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Posts Tagged ‘Artificial Intelligence’

Intelligent user interfaces 2017 (IUI2017)

Posted by dgarijov on March 30, 2017

I have just returned from an amazing IUI2017 in Limassol, Cyprus and, as I have done with other conferences, I think it would be useful to share a summary of my notes in this post. This was my first time attending the IUI conference, and I am gladly surprised with both the quality of the event and friendliness of the community. As a Semantic Web researcher, it was also very positive to learn how problems are tackled from a human-computer interaction perspective. I have to admit that this is often overlooked in many semantic web applications.IMG_20170312_131824098

What was I doing in IUI2017?

My role in the conference was to present our paper towards the generation of data narratives, or, in a more ambitious manner, our attempt to write the “methods” section of a paper automatically (see some examples here). The idea is simple: in computational experiments, the inputs, methods (i.e., scientific workflows), intermediate results, outputs and provenance are explicit in the experiment. However, scientists have to process all these data by themselves and summarize it in the paper. By doing so, they may omit important details that are critical for reusing or reproducing the work. Instead, our approach aims to use all the resources that are explicit in the experiment to generate accurate textual descriptions in an automated way.

I wanted to attend the conference in part to receive feedback on our current approach. Although our work was well received, I learned that the problem can get complex really quickly. In fact, I think it can become a whole area of research itself! I hope to see more approaches in the future in this direction. But that is the topic for another post. Let’s continue with the rest of the conference:


The conference lasted three days, with one main keynote opening each of them. The conference opened with Shumin Zhain, from Google, who described their work on modern touchscreen keyboard interfaces. This will ring a bell to anyone reading this post, as the result of their work can be seen on any Android phone nowadays. I am sure they will not have problems finding users to evaluate their approaches.

In particular, the speaker introduced the system to capture gestures to recognize words, as if you were drawing a line. Apparently, before 2004 they had been playing around with different keyboard configurations that helped users write in a more efficient manner. However, people have different finger sizes, and adapting the keyboard to them is still a challenge. Current systems have several user models, and combine them to adapt to different situations. It was in 2004 when they came with the first prototype of SHARK, a shape writer that used neural networks to decode keyboard movements. They refined their prototype until achieving the result that we see today on every phone.

However, there are still many challenges remaining. Smart watches have a screen that is too small for writing. And new formats without screen such as wearable devices or virtual reality don’t use standard keyboards. Eye tracking solutions have not made significant progress, and while speech recognition has evolved a lot, it is not likely to replace traditional writers any time soon.

The second speakers was George Samaras, who described their work to personalize interfaces based on the emotions shown by the users of a system. The motivation for this need is that currently an 80% of the errors of automated systems are due to human mistakes rather than mechanical ones, especially when the interfaces are complex, such as in aviation or nuclear plants. Here cognitive systems are crucial, and adapting the content and navigation to the humans using them becomes a priority.

The speaker presented their framework to classify users based on the relevant factors in interfaces. For example, the verbals prefer textual explanations, while imagers like image explanations for e.g., browsing results. Another example is how users prefer to explore the results: we have the wholist, who prefer a top down exploration, versus the analysit, who would rather go for bottom up search. This is can become an issue in collaborations, as users that prefer to perceive the information in the same way may collaborate more efficiently together. A study performed over 10 years with more than 1500 shows that personalized interfaces lead to a faster task completion.

Finally, the speaker presented their work for tackling the emotions of users. Recognizing them is important, as depending on their mood, users may be keen to see the interface in one way or the other. They have developed a set of cognitive agents, which aim to personalize services and persuade users to complete certain tasks. Persuasion is more efficient when taking into account emotions as well.


The final keynote was presented by Panos Markopoulos, who introduced their work on hci design for patient rehabilitation. Having a proper interaction with patients (in exercises for kids and elderly people, arm training for stroke survivors, etc.) is critical for their recovery. However, this interaction has to be meaningful or patients will get bored and not complete their recovery exercises. The speaker described their work with therapists to track patient recovery in exercises such as pouring wine, cleaning windows, etc. The talk ended with a summary of some of the current challenges in this area, such as adapting feedback from patient behavior, sustaining engagement on the long run or personalization of exercises.


  • Recommendation is still a major topic in HCI. Peter Brusilovsky gave a nice overview of their work on personalization in the context of relevance-based visualization, as part of the ESIDA workshop. Personalized visualizations are now gaining more relevance in recommendation, but picking the right visualization for users is still a challenge. In addition, users are starting to demand why certain recommendations are more relevant, so non-symbolic approaches like topic modeling present issues.
  • Semantic web as a means to address curiosity in recommendations. SIRUP uses LOD paths with cosine similarity to find potential connections relevant for users.
  • Most influential paper award: Trust in recommender systems (O’Donovan and Smyth), where they developed a trust model for users, taking into account provenance too. Congrats!



