In recent years, the world has been experiencing a period of intense change, associated with constant breakthroughs in technology. Artificial intelligence, blockchain, AR, 5G, quantum computing – these terms no longer seem distant or abstract – they are becoming part of our everyday lives. Technologies that once seemed like science fiction are now all around us, changing the way we work, communicate and live.
What horizons will these technologies open up for us in the near future? How will our habits and needs change in light of the rapid development of the IT industry? Let’s look at the key trends to better understand what lies ahead.
Top promising technologies 2024
Many people have heard about IoT technology, Bitcoin, artificial intelligence and NLP. But what is behind these broad concepts and what other innovations can we expect in the field of AI and automation?
Digital Twins
A digital twin is an exact virtual copy of a real-life object, system or process. The combination of digital twins with IoT and AI allows you to monitor the behavior of an object in real time.
Imagine that you have a toy airplane. You want to create an exact replica of this airplane on a computer so that you can study its design and flight behavior. You photographed and measured every detail of it. Then you recreated it on the computer – this is the digital twin of the aircraft.
What makes digital twin technology special? Digital Twins can update in real time. If you add new parts or change the shape, the digital twin will update as well. This allows scientists and engineers to test new ideas and approaches without making changes to the actual research object.
Digital twins are used for a variety of things: engineers create virtual twins of machines to test their performance before production begins; Doctors can create digital twins of patients to predict disease risks and develop personalized treatment approaches. In modern cities, twins are used for traffic control and weather forecasting.
The Digital Earth Project | NASA
NASA is leading a project called Digital Earth, which uses digital twins to monitor and analyze changes on Earth. This project combines data from various satellites, survey systems and sensors to create highly detailed digital models of our planet. Digital twins of the Earth allow scientists and ecologists to analyze changes in climate, forests, water resources and other ecosystems. They are also used to predict the effects of natural disasters such as floods, fires and sea level changes. This information helps develop informed strategies for the sustainable management of the Earth’s natural resources.
Federated Machine Learning
Federated machine learning is a method that allows machine learning models to be trained directly on smartphones (or laptops), without the need to transmit user personal data to a central server.
Here’s how it works: Instead of sending data to a server, the machine learning model is sent to users’ devices. Then each device trains the model on its own data, without transferring it anywhere. Only updated model parameters are sent back to the server, where they are combined with other updates from other devices. This allows you to create a common model that takes into account the data of all users, without compromising their privacy.
By distributing data storage and training models, federated learning helps reduce the risks of cyberattacks and hacks associated with data centralization. Each device processes only its own data, making the process less vulnerable to massive security breaches.
This technology originates from conventional machine learning, but has been adapted to work with distributed data and take into account the increased requirements for privacy and security in such areas as FinTech and MedTech. With the advent of AI, it has become almost impossible to control systems’ access to the data of ordinary users, so companies that value their reputation use federated ML to ensure the security of their clients.
There is even an annual conference, Federated Learning for Data Privacy and Confidentiality, that brings together top experts to discuss new research and applications in the field.
Forecasts for the development of federated ML – possible integration with blockchain technology, multiplayer training and collective intelligence, widespread use in secure and confidential environments.
Humanoid robots and collaborative robots (cobots)
Humanoid robots are autonomous machines that resemble humans in both appearance and function. They are designed to perform tasks that are typically performed by humans, which is why they are becoming increasingly popular in various fields.
They can mimic human movements and actions, making them ideal for tasks that require flexibility and dexterity.
Collaborative robots (or cobots) are robots specifically designed to safely interact with people in the workplace. Unlike traditional robots, which work in isolated areas, cobots work side by side with humans to help them perform a variety of tasks.
- Sophia by Hanson Robotics
One of the most famous humanoid robots that can conduct conversations, recognize faces and express emotions. - Atlas from Boston Dynamics
A robot capable of running, jumping and performing complex acrobatic movements, demonstrating a high level of mobility and coordination. - ASIMO from Honda
One of the first humanoid robots, capable of walking, running and interacting with the environment.
Cobots are commonly used for assembly, packaging, and quality control in manufacturing. Their widespread use in agriculture, construction and services is predicted.
4D printing technology
We are sure that today few people will be surprised by the term 3D printing. Many have seen people even building houses using 3D printed materials.
What is 4D printing?
4D printing is an extension of 3D printing, where printed objects can change their shape or properties over time when exposed to external factors such as temperature, light or water.
Why did 4D printing technology arise? The emergence of 4D printing stems from the need to create more functional and adaptive materials and structures. While 3D printing allows you to create static objects with complex shapes, 4D allows objects to change and adapt to their environment.
4D printing is used to create medical implants and devices that can adapt to changes in a patient’s body. For example, self-healing stents or artificial tissues that can change their shape in response to physiological conditions.
4D printing is also used to create materials that can adapt to environmental changes, such as changes in temperature or humidity. This could be useful for creating smart building facades that change their properties depending on weather conditions. Surprisingly, this is reality.
