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Interactive Machine Learning: Experimental Evidence for the human-in-the-loop

Recent advances in automatic machine learning (aML) allow solving problems without any human intervention, which is excellent in certain domains, e.g. in autonomous cars, where we want to exclude the human from the loop and want fully automatic learning. However, sometimes a human-in-the-loop can be beneficial – particularly in solving computationally hard problems. We provide new experimental insights [1] on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, an Ant Colony Optimization (ACO) framework was used, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind and it is a wonderful experimental platform for explainable AI.

[1] Holzinger, A. et al. (2018). Interactive machine learning: experimental evidence for the human in the algorithmic loop. Springer/Nature: Applied Intelligence, doi:10.1007/s10489-018-1361-5.

Read the full article here:
https://link.springer.com/article/10.1007/s10489-018-1361-5

 

 

 

AI, explain yourself !

“It’s time for AI to move out its adolescent, game-playing phase and take seriously the notions of quality and reliability.”

There is an interesting commentary with interviews by Don MONROE in the recent Communications of the ACM, November 2018, Volume 61, Number 11, Pages 11-13, doi:

Artificial Intelligence (AI) systems are taking over a vast array of tasks that previously depended on human expertise and judgment (only). Often, however, the “reasoning” behind their actions is unclear, and can produce surprising errors or reinforce biased processes. One way to address this issue is to make AI “explainable” to humans—for example, designers who can improve it or let users better know when to trust it. Although the best styles of explanation for different purposes are still being studied, they will profoundly shape how future AI is used.

Some explainable AI, or XAI, has long been familiar, as part of online recommender systems: book purchasers or movie viewers see suggestions for additional selections described as having certain similar attributes, or being chosen by similar users. The stakes are low, however, and occasional misfires are easily ignored, with or without these explanations.

“Considering the internal complexity of modern AI, it may seem unreasonable to hope for a human-scale explanation of its decision-making rationale”.

Read the full article here:
https://cacm.acm.org/magazines/2018/11/232193-ai-explain-yourself/fulltext

 

 

What if the AI answers are wrong?

Cartoon no. 1838 from the xkcd [1] Web comic by Randall MUNROE [2] describes in a brilliant sarcastic way the state of the art in AI/machine learning today and shows us the current main problem directly. Of course you will always get results from one of your machine learning models. Just fill in your data and you will get results – any results. That’s easy. The main question remains open: “What if the results are wrong?” The central problem is to know at all that my results are wrong and to what degree. Do you know your error? Or do you just believe what you get? This can be ignored in some areas, desired in other areas, but in a safety critical domain, e.g. in the medical area, this is crucial [3]. Here also the interactive machine learning approach can help to compensate or lower the generalization error through human intuition [4].

 

[1] https://xkcd.com

[2] https://en.wikipedia.org/wiki/Randall_Munroe

[3] Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis & Douglas B. Kell 2017. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923. online available: https://arxiv.org/abs/1712.09923v1

[4] Andreas Holzinger 2016. Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Informatics, 3, (2), 119-131, doi:10.1007/s40708-016-0042-6. online available, see:
https://hci-kdd.org/2018/01/29/iml-human-loop-mentioned-among-10-coolest-applications-machine-learning

There is also a discussion on the image above:

https://www.explainxkcd.com/wiki/index.php/1838:_Machine_Learning

 

 

Yoshua Bengio emphasizes: Deep Learning needs Deep Understanding !

Yoshua BENGIO from the Canadian Institute for Advanced Research (CIFAR) emphasized during his workshop talk entitled “towards disentangling underlying explanatory factors”  (cool title) at the ICML 2018 in Stockholm, that the key for success in AI/machine learning is to understand the explanatory/causal factors and mechanisms. This means generalizing beyond identical independent data (i.i.d.); current machine learning theories are strongly dependent on this iid assumption, but applications in the real-world (we see this in the medical domain!) often require learning and generalizing in areas simply not seen during the training epoch. Humans interestingly are able to protect themselves in such situations, even in situations which they have never seen before. See Yoshua BENGIO’s awesome talk here:
http://www.iro.umontreal.ca/~bengioy/talks/ICMLW-limitedlabels-13july2018.pptx.pdf

and here a longer talk (1:17:04) at Microsoft Research Redmond on January, 22, 2018 – awesome – enjoy the talk, I recommend it cordially to all my students!

IEEE DISA 2018 in Kosice

The IEEE DISA 2018 World Symposium on Digital Intelligence for Systems and Machines was organized by the TU Kosice: http://www.disa2018.org

Here you can download my keynote presentation  (see title and abstract below)
a) 4 Slides per page (pdf, 5,280 kB):
HOLZINGER-Kosice-ex-AI-DISA-2018-30Minutes-4×4

b) 1 slide per page (pdf, 8,198 kB):
HOLZINGER-Kosice-ex-AI-DISA-2018-30Minutes

c) and here the link to the paper (IEEE Xplore)
From Machine Learning to Explainable AI

d) and here the link to the video recording
https://archive.tp.cvtisr.sk/archive.php?tag=disa2018##videoplayer

Title: Explainable AI: Augmenting Human Intelligence with Artificial Intelligence and v.v

Abstract: Explainable AI is not a new field. Rather, the problem of explainability is as old as AI itself. While rule‐based approaches of early AI are comprehensible “glass‐box” approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. The introduction of probabilistic learning methods has made AI increasingly successful. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models they lack still explicit declarative knowledge. For example, words are mapped to high‐dimensional vectors, making them unintelligible to humans. What we need in the future are context‐adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real‐world phenomena.
Maybe one step is in linking probabilistic learning methods with large knowledge representations (ontologies), thus allowing to understand how a machine decision has been reached, making results re‐traceable, explainable and comprehensible on demand ‐ the goal of explainable AI.