IUI 2017 had 193 participants this year, almost half of them students (86); and an acceptance rate of 23% (27% for full papers). You can check the program for more details. I usually prefer this kind of conferences because they are relatively small, you can see most of the presented work without having to choose and you can talk to everyone very easily. If I can, I will definitely come back.

I also hope to see more influence of Semantic Web techniques to facilitate some of the challenges in HCI, as I think it there is a lot of potential to help in explanation, trust or personalization. I look forward to attending next year in Tokyo!

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AAAI 2017

Posted by dgarijov on February 16, 2017

The Association for the Advancement of Artificial Intelligence conference (AAAI) is held once a year to bring together experts from heterogeneous fields of AI and discuss their latest work. It is also a great venue if you are looking for a new job, as different companies and institutions often announce open positions. Last week, the 31st edition of the conference was celebrated in downtown San Francisco, and I attended the whole event. If you missed the conference and are curious about what was going on, make sure you read the rest of this post.


But first: what was I doing there?

I attended the conference to co-present a tutorial and a poster.

The tutorial was a training session called “The scientific paper of the future”, which introduced a set of best practices on how to describe data, software, metadata, methods and provenance associated with a scientific publication, along with different ways of implementing these practices. Yolanda Gil and I presented, but Gail Clement (lead of AuthorCarpentry at Caltech library) joined us as well to describe how to boost your research impact in 5 simple steps. I found some of her materials so useful that I have finally opened a profile on ImpactStory after her talk. All the materials of our talk are online, so feel free to check them out.


From left to right: Gail Clement, Yolanda Gil and me

The poster I presented described the latest additions of the DISK framework. In a nutshell, we have adapted our system for automating hypothesis analysis and revision to operate on data that is constantly growing. While doing this, we keep a detailed record of the inputs, outputs and workflows needed to do the revision of the hypothesis. Check out our paper for details!


Ok, enough self-promotion! Let’s get started with the conference:


In general, the quality of the keynotes and talks was outstanding. The presenters did a great job and effort to talk about their topics without jumping into the details of their field.

Rosalind Piccard started the week by talking about AI and emotions, or, using her own terms, “affective computing”. Detecting the emotion of the person interacting with the system is pivotal for decision making. But recognizing these emotions is not trivial (e.g., many people smile when they are frustrated, or even angry). It’s impressive how sometimes just training neural networks with sample data is not enough, as the history of the gestures play an important role in the detection as well. Rosalind described her work for detecting and predict emotions like the interest of an audience or stress. Thanks to a smart wristband they are able to predict seizures and breakouts in autistic kids. In the future, they aim to be able to predict your mood and possible depressions!

On Tuesday, the morning keynote was given by Steve Young, who talked about speech recognition and human-bots interaction. Their approach is mostly based on neural networks and reinforced learning. Curiously enough, this approach works better on the field (with real users) than with simulated results (for which other approaches work better). The challenges in this area lie in determining when a dialog is not accurate, as users tend to lie a lot when providing feedback. In fact, maybe the only way of knowing that something went wrong in a dialog is when it’s too late and the dialog has failed. As a person working on the Semantic Web domain, I found interesting that knowledge bases are an uncharted territory in this field at the moment.

Jeremy Frank spoke in the afternoon session for IAAI. He focused on the role of AI on autonomous space missions where sometimes the communications are interrupted and many anomalies may occur. The challenge in this case is not only to be able to plan what the robot or ship are going to do, but to monitor the plan and explain whether an order or a command did what it was actually supposed to. In this scenario, having new software becomes a risk.

On Wednesday, Dmitri Dolgov was in charge of talking about self-driving cars. More than 10 trillion miles are travelled every year across the world, with over 1.2 million casualties in accidents that are 94% of the time a human error. The speaker gave a great overview of the evolution of the field, starting in 2009 when they wanted to understand the problem and created a series of challenges to drive 100 miles in different scenarios. By 2010, they had developed a system good enough for driving a blind man across town, automatically. In 2012, the system was robust enough to drive in freeways. By 2015, they had finally achieved their goal: a complete driverless vehicle, without steering wheel or pedals. A capability of the system that surprised me is that it is able to read and mimic human behavior in intersections or stop signs without any trouble. In order to do this, the sensor data has to be very accurate, so they ended up creating their own sensors and hardware. As in the other talks, deep learning techniques have helped enormously to recognize certain scenarios and operate accordingly. Having the sensor data available has also helped. These cars have more than 1 billion virtual miles of training, and they are failing less and less as time goes by.


