Covering Disruptive Technology Powering Business in The Digital Age

The world is changing faster than ever before. The past century has seen more profound changes to our society and economy than any other time that came before. At the core of today’s technological transformation is a set of critical technologies, spearheaded by Artificial Intelligence, or AI. 

AI deals with how the human brain operates, seeking to apply those biological principles to computing. It represents a huge departure from traditional computing and programming, where a human needs to feed the machine an input, output and logic. With AI, the machine only needs an input—it is then able to figure out the rest by itself.

Many associate AI with science-fiction. But this technology is not something that we might encounter in a distant future—it is very much here already. AI has given us machine learning, neural networks and deep learning, techniques shaping every field of economic activity and human endeavor, from economics to arts and entertainment, and from the way we shop to how we interact.

The impact of AI is not only evident—it is profound. It is changing the way we manufacture, ship, market, and use products. Its impact on the world cannot be overstated. Facebook, Netflix and Amazon, to name but a few, use AI to create powerful algorithms capable of accurately predicting consumer behaviour.

Types of AI

A host of techniques are associated with AI, such as machine learning and neural networks, and they are used across the whole spectrum of tech jobs and areas of economic activity. However, broadly speaking, we can divide AI into four categories according to the levels of complexity involved:

1. Reactive machines that use data to draw conclusions. A good example of this is chess software.
2. Machines that use live data to interpret a situation and make decisions. Self-driving cars can be put in this category.
3. Machines that understand that every entity possesses its own set of underlying concepts—motives, intentions and emotions—also known as ‘theory of mind.’ We haven’t reached this level yet.
4. Self-aware systems. This would be the final stage—AI that not only knows of the existence of other systems, but that is also aware of its own.

Artificial General Intelligence

Machines can be very good at specific tasks, surpassing humans in many situations. For example, modern chess engines that run on supercomputers are capable of defeating even the best human players. However, there is no machine today with the depth and breadth of skills and cognition that human brains are capable of. If such a device existed, it would be what we call Artificial General Intelligence or AGI.

Conversational Intelligence

Conversational AI, or chatbots, is another technology that has taken the world by storm in the last few years. These chatbots have reached such a level of sophistication that, in many cases, it is genuinely hard to tell them apart from their human counterparts. Chatbots allow firms to quickly and efficiently address customers’ main questions and problems. 

As they replace humans, these chatbots have saved companies millions of dollars in salaries and have improved the customer experience immensely. The most advanced of them are capable of answering open-ended questions very much as a human would.

An excellent example of a company that uses this technology is Sephora, the French cosmetics manufacturer. Visitors to its website can “try on” its lipstick and eyeshadow on a photo of themselves that they share with a bot. The bot’s AI technology identifies the user’s facial features and uses augmented reality to apply these makeup tests.

Machine Learning

Machine learning is an AI technique that creates systems that learn and grow based on experience. Through machine learning, computers can automatically adapt their processes without targeted programming. The applications of this technique are wide-ranging and are currently being used in the e-commerce, banking and medicine industries, among many others.

Marketers are using machine learning to collect and analyze huge amounts of data. This allows them to run targeted campaigns that attract more buyers. The technique is also used for dynamic pricing. Firms readjust prices based on various factors—such as competitor pricing, product demand and day of the week—which are considered all at once. 

E-commerce retailers use machine learning to collect large amounts of data and make sense of it to provide an experience similar to what a customer would have in the store. 

Companies that use machine learning to implement these strategies have seen sales increases by 6 to 10 percent compared to companies that do not use these techniques, according to a study by Boston Consulting Group.

Cogito is a prominent example of a company utilizing machine learning to improve customer service. The company fuses machine learning and behavioral science to make phone professionals’ jobs easier and make voice calls a more enjoyable experience for customers.

Deep Learning

Deep learning is based on the idea of letting machines teach themselves. This AI technique enables computers to perform high-level thought and abstraction, such as image recognition. The goal is to mimic the way a human brain functions. It uses deep neural networks in which data is passed along. These networks adapt according to whatever information they are processing so that they are always becoming more efficient.

Deep learning has applications in every industry. In the medical field, for example, it is being used to help doctors diagnose lung cancer. The Chinese startup Infervision is using deep learning and image recognition to diagnose possible lung cancer with X-rays more accurately and efficiently than ever before.

Neural Networks

Neural networks are an AI technique modeled after connections in the human brain. It creates systems that are capable of learning and improving over time. While deep learning concerns the transformation and extraction of features that attempt to establish a relationship between stimuli and neural responses in the brain, neural networks use neurons to transmit input and output values through connections.

In 2014, Apple adapted Siri’s voice recognition technology to use neural networks. Google has also used the technology on Google Translate.

Is AI safe?

Unlike many a science-fiction flick would have you believe, we are far from a future in which machines become sentient and decide to take over the world. However, most devices that use AI are connected to the Internet, which exposes these systems to common cybersecurity threats like data breaches. Right now, the most significant danger with this technology is that you might get your precious data stolen!

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