The grass is always greener…

As a researcher, you travel a lot. It’s usually a lot of fun, but… Umeå is so far from everything. It’s therefore no surprise that over 90% of the carbon dioxide emissions of Umeå University is caused by traveling employees. With a better world in mind, I therefore spend my last two days in a train, traveling from Umeå-Sundsval-Uppsala-Stockholm-Lund-Copenhagen-Hamburg-Ostnabruck-Hengelo-Zwolle-Leiden. From white to grey to brown to green and greener.

Of course I wanted to take advantage of the undisturbed time in the train and work on proposals, read literature and have great thoughts. So far so good, but after some hours, batteries got low and I realized that my train was not equipped with power outlets. Neither was the next one nor the next and the next, etc.

So I ended up staring out of the window, sticking to ‘having great thoughts’, and doubting about whether I need to take up holidays to compensate for the not being able to work or not. However, soon I found my brain wandering off. It simply can’t stop being a researcher, so it started analyzing the slowly changing landscape: Snow gone after Uppsala, the fist daffodils around Copenhagen and ice-cream-eaters in Hengelo. As the weather changed from snow to rain to sunshine, my hypothesis-addicted brain quickly found an explanation. More difficult to explain was: the stones (flyttblock) being rounder in Lund than in Umeå, and the grass being greener in Amsterdam than around Lund. Because I had nothing better to entertain my brain, I found myself playing the game of thought experiments.

There are three basic designs that underlie most experiments. The first one is based on comparing groups. The second approach extends on that, and looks for relations over a gradient. Instead of comparing two averages (are stones really more round in Lund than in Umeå), one can than draw lines and see trends (As I did along the climate gradient). However, both these approaches encounter problems when your locations differ in too many things. The classic example of this pitfall is that you can find a strong relation between number of crime victims and numbers of hamburgers eaten. Of course there is no causal effect there, but both hamburger consumption and number of crime victims may both be driven by city size. To overcome this ’co-founding’ problem, you can do a ‘manipulative experiment’, and for instance add nutrients to see if it affects the greenness of the grass, or that temperature also is important there.

I assume that many researchers play this game of thought experiments and if you find yourself sitting in a train with low batteries and nothing better to do, you are welcome to contribute.  Overall, I enjoyed my long, long journey south, as it all trains ran smoothly in time and I met many interesting people. But next time, I’ll be better prepared so I can keep my brain entertained with work rather than with stones, the weather and grass.

Back to work at the Advanced Light Source

The holidays are over and 2019 begins – I hope it’ll be a year that brings implementation of the compromises made in Katowice. For me, the coming year brings more work over at the Advanced Light Source (ALS) synchrotron in Berkeley including standard compound measurements, updating sample stage capabilities at the beamline, and applications for beam time for the autumn season. Going to and from work, there are some famous landmarks to look at from the Lawrence Berkeley National Laboratory site up the hills from Berkeley as shown in the image below.

View from Lawrence Berkeley National Laboratory towards the Bay area. Silhouette of San Francisco is visible to the left of centre, the small island of Alcatraz with Golden Gate bridge behind it is off to its right. The Campanile (bell tower) at the bottom centre is the Sather tower located in University of California, Berkeley campus.

As I was with my family during the holidays and showed some pictures like the one above, I was asked how it is to work abroad as a research scholar. So far, it’s amazing how the common scientific language enables conversation on complex issues. Compared to my earlier stay in Wien, Austria, the work over here in USA provide an added level of complexity through the time zone differences. It hasn’t been uncommon to start with phone meetings during European office hours from around 2-3 in the morning and then continue my work day until I go home from ALS at about 18-19 in the evening. I’m not typically a morning person so this has been a bit of change, fortunately my roommates didn’t complain about early morning discussions.

Other than such things that mostly concern personal situation, I’ve seen significant differences in matters that determine your day-to-day work between the countries, but also between Swedish universities. Seemingly mundane things like purchasing a small piece of equipment isn’t a pain for the individual researcher in Austria or the US in my experience. By comparison, the Swedish system of procurement of seemingly cheap equipment that is required for studies in natural sciences or technology probably does hamper scientific progress in Sweden.

