The public cloud is headed for an unprecedented phase of maturity with universally high growth rates and sinking barriers to entry. Artificial intelligence (AI), machine learning and augmented analytics are now widely used, and as these fields become more accessible, a new, more robust generation of AI applications is being born. Things are becoming increasingly autonomous. With this next stage of digital transformation imminent, the rise of autonomous objects is fast approaching. Autonomous objects are characterized by the fact that they act independently with each other or with people in an extended ecosystem and are a result of further advances in the areas of AI, network technology, and cloud and edge computing. But all jokes of robots replacing humans aside; what are the real-world applications of such technology, and what will this automated future look like?
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There’s an AI for that
This journey is not unfamiliar, we’ve seen countless times before the power of advancements in certain technology and the life-enhancing ability it can bring. Look at the success story of apps – this began with the triumphant advancements in mobile devices. These highly specialized, relatively small programs offered practical support for real, everyday problems – so much so that “there’s an app for that” has become a common saying. It’s extremely likely that there will be a similar development in the field of AI, but instead of a comprehensive super AI, we will see the future in modular AIs. This will change the way we process and consume information and in ten years, we will have an AI for everything, just as there is a mobile app for any scenario today.
An automated future
This new autonomy of things should not be something to fear, it should be embraced, as being open to an automated future will allow for efficiency across countless applications. From autonomous household appliances and driverless transport systems, to warehouse management drones and damage detection technology in buildings and wind turbines – autonomous objects are opportunities for companies to enhance their processes and save valuable time and money.
One promising, real-world example of an autonomous object in practice is the mobile robot SPOT, from Boston Dynamics. This robot can climb stairs on four legs, collecting high-resolution image data that is analyzed for preventative maintenance. As terrifying as a four-legged robot sounds, the technology is proving to be highly valuable and has been adapted to provide an automated vehicle-damage-detection solution for rental or leasing companies.
When used in vehicle inspection, this automated technology can help streamline an otherwise time-consuming and costly manual process of recording damage of recently returned rental cars. Thanks to image and spatial recognition, combined with the necessary computing power, SPOT can independently navigate around the vehicle and record its condition. Using computer vision, SPOT moves freely through the parking area and scans the license plates to find the right vehicle. Once detected, it walks around the vehicle to record its condition by continuously collecting visual data with its camera and sensors. This information is uploaded to the cloud, where advanced image recognition and machine learning algorithms perform the damage detection. All damages are saved in the return protocol and can be accessed by employees and presented to the customer for approval via display in an easy-to-navigate app – after all, there’s an app for everything, right?
And it doesn’t stop there. Further application scenarios of autonomous objects can be found in building information management as well as plant monitoring. A robot’s mobility and autonomy can enable precise visual and acoustic measurements in addition to gas detection in hard-to-reach areas. In doing so, with the help of predictive maintenance models and deep-learning algorithms, potential threats to the environment and health and safety can be detected. Furthermore, autonomous objects can also prove to be highly beneficial when applied to logistics and supply chain management. Autonomous drones are being developed to execute the stock count with greater accuracy and minimal human intervention, redefining the way companies perform this once laborious task.
One area not to be underestimated is that of the use of AI-supported tools in revolutionizing software development. The growing demand for software solutions has made the glaring shortage of skilled workers increasingly visible. However, the use of Natural Language Processing and AI-assisted programming now make it possible to develop software without extensive programming knowledge, virtually as a layman. With that in mind, AI is opening up new worlds for those who write and compile software – it can support developers with algorithms that automatically generate code in different application scenarios, using advanced programming language processing capabilities. If such low-code, no-code approaches are taken further with the possibilities of machine learning, eventually there could be machines that program themselves, accelerating the transformation process.
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The trend of Quantum Machine Learning
Promising developments in hardware is also something to take a note of, with the combination of machine learning and quantum computing in particular holding enormous potential. Today, quantum computers are already able to solve certain problem classes many times faster than traditional computers. Especially in complex optimization problems, the so-called “quantum supremacy” becomes apparent, from which machine learning can also benefit, with faster processing of larger amounts of data, exponential acceleration of model training and pattern recognition. These advantages can also be used in hybrid approaches to increase precision and speed. Additionally, an extensive range of quantum development libraries are available that allow the strengths of quantum hardware to be tested and exploited with well-known programming languages such as Python.
These were the focus topics of Reply’s Xchange event earlier this year, which highlighted the state of tomorrow’s technologies, including quantum and spatial computing, new human-machine interfaces, biotagging technologies, 5G and mobile robots. In addition to autonomous objects, we wanted to illuminate the tech trends that are shaping our futures, from lectures and workshops addressing digital solutions with spatial computing, to discussions on the Internet of Things (IoT) in all its forms. Considering the breadth of these technological advancements and the rate in which they are developing, it’s evident that the next stage of digital transformation is well underway. The autonomous movement is just the beginning, and soon AI will influence many, if not all, aspects of our existence.
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Filippo Rizzante, CTO, Reply