Whether by accident or design, the details of Google's plans for artificial intelligence (AI) have been elusive. In some cases, there's no real mystery, just nothing all that exciting to talk about. AI technology is the foundation of the company's search engine, and the most obvious reason for Google's high-profile, $400M acquisition of DeepMind in 2014 is to use the UK firm's expertise in deep learning--a subset of AI research, but more on that later--to bolster that core capability. But the Googleplex has absorbed other bright minds from the field of AI, as well as some of the most buzzed-about companies in robotics, with only some of that collective braintrust officially allocated to driverless cars, delivery drones or other publicly announced robotics or AI-related projects. What, exactly, are Google's AI experts up to?

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer.

Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference. Both terms crop up very frequently when the topic is big data, analytics, and the broader waves of technological change which are sweeping through our world. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider "smart."

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.

Survival: "Our entire existence is based on having the best detection mechanism possible," says Valerie Bannert-Thurner at Nasdaq. Certain things make Valerie Bannert-Thurner raise an eyebrow when looking for signs of bad behavior on the Nasdaq exchange. "I like the example of excessive cheering because the guys just can't help themselves but cheer," said Bannert-Thurner, who is senior vice president and head of risk and surveillance at Nasdaq. Another worrisome indicator is seemingly too-good-to-be-true trading profits. "If people are excessively profitable given how they trade and in comparison to everybody else trading the same instruments with similar styles, then we ask, is this luck or something else?" Bannert-Thurner said.

Our society went from an agrarian economy to mass-producing affordable goods using steam power, electricity and, eventually, computers and automation. We've gone from the horse and buggy to the Model T, and now we're on to self-driving cars! The smart factories of the Industry 4.0 era will be powered by the internet of things, cloud computing and cyber-physical systems (CPS) technologies. Cyber-physical systems are powered by enabling cloud technologies which allow intelligent objects and cloud-based programmatic modules to communicate and interact with each other. These new cyber-physical manufacturing facilities use robotics, sensors, big data, automation, artificial intelligence, virtual reality, augmented reality, additive manufacturing, cybersecurity systems and other cutting-edge technologies to deliver unprecedented flexibility, precision and efficiency to the manufacturing process.

The United Nations decided to formally address the issue of killer robots. At the International Convention on Conventional Weapons in Geneva, the 123 participating nations voted to form a group in 2017 of governmental experts to look at lethal autonomous robots that can select targets without human control, which could lead to a ban, reported Human Rights Watch. Many of Silicon Valley's elite, including Steve Wozniak and Elon Musk, have expressed concern over the development of killer robots. Musk and Wozniak both signed on to a letter last year urging the UN to take up the issue, calling for an international ban on the creation of lethal autonomous weapons. Stephen Hawking and leading AI researchers -- including University of California Berkeley computer scientist Stuart Russell, Google Director of Research Peter Norvig and Microsoft Managing Director Eric Horvitz -- were among the over 1,000 scientists who signed the letter calling for a killer robot ban.

When I was a scrawny little chap, shortest in my high school class, I always wanted a super power. Wanted doesn't capture the feeling. I would have given a limb for a super power. I read a lot of books back then (and now) and landed on a super power that had something to do with the brain. I eventually landed on Prof. Xavier of the X-Men.

Talk to someone with programming skills and discuss any subject about deep learning with them so that you could quickly jump in as a newbie. Though some people figure out various libraries embedding math is used universally, you needn't understand the theory to implement deep learning tasks, I still recommend you learn some math knowledge like partial derivative. Some resources could give you a good starting point like Stanford's online course CS231n, Deep Learning at Oxford 2015and Andrew Ng's Coursera class. Also, some interesting online books like Neural Networks and Deep Learning could also give you an assistance to deep learning. Facilities and toolkits should also be available.

Machine Learning is the foundation for today's insights on customer, products, costs and revenues which learns from the data provided to its algorithms. Some of the most common examples of machine learning are Netflix's algorithms to give movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend products based on other customers bought before. Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables.