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A20-icon.jpg' alt='Drivers Allwinner Technology' title='Drivers Allwinner Technology' />The dawning of the AI age. I dont think many would disagree that the key technology trend of the 2. However today I think we are on the verge of a huge shift towards another driving paradigm smartness. Mobile has had huge social benefits from connecting us to help and community, to enabling access to rich information everywhere. Yet there are also downsides over stimulation and over reliance perhaps being two. What will the effect of the next wave of devices be Already Im sure youve heard of the prediction that cars intelligent enough to drive themselves will lay waste to professions that involve human drivers. But the knock on effects will be even greater. The insurance industry, car parking providers, car manufacturers, leasing companies all will change. And what will drivers do when they no longer drive The rise of in car entertainment and lifestyle options will surely follow. Driving is just one part of our work that autonomous systems will affect. Science fiction writers like Iain M. Banks talk about a post scarcity future where lives are driven by the pursuit of purpose rather than work or money. Will that be the end result of our smarter machines In Banks future, even government has been replaced by AI. Artificial intelligence is a very broad topic. It covers everything from smart sensors and fully unmanned cars through to complex intelligences we cant yet create but to which we can already see a potential path. Consider AI as being a set of component technologies machine learning, deep learning, CNN etc. But whatever you call them, they add up to smarter machines, with that smartness residing to some extent in local devices like cameras, consumer products or robots, and to some extent in the cloud, depending on the application. Whether its platooning, crowdsourced parking, smart things, Massive Machine Type Communications, Io. T, SLAM and more AI is a component today and will be more so tomorrow. Welcome to the machine. Today, we can easily talk to a machine and expect intelligent, albeit not Turing test passing, answers. Voice controlled things are now part and parcel of the everyday, bringing added convenience and increased productivity. By combining three technologies Io. T, AI and 5. G, the transformed smart things can enable an experience which just a few years ago was the preserve of science fiction. Our devices will start to experience us understanding where we are, what we are saying, what we actually want even when we express it imperfectlyAI is real machines can now drive like us, play games like us and see like us. But this isnt necessarily like the dark future we see in the TV show Westworld this is about how to make our day to day lives better. While the self awareness experienced by the machines of Westworld is still in the future, today we all need to be talking about the intersection of smartness and devices, whether its across robotics, autonomous devices, smart home, or the Internet of Things. Inside the head of AIThere are many kinds of AI, and they each need different techniques and technologies. Reactive machines we can do well today using rules based AI. Amazon S3 File Upload Api Console'>Amazon S3 File Upload Api Console. A reactive machine perceives and reacts to stimuli but has no internal concept of itself or the world. Systems we are involved in today, like autonomous cars, go beyond this to have a sense of time and memory, which they need to keep track of real world objects and events. But they dont really have what we would call experience they arent learning to handle new situations, just to improve their handling of known ones. To go beyond this, AI needs not just a system of functions but a Mind that can adjust and learn and even ultimately achieve consciousness and have an original thought, a concept of others, and finally self awareness. The technology for this is not impossible to conceive of today it is basically advanced handling of memory, learning, and decision making all areas that AI researchers currently work in, though without so far the success and sophistication to build fully aware AI. At Imagination, we are using our Power. VR GPU compute technology to help drive the machine learning revolution. Our mission is to enable machine learning in low power, cost effective platforms where to date it has not been possible to support the type of computational load necessary. We see two models out there the smartphone world where the GPU in these platforms is being used to enable smarter AI apps and autonomous loosely connected sensors with AI at both the sensor level and in the cloud. With applications such as autonomous vehicles, more intelligent security systems, and context aware consumer goods, this technology is rapidly emerging. Power. VR is focused on applying technologies like CNN because GPUs are highly applicable to this sort of highly parallel data driven computation. CNN is very real and here to stay. Huge samples of data being stored and trained in the cloud can then be applied at the level of devices like cameras, robots and drones to recognise, manipulate, avoid and engage with objects in the world. In autonomous vehicles, the ability to recognise a very large number of objects, from road signs to cats, and to understand them in a range of contexts, has to happen instantaneously within the system. In those devices, we need to enable comprehensive real time in system intelligence, which is something a GPUs highly parallel nature makes it ideal for. This applies not just to auto, but also embryonic markets like security and retail cameras we are enabling the intelligence as close to the edge as possible, at low power and low cost. Accelerated and connected minds. We humans have limited inputs our eyes, our ears, perhaps taste and smell. We have minds that wander but are in one place. The intelligence of our future machines will be very different. For a start, we would like them to be focused on the job at hand and yet, architecturally in embedded, they are likely to be a distributed architecture with parallel, loosely coupled processors at nodes across the system we dont want them to expend energy when they dont need to and we will rely on them to be safe and highly reliableespecially if they are our acting as our future chauffeurs. They will have different priorities than us, with their minds being layered with core tasks at the base, then a hierarchy of options in the subsumption architecture model proven already in robotics. But like us, they will think about some common concerns are they safeAre they healthy How can they adjust to make their lives simpler and more efficient  They will become increasingly self organising. Our partners possess the algorithms and domain knowledge to understand these inputs and apply them to applications. Our role is to create hardware accelerators for that learning and understanding and ensure real time response to the huge amounts of data coming in from those inputs. For example, in autonomous vehicles, its of no use for our partners to have advanced collision avoidance systems if we cannot provide real time response. This is an area in which our multi threaded MIPS CPUs excel. And there are other kinds of minds coming into play for example in Io.