Articles for category: AI Tools

March 27, 2025

ikayaniaamirshahzad@gmail.com

Why Generalists Will Survive the Gaming Industry’s Collapse

Repost from HackerNoon: The video game industry, once a beacon of innovation and opportunity, is now facing an unprecedented crisis. Mass layoffs have become the norm, with thousands of developers losing their jobs due to corporate restructuring, overambitious projects, and poor financial planning. This turmoil has exposed deep flaws in the industry’s hiring and business models, particularly its over-reliance on hyper-specialized talent. In an era of uncertainty, game developers must rethink their career strategies, and becoming a generalist is emerging as a critical survival skill. For decades, game studios have sought out ultra-specialized professionals who excel in highly specific fields,

March 27, 2025

ikayaniaamirshahzad@gmail.com

The Skills Extractor Library – ESCoE : ESCoE

By Elizabeth Gallagher, India Kerle, Cath Sleeman and Jack Vines Introduction There is no publicly available data on the skills that are commonly required in UK job adverts. As a result, there is very little understanding of either the skill specialities that exist in different regions in the UK or the skills required for given occupations. Considering this, Nesta set out to start collecting UK job adverts and developing algorithms to extract structured information from them in 2021. Two years on, Nesta’s Open Jobs Observatory (OJO) has collected over five million job adverts. This project helps us to provide insights

March 27, 2025

ikayaniaamirshahzad@gmail.com

Deploy models on AWS Inferentia2 from Hugging Face

AWS Inferentia2 is the latest AWS machine learning chip available through the Amazon EC2 Inf2 instances on Amazon Web Services. Designed from the ground up for AI workloads, Inf2 instances offer great performance and cost/performance for production workloads. We have been working for over a year with the product and engineering teams at AWS to make the performance and cost-efficiency of AWS Trainium and Inferentia chips available to Hugging Face users. Our open-source library optimum-neuron makes it easy to train and deploy Hugging Face models on these accelerators. You can read more about our work accelerating transformers, large language models

March 27, 2025

ikayaniaamirshahzad@gmail.com

FDA Compliance in Software Development: Cases Where Poor Software Quality Led to Costly FDA Rejections

If you work in pharma, you know how much time and money go into drug development. The last thing you want is a painful FDA rejection, one that not only costs your company millions but also delays critical treatments for patients who need them. Regulatory rejections can happen for many reasons, such as insufficient clinical evidence, manufacturing and quality concerns, incomplete applications or missing data, and general safety issues. However, in recent years, poor software quality has become a growing yet often overlooked factor. By “software,” we mean not just applications, but also programming environments, languages, and the tools used

March 27, 2025

ikayaniaamirshahzad@gmail.com

Thoughts on setting policy for new AI capabilities

Thoughts on setting policy for new AI capabilities. Joanne Jang leads model behavior at OpenAI. Their release of GPT-4o image generation included some notable relaxation of OpenAI’s policies concerning acceptable usage – I noted some of those the other day. Joanne summarizes these changes like so: tl;dr we’re shifting from blanket refusals in sensitive areas to a more precise approach focused on preventing real-world harm. The goal is to embrace humility: recognizing how much we don’t know, and positioning ourselves to adapt as we learn. This point in particular resonated with me: Trusting user creativity over our own assumptions. AI

March 27, 2025

ikayaniaamirshahzad@gmail.com

Forecasting Bones or No Bones Days with Community Core

Each morning, the world waits to see if a 13-year-old pug named Noodle is having a “Bones day” or a “No Bones day.” Over the last few months, Noodle’s owner, Jonathan, has chronicled declarations of Nones/No Bones days via TikTok, which depend on Noodle’s reaction after Jonathan picks him up from his dog bed. If Noodle remains standing when Jonathan pulls away, it is a Bones day. This has come to symbolize a productive day and the permission to take risks. However, if Noodle falls down, it is a No Bones day, when viewers are encouraged to indulge in self-care

March 27, 2025

ikayaniaamirshahzad@gmail.com

Data Science and Agile (Frameworks for Effectiveness)

This is the second post in a 2-part sharing on Data Science and Agile. In the last post, we discussed about the aspects of Agile that work, and don’t work, in the data science process. You can find the previous post here. Follow-up: What I Love about Scrum for Data Science A quick recap of what works well Periodic planning and prioritization: This ensures that sprints and tasks are aligned with organisational needs, allows stakeholders to contribute their perspectives and expertise, and enable quick iterations and feedback Clearly defined tasks with timelines: This helps keep the data science team productive

March 27, 2025

ikayaniaamirshahzad@gmail.com

🔥 Introducing Docker Model Runner – Bring AI Inference to Your Local Dev Environment

Imagine running LLMs and GenAI models with a single Docker command — locally, seamlessly, and without the GPU fuss. That future is here. 🚢 Docker Just Changed the AI Dev Game Docker has officially launched Docker Model Runner, and it’s a game-changer for developers working with AI and machine learning. If you’ve ever dreamed of running language models, generating embeddings, or building AI apps right on your laptop — without setting up complex environments — Docker has your back. Docker Model Runner enables local inference of AI models through a clean, simple CLI — no need for CUDA drivers, complicated

March 27, 2025

ikayaniaamirshahzad@gmail.com

CyberSecEval 2 – A Comprehensive Evaluation Framework for Cybersecurity Risks and Capabilities of Large Language Models

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate potential risks of Large Language Models (LLMs). Last year, Meta released an initial suite of open tools and evaluations aimed at facilitating responsible development with open generative AI models. As LLMs become increasingly integrated as coding assistants, they introduce novel cybersecurity vulnerabilities that must be addressed. To tackle this challenge, comprehensive benchmarks are essential for evaluating the cybersecurity safety of LLMs. This is where CyberSecEval 2, which assesses an LLM’s susceptibility to code