About

GoodAI was founded in 2014 with a $10M personal investment from Marek Rosa. Our long-term goal is to build general artificial intelligence that will automate cognitive processes in science, technology, business, and other fields. We conduct our own research, advocate fundamental AI research at the EU governmental level, and forge a community of like-minded groups through the GoodAI Grants program.

Team

Meet our team

Marek Rosa

Marek Rosa

Founder and CEO

Marek Rosa, CEO/CTO, founded GoodAI with a personal investment of $10M. He has set the long-term vision for GoodAI and directs the focus of the company, leading both the technical research and business sides. He takes a hands-on approach to our daily research and development as a researcher and programmer himself.

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Olga Afanasjeva

Olga Afanasjeva

COO

Olga is an AI evangelist with a background in arts and social sciences, pursuing her passion for discovery and redefining the limits of what is possible. She leads the day to day activities of GoodAI.

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Jan Feyereisl

Jan Feyereisl

Senior Research Scientist

Together with his colleagues, Jan is actively investigating various aspects of the Badger architecture, focusing on facets at the intersection of various disciplines ranging from machine learning, complexity science and computational mechanics all the way to psychology. Jan is particularly interested in the dynamics of collective computation & learning. He is also the Executive Director of the AI Roadmap Institute.

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Martin Poliak

Martin Poliak

Senior Research Scientist

Ph.D. in Computer Science, Martin helps research different flavors of Badger architecture and works closely with other team members on the design of testing tasks used both for development and benchmarking. In previous internal projects, he frequently held a lead position. He has been with GoodAI for over 4.5 years.

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Lucie Krestova

Lucie Krestova

HR Manager

Lucie Krestova is responsible for recruitment and Human Resources at GoodAI and our sister company Keen Software House.

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Christine Lee

Christine Lee

Technical Writer / PR Manager

Christine is a communications specialist with a background in design, branding, and content creation. She handles GoodAI's public relations and publishing.

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Simon Andersson

Simon Andersson

Senior Research Scientist

Simon is part of the Badger architecture team. Before joining GoodAI, he worked in natural language processing in connection with speech recognition and knowledge representation and in computer vision, most recently at Trento University, Italy and Université Joseph Fourier, Grenoble, France.

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Jaroslav Vitku

Jaroslav Vitku

Senior Research Scientist

Ph.D. in Artificial Intelligence and Biocybernetics, actively working on R&D of the Badger architecture. Recently familiar with words/abbreviations like Deep RL, Multi-Agent RL, ES, GA, DQN, DDPG, MADDPG, Gym, open-endness, POET, PyTorch, AWS, etc..

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Petr Hlubuček

Petr Hlubuček

Research Scientist

Petr has a background in Computer Science and Biology and is currently working on research of Badger architecture. He is interested in interpretability and visualization of RNN-based models. Lately he has been working with attention, meta-learning, program induction.

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Joseph Davidson

Joseph Davidson

Senior Research Scientist

Ph.D in Computer Science from the University of Glasgow. Joe is currently interested in the discovery of learning algorithms which can themselves learn and adapt to biases in the data. While this is primarily to facilitate the development of the Badger architecture as a lifelong learning machine, it also serves Joes true dream of being able to get out of hyperparameter tuning.

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Shantesh Patil

Shantesh Patil

Senior Game Designer

Shantesh is a Game Designer with over a decade of experience working on several titles across larger studios like Ubisoft as well as leading teams at smaller independent studios. He has a strong interest in crafting interactive experiences that connect with players on an emotional level. Currently leads the design effort on the AI Game project.

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Jan Štafa

Jan Štafa

Research Engineer

Jan is a software developer interested in games and AI. He is currently working in GoodAI as Research engineer. Together with his colleagues, he is developing the new AI Game. He has master's degree in software engineering and he has been working on many commercial projects ranging from banking applications to automotive and applied AI.

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Dominik Čech

Dominik Čech

Game Designer

Dominik is a passionate gamer and game designer interested in creating unique game experiences. He has a Bachelors degree in Game Design and Production from Abertay University. He currently works on the AI Game.

