Why would decision makers attend these masterclasses? To make informed business decisions on and around cognitive technologies, decision makers must understand the foundations, as well as the context, of these technologies.
What do we offer? The IBM Benelux Center for Advanced Studies (CAS) teamed up with our long term collaborators in academia to deliver two 2-day masterclasses to educate decision makers about the technology. The first day of a masterclass will be at the university, covering the academic basics in an accessible way, while the second day is at IBM providing a more industrial angle of the topic.
The first masterclass, titled Foundations of Cognitive Computing, is delivered together with the Vrije Universiteit Amsterdam featuring renowned professors such Lora Aroyo, Guszti Eiben, and Frank van Harmelen (final list to be confirmed), but we will also have talks by IBM Research (Ken Barker), and demonstrations of past and present projects delivered locally by CAS. The preliminary dates for this Masterclass are 16-17 November 2017.
Topics covered (subject to change upon demand):
The past, present and future of Artificial Intelligence
Introduction to Cognitive Computing and Watson
How do Cognitive Systems learn?
Where is Watson now?
AI for the Masses (AI services in the cloud)
When AI Goes Bad (Ethics)
Demos and Corresponding Deep Dives
The second masterclass, titled The Internet of Everything and Everyone, is delivered with TU Delft, containing talks by prof. Geert-Jan Houben, dr. Alessandro Bozzon and several other researchers and students at the university, but also from IBM (John Cohn, Victor Sanchez, etc), CAS researchers and students. The date for for this masterclass are7-8 December 2017.
Topics covered (subject to change upon demand):
Data collection for Cognitive systems:
Solving real problems with IoT and with human computation
Data Science to connect machines and humans
Demos by students, faculty and IBM on how the technology can be used
What benefits can the masterclasses bring? By gaining and understanding of what the technology is based on and what it can do, attendees can engage in deeper, more meaningful conversations about Cognitive, IoT, etc. They might become inspired to start projects using the technology, or perhaps the class can help them become convinced about the value a proposed IBM project.
Interested? Contact us via firstname.lastname@example.org, or via Zoltan Szlavik and Benjamin Timmermans directly.
The first keynote was presented by Geoff Mulgan, CEO of NESTA. He set the context of the conference by stating that there is a problem with technological development, namely that it only takes knowledge out of society and does not put it back in. Also, he made it clear that many of the tools we see today like Google Maps are actually nothing more than companies that were bought and merged together. This combination of things is what creates the power. He also defined what the biggest trends are in collective intelligence: the observation e.g. citizen generated data on floods, predictive models e.g. fighting fires with data, memory e.g. what works centers on crime reduction, and judgement e.g. adaptive learning tool for schools. Though, there are a few issues with collective intelligence: Who pays for all of this? What skills are needed for CI? What are the design principles of CI? What are the centers of expertise? These are all not yet clear. However, what is clear is that there is a new field emerging through combining AI with CI: Intelligence Design. We used to think systems resolve this intelligence, but actually we need to steer and design it.
In a plenary session there was an interesting talk on public innovation by Thomas Kalil. He defined the value of concreteness as things that happen when particular people or organisations take some action in pursuit of a goal. These actions are more likely to affect change if you can articulate who would needs to do what. He said he would like to identify the current barriers to prediction markets and areas where governments could be a user and funder of collective intelligence. This can be achieved through connecting people that are working to solve similar problems locally, e.g. in local education. Then change can be driven realistically, by making clear who needs to do what. Though, it was noted also that people need to be willing and able for change to work.
There were several interesting talks during the parallel sessions. Thomas Malone spoke about using contest webs to address the problem of global climate change. He claims that funding science can be both straightforward and challenging, for instance government policy does not always correctly address the need of a domain issues, and even conflicts of interest may exist. Also, fundamental research can be tough to convince the general public of its use, as it is not sexy. Digital entrepreneurship is furthermore something that is often overlooked. There are hard problems, and there are new ways of solving them. It is essential now to split the problems up into parts, solve each of them with AI, and combine them back together.
Also Mark Whiting presented his work on Daemo, a new crowdsourcing platform that has a self-governing marketplace. He stress the fact that crowdsourcing platforms are notoriously disconnected from user interests. His new platform has a user driven design, in order to get rid of the flaws that exist in for instance Amazon Mechanical Turk.
Daniel Weld from the University of Washington presented his work on argumentation support in crowdsourcing. Their work uses argumentation support in crowd tasks to allow workers to reconsider their answers based on the argumentation of others. They found this to significantly increase the annotation quality of the crowd. He also claimed that humans will always need to stay in the loop of machine intelligence, for instance to define what the crowd should work on. Through this, hybrid human-machine systems are predicted to become very powerful.
