Steven Hawking’s take on AI

I’m reading Brief Answers to the Big Questions, Steven Hawking’s last book published in 2018. One of the questions he takes on is, “Will artificial intelligence outsmart us?” His answer is more nuanced than some media outlets give him credit for.

Hawking sees huge potential for artificial intelligence, especially in partnership with human cognition and if properly aligned with human interests.

If we can connect a human brain to the internet it will have all of Wikipedia as its resource.

Steven Hawking, Brief Answers to the Big Questions

However, he acknowledges that creating this alignment is tricky and there are many risks in a technology that can quickly surpass human abilities and exponentially develop itself.

His key advice is that humanity needs to seriously consider the risks and impact of artificial intelligence alongside developing it if it is to be a beneficial rather than a destructive force.

For my notes on this book as well as the list of books I’ve read and my reviews, visit my Goodreads page.

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IBM Watson, five years later

In 2014 I wrote about my excitement over a demo of IBM Watson. I have recently heard about some familiar consumer tools that are using Watson and I revisited my post to see what’s changed over five years.

There are great case studies on IBM Watson here. It’s no surprise that this technology has gotten a lot of traction over the last five years.

One caveat I had when reviewing this tool in 2014 was that the data is only as good as how it’s communicated, and this still stands. Many analytics tools are doing a much better job of showing the quality of the underlying data and how reliable the predictions are, but there is still a level of understanding that is required when handling big data sets.

Public policy and AI

One of the largest AI conferences is NeurIPS, which happens annually in December. I spent some time browsing the presentations from the 2018 conference and found an interesting presentation by Edward W. Felten from Princeton University titled, “Machine Learning Meets Public Policy: What to Expect and How to Cope.”

Felten kicks off his talk by highlighting the messaging that people in public policy are hearing about AI, and overwhelmingly, it is a warning to put regulations in place. People like Henry Kissinger and Elon Musk have already sounded the alarm to policy makers.

His thesis is that the best policies will come out of technical people partnering well with policy makers, with both sides trusting the others’ expertise. This comes from being engaged and constructive in the policy making process over time.

It was interesting to see push back on this thesis by some attendees. One counterpoint was that many industries have self-regulating bodies, such as FINRA, and that this could be an option for machine learning. However, Felten pushes back on this because self-regulating bodies work well when they have public accountability, and they are easily replaced when not accountable.

AI blogs and newsletters for businesspeople

Keeping it brief for a Saturday post. I wanted to share some content I follow for information on AI developments. A challenge I’ve found as I learn about AI is that a lot of content skews technical – the intended audience is programmers, not businesspeople. The sites I’ve shared below are relevant to those focused on implementation and investment rather than creation. I may update this over time as I find additional sites.

  • Artificial Intelligence Weekly: Download of relevant stories and investment news
  • VentureBeat’s AI Channel: Good coverage and a weekly newsletter recap from Khari Johnson, the AI staff writer
  • Work-bench blog: Work-bench is a enterprise technology focused VC in New York City. They’re not exclusively focused on AI but it’s gotten a lot of attention lately and often comes up in their blog posts.

Fun AI: Iconary

I’m ending the week on a light note with an AI game I found. Iconary is a Pictionary-like game developed by AllenAI in Washington. I had a lot of fun drawing and guessing and it’s surprising to see how closely my perception and guesses match my AI opponent, Allen.

This is the kind of thing that is so tricky for AI – reading meaning into symbols. AI can recognize a tree as a tree, but can it recognize a group of trees as a forest? This one can!

Here’s a great write-up on the game and the impressive AI behind it on TechCrunch.

Welcome to AI February!

Having touched companies’ recruiting processes in some capacity the entirety of my career, I’ve been fascinated with the ways recruiting has evolved in that time. As a snapshot of this change, ten years ago as a new recruiter, I sometimes called an advertising agency to help post retail openings in the local newspaper. Today in recruiting operations, we use Google Analytics and tracking pixels to see performance of online postings, and programmatic advertising can automate some placement decisions.

One of the most exciting advancements in the field is artificial intelligence. Already there are HR tech startups touting their use of AI to enhance and improve the recruiting experience. The tagline is generally something about removing human bias and increasing efficiency – great arguments to move towards technology!

As a data analyst and a former recruiter, I am both excited and skeptical. Can these tools do what they promise? Do they truly apply AI or is this advanced statistics dressed up? And finally – aren’t people a critical component of the hiring practice?

To dive into some of these questions and familiarize myself with the state of AI, I’ve decided to commit my February to learning more about it. Every day throughout February, I will learn something about artificial intelligence and share it here. I’m focused on the following areas:

  • Defining artificial intelligence. What counts as artificial intelligence? What is the difference between AI and machine learning or are they interchangeable?
  • What’s the state of artificial intelligence in recruiting? Who is already doing this and how successful is it?
  • What are the promises and pitfalls of AI?

I realize that these three areas are rich enough that I could probably devote a month (or more!) to each. My intention here is to more broadly explore, though, so I’ll touch on all of these in one short month.

I’m looking forward to this month of learning and hope you follow along! Feel free to share any interesting tidbits, resources or your own interest with me here or via email or Twitter. Here’s to learning!