Wall Street is predicting a rough 2024 for Apple's iPhone franchise because of a lack of interesting new hardware features. Could artificial intelligence software make an iPhone 16 shine brighter?
Some Apple stock bulls think so. Morgan Stanley analyst Erik Woodring this month opined that 2024 "will be the year that Apple's 'Edge AI' opportunity comes to fruition," and that it could power the new crop of iPhones this fall to greater heights.
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Apple's iPhone sales, led by the current iPhone 15, are expected to decline by about 2% this year, according to estimates compiled by FactSet Systems, to 229 million units, as the current iPhone cycle underwhelms with merely iterative hardware features.
But come 2025, wrote analyst Woodring, current Wall Street expectations for growth of 4%, to 237 million units, could turn out to be 15% higher if an iPhone 16 has enhanced AI capabilities.
"If we are correct, and new LLM-enabled software features drive an upgrade cycle, then we see the potential for up to 15% upside to our FY25 iPhone shipment forecast," wrote Woodring. The acronym "LLM" refers to "large language models" such as OpenAI's GPT-4.
Woodring speculates that the world will see details at Apple's Worldwide Developer Conference this summer, "highlighted by an LLM-powered Siri 2.0 and a broader GenAI-enabled operating system that has the potential to catalyze an iPhone upgrade cycle."
Why is "LLM-powered" such a big deal? To use large language models akin to OpenAI's GPT-4 requires a phone to go back and forth to the network, sending prompts and retrieving responses. Even on a desktop computer with an ethernet connection, the round-trip means waiting a while for a response. In a mobile device on a cellular network, relying on the cloud connection could result in one of those awkward moments where Siri seems brain-dead.
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Instead, what's needed is to eliminate the cloud reliance and move more of the LLM processing locally, on the device. Apple already has what it calls the "Neural Engine" in the iPhone, a separate collection of circuits for running AI. However, the AI tasks performed by the Neural Engine -- tasks much less demanding than an LLM -- are likely to involve very carefully defined functions such as face recognition, where the use of the circuits has been carefully curated.
Taking an off-the-shelf large language model and running it locally is bound to be a much more demanding task.
Woodring bases much of his enthusiasm about this year's AI on a paper published this month by Apple researchers Keivan Alizadeh and colleagues, titled, "LLM in a flash: Efficient large language model inference with limited memory," which is posted on the arXiv pre-print server.
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The crux of the paper is that LLMs take up a lot of memory, and Apple has found a clever way to use the vast storage of the resident flash memory -- the stuff that holds the iPhone's files. With special software, an LLM can be easily moved into and out of main memory, DRAM, with the illusion of having a lot more DRAM than is typical on the phone.
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