Virtually all of the Big Tech companies — Microsoft, Google, and Facebook have invested millions of dollars and hired top talent to build AI solutions. Yet, many of them have ended in disaster:

• Microsoft’s AI, Tay, was a chatbot deployed on Twitter to have playful and casual conversations with its followers. Instead, it began to take on the form of a raging bigot, spewing racist and misogynistic tweets.
• Google’s Panorama feature promised to stitch photos together to create one continuous image. Instead, it would superimpose people into landmarks, creating wonderfully bad photos.
• Facebook created a chatbot that they had to quickly dismantle because they soon began to speak their own…

Data for Change

The tech giants — Amazon, Microsoft, Google, Uber, Facebook — all spend millions of dollars on AI solutions. Yet, many end in failure:

• IBM Watson was pulled out of medical institutions when it began to frequently give bad advice to cancer patients.
• Amazon’s hiring AI showed bias towards hiring men because it learned that good hires tended to use verbs that were commonly found on male engineers’ resumes.
• Uber’s autonomous driving AI struck and killed a woman because it failed to classify her as a pedestrian

Although each of these systems were designed and built to accomplish a task in good faith, they each failed because of unintended predictions. …

I’ve been through 3 startups in my career, either as an early employee or as a founder. Two of those startups ended up “failing” while the third one was acquired.

Needless to say, like you, I’ve spent many nights reading entrepreneurial books and blogs in hopes of learning the secret to building successful startups — only to find the same information in every book: build an MVP and find product-market fit.

These statements are then usually followed by an example of somebody who had an idea, built an MVP (minimum viable product), hit 1 million users in a week and then acquired a year after. …

(Don’t have time to read it all? I’ve summarized the entire article in the conclusion section!)

During my PhD in Organizational Psychology I always found myself reading about Data Science — thinking, it’d be really cool to be a data scientist... But, at the time, I couldn’t write a lick of code, so I chalked it up as a dream I’d achieve in another lifetime.

My worries about learning data science came in two forms: 1) Data Scientists code, I don’t, I’ll never become one, and 2) I’m doing a PhD in an unrelated topic, why throw it away?

It was around this time I was perusing LinkedIn and noticed an update, my friend who had been working as a recruiter for 5 years took a position at Google. …

Background:

On October 17th, 2018, cannabis became legal in Canada.

As an entrepreneur, I’m always reading about the latest tech startups, following how markets are developing and sniffing out emerging opportunities. As a data scientist, I’m always looking for data driven solutions to problems I’ve identified.

As a resident of Toronto, naturally, I began to look at the cannabis market.