There are other examples, but since I’m looking at equipment upgrades here at ALS has become so clear that the focus here is placed on how science could best benefit from whatever is purchased – not whether a disgruntled manufacturer could challenge the process. The contrasting sluggishness in Sweden is obvious where procedure is seemingly regarded more important than science, and this comes at a huge cost. Staff from all levels are putting in hours to create procurement documents, there is a lack of progress in projects with deadlines that are in need of the equipment, and in the worst cases post-docs or guest researchers are not able to acquire data they need for publication before they have to leave their position. As a researcher who relies heavily on experimental and analytical equipment, I sincerely hope that the scientific outcome will be prioritized in Sweden as well in the future.

This is one of the lessons I’ve learned so far while working abroad, that there should always be room to reflect on whether a procedure benefits the goals or not. With these words, I wish you a great 2019!

Suna Bensch

The story about the two waiting robots

If you read my last blog post and, like I do, love long theater performances during which ”nothing happens, twice” (Vivian Mercier about Samuel Beckett’s Waiting for Godot (best play ever)), then you’ll probably enjoy my story about the two waiting robots 🙂



The AI hypes and the reality of AI capacity

Artificial Intelligence or AI: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it (check out Dan Ariely for the original).

In this post I try to describe development in AI in context of its past and current hype cycles along with the actual AI capacity.

AI, which is a subfield of computer science that deals with the simulation of intelligent behaviours in machines, has its origins in the 1950’s. In 1956, John McCarthy organized the Dartmouth conference, inviting leading researchers to discuss ideas around intelligent machines. The term ”Artificial Intelligence” was coined during that conference. What then followed, was a time during which fundamental breakthroughs were expected in AI, and therefore a lot of research funding was invested, and accordingly a lot of AI research was conducted. The excitement around AI lasted until around 1974 when the levels of expectations on AI, and actual achievements did not match – and a phase of disillusionment followed. This phase between 1974-1982 is known as the AI winter. Research funding was withdrawn and accordingly research in AI decreased substantially.

From 1983 until today, AI is experiencing a revival (first gradually and since 2011 rapidly). This revival is mainly due to the advancement in machine learning methods applied to areas such as image analysis and natural language processing. This advancement can be attributed to today’s fast computers on which huge neural networks can process huge data sets. This technology trigger lead to a renewed enthusiasm around AI, similar to the one in the 1950’s. The advancements of AI research have mainly been demonstrated in games where machines beat humans (e.g. Chess, Go), natural language processing, self-driving cars, and medical diagnosis (i.e. medical data analysis).

Now let’s look at hype cycles. The Gartner hype cycle illustrates how technology hypes are triggered by technological advancement, leading to a peak of inflated expectations which is followed by a drop into a trough of disillusionment. After this follows a slope of enlightenment and eventually a plateau of productivity where expectations adapt to the reality of the technological capacity.


Gartner hype cycle

If I sketch AI’s historical and current development I need two Gartner hype cycles. AI’s first hype was between 1956 and 1974 and a second between 2011 and 2018 (follow the black line in the sketch below). We see that expectations rise, fall and flatten. If the Gartner hype cycle is correct, we will experience a second AI winter in the near future.

Now I will expand the sketch with a curve showing the progress of actual development of AI technology (follow the red line in the sketch):

The red line illustrates that a jump in increased AI capacity causes expectations to inflate. On the way up to the peak, expectations rise faster than the actual AI capacity. During the AI winter, technology still developed, but at a slower pace (since research funding was substantially decreased) until the two lines converged on the plateau of productivity. The breakthroughs in AI the last couple of decades caused the current peak of inflated expectations, which, after a possible second AI winter, will converge with the actual AI capacity. But first we should be prepared to put on some warm winter clothes to survive the approaching winter! Brrrr.


Check out the Gartner Hype Cycle for Emerging Technologies 2018.

If you want to learn more about the AI hype cycles, read Alok Aggarwal’s excellent assessments about the first AI hype and the current AI hype.




Mikael Hansson

I’m Dr. Bonnie Barstow but my Peppers are not KITT

When I was a little girl I tried to watch every episode of the American television series Knight Rider, not because of the male main character who fought injustice and crime, but because of Dr. Bonnie Barstow, the female chief technician of KITT. KITT was an artificially intelligent self-driving car that could understand and communicate in natural language, and one could remote-connect to it via a wrist watch.

