Artificial Intelligence

A LinkedIn post with little traction led to a surprising AI collaboration – proof that sharing online can spark unexpected opportunities.

Themes from AI Con 2024: data moats, stealthy AI use, Chatty’s UX revolution, and enduring fundamentals.

Building AI is hard and expensive. For most companies, the path to AI success is building with third-party AI interns and cheap AI cogs.

Podcast chat on the reality of Data & AI and my consulting focus: Helping climate & nature tech startups ship data-intensive solutions.

My views on separating AI hype and bullshit from the real deal. The general ideas apply to past and future hype waves in tech.

The real world of AI/ML doesn’t fit into a neat diagram, so I created another diagram and a maturity heatmap to model the mess.

Video and key points from the second part of a webinar on a startup’s first data hire, covering tips for defining the role and running the process.

Video and key points from the first part of a webinar on a startup’s first data hire, covering data & AI definitions and high-level recommendations.

Asking identical questions about my MagicGrantMaker idea yielded near-identical responses from the top chatbot models.

Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.

Quotes from Demetrios Brinkmann on the relationship between MLOps and DevOps, with MLOps allowing for managing changes that come from data.

Since we’re far from a utopia where data issues are fully handled by AI, this post presents six questions humans can use to assess data projects.

Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).

Overview of the book The Economy of Algorithms by Marek Kowalkiewicz.

Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.

It’s easy to prototype with AI, but production-grade AI apps require even more thorough testing and observability than traditional software.

Discussing the use of AI to automate underwater marine surveys as an example of the uneven distribution of technological advancement.

Discussing considerations that arise when attempting to automate the extraction of structured data from PDFs and similar documents.

Classifying startups as ML-centric or non-ML is a helpful exercise to uncover the data challenges they’re likely to face.

Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with a no-code approach.

Getting ChatGPT Plus to elaborate on possible book content and produce a PDF cheatsheet, with the goal of learning about its capabilities.

Reflecting on an interview with Jason Warner, CEO of poolside.

Shifting focus to ‘Data & AI for Impact’, with more startup-related content, increased posting frequency, and deeper audience engagement.

Replacing ‘artificial intelligence’ with ‘automation’ is a useful trick for cutting through the hype.

Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.

Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.

Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.

Bing Chat recently quipped that humans are small language models. Here are some of my thoughts on how we small language models can remain relevant (for now).

My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.

Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.

Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.

Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.

Updating my definition of data science to match changes in the field. It is now broader than before, but its ultimate goal is still to support decisions.

An overview of my PhD in data science / artificial intelligence. Thesis title: Text Mining and Rating Prediction with Topical User Models.