Employees who are already living the future of work

Curious about how AI technology might change your job? The NYT offers a glimpse at how algorithms are changing traditional roles. In retail, fashion buyers who are normally tasked with making purchasing decisions, are increasingly using algorithms to do the task. These algorithms make fashion decisions and predict the next big trend, a task normally associated creative geniuses. With so much consumer data, predicting trends and stock levels is left to the machines, no intuition needed.

“Retailers adept at using algorithms and big data tend to employ fewer buyers and assign each a wider range of categories, partly because they rely less on intuition.

At Le Tote, an online rental and retail service for women’s clothing that does hundreds of millions of dollars in business each year, a six-person team handles buying for all branded apparel — dresses, tops, pants, jackets.”

The result is two-fold: the industry is using fewer buyers in the decision-making process and retailers are increasingly hiring people who can “stand between machines and customers.” The article notes that there are plenty of areas where automation can’t do the job. Negotiating with suppliers, assessing fabric transparency, and styling all need a human touch.

Instead of replacing all the humans, algorithms are changing how we work.  As a result, future roles (and managers) will demand employees who understand understand how to use algorithms to make decisions that improve the final product, while also understanding the limitations of the technology.

In the future of work (which is already here and we need a better phrase), we’re going to need a lot more of these employees.

Do employers care about your online certificate?

Recently I came across a certificate in Higher Education Administration from Northwestern. For $19,975, I can “deepen (my) understanding of the field and expand (my) networks.” Details on career outcomes or paths are notably absent. Instead the page offers the basics of college career services: “access to ongoing professional development support, one-on-one career coaching, academic advising and networking opportunities.”

The certificate reminded me of a Northwestern ad I saw last year promoting a $10,000 global mobility online certificate. The ad was marketed towards future international education professionals. As someone who has worked in international education for over a decade, I got a bit riled up. Aside from the fact that you don’t need a $10K certificate to get a job in international education, the program’s career preparation promises were lackluster at best. The lack of testimonials from employers raving about the certificate, or explaining how the certificate signaled a candidate’s competitiveness, was telling.

Despite my frustrations with certificates with lackluster career promises, I recognize the role certificates play as career paths and institutions adapt to changes in the market. Certificates are revenue generating programs which help institutions shore up revenue from diverse sources. Certificates also provide an attractive option to employees who want to upskill or change careers. They usually take under a year to complete. Certificates are frequently associated with a university brand name. While affordability varies by institution and certificate program, they’re cheaper than a full degree and they qualify for financial aid.

However, data on career outcomes from non-degree credentialing – i.e. certificate holders – is hard to come by. Employers’ attitudes towards certificate holders are difficult to pin down, which makes it hard to know if certificates hold their value in the market or even determine the ROI on a $20K certificate.

Thankfully we’re a bit closer to understanding employer attitudes to non-degree credentials thanks to a new report by Burning Glass Technologies. A recently released report, The Narrow Ladder: The Value of Industry Certifications in the Job Market, examines how employers use certifications (not certificates) in the hiring process. Using their vast database of over 700 million job postings, Burning Glass Technologies examines the types of certifications that employers value, along with the skills and salary bump employees receive post-certification. It’s well worth a read for anyone who advises students or mid-career professionals about their upskill options.

“It’s not that the “non-degree” credentials are rare; more than a quarter of the employed U.S. population holds a license or certification, on top of any degrees they may hold. Certifications can be precisely tuned to industry needs, and they hold the promise of reducing the need for employers to rely on imperfect proxies, like college degrees. In certain occupations, certifications outline career ladders that define industries and give employers and job seekers alike guidance about what skills are necessary to advance.
Those occupations, however, are the exception, and if the nation is to close the skills gap, perhaps they should become the norm.”

Though the report focuses on certifications, its analysis provides material for examining certificate programs as well. Most importantly, it provides a clear difference between between certifications and certificates. The report examines employer attitudes towards certifications, which are “awarded by a certifying body, often an industry association or trade group, based on an examination process assessing whether an individual has acquired the designated knowledge, skills, and abilities to perform a specific job.” This differs from certificates, which the report defines as “short-term, professionally oriented credentials awarded by an educational institution (as opposed to an industry body) based on completion of specific coursework.” 