Which 10 year old’s heart wouldn’t start beating faster by this? I wanted to be like Dr. Bonnie Barstow when I grew up, building technology and machines that were so intelligent, that humans could talk to them!

Eventually, I started my university education and focused on natural language processing or how to make machines understand and communicate in natural language. Back then (about 20 years ago) we studied linguistic theories in order to understand the underlying principles of how humans process natural language, and then developed so-called finite state methods (e.g. formal grammars, graphs and automata) that could be implemented in machines.

Since recently, everyone in natural language processing (or in any other research area of your choice) seem to use machine learning methods to solve problems: given a problem statement x and a machine learning algorithm y, apply y to x, and obtain very good and fast results. The only tiny remaining question is: why do we obtain these results?

Do machines today speak and reason like humans do due to the omnipresent machine learning methodology? No, they don’t. Did we make progress? Yes and no. We obtain faster and better results for specific problems, but those who want to understand some principles of human cognition, do not gain the scientific insight they seek.

Currently I’m working with colleagues at the Department of Computing Science, and three Pepper robots on developing dialogue management approaches that combine machine learning methods and finite-state methods. Machine learning algorithms serve us well for problems that involve mining patterns or correlations over large data sets, and finite-state methods give us the understanding that we, as scientists, naturally seek. I believe that the key to a new advancement in AI is combining methodological approaches, so that we get reliable and fast results, combined with insight into stated problems and their solutions.

I want to share an advice by Marvin Minsky (one of the pioneers of AI) who said ”If you just have a single problem to solve, then fine, go ahead and use a neural network. But if you want to do science and understand how to choose architectures, or how to go to a new problem, you have to understand what different architectures can and cannot do”.

Over the past years, I obtained my PhD, tried out various methodological approaches, worked with Pepper robots, and I can say, that I am Dr. Bonnie Barstow but my Peppers are not KITT. Not yet.

Photo: Mikael Hansson


Trip with the Alpine Ecology course

Did you know that Trollsjön (or Rissájávri in Northern Sami) is the clearest and cleanest lake in Sweden? On June 27, we meet with the students from the introductory course on Alpine Ecology (7,5 credits), held at CIRC via Umeå University, and went together to Kärkevagge and Trollsjön. The aim of today was to learn about alpine pollination and to study alpine bumblebees. Though, Yrsa’s and my main aim was to photographically catch the stunning landscape of Kärkevagge and the students in action. While none of us are experts of the area, the course lecturer, Stig-Olof, told us all about it’s ecological and biological features. While running around with a camera on stones and creeks and trying to catch up with the students catching bumblebees is not the easiest task, the pictures still ended up quite beautiful – with the help of an incredible view. To see all the photos from the field day with the Alpine Ecology students, see CIRC’s Facebook page or Instagram!


First Week as Communication Interns in the Arctic!

First week at the Climate Impact Research Centre (CIRC, part of the Department of Ecology and Environmental Science at Umeå University) and I am already in love with the Swedish mountains! Before going to Abisko, I had only seen the mountains from the train window going up to Narvik. This summer, Sebastian Enhager (see his profile) and I will spend three months here in Abisko, about half an hour from Riksgränsen and about seventeen hours (by car) from both of our hometown, Stockholm, assisting as CIRC’s new communication interns.

This first week at the research station has involved a lot of new faces, exciting events and already a lot of field work, making use of our new hiking boots. During these first days in Abisko, we participated in the grand opening at Naturum with representatives from Gabna Sapmí village, Norrbotten administrative board and the Swedish Government. Taking photos of the new app ”Norrbottens naturkarta” (”Norrbotten’s nature map” in English), the new exhibition at Naturum and the launch of the new gate to Kungsleden, as well as starting up and promoting the event on CIRC’s communication channels (see CIRC’s Instagram, Facebook and webpage).

The remaining days this first week have involved taking photos and producing content from several field works, on a boat in Stordalen mire and the beautiful lake Torneträsk as well as hiking on the Nuolja mountain and in Abisko National Park. The amount of different projects being carried out at CIRC is impressive. During this first week, we have only been able to touch upon the surface of these activities. Additionally, this week we have awed to the Midsummer sun never going down and danced around the Midsummer pole. And tomorrow, we will participate in and contribute with communicative content to a fairly new project called Participatory Mapping, where Swedish Television (SVT) also will join.

