A.I. Beyond the basics.
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:
- Cloud computing,
- Cyber-physical systems,
- Cognitive computing.
- I.O.T. - the internet of things,
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.
Types of A.I.
- Machine learning (ML)
- Deep learning
- Computer vision
- Natural language processing (NLP)
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.
Valuable subsets of AI already at work in business.
Machine Learning
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:
- Supervised (the machine learns by ingesting labeled data)
- Unsupervised (the machine learns from data on its own).
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.
Deep 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.
Computer Vision
Computer vision is the field of AI that trains computers to understand visual data; basically, it enables machines to "see." By using deep learning models, computer vision is aimed at recognising digital images in a given context — identifying and then classifying them. Some computer vision systems are 99% accurate, thanks to the tsunami of visual data available for use in training machines today together with a wealth of computing power for analysis. Many of these are much better than humans at detecting and reacting to visual input.
The strength of computer vision technology is turning raw image data into higher-level concepts so that humans or computers can interpret and act upon them. Any area where humans are required to view and make decisions in regards to image data is relevent to computer vision. A deep learning model can develop an accurate performance model that enables the computer to perform the task of a human faster and to a much higher degree of accuracy. One example would be it's use in multi storey car parking. As vehicles enter the premises the A.I. observes each car's registration plate and immediately logs the customer as arrived. When they leave the time is instantly calculated for that visit and a charge instantly applied to the customers account. All of this happens without a human being required in the process.
Natural Language Processing
This is the category of AI that enables computers to understand, interpret and manipulate human language. The earliest applications were rules-based, but today NLP may be powered by ML, deep learning or both. There are a number of NLP subcategories, including natural language understanding, which involves reading comprehension by machines, and natural language generation, whereby computers transform data into human words.
NLP is the type of AI you want if you're dealing with unstructured speech and text datasets. Because NLP can extract key words and phrases, interpret intent and even generate responses, businesses can employ it when developing intelligent assistants or chatbots, automating tasks related to complex documentation, or analysing data in social media feeds or customer support calls. In the legal field NLP can be used to anaylse and summarise contracts, reports or correspondence. Given it's ability to both analyse and generate language, NLP can also be useful in coordinating threat detection, investigation and response. In the future, NLP could be used to scan for bugs in software code (which is written in a language, after all)
The immediate future and A.I.
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!
