Updates from Google Next meet 2018: Artificial Intelligence is helping so many enterprises these days, be it analytics, and be it forecasts. What if these machine learning tools are combined with the Internet of things (IoT), the technology that enables communication between devices! Well certainly, technological advancements take no time, and for this particular one, Google has made that advancement.

In this great era of new technologies, it is important to focus on extensions of existing projects. Companies focusing on AI and IoT based products should know how to get more from their existing projects. Right now, Google is broadening the bandwidth of Artificial Intelligence and machine learning applications which now extend to the Internet of Things, least to say increasing the efficiency of IoT technology with manifolds making them way more smarter and dependable.

On Wednesday a brand new technology was proposed, The Edge Tensor Processing Unit (Edge TPU), a custom (Application specific integrated circuit) chip that operates TensorFlow framework of machine learning in edge devices. Machine learning modules are now exercised in a lighter version to enable usage on varied devices.

The Google Cloud’s features of AI are even extended to communication gateways and devices in connection, by the IoT cloud of edge devices. It performs over two platforms mainly Android Things or any Linux enabled OS. Further, it comprises two other runtime software assembles, called the Edge IoT Core and Edge ML. The former induces secure connection between cloud and edge devices for enabling updates from software and firmware and manages data exchange to cloud IoT core. The latter, Edge ML uses a lighter version of TensorFlow which runs pre-installed machine learning framework and modules without calling the server i.e. locally, which as a result reduces latency and enables higher versatility operations of edge devices.

Looking into hardware assembly, the custom (Application specific integrated circuit) chip’s new design enables running of the lighter version, The TensorFlow Lite on edge devices and induces machine learning in the cloud along with Google’s Edge TPU Cloud.

“Normal sensors now induce more application than just collecting data, they are now capable of making local decisions in real time,” says Antony Passemard, Head (Product Management) for Cloud IoT at Google explained in his presentation for the press on Tuesday.

Google is planning to have a development kit module of Edge TPU, to be released in October.  This will help developers to start with the creation of IoT solutions with the help of brand new chip.

All cloud providers are now looking for ways to incorporate their machine learning platforms with more efficient tools and technologies to help enterprises extract more intelligence from their data sets and IoT devices. Since now, the new technology is the smarter technology; the more your technology does work for you the more efficient we call it.

With similar initiatives, Amazon web services caught our attention with SageMaker last year, which helped in increasing ease in ML along with some additional tools that speed up their process announced this month.

Microsoft Corp another tech giant also did not lack behind and introduced their deep learning acceleration platform called the BrainWave last year.