Computer Science: Research Guide

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure "general intelligence."

As we come to the end of a year in which half the world's population went to the polls, EPFL researchers developed and field-tested a groundbreaking new technology to protect remote electronic voting or e-voting from voter coercion and vote buying.

A recent perspective published Nov. 13 in Intelligent Computing, asserts that today's artificial intelligence systems have finally realized Alan Turing's vision from over 70 years ago: machines that can genuinely learn from experience and engage in human-like conversation.

Artificial intelligence that is as intelligent as humans may become possible thanks to psychological learning models, combined with certain types of AI. This is the conclusion of Robert Johansson, who in his dissertation, from Linköping University, has developed the concept of "Machine Psychology" and how it can contribute to AI development.

When it comes to digital access and internet technologies, some languages are still more equal than others. Speakers of majority languages, who type in English or text in Korean, assume their message will be transmitted accurately. But Indigenous language communities don't share this same confidence.

Researchers at University of North Carolina at Chapel Hill and University of Maryland recently developed MyTimeMachine (MyTM), a new AI-powered method for personalized age transformation that can make human faces in images or videos appear younger or older, accounting for subjective factors influencing aging.

Modern computers are a triumph of technology. A single computer chip contains billions of nanometer-scaled transistors that operate extremely reliably and at a rate of millions of operations per second.

Among the many artificial intelligence and machine learning models available today for image translation, image-to-image translation models using Generative Adversarial Networks (GANs) can change the style of images.

More than one in four people currently integrate speech recognition into their daily lives. A new algorithm developed by a University of Copenhagen researcher and his international colleagues makes it possible to interact with digital assistants like "Siri" without any internet connection. The innovation allows for speech recognition to be used anywhere, even in situations where security is paramount.

Researchers at the University of Waterloo's Institute for Quantum Computing (IQC) have found that quantum algorithms could speed up generative artificial intelligence (AI) creation and usage.

Research has long shown that humans are susceptible to "social identity bias"—favoring their group, whether that be a political party, a religion, or an ethnicity, and disparaging "outgroups." A new study by a team of scientists finds that AI systems are also prone to the same type of biases, revealing fundamental group prejudices that reach beyond those tied to gender, race, or religion.

Second-language speakers who come to Austria with a good knowledge of German usually find it difficult to understand the local dialects. Similarly, speech recognition systems often fail to decode regionally accented word choice and pronunciation.

To efficiently compute where waves of light, sound, or earthquakes will go when scattered by irregular obstacles is useful in various fields but difficult and expensive to do, even using recent machine learning techniques. To improve the scalability and practicality of such computations, Laurynas Valantinas and Tom Vettenburg, researchers at the University of Dundee in the UK, mapped the wave equations onto the structure of a recurrent neural network. Its minimal memory requirements allowed them to scale up wave scattering calculations by two orders of magnitude or more.

Quantum computers differ fundamentally from classical ones. Instead of using bits (0s and 1s), they employ "qubits," which can exist in multiple states simultaneously due to quantum phenomena like superposition and entanglement.

Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should trust a model's predictions.

The capabilities of large-scale pre-trained AI models have recently skyrocketed, as demonstrated by large-scale vision-language models like CLIP or ChatGPT. These typical generalist models can perform reasonably well in tasks covering a large variety of fields, which has paved the way for their widespread adoption by the public. However, such versatility no doubt comes at a cost.

Train delays are not only a common frustration for passengers but can also lead to significant economic losses, especially when they cascade through the railway network. When a train is delayed, it often triggers a chain reaction, turning minor issues into widespread delays across the system. This can be costly.

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?

A new machine-learning weather prediction model called GenCast can outperform the best traditional forecasting systems in at least some situations, according to a paper by Google DeepMind researchers published today in Nature.

A new coding tool will help make it easier for children to portably program a popular educational micro-computer on the fly. Researchers from Lancaster University's School of Computing and Communications working in partnership with researchers at Microsoft have evaluated a new programming tool called MicroCode.

Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.

When you visit a hospital, artificial intelligence (AI) models can assist doctors by analyzing medical images or predicting patient outcomes based on historical data. If you apply for a job, AI algorithms can be used to screen resumés, rank job candidates and even conduct initial interviews. When you want to watch a movie on Netflix, a recommendation algorithm predicts which movies you're likely to enjoy based on your viewing habits. Even when you are driving, predictive algorithms are at work in navigation apps like Waze and Google Maps, optimizing routes and predicting traffic patterns to ensure faster travel.

A trio of AI researchers from Cornell University, Signal Foundation, and Now Institute have published a Perspective piece in the journal Nature, arguing that well-known open LLMs are not nearly as open as their makers claim.

A common challenge for marketers is understanding and engaging with people who have "thin" data profiles, such as donors with little demographic information or brief interaction histories that leave organizations without a clear picture of their interests.

For the first time, CISPA researcher Aleksei Stafeev presents a study that systematizes the knowledge about tools for the automated analysis of websites, so-called web crawlers, in the field of web security measurement.

Large language models (LLMs), such as Open AI's renowned conversational platform ChatGPT, have recently become increasingly widespread, with many internet users relying on them to find information quickly and produce texts for various purposes. Yet most of these models perform significantly better on computers, due to the high computational demands associated with their size and data processing capabilities.

The large language model of the OpenGPT-X research project is now available for download on Hugging Face: "Teuken-7B" has been trained from scratch in all 24 official languages of the European Union and contains 7 billion parameters.

A design structure matrix (DSM) is a visual analysis tool used for system modeling. Simple and concise, it uses an NxN square matrix to represent the relationships between N system elements. Data collection for DSM generation is traditionally done via surveys and interviews, but because these require domain experts and a lot of hours or even days to go through product models, the process becomes lengthy and expensive.

Fields ranging from robotics to medicine to political science are attempting to train AI systems to make meaningful decisions of all kinds. For example, using an AI system to intelligently control traffic in a congested city could help motorists reach their destinations faster, while improving safety or sustainability.

To make sense of the world, most humans rely in great part on their vision. Recent research suggests that the human visual system is hierarchical, meaning that it processes information on different levels, ranging from the low-level processing of sensory stimuli to the high-level processing associated with more advanced cognitive abilities.