Causal inference is an important and active area of artificial intelligence research today. Indeed, no less than Turing award winner Yoshio Bengio lists causal reasoning as a top priority, as does his co-Turing award winner Yann LeCun, who writes that “Lots of people in ML/DL know that causal inference is an important way to improve generalization. The question is how to do it“. And Judea Pearl’s The Book of Why is a groundbreaking advance in this important discipline. While valuable, these initiatives overlook a much easier formulation of causal reasoning—you might call it “low hanging fruit ..read more

Most conversations between decision makers and data professionals begin back-to-front If you’re a decision maker who wants to make the best use of data and other information resources, such as artificial intelligence (AI), to support your decisions, chances are that conversations between you and your data analysts is happening back-to-front. Data analysts should encourage us to explain, “Here’s what I need you to do for me…” Instead, so often they begin with: “Here’s what we have for you…”. The solutions that the analysts produce offer insights that might be relevant. But they do not actually ..read more

As data storage and management becomes less expensive, many organizations are tasked with being “data-driven”. What does this mean in practice? For many, using data to inform important organizational decisions is an important goal. This is the role of Decision Intelligence (DI): a practice that bridges decisions to data. However, it’s not always clear how data connects to decisions. Decision Intelligence treats decision making as a thought and/or simulation process, which is meant to match as effectively as possible a chain of cause-and-effect links from actions to outcomes. For instance ..read more

Forbes reports that “most AI projects fail”; Techrepublic puts the number at 85%. How do we reconcile this reality with claims of AI as representing an “existential threat” such as those from Elon Musk and futurism.com? Often called AGI (Artificial General Intelligence), the idea is a general-purpose, human-like intelligence like Hal from 2001 or the Terminator. Here’s what’s going on. First, all new technology by its nature is disruptive, and AI today is no exception. The cotton gin, the telephone, and certainly the internet and personal computers all created massive shifts in employment and ..read more

In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word “decision” to mean a lot of things. Let’s tease out some distinctions: Decision Type Name Question answered Primary information Source Typical success criterion Typical method A ML classification “Decisions That”: “What is this picture?” “What disease does this person have?” “Is this a cat?” Data True positive, true negative Supervised learning B ML regression “Decision about a prediction”: “What will be the Covid-19 incidence next month?” “What will be this security’s price ne ..read more

The introduction of the cotton gin wasn’t accompanied by an entire genre of Hollywood movies dedicated to the gin “singularity”. Nor did we usher in Golden Age of telecommunications with blockbuster killer “phone web” stories. Artificial Intelligence is different. Like other disruptive technologies, it is having far-ranging effects, good and bad. But uniquely, quality AI information is clouded by the AI apocalypse narrative. If you google the field, you’ll be challenged to separate medical imaging wheat from AGI chaff. (Don’t tell anyone, friends, there’s no magic here, it’s just math.) AI al ..read more

Have you ever wondered just how many decisions you will make today? In your lifetime? Though difficult to measure, researchers have estimated that the average adult makes anywhere from a few thousand to 35,000 decisions in a single day. While the estimates range tremendously due to the complexity of deciphering between decisions and automatic reflexes, it is undisputed that people make a lot of decisions every single day. A different way to understand just how active our brains are is to look at a more easily measured metric, the energy consumption of the brain—specifically the amount of gluc ..read more

Are you thinking: “I’ve heard that AI can provide some value to my organization?” but wondering where to start? You might be a CIO or other technical leader, looking to avoid being left in the dust behind this important technology. Based on our experience delivering dozens of AI projects (thousands of models) over 30 years, there are three approaches that you can take, each with their pros, cons, classic mistakes, and best practices. Buy What it is: In this strategy, you buy an AI solution from a vendor. It might be packaged within an application, or you might obtain AI value through an API ..read more

Like it or not, technology is everywhere. We have long ago passed a time where computers were relegated to men in white coats in distant rooms. It’s now in our pockets, in our faces, and mediates many of our relationships. Yet, as Shoshana Zuboff explains in The Age of Surveillance Capitalism, recent years were characterized by AI and other manipulative technologies monitoring and controlling us, rather than the other way around. As an AI professional who cares about the problems raised by AI systems, not just at the individual level but at the interface between technology and whole-system ..read more

I wrote previously about the emergence of applied, agile, assured AI. This post is a deeper dive, explaining the diagram, below, in more detail: Timeline of computer science and data science showing that DS is reaching milestones that CS reached a while back Over the years, computer science has evolved through four eras, which are playing out as well, with some delay, in data science (my emphasis here is the AI/ML/DI side of data science, because the pure data side is shared with CS): R&D: Here, the goal was to create CS to start with and, ultimately, to prove that the technology ..read more