Data moats, stealthy AI, and more: AI Con 2024 notes
Themes from AI Con 2024: data moats, stealthy AI use, Chatty’s UX revolution, and enduring fundamentals.
Artificial Intelligence
Don't build AI, build with AI
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.
Data, AI, humans, and climate: Carving a consulting niche
Podcast chat on the reality of Data & AI and my consulting focus: Helping climate & nature tech startups ship data-intensive solutions.
AI hype, AI bullshit, and the real deal
My views on separating AI hype and bullshit from the real deal. The general ideas apply to past and future hype waves in tech.
AI/ML lifecycle models versus real-world mess
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.
Your first Data-to-AI hire: Run a lovable process
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.
Learn about Dataland to avoid expensive hiring mistakes
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.
Exploring an AI product idea with the latest ChatGPT, Claude, and Gemini
Asking identical questions about my MagicGrantMaker idea yielded near-identical responses from the top chatbot models.
Is your tech stack ready for data-intensive applications?
Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.
Dealing with endless data changes
Quotes from Demetrios Brinkmann on the relationship between MLOps and DevOps, with MLOps allowing for managing changes that come from data.
AI ain't gonna save you from bad 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.
Plumbing, Decisions, and Automation: De-hyping Data & AI
Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).
Adapting to the economy of algorithms
Overview of the book The Economy of Algorithms by Marek Kowalkiewicz.
Assessing a startup's data-to-AI health
Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.
AI does not obviate the need for testing and observability
It’s easy to prototype with AI, but production-grade AI apps require even more thorough testing and observability than traditional software.
Artificial intelligence, automation, and the art of counting fish
Discussing the use of AI to automate underwater marine surveys as an example of the uneven distribution of technological advancement.
Questions to consider when using AI for PDF data extraction
Discussing considerations that arise when attempting to automate the extraction of structured data from PDFs and similar documents.
Two types of startup data problems
Classifying startups as ML-centric or non-ML is a helpful exercise to uncover the data challenges they’re likely to face.
Avoiding AI complexity: First, write no code
Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with a no-code approach.
Nudging ChatGPT to invent books you have no time to read
Getting ChatGPT Plus to elaborate on possible book content and produce a PDF cheatsheet, with the goal of learning about its capabilities.
Future software development may require fewer humans
Reflecting on an interview with Jason Warner, CEO of poolside.
New decade, new tagline: Data & AI for Impact
Shifting focus to ‘Data & AI for Impact’, with more startup-related content, increased posting frequency, and deeper audience engagement.
Artificial intelligence was a marketing term all along – just call it automation
Replacing ‘artificial intelligence’ with ‘automation’ is a useful trick for cutting through the hype.
Google's Rules of Machine Learning still apply in the age of large language models
Despite the excitement around large language models, building with machine learning remains an engineering problem with established best practices.
Was data science a failure mode of software engineering?
Yes, data science projects have suffered from classic software engineering mistakes, but the field is maturing with the rise of new engineering roles.
How hackable are automated coding assessments?
Exploring the hackability of speed-based coding tests, using CodeSignal’s Industry Coding Framework as a case study.
Remaining relevant as a small language model
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).
ChatGPT is transformative AI
My perspective after a week of using ChatGPT: This is a step change in finding distilled information, and it’s only the beginning.
Causal Machine Learning is off to a good start, despite some issues
Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.
Building useful machine learning tools keeps getting easier: A fish ID case study
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.
Use your human brain to avoid artificial intelligence disasters
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.
Defining data science in 2018
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.
My PhD work
An overview of my PhD in data science / artificial intelligence. Thesis title: Text Mining and Rating Prediction with Topical User Models.