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Articles by Boris Ruf
AI Image Generators: This is How Much Electricity Generating Images Requires
An international team of researchers from Stanford University and the insurance group Axa has investigated how the energy consumption of diffusion models, the architecture on which image-generating AI systems are based, can be systematically predicted. Popular examples include DALL-E, Midjourney, or Google's Nano Banana.
KI-Bildgeneratoren: So viel Strom braucht das Generieren von Bildern
Ein internationales Team von Forschern der Stanford University und des Versicherungskonzerns Axa hat untersucht, wie sich der Energieverbrauch von Diffusionsmodellen systematisch vorhersagen lÀsst, also der Architektur, auf der bildgenerierende KI-Systeme aufbauen. Beliebte Beispiele sind DALL-E, Midjourney oder Googles Nano Banana.
KI-Chatverlauf zurücksetzen senkt CO₂-Ausstoß
Ein Forschungsteam des Versicherungskonzerns Axa hat untersucht, welchen Einfluss Anwender von KI-Chatbots auf ihren CO₂-Fußabdruck ausüben können. In ihrem Fachartikel Mitigating the Carbon Footprint of Chatbots as Consumers, der bei Springer Nature veröffentlicht wurde, formulieren die Forscher einen verblüffend einfachen Tipp: Sobald das Thema wechselt, sollte ein neuer Chat gestartet und somit der Chatverlauf zurückgesetzt werden.
COâ‚‚-Bilanz von KI-Projekten ermitteln
KĂĽnstliche Intelligenz verbraucht viel Energie. Einerseits beim iterativen Training von KI-Modellen, bei dem man groĂźe Datenmengen verarbeitet und Millionen von Parametern justiert, andererseits aber auch beim Betrieb. Denn jede Abfrage aktiviert Millionen von Parametern, durch die das Modell den richtigen Output erzeugt.
Nachhaltigkeit und KI: Wie intelligente Modelle das Klima beeinflussen
KI-Modelle können dazu beitragen, den Klimawandel einzudämmen und seine Folgen zu bewältigen. So ermöglichen präzise Datenanalysen und Vorhersagen einen effizienten Einsatz von Ressourcen und helfen Landwirten, ihre Erntezeiten optimal zu planen. Die Meteorologie, insbesondere die Vorhersage extremer Wetterereignisse, wird durch neue Algorithmen revolutioniert.
Der Energiehunger generativer KI
iX 7/2025 S. 48 Titel Kosten von KI iX-tract Die Fähigkeiten großer Sprach- und multimodaler KI-Modelle erfordern eine enorme Rechenleistung. Neben den Anforderungen an die Stromnetze und die Energieerzeugung geht das häufig mit einem hohen CO₂-Ausstoß einher. Entwicklungen bei der Modellarchitektur könnten den Aufwand verringern, derzeit setzen Modellbetreiber und Hardwareanbieter jedoch eher auf Symptombekämpfung.
CO₂-Bilanz von KI-Projekten ermitteln
iX 7/2025 S. 52 Titel Kosten von KI iX-tract Am einfachsten lässt sich KI nutzen, wenn man auf eines der vielfältigen Angebote in der Cloud zurückgreift. Besonders bei den proprietären Modellen schweigen sich die Anbieter allerdings bei Details zum Betrieb aus, auch was den Energiebedarf angeht. Muss man Reportingpflichten nachkommen, bleibt nur das Schätzen des Ressourcenbedarfs und der dadurch anfallenden Emissionen.
KI - eine enorme Belastung für die Stromnetze
Während das Aufkommen der KI (Künstliche Intelligenz) eine große Anzahl Sektoren revolutionieren und nie dagewesene wirtschaftliche Möglichkeiten eröffnen könnte, hat ihre Energieintensität ernste Umweltbedenken aufgeworfen. In Reaktion darauf fördern Technologieunternehmen sparsame KI-Praktiken und unterstützen Forschungsbemühungen, die sich auf die Reduzierung des Energieverbrauchs konzentrieren.
Why frugal AI alone won't fix AI's energy problem
A robot arrives on stage as Nvidia CEO Jensen Huang introduces new products during a keynote session at the SAP Center in San Jose, California, March 18. AFP-Yonhap By Boris Ruf PARIS – While the rise of AI could revolutionize numerous sectors and unlock unprecedented economic opportunities, its energy intensity has raised serious environmental concerns. In response, tech companies promote frugal AI practices and support research focused on reducing energy consumption.
