Supermind.Design

While the initial use of geospatial #AI driven Augmented Reality like what Google just announced with Maps (starting with Paris and Singapore) is going to be aimed at marketing and sales, imagine the possibilities when the creation tools will be given to millions of “citizen creators” who can build their experiences for their own neighborhood.

Innovation very often comes from recombining adjacent ideas. Unfortunately, our search engines for knowledge don't typically show ideas adjacencies - or if they do, they do that as a black box that discourages deliberate exploration. That's why I believe that in the future, we will have increasingly better knowledge exploration through the mining of knowledge graphs facilitated by AI. This example from BCG Henderson Institute (Idea Explorer) is interesting - they use the corpus leadership publications to enable the reader to discover following a more interesting path than just a bunch of links. Imagine what you could do when you applied some of the same principles to the entire knowledge that is publicly accessible. You could access - and engage with - our #collectiveintelligence.

Happy Earth Day. For a reminder of how Earth functions as a collectively intelligent system, have a look at the studies below. It is just extraordinary - insightful, humbling, inspiring. And a reminder that our technology can help Earth function better - not just worse. Think of monitoring, modeling, and pricing of externalities in our markets. H/T Rafael Kaufmann Nedal, whose work in this space should be of interest to some.

Check out this job at Beautiful Places AI: Founder Associate

This (link in the comments) is an insightful read from Ajay Agrawal and colleagues. It argues that the “Turing trap” debate of obsession with #AI (and other information technology) automation at the expense of work on augmentation, and its implication on inequality, misses some key aspects. “One person’s automation is someone else’s augmentation”, for instance when talking about doctors’ diagnoses which, if partially automated, could become easier to unbundle from (scarce, expensive) doctors and accessible to (somewhat more available) nurses. The implication is that designing for automation of certain tasks could result in a more widespread positive impact than initially thought. They call it Turing Transformation. At the #CollectiveIntelligence level, the net effect of these technologies is still an augmentation one.?

What are the use cases for #AI (predictive and generative) in specific processes? That's a critical question that many ask themselves these days when they try to apply it. Remarkably, I didn't find a tool to help do that - so I created Apta, a simple OpenAI GPT, shaped its collaborative workflow so it asks you questions before giving answers and fed it with examples. Try it for yourself - the link is in the comments. You will need a ChatGPT Plus account. This is just a simple prototype, inspired by the work we do at MIT's Center for Collective Intelligence: imagine what could be done with more concerted work.

Are we on the edge of a new category of HR jobs: AI work engineers? Ryan Roslansky the CEO of LinkedIn published yesterday an interesting article in Harvard Business Review on Talent Management in the Age of AI. In the article he made a prediction of an entirely new category of jobs centered around the idea of “AI transformation,” where teams are tasked with figuring out which AI tools are the right tools to grow the business and identify as a result the right workforce strategies to keep business teams agile and engaged. As LinkedIn CEO he expects the battle for talent in those roles to be fierce and already begun. It’s clear that companies who use AI to automate their operations solely to reduce employee numbers will only achieve short-term productivity gains. There is a risk that re-thinking and re-engineering work as a result of AI will mean going back to the era of time and motion studies of the late nineteenth and early twentieth century. In other words a modern form of so-called scientific management to identify what part of the process can be “AI’ed” with the risk of making humans even more a cog in the (information)machine. To truly capture the full business benefits of Generative AI we need to start with human engagement, agility and business value as Ryan Roslansky points out and engineer the AI solution in collaboration with the source that in the end makes the difference: humans. https://lnkd.in/ePurNbUH