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Reham Bukhari

Reham Bukhari

Advance QA Engineer

Reham holds MS in Computer Science degree and has 14 years of experience working in QA within the Financial, Security and Health sectors. She has strong experience in functional, usability, regression, performance, security and automation testing.

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Ryan Camilleri

Ryan Camilleri

Unity / AI Developer

Ryan is a game programmer focused on building robust software with efficient design. He is passionate about applying immersive technologies in games, particularly those of Augmented & Virtual Reality. This passion lead him to win the best VR Prototype Award for the Game Development World Championship in 2021, with his Bachelors dissertation. Currently, Ryan forms part of the team working on the AI Game project.

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Advisors

Meet our advisory team

Šimon Šicko

Šimon Šicko

CEO, Pixel Federation

Simon Sicko is the co-founder and CEO of the Slovak company Pixel Federation - an independent game developer and publishing company. With more than 200 employees it belongs to one of the largest Game Studios in Central Europe. Šimon is an experienced entrepreneur and provides GoodAI with strategic guidance.

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Lucia Šicková

Lucia Šicková

CLO, Pixel Federation

Lucia is Chief Learning Officer at the Slovak company Pixel Federation. She is an experienced Project Manager with a unique skill-set in the area of Human Resources and IT Management. He key focus is on learning, education, personal growth. She is advising GoodAI on strategic matters.

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Dr. Wendelin Böhmer

Dr. Wendelin Böhmer

TU Delft

Assistant Professor Böhmer's research interest focuses on the intersection between inductive and deductive reasoning in artificial intelligence. He studied computer science and received a PhD at the Technical University of Berlin and worked as a postdoc for the University of Oxford.

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Jakob Foerster

Jakob Foerster

Oxford University

Jakob Foerster is an associate professor at the University of Oxford. His work helped bring deep multi-agent reinforcement learning to the forefront of AI research.

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Our approach

The guiding principles of our AI research revolve around an AI which can accumulate skills gradually and self-recursively. To achieve this vision, we have developed a research roadmap and the Badger architecture that maps out our work with growing network topologies and modular networks. Our Grants Program, the Roadmap Institute, and the General AI Challenge leverage the power of collaboration to tackle open research problems together.

FAQs regarding general AI and GoodAI

What is general artificial intelligence and how can it be useful?

AI (Artificial Intelligence) is a software program that is able to learn, adapt, be creative and solve problems. While narrow AI is usually able to solve only one specific problem and unable to transfer skills from domain to domain, general AI (AGI) aims for a human-level skill set.

No one has developed general AI. With general AI we will be able do so many things we simply cannot do with our current level of technology. We will automate science, engineering, production, manufacturing, robots, entertainment, anything you can think of, and more. General AI will help us become better people, augment our own intelligence, and recursively self-improve ourselves.

How can we build and educate AGI? How can we do it fast?

General AI is complicated to design from scratch, especially if we want to teach it everything at once (so-called ‘end-to-end’). It is more feasible to do if the whole problem of learning and designing is deconstructed into several (less  complicated)  “sub-problems” which we know how to tackle. For example, it is clear that we want the AI to understand and remember images, so it needs the ability to analyze them and a memory to store data. We want it to be able to communicate with humans, so It will need to write, read and understand language. It will also need to learn and adapt to new things, and much more. We call solutions for each sub-problem skills.

A skill can be seen as an ability or heuristic which helps the AI solve a particular problem. Importantly, each skill can also be used for learning other skills, significantly reducing the search space for solving other sub-problems.

Skills can range from simple and concrete (like the ability to recognize faces, add numbers, open doors, etc.) to more abstract ones (like the ability to build a model of the world, to compress temporal / spatial data, to receive an error signal and adapt accordingly, to acquire new knowledge without forgetting older knowledge, etc.). Skills also provide a simple way to measure how the system works, as it is clear how to measure which system is better in understanding speech, classification, and game playing. However, evaluating general AI as a whole is still unclear.

