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IBM Watson Hosts World's First Cognitive Dance Party

IBM Watson Helps Curate World’s First Cognitive Dance Party

Folsom Street Foundry, a bar in San Fransisco, that typically hosts events that stretch into the wee hours of the morning recently held a futuristic party powered by IBM Watson that started a little before the sun rose last Wednesday.

Daybreaker, an organization that holds early morning dance events in 15 cities across the world, held the first ever “cognitive dance party,” powered by IBM’s artificial intelligence engine Watson. And with it, dancing with reckless abandon for two hours before rushing off to work just got a real high-tech twist, thanks to artificial intelligence and machine learning.

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IBM Watson is one hell of a machine and typically finds its application in hospitals, banks, and classrooms. Taking a departure from the routine, it found itself showing off its playful side to early workouters in the Bay Area.

Hey, who said machines can’t get spunky?

Those participating were asked to link their Twitter handles or fill out a questionnaire upon registering for the event, which IBM Watson then analyzed through its Personality Insights program to curate an event that would take care of people with all types of personalities. So that even morning grumpies like some of us could end up having some cardio-fun in the morning!

Based on the personality assessments carried out, Watson then visibly categorized people into three groups: those which it found to be the most expressive of the lot were coded yellow, red for the outgoing, and purple for the most conscientious. Dancers turned up at the venue in these colors.

 
IBM Watson Color Coded Dancers at the Party

Color-coded Dancers at the Cognitive Dance Party

Based on the strongest personalities identified for the group that turned up, Watson decided their workouts (yoga, capoeira, or high-intensity interval training), tailored light to reflect the collective mood of the group on the dance floor, customized breakfast recipes, and pulled live data from Twitter to reflect the overall mood of those attending on an LED sun that rose as the sun rose outside.
 
IBM_Watson_Daybreaker_Sunrise

IBM’s Watson-Powered LED Sun rising at the Folsom Street Foundry

 
And that’s not it, the club even put systems in place that could harness the kinetic energy of 500 plus people jamming on the dance floor to power Watson!
Seriously, like whaaa!
Daybreaker’s CEO, Radha Agrawal said, “In many ways, we started Daybreaker out of frustration that no one was dancing at nightclubs anymore and connecting authentically because everyone was on their cell phones and digitally divided. This is the first time I’ve seen technology enhance a dance party experience instead of take away from it.”
Indeed!
“This is a chance for us to provide a hands-on experience that shows how Watson works and how [people] can use it to come up with new ideas,” Gabrielle Gugliocciello, the IBM spokeswoman added.
“Pushing the boundaries of people’s imagination is a central part of extending this impact, and events like Daybreaker help us inspire the thinking that fuels this potential.”
What happened on Wednesday night at Folsom Street Foundry was that IBM Watson very carefully combed through the participants’ personalities and custom-made a fitness event for them.

