The Discovery Lab: the pearls and perils
A recent European study on University-Business Cooperation (UBC) suggested that joint research can have major benefits. Among those are “innovation with a longer-term horizon, shorter-term problem solving, data for high quality research and the ability to bring research into practice creating impact”. Max Welling explored the necessity of the role of corporates in AI ecosystems in his blog.
Local innovation ecosystems – such as Amsterdam Data Science (ADS) – can act as catalysts for regional competitiveness. Campuses can act as “platforms or hubs” – as with the Innovation Centre for AI (ICAI) Labs, and these in turn drive societal and economic impact for governments. These hubs are often the result of collaboration between academia and commercial businesses. The benefits are great, but getting such collaborations off the ground can be tricky – so what are the secrets to a fruitful collaboration?
The Discovery Lab, established a year ago, is a collaboration between Elsevier, the University of Amsterdam and the VU Amsterdam, with support from ADS. The lab is the latest step in a collaboration that started about 10 years ago, inspired by a joint vision of using AI and knowledge graphs to make research easier.
Trust is the Key Element to any Successful Partnership
The creation of the Discovery Lab was an exercise in trust, not just building trust between collaborators but also using that trust to develop an idea into something tangible. Although it may sound trivial, personal trust is essential, starting with the initial “click” that gets the partners excited about an idea. Collaboration between organisations is ultimately a collaboration between individuals.
Success Factors to Launch the Discovery Lab
Looking back on our experiences with ADS and ICAI, three success factors were dominant:
1. Building credibility and showing reliability
Getting to know each other’s capabilities over time helps clarify which partners are well suited to which projects. Before big investments can be made from either side, we used joint funding applications, master student projects and smaller contract research work. Although this takes longer, it is a strong foundation. Together with ADS, we helped shape an ecosystem around this work that allowed all partners to explore ideas with each other and shape the joint excitement – before making bigger investments. Within the Discovery Lab we consolidated and connected various joint projects around research knowledge graphs.
2. Legal is necessary, but should not get in the way
The most frequent complaint I hear across collaboration set-ups is about legal matters delaying and complicating a partnership. Involving legal early in the process – as people often suggest – can actually complicate the partnership at the wrong time. If they’re involved too late it can delay the process at the end. So, how do you find the right balance and how do you actually benefit from the legal queries raised in building trust?
In my experience, what holds the process back is that business priorities are not sufficiently detailed and clear enough to state what is key to the business/research and what is optional. For example, in IP what risk is the business willing to take and is the business able to actually absorb the results of the research? on the university side: what commitments, e.g. in terms of liabilities or in terms of AI ethics, can a university and a researcher make? And there are differences in style, e.g. what level of detail needs to be agreed upon and what can be left to later negotiations.
The challenge is how to formalize working with uncertainty and whether you’re open to defining the approach, the process, the principles of work and the resources rather than the outcomes. The more obscure outcomes and processes are, the more likely that legal issues appear.
3. Challenges with an evolving research landscape
The challenges to establishing the Discovery Lab were sometimes unexpected: too little transparency of government decision criteria being the main issue we faced. The key to overcoming these challenges was to always have a fast and flexible response in collaboration with our partners. Different methods of doing so may need to be tested, but with a strong foundation of trust between our collaborators meant we were able to face these challenges head on.
The Discovery Lab one year on
The Discovery Lab has been active for a year, and our joint efforts have really paid off. Despite the difficult circumstances due to COVID-19 adversity, there is active collaboration between the university researchers and Elsevier’s data scientists in the Lab. The first joint experiments are being conducted, the first publications are already out, and we’re actively connecting research in the Lab with business activities. We hope that our experiences in building the Discovery lab will help others to build similarly successful collaborations within the ADS ecosystem.
Why we need the UvA Data Science Centre
Accelerating science through data
As scientists and researchers, we are already immersed in what Turing Award winner Jim Gray termed the 4th paradigm: the use of data-intensive techniques to attack the hardest problems in research. This can clearly be seen in how researchers have tackled the many, sometimes unexpected, problems that have arisen from the COVID-19 pandemic.
For example, what is the real-time economic impact of COVID-19? Through the use of novel data sources and data science techniques, Raj Chetty and his team at Harvard have been answering that question. During the Amsterdam Data Science meetup series on COVID-19, we saw how data science is being used to answer diverse research questions, such as understanding the virus’s impact on our transportation system or building better predictive models for the disease.
At the University of Amsterdam (UvA), rarely a week goes by where I don’t see my colleagues in a variety of disciplines engaging with data to do impactful science. Recent examples include:
The driving forces behind the Data Science Centre
Given these compelling examples of data-driven research already taking place at the university, we asked ourselves how can we further support and expand the use of such techniques in all research domains across the UvA?
In our discussions with researchers and faculties, one of the clear needs was for skilled data scientists and engineers to be part of research teams. We see this from Chetty’s experience, where the availability of data scientists and engineers as part of the team was central to being able to answer the research question. They were able to develop the required data pipelines, integrate heterogeneous datasets and anonymize data correctly. Personally, it’s clear to me that this team science oriented approach is critical for data-driven research.
The second area where we saw opportunities for acceleration was interdisciplinary cooperation oriented around data. We know that from large scale studies that communities arise around datasets and that these communities often draw from different disciplines and methodological backgrounds. For example, in the AI4Science lab, researchers who study methods of causal discovery are working with those who work on gene regulatory networks to further research in both areas. In the case of studying urban environments mentioned above, urban geographers and political scientists came together around street view datasets.
To address these needs, the UvA will launch a Data Science Centre (DSC) in 2021. The centre was inspired by similar initiatives at NYU, Berkley and more locally by the Netherlands e-Science Centre and the University of Maastricht. However, we aim to have our own unique flavour that will leverage the UvA’s strengths.
