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Artificial intelligence research at
Keen Software House
Marek Rosa, Dušan Fedorčák, Martin Bálek
Keen Software House
March...
Introduction
• About us
• Project history
• How does our team work
• Why general artificial intelligence?
• Long-term goal...
About us
• Interest in AI and robotics
since age of 15
• How to achieve it?
– Space Engineers
– Medieval Engineers
– *** E...
Project history
• Started in 1/2014
• Examining various AI approaches
– from biologically based neurons (e.g. spiking)
– t...
How does our team work
• Milestones vs. autonomous research to pursue
creative solutions
• 2 team meetings each week (upda...
Why general artificial intelligence?
• Narrow vs. general AI
• Highest “return on investment” (ROI)
possible => high-risk ...
Long-term goal
• Long-term goal: human-level AI in 10 to 50 years
• What is general AI?
– Artificial brain that can percei...
R&D short-term goals
• Already accomplished:
1. AI that learns to play Pong
• Unstructured input (screen pixels and
reward...
Commercial short-term goals
• AI company
• AI platform/ecosystem
– Brain Simulator
– Marketplace
• AI module developers
• ...
Open problems
• AI safety => friendly AI and collaboration
• Robots will take our jobs! => invest in AI
• Many problems ar...
What you can do for yourself?
• You can invest in AI companies
• Every $1 invested today will return 1,000,000 times
• Joi...
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Artificial general intelligence research project at Keen Software House (3/2015)

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

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

  1. 1. Artificial intelligence research at Keen Software House Marek Rosa, Dušan Fedorčák, Martin Bálek Keen Software House March, 2015
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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