Short presentation at University of Plymouth as part of the UK's Robotics Week

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© Ian Phillips 2017
© Ian Phillips 2017
https://ianp24.blogspot.com
Robotics, Artificial Intelligence and Society
Robotics Week
Centre for Robotics and Neural Systems (CRNS)
University of Plymouth
29jun17
Prof. Ian Phillips CEng, FIET, FIMA, SMIEEE
Independent Technology Consultant & Philosopher
(Formerly: Principal Staff Eng’r. @ ARM Ltd, UK)
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Visiting Prof. with...

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© Ian Phillips 2017
Leonardo ‘Robot’: 1495
§ Leonardo's Mechanical Knight was a
humanoid automaton designed and
possibly constructed by Leonardo da
Vinci around the year 1495 (Showing
inner workings)
§ The term “Robot” was first used to
denote fictional automata in the
1921 play Rossum's Universal Robots
by Karel Čapek. Derived form the
Czech "robota", meaning servitude.
The play, replaced the popular term
"automaton" with "robot."

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© Ian Phillips 2017
First Robot in film: “Metropolis” by Fritz Lang 1927
http://www.openculture.com/2012/07/imetropolisi_restored_watch_a_new_version_of_fritz_langs_masterpiece.html

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© Ian Phillips 2017
Not What They Seem ...
§ As humans we quite literally take most things at face-value!
We turn-off our imagination at the User-Interface!!
§ So we assume a lot about what a
Robot can do, because it looks
human (The Uncanny Valley)

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© Ian Phillips 2017
Not so cute ...
§ text

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© Ian Phillips 2017
iCub @ Uo. Plymouth. Prof A. Cangelosi

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© Ian Phillips 2017
Intelligence and Artificial Intelligence
§ Controlling your Robot ...
§ A simple feedback loop, involving a Neural
Network ...
§ Biological
§ Electronic/Software

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Brain vs Technology
§ The whole human brain contains ~100 billion biological neurons ...
... around 20 billion neurons in the cerebral cortex (The wrinkly, brainy bit on the outside)
§ With around 10,000 interconnections per neuron
§ Total Power dissipation around 20W (A small lightbulb). Weight 1.3Kg.
§ Everybody knows how to program it!
§ The Spinnaker machine (Uo. Manchester. Prof S.Furber et all) ...
§ Emulates 1 billion (1 thousand million) real-time neurons using special electronics and software
§ With up to 10,000 weighted interconnects per neuron
§ Power dissipation ~40kW (A small factory). Weight 1 Ton
§ Spinnaker neuron around 200,000 times less power efficient than biology
§ Nobody knows how to use/program more that a few % of these neurons!
§ iCub (Uo. Plymouth. Prof A.Cangelosi et all) ...
§ Uses a ~100,000 electronic neurons in a Deep Neural Network
§ Implemented in an Nvidia AI Box (3U 19” rack) ... At the end of an umbilical from the ‘Robot’
§ iCub has no intelligence ... It is just an Input/Output device, with a cute wrapper.

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© Ian Phillips 2017
Spinnaker
§ University of Manchester (Prof. Furber et all)
§ 1 Billion electronic real-time Neurons in a Network
§ ~1% of a human brain
§ Consumes 40kW (Would need ~8MW for 100B!)
§ Human brain needs 20W
Half a full Spinnaker implementation
518,400 ARM Processors.
~500 Million Neurons ... “A few mouse brains”
18 ARM Processors.
~20,000 Neurons ... “Small insect brain, eg drosophila” 864 ARM Processors (48-node)
~1 Million Neurons ... “A pond snail”

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iCub’s External Brain
§ Robot Developers/Researchers tend not to show the umbilical
for ‘mental’ (and/or physical) support
... Being human we assume those to be like our own.
§ But the ‘Body’ is just convenient arrangement of dumb
Input/Output devices, to interface to the external world
... The ‘Brain’ is in the out-of-sight box!
... Robots are autonoma, which are good at specific tricks!
NVIDIA DGX-1 ‘Essential Instrument of AI Research’

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Tegra Jetson TX2 AI Board – State-of-the-art Mar17
§ Jetson TX2 brings powerful AI capabilities at the edge, making possible a new class of
intelligent machines,” said Deepu Talla, vice president and general manager of the Tegra
business at NVIDIA. “These devices will enable intelligent video
analytics that keep our cities smarter and
safer, new kinds of robots that optimize
manufacturing, and new collaboration that
makes long distance work more efficient.”
§ The Pascal chip uses 256x GPU cores
alongside two of NVIDIA's customised
Denver 2x 64bit ARM processors and
4x ARM A57 cores.
§ Costing around £700, the TX2 supports real-time video
processing and image recognition for up to 6, 4kx60fps video streams.
Its 8Gbytes of LPDDR4 memory has a 58.3 Gbyte/s data rate.