The development of 4D printing requires the use of smart materials such as hydrogels, thermoset polymers, alloyed metals and other shape memory materials. To create such objects, specialized software and technologies are used to program changes in the shape and properties of the material.
In 2024, this technology is already showing its practical value, but its potential is only just beginning to unfold. In the future, we can expect new materials and applications to emerge that will further expand the capabilities of this innovative technology.
Virtual power plants
With the growth of renewable energy sources and widely distributed energy resources, the question of how to manage them effectively has arisen.
Virtual Power Plants (VPPs) are an innovative approach to energy resource management that is gaining increasing acceptance in the modern energy industry.
Virtual power plants combine distributed energy sources, such as solar panels and wind turbines, into a single network controlled by software. This allows optimization of energy production and consumption, improving the stability and reliability of power systems.
These systems also provide flexibility in load regulation and provide reserve power to maintain network stability or unexpected situations.
In the US, virtual power plants are becoming an important element of the sustainability strategy. Companies like Tesla and Sunrun offer solutions for households and businesses, allowing them to participate in VPP and earn rewards for doing so.
The use of VPP helps reduce the cost of energy production and transmission, making energy economical and affordable for everyone.
Neuro-symbolic AI
Neuro-Symbolic AI combines two approaches: neural networks, which are used to learn from large amounts of data, and symbolic processing.
The main idea is that systems can not only learn from examples, but also use logic and knowledge to make decisions. For example, analyze medical data and predict diagnoses, taking into account standard medical rules.
The advantage of the neuro-symbolic approach is the ability to solve complex problems that require deep understanding and logical thinking. This can be useful wherever it is important not just to process data, but also to draw meaningful conclusions.
Quantum-resistant cryptography
Quantum-resistant cryptography is special encryption methods created to protect data from future calculations on quantum computers. These methods use new mathematical approaches that remain safe even as quantum technologies advance.
What methods does quantum-resistant cryptography use? For example, random numbers. Thus, this approach uses quantum physical processes to generate purely random numbers. This creates numbers that are impossible to predict or reproduce.
The future of quantum-resistant cryptography promises further growth and innovation in data security. It is expected that with the development of quantum computing technologies and the expanding use of quantum algorithms, the need for effective information protection from new types of attacks will only increase.
In the first half of 2024, the world experienced an increase in a series of ransomware attacks, as well as data leaks aimed at data theft and extortion.
According to Exploding Topics, more than 800,000 people fall victim to cyberattacks every year. As of the first quarter of 2024, cybercriminals are creating about 1 million phishing sites per month – almost 7 times more than in the second quarter of 2020.
Fog Computing
Fog Computing is part of the Edge Computing concept, which we have already written about, but they have different approaches to data processing.
Fog Computing and Edge Computing are two related concepts that help process data closer to where it is created, improving network speed and efficiency. Edge Computing focuses on processing data at the very edge of devices, such as sensors or mobile devices, allowing you to quickly respond to changes and process small amounts of data on the spot.
Fog Computing extends this idea by creating compute nodes and servers that are closer to the edge of the network than traditional centralized data clouds. This makes it possible to process and analyze large amounts of data, for example to monitor large industrial systems or manage smart cities (thus, this technology is directly used in IoT).
The application of Fog Computing can be seen in various industries that require complex data processing at a distance from centralized servers. For example, in industry for monitoring production processes or in the transport industry for managing traffic flows.
Imagine that you have a smartphone that is connected to IoT devices in your home. These devices collect data about the temperature, safety, and overall health of your home.
Edge Computing on your smartphone can process raw data, such as determining that the temperature in a room is above a set threshold or detecting movement on a camera. This data can be processed directly on the device, allowing you to intervene quickly.
Instead of sending all your data to the cloud for long-term storage and analysis, Fog Computing on your smartphone can aggregate and analyze data in greater depth. For example, the system may detect that the temperature in your room has been consistently higher than normal for the past few days, which could indicate an air conditioning problem.
Next, Fog Computing can make decisions on its own, such as sending a notification to your smartphone or turning on a smart thermostat to automatically regulate the temperature. Thus, managing your home becomes more autonomous and faster thanks to Fog Computing level processing on your device.
Magnetics for Computing
Research in the field of magnetic materials and devices leads to the creation of new types of memory and logic elements for computers. Technologies like MRAM (magnetoresistive random access memory) offer high speed, power efficiency and radiation resistance.
In hard disk drives (HDDs) and SSDs, magnetic materials provide significant capacity at a relatively low cost. This makes them attractive to different user segments (home PCs, server systems or cloud data storage).
Magnetic materials are used in magnetic resonance imaging (MRI) scanners, which are essential for accurate diagnosis and monitoring of diseases. Constant developments in the field of medical technology require further improvement of magnetic materials to improve image quality and reduce research time.
In the electronics industry, magnetic materials are being used to create more precise sensors and actuators, helping to advance the Internet of Things (IoT) and improve automated systems.
Today,
Technology has become much closer and more accessible than just a few years ago. And modern people, regardless of age, more openly accept technical innovations, turning them into part of their lives.
Whether to use the latest achievements of science and technology is your choice.