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Human-in-the-loop AI

Very interesting: in the recent April, 5, 2018, TWiML & AI (This Week in Machine Learning and Artificial Intelligence) podcast, Robert Munro reports on the newly branded Figure Eight [1] company, formerly known as CrowdFlower. Their Human-in-the-Loop AI platform supports data science & machine learning teams working on various topics, including autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more, and most recently on disaster response and epidemiology. This is a further proof on the importance of the human-in-the-loop interactive machine Leanring (iML) approach! Awesome discussion led by Sam Charrington:

This fits well to the previous discussion with Jeff Dean – who emphasized the importance of health and the limits of automatic approaches including deep learning, listen directly at:

[1] https://www.figure-eight.com/resources/human-in-the-loop

 

A good proof of the importance of the HCI-KDD approach, worth: 2,1 Billion USD !

Our strategic aim is to find solutions for data intensive problems by the combination of two areas, which bring ideal pre-conditions towards understanding intelligence and to bring business value in AI: Human-Computer Interaction (HCI) and Knowledge Discovery (KDD). HCI deals with questions of human intelligence, whereas KDD deals with questions of artificial intelligence, in particular with the development of scalable algorithms for finding previously unknown relationships in data, thus centers on automatic computational methods. A proverb attributed perhaps incorrectly to Albert Einstein illustrates this perfectly: “Computers are incredibly fast, accurate, but stupid. Humans are incredibly slow, inaccurate, but brilliant. Together they may be powerful beyond imagination” [1].

An article published on February, 18, 2018 by David Shaywitz [2] from Forbes reports on the recent purchase of  the oncolology data company Flatiron Health for the enormous sum of 2,1 Billion USD (remember: Deep Mind was purchased by Google for a mere 400 million GBP 😉

This supports a few hypotheses which I try to convince my students all the time (but they won’t believe me unless Google is doing it 😉

a) those who can turn raw health data into insights and understandable knowledge can produce value
b) data – and particularly big data – is useless for the decision maker, what they need is reliable, valuable and trustworthy information
c) for the complexity of sensemaking from health data we (still) need a human-in-the-loop:  Humans (still) exceed machine performance in understanding the context and explaining the underlying explanatory factors of the data
d) consequently this is a good example for the business value of our HCI-KDD approach: Let the computer find in arbitrarily high-dimensional spaces what no human is able to do – but let the human do what no computer is able to do: BOTH working together are powerful beyond imagination!

Flatiron Health [3] is a company which is specialized on health data curation, supported by technology of course, but mostly done manually by human experts in the Mechanical Turk style. Remark: The name mechanical turk has historic origins as it was inspired by an automatic 18th-century chess-playing machine by Wolfgang von Kempelen,  that beats e.g. Benjamin Franklin in chess playing – and was acclaimed as “AI”. However, ti was later revealed that it was neither a machine nor an automatic device – in fact it was a human chess master hidden in a secret space under the chessboard and controlling the movements of an humanoid dummy. Similarly,  services which help to solve problems via human intelligence are called “Mechanical Turk online services”.

[1] Holzinger, A. 2013. Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, Alfredo, Kittl, Christian, Simos, Dimitris E., Weippl, Edgar & Xu, Lida (eds.) Multidisciplinary Research and Practice for Information Systems, Springer Lecture Notes in Computer Science LNCS 8127. Heidelberg, Berlin, New York: Springer, pp. 319-328, doi:10.1007/978-3-642-40511-2_22

[2] https://www.forbes.com/sites/davidshaywitz/2018/02/18/the-deeply-human-core-of-roches-2-1b-tech-acquisition-and-why-they-did-it/#6242fdbc29c2

[3] https://flatiron.com

Federated Collaborative Machine Learning

The Google Research Group [1] is always doing awesome stuff, the most recent one is on Federated Learning [2], which enables e.g. smart phones (of course any computational device, and maybe later all internet-of-things, intelligent sensors in either smart hospitals or in smart factories etc.) to collaboratively learn a shared representation model, whilst keeping all the training data on the local devices, decoupling the ability to do machine learning from the need to store the data centralized in the cloud. This goes beyond the use of local models that make predictions on mobile devices (like the Mobile Vision API and On-Device Smart Reply) by bringing model training to the device as well – which is great. The problem with standard approaches is that you always need centralized training data – either on your USB-stick, as the medical doctors do, or in a sophisticated centralized data center.

