After decades of false starts, artificial intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize. AI's latest breakthrough is being propelled by machine learning--a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.

Ophir Tanz is the CEO of GumGum, an artificial intelligence company with particular expertise in computer vision. GumGum applies its capabilities to a variety of industries, from advertising to professional sports across the globe. Ophir holds a B.S. and a M.S. from Carnegie Mellon University and currently lives in Los Angeles. Cambron Carter leads the image technology team at GumGum, where he designs computer vision and machine learning solutions for a wide variety of applications. Cambron holds B.S. degrees in physics and electrical engineering and an M.Eng. in electrical engineering from the University of Louisville.

Artificial intelligence already plays a major role in human economies and societies and it will play an even bigger role in the coming years. To ponder the future of artificial intelligence is thus to acknowledge that the future is artificial intelligence. This will be partly owing to advances in "deep learning," which uses multilayer neural networks that were first theorized in the 1980s. With today's greater computing power and storage, deep learning is now a practical possibility, and a deep-learning application gained worldwide attention in 2016 by beating the world champion in Go. Commercial enterprises and governments alike hope to adapt the technology to find useful patterns in "Big Data" of all kinds.

Understanding how today's AI works might seem overwhelming, but it really boils down to two concepts you probably have heard of before: "machine learning" and "deep learning". Neither are brand new ideas, but the way they're used seems to constantly evolve. Machine learning and deep learning are how Netflix knows what you might want to watch next, or how Facebook can recognize your friends' face in a photo, or how a support agent can figure out if you'll be satisfied with your customer service. So what are these buzzwords that still dominate the conversations about AI, and how exactly are they different? And what do they mean for customer service?

The combination of all of these factors means we are at the start of a new and exciting age of advancement in Artificial Intelligence, one that will have a profound effect on every person's life, but also many of these advancements will be so embedded in our everyday lives that they may mostly go unnoticed.

Breaking the Enigma was an incredible feat - it even inspired the 2014 film The Imitation Game starring Benedict Cumberbatch as Alan Turing. Turing was one of the most important figures in the project. He also introduced the notion of Turing-completeness. In an ironic twist, we'll be using a Turing-complete algorithm (the LSTM) to decode the Enigma.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

The next time you come across a picture of a ravishing dish on Instagram or WeChat that whets your appetite but you can't exactly make out what it is made of, don't wrack your brain trying to guess the recipe. An AI system unwrapped earlier this week helps those with culinary curiosity find the right ingredients of an unknown dish and offers step-by-step instructions how to make it just by analyzing a photo they upload online. Researchers from the Polytechnic University of Catalonia, Massachusetts Institute of Technology (MIT) and Qatar Computing Research Institute have developed a deep-learning algorithm that can whip out a recipe just by "looking" at a photo of the dish. They fed the neural network one million recipes, along with one million photos of their final outcome, from popular websites like Allrecipes.com and Food.com to create a huge database they dubbed, Recipe1M, accessible through a web portal they called Pic2Recipe. With a single click of a button, the website allows users to upload a photo of the mystery dish and then the system, using machine learning, goes through the massive mounds of data to analyze it. It then predicts a list of possible ingredients along with their relevant recipes, then ranks them based on how certain the AI is they match the image.

We are excited to announce our next MIIA event that will be hosted by PwC in V&A Waterfront, Cape Town on the 16th of August 2017 at 6pm. The theme of the event is "Data Science Applications to help businesses thrive in the Smart Technology Era" and we have speakers from PwC, Dimago, Cortex Logic and UCT. The Machine Intelligence Institute of Africa (MIIA) has also recently partnered with the Africa Data Forum and will be participating in the upcoming All Things Data Conference at the Westin, Cape Town on 29-30 August 2017. MIIA has decided to join forces with the Africa Data Science Association and will also have Board representation in this non profit organization that aims to ensure alignment between the academia and corporates on data science curriculum. We are excited to have many of the MIIA community members also participating in the upcoming Deep Learning Indaba at Wits, Johannesburg from 10-15 September 2017.

This post is adapted from Section 3 of Chapter 9 of my book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future. You can read the first part here: The Limitations of Deep Learning. Given what we know of how deep nets work, of their limitations, and of the current state of the research landscape, can we predict where things are headed in the medium term? Here are some purely personal thoughts.