 

 

 

Judea Pearl on explainable-AI: teach machines cause and effect

To build truly intelligent machines, teach them cause and effect, emphasizes Judea PEARL in a recent Quanta Magazine article (May, 15, 2018) posted by Kevin HARTNETT. Judea Pearl won in 2011 the Turing Award (“the Nobel Prize in Computer Science”) and just published his newest book, called “The book of why: the new science of cause and effect”, wherein Pearl argues that AI has been handicapped by an incomplete understanding of what intelligence really is. Causal reasoning is a cornerstone in explainable-AI!

Read the interesting article here:
https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515

The book is also announced by the UCLA newsroom, along with a nice interview see:
http://newsroom.ucla.edu/stories/artificial-intelligence-pioneers-new-book-examines-the-science-of-cause-and-effect

 

Microsoft boosts Explainable AI

Microsoft invests into explainable AI and acquired on June, 20, 2018 Bonsai, a California start-up, which was founded by Mark HAMMOND and Keen BROWNE in 2014. Watch an excellent introduction “Programming your way to explainable AI” by Mark HAMMOND here:

and read read the original story about the acquisition here:

https://blogs.microsoft.com/blog/2018/06/20/microsoft-to-acquire-bonsai-in-move-to-build-brains-for-autonomous-systems

“No one really knows how the most advanced algorithms do what they do. That could be a problem.” Will KNIGHT in “The dark secret of the heart of AI”

https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai

The Problem with explainable-AI

A very nice and interesting article by Rudina SESERI in the recent TechCrunch blog (read the orginal blog entry below): at first Rudina points out that the main problem is in data; and yes, indeed, data should always be the first consideration. We consider it a big problem that successful ML approaches (e.g. the mentioned deep learning, our PhD students can tell you a thing or two about it 😉 greatly benefit from big data (the bigger the better) with many training sets; However, it certain domain, e.g. in the health domain we sometimes are confronted with a small number of data sets or rare events, where we suffer of insufficient training samples [1]. This calls for more research towards how we can learn from little data (zero-shot learning), similar as we humans do: Rudina does not need to show her children 10 million samples of a dog and a cat, so that her children can safely discriminate a dog from a cat. However, what I miss in this article is something different, the word trust. Can we trust our machine learning results? [2] Whilst, for sure we do not need to explain everything all the time, we need possibilities to make machine decisions transparent on demand and to check if something could be plausible. Consequently, Explainable AI can be very important to foster trust in machine learning specifically and artificial intelligence generally.

[1] https://link.springer.com/article/10.1007/s40708-016-0042-6

[2] https://ercim-news.ercim.eu/en112/r-i/can-we-trust-machine-learning-results-artificial-intelligence-in-safety-critical-decision-support

 

 

MIT emphasizes the importance of HCI for explainable AI

In a joint project “The car can explain” with the TOYOTA Research Institute  the MIT Computer Science & Artificial Intelligence Lab  are working on explainable AI and emphasize the increasing importance of the field of HCI (Human-Computer Interaction) in this regard. Particularly, the group led by Lalana KAGAL is working on monitors for reasoning and explaining: a methodological tool to interpret and detect inconsistent machine behavior by imposing constraints of reasonableness. “Reasonable monitors” are implemented as two types of interfaces around their complex AI/ML frameworks. Local monitors check the behavior of a specific subsystem, and non-local reasonableness monitors watch the behavior of multiple subsystems working together: neighborhoods of interconnected subsystems that share a common task. This enormously interesting monitoring consistently checks that the neighborhood of subsystems are cooperating as expected. Insights of this projects could also be valuable for the health informatics domain:

https://toyota.csail.mit.edu/node/21

Google Brain says Explainability is the “new deep learning”

There is a very interesting interview in the Talking Machines*) series from May, 31, 2018. Katherine GORMAN interviews Maithra RAGHU **) from the Google Brain Team, where she mentioned that “explainability is the new deep learning”, and it is particularly important for health informatics, where it is important to re-trace, re-enact and to understand and explain why a machine decision has been reached. This is super for us, because when I tell my students that this is important, nobody believes me; but now I can emphasize that not I am saying that, but Google Brain is saying it. Excellent.

However, the whole field needs a lot of work, before we can provide useable solutions for the end-user in daily routine (e.g. a medical doctor); urgently needed are approaches to explainable User Interfaces and most of all a research framework for testing explainability.

*) Talking Machine is an excellent, highly recommendable Podcast series, founded by Katharine GORMAN and Ryan ADAMS in 2015 and now run by Katharine together with Neil LAWRENCE (who leads the Amazon Research in Cambridge, UK).

**) Maithra RAGHU is currently a PhD working with Jon KLEINBERG at Cornell (see http://maithraraghu.com ), where she is doing extended research with the Google Brain Team, see: https://ai.google/research/teams/brain
Maithra has published some very interesting papers, e.g.: Maithra Raghu, Justin Gilmer, Jason Yosinski & Jascha Sohl-Dickstein. SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. Advances in Neural Information Processing Systems, 2017. 6078-6087.
or this is also very interesting:
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein & Surya Ganguli. Exponential expressivity in deep neural networks through transient chaos. Advances in neural information processing systems, 2016. 3360-3368.