AI is going to wreck your carefully planned career

Yesterday I presented to a group of undergraduate students at PSU about the future of work and the coming changes to the workforce. As someone who regularly talks about the future of work this was the first time I’ve stood in front of soon-to-graduate students and tell them they’ll need to become lifelong learners because artificial intelligence. It’s a bit of an awkward message to deliver. They’re in their last term, weeks aways from finishing up four years of learning, working, and preparing for their next career move. They are ready to take on the world with their new skills. And I’m telling them they’re going to need to keep learning, upskilling, post-college.

But the students were game for the discussion and asked solid questions about my business plan and online courses.

The experience, however, highlights one of the biggest challenges I have right now. Everyone working in future of work spaces is working to educate employees and students about the coming changes to the workforce. Despite the blazing headlines about robots taking our jobs, the subject (or fear?) isn’t tangible enough to stick. How do we get people to shift from outdated career models and thinking to commit to lifelong learning and upskilling? How do we get people to see how artificial intelligence is changing the workplace and our jobs, if they aren’t yet feeling affecting by the technology?

Predictive analytics and algorithmic decision making happen outside of our view, behind the scenes of our daily lives. Yet we are increasingly influenced by these invisible algorithms from what we see in our newsfeeds to what prices we pay for flights. Algorithms are shaping our workplaces too. From managers that monitor employees using predictive analytics, to algorithms that rank resumes, to smart platforms that determine how we get hired, these technologies shape our career decisions and job search outcomes.

Yesterday I asked if any of the students had experienced an interview using the HireVue platform. One had. I asked if she knew she was being evaluated by algorithms. She responded that she wasn’t, and the audible, “Whaaaat?” and gasps from the audience indicated most students weren’t aware either. Job seekers need to know about the technology that’s being used to evaluate them. 

For yesterday’s talk I put together the resources to help students understand the coming changes, the technology, and how to prepare for an ambiguous career. If you’ve seen the headlines about robots taking our jobs and want to get beyond the headline hype, check out the resources below.

Start with the video below as an introduction to the subject.

BONUS WATCHING: Learn about the digital skills gap

Next, play with this fun tool: Willrobotstakemyjob.com

If you have extra time, dive into this episode, McKinsey Global Institute Podcast: How will automation affect jobs, skills, and wages? It’s a bit dry because it’s consultants talking but it’s worth understanding in depth just how dramatic of a shift is coming to the workforce. Here’s a quote from the episode to put it in perspective:

It’s something that has been a bit of a mantra in the educational field. Everyone is going to have to be a student for life and embark on lifelong learning. The fact is right now it’s still mainly a slogan. Even within jobs and companies there’s not lifelong training. In fact what we see in corporate training data at least in the United States, is that companies are spending less. As we know right now people expect that they get their education in the early 20s or late 20s and then they’re done. They’re going to go off and work for 40, 50 years. And that model of getting education up front and working for many decades, without ever going through formal or informal training again is clearly not going to be the reality for the next generation.

Continuing on that theme is another article by McKinsey, Getting Ready for the Future of Work, which is worth reading if only for this shocking quote right here:

The time it takes for people’s skills to become irrelevant will shrink. It used to be, “I got my skills in my 20s; I can hang on until 60.” It’s not going to be like that anymore. We’re going to live in an era of people finding their skills irrelevant at age 45, 40, 35. And there are going to be a great many people who are out of work.

Then spend some time reading about how artificial intelligence is changing the way we find and get jobs. Start with, AI is now analyzing candidates facial expressions during job interviews. Then read about my experience trying to interview with a chatbot. Finally, put it all together in The grim reality of job hunting in the age of AI.

And if this all has you thinking, holy shit, am I at risk of being irrelevant?!?! read, How to Stay Relevant in Today’s Rapidly Changing Job Market.

While you’re at it sign up for early access to FutureMe School because we exist to smash traditional career narratives and prepare you for this new world of work.

If you’re super interested in understanding AI in depth from a non-tech perspective, and want your mind blown while being entertained by stick figure comics, read with this fabulous introduction to the subject: The AI Revolution part 1 and part 2. They are both long reads so settle in.

So about that graduate program you’re thinking about doing

Nearly 30% of professionals believe their skills will be redundant in the next 1-2 years, if they aren’t already, with another 38% stating they believe their skills will be outdated within the next 4-5 years. – LinkedIn Economic Graph

Has anyone told the students who are putting down 10K for graduate certificates or taking on $90k in debt to pursue uncertain career paths that are at risk for AI disruption? Who’s working to make sure that these programs – especially those outside of elite schools – prepare students for emerging jobs?

Who is responsible for that discussion? Admissions? Career services? Deans?

Hiring practices are about to get even more opaque

All that advice about plugging keywords into your resume to make sure it passes the ATS systems is about to be useless. Here’s an excerpt from AI for Recruiting: A Definitive Guide to for HR Professionals by Ideal.com, a AI-powered resume screening and candidate tracking solution for busy recruiters.

Intelligent screening software automates resume screening by using AI (i.e., machine learning) on your existing resume database. The software learns which candidates moved on to become successful and unsuccessful employees based on their performance, tenure, and turnover rates. Specifically, it learns what existing employees’ experience, skills, and other qualities are and applies this knowledge to new applicants in order to automatically rank, grade, and shortlist the strongest candidates.The software can also enrich candidates’ resumes by using public data sources about their prior employers as well as their public social media profiles.

Now for all the questions: What are the “other qualities” that they measure? How much weight do they give to experience vs. skills? How much data does a company need to use these algorithms effectively? How does a company without loads of data use this technology? Who decides which data to use? Who reviews the training data for accuracy and bias – the company or the vendor? How does this company avoid bias, especially if people who advance are all white men (due to unconscious bias in the promotion process)? What data points are most valuable on candidates social profiles? Which social profiles are they pulling from? Are personal websites included? Which companies are using this technology? Are candidates without publicly available social media data scored lower? Of the companies using these technologies, who’s responsible for asking the questions above?

This technology gives a whole new meaning to submitting your resume into a black hole.

Do you ever feel like you need to go back to school so you can catch up?

This thirst for AI has pushed all AI-related courses on Stanford to way over their capacity. CS224N: Natural Language Processing with Deep Learning had more than 700 students. CS231N: Convolutional Neural Networks for Visual Recognition had the same. According to Justin Johnson, co-instructor of CS231N, the class size is exponentially increasing. At the beginning of the quarter, instructors for both courses desperately scramble to find extra TAs. Even my course, first time offered, taught by an obscure undergraduate student, received 350+ applications for its 20 spots. Many of the students who took these courses aren’t even interested in the subject. They just take those courses because everyone is doing it”

-excerpt from Confession of a so-called AI Expert.

The author, Chip Hyuen, is a third year student and TensorFlow TA at Stanford. She’s got a fab internship at Netflix and a killer writing style. The full article is a must-read, in part so you can fully appreciate the last sentences:

“Maybe one day people would realize that many AI experts are just frauds. Maybe one day students would realize that their time would be better spent learning things they truly care about. Maybe one day I would be out of job and left to die alone on the sidewalk. Or maybe the AI robot that I build would destroy you all. Who knows?”