General AI will essentially be a system that exhibits a very large set of skills. Some of those skills might be hard coded by programmers, but most will be learned. Take, for example, the evolution of humans. Evolution provided us with some hardcoded skills or predispositions, but most of what we know we need to learn during our lifetimes – from our parents, the environment, or society. Those skills cannot be hardcoded because humans, just like an AI, need to be able to adapt to unknown future situations. Sometimes it is also easier to teach the desired skill than to add it as a part of the design. On the other hand, letting the AI discover all skills by itself would be slow and inefficient. This means that our job is to identify essential skills and find the most efficient ways to transfer them to a general AI system – by hardcoding them or by teaching them. It is not necessary to find the best skills. Any skills which have the desired properties and which enable the AI to further learn and improve itself can move us closer to general AI.

Just like an AI has to use efficient methods when searching for problem solutions, AI researchers must also look for efficient shortcuts to narrow the search for the general AI architecture, optimal curriculum, etc., as we can’t effectively explore the entire space of potential solutions. We can, for example, draw inspiration from evolution, animal brains, or other systems designs. Part of the problem is also what general AI architecture and skill set is easier for us to attain now, with our current knowledge and resources. The framework, roadmap, the Institute are all part of a method for narrowing the search for the architecture and the curriculum to teach it.  Individual sub-problems can be also outsourced to other researchers and institutions.

We can ask questions like, “What is the minimal skill set that is sufficient for human-level AGI?” If we can optimize the process by cutting out all unnecessary skills, we can get to our goal faster. On the other hand, the learning algorithm alone wouldn’t be sufficient; we also need thousands or millions of learned skills for the particular domain. Without them, the AI wouldn’t be able to start solving the problems we need. For example, driving a car is not a crucial skill for a researcher AI living only in the world of internet and scientific publications, but a skill such as the ability to generalize to similar, but previously unseen situations is universal, and falls into the category of necessary skills for every general AI).

How do we understand intelligence?

Intelligence is a problem-solving tool that searches for solutions to problems in dynamic, complex and uncertain environments. From a computational point of view, all problems can be viewed as search and optimization problems and the goal of intelligence (or an intelligent agent) is to narrow the search space in order to find the best available solution with as few resources as possible.

Intelligence achieves this by discovering skills (heuristics, shortcuts, tricks) that narrow the search, diversify it, and help steer it towards areas that are potentially more promising.

One of the most useful skills is the capacity to gradually acquire new skills – which helps in exploiting accumulated knowledge in order to speed up the acquisition of additional skills, the reuse of existing skills, and recursive self-improvement. This way, the intelligent agent slowly creates a repertoire of skills that are essentially building blocks for new, more complex skills.

An intelligent agent operates with limited resources (time, memory, atoms, computation cycles, energy, etc.), which is another constraint put on intelligence, favoring skills that use fewer resources.

Gradual and guided learning also helps narrow the search, because at each step, an intelligent agent has to search for a new solution only within a small and useful area, decreasing the number of candidate solutions, thereby reducing the complexity of the search space. On the other hand, if there was no gradual or guided learning and the agent were expected to find a solution to a complex problem too far from its current capabilities, it might never find the solution.

What are our objectives and goals in terms of AI safety?

Teaching the AI through gradual and guided learning, where we fine-tune individual learning tasks in order to teach the AI desired skills (behaviors), will allow us to have more control over the behaviors it will use later to solve novel problems. The AI’s behavior will, therefore, be more predictable.

In this way, we can imprint positive human biases into the AI, which will be useful for future value alignment (between AI and humans) – one of the important aspects of AI safety.

Why is "gradual" good?

If we have a hard task, a good way to solve it is to break it down into smaller problems which are easier to solve. The same is true for learning. It is much faster to learn things gradually than try to learn a complex skill from scratch. One example of this is is a hierarchical decomposition of a task and gradual learning of skills from the bottom of the hierarchy to the top.

For instance, if you have a newborn child and you give it a task to learn how to get to the airport, the chance that it will learn to do it is really small because the space of possible states and actions is just too large to explore in a reasonable amount of time. But if you teach it gradually with small tasks, for instance how to crawl and then walk, you increase the chances of success, as it can use these skills to try to get to the airport.

We want to build systems that learn gradually. Furthermore, we want to guide their learning in a correct way. Guided learning means showing the system what things make sense to learn and in what order. This reduces the necessity for exploration even further.

Basically, you show the child that it makes sense to learn how to walk and open doors, and only then to try to get to the airport.

