Machine learning algorithms created using Qeexo's AutoML can now be deployed on Arduino Nicla Sense ME with Bosch BHI260AP and BME688 sensors.

Since the HAL9000 and Star Trek's M-5 Multitronic, the power and capabilities of AI have always been oversold by both Hollywood and Silicon Valley. Although we're still waiting on machines that can carry on an intelligent conversation, AI has been creeping into many objects in our everyday lives behind the scenes, making them more useful and proactive. People are most familiar with the intelligent assistants built into devices like the Amazon Echo, Google Nest Hub and Apple HomePod, but as I wrote more than three years ago, these rely on cloud backend services for most of their smarts, using local hardware primarily to recognize their wake word and listen for follow-up questions. The combination allows surprisingly sophisticated deep and machine learning models to run on embedded systems. Until recently, shoehorning AI software into a battery-powered device has required data scientists skilled in working with the constraints of an embedded SoC, but recent advances in AI development and automation frameworks, categorically termed TinyML, greatly expands the realm of smart devices.

"Qeexo’s Runtime-Free Architecture for Efficient Deployment of Neural Networks on Embedded Targets" Rajen Bhatt Director of Engineering Machine Learning, Qeexo Co Neural networks, including convolutional, feed-forward, recurrent, and convolutional-recurrent, are increasingly popular due to their recent successes in AI applications. Developing neural network models for tinyML applications can be very cumbersome due to constraints of embedded targets having low-power MCUs. Qeexo has developed a runtime-free architecture for efficiently converting TensorFlow-and-PyTorch-generated models to target libraries. This approach builds models which are orders of magnitude smaller than TensorFlow Lite Micro and does not compromise on latency or inference performance. "Democratization of Artificial Intelligence (AI) to Small Scale Farmers - a framework to deploy AI Models to Tiny IoT Edges that operate in constrained environments" Chandrasekar Vuppalapati Senior Vice President - Products & Programs Hanumayamma Innovations and Technologies Inc. Big Data surrounds us. Every minute, our smartphone collects huge amounts of data from geolocations to the next clickable item on an ecommerce site. Data has become one of the most important commodities for individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have been largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only isthis a huge missed opportunity for big data companies, it is one of the significant obstacles in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the talk is to present the TinyML framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution.

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that's attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code. Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the company's co-founder and CEO, the original plan called for Qeexo to be a machine learning application company. The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate.

Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that's attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code. Qeexo emerged from Carnegie Mellon University in 2012, just at the dawn of the big data age. According to Sang Won Lee, the company's co-founder and CEO, the original plan called for Qeexo to be a machine learning application company. The company landed a big fish, the Chinese mobile phone manufacturer Huawei, right out the gate.

"Everybody uses pinch and zoom, because it's easy to remember," Sang Won Lee, CEO of a gesture control company called Qeexo told Digital Trends. "The reason we don't use three- or four-finger gestures is because we can't remember what they do -- but you can probably hold a computer mouse with your eyes closed, right?" Try making that shape with your hand right now, and we're pretty sure you'll succeed. Now imagine using that same gesture to summon up a virtual mouse on the screen of your tablet; or in the future, an interactive tabletop, complete with left and right buttons, and that always-helpful scroll wheel. Such natural movements could transform the way we interact with touchscreens, especially large ones.