The Future of Work from an L&D perspective

As stewards of your company’s value, you need to understand how to get your people ready—not because it’s a nice thing to do but because the competitive advantage of early adopters of advanced algorithms and robotics will rapidly diminish. Simply put, companies will differentiate themselves not just by having the tools but by how their people interact with those tools and make the complex decisions that they must make in the course of doing their work. The greater the use of information-rich tools, the more important the decisions are that are still made by people. That, in turn, increases the importance of continuous learning. Workers, managers, and executives need to keep up with the machines and be able to interpret their results. – Putting Lifelong Learning on the Agenda,McKinsey Insights

Here’s a company that’s living that advice:

“The future of learning sabbaticals at Buffer is closely tied with our desire to help create the future of work. There’s a quote from Stephanie Ricci, head of learning at AXA that’s really powerful in explaining how much impact learning will have for employees in the future:

“By 2020, the core skills required by jobs are not on the radar today, hence we need to rethink the development of skills, with 50% of our jobs requiring significant change in terms of skillset”

That is a huge amount of jobs that will require new skills and for organizations and workers that means a lot of learning and developing.”

Why this company implemented a learning sabbatical for its employees, FastCo

Are your skills still relevant?

Depending on when you graduated college, they might not be:

“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.” – Getting Ready for the Future of Work


How was this algorithm designed?

Algorithms are everywhere. They make decisions for us and most the time we don’t realize it. Remember the United story where the passenger was violently ripped out of his seat? The decision to remove that specific was the result of an algorithm.

As more algorithms shape our life we must ask questions like who’s designing these algorithms, what assumptions do these designers make, and what are the implications of those assumptions?

So I’m giving a huge shout out to the podcast 99% Design for their episode on how algorithms are designed.

The Age of the Algorithm

Featuring the author of Weapons of Math Destruction, the episode takes a look at the subjective data used for algorithms that determine recidivism rates and reject job applicants. The examples used and questioned raised in this episode should have us asking more questions about the people and companies designing the algorithms that run in the background of our online and offline lives.

“Algorithms … remain unaudited and unregulated, and it’s a problem when algorithms are basically black boxes. In many cases, they’re designed by private companies who sell them to other companies. The exact details of how they work are kept secret.”

Do AI company founders watch Black Mirror?

“Cameras are no longer just for memories but are fundamental to improving our daily lives – both in our personal and professional lives.” – It’s Coming, The Internet of Eyes will allow objects to see, The Next Web

Read the glowing article above where founders gush over a soon-to-be world in which all inanimate objects have tiny cameras that monitor our everyday movements. How does it make you feel? Is this the first time you’ve ever heard of the Internet of Eyes?

“Similar to the Internet of Things, the IoEyes is a network of cameras and visual sensors connected via the internet enabling the collection and exchange of visual data on a scale unimaginable before.”

This was the first time I’ve heard of the Internet of Eyes (IoEyes) and it’s absolutely terrifying. Equally terrifying are the founders who believe “IoEyes will only have a positive effect on society as a whole.” These guys seem to be clueless about the negative impact these technologies will have on society. You’d think there’d be a second thought on the “trillions of frames of potentially actionable data” they’re sucking up when data breaches are happening at record paces. Or maybe the founders just don’t care because profit&brand. And they’re doing it all to give us a better quality of life, to give us things like better data from our toothbrushing experience:

Imagine performing a simple daily task and knowing what’s going on inside your body.A real-time visual feed of you brushing your teeth will generate not just one visual signal but millions of layers of signals, including analyzing heart rates, blood conditions, DNA structure, temperature, and emotional state.”

Regardless, these founders (and maybe tech journalists) need to take a break from building (and reporting on) the future of surveillance for a bit of Netflix and chill with Black Mirror. Black Mirror is notorious for it’s dark take on how technologies affect society. Their episodes stay in your head way beyond episode. The series makes you rethink the impact of technologies in a visceral way. Every time I read an article like the one above it makes me wonder if any of these founders watch the show.

So my Netflix and chill recommendation for the founders is as follows. Start with the episode, The Entire History of You. Then move on to Nosedive followed swiftly by Shut up and Dance. Throw in the Christmas episode for fun.

Then get back to me about how positive these technological advances are for society.

PS: IoEyes also helping to reinforce those pesky gender stereotypes and support controlling personalities:

“The benefits of biometrics and sensors offer invaluable support. From deterring people from driving when they are too intoxicated, to making sure your teenage daughter isn’t bringing home that boy you don’t like when you aren’t around.” 


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?”

Ellen Pao is such a bad ass it makes my head explode

When I first got the three pages of specs for a chief-of-staff position at Kleiner Perkins in 2005, it was almost as if someone had copied my résumé. The list of requirements was comically long: an engineering degree (only in computer science or electrical engineering), a law degree and a business degree (only from top schools), management-consulting experience (only at Booz Allen or Bain), start-up experience (only at a top start-up), enterprise-software-­company experience (only at a big established player known for training employees) … oh, and fluency in Mandarin.”

That’s Ellen Pao’s career in the elite of the elite from a must-read excerpt of her upcoming book, Resent, which details the intense harassment she experienced at Kleiner Perkins Caufield & Byers.