Hila Lifshitz-Assaf of NYU Stern School of Business gave an interesting talk on changing innovation processes. The process of innovation has changed from a lane inventor, to labs, to collaborative networks, and now into open innovation platforms. The main issue with this is that the best practices of innovation fail in the new environment. In standard research and development there is a clearly defined and selectively permeable, whereas with open innovation platforms this is not the case. Experts can participate from in and outside the organisation. It is like open innovation: managing undefined and constantly changing knowledge in which anyone can participate. For this to work, you have to change from being a problem solve to a solution seeker. It is a shift from thinking: The lab is my world, to the world is my lab. Still, problem formulation is key as you need to define the problems in ways that cross boundaries. The question always remains, what is really the problem?
In the poster sessions there were several interesting works presented, for instance work on real-time synchronous crowdsourcing using “human swarms” by Louis Rosenberg. Their work allows people to change their answers through the influence of the rest of the swarm of people. Another interesting poster was by Jie Ren of Fordham University, who presented a method for comparing the divergent thinking and creative performance of crowds compared to experts. We ourselves had a total of five posters covering both poster sessions, which were received well by the audience.
As I am study advisor for the international students, I am also responsible for immigrants that study Computer Science bachelor at the Vrije Universiteit. In order to provide these future students with a clear picture of what they can expect, I gave a presentation about Computer Science, our program at the University, and things they should take into account as (international) students.
The presentation is available below, which is based on slides of Wan Fokkink. If you are an immigrant and would like to study Computer Science at the VU, you should get in touch with VASVU for a preparation year or visit the Computer Science website.
We present our latest work on the CrowdTruth framework, titled “Human Computing for the Real World”, at the ICT Open 2017 conference on 21st and 22nd of March 2017. I made a new video that demonstrates the different aspects of the framework for dealing with ambiguity in data, crowdsourcing of human interpretations, and evaluating disagreement between annotations.
Our demo of ControCurator titled “ControCurator: Human-Machine Framework For Identifying Controversy” will be shown at ICT Open 2017. In this demo the ControCurator human-machine framework for identifying controversy in multimodal data is shown. The goal of ControCurator is to enable modern information access systems to discover and understand controversial topics and events by bringing together crowds and machines in a joint active learning workflow for the creation of adequate training data. This active learning workflow allows a user to identify and understand controversy in ongoing issues, regardless of whether there is existing knowledge on the topic.
The course is run by Lora Aroyo, Anca Dumitrache, Benjamin Timmermans and Oana Inel from the VU, and Robert-Jan Sips and Zoltan Szlavik from IBM. In the course the students were challenged by Amsterdam Marketing to solve the issue of the increasing overcrowdedness of tourists in the city center of Amsterdam. The city is culturally rich with many places to visit, yet most visitors cluster around a limited set of popular locations. The students came up with ideas to motivate visitors to spread in the city and provide them with relevant information for their visit.
Today I gave a talk in our Weekly AI meeting on the topic of ControCurator. This is a project that I am currently working on, which has the goal to enable the discovery and understanding of controversial issues and events by combining human-machine active learning workflows.
In the talk I explained the issue of defining the space of a controversy, and how this relates to for instance wicked problems. You can see the slides below.
Brainstem tumors are a rare form of childhood cancer for which there is currently no cure. The Semmy Foundation aims to increase the survival of children with this type of cancer by supporting scientific research. The Center for Advanced Studies at IBM Netherlands is supporting this research by developing a cognitive system that allows doctors and researchers to quicker analyse MRI-scans and better detect anomalies in the brainstem.
In order to gather training data, a crowdsourcing event was held at the festival Lowlands, which is a 3-day music festival that took place from 19-21 August 2016 and welcomed 55k visitors. At the science fair, IBM had a booth that hosted both this research and showcase of the Weather stations of the Tahmo project with TU Delft.
In the crowdsourcing task, the participants were asked to draw the shape of the brainstem and tumor in an MRI scan. Gathering data on whether a particular layer of a scan contains the brainstem and determining its size should allow a classifier to recognize the tumors. Furthermore, the annotator quality can be measured with the CrowdTruth methodology by analysing the precision of the edges that were drawn in relation to their alcohol and drug use that we collected. The hypothesis is that people under influence can still make valuable contributions, but that these are of lower quality than sober people. This may make the reliability of online crowd workers more clear, because it is unknown under what conditions they make their annotations.
The initial results in the heatmap of drawn pixels give an indication of the overall location of the brainstem, but further analysis will follow on the individual scans in order to measure the worker quality and generating 3d models.
From 2 to 16th of July we organized the Big Data in Society Summerschool at the Vrije Universiteit Amsterdam. As part of our Collaborative Innovation Center with IBM, we presented an introduction of the technical and theoretical underpinnings of IBM Watson and discussed the use of big data and implications for society. We looked at examples of how the original Watson system can be adapted to new domains and tasks, and presented the CrowdTruth approach for gathering training and evaluation data in this context. The participating students, which ranged from bachelor to PhD level, said they learned a lot from the lectures and found the practical hands-on sessions very useful.