Technology has had a profound impact on our lives. It has to allowed us to create astonishing tools and machines that make our lives easier and more secure, like cranes, the automobile or x-ray scanners. We have significantly reduced famine, plague and war, doubled our life expectancy, and live a much more comfortable life compared with just a couple of hundred years ago. Most of this can be attributed to technological advancements. Our intelligence together with our ability to create powerful tools has moved us to the top of the food chain. The invention of artificial intelligence, combined with other modern technologies, like Internet of Things, Big Data, and robots will now take us to new productivity levels, far beyond today’s possibilities.
The impact of artificial intelligence on technology, in general, is huge. It adds learning capability to machines and improves the decisions that machines need to make. This is done through software and neural networks, which have improved based on the huge data transfer capacities of recent computing generation, and the highly parallel computing capabilities of modern chips with specialized parallel processing architectures. All computers, machines, and robots are directed by software. The programs that run them define their performance and capabilities. The logic of software is based on input, some operations and calculations being done on the input producing an output.
Since artificial intelligence enables the machine to learn from this flow, from input to output, by being trained or by observing the results, the software can adapt itself to perform better in the future. Today this is often done through neural networks, whose multi-level pattern-recognitions create self-improving algorithms based on observing enormous data streams. These algorithms can yield better results than algorithms created by human developers in traditional ways, giving artificial intelligence an edge over human-created ones, especially in cases where there are many complex parameters, and large volumes of data over time. Better algorithms enable better software that even improves over time. This brings cognitive capabilities to other technologies running on software, and because most technologies run on software, the impact is huge.
Computer chips, sensors, the Internet, cloud computing, apps, robots, drones and augmented reality are some examples where artificial intelligence based software improves the performance. But also for more complex systems like enterprise resource systems, mobile phones, and large-scale traffic control systems, artificial intelligence advances the capabilities, performance, and quality of software and machines significantly. It also adds new capabilities like voice and image recognition that enable new functions and more efficient and convenient user interfaces. The capability of machines to learn also allows more complex use cases, where several steps are needed to perform a given task.
Initial technological areas where artificial intelligence has the most profound impact are Big Data, Internet of things and robotics. The increasing digitalization of many areas of our lives and businesses have created enormous data pools and constant data streams. The area of Big Data focuses on datasets that are so large or complex that traditional data processing application software is inadequate to deal with them. Big Data is often generated by combing application specific data with external data sources, thus making the data sets very complex and difficult to handle.
A simple example is using an app to go to a restaurant. Your smartphone knows the time of the day, your current location, as well as your destination. An algorithm can calculate the best route to get from A to B. This information can be combined with weather information, information about road traffic, public transportation options, taxi and Uber-like options or local bike sharing options as well as the availability of bike paths on the way. Predictive data can simulate how the situation is likely to change in the near and distant future. The complexity of handling such a digital service becomes enormous. Artificial Intelligence can make better sense of the data, identify patterns and learn from your behavior and the result. How much time did it actually take to reach the destination and how is it different from what was projected? What factors caused delays? An intelligent routing system based on artificial intelligence can produce significantly better results than simple routing algorithms. And it can improve over time as it learns what factors matter and what personal behaviors impact the result.
Other examples are understanding medical images, optimizing plant fertilization and watering, simulating climate change predictions and financial transactions. In companies, customer behavior, digital marketing and advertising and human resource performance are other examples of Big Data applications where artificial intelligence can play a big role.
More and more data sets are produced by real world sensors that have emerged through the growth of Internet of Things (IoT), where smart and connected little machines perform simple tasks in all parts of our lives. Scales, cameras, coffee machines, thermostats, gates, video surveillance systems are only a few such examples. IoT is characterized by inter-networking of physical devices. Often it contains sensors to generate data, an ability to communicate this data through the Internet and then acting on this data. A surveillance camera generates a series of pictures, uploads them to the cloud, where image processing software detects the alarming situation and informs the user via an app, while the camera sounds a built in alarm. Then based on the movement of objects, the camera can follow moving objects or can be controlled via an app from anywhere in the world. Both the application of IoT devices and the sheer amount of data they produce are creating a lot of use cases that benefit from artificial intelligence. Most of the recent devices are labeled smart or intelligent, like Nuki’s door knob that automatically opens your door as you approach with your smartphone, but just like the personal voice assistants, the intelligence can still be questioned. Financial services company IHS forecasts that the IoT market will grow from an installed base of 15.4 billion devices in 2015 to 30.7 billion devices in 2020 and 75.4 billion in 2025, so IoT becomes a big driver and beneficiary of artificial intelligence capabilities.
Another area where artificial intelligence is being deployed on a big scale is robots. The field of robotics, an interdisciplinary branch of engineering and science that includes mechanical engineering, electrical engineering, computer science, materials and others, has embraced artificial intelligence to give robots cognitive capabilities, going from simple pre-programmed capabilities to more complex, context-aware applications of high quality. Robots combine a lot of technological capabilities and also incorporate technologies like Big Data or IoT. Robots come in many shapes or forms like manufacturing machines, self-driving cars, and drones. These cognitive capabilities allow new use cases like monitoring crop health by drones, resulting in better fertilization and use of pesticides. The cost saving and quality improvements in comparison to traditional models, like human inspection or airplane monitoring, are huge. According to a study by Informa Economics, corn, soybean and wheat farmers could save an estimated $1.3 billion annually by using drones to increase crop yields and reduce input costs.
Robots are also an area of great dispute and cause of human anxiety. In the industrial revolution, people feared that automated machines would take away a lot of their jobs, and science fiction movies have often portrayed robots as stronger than humans, once they turn to their own self-created motives. If these motives are bad, the impact on humanity is existential. We should be aware that humans create the technology and give robots their tasks.
The new and evolving capability of artificial intelligence, and its impact on other technologies, has added a lot of complexity to an already difficult-to-understand and difficult-to-master landscape of technologies. The complexity of creating the best solution for a given use case and the impact for businesses if they get it wrong are limiting factors for take-up of the technologies in many areas. Fortunately, an ecosystem of software, tools, and services is being created at a fast pace, fueled by corporate and venture investments in the promising new area of artificial intelligence. One can find ready-to-use and proven solution in all areas of technology. If you are looking for complex industry solutions, if you are looking to optimize your enterprise processes, are looking for capabilities, tools and components like image recognition, voice understanding and translations, neural networks or core technologies like neural networks, there is a growing ecosystem of artificial intelligence-enhanced solutions and services that you can tap into.
Another ecosystem visualization focuses on core areas of enterprises:
Businesses now need to figure out how to create value for their customers using artificial intelligence and what business model makes it competitive and profitable.