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Marek Rosa - Inventing General Artificial Intelligence: A Vision and Methodology


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Machine Learning Prague 2016

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Marek Rosa - Inventing General Artificial Intelligence: A Vision and Methodology

  1. 1. Accelerating Towards General Artificial Intelligence: GoodAI and the Future of Humankind Marek Rosa CEO, CTO & Founder GoodAI and Keen Software House
  2. 2. About GoodAI • Interest in general AI since childhood • Development began in January 2014, within Keen Software House - Space Engineers + Medieval Engineers • My personal $10mil investment • Team of 30 researchers • Team members = co-owners of GoodAI
  3. 3. Our mission Develop general artificial intelligence as fast as possible, be helpful to humanity, and understand the universe.
  4. 4. Advantages of AGI • General AI vs narrow AI • Highest “return on investment” (ROI) possible => high-risk & high-reward • Recursively self-improving AI; exponential growth • Could be “our final invention” (in a good sense) • AI scientists, AI programmers, AI astronauts, AI …. • Solve the problems of humankind • Illness, death, climate change, rescue operations, exploring the universe • Everyone will benefit from AI (charities, corporations, individuals…)
  5. 5. Effect on Jobs • AI will be increasingly more skilled than humans at performing human tasks. Where will this lead? • Increased automation in our economy • Employers will start to prefer intelligent machine workers to human ones • Job replacement • Institute a universal basic income • Exit the human-based economy • No need to work to survive • Altruism • Investing in the AI future
  6. 6. What is Intelligence? • Learn, adapt, solve problems and achieve goals in a complex environment • Model of the world where only relevant parts are represented • Evolution vs intelligence - Intelligence is both faster and cheaper - Intelligence needs fewer resources • Maximize the chance of achieving goals in the future (goal / resources / time) BrainSensors Motors
  7. 7. Unified Brain Architecture • Our own all-in-one AI brain architecture - Composed of a network and sub-networks of "experts" (essentially small programs) - Purpose is to make a procedural representation of the world, past experiences, learned skills, predictions, plans, etc. • Not just integrating Deep Learning, Machine Learning, HTM, LSTM, or others as independent modules where each is focused on specifics - Look to the principles of intelligence, aim to understand them, and implement only those principles into our unified brain architecture - Building our own design, not on top of existing technology
  8. 8. Intrinsic Properties • One system manifests them all: - Over-generalization - Generalize-first - Analogy - Knowledge transfer + context switching: Reuse existing or modified programs (groups of experts) • Conflicting and Parallel Actions - Hierarchical long-term sequences - Actions (hierarchical) –> motor commands - Behaviors: internal, external, general to specific / concrete • Learning to learn • Additive learning, compositional learning • Online, continuous, lifelong learning • Unsupervised and supervised learning, puppet learning, guided and gradual learning, reinforcement learning • Pattern detection • Altering knowledge • Unified memory: working + short term + long term + episodic • Anomalies / novelty detection • One-shot learning • Pattern reconstruction • Detect uncertainty + confidence • Predictions – multiple and simultaneous • Perceptual consistency, continuity
  9. 9. Learned General Abilities • Visual attention / focus • Feature selection • Language? • Gratification delay • Mental time travel • Imagination • Active logic reasoning • Meta cognition • Third party intervention • Empathy • Mirroring • Recognize himself in the mirror • Abduction • Induction • Deduction • Imitation learning • Planning – long-term sequences of actions • Active learning • Proactive learning • Cooperation • Curiosity • Creativity • Imagination • Exploration • Exploitation
  10. 10. Learned Skills / Knowledge / Experience • Recognize apple, pear, door (locked / unlocked) • Apple tree produces apples...pear tree produces pears… • Open door • Close door • Day + night • …
  11. 11. School for AI • Set of simple game environments • Gradual and guided learning • Learning tasks • Communication • Can serve as an AGI benchmark • Sets the requirements for our Unified Brain Architecture!
  12. 12. Brain Simulator • In-house, collaborative platform for researchers, developers, and high-tech companies • Prototype and simulate artificial brain architectures • Share knowledge
  13. 13. Integration with Space Engineers
  14. 14. Future Business Applications • Useful to start with games (safe, low-risk environment) • Easy-to-implement applications • Later commercialization - Automating science, engineering, art, manufacturing, robotics, etc. • Paradox: need to reach human-level AI before commercializing?
  15. 15. Thank you! Keep in touch or join our teams! We’re hiring: Game Programmers Game Artists Game Writer …and more!