The basic idea is that the mobile device downloads the current modela and subsequently improves it by learning from data on the respective device, and then summarizes the changes as a small focused update. The remarkable detail is that only this update to the model is sent to the cloud (yes, here privacy, data protection safety and security is challenged see e.g. [3] – but this is much easier to do with this small data – as when you would do it with the raw data – think for example on patient data), where it is immediately averaged with other devicer updates to improve the shared model. All the training data remains on the local devices, and no individual updates are stored in the cloud.

The Google Group recently solved a lot of algorithmic and technical challenges. In a typical machine learning system, an optimization algorithm e.g. Stochastic Gradient Descent (SGD) [4] runs on a large dataset partitioned homogeneously across servers in the cloud. Such highly iterative algorithms require low-latency, high-throughput connections to the training data. But in the Federated Learning setting, the data is distributed across millions of devices in a highly uneven fashion. In addition, these devices have significantly higher-latency, lower-throughput connections and are only intermittently available for training.

This calls for a lot of further investigations with interactive Machine Learning (iML) bringing the human-into-the loop, i.e. making use of human cognitive abilities. This can be of particular interest to solve problems, where learning algorithms suffer due to insufficient training samples (rare events, single events), where we deal with complex data and/or computationally hard problems. For example, “doctors-in-the-loop” can help with their long-term experience and heursitic knowledge to solve problems which otherwise would remain NP-hard [5, 6]. A further step is with many humans-in-the-loop: Such collaborative interactive Machine Learning (ciML) can help in many application areas and domains, e.g. in in health informatics (smart hospital) or in industrial applications (smart factory) [7].

Read the original article, posted on April, 6, 2017,  here:
https://research.googleblog.com/2017/04/federated-learning-collaborative.html

[1] https://research.googleblog.com

[2] NIPS Workshop on Private Multi-Party Machine Learning, Barcelona, December, 9, 2016, https://pmpml.github.io/PMPML16/

[3] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., Mcmahan, H. B., Patel, S., Ramage, D., Segal, A. & Seth, K. 2016. Practical Secure Aggregation for Federated Learning on User-Held Data. arXiv preprint arXiv:1611.04482.

[4] Bottou, L. 2010. Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT’2010. Springer, pp. 177-186. doi:10.1007/978-3-7908-2604-3_16  (N.B.: 836 citations as of 08.04.2017)

[5] Holzinger, A. 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

[6] Holzinger, A., Plass, M., Holzinger, K., Crisan, G., Pintea, C. & Palade, V. 2016. Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to solve the Traveling Salesman Problem with the Human-in-the-Loop approach. In: Springer Lecture Notes in Computer Science LNCS 9817. Heidelberg, Berlin, New York: Springer, pp. 81-95, [pdf]

[7] Robert, S., Büttner, S., Röcker, C. & Holzinger, A. 2016. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Machine Learning for Health Informatics: Lecture Notes in Artifical Intelligence LNAI 9605. Springer, pp. 357-376, [pdf]

Image source: https://research.googleblog.com/2017/04/federated-learning-collaborative.html

 

Visualization of High Dimensional Data

Google is doing experiments with visualization of high dimenisonal data. This experiment helps visualize what’s happening in machine learning. It allows coders to see and explore their high-dimensional data. The goal is to eventually make this an open-source tool within TensorFlow, so that any coder can use these visualization techniques to explore their data.
Built by Daniel Smilkov, Fernanda Viégas, Martin Wattenberg, and the Big Picture team at Google:
This work is based on a method developed by Laurens van der Maaten & Geoffrey Hinton in 2008:
Maaten, L. V. D. & Hinton, G. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 11, 2579-2605, http://www.jmlr.org/papers/v9/vandermaaten08a.html
t-Distributed Stochastic Neighbor Embedding (t-SNE, spoken: Disney) is a (prize-winning) nonlinear technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional data sets into R2 or R3. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets (“big data”).
For details please refer directly to:
Compare this method to our own work on subspace clustering:

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