It was only a matter of time

Students are looking for ways to beat AI recruiting tools like HireVue. And now coaching services are offering help:

“A start-up called Finito claims it can coach candidates to beat AI for as long as it takes them to get a job — but at a total cost of nearly £9,000. Candidates are steered through interview dry runs and get tips on what skills are needed to get past robot selections, in sectors including finance, public relations and the arts. They then watch footage back to spot foibles that could be flagged up as nerves.”

Add helping students beat the AI recruiting process to the list of things career services needs to upskill.

beat AI recruiting

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.

WSJ now predicts whether you’ll subscribe to their site

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).

Preparing students for a fluid workplace

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.

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.

 

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.

Start upskilling for AI now

In 2017, roughly 70,000 postings requested AI skills in the U.S., according to our analysis of job postings. That’s a significant change, amounting to growth of 252% compared to 2010. Burning Glass also found that demand for AI skills is now showing up in a wide range of industries including retail, health care, include finance and insurance, manufacturing, information and professional services, technical services, and science/research. – Burning Glass Technologies

I’ve been seeing AI skills pop up in random job posts. I’ve wondered if it’s part of a bigger trend. It’s hard to get perspective since I’m not in the job market. Amazon leads the hiring for AI skills by a mile but GM, Accenture and Deloitte are also investing heavily. The most in-demand AI skills:

software developer/engineer, data scientist, data mining/data analyst, data engineer, computer systems engineer/architect, medical secretary, systems analyst, product manager and business management analyst.

Medical secretary threw me for a loop. Maybe because they’re working with new AI medical technology? Regardless it’s time to upskill.

 

AI for the doctor’s office

SmartExam acts as a virtual physician’s assistant – an automated medical resident, if you will – that enables primary care providers to deliver efficient remote care while cutting costs and improving outcomes… The intelligent software dynamically interviews patients, using answers to garner more information and support providers in the care delivery process… SmartExam lets providers achieve as much, or more, in a two-minute virtual patient visit as the 20 minutes of provider time needed for an office visit, the company said… “It allows clinicians to operate at the tops of their licenses,” said Constantini. “They can focus on what they do best — diagnosis and treatment.” – Bright.MD raises another $8M for “virtual physician’s assistant” SmartExam

I wonder if current medical students are taught how to integrate AI software into their training.

Harsh words, harsher realities

“You’re out of time. If you can’t already write a piece of code to find the longest palindrome in a string, you probably won’t be able to do so before the automation revolution deals a body blow to your banking job sometime around 2022. Cathy Bessant, the chief technology and operations officer at Bank of America, said as much in conversation with Bloomberg last month. If you’re a bank employee who’s technologically illiterate, Bessant said it’s no good rushing to do a few coding courses on the side. You’re too late: things are moving too fast. “The kind of skills that we’ll need have to be taught beginning at a much earlier age,” said Bessant. “Whether you can train the same worker at the same time you’re changing their job remains to be seen.” – Can’t Code? The only other thing that will save your job

Immediate thoughts: 

Does this include executives and leadership?

Are they doing any work to train their best and brightest in these skills?

Where will these bankers go now?

Does it even matter since this is the new reality:

“Huy Nguyen Trieu, the former head of macro structuring at Citi, told us he knows of a team of just four algorithmic traders who now manage 70% of the trades that were done by 140 people in 2010″

Luckily there’s a sliver of hope for bankers in form of soft skills:

“Not for nothing has Goldman Sachs president David Solomon been extolling the virtues of a well-rounded education that incorporates public speaking and communication. Just as banks need geeks, they’ll also need exceptionally charismatic individuals to act as the face of the new automated reality.”

Maybe I should launch a new workshop as part of my power skills series: How to Charm the Pants Off of Your Audience and Save Your Job