Data Science Centre activities
First, the DSC will embed data scientists and engineers throughout every faculty in the university. We will create a community where knowledge sharing is key. Data scientists and engineers will share knowledge and experience through weekly sessions at the Library. The Library has always served as an intellectual crossroads within universities where scientists acquire knowledge and skills making it a natural home for the DSC.
We aim to have 35 dedicated staff by 2025 and will offer training opportunities for faculty and staff members more broadly. This will ensure that faculty members will be able to act on new skills gained and the ideas and knowledge sharing happening in the DSC.
Second, following the success of collaborative interdisciplinary PhDs and researcher assistants (as seen in the UvA’s research priority areas, AI4 Science and ICAI Amsterdam labs), the DSC will adopt a similar program to innovate data science methods through engagement with hard domain specific research questions.
The Data Science Ecosystem
Just as the UvA helped spur the Amsterdam Data Science (ADS) ecosystem over five years ago, it is now harnessing the ecosystem to improve its broader research. The DSC will work together with ADS to facilitate data science related collaboration and networking in and outside the university. Indeed, this last point shouldn’t be underestimated: data driven research often requires collaboration with parties outside the research domain. Again, reflecting on the COVID-19 economy-tracking example, the data used is supplied by multiple commercial parties. Thus, connecting to the wider data science community through ADS is critical for enabling world-class research.
Using the strengths of the UvA and ADS ecosystem, I’m excited to see the new discoveries that the UvA Data Science Centre will make possible. If you are interested in collaborating with the centre, please contact DSC@uva.nl.
AI Technology for People
Amsterdam Data Science (ADS) has created a tremendous network in which academia, companies, and the municipality come together in meetups and other events. Furthermore, its newsletter reports news from the community, and ADS seed projects encourage collaboration between research and companies. These are powerful means to build an ecosystem around data science, a field in which various disciplines have to come together to be successful and where fundamental research and application in real world settings go hand-in-hand.
AI affecting the world in which we live
Recently ADS has added AI to their agenda and rightly so – AI is transforming the world at a very rapid pace. It builds upon the foundations laid out in big data research and data science. It introduces intelligence in the form of being able to really understand unstructured data such as text, images, sound, and video, and techniques for giving machines a form of autonomous behaviour.
Over the last months the knowledge institutes in Amsterdam (
AUMC,
CWI,
HvA,
NKI,
UvA,
VU), in conjunction with
Sanquin, the
Amsterdam Economic Board and the
City of Amsterdam, have formed a coalition and developed a joint proposition for AI in Amsterdam: “
AI Technology for People” building upon three foundational themes.
- Machine learning has been the main driver in the emergence of AI – and will continue to push it forward. Techniques include data-driven deep learning methods for computer vision, text analysis and search approaches that make large datasets accessible and knowledge representation and reasoning techniques to work with more human-interpretable symbolic information. Related activities include the analysis of complex organizational processes, and knowledge representation and reasoning techniques to work with symbolic information.
- Responsible AI is key to assuring that technology is fair, accountable and transparent (FAT). Methods need to prevent undesirable bias and all outcomes should be explainable through the identification of comprehensible parameters on which decisions are based. When high-impact decisions are involved, the reasoning behind them must be understandable to allow for ethical considerations and professional judgements.
- Hybrid intelligence combines the best of both of these worlds. It builds on the superiority of AI technology in many pattern recognition and machine learning tasks and combines it with the strengths of humans to deploy general knowledge, common sense reasoning and human capabilities such as collaboration, adaptivity, responsibility and explainability. Hereby combining human and machine intelligence to expand on human intellect rather than replace it. See the recent blog by Frank van Harmelen on the Hybrid Intelligence project.
The focus of AI Technology for People
The coalition focuses on three application domains.
- AI for business innovation: As described in Max Welling’s blog, research excellence has already inspired several international partners to start research labs in Amsterdam within the Innovation Center for Artificial Intelligence (ICAI). Other companies, both regional and inter)national, continue to follow suit. Amsterdam hosts the headquarters of major companies that rely on AI to innovate, many small- and medium-sized high-tech AI businesses and a strong creative industry. The city provides an ideal ecosystem in which business innovations – both small and large – can flourish.
- AI for citizens: With its multitude of cultures, large numbers of tourists, rich history, criminal element and intense housing market, Amsterdam has all the challenges and opportunities of other major world cities, but in a far smaller area. With the excellent availability of open data in the city, AI can be applied directly to improve the well-being of citizens – with the city itself becoming a living lab.
- AI for health: The coalition is building on the work of renowned medical research organisations such as Amsterdam UMC, NKI, Sanquin and the Netherlands Institute for Neuroscience. The cross-sectoral health-AI collaboration has also been institutionalized in other ways, such as through ecosystem mapping and Amsterdam Medical Data Science meet-ups, with all initiatives being bundled under Smart Health Amsterdam.
Achieving its Goals
To realize the above ambitions, the AI coalition partners not only plan to make their own major investments in AI, they also aim to attract significant external funding, for example through labs within the
Innovation Center for Artificial Intelligence (ICAI) and other funding instruments through the
National AI Coalition (NL AIC). Ecosystems in which science, policy, industry, and society (the quadruple helix) come together are the basis for these national initiatives. Amsterdam and the ADS ecosystem provide a successful regional example of collaborations in many forms, such as industry funded PhD students, joint appointments, professionally oriented education and partner meetings. ICAI, which has its headquarters in Amsterdam and labs all over the Netherlands, is a member of ADS and creates industry funded research labs that produce research presented at top international academic conferences. Let’s use the momentum to bring the ecosystem to the next level.