 With the rise of this next generation of computing has been the expansion of cyber-physical systems. With the internet of things, devices, objects and all types of technologies are becoming instrumented and networked to algorithms running in the clouds. With this next generation, computers are starting to come out of the world of well-structured data into the everyday world of unstructured environments where they use large amounts of data to create the context within which they can interpret new things and interact with the world in a fluid fashion. This next generation of robots, unlike previous ones, are safe for and can interact with humans in a more fluid fashion, what is called human-centered robotics. They have a sense of touch and are very precise. Amazon's warehouses that may have up to 10,000 robots assisting people in bringing them packages is one example. But the same evolution in computing that is developed in the platform model is also coming to robotics, where multi-purpose physical robotic capabilities can be delivered via a cloud platform that enables developers to bundle them into new processes and applications. Again, it can't be over-emphasized how important the developments of cloud computing have been to this whole equation. Faith A. Lee of Google Cloud presents it well when she notes, It took me a while, but I started to realize that cloud is the biggest computing platform humanity has ever created. And what is computing today? Computing is to make your data speak intelligently and to act intelligently to solve your problem or your customer's problem. So really, this marriage between AI and cloud is like this perfect vehicle to democratize AI. The combination of smart systems, cloud platforms and cyber-physical systems will revolutionize our technology landscape in the coming decades. With the rapid commodification of smart systems, connectivity to the cloud and sensing devices, more and more of our technologies will become cyber-physical from shopping trolleys to shoes to cars to whole houses. But also smart platforms will be plugged into whole infrastructure systems like the power grid, internet routing, city transport systems, taking in massive amounts of data and learning from it in order to optimize the system. This will be a massive source of technological disruption. Much media attention and public imagination is currently focused on robotics and individual cyber-physical systems. Although little robots cleaning our houses or delivering pizzas might be the most apparent manifestation of this change, the real innovation will be delivering these machine learning algorithms as a service to IoT platforms, the network, whole infrastructure systems, whether that's cloud analytics connected to the smart grid, transport networks or connecting an enterprise's whole supply chain up, or even the smart city itself. Take for example the mining industry that is currently going through a massive wave of automation as mining companies are rolling out autonomous trucks, drills and trains. From a control centre in Perth, Rio Tinto's employees operate autonomous mining equipment in Australia's remote Pilbara region, which is rich in minerals. 73 trucks, each the size of a small two-storey house, find their way around using precision GPS and looking out for obstacles using radar and laser sensors and work alongside robotic rock drilling rigs. From their single operation centre, they integrate information from all their mines, ports and rail systems and visualization technology gives their personnel a 3D display of all their operations. As the company says, these technologies take us ever closer to whole mine automation. Cloud analytics is a service model in which elements of the data analytics process are delivered through a public or private cloud. Cloud analytics applications and services are typically offered under a subscription basis or utility paper use pricing model. Google, Microsoft and Amazon also expect to increase their profitability and enhance their cloud services through associating it with their machine learning capabilities. Their strategy is to allow companies which are unable to develop machine learning solutions at their level to access their cloud-based ML services through APIs. One example of this is the apps recently developed by Microsoft for the facial recognition of Uber drivers. Companies like Uber already have machine learning platforms and increasingly these new technologies will be used to analyse the data coming from their car sharing platform to optimise where cars go, what route they take, how much they charge, essentially automate the most basic management activities of their platform. The platform model will be important in developing smart solutions in that it will enable different smart capabilities to be offered as modular utility functions that can then be plugged into and bundled together by enterprises according to their specific needs. Instead of having just one general purpose system, a platform model allows developers to draw upon specific capabilities and integrate them into their solution such as machine learning to recognise a facial or voice recognition software or advanced analytics for specific domains. Equally the platform plug and play model will work to commoditise smart systems making them available to almost any technology developer. APIs and developer toolkits are already offered by IBM that can be plugged into a wide variety of applications from health diagnostics to analysing data coming from transport systems. In such a way smart capabilities will flow to almost all types of physical technologies in the coming decades. These cloud-based platform model to smart systems will mean that through an internet connection even the smallest of computers like a mobile phone can operate like the most powerful computers in the world by simply sending the inputs to the cloud where it's processed and then output the information that's returned. This is quite an extraordinary phenomenon in that it means that the most powerful computing operations and algorithms can be accessed anywhere there's internet connection on the planet meaning that the most advanced technology of our age can be accessed and used virtually anywhere on the planet through just a mobile phone and internet connection whereas previously we put computing devices into the hands of people now we're putting supercomputers into their hands. The primary beneficial function of analytical systems will be in their management and optimisation of large complex networks they will be connected into whole transport networks power grids cities and possibly even whole urban networks analysing that data to make predictions, optimisations and adaptations APIs will make high-end machine learning capabilities available to all forms of devices and physical systems as one commentator noted APIs are not a dime a dozen, they're a dime a million. One aspect of this platform model is that it can harness fleet learning because any component is operating within a network when one smart system learns something then all have access to that new information almost immediately that kind of network effect means that the system can improve its capacities at an extraordinary rate the system will learn over time due to network effects and big data machine learning network components can help each other to create a wisdom of the crowds effect The CEO of Tesla explained fleet learning within their network the whole Tesla fleet operates as a network when one car learns something they all learn it he goes on to explain how each car using the autopilot system essentially becomes an expert trainer for how the autopilot should work the company's autopilot service is constantly learning and improving through machine learning algorithms because all of Tesla's cars have an always connected wireless connection data from driving and using autopilot is connected sent to the cloud and analyzed with software for autopilot Tesla takes the data from cars using the new automated steering or lane change system and uses it to train its algorithms Tesla then takes these algorithms, tests them out and incorporates them into their upcoming software in this way we can see how cloud platforms, machine learning and the internet of things can work in a synergistic fashion