The excerpt is worth the read in part because it challenges the assumptions we make about women who speak out on sexual harassment. It’s not just a woman who speaks up, gets fired, goes to court, loses, life goes on. Imagine having this happen to you when you spoke up about wrong-doing in your organization:

In response to my suit, Kleiner hired a powerful crisis-­management PR firm, Brunswick. On their website, they bragged about having troll farms — “integrated networks of influence,” used in part for “reputation management” — and I believe they enlisted one to defame me online. Dozens, then thousands, of messages a day derided me as bad at my job, crazy, an embarrassment. 

Corporate. Troll. Farms. Backed by people who have piles money like this:

That’s terrifying.

Ellen Pao is a fighter. A leader. A storyteller. And she’s a damn strong role model for women, especially those navigating those same elite circles.

Cutting through the edtech hype

My Stitcher app is crowded. Week after week I watch all the podcasts that could be slip by, unheard. I have too many favorites and not enough time for all of them. But one episode regularly makes the weekly cut: Leading Lines. Here’s how the podcast for edtech in highered describes themselves:

“We explore creative, intentional, and effective uses of technology to enhance student learning, uses that point the way to the future of educational technology in college and university settings. Through interviews with educators, researchers, technologists, and others, we hope to amplify ideas and voices that are (or should be!) shaping how we think about digital learning and digital pedagogy.”

The short version: they provide a much needed perspective on educational technology in higher education. The result is a podcast that dives deeper into how teaching and learning is evolving alongside new technology. It’s positively refreshing. I’ve learned about second-language learning with wikipedia, new technologies for that enhance engagement in the classroom, and designing MOOCs.

I’ve worked on both sides of the edtech sector: as a vendor and client. In 2010, I did business development for an international startup. I worked remotely for an international student recruiting platform which gave students all over the world direct access to universities. My days consisted of scouring websites for university contacts, pitching administrators on email, following up on leads, demoing the platform, and waiting. Lots and lots of waiting. I loved our product and was out to convince the world of higher education how we were going to solve their problems (or at least North and South America, my territory). The job was filled with equal parts rejection and learnings. I didn’t know the term edtech then; we positioned the company as a social tool as social media was all the new rage. Though the term wasn’t around, I embraced the edtech hype. I believed that technology could solve many issues in higher education (ignoring the fact I’d never actually worked in higher ed at that point). The startup eventually folded.

In 2014, when I started work in career services at Yale School of Management, I was on the other side of edtech as a potential client. I was on the receiving end of a lot of pitches in part because of the brand name. The thinking goes like this: if a company can claim Yale SOM as a client and post our public testimonial they can sway other schools to do the same. We did the same when I worked at a startup. I remember trying to close a Notre Dame deal to score a brand name to dangle in front of future clients. The strategy works. At Yale SOM my director always evaluated new tech starting with: Harvard/Booth/Wharton is using it, so we should take a look.

In the beginning I had much empathy for sales teams whose emails I regularly ignored. I was ridiculously busy. The emails and requests for time were competing with ambitious students and a department that loved emails and meetings with equal fervor. Occasionally an email would break through (the power of follow ups!) and I’d chat. But the empathy faded over time as I experienced the worst of edtech sales:

  • Vendors insisting that their dashboard would solve all my problems without actually listening to my problems
  • Vendors who insisted on following their script. Once a person launched into a lengthy explanation on the basic concepts of data collection, ignoring the fact we were an MBA career services office which collects and tracks data on every student for mandatory reporting purposes.
  • Vendors insisting on demos when the product had no fit in our department
  • Vendors pushing to move forward despite my statements that I made zero decisions and didn’t control the budget – I was merely an internal lobbyist and would advocate where possible.
  • Vendors casually ignoring my questions at conference booths until they saw Yale on my badge; then it was all ears and smiles. (I know it happens and I know how boring booth work is but the frequency in which it happened was so disappointing).
  • Vendors ignoring the platform fatigue issue in our department (at one point I had students using 6 platforms and even I was tired of platforms).
  • Vendors with no understanding of UX, a particularly large red flag considering we’re dealing with learning outcomes. If you don’t understand users, how can you support their learning outcomes? Grad Leaders is the worst offender in this case, despite their prominence in the market.

These are the worst offenders of course. To be fair, edtech sales is rough . Decision-making in higher education is opaque. You don’t know who makes the decisions and when. Sales cycles are notoriously long compared to the private sector. Rejection is almost a relief compared to the non-responses. I look back now at some of my sales emails and I cringe. I was definitely a shitty edtech sales person at times (thankfully I’ve improved).

Now I read most edtech coverage with a critical eye. I wonder: did they talk with users before creating their solution? Is their solution based on a real problem? How are users benefiting from this technology? So when I read the edtech news at EdSurge Highered and CB insights I like to balance it with the Leading Lines podcast. I’m also a fan of Hack Education Newsletter, a comprehensive yet critical take on edtech news (and policy).

My relationship with edtech is always evolving. I’ve flipped sides again, having launched a company in the edtech space and pitching universities. But having a critical perspective keeps me grounded as I build and pitch. Podcasts like Leading Lines remind me regularly to consider both the learner’s and administrator’s perspective when designing for education.