So about that graduate program you’re thinking about doing

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be redundant in the next 1-2 years, if they aren’t already, with another 38% stating they believe their skills will be outdated within the next 4-5 years.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

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

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

Hiring practices are about to get even more opaque

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

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

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

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

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

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

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

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

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