Artificial general intelligence research project at Keen Software House (3/2015)

1. Artificial intelligence research at
Keen Software House
Marek Rosa, Dušan Fedorčák, Martin Bálek
Keen Software House
March, 2015

2. Introduction
• About us
• Project history
• How does our team work
• Why general artificial intelligence?
• Long-term goal
• Short-term goals: R&D & commercial
• Open problems
• Second part (more technical), brain simulator
architecture – Dušan and Martin

3. About us
• Interest in AI and robotics
since age of 15
• How to achieve it?
– Space Engineers
– Medieval Engineers
– *** Engineers 
– Set up an AI team
– Shared common goal
• Actual R&D funds: $10mil

4. Project history
• Started in 1/2014
• Examining various AI approaches
– from biologically based neurons (e.g. spiking)
– to very artificial solutions
• Brain Simulator
• Milestones: Pong
• Team grown to 12 researchers and the plan is
30 or more…

5. How does our team work
• Milestones vs. autonomous research to pursue
creative solutions
• 2 team meetings each week (update, brainstorming)
=> knowledge sharing
• Rapid iterations => fail fast, fail often, fail forward
• Studying and experimenting
• Motivation: working on the most exciting scientific
challenge = meaningful work
• What’s next:
– Early access
– Openness
– Ecosystem
– External pressure

6. Why general artificial intelligence?
• Narrow vs. general AI
• Highest “return on investment” (ROI)
possible => high-risk & high-reward
• Recursively self-improving AI
• Exponential growth
• Market size: unlimited
• Could be “our final invention” (in a
good sense)
• AI scientists, AI programmers, AI
astronauts, AI ***
• Next step in evolution
• AI will change everything
• Everyone will benefit from AI
(charities, corporations, individuals…)
• The future will be awesome!
Credit: "The Singularity is Near"

7. Long-term goal
• Long-term goal: human-level AI in 10 to 50 years
• What is general AI?
– Artificial brain that can perceive, learn and adapt to the environment while
maximizing its short and long term rewards
– Sensors
• Input: visual, auditory, tactile, etc.
– Motors
• Output: e.g. sequence of muscle commands
– Motivations
• Input: reward and punishment
– Brain:
• architecture of AI modules that learn the patterns and sequences of signal
coming in and out of the brain; also patterns within the brain
• spatial and temporal
• seeking causalities and correlations
• finding associations
• working memory
• prediction for modules that can benefit from seeing the future
• long term memory
• goal selection and hierarchical goal execution (based on motivations)
• all this on multiple levels of hierarchy
• and more: feature extraction, generalization, abstraction, etc.
– Architecture: heterogeneous
• Learning
– Not hardcoded
– Online learning
– Learns by interacting with the environment and with itself – like children
– Learning from a mentor (mirroring). Doesn’t need to waste time by
exploring solutions that won’t lead to useful outcomes.
BrainSensors Motors

8. R&D short-term goals
• Already accomplished:
1. AI that learns to play Pong
• Unstructured input (screen pixels and
reward/punishment signals)
• AI has to extract useful features from the image,
causalities, correlations, select goals that lead to
increasing reward and avoiding punishment
• Google DeepMind
• Upcoming milestones:
2. AI that plays a game with a more complex
environment; delayed reward that requires
long-term goal following
3. AI that learns to play variety of games without
the requirement to “restart the brain”
4. Muscle control sequences, balancing
Gameboy Pong

9. Commercial short-term goals
• AI company
• AI platform/ecosystem
– Brain Simulator
– Marketplace
• AI module developers
• AI brain architectures
• Licensing to customers (robotic
firms, AI app developers)
• Investing in AI developers
– Community feedback
– Equity crowd-funding
• Our basic AI R&D will
continue in parallel

10. Open problems
• AI safety => friendly AI and collaboration
• Robots will take our jobs! => invest in AI
• Many problems are unsolvable by narrow AI and require
human intuition and knowledge (acquired from birth to
adulthood)
– Can AI fast-forward this process?
• What if our future human-level AI requires extreme
computational resources? (out of our reach). E.g.
simulating 100 billion biological neurons
– Moore’s law is on our side
– Better start the project today and hope that in 10+ years
hardware will be ready
– Maybe our implementation will use resources better than
the nature 

11. What you can do for yourself?
• You can invest in AI companies
• Every $1 invested today will return 1,000,000 times
• Join our team – we are always hiring
• AI Programmers / Researchers
• SW Engineers / Architects
• PR Manager / Evangelist
• Follow me: http://blog.marekrosa.org/
www.keenswh.com