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There is a growing need for investment in foundation AI technologies. Intensive study with possible close Computational limit And sub-regions like natural language are going against it Rude Technical Obstacle, Novel AI and machine learning technology have never been reasonably high demand.
NYU psychologist Gary Marcus, Google software engineer Francois Cholt, and AI Jerome Pesenti’s Facebook head argued that the lack of progress is not surprising as researchers face both algorithmic and scientific challenges. Even the most sophisticated AI models may suffer from catastrophic forgetfulness, or a lack of fertility, clarity, consistency, and reliability, as well as a tendency to suddenly forget learned information.
So Marak Rosa, a Slovak entrepreneur and computer programmer, was founded Gudaii, A company dedicated to the research and development of general artificial intelligence (AGI). He is the CEO and founder of Ken Software House, headquartered in Prague, the capital of the main republic.
Rosa founded GoodAI in 2014 with an investment of १० 100 million, followed by publicly announcing the company and its first research roadmap in 2015 and 2015, respectively. In 2017, he founded the General AI Challenge, a prize money of million 1 million to tackle critical research issues in “human-level” AI development.
Gudai now employs about 20 researchers and engineers. Its new initiative is the GoodAI Grant Initiative, which aims to fundraise in this area Curiosity And Continuous learning. To date, the Gudai Grant Initiative has provided more than अमेरिकी 500,000 osa – from all Rosa – to nine projects that Gudai considers part of its end roadmap as generic AI.
“What makes us different? [from other grant organizations] It is our openness and flexibility and our willingness to work with potential guarantors to come up with a suitable proposal, ”Gudai PR manager Will Millership told VentBrett in an email interview. “We don’t really want to be limited to what the Office of Community works with and so we also work with individual scientists, groups of researchers, private companies, and individual students. We do a lot to ensure that all of the project’s intellectual property is shared. That doesn’t mean being completely open. Every agreement should be based on respecting the educational and business interests of both GUAIs and grant recipients. ”
GoodAI grant projects
In December 2011, Rosa and Gudai’s team published Badger, which was defined by Gudai as a “modular lifelong learning” based on the same architecture theory. Badger, who outlines the direction of GoodAI’s research, seeks to create a system of AI agents capable of adapting to a growing, open-end range of tasks while re-using knowledge gained in previous tasks.
“Our goal is to develop a secure AI – as quickly as possible – to help humanity and understand the universe,” Millership said. “We see the creation of human-level unity as the greatest challenge for mankind and beyond the work of individual researchers or research groups. So we believe that cooperation – and not competition – is the best way forward. ”
Among the recipients of the GUAI grant is Deepak Pathak, an assistant professor at Carnegie Mellon University who is inspired by developmental psychology and specifically how curiosity drives the teaching of early human development. Another is Farran Elliott, Ph.D. Students at MIT’s Computer Science and Artificial Intelligence Laboratory, which aims to create AI models that generalize to new tasks in a new environment from little data and previous experiences.
Gudai’s ambition – AGI, or the imaginary intelligence of a machine with the ability to understand or learn a task – has its obstacles. Facebook chief AI scientist Yan Lacan believes that it does not exist, because there is no such thing as common sense. He argues that human intelligence is also very specific, requiring many different systems for different individual functions.
To counter this, GUAI recently released its latest research roadmap, which highlights some of the technical challenges associated with creating a human-level or general AI. GoodAI claims that AGI should “learn to learn” and engage in lifelong learning, on a continuous and gradual schedule. It also believes that AGI should engage in open exploration and self-discovery goals as well as normalize the “out of distribution” and expand new problems.
“Each of these features reflects learning patterns throughout human life and so we see them as the key to creating AI that is able to generalize new problems in different environments, just like humans do,” Millership said. “We [plan to] Work closely with guarantors during their projects, providing support if needed, and [put] In summer seminars, where all the guarantors can share their ideas and projects. We are working to create an international community of academics and researchers across the industry. ”
Despite recent successes in overcoming obstacles to AGI, it is clear that there will be a long and long way to go for more human-like AI. Yet efforts like GoodAI seek to accelerate progress by tapping into a wide pool of nonprofit organizations and open communities such as Continental AA and ElitheAI, AI and machine learning expertise.
Thanks for reading,
AI staff writer
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