Month: April 2025

:::info Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. ::: Table of … Read more

:::info Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. ::: Table of … Read more

:::info Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. ::: Table of … Read more

Table of Contents Goroutines Mutexes WaitGroups Condition Variables Goroutines Concurrency is at the heart of modern systems programming, and Go offers some of the most intuitive and powerful primitives to support it. This article offers a detailed exploration of Go‘s concurrency tools, ranging from goroutines and closures to the Go runtime scheduler accompanied by concise … Read more

:::info Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. ::: Table of Links Abstract and 1 Introduction 2 Background 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 4 … Read more

:::info Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. ::: Table of Links Abstract and 1 Introduction 2 Background 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 4 … Read more

Gig workers can be considered the backbone of our modern economy. Just think of how many times you’ve been saved by a rideshare driver, food delivery worker, or freelance specialist assisting with a niche task. However, more often than not, gig workers are left behind when it comes to financial services. Due to the non-traditional … Read more

:::info Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. ::: Table of Links Abstract and 1 Introduction 2 Background 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 4 … Read more

:::info Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. ::: Table of Links Abstract and 1 Introduction 2 Background 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 4 … Read more

:::info Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. ::: Table of Links Abstract and 1 Introduction 2 Background 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 4 … Read more

:::info Authors: (1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada; (2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; … Read more

KEY TAKEAWAYS WasmEdge functions as a stand-alone option. It replaces traditional OCI runtime systems with its own faster and lighter solution. These containers provide faster startups and reduced resource consumption. So, the performance is faster alongside minimal overhead expenses. The secure sandboxing of WasmEdge containers creates an improved security environment that cuts down system vulnerabilities … Read more

:::info Authors: (1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada; (2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; … Read more

Hello JavaScript Enthusiasts! Welcome to a new edition of “This Week in JavaScript“! Today, we’re covering a critical Next.js security patch you can’t afford to miss, OpenAI’s revolutionary 4o Image Generation, and Vue.js’s impressive growth stats—plus exciting new tools that’ll supercharge your development workflow! Next.js Middleware Security Vulnerability Next.js just patched a critical vulnerability (CVE-2025-29927) … Read more

Hello AI Enthusiasts! Welcome to the twelfth edition of “This Week in AI Engineering”! ChatGPT’s 4o brings powerful native image generation that sparked the viral “Ghibli effect,” and Tencent unveils the world’s first ultra-large Hybrid-Transformer-Mamba MoE model, Google’s Gemini 2.5 Pro achieves state-of-the-art performance with remarkable reasoning capabilities, Microsoft’s KBLaM integrates knowledge bases with linear … Read more