four very large-scale NLP models. WenYuan: China’s largest pre-training language model (supporting Chinese & English) for text categorization, sentiment analysis, reading comprehension, etc. It claims GPT-3 comparable performance on several important NLU tasks. WenLan:…
Automated Machine Learning has come a long way since Google Brain introduced NAS in early 2017. Amazon’s AutoGluon, Google’s AutoMLZero, Salesforce’s TransmogrifAI, the AutoML features of Azure, H2O, Scikit-learn, Keras & others (TPOT, DataRobot, etc.) are witnessing increased adoption. Modern AutoML systems generally focus on hyper-parameter optimization (HPO), neural architecture search (NAS), model selection & compression, and to a certain extent, meta-learning. They also…
Earlier this year, 55 researchers from 44 global institutions proposed GEM (Generation, Evaluation & Metrics), a new benchmark environment for Natural Language Generation. It evaluates models through an interactive result exploration system. This enables a much better understanding of model limitations & improvement opportunities, and does not misrepresent the complex interactions of individual measures. This…
Design Patterns are reusable, formalized constructs that serve as templates to address common problems in designing efficient systems. These enable the development of high-performance, resilient & robust applications. Widely-used design patterns, especially from the object-oriented paradigm, include:▪︎ Behavioral Patterns: Command, Mediator, Memento, Observer, Visitor, etc.▪︎ Creational Patterns: Builder, Factory, Prototype, Singleton, etc.▪︎ Structural Patterns: Adapter, Bridge, Composite, Decorator,…
Object detection is one of the most fundamental problems in Computer Vision, and has powered many of the significant advances in this field. It has applications in a wide range of areas such as advertising, driverless cars, healthcare, robotics, security, and others. This quasi-technical paper discusses the evolution, critical areas of research, and recent innovations…
Emotion AI, also known as Affective Computing or Artificial Emotional Intelligence, is an interdisciplinary field that operates at the intersection of Behavioral Science, Cognitive Computing, Computer Science, Machine Learning, Neuroscience, Psychology, Signal Processing, and others. This is one of the rapidly evolving areas of AI research today. At the basic level, Emotion AI refers to…
Philip E. Tetlock, one of the foremost global experts in forecasting behavior, conducted a comprehensive 20 year study of expert judgment in scientific literature, and concluded that the average expert performance was around the same as random guessing. Since this study was conducted between 1984 and 2004 – before the advent of Big Data, Deep…
Extraordinary threats create extraordinary opportunities. Research & Development programs offer a great mechanism to explore/exploit such opportunities, thus enabling significant long-term value creation. At the same time, R&D functions are usually among the first to get financially impacted when a crisis strikes. Navigating R&D organizations through financial crunches, high uncertainties, and other constraints while still…
The COVID-19 pandemic is an earth-shattering, black swan event. Personal lives, societies and businesses are getting severely impacted. As governments, companies, institutions, and other organizations around the world shift their focus and resource allocation from their regular objectives to controlling this pandemic and its impact, it also increases the risk of serious cyber-attacks from rogue…
“You see, but you do not observe.” Sherlock Holmes, A Scandal in Bohemia. Knowledge, not mere data, is the real deal. Data is abundant in organizations, but not always in the structure and form that can be easily processed, analyzed and leveraged. As a result, companies end up ignoring, intentionally or otherwise, most of the data…
A common grievance of most enterprises is that while data is abundant, there is not enough knowledge. Data is the symbolic representation of the observable properties of real-world entities and, on its own, yields limited practical value. Knowledge, on the other hand, is ‘meaningful data’ created through cognitive processing mechanisms. Generating actionable knowledge from raw…
An Asian publishing company embarked on a major transformation program to deploy AI-based analytics and business intelligence solutions across all their strategic business units. The program witnessed initial success during the proof-of-concept and early validation stages. This encouraged the company to initiate a phase-wise enterprise roll-out. However, the roll-out turned out to be a major…
“If you torture the data long enough, it will confess.” Ronald Coase, Nobel Prize Winner in Economics, 1991. Imagine a below-average shooter (the original story has a Texan) randomly firing at the side of a barn, and then painting bull’s-eyes around the tightest clusters of holes made by his gunshots. He then showcases his ‘marksmanship’…
Strategic Foresight refers to an organization’s ability to identify key drivers of change, and effectively prepare for multiple plausible alternative futures. The focus is not on predicting a singular future outcome, but on understanding how different versions of the future might shape up, and optimally channeling that knowledge to manage risk, create competitive advantage, and…
The Fourth Industrial Revolution (4IR) has begun. We are already witnessing massive disruptions in different forms and levels in most aspects of our businesses and lives. The determinants of business success are changing. Incremental and Non-R&D innovation will not be adequate for business leadership in this new industrial age. There is a compelling need to…
The broad theoretical foundations of Deep Learning were formed in the seventies and eighties, and evolved in the nineties and early 2000s. However, it was only in the early part of this decade that Deep Learning started emerging out of the confines of academia and research units, and into the world of enterprise adoption. The…