A Peek Into Advanced Mathematics: The Wedderburn-Artin Theorem

A Peek Into Advanced Mathematics: The Wedderburn-Artin Theorem

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.

Modern Algorithms: The Jepsen Test and Distributed Systems Safety

Modern Algorithms: The Jepsen Test and Distributed Systems Safety

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.

Opinion: Is eBPF the Future of Linux Kernel Extensibility?

Opinion: Is eBPF the Future of Linux Kernel Extensibility?

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 State of Quantum Optimization: Capabilities, Constraints, and the Road to Fault Tolerance

I. 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…

Building Complex Agentic Systems with WebAssembly

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…

Why Agentic Infrastructure Is the New Moat in AI: Systems Compound, Models Don’t

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…

Can We Predict Chaotic Systems Beyond the Lyapunov Horizon?

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…

Maximizing Compute Throughput, Memory Efficiency, and Parallelism in HPC Systems

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…

Quantum Error Correction: Stabilizing Unstable Qubit Systems

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…

Beyond VC Dimension & Rademacher Complexity: Revisiting Generalization with Kolmogorov Lenses

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…

Shor’s Algorithm: How Quantum Computers Could Break RSA Encryption

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,…

The Role of Graph Compilers in Modern HPC Systems

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…

Engineering Multi-Agent Systems: A Technical Playbook

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 Future of Java: Can it remain competitive in the coming years?

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…

Decoding the Hype of AI Chips

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…

Reinforcement Learning: A Catalyst for Next-Gen Mathematical Optimization

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…

Fully Homomorphic Encryption: The Future of Secure Computing

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…

A Technical Deep Dive Into CPU & GPU Internals

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 Mysterious Nature of Large Low-Shear-Velocity Provinces

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…

AI Research & Innovation in 2024, Vol. 2

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…

AI Research & Innovation in 2024, Vol. 1

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…

The Surprising Success of TiDE in Long-Term Time-Series Forecasting

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…

The Shannon-Khinchin Axioms, and Uncertainty in Complex Systems

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…

A Technical Analysis of the Efficiency of Dask

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:…

Monolith or Microservices? The Debate Continues

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…

Degree-Constrained Minimum Spanning Trees

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’s Spotlight, Meta’s LLaMA, and other innovations

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,…

VALL-E, ChatGPT for Medical Advice, and other innovations

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…

LangChain: A step towards building better LLM-based conversational applications

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…

Dealing With Systemic Risk Shocks In Complex Systems: Early Over-reactions & Extreme Measures Hold The Key

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…

NeurIPS 2022: My Top Two ‘Practically-Relevant’ Papers from the Outstanding 13

[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…

Will the Hubble Tension ever get resolved?

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…

The quantum entanglement of atomic clocks

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,…

Recommended AI Papers: August 2022

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:…

tACS Brain Simulation: A step towards sustainable memory enhancement

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…

Recommended AI Papers: July 2022

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…

Recommended AI Papers: June 2022

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:…

Recommended AI Papers: May 2022

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…

Are your AI-driven applications really secure?

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…

Recommended AI Papers: April 2022

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…

Recommended AI Papers: March 2022

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…

Recommended AI papers: Feb 16 – 28, 2022

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…

Recommended AI papers: Feb 1 – 15, 2022

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:…

Recommended AI papers: Jan 16 – 31, 2022

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…

The Cost-Competitiveness of Renewable Energy

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…

Next-Generation Engineering With WebAssembly

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…

The Significance of Lagrange Points

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…

Recommended AI papers: Jan 1 – 15, 2022

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…

The RISC-V Revolution: Why The Global Tech Community Needs To Pay More Attention To This

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,…

Combinatorial Evolution: The Convergence of Exponential Technologies

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…

Recommended AI papers: Dec 16 – 31, 2021

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…

Recommended AI papers: Dec 1 – 15, 2021

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…

Recommended AI papers: Nov 16 – 30, 2021

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…

Does the mysterious ‘Planet Nine’ actually exist?

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 –…

Recommended AI papers: Nov 1 – 15, 2021

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:…

Recommended AI papers: Oct 16 – 31, 2021

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…

Recommended AI papers: Oct 1 – 15, 2021

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:…

Kafka or RabbitMQ? The right messaging system for your Cloud-Native/Microservices application

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 10-year Astronomy & Astrophysics Decadal Survey Reports

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…

Architecting AI Applications

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: Still Relevant After 10 Years!

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…

The Trend of Micro-frontends: More Hype Than Practical Utility?

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…

Can Traversable Wormholes Be Real?

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:…

Pulses traveling faster than the speed of light

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…

Building Next-Gen Artificial Intelligence Systems Through Multimodal Machine Learning

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.)…

The Curious Representation Learning (CRL) Framework

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…

The Increasing Relevance of Low-Code Engineering

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…

A New Legal Framework for AI

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…

China’s Super-Scale Intelligence Model System

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:…

The Current State of AutoML

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…

The GEM Benchmark for Natural Language Generation

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 for building AI & ML Applications

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,…

Trends & Innovations In Object Detection

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…

The State of Play in Emotion AI

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…

Navigating R&D Organizations Through Economic Turbulence – Four Principal Strategies For A Strong (Post-Crisis) Emergence

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…

Using AI To Counter Zero-Day Cyber Attacks: A Security Imperative During The COVID-19 Global Crisis

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…

Knowledge Graphs in AI Development

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…

Is Technical Debt Derailing your AI-driven Transformation Program?

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…

Applied R&D And The Fourth Industrial Revolution

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…