There is a lot happening and a lot to do here, as you might notice from this attempt to describe one week here in one blog post. Follow Sebastian’s and my blog to get to know more about everything that is happening in the Arctic and the Swedish northern mountains.


Brewing beer on recycled water?

Conference participations are a recurring research activity. We are expected to disseminate, communicate and network. Latest example was a one-day conference in Stockholm about current state and future development of the environment. A lot of inspiring talks by scientists, representatives from industrial and governmental agencies. Here, I present my own summary:

  • The future energy sources are renewable (sun, sun, sun, wind and water). However, the present is still crude oil, coal and gas – a transition that will take time!
  • The carbon emissions within Sweden are decreasing, which is a positive development. However, considerable proportions of our food and goods are imported, so a growing part of “our” emissions are generated elsewhere.
  • Great focus on the feasibility to achieve the Global Sustainable Development Goals until 2030 – little focus that we fail badly to achieve important environmental goals on the national level…
  • Planetary boundaries and limited natural resources are obvious. However, the current global economy is still not adequately adjusted and regulated to that rather well-established fact.
  • There is a clear trend that environmental issues are moving from a focus on technology and environment towards behavior and policy.

And, on a personal note:

  • Travelling by train to Stockholm is a more than convenient alternative. Sustainability starts with your own choices!
  • No matter of color, the politicians that were represented at the conference seemed all to have very reasonable arguments concerning environmental politics. Obviously, there are soon elections in Sweden.
  • And, this week, a new beer was released, entirely brewed on recycled water (yes, the water leaving the sewage treatment plants in Sweden apparently has drinking water quality). It will cost me quite an effort, but I will have to try.

Further reading needed? Tillståndet i miljö 2018

To balance work and family…

One of those days… the school is closed today due to a planning day, and suddenly the kids follow half a day to the office. I appreciate their company, but don’t expect that you get anything done! So, they do some drawings and we are listening to Samir & Viktor while I try to write this text.

This morning, I promised them to visit my office and to make a session at the microscope to have a look at plankton. No, no, I am not one of those nerdy scientist-parents that try to introduce the kids to natural sciences at early age. The shark book by Sarah Sheppard that my kids received as a gift for Christmas a few years ago did the job much better than I do – so, they really ASKED to have a look at plankton!

Is it possible to balance work and family life as a scientist? I have eight years of personal experience, and had to learn a few things:
(1) Kids decrease the number of hours you spend in the office, but increase your efficiency (“good-enough approach”).
(2) You have to learn to say no (I am still can improve there).
(3) In case your parents live abroad, it is a tremendous advantage to have parents-in-law nearby which are willing to sort out some impossible days.
(4) And, let’s face it: your scientific output will – at least temporarily – decrease.

Any reason to complain? No. After all, also the life as a scientist is all about choices… Let’s go for ice-cream now!

Var det bättre förr?

Dealing with long-term environmental development, our research field faces regularly the challenge to evaluate the present-day conditions against a historical background. Often, the historical conditions are seen as pristine and unaffected by human perturbations, even though there exist many examples that also earlier cultures contributed heavily to the degradation of ecosystems and the environment. A well-known example is certainly the history of the Easter Islands, but there are also many examples in “our backyard”, as for instance the history of mining activities in the Bergslagen region, with tremendous environmental impact.

For the two decades of my own professional career within research and education, I may not yet be able to draw any firm conclusions whether things were better in the past. There was certainly less administration and reporting, but on the other hand, important tools such as the World Wide Web or E-mail systems were not yet available (by the way, never tell your students that you remember the time before E-mails and Cambro – they immediately think you are a dinosaur).

However, one research activity is very well suited for comparisons with the past. No matter what we are dealing with as environmental scientists, at some point we need to collect samples to describe and understand the environment or to test our hypotheses. How did early scientists carry out field work? One of the pioneers of Swedish sediment research was Gerard de Geer, and there exists a wonderful clip about field work from the 1930.

So, please enjoy below the insights in terms of sampling equipment and clothing (personally I appreciate the elegant headgear)! But, frankly, when it comes to data analysis, please ask yourself whether “det var verkligen bättre förr”!