Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intelligence applications like image recognition, self-driving cars and most recently the AlphaGo vs. Lee Sedol matches. Recently, Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis. For those of you that are new to the topic of Deep Learning, we have put together a list of ten common terms and concepts explained in simple English, which will hopefully make them a bit easier to understand. We've done the same in the past for Machine Learning and NLP terms, which you might also find interesting. In the human brain, a neuron is a cell that processes and transmits information.

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

Dugong expert Dr Amanda Hodgson estimates she has stared at more than 30,000 photographs of blue water. "It's really taxing on your eyes and it's hard to maintain concentration." The researcher from WA's Murdoch University has been scanning pictures captured by aerial drones in a search for dugongs, to work out their population, size and location. Globally dugongs are classed as "vulnerable to extinction" and are found in waters off the northern half of Australia. "There are areas where they're quite vulnerable because their habitat overlaps with coastal development."

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

Domino's is taking a page out of Santa's book on efficient winter transportation. The global pizza chain, which has already invested in high-tech delivery methods like autonomous robots, drones, "zero-click" ordering apps and satellite tracking to deliver food, is now training reindeer to transport pies. Reindeer delivery may be part of a contingency plan for Domino's Japan ahead of what is expected to be a particularly cold and snowy winter, RocketNews24 reports. The chain is attempting a trial period for performing training exercises in the city of Ishikari-- a particularly ice-prone area in Hokkaido-- to figure out if its reindeer delivery initiative is feasible. The technique will involve insulated pizza containers strapped to the animals' backs.

The same Microsoft technology currently used by healthcare organizations and yes, robots, is now being tested throughout the summer months by Cricket Australia, making it the first cricketing nation to integrate the company's team and player performance platform into its decision-making processes across fitness, game strategy, player recovery and team selection. As part of the new deal with Microsoft, Cricket Australia is now one of a few sports organizations worldwide experimenting with the new platform. At the recent Microsoft Australia Developer Conference, Chief Executive Officer Satya Nadella said he was "glad" with how Cricket Australia was planning to integrate "machine learning" into its overarching strategy. "Cricket is one of the richest sports when it comes to using machine data. But how could you harness the power of data to even start having a more intelligent informed conversation about performance of teams, performance of players?"

New Zealand-based drone manufacturer Altus Intelligence wants to make sure its US$39,000 survey drones don't end up as rubble. Most of Altus' customers use its flagship drone, the Long Range Extreme Weather (LRX), for construction and engineering surveying/mapping and expect a rugged, dependable vehicle to get the job done. That's where the LRX's three separate fail-safe systems come in. The first is a triple auto pilot design, meaning that if anything goes wrong with one of the GPS streams, the other two will take over. The LRX is also armed with eight staggered propellers.

Deep learning is not as complex a concept that non-science people often happen to decipher. Scientific evolution over the years have reached a stage where a lot of explorations and defined research work needs the assistance of artificial intelligence. Since machines are usually fed with a particular set of algorithms to understand and react to various tasks within a matter of seconds, working with them broadens the scope of scientific breakthroughs resulting in the invention of techniques and procedures that make human life simpler and enriching. However, in order to work with machines, it is important for them to understand and recognize things just the way the human brain does. For example, we may recognize an apple through its shape and colour. For a robot to go through the same cognitive process, it must be fed with programming structures to recognize the same.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.