Here’s a plug for a practice I’ve leaned on more and more to make sure I’m getting things done, particularly those important-but-hard-to-sit-down-and-do things.
I experience the textbook case of procrastination due to perfectionism. When I have a vision for how I want something to turn out, it’s often overwhelming for me to start or at least keep it going. The Pomodoro technique totally wipes this out.
Here’s a summary of the Pomodoro technique. Many advocates advise not using the timer on your phone but I always have with no issue. I also don’t stick to the rules about how many Pomodoros to do before taking a break. It’s rare that I find a block of clear time longer than 1-2 hours in my day.
This is why the Pomodoro technique works so well for me:
- Offers a low barrier-to-entry to just getting started. Rather than clearing my desk, reaching inbox zero and having a snack before starting a project (all sneaky ways to procrastinate!), I just start the timer and work. It’s only 25 minutes!
- Captures actual effort exerted. Recently I lamented that a project was taking the whole day. Then I realized I had only done two Pomodoros, with the rest of my time going to two phone meetings, a coffee and lunch. So in reality I had only worked on that thing for less than one hour. A much needed reality check!
I recommend trying it out, especially if you’re prone to procrastination.
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.
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.
It’s exciting to see the ways that AI can enhance recruiting capabilities, but just as important – and potentially more so – is how it impacts the job candidate’s experience. An article from last year on CNBC highlighted one candidate’s feelings of distance when interacting with an AI recruiting tool, in this case Hirevue: “It felt weird. I was kind of talking into the void.”
Anecdotally, I’ve heard a variety of reactions, more increasingly positive. As interacting with AI tools becomes a more common experience during the recruiting process, people become more comfortable with the idea that part of the process will be automated. I also suspect some generational differences; Millennials notoriously hate phone calls, and many of these AI solutions approximate a text conversation or a video chat.
A great approach to exploring these solutions is to look at the data. Are candidates less likely to continue with the recruiting process when presented with an AI tool? Do they rate their experience lower once these solutions are put in place? Individual companies can track this for themselves with some simple data collection.
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.
Happy Sunday! Keeping it light with an AI bot that was featured on This American Life: InspiroBot.
InspiroBot generates slogans over images and weirdly, sometimes spits out something profound… but more often something hilarious.
Above: The inspirational poster that InspiroBot created for me.
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.
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.
Google has great resources for learning more about AI, both for developers and businesspeople: ai.google.
Through this site, I watched a lecture by Margaret Mitchell on fairness in AI. There are many stories about unintended bias in AI tools. A recent article about Amazon’s challenge with this made a lot of noise in the HR community. There are different types of human bias that can manifest in data:
- Reporting bias: People report what they find interesting or notable so data doesn’t reflect real-world frequencies
- Selection bias: The training data for machine learning systems is not a random sample of the world but instead things we find interesting
- Overgeneralization: Conclusion is made based on limited information or information not specific enough
- Out-group homogeneity bias: We assume people in groups we don’t interact with every day are more similar to each other than those in our in-group
- Confirmation bias: Tendency to search for, interpret and favor information that confirms our own pre-existing beliefs and hypotheses
- Automation bias: Preference for suggestions from systems, as if they are somehow more objective than other sources, like humans.
There are methods for designing in fairness in machine learning but this must be intentional – AI is not inherently unbiased.
And now, the convergence of my two areas of interest: recruiting and AI. The companies in this space, many of which are startups, are finding novel ways to apply AI to the recruiting process. I’ll break these out into a few categories, reflecting the stages of recruiting: sourcing, assessment and candidate experience. Today I’m highlighting sourcing.
Sourcing is a natural fit for AI because it’s an expensive activity for recruiting organizations and there is so much data available on potential candidates.
In traditional talent sourcing, a recruiter (or sourcer) looks far and wide across a population to find relevant talent for an available job. Once a qualified person has been identified, the recruiter then attempts to engage this person to see if they will consider the job. There are a few obstacles here: first, the pool of potential talent may be very large and difficult to comb through; second, it may be difficult to identify best-fit candidates, and third, it may be difficult to engage or find people willing to engage.
AI is a great fit for AI because it’s a data-rich activity. Across the web, there’s social media profiles, participation on forums, articles and white papers. Content that could flag someone as a relevant fit for a job is nearly limitless. And within companies, there is plentiful data as well. Data on existing employees can suggest what skills work well for roles, and efficient mining of previous job candidates can lead to a future hire for a different job.
Here are a few companies applying AI to sourcing activities:
- LinkedIn: A leader in recruiting technology. LinkedIn Recruiter is a popular tool for recruiting organizations and AI is at the heart of the recommendations to recruiters when they are searching for new talent pools
- Entelo: A startup that applies predictive analytics to identify those most receptive to a job opportunity
- Restless Bandit: Analyzes resumes within a company applicant tracking system to match top candidates to open roles