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Sunday, December 24, 2017

2017 laid the foundation for faster, smarter AI in 2018 | Engadget - Robots

Photo: Cherlynn Low
Cherlynn Low, reviews editor of Engadget says, "We’ve made progress, but there’s still a long way to go."

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"AI is like the Wild West right now," Tim Leland, Qualcomm's head of graphics, told me earlier this month when the company unveiled its latest premium mobile chipset. The Snapdragon 845 was designed to handle AI computing tasks better. It's the latest product of the tech industry's obsession with artificial intelligence. No company wants to be left behind, and whether it's by optimizing their hardware for AI processing or using machine learning to speed up tasks, every major brand has invested heavily in artificial intelligence. But even though AI permeated all aspects of our lives in 2017, the revolution is only just beginning.

This might be a helpful time to clarify that AI is often a catch-all term for an assortment of different technologies. There's artificial intelligence in our digital assistants like Siri, Alexa, Cortana and the Google Assistant. You'll find artificial intelligence in software like Facebook's Messenger chatbots and Gmail's auto-replies. It's defined as "intelligence displayed by machines" but also refers to situations when computers do things without human instructions. Then there's machine-learning, which is when computers teach themselves how to perform tasks that humans do. For example, recently, an MIT face-recognition system learned how to identify people the same way humans do without any help from its creators.

It's important not to confuse these ideas -- machine-learning is a subset of artificial intelligence. Let's use the term machine learning when we're talking specifically about concepts like neural networks and models like Google's TensorFlow library, and AI to refer to the bots, devices and software that perform tasks they've learned.

One of the biggest developments as we head into 2018 is the shift from running machine-learning models in the cloud to your phone. This year, Google, Facebook and Apple launched mobile versions of their machine-learning frameworks, letting developers speed up AI-based tasks in their apps. Chip makers also rushed to design mobile processors for machine learning. Huawei, Apple and Qualcomm all tuned their latest chipsets this year to better manage AI-related workloads by offering dedicated "neural" cores. But barring a few examples like Face ID on the iPhone X and Microsoft Translator on the Huawei Mate 10 Pro, we haven't yet seen concrete examples of the benefits of chips tuned for AI.

Basically, AI has been improving for years, but it's mostly been cloud-based. Take an image-recognition system, for example. At first, it might be able to distinguish between men and women who look drastically different. But as the program continues training on more pictures in the cloud, it can get better at telling individuals apart, and those improvements get sent to your phone. In 2018, we're poised to put true AI processing in our pockets. Being able to execute models on mobile devices not only makes AI faster, it also stores the data on your phone instead of sending it to the cloud, which is better for your privacy.
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Source: Engadget


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