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Neural Networks for Counting & Classifying Plankton
§ Dr. P.Culverhouse, Uo. Plymouth started this work
nearly 30yrs ago!
§ Uses just a few hundred electronic-neurons
§ Still in use by fishery scientists today for ocean
health monitoring!
Plankton
Classification ...
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• 6
Small Neural Network

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Teaching an Artificial Intelligence
§ Training (teaching) involves ‘exposing’ a network
to thousands or millions of examples, then Noting
the useful Connections and Weightings between
the nodes which produce the expected output ...
§ Replicating a trained network is a
much simpler task and can can often
be implemented on much smaller
machine (eg Smartphone).
§ Adding a Learning Ability makes a
‘machine’ much more complex!

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The Dream ... The Nightmare

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Automotive Robots – Autonomous Vehicles
§ A practical AI Automotive Controller Neural Network will have to be independent of the
Internet, which will limit its size and sophistication ...
§ Its Neural Networks, will be capable of a limited degree of learning
§ Its never going to learn to write poetry,
§ It will never change its fundamental sensors or core algorithms
§ Practically they will have a ‘capability’ of a fraction of a million real-time neurons
§ Cost, Power and Reliability will be factors in this
§ Similar intelligence to a small insect or pond snail
§ <0.0001% of the capacity/capability of the human brain!
§ Comparing performance of AV-AI to the performance of the human brain is futile ...
... The human brain is just so, so, so much better!!
§ The human brain finds driving so easy that it is susceptible to doing other things at the same time!
§ An AV-AI will not be distracted; but will never be able to handle the full spectrum of driving situations
§ There is room for human/machine cooperation, but not for full Artificial Autonomy

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AV-AI Performance – Jan17
§ Deep3DBox (George Mason University, USA)
is amongst the best ...
§ Correctly identifies and orients 89% of cars, but
only identifies 74% bikes and orients 59%
§ “Bikes pose a complex detection problem because
they are relatively small, fast and heterogeneous”
(And don’t always obey rules)
§ Other AV-AI problems include Mixed precincts,
Single-track roads, Dirt-roads, Human gestures,
Weather, Roadworks.
§ Human accident rate is much lower than
current AV trial figures.
§ Western Fatality rate is ~5 per Billion Km (All roads)
§ AVs are 2-3 per few Million Km (On the best roads)
§ Long way to go before AV will be Autonomous

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© Ian Phillips 2017
Industrial Robots ...
§ Are very good at doing certain things (tricks) ...
§ Repetitive
§ Handling (Weight, Positioning, Maneuvering)
§ Not so good at others ...
§ Adapting (May be re-purposed to some degree)
§ Learning (Not easy to train)
§ Anything beyond their Design Limits

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Society’s Relationship with Robots
§ Using Robots and Autonoma ...
§ All the stuff which humans do which is ‘easy or repetitive’ will be replaced by automata at some stage
§ All the ‘very easy stuff’ has already been replaced ...
... Shovels with Diggers; Wheel-barrows with Conveyers; Tea-Ladies with Dispensers; Slide Rules with Computers.
§ The easier phases of driving ...
§ All the stuff which humans do which is ‘individual, adaptive or creative’ will be last on the list
§ Scientific Discovery, Engineering and Technology, being amongst the most irregular will be the last
§ The more difficult phases of driving ...
§ Creating Robots and Autonoma (Design and Reproduce) ...
§ Has ‘it all been done’ in Robotics and AI? ... No! We have hardly scratched the surface!!
§ We need more capable, more efficient machines; with better Architectures for both Hardware and Software
§ They have to be Researched and then Developed (The UK is a good place for this).
§ ‘Better Algorithms’ is Public-Speak for Lots-and-Lots of Innovation, Invention and Years of Work
§ Installing, Programming and Maintaining Robots and Autonoma ...
§ Somebody has to ‘care’ for them, which will remain an expert skill in its own right

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Conclusions ...
§ Robotics ...
§ Just Autonoma able to do particular and quite limited ‘tricks’
§ Artificial Intelligence ...
§ A useful tool to perform sophisticated-seeming data processing/analysis
§ But nowhere-near the pattern-matching ability of the human brain today (and for very many years)
§ Society ...
§ Must not get carried away with the Hype around AI, AV, Robots and Robotics
§ Must learn to understand the difference between Science and Science-Fiction
§ Significant things take significant time ...
§ Hype, market demand &/or public wishes; don’t make the Laws of Physics any easier to manipulate.
§ Career opportunities (Science and Engineering) ...
§ We are nowhere-near to knowing all there is to know about the laws of Physics and their Application.
§ There are many generations of Career Opportunities for all people with a brain ... imagination, application,
tenacity, sincerity, teamwork, ingenuity, communication and hand/eye skills.
§ Good Rewards ... Easily 2x national average salary, with travel and social opportunities.