Artificial intelligence research at Keen Software House

Marek Rosa, Dušan Fedorčák, Martin BálekKeen Software House

March, 2015

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

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

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…

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

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"

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

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

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

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

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