Onsdag 28 oktober
Computationally, nowadays any computer is incredibly powerful. As a
reference, the computational power of any of today's laptops surpasses
that of the 1996 world's fastest supercomputer. By analogy with cars, it
is as if everyone owned a Ferrari, a Lamborghini, or a Formula 1 car. And
the same way only selected drivers can race such supercars at more than
300Kms/hour, exploiting the full potential of a computer is a
challenging task that only few experts can perform. In most cases, users
rely on well known programming languages and compilers, trusting them to
"drive their computers fast enough". Is this really the case? Our
investigation illustrates that while programming languages are excellent
at computations with numbers, they still cannot compete with human
solutions when it comes to more advanced mathematical operations that
involve vectors and matrices. Our study aims at giving directions for
the future development of programming languages.
is professor for High-Performance and Automatic
Computing at Umeå University. Before moving to Umeå, he completed his studies in Italy and in the US, and was professor at RWTH Aachen, in Germany.
His research interests include the efficient use of computers to solve
mathematical problems, the automatic generation of algorithms, and computer-music. firstname.lastname@example.org
Why is it so difficult to remember how we got somewhere when following instructions from Google Maps? And is that even a problem (since we got there)? Taking navigation systems as an example, I will discuss the cognitive benefits and in particular the cognitive challenges with leaving tasks to a computer, i.e., with automation. As we will see, there are challenges resulting from a very different understanding of the world, and challenges resulting from us being pushed out of the task and at the same time being distracted by the machine. I will then highlight some of our work on alleviating these challenges: exploiting findings from cognitive science and AI methodologies to get computers to communicate more like humans, and to get humans back into the task in a smart way.
docent, heads the Cognitive Engineering group at the Department of Computing Science. The group explores cognitive aspects of interacting with autonomous systems and of human-in-the-loop computing. We aim to narrow the gap between human and machine by getting systems to use concepts and communication that are close(r) to humans’. email@example.com
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