We are living in a period of technological upheaval: within just a few years, the computer and the Internet have completely transformed the way we live and work. Find out here which of the new technologies are really important and why they will shape our everyday lives in the future.
Autonomous driving is becoming increasingly important. Cars are already taking many tasks off the driver’s hands: They can park on their own, keep in lane and match speed with the car in front. Every year, more functions are added here. It’s only a matter of time before steering will be completely automated.
The technology behind autonomous driving
Autonomous driving relies heavily on the use of various sensors, whose data is then analyzed in real time:
- Video cameras provide images of the surroundings. A front camera records the road, including its markings, traffic signs and road users. Other cameras provide images of the remaining three sides.
- An additional camera on the roof provides an all-round view. Parallel camera sensors can even record a three-dimensional image, helping the autonomous system to better estimate distances and speeds.
- Radar sensors measure the distance to other vehicles and other objects. Several sensors with different ranges are used for this purpose: High-range sensors measure distant objects, low-range sensors those in the near vicinity.
- Ultrasonic sensors are already used today as parking aids. They can reliably measure distances in the centimeter range.
- LiDAR sensors are a new technology. LiDAR stands for “Light Detection And Ranging.” They use laser beams for measurement, as opposed to radio waves in radar. Their advantage is their long range: they can “see” up to 200 meters away, even at night. They thus complement the less range-intensive radar sensors and the light-dependent video cameras.
- The GPS system provides the on-board computer with precise location data. For autonomous driving, road maps accurate to the centimeter and a fast navigation system are needed so that the automobile knows in real time at all times which lane it is currently in.
The data obtained is then integrally processed by computers with artificial intelligence. In this way, decisions on driving direction and speed can be made in the shortest possible time.
The benefits of autonomous driving
In an ideal world, the autonomous vehicle is the perfect driver: autonomous vehicles obey all traffic rules, coordinate with each other, react faster than any human to dangerous situations, and maintain an optimally energy-efficient driving style. They are never aggressive to other road users and do not behave recklessly.
In addition to this gain in safety and environmental friendliness, autonomous driving above all ensures more time. Passengers no longer have to pay attention to road traffic and can devote their time to other pursuits while driving. In addition, the importance also lies in the demographic development of our society: there are more and more older people who can no longer drive for health reasons, but at the same time do not want to do without mobility. For them, autonomous driving will replace today’s cabs and other costly transportation services.
The blockchain has become known through cryptocurrencies such as Bitcoin. But it is also gaining importance in other sectors of the economy, such as logistics or IT auditing.
The technology of the blockchain
The blockchain works with encrypted data that is chronologically chained together: A block is appended to an original data record, then another block, and on and on. The result is a history of data records that map financial transactions, for example.
The special feature of the blockchain is that all participants in the system have a copy of the entire database. This is referred to as a distributed ledger. If a new block – a new “link in the chain” – is now added, the blockchain on each participant’s computer is updated accordingly.
This leads to a high level of tamper-resistance: if a single copy of the data chain is tampered with, numerous correct copies still exist in the system. The manipulated data record is then simply sorted out. In addition, the sequence of the blocks is secured by a checksum.
The benefits of the blockchain
As mentioned, the blockchain forms the basis for so-called cryptocurrencies such as Bitcoin or Ethereum. The great advantage of these payment methods is that financial transactions can be processed directly and securely between two parties: The new technology provides both parties with reliable proof of whether a payment has arrived, for example. An intermediary such as a bank is therefore no longer necessary.
More recently, NFTs have been added as a variant: Non-fungible tokens are digital “tokens” in which a specific asset is stored – for example, a digital work of art or the forgery-proof scan of a property deed. These tokens are encrypted using blockchain technology so that no one can illegally copy them. Unlike cryptocurrencies, each NFT has its own value and is therefore unique worldwide.
In logistics, the blockchain offers the possibility of seamlessly documenting the entire path of a piece of goods from production to the retailer in a tamper-proof manner, without the need for an intermediary entity.
The blockchain can also be used to document safety-critical operations of software. In this way, highly sensitive data can be protected from manipulation. Application examples include electronic health records, contracts, military secrets or digital voting in elections.
Artificial intelligence (AI) is older than is commonly thought. As early as 1936, the Turing machine, a precursor to the computer, was developed. The term “artificial intelligence” was first used at a conference of scientists in 1956. And the first chatbot saw the light of day as “ELIZA” back in 1966.
However, AI only began to establish itself in everyday life from around 2011, for example through Apple’s voice recognition system “Siri”. In addition to the ever-improving hardware, this was primarily due to the emergence of the machine learning approach. Instead of predefining formal rules for a computer system, the system learns independently on the basis of the available data.
Machine Learning and its future
The most important achievement of machine learning is pattern recognition: For this purpose, thousands of images, text elements or sound bites, for example, are fed into the software together with the correct interpretation. From this, the software “learns” the correct mapping and can then apply these patterns to future data sets.
The most advanced variant of Machine Learning is Deep Learning, which is based on so-called neural networks. At the software level, they replicate the structure of the human brain, or more precisely, the interconnectedness of neurons in the nervous system.
