Today I tried the Google trick to read a WSJ article, Seven Jobs Robots Will Expand, whose title is clickbait for future of work people like myself. Most of WSJ is behind a paywall but normally you can access an article through a simple Google search. But it turns out WSJ closed their Google loophole some time back. In the course of researching why they did that (to get more subscribers obvi) and new methods to get around the paywall (there aren’t any) I found something far more interesting. WSJ has applied a machine learning model to predict whether or not you’ll subscribe to their paper. Based on that score they’ll decide whether or not to show you the article you requested. Visitors are a categorized into hot, warm or cold. More on this move from NiemenLab:
Non-subscribed visitors to WSJ.com now each receive a propensity score based on more than 60 signals, such as whether the reader is visiting for the first time, the operating system they’re using, the device they’re reading on, what they chose to click on, and their location (plus a whole host of other demographic info it infers from that location). Using machine learning to inform a more flexible paywall takes away guesswork around how many stories, or what kinds of stories, to let readers read for free, and whether readers will respond to hitting paywall by paying for access or simply leaving.
This is wild. I’m off to go play with new browsers to see if I can get that clickbait article (this is the only time I ever use sad Safari).
What tweaks could we make to the college curriculum that would help students prepare for the changing workforce? This quote from the article, The Global University Employability Ranking 2017, at the Times Higher Education, offers a clever solution:
“The way organisations have to work these days needs to be very fluid. In that kind of world it is important to have people who are really flexible, able to create networks within their organisations and very comfortable working in virtual teams and particularly [what we call] leading beyond authority: not necessarily having to get things done because they are in a team that has a boss,” he says.
But he is “not sure” that the implications of this are “well understood by the academic world and, therefore, when we throw a new graduate into [work] it can be quite overwhelming [for the graduate]”. One solution, he suggests, is for university courses to have more group projects, with assessment focused on the process that the participants go through, rather than the outcome.
Flourishing in such an environment requires “reflection and understanding”, and especially learning from mistakes, Saha says. He is sceptical that this aspect of professional competence is well explored in universities currently, but “in the working world, that is the bit that can be make or break”.
He’s spot on in his assessment and solution. Focusing on group work and assessing participants on their process, instead of outcomes, could go a long way to help students identify their strengths, weaknesses, and improve their leadership and collaboration skills. What really struck me in that sentence is that focusing on process, rather than outcomes, is the opposite of American business culture. American learning and working culture is focused specifically on outcomes – we’re obsessed with assessing programs. Managers evaluate employees based on their results, not collaboration.
I’ve never in my work life been on a team that was evaluated on how well they worked on a project together. It’s almost a revolutionary suggestion.
Hybrid jobs are all the rage currently and are some of the top paying jobs in the market right now. If you’ve got soft skills, business acumen, and technical skills, you’ve got the ticket to a high paying job.
Hybrid roles are super interesting to follow because they are so new. Their descriptions and responsibilities differ from one organization to another. This is particularly the case with AI interaction designers, a emerging job category I’m paying a lot of attention to lately (in part because I’m slightly obsessed with chatbot design as of late.) Diane Kim, who designs the friendly virtual assistant bot at x.ai, summed up this emerging field in her interview with Wendy and Wade, a career advising chatbot:
“The fact that AI Interaction Design is so new gives me the freedom to be experimental. I also have the unique opportunity to be part of defining an entirely new field. This is actually both what is most exciting and most challenging about my job…But it’s challenging because none of us really know what this is yet — we’re all figuring it out together. It’s really different from, say, being a recent grad in your typical UX role for a visual interface, with decades of research and best practices to follow. We don’t have the same industry standards or guidelines yet for conversational design, but the fun part is figuring them out as we go.”
So it’s within that context that I examined this AI chatbot writer role from JustAnswers.
The skill requirements on this role are massive. Let’s break it down.
You need quantitiatve and qualitative skills
You need to be a seriously good at writing (perfect tone!)
You need to understand Sales (identify (and contribute to?) revenue opps!)
You need be an experimenter – test and retest
You need mad research skills
You need the collaboration skills to work with diverse teams
You need to understand user experience
You need to dive into professional fields that requires years AND be required to anticipate which quesitons users would ask AND write the answers.
This is one hell of a robust skill set. That last ask – expert with diving into deep professional fields like medicine and law – really threw me off. Who is this person? And will you pay them a shit ton of money for this expertise and skill set?
It’s likely this job is like most job postings: crammed with all the ideal things. There is probably flexibility – an applicant doesn’t have to have all those things.
I’m curious about how much this role pays because writing is an underpaid profession. Some managers who don’t write assume it’s easy – after all they write emails and reports! Copy is everywhere and people assume it’s easy to produce. Thoughtful copy – the kind that strikes the perfect tone! – takes time and creativity to produce. People in quantitative fields tend to overlook that.
But bad writing, especially in AI conversation design, leads to awkward interactions with the product. For example this was my recent convo with a new recruiting bot Robo Recruiter:
If writing is underpaid but AI is a hot hot hot field, how much should we be paying our AI chatbot writers?
