Written by Miikael Lehtimäki and Ivan Dubrovin
AI Hub Tampere is a new research center hosted by Tampere University
and will bring together people from all aspect of AI research and bring
ai research closer to robotics and other uses. The teaching goals of the
center are to train people to develop AI, engineers to implement AI and
managers to outsource AI. The primary disciplines brought forward are
visual imaging, machines learning and audio & signal processing and
with experts of these areas working together they can all take strides
together.
With new advancements in visual imaging coming forth
AI becomes more capable of visually recognizing things, being able of
recognizing people, specific pieces of clothing and other equipment
along with reading emotions from faces. This comes in use for helping
social robots read situations and feelings of their subjects, the
ability to read items, people and faces together can help recognize
suicide or other attempts at self-harm along with being able to play
with toy or devices together with people.
With individualized
solutions for tasks and subject using the new algorithms and solutions,
including ones which enable learning from example and two-handed
manipulation of objects, robots and devices can learn unique ways to
accomplish tasks for unique situations, like taking time and its passing
into account knowing what is recent and what that means. Solution that
can come from this are throwing balls with two different children, the
robot can start with a general solution with the height and reach of the
child and specialize its throws for each to be catchable and if the
child threw balls last a long time ago, start by going easy on them,
learning what improves the mood of their owner, this combined with data
sharing between platforms can allow robots to try new or different
things to solve new problems, a robot that sees that you are in a bad
mood tries cartwheels, bringing you a ball, singing or playing a song or
sound for example.
Advancements in audio and signal processing
allows for the detection and categorization of individual sounds, from
speech, birds, cars and being able to isolate these sounds out from the
others. With these improvements robots can use simple audio receptors to
become far more aware of their surroundings, recognize people more
accurately and listen to multiple people at the same time, improving
social robots used in groups as they will get less confused from crowds,
able to recognize authorized commands from a specific individual even
in loud situations and better able to read situation context as in a
case of their owner playing sad or happy music or their tone of voice.
Alone
these directions can already bring great strides to social robotics in
general but together they bring us ever closer to the robot friend, an
ai companion that can learn to know you, knows what cheers you up,
suggest things to do, complains about things that annoy it and can tell
you to get them maintenance. Less a creepy android servant and more a
companion device that can journey through life together with you in this
ever-expanding digital world.
