Artificial Intelligence (AI) is making its mark as the latest and greatest of all previous information technologies. Many economists are suggesting that AI will support doubling the growth of the leading economies. It will also help increase labor productivity by as much as 40%.
The current model of the AI cycle quickly captures the explosion of data, and redistributes the intelligence closer to billions of end products. These products include cars, smartphones, and IoT devices. Three of the main areas where significant innovation will be traced back to AI are hardware, software, and data.
When it comes to hardware, the increased demands of machine learning, particularly deep learning, require specific technical architectures. That describes the recent boom in new hardware that presents new opportunities in the market, which started with graphic processing units by Nvidia, followed by companies such as Graphcore, and Nervana Systems, which later got acquired by Intel, and there’s much more. According to the curator of The Exponential View:
The Apple X phone uses the first Apple-developed GPU solution, called the A11 Bionic processor. Baidu announced in July deployment of Xilinx FPGA circuits to accelerate deep learning applications in its public cloud. Huawei is expected to launch an application processor that “combines CPU, GPU and AI functions” to bolster “smart computing.” Efinix, with major funding from Xilinx, is in the game to launch new Quantum programmable technology in 2018, chips that will squeeze AI into much smaller, more efficient point on the edge.
When it comes to AI and software, machine learning applications are comparable to factory operations. The user inputs data into the software, and the outputs are predictions such as items similar to what was originally inputted, when a delivery likely to reach its destination, or relevant items that the customer want to put in their shopping basket. In general, the outputs aren’t necessarily always correct (or perhaps the definition of correct changes), so the quality of the output consistently needs to be measured. And the key in the word learning is that a good machine learning deployment will learn to reduce the deviations of its output from the desired levels. Any company that is looking into using machine learning at any scale will need to deploy a machine learning pipeline, as Uber has done with Michelangelo.
In the case of AI and data- the magic takes place in getting most out of it to provide convenience, increased efficiency, and insights to the users. What makes them intelligent is the capability to learn from users’ behavior, looping it back into offering more personalized service. It’s not for nothing that data is now considered an even more valuable resource than oil. As such, the most powerful Internet firms, such as Google and Amazon, are at a major advantage because they have an enormous amount of data. The curator of The Exponential View also says, “Large swathes of industry are working double time to increase the range and quality of data inputs they can use to understand their business. While their sales data and inventory levels may be digitized, their footfall or in-factory behavior is not. Deployments in machine vision will create new classes of data for these firms — and in turn engineer new applications.”
We’re certainly living in exciting times to be witnessing the ripple effect of AI across numerous industries.