Artificial intelligence, Collaboration, Ecosystem
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.
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