- Google creates artificial intelligence to improve traffic lights and reduce pollution
- Apple and Meta would be the powerful new partners of generative artificial intelligence
Artificial intelligence is a fundamental pillar for the development of any technology today. This translates into a new opportunity for experts in computer science, software development and other related professions to acquire knowledge and improve their profile. Such as the case of Tiny Machine Learning (TinyML), an emerging concept that has even led Harvard to create specialized courses on the subject.
This specialized branch of machine learning aims to bring AI capabilities to low-powered, resource-limited devices, opening up a range of possibilities for applications in everyday life and in key sectors such as healthcare, agriculture, industry, and the smart home.
Why TinyML is relevant in artificial intelligence
TinyML, as the name suggests, focuses on developing machine learning models that can be run on microcontrollers and other devices with memory and power constraints due to their size.
Unlike traditional approaches that rely on powerful servers and constant connectivity to the cloud, this technology allows AI to operate locally on devices as simple as low-cost microcontrollers.
This technology democratizes access to artificial intelligence and significantly reduces latency, improving aspects such as privacy by processing data directly on the device. This is crucial for sensitive applications that require fast and reliable responses, such as personalized health monitoring, precision agriculture, or smart energy management in homes.
The growth of TinyML and its future impact
The TinyML field has seen exponential growth in recent years, supported by significant advancements in model compression algorithms, efficient architectures, and specialized hardware accelerators.
Key technologies such as TensorFlow Lite Micro and platforms such as Arduino and Google Tensorflow Lite for microcontrollers have been part of the pioneers on this path, facilitating the development and deployment of applications on a wide range of devices.
According to ABI Research projections, global shipments of TinyML devices are expected to reach 2.5 billion by 2030, with a potential economic value exceeding $70 billion.
This expansion represents an unprecedented opportunity for AI developers and professionals, because it promises to transform entire industries through intelligent and efficient solutions. It is an ideal option for those who want to improve their job profile and earn more money.
Practical applications that are changing lives
TinyML’s practical applications are proving to have a direct impact on people’s lives. For example, in the field of health, devices with this technology are being used to constantly monitor vital signs such as respiratory rate or the early detection of heart abnormalities, all with minimal energy consumption and without the need for constant connection to the cloud.
In agriculture, TinyML-based systems allow for accurate crop monitoring, optimizing water and fertilizer use to improve yields and reduce environmental impact. In the industrial field, its implementation facilitates the predictive maintenance of machinery, identifying problems that previously become costly breakdowns.
For people with disabilities, it is opening up new possibilities with devices such as smart sensors to monitor movements or gesture-based assistance systems for visually impaired people, improving their quality of life significantly.
TinyML Education & Training
For AI professionals and developers, studying TinyML represents a unique opportunity to differentiate themselves in an increasingly competitive and diversified job market. The ability to design and deploy machine learning models on low-power devices not only expands the reach of technology solutions, but also opens doors to new applications and emerging markets.
There are numerous educational resources available to learn about this technology, from free online courses like Google Codelabs to advanced majors offered by academic institutions like Harvard University and EdX.
These courses cover everything from the basic fundamentals of machine learning and data collection to advanced model optimization techniques and deployment on embedded devices.
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