In mainstream news (non-tech news) or the entertainment industry (movies and TV in particular), artificial intelligence (AI) appears to be a singular technology. This technology often takes a human form and is consigned to performing menial tasks or overtaking the world. This is far from reality, however.
In fact, AI is only one part of a much larger collection of technologies that are better described as machine learning. Those technologies include:
·         Artificial Intelligence
·         Machine learning
·         Cognitive analytics
·         Deep learning
·         Robotics process automation
As a result, when people say AI is the next big thing, they are usually referring to machine intelligence.
The Rise of Machine Intelligence
This placing of machine learning over AI is highlighted in an annual report on Tech Trends released recently by Deloitte. The Tech Trends 2017: The Kinetic Enterprise report cites analysis which predicts that businesses will invest over $30 billion in machine learning over the next two years. The Tech Trends report was based on a survey of 1,200 IT executives from across the world.
The Deloitte report says companies will do this for a range of reasons, from getting a deeper understanding of their customers to automating increasingly complex tasks. Many of these tasks are currently performed by people. Machine intelligence technologies, therefore, present significant benefits and opportunities for companies in every industry and sector.
According to the Deloitte report, three things collectively will be the catalyst for the growing use and development of machine learning technologies:
·         Big data – collecting data doesn’t present many problems for most companies. Understanding, analyzing, and then taking decisions on that data is a different issue. In fact, this is becoming harder the more data that is collected, and a lot of data is being collected – Deloitte says the amount of data collected by companies will double every year for the next two or three years. Machine learning will help make sense of that data and turn it into something that is actionable.
·         Computing speed and power – data is not enough on its own, and neither is the ability to understand and analyze it. Instead, you also need computing speed and power across every aspect of technology from the computer on your desk to the chip in an Internet of Things device. The computing speed and power we have now is better than ever before, and it continues to get better every day.
·         Improved algorithms – algorithms in machine learning technologies are simply getting better. In fact, the Deloitte reports says they are getting closer to simulating the thought processes of humans.
While AI is an important part of machine learning, companies that maximize the benefit of these new technologies will understand the much fuller scope of what can be achieved. Here it is in summary:
·         Machine learning is a much broader field than AI on its own
·         This presents far more opportunities for companies that look to implement and use such technologies
·         Machine learning also opens up possibilities for the use of these technologies in ways that had never been thought of before, particularly where the focus up to now has been on AI
Big Data and related technologies – from data warehousing to analytics and business intelligence (BI) – are transforming the business world. Big Data is not simply big: Gartner defines it as “high-volume, high-velocity and high-variety information assets.” Managing these assets to generate the fourth “V” – value – is a challenge. Many excellent solutions are on the market, but they must be matched to specific needs. At GRT Corporation our focus is on providing value to the business customer.