Another benefit of gradual learning is that it can be more general. We do not have to specify a single global objective function (the main goal of AI) at the beginning, because we are rather teaching universal skills, which can be used later for solving some new tasks.

In the case of the child, we basically start teaching it to walk and open the door, even if we don’t know it will need to get to the airport later, or to become a dentist, etc.

If we teach skills gradually, we have better control over the knowledge which is learned by the system. Later, if we specify a goal for the system, it is more likely that in order to fulfill it, it will try to use these already learned skills rather than inventing new behavior from the scratch. It means that in this way, we reduce the chances that the system would invent any unwanted or harmful strategy.

This is similar to teaching the child how to walk and open the door, and then to go to the airport. It is more likely that it will try to solve the task by walking and opening the door, rather than trying to learn a completely new skill (like flying) from scratch, because it would be just more difficult.

Performance benefits:

  • Optimizing a model that has few parameters and gradually building up to a model with many parameters is more efficient than starting with a model that has many parameters from the beginning. At each step, you only need to optimize/learn a small amount of new parameters.
  • There is no need to know the size of the network a priori
  • Network size can correspond to the complexity of given problems (there are no neurons or weights to prune)
  • Starting with a small network is faster (than the other way around)
  • Reuse of existing skills is made possible

What is a skill / heuristic?

A skill or a heuristic is any assumption about a problem that narrows and diversifies the search for a solution and points the search towards more promising areas. It is not guaranteed to be optimal or perfect, but sufficient to meet immediate goals.

Other names for a “skill” or “heuristic” are: behavior, strategy, ability, solution, algorithm, shortcut, trick, approximation, exploiting structure in data, and more.
Skills can also be considered biases which restrict behavior.

Some skills are simple (e.g. detecting a simple pattern such as a line or an edge) or complex (e.g. navigating through an environment).

What does the GoodAI Research structure look like?

The GoodAI Research team is made up of a few “architecture groups” each working on its own general AI prototype. Each group is designing their own curriculum (School for AI). However, we are aiming to align all of the teams in order to focus on problems similar to the training and evaluation talks from the Gradual Learning round of the General AI Challenge.

Our AI Safety team is studying: how we can advance safely with our technology, how to mitigate threats to our team and humankind as a whole, how to we can create an alliance of AI researchers committed to the safe development of general AI, developing our futuristic roadmap, and more.

The General AI Challenge team formulates the problems for each round of the General AI Challenge, and manages the day-to-day activities.

What is the Futuristic Roadmap?

GoodAI’s Futuristic Roadmap is our vision for the future and the specific step-by-step plan we will take to get there. The roadmap outlines challenges we expect to come across in the course of general AI  development and our efforts to keep AI safe, and how we will mitigate risks and difficulties we will face along the way.

Our futuristic roadmap is a statement of openness and transparency from GoodAI, and aims to increase cooperation and build trust within the AI community by inspiring conversation and critical thought about human-level AI technology and the future of humankind. While our R&D roadmap is focused on the technical side of general AI development, this futuristic roadmap is focused on safety, society, the economy, freedom, the universe, ethics, people, and more.

​You can find one of the most recent roadmaps here.

How is GoodAI different?

GoodAI stands apart from other AI companies because of our roadmap, framework, and big-picture view. We pursue general AI with a long-term, 10+ year vision, and remain dedicated to this goal. We will not be distracted by narrow AI approaches or short-term commercialization, though we are certain to find useful applications for our general AI technology along the way.

Our roadmap, framework, and experimental implementations are at a very early stage and should be taken as works in progress.  We are focused on the gradual accumulation of skills and recursive self-improvement. We do research in growing network topologies and modular networks and train and teach our AI in our School for AI.

We are optimizing the process of building and educating general AI.

How can we compete against bigger companies?

Our mission is to build general AI as fast as possible, but this is not a race.

It’s not about competition, and not about making money.

At GoodAI, we want to create a positive future for everyone. Developing general AI will be the most helpful thing in human history, and we want to help make this dream come true.

How does our work contribute to the fields of AI and general AI research?

There is a significant lack of unified approaches to building general-purpose intelligent machines. Comparable to the biological sciences, most researchers, universities and institutes still operate within a very narrow field of focus, frequently without consideration for the ‘big picture’.

