Nowadays we see that technology alone is rarely enough to unlock sustainable business growth. When a new technology is combined with a ‘new ways of doing business’, true value is created. This article identifies a number of emerging trends in artificial intelligence for 2018. Executives should learn to shape the outcome rather than just react to it.
1. Artificial Intelligence seen as commodity
2. Democratization of ‘Click’ Machine Learning
3. “Guys, these geeks are infiltrating”
4. Predictive Analytics on the loose

Welcome to the second chapter of ML lessons from Ng’s experience. This will be a short one and we will only be talking about Human Level Performance & Avoidable Bias. If you did not read the first chapter, here’s the link. Let’s get started! Oh and one more thing, this series is entirely based on a recent course by Andrew Ng on Coursera.

When data scientist Joel Grus wrote an article on using machine learning to solve the ‘fizzbuzz’ problem last year, most people saw it as an exercise in comedy, perhaps with a warning about the inappropriate use of AI. But we saw a deeper lesson. Certainly, you don’t need AI to solve fizzbuzz, so long as someone tells you the algorithm underlying the problem. But suppose you discover a seemingly random pattern like fizzbuzz output in nature? Patterns like that exist throughout real life, and no one gives us the algorithm. Machine learning solves such problems. This summer, I had an opportunity to interview with an AI startup that I really liked. And guess what? I was asked to solve fizzbuzz using deep learning. Long story short, I didn’t get the job offer.

Most of the artificial intelligence (AI) successes in recent years have occurred in narrowly defined problems such as image classification, computer vision, speech recognition, and machine translation, powered by the availability of large data sets, incredibly powerful computers, and supervised learning algorithms. However, general artificial intelligence – the holy grail of AI research – remains a far-off goal, one that will take decades to achieve. Many in the AI community believe major advances in unsupervised learning – the ability to learn from data without any labels – holds the key to general artificial intelligence.

Year over year, in the last classes before Christmas I ask my students to create a Christmas tree in R. Classes are about Techniques of data visualisation and usually, at this point, we are discussing interactive graphics and tools like rbokeh, ggiraph, vegalite, googleVis, D3, rCharts or plotly. I like this exercise because with most tools it is easy to create a barchart, but how good must be the tool and the craftsman to handle a christmas tree?

There was a time when Web scraping was quite a difficult task requiring knowledge of XML Tree parsing and HTTP Requests. But with the likes of libraries like beautifulsoup (for Python) and rvest (for R), Web scraping has become a toy for any beginner to play with.

Amazon Redshift is one of the hottest databases for Data Warehousing right now, it’s one of the most cost-effective solutions available, and allows for integration with many popular BI tools. Unfortunately, the status of the drivers compatibility is a little more shaky, but there is a way to make it work very nicely with R!