Below is a rough outline of the talk.What is machine collaboration? here are some early ideas :
Some worked out better than others
But the vision of using machines to augment our human limitations has been with us for millennia “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world.” – Archimedes
Big Data, NLP and predictive analytics will fundamentally change how we interact with machines. Let’s start off by examining some very human traits that describe how we learn and make discoveries.
Creativity Creativity can be described as the combination of two seemingly disparate entities or ideas in useful/novel ways. (show charts and animation) This mashup of things and ideas is how we invent new things. Sometimes what we invent is new to us, sometimes it is new to the world. Regardless of its impact, as we solve the problem at hand, both our understanding of the world and our understanding of the problem change and evolve. This in turn feeds our intuition.
What is intuition and its role in making decisions? The advantages of intuition and how we use it to make decisions This is your brain on chess – http://news.discovery.com/human/chess-experts-brain-activity-110121.html
Designing for creativity and intuition – show examples of how reducing cognitive load on the frontal cortex allows users to free up resources for deeper thinking or more meaningful interactions.
What it means for UI This is what’s so exciting about the era of data, because our limited ability to know the world via our single brain can be greatly augmented. We’re no longer limited to the connections we can make based on our own experiences. We can tap into the shared knowledge, relationship of all humankind very quickly. This broadens the traditional realm of HCI from designing for the goals and tasks of a user to designing for the limits and strengths of human cognition.How can we interact with big data systems, what roles would they play? some examples:
So how is this playing out in the real world? (show familiar examples, google graph, Siri, etc.)
Case study 1 – Sherlock A clinical decision support system based on natural language analysis from doctor’s notes. Document-based storage system Radiologists dictate their notes Resulting audio is translated into text Entities are extracted and enriched (i.e. the word “pneumonia” is recognized and associated with the SNOMED taxonomy) and sentiment analysis applied. Allows advanced queries such as “show me all patients where the doctor signaled there was no sign of pneumonia, but subsequent test results revealed pneumonia.”Case study 2 – HFinder A social knowledge and machine learning engine for discovering and rating bioinformatics hypotheses. It’s a system that creates hypotheses based on the aggregate information across domains to make scientific predictions. Scientists (the users) rate the hypotheses and debate their relative merit.
The Disappearing Interface Because of fundamental shifts in how we interact with knowledge and data, the interfaces we use to access them will need to become more like interacting with other humans or the natural world. Often, decision support systems are not well-integrated into existing workflows and as a result experience low adoption rates. We will need to push far beyond showing users things to click on and instead give them collaborators with whom they can make discoveries and further knowledge.
As we begin to depend on machines to collaborate, they will inevitably begin to take the form of our own image – and in some ways better. And the ways that we will communicate and interact with them will be more like interacting with the real world.
JD is a User Experience Lead at Salesforce where he specializes in search, online communities and collaboration. Prior work experience includes Director of UX at MarkLogic Inc, and NewCity, Inc. where he was the UX lead for the Virginia Bioinformatics Institute’s most successful project – resulting in a $27 million grant. (largest in the history of Virginia Tech) Awards include multiple IMA, CASE and ADDY’s. A graduate of Carnegie Mellon’s MHCI program, JD is frankly super stoked about how Big Data and design will affect our future.
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