The afternoon session was led by Kristen Grauman, an expert in computer vision who analyzed how image recognition works in unlabeled video. The key challenge in this case is to be able to learn from images in a more natural way, as animals do. It turns out that our movement is heavily correlated to our vision sense, to the point that if we don’t allow an animal to move freely when it’s growing up and viewing the world, it may be damaged permanently. Therefore, maybe machines should learn from images in movement (videos) to understand better the context of an image. The first results in this direction look promising, and the system has so far learned to track relevant moving objects in video, by itself.

The final day opened with a panel that I am going to include in the keynote group, as it has been one of the breakthroughs of this year. An AI has recently beaten all the professional players against whom it has played in Poker (one to one), and two of the lead researchers in the field (Michael Bowling and Tuomas Sandholm) were invited to show us how they did it. Michael started describing DeepStack and why Poker is a particularly interesting challenge for AI: while in other games like chess you have all the information you need at a given state to decide your next move, Poker is an imperfect information game. You may have to remember the history of what has been done in order to proceed with your next decision. This creates a decision tree that is even bigger than complex board games like Chess and Go, so researchers have to abstract and explore the sparse tree. The problem is that, at some point, something may have happened that wasn’t taken into account in the abstraction, and this is where the problems start.

Their approach for addressing this issue is to reason over the possible cards that the opponent thinks the system has (game theory and Nash equilibrium play a crucial role). The previous history determines distributions of the cards, while evaluation functions have different heuristics based on the beliefs of the players in the current game (deep learning is used to choose the winning situation out of the possibilities). While current strategies are very exploitable, DeepStack is one of the least, being able to make 8 times what a regular player makes while being able to run in a laptop during the competition (the training part takes place before).

Tuomas followed introducing Libratus, an AI created last year but evolved from previous efforts. Libratus shares some strategies with DeepStack (card abstraction, etc.), as the Poker community has worked together on interoperable solutions. Libratus is the AI that actually played against the Poker professionals and beat them, even when they had a 200K $ incentive for the winner. The speaker mentioned that instead of trying to exploit the weaknesses of the opponent, Libratus focused on how the opponent exploits the strategies used by the AI. This way, Libratus could learn and fix these holes.

According to the follow up discussion, Libratus could probably defeat Deepstack, but they haven’t played against each other yet. The next challenges are applying these algorithms to solve similar issues in other domains, and making an AI that can actually be part of a table and join tournaments (this may imply a redefinition of the problem). Both researchers ended up stating how supportive the community has been providing feedback and useful ideas to improve their respective AIs.

The last keynote speaker was Russ Tedrake (MIT Robot labs), who presented advances in robotics and the lessons learned during the three year DARPA challenge on robotics. The challenge had a series of heterogeneous tasks (driving, opening a valve, cut a hole in a wall, open and traverse a door, etc.). Most of these problems are faced as optimization problems, and planning is a key feature that has to be updated on the go. Robustness is crucial for all the processes. For example, in the challenge, the MIT robot failed due to a human error and an arm broke off. However, thanks to the redundancy functions, the robot could finish the rest of the competition using only the other arm. As a side note, the speaker also explained why the robots always “walk funny”: their center of mass. It facilitates the equations for movement, so researchers have adopted it to avoid more complexity in their solutions.

One of the main challenges for these robots is perception. It has to run constantly to understand the surroundings of the robot (e.g., obstacles), dealing with possible noise data or incomplete information. The problem is that, when a new robot has to be trained, most of the data produced with other robots is not usable (different sensors, different means for grabbing and dealing with objects, etc.). Looking how babies react with their environment (touching everything and tasting it) might bring new insights in how to address these problems.

My highlights

-The “AI in practice” session that occurred on Sunday was great. The room was packed, and we saw presentations from companies like IBM, LinkedIn or Google.

I liked these talks because they highlighted some of the current challenges faced by AI. For example, Michael Witbrock (IBM) described how despite the advances in Machine Learning applications, the representations used to address a problem can barely be reused. The lack of explanation of deep learning techniques does not help either, specifically in diagnosing diseases: doctors want to know why a certain conclusion is reached. IBM is working towards improving the inference stack, so as to be able to combine symbolic systems with non-symbolic ones.

Another example was Gary Marcus (Uber labs), who explained that although there has been a lot of progress on AI, AGI (artificial general intelligence) has not advanced that much. Perception is more than being able to generalize from a situation, and machines are currently not very good at it. For example, an algorithm may be able to detect that there is a dog in a picture, and that the dog is lifting weights, but it won’t be able to tell you why this picture is unique or rare. The problem with current approaches is that they are incremental. Sometimes, there is a fear to step back and look at how some of our current problems are addressed. Focusing too much on incremental science (i.e., improving a small percentage of the precision of the current algorithms), may lead to get stuck in local maximums. Sometimes we need to address problems from different angles to make sure we make progress.

