One in three smartphones — roughly over half a billion — shipped in 2020 will have machine learning and artificial intelligence (AI) capabilities at the chipset level, new research said. According to Counterpoint’s ‘Components Tracker Service’, Apple, with its Bionic system on chip (SoC), will drive native AI adoption in smartphones, making the iPhone maker a leader in the AI-capable chip market through 2020.
Huawei, with its HiSilicon Kirin 970 SoC, is second in the market after Apple with AI-capable smartphones. “The initial driver for the rapid adoption of AI in smartphones is the use of facial recognition technology by Apple in its recently launched iPhone X,” said Counterpoint Research Director Jeff Fieldhack.
“Face recognition is computationally intensive and if other vendors are to follow Apple’s lead, they will need to have similar on-board AI capabilities to enable a smooth user experience,” he added.
Qualcomm is expected to unlock AI capabilities in its high to mid-tier SoCs within the next few months. Qualcomm should be able to catch up with Apple and Huawei and is expected to be second in the market in terms of volume by 2020, followed by Samsung and Huawei.
“With advanced SoC-level AI capabilities, smartphones will be able to perform a variety of tasks such as processing natural languages, including real-time translation; helping users take better photos by intelligently identifying objects and adjusting camera settings accordingly,” explained Counterpoint Research Director Peter Richardson.
Machine learning will make smartphones understand user behaviour in an unprecedented manner. Analysing user behaviour patterns, devices will be able to make decisions and perform tasks that will reduce physical interaction time between the user and the device.
“Virtual assistants will become smarter by analysing and learning user behaviour, thereby uniquely serving each user according to their needs,” Richardson added. According to Research Analyst Shobhit Srivastava, there is also growing potential in for AI-capable devices to play a key role in health care.
“Machine learning algorithms can be used to generate health and lifestyle guidance for users by analysing combinations of sensor data and user behaviour,” said Srivastava.