In 1950, computer scientists Alan Turing developed a way of testing a machine’s ability to exhibit intelligent behaviour, equivalent to humans. Since the Turing Test was first used, the world has become fixated on the possibility that, one day, a computer could function like a human being.
Here Jonathan Wilkins, marketing director at obsolete industrial automation parts supplier, EU Automation discusses how recent developments in machine learning are influencing industry.
Machine learning is a concept that has been around for many decades. In machine learning, the computer doesn’t rely on rule-based programming, rather the algorithms can adapt and learn from the data. This means that manufacturers using this software don’t need to rely on the time and expense of dedicated data analysts to find patterns and make predictions.
Despite its long history, recent developments in machine learning are carving the way factories of the future will operate.
As of yet, there is no machine that functions exactly like a human. Because of this, many businesses choose to adopt a human-in-the-loop model when it comes to machine learning. When a machine first analyses a piece of data and gives it either a label or an action, it assigns a confidence score that dictates how sure it is that it has made the correct decision. If the score is below a pre-assigned value, it sends the data to a human annotator to make a judgement. This judgement is then fed back into the machine learning algorithm to make it smarter.
Self-driving cars are a good example of human-in-the-loop computing. Tesla recently launched an automated driving mode that allows the vehicle to drive itself, but insists a human keeps their hands on the wheel. When the machine learning vision system senses doubt, it hands control back to the driver.
The world’s data doubles every two years, while the cost of storing that data declines at roughly the same rate. This is also true for digital data and cloud storage, which means more features and better machine learning models will be created in the next few years.
Based on this model, intelligent applications that can generate higher quality data will have an unfair advantage from their data flywheel — more data leading to better models, leading to better user experience, resulting in more users, which in turn means machines can collect more data. For companies to stay ahead, they need to make the most of every set of data they receive.
We’ve come a long way since the invention of the Turing Test in 1950, but it’s fair to say that no one is certain where artificial intelligence and machine learning will take us. One thing we do know is that, to keep up with other sectors, manufacturing must make the most of the technology available today.