A couple weeks ago we discussed how to perform multi-label classification using Keras and deep learning. Today we are going to discuss a more advanced technique called multi-output classification. And how are you supposed to keep track of all these terms? You can even combine multi-label classification with multi-output classification so that each fully-connected head can predict multiple outputs! If this is starting to make your head spin, no worries -- I've designed today's tutorial to guide you through multiple output classification with Keras. It's actually quite easier than it sounds. That said, this is a more advanced deep learning technique we're covering today so if you have not already read my first post on Multi-label classification with Keras make sure you do that now. From there, you'll be prepared to train your network with multiple loss functions and obtain multiple outputs from the network.

When Azeem asked me to give a talk at CogX, he asked me to focus on just a single point that I wanted the audience to take away. A few years ago my priority would have been convincing people that deep learning was a real revolution, not a fad, but there have been enough examples of shipping products that that question seems answered. I knew this was true before most people not because I'm any kind of prophet with deep insights, but because I'd had a chance to spend a lot of time running hands-on experiments with the technology myself. I could be confident of the value of deep learning because I had seen with my own eyes how effective it was across a whole range of applications, and knew that the only barrier to seeing it deployed more widely was how long it takes to get from research to deployment. Instead I chose to speak about another trend that I am just as certain about, and will have just as much impact, but which isn't nearly as well known.

HANOVER, GERMANY - APRIL 25: Close up of the digital display while a camera and radar system assists as artificial intelligence takes over driving the car during tests of autonomous car abilities conducted by Continental AG on the A2 highway on April 25, 2018, near Hanover, Germany. Israeli artificial intelligence (AI) startup, Hailo Technologies, has closed a $12.5 million series A from Maniv Mobility, OurCrowd, and NextGear to develop a chip for deep learning on edge devices and processing of high-resolution sensory data in real time. According to a report from Markets and Markets, edge computing will be worth $6.72 billion by 2020, and IC Insights reported that integrated circuits in cars are expected to generate global sales of $42.9 billion in 2021. In 2017, McKinsey reported in the study, Self Driving Car Technology: when will robots hit the road?, that ADAS systems grew to 140 million in 2016 from 90 million units in 2014. "Because of the low latency required for autonomous driving and advanced driving assistance, deep learning with convolutional neural networks, running on in-vehicle hardware, is necessary," offers Tom Coughlin, IEEE Fellow and President at Coughlin Associates.

Our world is becoming increasingly complex. Every day an immense amount of data is generated, which, if properly understood, can help us make better sense of our world. Until recently, we did not have the means to make proper sense of all of this data. But recent developments in artificial intelligence have changed this. Now, we are able to draw meaningful insights from enormous data sets and use these to make better decisions.

Over the past few years, the internet has been inundated with thousands of articles proclaiming the new age of data and how it interacts with and drives artificial intelligence (AI). As a result, the three terms data science, machine learning, and deep learning have transitioned almost overnight from buzzwords to standard vocabulary, and have become synonymous with the direction that society is moving in. In the olden days, it was called statistics. But now it has morphed and grown, like Thanos's chin, until it became'data science'. Today, top-flight universities offer degrees in it and everyone is calling it a career path that will never fail.

COLLAB. are a specialist Big Data and Data Science recruitment agency, but were not here to talk about that! Please find an overview of some of the most interesting content we have shared over the last month below. Not everyone who can talk about "entropy loss" has the engineering skills to back it up... Read more here With cloud object stores becoming the de facto data lakes, it can be hard when it comes to finding and accounting for all the data... Read more here Data is instigating change and giving rise to a new data-driven economy... Read more here Rice University created a deep learning, software coding application called BAYOU that can help human programmers work with APIs... Read more here Are you sure about your analytics initiative is delivering the value it's supposed to? Well, CEOs that don't have the knowledge that businesses are analytics driven... Read more here Information security, data science and cloud computing skills are the most sought-after talents in the marketplace today... Read more here Thanks you for taking the time out to read through COLLAB.'s monthly newsletter. If you'd like to subscribe, please do so HERE.

Artificial intelligence (AI) is best known for its ability to see (as in driverless cars) and listen (as in Alexa and other home assistants). My colleagues and I are developing an AI system that can smell human breath and learn how to identify a range of illness-revealing substances that we might breathe out. The sense of smell is used by animals and even plants to identify hundreds of different substances that float in the air. But compared to that of other animals, the human sense of smell is far less developed and certainly not used to carry out daily activities. For this reason, humans aren't particularly aware of the richness of information that can be transmitted through the air, and can be perceived by a highly sensitive olfactory system.

As AI and robotics develop side by side, the medium-term future will see infant-like robots that can learn for themselves and crow-like machine intelligences that can teach themselves to use tools, claims a leading AI researcher. But their progress will go far beyond there. In 2018, most people are worried that AI and robotics might automate their jobs and leave them scrabbling in the gig economy for a regular wage. But one AI expert believes that the technologies' ambitions are much bigger than that: in the centuries ahead, AI and robots will "emigrate" from Earth and communicate with each other across the universe, he says. But long before then, general artificial intelligences will emerge that can be taught like human children, and which can teach themselves about how the world works through "power play" and "artificial curiosity", he said.

Researchers have developed a deep learning algorithm that can automatically identify, count and describe animals in their natural habitats. A new paper, published in Proceedings of the National Academy of Sciences (PNAS), decribes how the cutting-edge artificial intelligence technique can automatically describe photographs that have been collected by motion-sensor cameras on deep neural networks. The result is a system that can automate animal identification for up to 99.3 per cent of images while still performing at the same 96.6 per cent accuracy rate of crowd-sourced teams of human volunteers. "This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyse the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behaviour into'big data' sciences," explained Jeff Clune, the senior author of the paper and Harris Associate Professor at the University of Wyoming. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems."

IMAGE: This is the Rice University's PlinyCompute team includes (from left) Shangyu Luo, Sourav Sikdar, Jia Zou, Tania Lorido, Binhang Yuan, Jessica Yu, Chris Jermaine, Carlos Monroy, Dimitrije Jankov and Matt... view more HOUSTON -- (June 11, 2018) -- Computer scientists from Rice University's DARPA-funded Pliny Project believe they have the answer for every stressed-out systems programmer who has struggled to implement complex objects and workflows on'big data' platforms like Spark and thought: "Isn't there a better way?" Rice's PlinyCompute will be unveiled here Thursday at the 2018 ACM SIGMOD conference. In a peer-reviewed conference paper, the team describes PlinyCompute as "a system purely for developing high-performance, big data codes." Like Spark, PlinyCompute aims for ease of use and broad versatility, said Chris Jermaine, the Rice computer science professor leading the platform's development. Unlike Spark, PlinyCompute is designed to support the intense kinds of computation that have only previously been possible with supercomputers, or high-performance computers (HPC). "With machine learning, and especially deep learning, people have seen what complex analytics algorithms can do when they're applied to big data," Jermaine said.