Watson’s Personality Insights

We all know how we secretly love playing those silly games on social media to figure out what personality type we are. Now, imagine having a powerful artificial intelligence-based machine figuring it out for you simply based on the way you write or post on social media.
And, yes, when you use Watson’s Personality Insights, you will receive an answer which is way more satisfying than a disappointingly “optimistic.”
You can do try this out too, by either submitting your Twitter account or pieces of your writing that reflects your “daily experiences, thoughts, or responses” here. The output then gives you percentile scores for your personality, needs, and values.
Percentiles are a more accurate representation when comparing two things, in this case, comparing you to a broader population. For example, 89% on Extraversion under Personality traits does not mean that you are extroverted 89% of the times, it means that you have a tendency to remain more extroverted when compared with 89% of the population sampled.
And the sample population is mostly Twitter users as seen at Daybreakers’ cognitive dance party event.
An underlying principle in psychology and marketing says that language is a reliable indicator of personality traits, emotional states, social compatibility, among other things, and what one writes on social media is a treasure trove for intelligence-based machines like Watson.
Studies have found out that quick-responders are usually those who prefer a state of excitement in their lives and those who defer from responding immediately usually score high on cautiousness. Similarly, people who score high on modesty, openness, and friendliness are more likely to share posts or Tweets instead of Tweeting.
So How Does Mr. Smarty-Pants Watson Do It?
By now most of us have an idea of how AI is transforming our lives for the better. Thinking machines are increasingly becoming a part of daily decision-making. (Hey, the human brain still rocks, no shade given to here!)
But looks like we won’t stop at anything. The thirst for faster and more complex evidence-based decision-making capabilities and better management of data has given rise to the force behind such emerging trends. Whether you realize it or not, you are already a part of the next big evolution in big data: an era of cognitive computing.
Cognitive computing enables people to create a profoundly new kind of value, finding insights locked away in volumes of data. Whether we consider a DJ curating a playlist or a chef creating new recipes, they need new approaches to putting into context the volume of information they deal with on a daily basis in order to derive value from it.
Touted as the next big thing, IBM Watson is essentially a supercomputer that combines AI with state-of-the-art analytics software and essentially works as a question–answering machine. It’s named after IBM’s founder, Thomas J. Watson, and was conceived much earlier than you think.
Hard-pressed by his boss to come up with the “next big challenge,” IBM’s Charles Lickel in 2004 stumbled across a game of Jeopardy! playing out on a telly at a bar one discrete night. Fast-forward to 2011, IBM’s Watson, developed by Lickel and his team of researchers, defeated Ken Jennings and who had held the longest winning streak on Jeopardy!
Jeopardy! covers a mind-boggling variety of questions, so rigging the system to operate within a single domain was never enough. The clues in the quiz show are usually presented in a complex language and often contain puns and cultural references that obscure the meaning of what is really being asked. Furthermore, they needed to equip Watson with advanced functionality that allowed it to come up with a viable response for every wrong answer.
Watson has been programmed to reason out problems like a human does. When we make decisions, we go through four key steps: First, we observe visible phenomenon and bodies of evidence, second we use our knowledge to interpret what we are seeing to generate hypothesis for what it means, third we evaluate which hypothesis are right or wrong, and finally we decide.
And just how we become better decision-makers with time, Watson uses similar processes to reason out the information given to it. And unlike other analytical systems that need a structured database to pull in information from, Watson is smart enough to make use of unstructured data, which is 80% of the data today.
And what is this data really? It’s nothing but what is created by us to be consumed by us: newspaper and blog articles, research data, posts, Tweets, etc.
This cognitive computing technology was made to process at a rate of 80 teraflops (trillion floating-point operations per second). In order to stand a chance against a high-functioning human brain’s ability to process and answer questions, Watson accesses 90 servers with a data storage of over 200 million pages of information, which it processes against six million logic rules!
And as expected from any supercomputer, IBM Watson occupies the space required by at least 10 refrigerators.

Watson’s Specifications

  • Apache UIMA (Unstructured Information Management Architecture) frameworks, infrastructure for the analysis of unstructured data
  • Apache’s Hadoop: Java-based programming framework that helps analyze large datasets
  • SUSE Enterprise Linux Server 11: Fastest available Power7 processor OS
  • 2,880 processor cores
  • 15 terabytes of RAM
  • 500 gigabytes of preprocessed information
  • IBM’s DeepQA software: Helps with information retrieval that incorporates Natural Language Processing (NLS) and machine learning.

Watson has come a long way since its early days, it can now host dance parties at clubs and give you tailor-made work-outs and diet plans on the side!
Learn more about machine learning and big data and Hadoop ecosystem at AcadGild, and let your imagination run wild with IBM Watson!
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One Comment

  1. The psychology of dance is the set of mental states associated with dancing and watching others dance. The term names the interdisciplinary academic field that studies those who do. Areas of research include interventions to increase health for older adults, programs for stimulating children’s creativity, dance movement therapy, mate selection and emotional responses.
    Ballet and Indian dance
    Indian Dance
    Untrained, frequent spectators and novices of ballet or Indian dance watched videos of ballet, Indian dance, and non-dance soloists. Motor-potentials (MEPs) in their hands and arms were used to measure corticospinal excitability. In ballet, the arms are used frequently, while the hands are used in Indian dance. Participants had higher MEPs in their arms when watching ballet compared to Indian dance. Ballet spectators’ visual experience, not motor experience, drove motor resonance.
    Position sense
    Dancers have more accurate position sense than non-dancers allowing them to rely more on position sense than vision.Experts, amateurs and novices differ in their mental representations based on spatial parameters. Subjects in each group viewed clips of basic classical ballet steps and noted the spatial parameters of basic action concepts (BACs), which are mental representations of each part of a movement. For the pas assemblé, amateurs and experts clustered the movement into functional phases, but for the pirouette, only experts had adequate spatial parameters. This suggests that experts retain spatial parameters in their long-term memory and that dancers use mental imagery to memorize long complex phrases.

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