by Robert Triggs, Android Authority:
If virtual assistants have been the breakthrough technology in this year’s smartphone software, then the AI processor is surely the equivalent on the hardware side.
Apple has taken to calling its latest SoC the A11 Bionic on account of its new AI “Neural Engine”. Huawei’s latest Kirin 970 boasts a dedicated Neural Processing Unit (NPU) and is billing its upcoming Mate 10 as a “real AI phone“. Samsung’s next Exynos SoC is rumored to feature a dedicated AI chip too.
Qualcomm has actually been ahead of the curve since opening up the Hexagon DSP (digital signal processor) inside its Snapdragon flagships to heterogeneous compute and neural networking SDKs a couple of generations ago. Intel, Nvidia, and others are all working on their own artificial intelligence processing products too. The race is well and truly on.
There are some good reasons for including these additional processors inside today’s smartphone SoCs. Demand for real-time voice processing and image recognition is growing fast. However, as usual, there’s a lot of marketing nonsense being thrown around, which we’ll have to decipher.
AI brain chips, really?
Companies would love us to believe that they’ve developed a chip smart enough to think on its own or one that can imitate the human brain, but even today’s cutting edge lab projects aren’t that close. In a commercial smartphone, the idea is simply fanciful. The reality is a little more boring. These new processor designs are simply making software tasks such as machine learning more efficient.
These new processor designs are simply making software tasks such as machine learning more efficient.
There’s an important difference between artificial intelligence and machine learning that’s worth distinguishing. AI is a very broad concept used to describe machines that can “think like humans” or that have some form of artificial brain with capabilities that closely resemble our own.
Machine learning is not unrelated, but only encapsulates computer programs that are designed to process data and make decisions based on the results, and even learn from results to inform future decisions.
Neural networks are computer systems designed to help machine learning applications sort through data, enabling computers to classify data in ways similar to humans. This includes processes like picking out landmarks in a picture or identifying the make and color of a car. Neural networks and machine learning are smart, but they’re definitely not sentient intelligence.
When it comes to talk of AI, marketing departments are attaching a more common parlance to a new area of technology that makes it harder to explain. It’s equally as much an effort to differentiate themselves from their competitors too. Either way, what all of these companies have in common is that they’re simply implementing a new component into their SoCs that improves the performance and efficiency of tasks that we now associate with smart or AI assistants. These improvements mainly concern voice and image recognition, but there are other use cases, too.
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