In this way, it should be possible for AI to analyze data sets that were previously difficult to capture. This applies in particular to sensory data, for example with regard to the interpretation of human facial expressions or body postures.
The benefits of artificial intelligence
AI is now used in almost all technological areas. Particularly important fields are:
- Medicine: AI helps to make better diagnoses, for example by interpreting X-ray images more accurately than a human could.
- Image recognition: AI can be used to identify, classify and optimize objects in images. A well-known example of this is Google Lens.
- Speech recognition: Spoken language is converted into text using AI. Similarly, AI is used in translation programs such as DeepL. Text in images is also “read” by AI, making PDF documents searchable, for example.
- Video technology: AI can automatically classify videos and recognize objects or even people in them. For example, criminals can be identified more quickly on surveillance videos.
Industry: AI can be used to automate many manufacturing processes as well as internal logistics. AI-based test algorithms reduce the error rate in manufacturing and thus increase the output ratio.
- Financial institutions: AI has found its way into both the investment and insurance sectors, where it performs numerous analytical functions.
- Automotive: As described above, AI forms the backbone of autonomous driving.
Conventional computers are subject to a limitation: they only know the states 1 and 0. If it were possible to distinguish more than merely two states, computing power would increase greatly.
Researchers are trying to implement this idea using quantum computers. These work with quantum bits, also called “qubits.” Qubits are capable of representing superpositions of the states 0 and 1 as well as various intermediate states.
Theoretically, quantum computers are very powerful thanks to these properties. However, they are currently still struggling with a high error rate. Once this problem is solved, quantum computers may even be able to crack cryptographic codes. In 2019, Google already reported that its quantum computer solved a problem in just over 3 minutes that would have taken a conventional supercomputer 10,000 years.
All leading IT corporations are now researching quantum computers. These include Amazon Braket, Microsoft Azure Quantum, Google Quantum AI and IBM Quantum Computing.
Virtual and augmented reality
Virtual reality (VR) and augmented reality (AR) are increasingly making their way into our everyday lives. Trend researchers predict a great future for both new technologies because they make work more efficient, games more impressive and human interactions more multifaceted in many areas.
The difference between VR and AR
VR glasses such as Oculus Quest, HTC VIVE Focus or HP Reverb immerse the user in artificial worlds. Both eyes are operated by one digital display each, creating a three-dimensional image. This approach is known as virtual reality.
Data glasses like Google Glass can superimpose real-time information on real objects that the user is looking at. The smartphone can similarly search for information about objects that the camera is recording. This is referred to as augmented reality.
Areas of application of Virtual Reality
Virtual Reality deals with simulations. Thanks to VR glasses and increasingly sophisticated sensors, data gloves and even full-body suits, the user experiences an artificial reality that feels extremely real.
This realistic simulation is becoming increasingly important in the following areas:
- Gaming: Computer games are the most popular and probably the most common application. Increasingly, game manufacturers are developing pure VR games that can be played entirely without control devices thanks to the new technology.
- Industry and product development: VR applications can already be found in planning, in the visualization of work processes, and in the manufacturing of products. In particular, a company can save a lot of money and time thanks to VR prototyping.
- Medicine: Virtual reality enables high-precision surgery and remote operations. In addition, VR is often used to train and educate medical professionals and to practice delicate procedures.
- Learning: In an increasing number of fields, VR applications support understanding, learning and training. The spectrum ranges from industrial work to driving locomotives and airplanes to astronautics.
- Architecture and building technology: Building models can be viewed inside and out using VR – in some cases even with the prospective buyer’s individually selected furnishings, materials and room layout. The basis for this is a uniform standard for the digital recording of buildings, Building Information Modeling (BIM).
Areas of application for augmented reality
There are also a variety of applications for AR. Here are three examples:
- Games: Game characters are superimposed in real environments. The game principle has become known through Pokémon GO.
- Smartphone: A good example in this area is Apple’s Animoji. Users record their own facial expressions and voices, which are then transferred to an animated object, such as the face of a panda bear.
- Shopping: With the “IKEA Place” app, any furniture from the catalog can be projected realistically into the user’s own home.
Cloud and Edge Computing
Cloud computing is already widespread today, but it forms the basis for many future innovations:
- Serverless IT architecture: in the future, companies will no longer need servers, but will use a cloud infrastructure that they can scale at will.
- Artificial intelligence: AI requires huge amounts of data (Big Data), mostly in unstructured form. In the future, these will be stored in the cloud and retrieved from there, so that only the results need to be transmitted to the end devices.
- Smart City: In the city of the future, cars, buses and trains will drive autonomously, buildings will optimize their infrastructure independently, and digitization will make it easier to save energy. The corresponding data will be stored in the cloud.
- Agriculture: Automation and precision farming promise more sustainable and at the same time more cost-efficient cultivation of the soil. To achieve this, the producers involved exchange information via cloud tools and share data on germination rates, for example.
Edge computing is an extension of cloud computing. Here, all data is no longer transferred to the cloud and processed there. Rather, much of the data processing already takes place on the device itself, so only the results of the computation need to be transmitted to the cloud. In this way, the latency of intelligent devices can be reduced, leading to faster reaction times in autonomous driving, for example. Edge computing thus forms the basis for the Internet of Things (IoT).
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