The teams are set, the judges have confirmed. Details on the fake company has been shared. It is time to see how the professionals pitch a tech startup. The area’s best startup incubators and accelerators are facing off in this head to head competition to take home the awesome Unicorn Cup and bragging rights.
The teams are:
Thomas Schumann and Patti DuBois from CEI
Nate Mortenson from Tallwave
Wiley Larson from ASU
Lauren McDannel and John Johnson from Seed Spot
Our distinguished panel of judges consists of
Rebel Brown of Cognoscenti
Carine Dieudé of Altima Business Solutions
Jim Goulka of ATI
Christie Kerner of ASU
David McCaleb of ATI
Perfect Pitch is a contest where teams present the same fictitious technology startup company. A group of expert judges will determine who gave the best pitch. The event is part of PADT’s Nerdtoberfest celebration of engineering and manufacturing in Arizona, and takes place from 4:30-6:00 on Thursday, October 27th at our Tempe offices.
Everyone is invited! We will have an overflow area set up if we get more than can fit in our seminar room where you can watch live. We will also be streaming the event live to the world (watch this blog and social media for the link).
At barqk!, we deploy the latest cloud based machine learning and big data algorithms to convert your dog’s barking into words on your mobile device so that you can understand your pet’s needs, if they are sick, and be made aware of danger.
Dog owners face significant problems communicating with their pets. Although you can train a dog to obey commands, the dog cannot tell it’s owners what it needs or wants. This leads to significant stress for the owner and may lead to death when the animal cannot communicate an obvious and present danger.
Barqk! has created a cloud connected wearable device for dogs that records their barking and uses machine learning and big data algorithms to convert dog-speak into human-speak. The translated words are sent via text or through our app to the owner’s phone. Initially the owners provide feedback to the network, and the responses of all owners to every dog’s bark are collected as big data then fed through our proprietary algorithms that use Bessel functions and advanced machine learning approximations to develop a consensus on what a given bark means. Over time a translation for each dog will be developed and we expect 87% accuracy.