Why AI’s energy demand needs transparency, not just efficiency
By Boris Ruf While the rise of artificial intelligence (AI) could revolutionize numerous sectors and unlock unprecedented economic opportunities, its energy intensity has raised serious environmental concerns. In response, tech companies promote frugal AI practices and support research focused on reducing energy consumption, but this approach falls short of addressing the root causes of the industry’s growing demand for energy.
Por qué la IA frugal por sí sola no solucionará el problema energético de la IA
Si bien el auge de la IA podría revolucionar numerosos sectores y generar oportunidades económicas sin precedentes, su intensidad energética ha suscitado serias preocupaciones ambientales. En respuesta, las empresas tecnológicas promueven prácticas de IA frugales y apoyan la investigación centrada en la reducción del consumo de energía. Pero este enfoque se queda corto a la hora de abordar las causas profundas de la creciente demanda de energía de la industria.
Frugal AI not enough to fix new tech's energy problem
While the rise of artificial intelligence ( AI ) could revolutionize numerous sectors and unlock unprecedented economic opportunities, its energy intensity has raised serious environmental concerns. In response, tech companies promote frugal AI practices and support research focused on reducing energy consumption. But this approach falls short of addressing the root causes of the industry's growing demand for energy.
Frugal AI not enough to fix new tech's energy problem
Understanding ESG / TechTalk / Viewpoint Frugal AI not enough to fix new tech's energy problem To curb AI's rising energy consumption, the industry is promoting the concept of frugal AI, encouraging businesses and individuals to use AI models more selectively.
Why Frugal AI Alone Won’t Fix AI’s Energy Problem | by Boris Ruf - Project Syndicate
To curb AI’s rising energy consumption, the industry is promoting the concept of frugal AI, encouraging businesses and individuals to use AI models more selectively. But focusing solely on end users is misguided, given that the primary drivers of AI’s energy demand are suppliers. PARIS – While the rise of AI could revolutionize numerous sectors and unlock unprecedented economic opportunities, its energy intensity has raised serious environmental concerns.
Carbon Footprint: Why Common Claims May Not Be Accurate
As the climate crisis continues to unfold with natural disasters occurring more frequently, the need to take action to reduce greenhouse gases (GHGs) has become increasingly urgent. One of the key challenges is understanding the impact of our daily activities on the environment. However, identifying the individual reduction potential can be complex and difficult to understand, as hidden, embedded emissions often occur somewhere else and are hard to visualize.
So How Fair Is Your AI, Exactly?
Collecting and analyzing data through AI has become standard, and machine learning is improving business performances in many areas today. However, numerous cases of AI machine bias have been exposed in recent years, and new examples emerge on a regular basis. This is one of the main pitfalls of AI: if the training data include any kind of bias, the algorithm will incorporate and enforce it — potentially harming sensitive subgroups defined by gender, religion, ethnicity, or age.
So How Fair Is Your AI, Exactly?
Collecting and analyzing data through AI has become standard, and machine learning is improving business performances in many areas today. However, numerous cases of AI machine bias have been exposed in recent years, and new examples emerge on a regular basis. This is one of the main pitfalls of AI: if the training data include any kind of bias, the algorithm will incorporate and enforce it - potentially harming sensitive subgroups defined by gender, religion, ethnicity, or age.
To Guarantee Impartial AI Decisions, Lady Justice Needs to Blink
Systematic, unequal treatment of individuals based on their membership of a sensitive group is considered discrimination. For example, when people face unequal or disadvantageous treatment simply because they are male or female, this is considered gender discrimination. When job applicants or employees are treated less favourably because of their age, we talk about age discrimination.
To Guarantee Impartial AI Decisions, Lady Justice Needs to Blink
Systematic, unequal treatment of individuals based on their membership of a sensitive group is considered discrimination. For example, when people face unequal or disadvantageous treatment simply because they are male or female, this is considered gender discrimination. When job applicants or employees are treated less favourably because of their age, we talk about age discrimination.
How Prejudice Creeps into AI Systems
Machine learning (ML) algorithms identify patterns in data. Their major strength is the desired capability to find and discriminate classes in training data, and to use those insights to make predictions for new, unseen data. In the era of “big data”, a large quantity of data is available, with all sorts of variables. The general assumption is that the more data is used, the more precise the algorithm and its predictions become.
Biased AI, a Look Under the Hood
In order to gain a better understanding of the background to this problem, let us first introduce some fundamental knowledge about machine learning.
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