This month’s update is a feast for your senses with a special section on podcasts as well as the usual papers and updates. As always, if you’ve come across any books, research papers, articles or other resources that you think we should have featured in this blog, please let us know in the comments below.

This month saw the launch of Nesta’s new podcast Future Curious with an inaugural episode about Collective Intelligence. Featuring contributions from Chris Lintott of the citizen science platform Zooniverse, Gregory Landau the CEO of the Regen Network, a new initiative aiming to reshape the ecosystem around agriculture and PatientsLikeMe advocate Christine Von Raesfeld, it introduces some of the ideas we’re most excited about when it comes to humans and machines working together to tackle social problems.

There seems to be something in the air recently! We noticed that other podcasts were also engaging with collective intelligence, ranging from more theoretical discussions covering current schools of thought on distributed governance and collective consciousness, to those focussed on what we can understand about emergent collective behaviour by studying ant colonies. We also enjoyed a recent interview with Scott Page about his new book Model Matters, that made reference to research on diversity and collective wisdom. Recent offerings from Sage and RSA have further contributed to the collective podcast fever. Respectively, they look at text analysis by volunteer crowds to build legitimacy and combat fake news with founder of Public Editor, Nick Adams, and how we develop strategies for long-term societal thinking and collective decision making with the psychologist Steven Johnson.

This month also saw the release of a special series on AI for the benefit of society by This Week in Machine Learning (TWiML), which chimed with thoughts we previously outlined about the potential of AI for good. The theme-music for TWiML is enough to make you believe that we can change the world but this set of recent episodes further convinced us that we’ll achieve it through a combination of human and machine intelligence. Some of our favourite examples of collective intelligence came from Justin Spelhaug who spoke of AI for humanitarian action, including their involvement in a multi-stakeholder partnership between World Bank and industry to better predict famine at the community level or a new project that mobilises inputs from distributed volunteer networks in combination with machine vision recognition to enable more effective assessment and resource management during disaster relief.

A few oldies we’d recommend are:

• An interview with MIT's Cesar Hidalgo from 2017 focusing on how to accumulate knowledge in groups. In his own words: “anything that is worth doing cannot be done by individuals but has to be executed by teams”.
  
• Geoff Mulgan in conversation with Matthew Taylor of the RSA in early 2018 about How Collective Intelligence Can Change the World and other ideas introduced in his latest book, Big Mind.
  

We’re also looking forward to the podcast of the Smart Enough City event held at the Berkman Klein Centre at Harvard. We think that cities offer a perfect test-bed for hybrid collective intelligence systems that build as much on community based knowledge as smart technologies. If you haven’t seen them yet, check out our 2019 prediction for City Brains and our case for rethinking the labour market laid out in the Open Jobs concept.

• A special issue of JCOMM examining user design principles and socio-technical systems of citizen science projects that use digital technologies peaked our interest. From webcomics and online games to the ways that technology design can translate into subsequent real world action, we recommend exploring the many worthy articles in this volume.

• This fascinating study analysed the patterns of activity displayed by teams in science and technology over the last 50 years. They show that smaller teams play a disruptive role building on older ideas, while large teams drive the development of recent and popular research. The study uses some interesting metrics including a novel quantification of disruption and the Sleeping Beauty index (!) to measure impact. The final verdict? Science and technology needs a diverse ecosystem accommodating teams of different sizes.
  
  
• As one of the best known citizen science initiatives in the world, you might think the Zooniverse team would be content to just rest on their successful model. Happily their curiousity is pushing them to explore new ways to optimise their process, drawing on the possibilities offered by human-machine collaboration. We like this paper for its recognition of the design challenges that might change the nature of serendipitous discovery and volunteer motivations.
  
  
• The collective intelligence academic community were early contributors in developing the evidence base that makes the case for diversity in teams. The latest research from Anita Woolley’s team at Carnegie Mellon takes this one step further to demonstrate that cognitive style diversity is correlated with improvements in long-term group learning... but there’s a limit. When individual’s cognitive styles are too different, team learning is compromised. Carnegie Mellon will be hosting this year’s annual ACM CI conference in June, where we hope we’ll learn more about the next steps for this research.
  
  
• Did you ever hear about the rocket engineers who worked with an origami expert to discover how to fold a large solar array into a narrow rocket? Human ingenuity is often built on interdisciplinary transfer of ideas...or so argues a recent paper looking at the importance of analogy for innovation. The authors outline the common pitfalls of fixation, scalability and complexity that inhibit our ability to abstract away from a problem and suggest how crowd-driven or AI-based approaches might help us to overcome them.

We know that you’re mostly here for our awesome monthly updates but we occasionally write other blogs too. This month’s highlights were a working paper exploring future models forcollaborative partnerships around data, how to blend online and offline processes for a more collectively intelligent democracy, and our thoughts on integrating data with other forms of knowledge to improve diagnosis and survival in cancer.

Crowd Forecasting challenge: At the start of the year we teamed up with BBC Future to bring you our Brexit and Beyond crowd predictions challenge. Have your say on two new questions about the use of CRISPR technology and the likelihood of commercial space flight in 2019. If you register in the next couple of weeks you can opt in to join a forecasting team with the chance to be allocated official forecasting training to help improve your skills.

This is the third blog of our “What we are reading” series. Check out January’s edition here.