Get a Glance of Smart Wearable Device Market
Wearable devices maintain rapid growth
Gartner predicts that shipments of wearable devices will jump to 453 million in 2022. For example, by 2022, loads of ear-worn devices, head-mounted devices, and smartwatches will reach 158 million, 80.18 million, and 115 million units, respectively, with a five-year compound growth rate of 49.1. %, 33.3%, 22.7%, ear-worn devices will replace smartwatches as the essential product in the field of wearable devices. By 2022, headset devices such as Apple AirPods will account for more than 30% of the wearable device market.
The Wearable AI market is growing faster. The increasing demand for AI assistants, the emergence of the Internet of Things technology, the integration of wireless technology, the growth prospects of wearable component technology, and the increase in consumer preference and demand for advanced wearable devices, are all the main driving forces for the growing global wearable market. MarketsandMarkets predicts that the worldwide wearable AI market in 2018 is expected to be 11.5 billion U.S. dollars. By 2023, this figure is expected to reach 42.4 billion U.S. dollars, with a compound annual growth rate of 29.75% during the forecast period (2018–2023).
With the introduction of AI technology, the functions of Bluetooth headsets will be more ever-changing. It will also be one of the focuses of competition among various manufacturers.
What sparks might it collide?
It can be roughly divided into these:
(1) Intelligent noise reduction will become standard
(2) Sports earphones will add more detection functions
(3) The headset can also identify who you are: voiceprint ID and AI voice
(4) Customized EQ
(5) More AI functions, such as intelligent real-time translation, listening and translation processes, etc.
Why the High-Quality Training Data is so Important to AI Machine Learning?
The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed. Indeed, data has an important role. Thus, all developers from Google and Microsoft to ordinary individual developers are paying a lot of attention to the high-quality labeled data.
In the current practice of AI applications, different level of data quality demonstrates the value of AI solutions with a very obvious gap. If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
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