– AI in games is a thing! Over the years I have seen some approaches that aim to develop smart players, but attending this tutorial was one of the best experiences in the conference. Julian Toeglius gave an excellent overview/tutorial of the state of the art in the field, including how a simple A* algorithm may almost be a perfect player for Mario (if we omit those levels when we need to go back), how games are starting to adapt to players, how to build credible non player characters and how to create scenarios that are fun to play automatically. Then he introduced other problems that overlap with many of the challenges addressed in the keynotes: 1) How can we produce a general AI that learns how to play any game? And 2) how can we create a game automatically? For the first one, I found interesting that they have already developed a benchmark of simple games that will test your approach. The second one however is deeper, as the problem is not creating a game, or even a valid game. The real problem in my opinion is creating a game that a player considers fun. At the moment the current advances consist on modifications of existing games. I’ll be looking forward to reading more about this field and its future achievements.


– AI in education: Teaching ethics to researchers is becoming more and more necessary, given the pace at which science evolves. At the moment, this is an area often overlooked in any PhD or research program.

– The current NSF research plan is not mute! Lynne Parker introduced the creation of the AI research and development strategic plan, which expects to remain untouched even after the results of the latest election. The current focus is on how AI could help to the national priorities: liberty (e.g., security), life (education, medicine, law enforcement, personal services, etc.) and pursuit of happiness (manufacturing, logistics, agriculture, marketing, etc.). Knowledge discovery and transparent and explainable methods will help for this purpose.

– Games night! Great opportunity to socialize and meet part of the community by drawing, playing puzzles and board games.


– Many institutions are hiring. The Job fair had plenty of participating companies and institutions, but it was a little bit far away from the main events and I didn’t see many people attending. In any case, there were also plenty of companies with stands while the main conference was happening as well, which made it easy to talk to them and see what were they working on.

– Avoid reinventing the wheel! There was a cool panel on Expert systems history. Sometimes it is good to just take a step back and see how they analyzed research problems in the past. Some of their solutions still apply today

– Ontologies and Semantic Web were almost non-present in the whole conference. I think I only saw three talks related to the topic, about evolution and trust of knowledge bases, detection of redundant concepts in ontologies and the LIMES framework. I hope the semantic web community is more active in future editions of AAAI.

– Check out the program for more details on the talks and presentations.


Attending AAAI has been a great learning experience. I really recommend it to anyone working on any field of AI, especially if you are student or you are looking for a job. I also find very exciting that some of the problems I am working on are also identified as important by the rest of the community. In particular, the need of creating proper abstractions to facilitate understanding and shareability of current methods was part of the main topic of my thesis, while the need for explanation of the result of a certain technique is applied is highly related to what we do for capturing the provenance of scientific workflow results. As described by some of the speakers, “Debugging is a kind of alchemy” at the moment. Let’s turn it into a science.

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Making robots behave

Posted by dgarijov on March 24, 2014

I normally write about things that are somehow related to what I do, but last week I attended to a seminar that I really enjoyed, and I think it is worth making a short summary here. The title was “Making Robots Behave”, and it was presented by Leslie Pack, (MIT).
As you may have already guessed, the seminar was about Artificial Intelligence and robot behavior. In particular, the problem they wanted to address is how to solve robot uncertainty. The robot has a state estimation from which it creates its beliefs and uses the result to pick up the next action. Then a planner takes the action into consideration and tells the robot to execute the next planned action. From the result of the action a new input is generated and the robot re-estimates the next state, initiating the cycle again. A simple feedback loop! (For more details I recommend you to have a look at the talk, available online).
One of the cool things about the demo we saw is that most of the time you didn’t knew how the robot was going to act next. It depended on the reads the sensor gave to it in each moment, plus the planning algorithm and the feedback from its previous actions (i.e., its own knowledge of the world surrounding it). Sometimes the robot even moved its arms away because they were in the middle of its field of view.
I enjoy this kind of things because it reminds me somehow to a science fiction tale (the last question by Isaac Asimov) where humans ask a computer how to lower the entropy of the universe. After thousands of years, when the computer gets the response, it ends up knowing how to answer questions like the meaning of life. In the robot’s particular scenario the universe is the room, where it enters knowing nothing. When it exits, it is able to recognize the environment and the different objects found in its way. Whether the robot can learn from this experience and teach the actions to take in similar scenarios is something that we will have to wait to see.

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