Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. One of the most difficult things about detecting manipulated deepfakes and photos is that digital photo files aren't coded to be tamper evident. But researchers from New York University, as well as other researchers and start ups around the world, are starting to develop strategies that make it easier to tell if a photo has been altered, as well as finding new ways to prevent your likeness from being deepfaked, opening up a potential new front in the war on fakery. Forensic analysts have been able to identify some digital characteristics they can use to detect meddling, but these indicators don't always paint a reliable picture of whatever digital manipulations a photo has undergone. But what if that tamper-resistant seal originated from the camera that took the original photo itself?

I worry that when writing these columns, I sometimes start by meandering my way off into the weeds, cogitating and ruminating on "this and that" before eventually bringing the story back home. So, on the basis that "a change is as good as a rest," as the old English proverb goes, let's do things a little differently this time. Take a look at the image below. What do you see in addition to the penny piece? What I see is a Mantis AI-in-Sensor (AIS) System-on-Chip (SoC), where the "AI" portion of this moniker stands for "artificial intelligence."

If you're wondering how good your next phone's camera is going to be, it'd be wise to pay attention to what the manufacturer has to say about AI. Beyond the hype and bluster, the technology has enabled staggering advances in photography over the past couple of years, and there's no reason to think that progress will slow down. There are still a lot of gimmicks around, to be sure. But the most impressive recent advancements in photography have taken place at the software and silicon level rather than the sensor or lens -- and that's largely thanks to AI giving cameras a better understanding of what they're looking at. Google Photos provided a clear demonstration of how powerful a mix AI and photography would be when the app launched in 2015.

Artificial Intelligence (AI) and Machine Learning (ML) are changing everything in the electronics industry. Engineers are now evaluating how to design and train intelligence solutions in everything from sensors, to smartphones, to networks, to cloud data centers. However, just as there has never been a single processor for every application or workload, so too is there no single solution for AI. Qualcomm appears to be hedging its bets with a distributed AI solution the company calls the Artificial Intelligence Engine (AIEngine) and a dedicated AI accelerator, that was just announced at a company sponsored event in China. At the moment, most of the training of artificial neural networks is done in data centers.

Expensive flagship phones won't be the only way for you to play with advanced features like AR Emoji, Animoji and Face ID much longer. Qualcomm is making it easier for companies to create mid-range smartphones that pack those functions by launching a new mobile processor. The Snapdragon 710 will come with a multi-core AI Engine and support neural network processing, as well as image signal processors and graphics units that are typically found in higher-end chipsets. The 710 is the first of the 700-series, which was announced at MWC this year, and will sit above options like the 600- and 400-ranges but below top-tier chips like the Snapdragon 845. The Snapdragon 710 is a 10nm chipset that features a multi-core AR engine for on-device neural networking processing, as well as a Spectra 250 image signal processor that enables things like multi-frame noise reduction and AI camera features like video style transfer and active depth sensing for artificial bokeh.

It's been a strong fortnight for machine intelligence fans, starting with Arm's Robert Elliot and Mark O'Conner publishing a white paper on the company's Arm NN machine learning platform and its optimisations for use on low-power embedded devices. "We expect machine learning to become a natural part of programming environments, with tiny embedded neural networks being part of program execution," the pair explain of the inspiration behind Arm NN. "To prepare for this, we've developed a low-overhead inference engine with the ability to import a file produced by a handful of machine learning frameworks. This supports a'write once, deploy many' approach to development, with the same framework able to target the Cortex-A class cores used in high-end mobile as well as the Cortex-M class cores used in processing environments with very small memories. We've spent significant effort to make sure that good performance is achieved on all of these processors It enables efficient translation of existing neural network frameworks, including TensorFlow and Caffe, allowing them to run efficiently, without modification, across Arm processing platforms. The inference engine can be distributed to different devices while taking advantage of the key optimizations of each."

Artificial intelligence is permeating everybody's lives through the face recognition, voice recognition, image analysis and natural language processing capabilities built into their smartphones and consumer appliances. Over the next several years, most new consumer devices will run AI natively, locally and, to an increasing extent, autonomously. But there's a problem: Traditional processors in most mobile devices aren't optimized for AI, which tends to consume a lot of processing, memory, data and battery on these resource-constrained devices. As a result, AI has tended to execute slowly on mobile and "internet of things" endpoints, while draining their batteries rapidly, consuming inordinate wireless bandwidth and exposing sensitive local information as data makes roundtrips in the cloud. That's why mass-market mobile and IoT edge devices are increasingly coming equipped with systems-on-a-chip that are optimized for local AI processing.

Not surprisingly, this year's smartphones feature faster processors than those from last year--that happens every year. But what is new this year is the predominance of machine learning features that just about every processor vendor is touting as a way of differentiating their devices. This is true for the phone vendors who design their own chips, the independent or merchant chip vendors who sell processors to phone vendors, and even the IP makers who design the cores that go into the processors themselves. First a little background: all modern application processors include designs (often referred to as intellectual property, or IP) from other companies, notably firms like ARM, Imagination Technologies, MIPS, and Ceva. Such IP can appear in various forms--for example, ARM sells everything from a basic license for its 32-bit and 64-bit architecture, to specific cores for CPUs, graphics, image processing, etc., that chip designers can then use to create processors.

When Huawei presented the first smartphone processor with an AI unit at the IFA with the Kirin 970 from its subsidiary Hisilicon, the Chinese company captured the public's attention. The Neural Processing Unit (NPU) on the SoC is specifically responsible for the functions that can be performed faster and better with AI than with a traditional processor. At present, this mainly includes functions related to photography, image recognition and processing. First of all, artificial intelligence is the wrong term: considering today's so-called AI applications, at the core they're really using machine learning. A convolutional neural network (CNN) learns to recognize patterns on the basis of a large amount of raw data--completely independently.

The next-generation mobile processor that you'll most likely find in many of next year's major flagships is here. At its second annual tech summit today, Qualcomm unveiled the Snapdragon 845, which is its latest "premium" mobile CPU. The chipset will retain the same 10nm footprint as its predecessor, but feature revamped architecture that brings about new features like 4K HDR video capture on smartphones and improved AI processing. Qualcomm said it's focusing on AI, immersion, security and connectivity with the new chip. Immersion basically means the ability to capture and render the world around you at high resolutions and quality for more realistic results.