I’m crowdsourcing your answers below in the comments: how much do you think this job pays? Do you think it pays as much as a machine learning engineer? As a product manager?
I’ve been obsessed with the podcast Note to Self ever since I heard about their Bored and Brilliant challenge, a challenge to get people off their phones and think creatively (I appreciated the one small observation challenge as I ride a lot of public transport and it was a fab way to pass the time). It seems almost cliche to talk about the impact technology has on our lives now; we’re all aware of it. But that awareness has made us less likely to talk about it (or maybe it’s just because we’re less likely to engage in conversations in general because of phones and technology.) Note to Self is the public conversation about how we as individuals and society engage with technology. It’s not judgy or preachy. It’s more observation and discussion. The topics stay with you post-episode. As I scroll through endless family phones on Facebook/IG, I constantly think about the episode, What to Think About Before Posting Family Photos. I can’t link to the episode so here’s the excerpt:
We asked how you share personal photos. Here’s what we learned from your 1,200 (!) answers. Psychologist Guy Winch joins Manoush to untangle our mixed posting emotions. Because our grams are complex. A trans listener is thankful his parents didn’t post during his teen years. A mom doesn’t understand her daughter’s online brand. A son wishes his dad included him in family snapshots. Nothing is just a pretty picture. Plus, the wonderful Charlotte Philby, former editor of Motherland magazine. Her family posts were part of her “brand” – until she stopped gramming cold turkey
Two weeks ago, Note to Self did a brilliant week featuring “Women Owning It Online.” The line up of people was diverse. The conversations fascinating. The host, Manoush Zomorodi, talked with YouTube influencer Lele Pons (who has over 20 million subscribers!!!), the talented Transparent star Trace Lysette, the artist Amy Sherrard (who painted Michelle Obama!), bad ass foreign correspondent Christiane Amanapour, the artist Barbara Kruger who oozes a gives zero fucks charm, and gif designer/artist (what a job!) Jasmyn Lawson.
I can’t seem to link to any of the individual episodes but just download them all in your favorite app. The interviews are motivating and perspective-shifting. They’re also brilliant escapism from the endless (doom-filled) news cycle.
Brent (a pseudonym) is 52, his youthful appearance the result of rhinoplasty and a modified lower face-lift. He took a week off from a previous job to get the surgery. “Knowing I’m going back in to fight for another two or three jobs and that I’m going to be surrounded by a bunch of thirtysomethings,” he says, “my take was: I don’t have a problem looking 10 or 15 years younger than I am.” – The Brotox Boom: Why More Men Are Turning to Plastic Surgery
The term career seems so quaint when you read a phrase like “fight for another two or three jobs” from a 52 year old. Age discrimination is real and I feel for these bros or anyone who has to compete with 30 year olds to remain relevant. Maybe we shouldn’t fear the robots so much as the youth.
And maybe tech should start including plastic surgery as part of the benefit package to help them win the war for talent.
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.
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?
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
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.
I’m a liberal arts grad. I love words and language. I teach soft skills. Qualitative data is my jam. I’m also obsessed with machine learning (ML) and artificial intelligence (AI).
In 2015 I tumbled down the AI rabbit hole after discovering a long read on the fabulous site Wait But Why. The site explains complex ideas paired with hilarious stick figures. The two part series on AI, The Artificial Intelligence Revolution, was my gateway article to the world of AI, and later ML as part of AI.
So far my self-directed learning journey has only included reading about AI and writing about its affectonhiring and the future of work. I can’t code in Python (with zero plans to do anything with R). My data background includes data analytics, cleaning data, and putting it into Tableau but nothing close to data scientist. I also have no interest in going that far professionally. As a non-tech person trying to access ML/AI, it’s been a challenge to figure out where I fit in. I’ve uncharacteristically avoided meetup groups or conferences on the subject since I don’t have the tech skills.
Not me.
Last month I changed that. I got tired of reading. I wanted idea exchanges. So I attended a ML/Al unconference in PDX. And hot damn I found my people!
An unconference is the opposite of the standard conference setup. Instead of corporate-sponsored keynotes paired with bland chicken and an abundance of shy speakers who read PowerPoints, the participants chose the content. We pitched and voted on what they wanted to talk about. The result was facilitated conversations about subjects we were curious about and a format that flowed. It was the ideal setup for idea exchange and learning.If you’re conference weary an unconference will restore your faith in professional development.
Many people at the unconference were data scientists or computer scientists, and some working on ML projects. A few were students or job seekers. I met one other person who is like me, a communications expert without a technical background who works for a machine learning platform, BigML (and they’re doing rad stuff).
In our sessions we covered a roving range of topics about ML/AI: novel data sets, making AI more accessible to the masses, establishing trust with users, data security, AI decision making re: self-driving cars and the Arizona accident, becoming a data scientist and machine learning engineer, the future of companies and jobs (my pitch!), learning ML/AI as a new person (do you learn the math, the code, or find a project first? plenty of debate on this!), and plenty more side conversations that spilled out of the main sessions.
