Essay

Digital transformation, also dubbed as Industry 4.0, is in full swing. AI transformation, however, is just at the beginning, raising many societal and individual issues such as the replacement of human researchers with intelligent machines.

Many scientists are arguing that the latest techniques in machine learning and AI represent a fundamentally new way of doing scientific research.

One such approach, known as generative modelling, can help identify the most plausible theory among competing explanations for observational data, based solely on the data without any preprogramed knowledge of what physical processes might be at work in the system under study.

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One of the founding fathers of AI, Marvin Minsky was once questioned about machine emotions and said:

“The question is not whether intelligent machines can have any emotions, but   whether machines can be intelligent without any emotions”.

We have reached the point where computational methods can be applied to process the expression of emotions that occur with human interaction. However, this does not imply that intelligent machines have emotions.

For artificial intelligence to have human emotions, we would not only have to recreate the human brain but also its senses, body and consciousness.

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Artificial Neural Networks (ANNs) and Machine-Learning Algorithms (Deep Learning) are applied to detect deviations from a given norm or to predict outcomes based on the analysis of historic data. However, this neuroinformatic, mathematical approach to simulate the human brain is reaching its limits.

Decision-making provides one good example how the application of integrated AI (neuromathematics, neurobiology and neurophilosophy) can improve the decision-making process.

Regardless which process is applied – rational, intuitive or emotional – understanding the functionality of the human brain holds the key to enhancing the quality of human decision-making.

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Whether consciously aware or unaware, the brain correlates and selects information, associate’s meanings, and influences motivation, value judgment and goal-directed behavior.

It is the combination of neuroscience getting involved in studying consciousness and philosophy becoming more open to studying natural sciences, including neuroscience, that has brought about a more promising research environment leading to the emergence of neurophilosophy.

The most powerful artificial intelligence algorithms remain distinctly un-self-aware, but developments towards conscious thought processing are already happening, eventually providing a conscious machine.

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DeepMind, the world’s largest research-focused artificial intelligence operation, is losing money fast. The rising magnitude of DeepMind’s losses is impressive, more than USD 1 billion in the past three years, mostly related to its ongoing hiring of top researchers worldwide at very high salary levels with a current headcount of over 850 employees.

Advances in deep reinforcement learning have fueled DeepMind’s impressive victories in Go and the computer game StarCraft. However, this has hardly advanced DeepMind’s declared goal of being the world-leader in Artificial General Intelligence (AGI).

Today AI-research covers Neuroinformatics, Neurobiology and Neurophilosophy. Is DeepMind on the right track?

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Artificial General Intelligence (AGI) can be defined as the ability of a machine to perform any task that a human can. Although existing applications highlight the ability of AI to perform tasks with greater efficiency than humans, they are not generally intelligent.

Many researchers believe that AGI will be reached sometime this century, others consider AGI to be a myth. Reflecting on theory of mind provides one way of testing AGI’s viability.

Without some sort of radical breakthrough in design, sentient machines that can comprehend and communicate empathy and emotions will remain science fiction. Life is analogue not digital. The idea that digital intelligence can reach biological intelligence is likely to remain a myth.

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Networked-AI is a tool to support humans, it is human-centered. While connectivity and reach of the network are global, the application of Networked-AI is executed locally, contributing to the prosperity of the community and the national budget.

Networked-AI is an emerging property driven by three scientific disciplines: Information Technology, Neuroscience and Neurophilosophy.

The distribution of digital knowledge is a development process that will take place over many years to come. To follow and understand this fundamental change, AI needs to be demystified to the point where any individual with an average IQ can comprehend what is happening.

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Culture is vital because it enables its members to function with one another without the need to negotiate meaning at every moment.

As our daily lives are more and more penetrated and influenced by networked AI, its consequences require reflection. This mindset is a must to take advantage of the ever-growing usability and capability of networked AI.

To successfully adapt to a cultural shift requires foremost two things: life-long education and the implementation of ethics that define and secure our values in a new age of human existence.

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Artificial intelligence has been borrowing knowledge about the brain’s functionality since its early days, when computer scientists and psychologists developed algorithms called neural networks that loosely mimicked the brain. Those algorithms were frequently criticized for being biologically implausible.

Complementing the effort to model neural activity in the brain through biological experiments, one can train deep-learning networks to solve problems the brain needs to solve.

As a result, our knowledge about the biology of human brains and its AI data-scientific interpretation sets the stage for solving one of the last mysteries of human physiology.

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As science continuous to advance in high gear, the pace of knowledge-generation requires a revamp of our educational system.

Human-centered AI is crucial in supporting humans in lifelong learning. Both the content to be taught as well as the technological means to educate, will disrupt the current business model of our educational infrastructure whose foundation goes back to the middle ages.

The application of AI in education will improve the quality and retention rate and significantly lower the cost, a typical scenario for well-funded Start-ups and Spin-offs to incubate a new age in lifelong, personalized education.

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