We believe that our approach is a way to step out of this cycle and provide a fresh, unified perspective on building machines that learn to think. We hope to achieve this in a number of ways, each of which are equally relevant and essential for tackling different aspects of the building process:

  • Our framework provides a unified collection of principles, ideas, definitions and formalizations of our thoughts on the process of developing general AI. This allows us to amalgamate all that we believe is important to define as a basis on which we and others can build. It will act as a common language that everyone can understand, and provide a starting point for a platform for further discussion and evolution of our ideas.
  • Our roadmap is a principled approach to clearly outlining and defining a step-by-step guide for obtaining all abilities and skills that a human level intelligent machine needs to possess. This includes their definitions, as well as the gradual order and way in which to achieve them through curricula of our ‘School for AI’.
  • Our School for AI provides learning curricula — a principled, gradual and guided way of teaching a machine. This approach differs significantly from current approaches of narrow-focused and fixed datasets. We believe that gradual and guided learning are essential parts of data-efficient learning that are paramount to quick convergence towards a level of intelligence that is above current standards.
  • To compare and contrast existing approaches and roadmaps and foster more effective distillation of knowledge about the process of building intelligent machines, our AI Roadmap Institute is a step towards an impartial research organization advancing the search for an optimal protocol for  achieving general artificial intelligence.
  • Last but not least, our software infrastructure is comprised of our large-scale and highly parallel Arnold Simulator, able to handle extremely dynamic network topologies, as well as various learning environments. It was developed specifically for the numerous curricula of our School for AI, and serves as an ideal platform for transforming our conceptual ideas to practical implementations with tangible results.

Using the language of our principles, the above are simply a set of heuristics for steering our search for general AI that we believe are important and will help us achieve a significantly faster convergence towards developing truly intelligent machines.

History

The story of our success

  1. August 2020

    GoodAI Grants is launched

    $300,000 AI Grant fund is launched for researchers interested in tackling some of the open questions related to GoodAI’s Badger Architecture.

  2. August 2020

    Meta-learning & Multi-agent Learning Workshop

    GoodAI hosts the Meta-learning & Multi-agent Learning Workshop with over 60 participants from across the world take part.

  3. July 2020

    Move to Oranžérie

    GoodAI and sister company Keen Software House move to new headquarters The Oranžérie.

  4. December 2019

    Badger Architecture released

    GoodAI published the Badger Architecture which was a culmination of five years of work at GoodAI. Read more about it.

  5. August 2018

    Human-Level AI conference

    GoodAI hosts the Human-Level AI conference in Prague with over 500 participants from across the world.

  6. July 2018

    Solving the AI Race results

    $15,000 of prize money awarded to the finalists of the General AI Challenge Solving the AI Race round.

  7. January 2018

    Launch of Solving the AI Race round

    Launch of the Solving the AI Race round of the General AI Challenge.

  8. September 2017

    Gradual Learning results

    Results of General AI Challenge Round 1: Gradual Learning.

  9. February 2017

    Launch of General AI Challenge

    Launch of the General AI Challenge Round 1: Gradual Learning. GoodAI pledge $5mil in prize money over coming years to tackle crucial research problems in human-level AI development.

  10. August 2016

    GoodAI Research

    R&D team grew to 20 researchers, scientists, programmers. Major progress update released: Framework, AI Roadmap Institute, Roadmap, GoodAI Applied.

  11. January 2016

    GoodAI Research

    New focus on roadmapping announced. Research shifted in the direction of growing topology architectures that support the gradual accumulation of skills. Start of design and development of the School for AI. Start of development of Arnold Simulator.

  12. July 2015

    Public launch

    GoodAI was announced to the public as a company. Start of new unified approach to building general AI. Commitment to focus our research on the bigger picture. Brain Simulator was released.

  13. April 2015

    The beginning

    First information about the project was made public.

  14. January 2014

    The beginning

    GoodAI, a general AI research and development initiative, started with a personal $10M investment from CEO/CTO Marek Rosa.

  15. September 2013

    Marek Rosa releases Space Engineers

    Marek Rosa’s Keen Software House released the game Space Engineers. The success of the game allowed Marek to create GoodAI.

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