From Here to Human-Level AGI in Four Simple Steps According to Ben Goertzel

AGI  

⯀ Artificial general intelligence researcher and entrepreneur Ben Goertzel recently gave a talk at the Czech Institute of Informatics, Robotics, and Cybernetics on how to get to human-level machine intelligence. The presentation, while high-level contains a lot of interesting ideas on the topic.

Artificial intelligence researcher and entrepreneur Ben Goertzel recently gave a talk at the Czech Institute of Informatics, Robotics, and Cybernetics on how to get to artificial general intelligence (AGI).

Goertzel is one of the world’s foremost experts in Artificial General Intelligence, a subfield of AI oriented toward creating thinking machines with general cognitive capability at the human level and beyond. He also has decades of expertise applying AI to practical problems in areas ranging from natural language processing and data mining to robotics, video gaming, national security and bioinformatics. He has published nearly 20 scientific books and 140+ scientific research papers, and is the main architect and designer of the OpenCog system and associated design for human-level general intelligence.

The talk (full video below) is pretty high-level, but does explore many facets of machine learning and AGI.

"We need to backward chain from the Singularity to the present and look forward chain from the present time toward what we are trying to achieve," states Goertzel.

 He starts the talk with a review of the different approaches researchers are taking now to build AGI. Shane Legg, one of the founders of DeepMind, used to work for Goertzel, is following a biologically-inspired method, so far quite successfully.

Goertzel's plan with his OpenCog project, and the offshoot startup Singularity.NET, is what he calls an "integrated approach." Part of the problem as well is that the AI field has siloed into discrete areas of study that do not interact with the other research focuses well enough.  OpenCog, part of the integrated approach, stores and manipulates knowledge in the form of complex graphs. Goertel thinks this is what the brain is doing at a very high level.

For Goertzel and his team, deep neural networks are only part of the solution to the problem of creating AGI. "The AI literature is also very long and deep, but there are also lots of other things of value in it," he quips. Despite all the hype around deep learning systems today, he states that only by integrating the other aspects of AI will human-level intelligence be recreated in silico.

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The other important aspects of AI research he points out are:

• Evolutionary learning
• Logic systems

and

• Stochastic local search

"In the end if you want to make something intelligent, you are going to have to make something that can learn from its own experiences and observations," states Goertzel. These include the concepts of 'self' and 'will/volition.'

"In the end if you want to make something intelligent, you are going to have to make something that can learn from its own experiences and observations."

According to Goertzel, to get from where we are to true AGI will require advances in (at least) four different aspects.

First, it will require coordination of different AI agents at various levels of specificity into an overall complex, adaptive AI network — which is the problem addressed by the SingularityNET blockchain-based AI framework.

Second, it will require bridging of the algorithms used for low-level intelligence such as perception and movement (e.g. deep neural networks) with the algorithms used for high-level abstract reasoning (such as logic engines).

Third, it will require embedding of AI systems in physical systems capable of interacting with the everyday human world in richly nuanced ways — such as the humanoid robots being developed at Hanson Robotics. Sophia, the well known humanoid robot, that was recently parodied this season on Silicon Valley, is one of their most famous examples.

As Goertzel demonstrates in the video, Sophia is now also training people to meditate.

Fourth, it will require the development of more sophisticated methods of guiding abstract reasoning algorithms based on history and context (an area lying at the intersection of AGI and automated theorem proving).

Fortunately, while none of them are actually simple, all of these aspects of the AGI problem are topics of active research by outstanding teams around the world, making it plausible that AGI at the human level and beyond will be achieved during our lifetimes.

Along these lines, SingularityNET and AI Decentralized have announced a new group: DAIA or the Decentralized AI Alliance. DAIA is said to be an open industry alliance aimed at fostering the development of decentralized AI technologies. DAIA is said to be founded on the premise that making AI more decentralized, democratic and participatory is the right path for minimizing issues like these, ensuring that AI is applied for the greater good.

Goertzel is the CEO of SingularityNET (a blockchain based AI platform company), and the Chief Scientist of Hanson Robotics, a robotics company that creates the world’s most advanced humanoid robots. Ben also serves as Chairman of the Artificial General Intelligence Society, which hosts the annual AGI research conference series, and the OpenCog Foundation.

Before relocating to Hong Kong in 2011, Goertzel held executive roles at AI consulting and product development firms in Washington DC (CEO, Chairman and Chief Scientist at Novamente LLC and Biomind LLC) and New York City (CTO at Webmind Inc.). Prior to that, he served as faculty in mathematics at the University of Nevada Las Vegas, in cognitive science as the University of Western Australia, and in computer science at Waikato University in New Zealand, at the City University of New York and at the University of New Mexico in Albuquerque. Dr. Goertzel holds a PhD degree in mathematics from Temple University in Philadelphia.

Note, follow up questions to the presentation are available as well. 

SOURCE  CIIRC ČVUT

By  33rd Square