Geoffrey Hinton, aka the Godfather of AI, has been instrumental in the AI revolution we are now living in. However, he’s not content just to rest on his laurels and has dream up something new: capsule networks.

Check out this excerpt from an article in the Seattle Times.

With his capsule networks, Hinton aims to finally give machines the same three-dimensional perspective that humans have — allowing them to recognize a coffee cup from any angle after learning what it looks like from only one. This is not something that neural networks can do.

Here’s an interesting article in Forbes on how John Deere is using computer vision to optimize agricultural output.

In just 30 years’ time, it is forecasted that the human population of our planet will be close to 10 billion. Producing enough food to feed these hungry mouths will be a challenge, and demographic trends such as urbanization, particularly in developing countries, will only add to that. Intelligent […]

In this interview with Geoffrey Hinton, Martin Ford asks the pioneering AI researcher about the economics of a world dominated by AI and what to do about making sure the future is for everyone.

If you can dramatically increase productivity and make more goodies to go around, that should be a good thing. Whether or not it turns out to be a good thing depends entirely on the social system, and doesn’t depend at all on the technology. People are looking at the technology as if the technological advances are a problem. The problem is in the social systems, and whether we’re going to have a social system that shares fairly, or one that focuses all the improvement on the 1% and treats the rest of the people like dirt. That’s nothing to do with technology.

I am excited that I can now finally talk about this initiative publicly: an online AI training course geared towards business decision makers.

Is your company AI-ready? Learn more about AI strategy, culture, responsibility, and technology through our insightful AI leadership series.

Forbes points out that the term “Big Data” has been eclipsed by “Data Science” in the hype cycle. However, the Great Hype Cycle resembles Game of Thrones and I think we can all agree that “AI” or “Machine Learning” is next to sit on the Iron Throne of Hype.

In a world in which “big data” and “data science” seem to adorn every technology-related news article and social media post, have the terms finally reached public interest saturation? As the use of large amounts of data has become mainstream, is the role of “data science” replacing the hype of “big data?”

With the rise of Machine Learning came the rise of developer tools and libraries. What are they good for and what are the top ones that every data scientist and ML engineer should know. This article sheds some light on those questions.

A deep learning framework is an interface, library or a tool which allows us to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. They provide a clear and concise way for defining models using a collection of pre-built and optimized components.

Predicting the stock market is one of the most difficult things to do given all the variables. There are numerous factors involved – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict accurately.

In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM.

While Uber is known as one of the pioneers in self-driving vehicels, its autonomous vehicle division has been a source of contention for investors. TechCrunch recently reported numbers that were less than flattering : The ride-hailing company was spending $20 million a month on developing self-driving technologies.The Wall Street Journal estimates that Uber spent about $750 million on building out self-driving technologies before scaling back in 2018.

However, all is not bleak: Uber ’s autonomous vehicle unit may be about to get a massive ($1 billion+) cash injection?

It’s highly possible, according to news reports indicating a group of investors including SoftBank Group is putting money into the division. The Wall Street Journal reported last night that Uber, more formally known as Uber Technologies Inc., was in “late-stage” discussions with a consortium that would invest in the startup’s self-driving vehicle division.

Jerry Chi, Data Science Manager at SmartNews, has compiled a list of “mind-blowing” ML/AI breakthroughs of the last year or two.  I have to say that I agree with most of his choices.

Compared to other fields, machine learning / artificial intelligence seems to have a much higher frequency of super-interesting developments these days. Things that make you say “wow” or even “what a time to be alive!” (as the creator of Two Minute Papers always says)