This distinction is important since few people outside of mobility circles realize the difference. There is a critical difference between these types of upskilling. With such similar terms an employee looking to upskill could be forgiven for thinking a university certificate in higher education administration will provide the same signal to future employers and salary bump as a CISCO Cisco Certified Network Professional certification (it doesn’t). The former is a revenue generation program from a university with little focus on skill building and an unclear career trajectory. The latter is an industry-approved career training model with clearly defined career paths.

What struck me most from this report was the role that certifications played in outlining both the skills and career paths that job seekers and employers agree on. Certifications are built from industry needs. Here’s an example of the skills needed for a AMA Digital Marketing Certification:

Are university certificate programs mapping their content offerings to industry needs? Maybe but we don’t know. The report also finds that employers value certifications that improve technical skills. Do employers feel the same about certificates? Hard to know.

On top of that, the report finds that employers vastly prefer certifications over certificates.

In 2015, the demand for certifications is approximately 1.5 million job postings, whereas only about 130,000 postings ask for certificates.

Is it possible that employer demand for certificates aren’t as in-demand as universities promise? Again, we don’t know, but this stat and the lack of employee perspectives in program marketing for certificates is telling.

Among the most important takeaways from the report, however, is this nugget:

Relatively few certifications actually have market value, and there is a shortage of easy-to-find information to sort out which credentials are pathways and which are blind alleys. More transparency in the certification market can significantly improve the returns people receive on their certification investments.

Finding out which credential pathways are legitimate is difficult. I’d argue the same for certificate programs. Will a certificate in higher education administration make a candidate more desirable than a candidate with 5+ years working in higher education? Will a certificate provide a salary bump or launch a job seeker into a more senior role? Will a certificate ensure the skills learned are still relevant in the next five years? The lack of this data makes it tough to answer these questions.

Since we don’t have those answers yet, it’s up to the job seeker/future certificate student to ask the hard questions before taking on a certificate. So for job seekers thinking about getting any certificate – online or in person – ask yourself these six questions before committing:

  • Does this certificate add to or improve your technical skills?
  • Does this certificate put you on a path to a hybrid job?
  • Does this certificate map to industry needs?
  • Does this certificate frequently appear as a requirement in your future job posting?
  • Will this certificate give you a salary or title bump? 
  • Will this certificate be relevant in five years? 

If you can’t answer these questions on your own or through a Google search, ask admissions. You’re investing in a certificate; it’s perfectly fine to ask about career outcomes. Ask to speak to participants in the program (don’t rely on testimonials). Look at LinkedIn profiles of certificate holders to understand their career paths. If you don’t get a clear answer, consider other options that are either cheaper (i.e. MOOCs), bootcamps, or certificate programs that detail the results.

Employees will need to upskill throughout their career. Certifications and certificates are one of many paths to do so. To make sure they’re actually beneficial to job seekers, we need a lot more data like the recent report from Burning Glass Technologies.

Higher education should do their part by ensuring their certificate programs bring career outcomes data – or employer perspectives towards their certificate – to the forefront of their marketing and information websites. Because right now career outcomes from all these certificate programs basically look like this:

Certificate programs career outcomes page

Want job security? Become a data translator

In my last role I talked with MBA recruiters about their hiring needs on the regular. When I asked what they were looking for in a candidate the most common answer was: people that can work with data. The need for data-savvy candidates spanned industries and roles. An MBA doesn’t guarantee someone has experience working with data. At the time MBAs were still trying to upgrade their curriculum to include this skill. Yet overwhelmingly hiring managers wanted people who understood how to work with data. These conversations happened in 2016. Now the need is even greater.

Data powers modern organizations. Your ability to identify relevant data, evaluate it, work with it, and communicate what actions to take based on it, is crucial to staying relevant in the business world. And this isn’t just for MBAs – this goes for anyone working in a business organization.

