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The Types of Innovation that Artificial Intelligence In Makes Possible

All innovation has a common starting point: a vision. To create a truly disruptive concept, businesses need to imagine how they can drive improvement in unconventional ways. Today, companies are exploring different types of innovation through technology, with the objective of cutting costs, driving productivity, and boosting revenue. For instance, 38% of business executives have reported that they already use artificial intelligence into their workflow. Meanwhile, a further 62% percent said that they intended to implement a program this year. Looking to 2019, all companies should consider how artificial intelligence can improve business performance. Below, we introduce the AI technologies that benefit business and the basics of how they work.

3 types of innovation that your business can benefit from

1. Virtual customer support agents

A virtual customer support agent is a program that can answer queries, process orders, and create alerts using natural language processing. For example, the most common virtual customer support programs are chatbots. 

2. Machine learning

Machine learning is a branch of computer science that explores how computers can learn. By writing algorithms, application programming interfaces, and leveraging big data, machine learning platforms are improving exponentially. Currently, machine learning is mostly used for classification and predictive analytics.

3. AI-optimised hardware

One of the most exciting new types of innovation is AI-optimised hardware. Thanks to new central processing units designed specially to handle AI-oriented tasks, these silicon chips are set to appear in more mobile devices in the future.

The AI learning models behind all types of innovation

These new types of innovation would not have been possible without the synthesis of two crucial factors: big data and powerful graphics processing units (GPUs). Together, this technology can perform complex computations that facilitate deep learning.

As a result, researchers hope to create AI systems that can go beyond mimicking human thought processes. Although they’re unlikely to achieve this in the next wave of AI innovation, it’s in the pipeline. Meanwhile, here are the key types of deep learning processes currently in existence.

  • Unsupervised learning

Unsupervised learning is based on the premise that humans learn by example. For instance, a human will see an example of a process once or twice and then perform the task themselves. Through these principles, developers have created algorithms that allow machines to catalogue examples unsupervised.

  • Reinforcement learning

Reinforcement learning works through “rewarding” programs for taking the correct action. As summarised by IBM’s Distinguished Researcher Murray Campbell, “as the system takes action in an environment, it’s given rewards if it does good things and penalties if it does bad things. This is much easier to provide in terms of supervision, but it still requires supervision.”

  • Active learning

Active learning enables programs to analyse data without human direction. Whereas commonly programmers would feed a program a mass of labeled data, active learning allows the program to make sense of the information itself.


The most significant economic impact of artificial intelligence is its potential as a new method of invention. Looking to the future, this could significantly augment the nature of the innovation process. Although new types of innovation with broad applications don’t appear very frequently, AI could also dramatically change the cost of R&D. Currently, researchers are only examining a tiny proportion of what’s possible – and artificial intelligence could be the key to discoveries we couldn’t otherwise make. Ultimately, AI’s principal legacy may not be chatbots ability to answer questions, but its ability to change the way we innovate.

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