Artificial Intelligence or Machine Learning?
In 1950, Alan Turing proposed the Turing Test (Mueller, 2016). The test is passed when a computer can communicate with a human, and the human believes they were not talking to a computer but to another human. In 1956, John McCarthy coined the phrase ‘Artificial Intelligence’, and a new field in computer science was born (John Paul Mueller, 2018). Artificial intelligence (AI) research flourished but was ineffective until around 1980. Then, between 1980 and 2000, integrated circuits moved computers, artificial intelligence, and machine learning from science fiction to science fact.
AI is the capability of a machine to imitate intelligent human behavior (Merriam-Webster, 2018). Importantly, just saying that a technology, a device, or an application has artificial intelligence does not say anything about that technology or about how it resolves challenges or problems. Machine learning can be used to achieve artificial intelligence by creating systems that can recognize complex patterns or make intelligent decisions based on data. Applications of machine learning are being used in self-driving cars and cameras with facial recognition. These two examples, driving a car and recognizing faces, are activities that humans are already good at. What does the future hold and what comes next? Real-life applications of machine learning and AI that go beyond what a human can achieve alone.
Widex has introduced SoundSense Learn in Widex Evoke, the world’s first hearing aid that uses real-time machine learning to empower end users to make adjustments based on their preferences and intentions in different environments. This is made possible by using a distributed computing approach, leveraging the power of a smartphone connected to a hearing aid to incorporate a live machine-learning application. The power of machine learning combined with a simple user interface frees the end user to focus on the quality of the sound without having to learn and manipulate numerous adjustment parameters. Via a simple interface, SoundSense Learn (fig. 1) can automatically learn and meet the enduser’s preferences and intentions. Using machine learning allows the hearing aid to achieve this auditory intention because it is driven by the end-user and their preference in that moment.