As an non-tech outsider it’s a bit intimidating to participate in such a cutting-edge tech space. I think ML/AI people forget that at times. One of the guys I met at the conference noted that when you’re an expert it’s hard to remember how hard it is for others to start in your field. I’ll add that this goes double if you’re in a quant and code heavy field like machine learning. Luckily most everyone at the unconference made it easy to participate (as did the unconference format).
My main takeaway though is that you don’t need to be a software engineer, data science expert, or code wizard to understand ML/AI.
So for all the people who are curious about ML/AI but don’t know how to start engaging in these communities, here’s how.
Learn the basics: Know the difference between machine learning and AI; understand the difference between Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence; understand the basics of data science. There are no shortage of intro articles and videos on the subject (two examples below).
Prior to the uconference I was slightly worried I’d be left out of the conversation if it turned to technical. I prepared by returning to a set a YouTube videos I’d skimmed a while back: Fun and Easy Machine Learning. The YouTube list animates over 15 models to better understand machine learning.
Ignore the math and coding right now: Unless you want to become a data scientist or machine learning engineer, ignore it. You don’t need it to understand the basics or to explore products or impacts of ML/AI. For example, the Fun and Easy Machine Learning series sometimes dives into the math behind the models. Treat it as you would a foreign language; when you don’t the meaning keep moving forward and focus on what you do understand. Fill in the blanks later.
Read everything about ML/AI in the area you’re interested in. ML/AI for non tech people is a huge field. So narrow it down. Start with general articles about artificial intelligence and learn about it’s expected impact. The World Economic Forum has good articles with a global perspective. For business impacts, check out this history of ML/AI technology by industry/verticals. Then head over to CB Insights to study ML/AI companies (and subscribe to their newsletter as they’re cutting edge everything). Then pick an industry that interests you. Either one that you work in or one that you want to work in. Read everything you can about how machine learning is affecting that industry (it’s affecting all of them – right now finance, healthcare, and insurance are some of the industries talked about the most.) Explore products and platforms in that industry that use ML/AI. Read case studies. I study the future of work. So I read everything I can about ML/AI and it’s affect on workers and organizations: McKinsey, AXIOS, MIT, plus I play with HR Tech.
Avoid the hype. It’s easy to get caught up in the shiny promised of AI. Instead, pay attention to counter narratives, often published outside of the tech reporting ecosystem. Find the counter narrative about AI in your field. I read the amazing research and work by Audrey Waters at Hack Education for a counter narrative to AI edtech hype. Explore bias in ML/AI. Understand how AI isn’t neutral and that gender and race bias is coded into AI systems. Weapons of Math Destruction is an excellent book (and 99% Design has a good podcast on it). We need diverse perspectives and people in ML/AI fields to fight these bias, and non-technical people are part of that fight.
Take a course: FutureLearn, an online learning platform with a name after my own heart, offers an Intro to Data Mining course where you’ll learn the basics of classification algorithms. It’s a smooth intro to applied machine learning. They also offer an advanced course to build your skills further.
Go to an event and talk to people: This is the intimidating part. But get over it, embrace the awkwardness, and commit to asking curious questions. Remind yourself of the things that you know. Write down the things that you want to learn. Talk to people until you get the answers to your questions. Ask people how they got into their work, what impact they’re having, and how they’d explain their work to a non tech person. Tell them you’re curious. Some people will just talk at you. Others will teach you. Keep in touch with the people who teach you and simply move on from the ones who talk at you.
Get a project: This builds on not worrying about the math and coding. Instead, get a project. What problem do you want to solve? What problem does your organization need to solve? What data is available? What data is missing? How could ML/AI solve your problem? Starting there will help you lead you in the right direction. You might not have an answer right away. That’s ok. It make take a while to solve it. But that’s the point. You’re learning. Ambiguity is part of the process. So ask around your workplace. Visit the data science or computer science team in your organization (assuming you have one). Find a data scientist in your network or at ML/AI events and ask them how they’d solve your problem. Ask them to break it down. Ask a computer science student what they think.
Start with curiosity, ignore the part about not having a technical background, and see where it takes you.
Universities are now collecting loads of data on students from physical whereabouts, to courses progress, to when they get online, to even what they do when they’re online.
The president of Purdue penned an op-ed to challenge higher education (and hopefully edtech) to think critically about how we use students’ data especially when it comes to behavioral nudging, lest we end up with a Chinese-like social rating system:
Somewhere between connecting a struggling student with a tutor and penalizing for life a person insufficiently enthusiastic of a reigning regime, judgment calls will be required and lines of self-restraint drawn. People serene in their assurance that they know what is best for others will have to stop and ask themselves, or be asked by the rest of us, on what authority they became the Nudgers and the Great Approvers. Many of us will have to stop and ask whether our good intentions are carrying us past boundaries where privacy and individual autonomy should still prevail.