Thankfully you don’t have to be a data scientist to work with data. There are plenty of data-based opportunities that aren’t as hardcore as a data scientist. Some of those opportunities are summed up nicely in this HBR post, You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role

Companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators.

Data translators are exactly what they sound like: people who can translate data into meaning. These are the employees who bridge the “technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers.” They’re natural communicators and collaborators. They adapt and understand business goals across teams. Data translators have major soft skills with a solid foundation in analytics. They’re are also highly employable. IBM estimates that by 2020 over 2 million analytics roles will need to be filled. Those organizations are going to need a shitton of data translators.

According to the HBR article above, the best hires come from inside the organization. This means you’ve got a chance at positioning yourself for this future-proof role.

If you’re not using data in your current job you have two options: find another role so your skills remain relevant or create your own data translator role within your department. This is a new, evolving role. Data translators may not currently exist in your organization. Or they may exist but operate under a different job title.

Prepare for the role by exploring opportunities inside your organization to work with data. Get to know your data science team (if there is one). Start a conversation with your boss about your involvement in data-driven projects. Ask about the departments goals. Ask which data is already analyzed and used to support business goals. Identify which data-driven projects exist on your team and then find a way to get involved or at least shadow the project. Create your own data viz project by watching YouTube videos about Tableau and using relevant data from your department. Present to your team about your findings. Then identify a department that you collaborate with regularly. Get to know their business goals and how they work with data to make strategic decisions. The ideal data translator works seamlessly across departments. Getting to know the people in other departments – as well as their business goals – will position you well for any data translation job. Also, you can supplement all of this with online courses. Coursera and FutureLearn have excellent options.

Your ability to work with data is a must-have skill. You need it if you want to move up. But you also need the skill to ensure your relevance in the next 5 years of workplace evolution. If you don’t have the skills and experience to work with data this is the time to start upskilling and adding data analytics to your skill collection.

Don’t trust employers with your career plans

Here are two brutal quotes from an Axios post reporting on executives’ attitudes towards general pay raises and employee retraining. There were made during a conference for CEOs titled “Technology-Enabled Disruption: Implications for Business, Labor Markets, and Monetary Policy.”

“Executives of big U.S. companies suggest that the days of most people getting a pay raise are over, and that they also plan to reduce their work forces further.”

Damn. And then:

The moderator asked the panel whether there would be broad-based wage gains again. “It’s just not going to happen,” Taylor said. The gains would go mostly to technically-skilled employees, he said. As for a general raise? “Absolutely not in my business,” he said.

The CFO of AT&T also said that he doesn’t have a need for so many call center employees or guys that install their cables.

The message is pretty clear: employers don’t need you.

The idea that employees should be loyal to companies is a hold over from traditional career narratives. We’re still waiting for old school career narratives to catch up the present reality of work. But in the meantime it’s a good reminder that companies aren’t looking out for your best professional interest. Waiting for your employer to give you a raise, direct you to the next step, or reward you for your hard work – that’s not going to happen. Instead, it’s going to be up to you to figure out your next move and make sure you have the skills to get a pay upgrade. Don’t expect your employer to do it.

Your job search is becoming less human. Here’s how to adapt.

Imagine you’re a job seeker looking for a job. You submit your resume to a company’s website.

Your resume is scanned by AI that is evaluating your resume against the job description. It’s also comparing it to the employer’s existing database of current employees’ qualifications. Your new format and design don’t matter. Neither does your keyword stuffing to beat the ATS.

The algorithm also pulls in a few publicly available data points about you, like your social media profiles. It scores you based on that data and your resume. Your score puts you above the competition. Your resume isn’t reviewed by a recruiter.

Next you get a text on your phone. It’s the company and they’re asking if you have time to answer a few questions. You answer a few basic questions about your professional experience and interest in the role. You realize it’s a chatbot half way through but you’re just happy to avoid the awkward phone interview.

You make the cut again. You receive an automated email with a link to an online video interview platform and instructions. You record your answer to common behavioral questions. It’s awkward to stare at yourself on the screen. There are no visual or verbal cues to see how your answers land. Your responses are recorded. An algorithm analyzes the video, reviewing your micro expressions and looking at 25,000 possible data points to evaluate your personality and fit within the company. Your video response is scored by the algorithm.

