Mind-mapping AI in energy trading

Dig deeper into the world of artificial intelligence

Artificial intelligence (AI) is ever-present in our modern world, but differences still exist in the definitions of the technology and related subdivisions. To help you understand the concepts, we created a map with the most important categories and terms.

AI in stages

AI can be understood as the ability of machines to demonstrate intelligence similar (or equal) to that of humans. Therefore, we can categorize technologies by how well they emulate human intelligence and characteristics. The three main types of AI are:

Artificial Narrow Intelligence (ANI): Also called weak AI, this type specializes in one individual area, completing one single task. It accomplishes this one task very well and sometimes even outperforms humans in repetitive functions. We are surrounded by weak AI, from Google's search engine algorithms and the apps on our smartphones to streaming services and drone robots. As of today, ANI is the only AI in existence.

Artificial General Intelligence (AGI): Often referred to as strong AI, this refers to a machine with human-level intelligence. It can match or surpass human capabilities across all cognitive tasks, including attending school and university. Because the human brain is so complex, AGI is difficult to develop and is yet to be achieved, although many experts argue that commercial AI today is already showing signs of AGI. Estimates for the large-scale rollout of this technology range from the late 2020s to the second half of the century.

Artificial Super Intelligence (ASI): With this cognitive degree, the technology can self-improve and surpass humans in many or all areas. This stage stretches the limits of our imagination and is difficult to conceive of today. Although likely hundreds of years from being developed, ASI already prompts ethical debates on how far humankind can and should go in its pursuit of AI without losing control.

AI techniques: Algorithmic approaches to AI functions

Algorithms automate processes by mathematically standardizing technical procedures and rules. In simple terms, algorithms constitute a calculation rule according to the principle "if...then". A mathematical formula solves a narrowly defined question or structures a process. Today, AI is basically a combination of different algorithms that work in a specific area with large amounts of data or have been trained with them. Some of the most important algorithmic approaches for implementing AI functions are listed below.

Machine learning: The term machine learning (ML) is often used as a synonym for AI. Although closely related, they are not identical. ML is actually a subfield of AI. It consists of techniques that enable a software program to gain insights from data. Algorithms can then recognize patterns and regularities in datasets and develop solutions from them.

Classification and regression trees: A process or set of rules configured as a series of decisions in a tree structure. It illustrates how to predict the values of a focus variable based on other attributes. The outcome of a classification tree is a discrete value, such as the class to which data belongs.

Fuzzy logic: A decision-making approach based on multivalued rather than binary logic ("true" and "false"). A two-valued logic often considers 0 as false and 1 as true. However, fuzzy logic deals with truth values between 0 and 1, and these values are referred to as intensity (degree) of truth.

Neural network: Algorithms modeled after the human brain. This abstracted model of connected artificial neurons organizes complex tasks in successive layers of functions, each layer using the output of the previous one as input.

Deep learning: A subarea of machine learning that uses neural networks. Training methods that draw and analyze large amounts of data are used to create artificial intelligence. Based on existing information and the neural network, the system can repeatedly link what it has learned with new content and learn again. Most deep learning models are implemented by increasing the number of layers in a neural network.

Bio-inspired approaches: Inspired by biological systems and a wider range of biological structures capable of autonomous self-organization. These algorithms mimic biological mechanisms to better adapt decisions to new problems, new data, and swarm intelligence.

Reinforcement learning: A subset of machine learning based on a reward-punishment system. Agents learn how to select an action from an action space in a given environment to maximize rewards over time.

Supervised learning: The most widely adopted form of machine learning. This approach uses labeled input-output pairs to train algorithms to learn mapping from inputs to outputs and predict outcomes for new data.

Support vector machine: A supervised learning algorithm that analyzes labeled/grouped data, identifies the data points that are most challenging to the group, and determines how to separate the different groups and classify unseen data points.

Unsupervised learning: A type of machine learning algorithm that finds and analyzes hidden patterns or commonalities in unlabeled or unclassified data. Unlike supervised learning, the system isn't fed a predefined set of classes but rather identifies patterns and creates labels/ groups in which it classifies the data.

AI functional applications: Functions performed by AI techniques

AI functional applications cover the operations performed by AI techniques, independent of the sector (application field) in which they're used. They can be categorized as follows:

Augmented reality: Expands the perception of reality for humans. Augmented reality is computer-aided and offers users additional information that is integrated or superimposed into the visual and auditory representation of the world on an elemental level. Texts, animations, images, videos, and graphics are examples of extension elements. Augmented content is typically accessed via gesture control or touchscreens.

Biometrics: Refers to the science of measuring and analyzing biological characteristics. Biometric procedures, such as fingerprinting or facial recognition, are often used to identify or verify individuals.

Knowledge representation and reasoning: A computer processes real-world information and utilizes the learned or encoded knowledge to solve complex problems, for example, diagnosing a medical condition.

Distributed AI: Deals with systems formed by groups of individual components called agents. The system is specified by its individual agents, the interactions between them (described by a programming language), and the environment around them. The individual agents cooperate to achieve specific goals.

Object tracking: Determining (estimating) the positions and other relevant information of moving objects in image sequences commonly used in image retrieval, surveillance, and automated vehicle parking systems.

Planning/scheduling: Strategies or action sequences usually executed by intelligent agents, autonomous robots, and unmanned vehicles.

Predictive analytics: The process of predicting unknown events in the future using various statistical methods and analysis of current and historical data.

Robotics: Deals with the design, creation, control, production, and operation of robots, i.e., machines that can move independently and perform various activities. Anthropomorphic or humanoid robots also involve the production of limbs and skin, as well as facial expressions, gestures, and natural language capabilities.

Speech analysis: Recognition, evaluation, classification, and storage of speech information. The stored information is then put in relation to the spoken words, their meaning, and the characteristics of the speaker.

AI application fields

AI is found in end products and services but also in areas with high automation potential. Some of the most important fields include:

Life and medical sciences: Automated systems with machine learning offer promising applications for enhanced diagnostic support and image analysis. AI can be leveraged for personalized treatments and earlier detection of potential emergency scenarios (e.g., pandemics).

Production: AI algorithms can help companies manage the increasing complexity of development processes and quality regulations. This includes improved monitoring and automated adjustments of production processes, as well as optimization of the production and supply chain.

Transport and logistics: Fuzzy logic and other AI approaches have enabled better traffic management and increased road safety. They can also be used in crewless cargo ships and for fully automated package delivery.

Finance: AI is already deeply integrated into finance: From the approval of loans and the management of assets to risk assessment and prevention/detection of money laundering and fraud.

Technology, media, and communication: Media applications are diverse, ranging from improved archiving, search, recommendations, and generation of custom content in advertising and marketing.

Energy industry: Decentralization, the connection of consumers, and a growing share of fluctuating renewable energies are contributing to the increasing complexity of the energy system. AI can help handle the enormous data streams that are generated, optimize the system, and cater to customer needs. Classic application areas include the power grid, power consumption, and electricity trading.

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