The art of music permeates the globe as a universal expression, but as of late, advances in AI and IoT are elevating it from simply an art form to a whole new echelon rooted in science. What if listening to music was more than just a leisure pastime, and a song was more than just a choice of preference. Rubato.life, a new LA-based startup, released their beta application SDK last week that enables wearable devices
The current state of today’s connected, information-overloaded era compounded by the uncertainty and hardship due to the COVID19 pandemic weighs heavy on the world, often culminating into heightened stress. Among other afflictions, in numbers, stress is the leading proxy killer in American society and caused nearly in costs to businesses globally last year. As of late, the unconventional yet increasingly popular practice of wellness has been gaining , user and financial endorsement for effectively reducing
Datomize, a Tel Aviv-based startup has come out of stealth mode today to democratize machine learning with a code-free AutoML platform, and pre-seed funding raised from early-stage venture capital firm Taya Ventures, led by serial entrepreneur Kobi Marenko. The startup’s AutoML platform expedites the pipeline of machine learning model development, covering a host of steps, from data enrichment to deployment. Building on top of the machine learning expertise that’s afforded Marenko successes in his previous
The power of voice as a command interface to your online shopping desires is undeniably convenient. The combination of over integrated with voice assistant technology expected for 2020, and equally massive voice queries gives credence to the market fit behind voice search. But as the disparity between sales conversion rates for online e-commerce stores (2%) and retail stores (20-40%) persists, and despite voice based sales totaled in 2018, voice technology has yet to disrupt retail
As the deep learning market matures, developers are transitioning their trained models for use in production, ushering in a new set of challenges. Among the difficulties are the need to deploy models built in the lab on commercial hardware with its many computational limitations. These real-world restrictions have a direct impact on the models’ accuracy. With the looming uphill battle AI developers will soon face in continuously optimizing both software and hardware components at inference