Then you get an email from the recruiter. You’ve passed all the steps. They’d like to invite your for a day in the life experience at their company.

The visit is the first and maybe the last opportunity you’ll have to interact with a person in the entire search.

Back to reality. The situation above isn’t totally hypothetical. It’s the new job seeker reality. Companies are adopting HR tech that uses AI and automation to make the hiring process more efficient. It’s also making the hiring process less human. In 2017 Companies like Unilever and JP Morgan started automating their hiring process. Here’s what hiring looks like at Unilever:

Candidates learn about the jobs online through outlets like Facebook or LinkedIn and submit their LinkedIn profiles — no résumé required. They then spend about 20 minutes playing 12 neuroscience-based games on the Pymetrics platform. If their results match the required profile of a certain position, they move on to an interview via HireVue, where they record responses to preset interview questions. The technology analyzes things like keywords, intonation, and body language, and makes notes on them for the hiring manager. All of this can be completed on a smartphone or tablet.

If the candidate makes it through these two steps, they are invited to a Unilever office to go through a day-in-the-life scenario. By the end of the day, a manager will decide whether they are the right fit for the job.

A fundamental shift in hiring is under way and it’s powered by machine learning. From resume screening powered by AI to interview chatbots to predictive analytics that determine who’s most likely to leave a job, the list of startups transforming the hiring process is long. Over half of HR tech investments in 2017 went to companies offering products and services powered by AI.

For example, Entelo is an AI recruiting platform that uses machine learning to determine whether you’re a fit for their organization. The company’s knowledge base provides a few hints on how the AI will score you:

All of these new HR technologies are changing how you get hired for a new job. To succeed you must learn the new tools and adapt. Here’s how to start.

Don’t stop at resumes 

HR tools are evolving to evaluate you on more than just your resume. Tools like Entelo assess your social media data as part of your potential employability:

AI Recruiting on Entelo

The more work and knowledge that you share online will benefit you. So start by producing small bits of content. Create a personal website, show off a portfolio online, write small blog posts, or share articles related to your professional interests so you can be found, and evaluated, online.

Be curious about HR technology 

Explore the range of new HR technology that’s being used in the hiring process. Get curious about how these tools are used. Check out new AI tools to help job seekers. Tools like Jobscan and VMOCK are valuable resources that use machine learning to help your improve your resume. There’s even a promise of a chatbot to help you navigate your career (though it seems like it’s in permanent secret mode, so no proof that it actually does that yet).

Next research which companies are using AI technology for hiring so you can prepare accordingly. Right now large companies with large resume volumes are the ideal customers for automation tools. Smaller businesses and startups aren’t there, yet. Most HR tech products list which companies use their services. Before you apply, email a recruiter or ask a current employee about their hiring process so you know up front whether you’ll be engaging with a machine or a human.

600+ companies in 140 countries use HIreVue.

Ask hard questions about AI and HR technology 

This tech is brand spanking new. There are plenty of ethical questions about the use of AI and reinforcing bias in recruiting that need sorting out.  Job seekers can start asking hard questions too. Sometimes it’s as simple as asking how.

How do these platforms reinforce existing bias? How is current employee data used in the hiring process? How are candidates being scored by the algorithm? How are candidates screened out of the process? How do candidates rank if they don’t have online profiles or publicly available data for algorithms to find? How can a job seeker beat the AI system? How much do recruiters and hiring managers trust their AI systems?

Then ask yourselves the hard questions:  Are you getting all the information you need in the hiring process – company culture, opportunity for growth, management styles –  to make an informed decision? Does an automated candidate experience make you more or less likely to want to work for a new company?

Become an actor

One question they get frequently, said Lindsey Zuloaga, director of data science at HireVue, is if an applicant is able to trick the A.I. Her answer: “If you can game being excited about and interested in the job, yes, you could game that with a person as well,” she said. “You’re not going to game it without being a very good actor.”

