Author: Peter Rudin

OpenAI Disbands Its Robotics Research Team

Posted by Peter Rudin on 23. July 2021 in News No Comments

OpenAI has disbanded its robotics team after years of research into machines that can learn to perform tasks like solving a Rubik’s Cube, shifting its focus to other domains, where data is more readily available.

“Because of the rapid progress in AI and its capabilities, we’ve found that other approaches, such as reinforcement learning with human feedback, lead to faster progress in our reinforcement learning research”, OpenAI cofounder Zaremba said.

Moving away from robotics might also reflect the economic realities the company faces. DeepMind, the Alphabet-owned AI research lab, has undergone a similar shift due to rising R&D cost in favour of work with applications, like protein shape prediction.

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Is Artificial Intelligence Changing Maslow’s Pyramid of Needs?

Posted by Peter Rudin on 16. July 2021 in Essay No Comments

Abraham Maslow’s theory of human motivation, now over 80 years old, continues to have a strong influence on business and economic issues. Although the paper was written for psychologists, it has created a significant impact on management theory.

In contrast to Maslow’s hierarchy of needs towards self-actualization, one can apply an intelligence-focused view, where wisdom represents the highest level of human fulfilment

The mindset of Maslow’s hierarchy remains valid, provided the basic-level needs are expanded with internet connectivity. The challenge we face is to explore the ‘mystery’ of wisdom with psychological, philosophical and scientific expertise, including AI.

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Robots May Soon Be Able To Reproduce

Posted by Peter Rudin on 9. July 2021 in News No Comments

A team of researchers have recently demonstrated a fully automated technology to allow physical robots to repeatedly breed, evolving their artificial genetic code over time to better adapt to their environment.

The idea of digital evolution – imitating biological evolution in software to successively breed better and better solutions to a problem over time – is not new.

While scientists have always been confident that digital evolution could be effective as an optimisation tool, its creativity in producing original and unusual design has been more surprising. Unlike natural evolution which is driven simply by the goals of “survival and reproduction”, artificial evolution can be driven by specific targets.

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With Embodiment and Sensors to Artificial General Intelligence?

Posted by Peter Rudin on 2. July 2021 in Essay No Comments

Embodied cognition, the idea that the mind is not only connected to the body but that the body influences the mind as well, is a rather new concept in cognitive science.

Research shows that embodiment and learning correlate in achieving cognitive intelligence. Going one step further, DeepMind, Alphabets’ AI research unit has set forth the hypothesis that the promise of awards will enhance this learning process with AI based reinforcement learning.

However, there are other intelligences which are beyond cognition, supporting the fact that across the entire spectrum of human’s intellectual capacity, humans cannot be replaced by intelligent machines.

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Will Reinforcement Learning Achieve General AI ?

Posted by Peter Rudin on 25. June 2021 in News No Comments

In a new paper, scientists at Google’s DeepMind AI research unit argue that intelligence and its associated abilities will emerge not from formulating and solving complicated problems but by sticking to a simple but powerful principle: reward maximization.

Reinforcement learning formalises the problem of goal-seeking intelligence. The general problem may be instantiated with a wide and realistic range of goals and worlds, corresponding to different reward signals to maximise in different environments.

The researchers also acknowledge that learning mechanisms for reward maximization is an unsolved problem that remains a central question to be further studied in reinforcement learning.

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Quantum Mechanics: the Next Challenge beyond AI?

Posted by Peter Rudin on 18. June 2021 in Essay No Comments

One fundamental different mindset between quantum mechanics and classical mechanics can be defined as follows: Quantum mechanics describes the dynamics of ideas, whereas classical mechanics describes the dynamics of machines.

In a business context quantum management implicates the integration of serial and associative thinking in analogy to quantum processes like entanglement. 

A first step is to understand quantum’s potential for an organization’s product and service offering and  to review the management processes required for a mind-shift towards quantum management in response to  the limits experienced with AI-focused applications.

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Will AI Succeed? Quantum Theory Suggests Otherwise

Posted by Peter Rudin on 11. June 2021 in News No Comments

Will artificial intelligence one day surpass human thinking? The rapid progress of AI has raised concerns that its abilities will grow uncontrollably, eventually wiping out humanity.

One argument against the possibility reaching this level of intelligence comes from the fact that AI would need to accurately predict the future.

Quantum Theory, one of modern science’s key concepts for explaining the universe, says that predicting the future may not be possible because the universe is random. The next Singularity2030 Essay ‘Quantum Theory to Overcome the limits of AI’, to be published on June 18, 2021, augments this discussion. 

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From Intelligence to Wisdom; what about Motivation and AI?

Posted by Peter Rudin on 4. June 2021 in Essay No Comments

Motivation is the process that initiates, guides and maintains goal-oriented behavior while wisdom represents the highest level of intelligence one could be motivated to achieve.

Wisdom resists automatic thinking and grasps a deeper meaning of what is known but also understands the limits of knowledge. Hence, enhancing Artificial Intelligence (AI) with Artificial Wisdom (AW) will open new conceptual models for reaching human-level AI.

The design principles for AW-systems are in line with the ongoing paradigm-shift in AI-research, moving from a deterministic massive data-driven, pattern-recognition model to a human-learning model.

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Google with a New Species of Search Engine

Posted by Peter Rudin on 28. May 2021 in News No Comments

Search results have improved leaps and bounds over the years. Still, the approach is far from perfect.

Though they have their own shortcomings large language models like GPT-3 are much more flexible and can construct novel replies in natural language to any query or prompt.

A Google team suggests that the next generation of search engines might synthesize the best of all worlds, folding today’s top information retrieval systems into large-scale AI.

A key advance would be moving beyond algorithms that only model the relationships between terms (such as individual words) to algorithms that also model the relationship between words in an article.

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With Self-Reflection to better Decisions, what about AI?

Posted by Peter Rudin on 21. May 2021 in Essay No Comments

Self-reflection is a process of communicating internally with oneself. Sensing and recording of this process provides a means for developing an intelligent  ‘robot’ that mirrors our own life experiences, interacting with the outside world similar to the way a child learns.

Realtime sensing of behavioral and physical body data is rapidly advancing. The primary design-goal of this ‘robot’ is to support humans to overcome limits of learning and to provide support in the decision-making process. 

Motivation and purpose have to remain the driving authority that controls this process. How much authority we want to give these ‘assistants’ in carrying out specific tasks is up to us, respectively the organization engaged in AI-supported automation.

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