PhD

Essential knowledge, abilities, and practices with AIImage by the author created using Midjourney.This article is the second in a series introducing social work students to generative AI. I recommend starting with the first article, which you can find here:Mastering AI tools like ChatGPT offers MSW students a range of career advantages. Proficiency in these technologies streamlines work processes, increasing efficiency and accuracy in tasks like case management, data analysis, and resource allocation. As technology…

Navigating the AGI FrontierGenerated using Stable Diffusion with the prompt “Depict a scene where a human and an AGI entity work together, symbolizing a future where artificial general intelligence collaborates with humans to solve complex problems.”The advancements in AI systems like ChatGPT and GPT-4 have led to questions about whether the Turing Test is an adequate measure of human-like intelligence. This article explores two possible scenarios for the future of Artificial General Intelligence (AGI): AGI fully…

A need for changeImage from Midjourney created by the author.As a data-driven social work professor, I am preparing for the transformative impact that AI technologies will have on our field. While AI won’t replace social workers, it will significantly reshape research, practice, and education.Princeton University economist Ed Felten and his colleagues developed a unique metric called AI occupational exposure. This measure highlights the impact of AI on specific occupations by connecting ten AI applications (such as…

A gradient-based reinforcement learning algorithm to learn deterministic policies for continuous action spacesPhoto by Jonathan Ford on UnsplashThis article introduces Deep Deterministic Policy Gradient (DDPG) — a Reinforcement Learning algorithm suitable for deterministic policies applied in continuous action spaces. By combining the actor-critic paradigm with deep neural networks, continuous action spaces can be tackled without resorting to stochastic policies.Especially for continuous control tasks in which randomness…

One of the most popular Python libraries for dealing with machine learning tasks is scikit-learn. It went public in 2010 and has since been essential for implementing popular supervised ML algorithms like logistic regression, random forests, and support vector machines.When writing code in scikit-learn, you can use a feature called pipelines. This feature allows you to bundle up several of the steps in the machine learning process into a single component. The use of pipelines is one of the single most determining factors…

The “spirit” is right; the body has many flawsPhoto by Álvaro Serrano on UnsplashLast Tuesday, I received from the Future of Life Institute an email asking me to sign a petition to pause giant AI experiments. When I signed the letter, the organizers asked us to keep it confidential until its publication moment arrived. At that time, I didn’t expect it to raise so much news, comments, articles, and more.Shortly after its publication, I was contacted by a couple of news outlets, one from Argentina and the other one from…

A paradigm shiftASCII art of a puppy produced by the author using ChatGPT.Artificial intelligence (AI) has advanced incredibly fast in recent years, giving rise to powerful tools known as generative AI and large language models. Generative AI is artificial intelligence that can create new content, such as text, images, or music, based on patterns learned from existing data. Large language models, like OpenAI’s GPT series, are a subset of generative AI that have been specifically trained on vast amounts of text to…

A Python implementation of Q-learning to solve the Taxi-v3 environment from OpenAI Gym in an animated Jupyter NotebookPhoto by Alexander Redl on UnsplashThe goal of the Taxi Environment in OpenAI’s Gym — yes, from the company behind ChatGPT and Dall⋅E — is simple and straightforward, making for an excellent introduction to the field of Reinforcement Learning (RL).This article provides a step-to-step guide to implement the environment, learn a policy using tabular Q-learning, and visualize the learned behavior in…

Intravenous contrast enhancement is fundamental to CT imaging. Here’s how it might be affecting your CT image datasets“Brain CT in the style of Vincent Van Gogh”. DALL-E 2.The use of contrast is ubiquitous in radiologic imaging and yet, there’s surprisingly little helpful information on the topic online. Here, I’ll describe the basics of IV contrast, how it appears in CT images, and how it might affect your imaging dataset.As a data scientist working on medical imaging studies, it is important to understand how contrast…

A personal case study with free code to plot personalized contour plots of heart rate, speed, and VO₂ Max with Fitbit to track and improve your fitnessVO₂ Max over the past 3+ years (Image by Author)In this post, we will look at how Fitbit measures your VO₂ Max, track how it changes over time, and derive useful insights to give a better steer on how to improve it over time. You will also see how I have used a simple concept of multiple regression to better understand how Fitbit derives this measure from speed, and heart…