To succeed in the future of work you need emotional intelligence. As more employers delegate emotional intelligence screening to automated tools you need to ensure you’re expressing that emotional intelligence. Start by recording yourself so you know how you look, talk, and express yourself on screen. Pay attention to your tone, body language, facial expressions. Learn how to build your soft skills to improve your emotional intelligence. Then consider taking a few acting or improve lessons to get comfortable expressing yourself.

Cultivate those professional relationships

Interestingly none of the articles praising new HR Tech have addressed internal referrals, the secret sauce to getting noticed in the job search. Will recruiters eschew a recommendation from a human in favor of their AI scoring system? Do AI hiring platforms incorporate internal recommendations into their scoring model? We don’t know. So for now we can assume that internal referrals via professional relationships might be a way to beat the AI system ( or at least, get around it). More importantly though those professional relationships will take on greater importance the more automated the hiring process becomes. Conversations with people inside of companies give you valuable insights and a feel for company culture, making up for the insights you lose in an automated process.

Sharpen your persuasion skills 

We’re not in a fully automated hiring process (yet). Job seekers still have a chance to engage with humans during their search. But the hiring process is evolving and making some career advice outdated. When you finally get in front of an employer it might not be what you expected (i.e. those behavioral interview questions you memorized might not be so helpful).

However one thing won’t change: once you are in front of a human you still have to persuade them that you’re the best person for the job. Your job search is act of persuasion. Learn the new automated systems and then focus on building your persuasion skills. Reflect on what the companies needs and how you meet that need. Learn how to tell an engaging professional story that connects your interests to your future team’s needs. Show employers your intellectual curiosity and passion as you ask questions about the role.

In the end, we need to all pay attention to the way hiring is changing. With millennials looking at a lifetime of job hopping, we’re going to have adapt fast to new hiring processes. As this article so cleverly points out, those “first impressions so carefully emphasized by career coaches are now being outsourced to artificial intelligence.”

So join me over at FutureMe School as we rethink how job seekers engage with employers. We’re smashing traditional career narratives and we’d love for you to be part of it.

More career advice like this please

There’s a lot of bad career advice masquerading as good advice. Much of it stems from outdated notions about careers. Advice like “stick with a job at least two years” and “don’t job hop, it’ll hurt your resume!” is meant for old school careers where companies invested in employees. It was meant for a time when people stayed with companies 5, 10, even 15 (!) years.

This advice is dead wrong.

It keeps people in miserable jobs.

And there’s no need for it in the new world of work.

This perspective was most expertly summed up in the tweet thread below:

If you’ve got a bad manager or work in a toxic environment, leave. I don’t care if you’re two months into a new job, if you have the means to leave, gtfo. Don’t waste your time because it’ll look bad on your resume. Don’t stick with it to tough it out. It’s not worth your time or sanity, especially if you’re earlier in your career. It’s totally ok to make a mistake. (Note: not everyone has the means to escape; this is advice for those who do)

Instead, put all your energy into leaving asap. Build a story that explains the honest reasons why you left (bad work culture is a perfectly ok reason to leave). Build relationships with people inside companies that are known for having good work cultures. Learn what you like in a manager. Ask people what their managers are like during careful informational interviewing. Read Glassdoor reviews.

But don’t stay at shitty jobs just because of the fear of being perceived as a job hopper. With the number of workers who work in the gig economy, the increase of job seekers with side hustles, a tight labor market, new job types, there’s a lot more fluidity in your career. Employers can work with job hoppers. It’s not worth it to stay.

So hey, if you’re in this position, start plotting your escape.

How much should this AI Chatbot Writer job pay?

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.

Chatbotjob Chatbotjob

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?

Write your answer below.

Then see what Paysa pegs the going salary rate in San Francisco.

 

The future of work: Plastic surgery for tech bros

 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.

Artificial intelligence is going to wreck your carefully planned career

Yesterday I presented to a group of undergraduate students at PSU about FutureMe School 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.

How to learn about ML/AI if you don’t have tech skills

Art by AI

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 affect on hiring 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).

Here’s a helpful Quora answer about the differences between a data scientist and a machine learning engineer. 

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.