If you have already grasped the basics of what artificial intelligence and machine learning is but want the next level of understanding then read on.
Industry 4.0
Industry 4.0 is the common name given to the current trend in automation and data management across leading manufacturing technologies.
This includes:
Industry 4.0 is widely recognised as being the 4th industrial revolution. Industry 4.0 nurtures the idea of "smart factories." Within the technologically structured smart factory, cyber-hardware systems oversee real world physical processes, create virtual copies of the real world and make decisions on decentralized basis. Over the internet of things, cyber-real world systems will cooperate & communicate with one another and also with humans in real time across organisational services offered, internally and used by all members of the value chain.
Getting a grasp on the different types of AI — What they are, How they work & where they should and shouldn't be used is critical for business.
What it is: Machine learning is the most prevalent subset of AI in most organisations and has been employed in business for some time. ML includes techniques that enable computers to learn from data supplied and apply that learning without any human involvement.
ML techniques can be classified as:
When you hear a business emphasize it's machine learning capabilities, though, understand that ML itself is a very broad category (though slightly less broad than AI). As CompTIA explains it: "There is no single approach or algorithm that defines machine learning; instead, there are many different methods being used to produce machine learning capability. This means an end user needs to know the details behind the specific machine learning process they are implementing. What is M.L. good at? Machine learning, in various forms, is good at identifying patterns within data. Infinitely faster than a human analyst ever could. Business applications: ML may be used to identify patterns in processes or derive insight from data to build models for performance processes. Many processes humans currently execute can be replaced by an A.I. by utilising machine learning.
What it is?: A subset of machine learning.
deep learning is a statistical approach capable of enabling computers to solve even more complex problems. Unlike shallower categories of AI, a deep learning approach involves the machine ingesting large quantities of data (over and over again) to train a multi-layered deep neural network (DNN) designed to mimic the biological structure and performance of the human brain. Once the DNN observes enough labeled data, it can successfully identify or categorize new, unlabeled data
What is it good at?
Basic machine learning is actually pretty bad at some tasks that are second nature for maturer humans, for instance; seeing differences between a picture of a cat or a dog or classifying voices as being male or female. That's where deep learning comes in; it excels with unstructured data like images, audio and natural language.The more data the deep neural network is exposed to, the better it gets at identifying and classifying features. This is what powers our shopping recommendations, Our intelligent home assistants and our autonomous driving capabilities.
Deep learning Business applications:
There are many opportunities for applying deep learning technology in business. One important task that deep learning can perform is e-discovery. For example, large investment houses like JPMorgan Chase are using deep learning based text analytics for insider trading detection and government regulatory compliance. Hedge funds use text analytics to drill down into massive document repositories for obtaining insights into future investment performance and market sentiment. The use case for deep learning based text analytics revolves around its ability to parse massive amounts of text data to perform analytics or yield aggregations. Any business holding an archive of data stands to immediately benefit from the deployement of machine learning data models for predictive, analytical and performance algorythims.
All the hype around AI, machine learning, computer vision and natural language processes exacerbates the feeling that if you don't get started now, you'll be left behind. The speed at which this is all evolving especially delivers an unavoidable feeling of urgency. CIOs and business leaders can't afford to become paralysed by these fears.
The good news is that you don't need to be a large, tech-forward enterprise to win the AI race.
You just need to be smart about how you start. Technology officers positioning their companies to take advantage of AI now are building a foundation of data, technology, and talent that their competitors are not. They know that, like many overhyped technologies before it, AI is just a tool—one that will work only if you know where and how to wield it. Thats where we come in!
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