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Iris is our baby AI and she is learning as fast as she can. But how does she do it?

A lot of people have asked us about the process she follows when linking open access science to any TED talk we ask her about. The short answer is simple: she has been designed to learn like a baby human – using the capacities of her algorithmic brain!

What does this mean exactly? Well, to answer that we need to get into more details. First of all, Iris has very large storage and processing capacities. She can read the transcripts of all the TED talks to date in no time.

While doing this reading Iris performs an elaborate frequency analysis over the text. As a first step she tries to make sense of the different words she encounters to figure out which are the most important ones – the ones that she needs to pay closer attention too. This frequency analysis is more complicated than mere word counts. It analyzes the dynamics of the words in the text and their context. Iris can do and in fact does quite better than that.

Once the frequency analysis is performed Iris does what her machine-learning teachers refer to as feature extraction: this process includes combining the words in clusters to find contextually similar groups of words. She then uses those clusters to find what the words mean in this context (e.g. the word charge implies something different in electrical engineering than it does in chemistry).

Throughout this process she also looks at synonyms to expand the categories and get a broader understanding of the topic – always within the same context. She also performs filtering to disregard words that are irrelevant to the context.

As a next step Iris runs a generalization over the entire set of TED talks – she has been lucky enough to be exposed to this privileged body of knowledge to form her first notions of the world! With this exercise she finds a more precise definition of each word in its respective context.

Once that process is completed Iris organizes the concepts in hierarchies, to be able to more easily grasp and represent the context to the user communicating with her. It is important to note that our baby AI creates flexible hierarchies –not humanly pre-built ones–, expressing patterns across different research disciplines that she sees from her very own, direct experience.

Lastly, Iris structures the results of her thinking and presents them to users through a particular type of Voronoi tree maps. This data visualization approach displays hierarchical data by partitioning a polygon continuum. The polygon areas are proportional to the relative weights of their respective nodes.

Anita Schjøll Brede

I do things I don’t know how to do. The past 10 years my career has spanned 8 industries including developing an e-learning tool in Silicon Valley, performing theater for babies, reducing energy consumption in the process industry through heat exchanger network optimization, getting 30 (mainly middle-age male) engineers to dance to ABBA in front of their co-workers, facilitating solar light business creation in Kenya, being in the center of several startups crashing and burning, organizing entrepreneurial conferences and trying to disrupt the recruitment industry. I also dropped by 6 universities on the way. And built a race car.

Summer 2015 I was admitted to the amazing Graduate Studies Program at Singularity University, and my outlook on life, innovation, technology, the world and our future has not been the same since.

Ask me to talk about AI, self-driving cars, genomics, robots, colonization of mars, personalized medicine and the general craziness of the future – and how we can use this craziness to make the world better – and you’ll make me very happy!

I have a passion for entrepreneurship and creating ventures, and it is the process in itself – starting from a vague idea and moving towards building something real that makes a difference in people’s lives – that inspires me.

Another passion of mine is presentations and any kind of public speaking. If I have the chance to be in front of a crowd, I’ll take it.

I am decently nomadic, though Norway is where most of my physical belongings are, and where I oddly feel somewhat at home.

View Anita Schjøll Brede’s profile on LinkedIn

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Ambition

50.000 users in the next two years.
• Gamification of A.I.

Challenge

Open up access to scientific research

Need for money: 1st round next 3 months

Publishing industry is closed, wants to break it open
• Just like music industry, with Napster, Pandora, Spotify
• Upload own papers by scholars

Competition so far:
Sidehub (Russia) illegal: way to circumvent the paywall
Google Scholar: But, you need to know what you’re looking for. You need to know the terminology and/or the title. Plus, finding all the related areas takes you hours. We give the results fast, with context for non-experts and avoid a “tunnel vision” through exploring more broadly.

Creative Minds

In the next few days the group of carefully selected Creative Minds will be named.

Pre-spectives

  1. Can we brainstorm on gamification of A.I.?
  2. How should we position ourselves and what is our best way to additional funding?
  3. What target groups would be benefit and how can we work with them to create demand on a larger scale?

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