Essay

The obsession with creating bigger datasets and bigger neural networks has side-lined some of the important questions and areas of research regarding AI. We need new models to advance AI.

Artificial neural networks rely on the point model, treating neurons as nodes that tally inputs and pass the sum through an activity function. Neuroscientists have discovered that dendrite compartments which are part of a neuron can also perform computations that mathematicians had categorized as unsolvable.

Lack of causality is one of the shortcomings of current machine learning systems. Systems that compose and manipulate named objects and semantic variables with causal structures will overcome these limits of AI.

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Members of the first human groups shared the instinct to combine their respective information and expertise to meet survival tasks they could not possibly meet individually. Those early forms of collective intelligence (CI) gave rise to language and tools which, in turn, enabled new forms of collective intelligence that could absorb more existential complexity.

According to Thomas Malone, Director of the Center for Collective Intelligence at MIT, web-based software tools allow people to interact and collaborate in new ways. The relationship between CI and AI defines ACI as a new research area with a few major drivers.

One issue that must be solved relates to the problem of digital identity. We must create an identity system in which human beings can control their identity.

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While educational institutions are overwhelmed by an onslaught of new students reaching for a degree in machine-learning, there is growing concern among members of the AI community that machine-learning and deep neural networks (DNNs) are flawed with severe problems.

Most advances in the field are associated with creating bigger neural networks and training them with more and more data. Excitement has blinded research to one of the fundamental problems that AI technology still suffers from: CAUSALITY.

DNN algorithms are powerful, but to think that they ‘think and learn’ in the same way as humans do is incorrect.

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With the resurgence of AI in the late nineties, applying Deep Learning to solve specific cognitive problems, stipulates the question what constitutes intelligence? How can one create intelligence artificially if we have no precise definition of what intelligence is?

Applying a framework like Francois Chollet’s Abstraction and Reasoning Corpus (ARC) is likely to empower AI research with a new perspective on defining and evaluating intelligence.

Measuring AI performance against human intelligence will eventually advance AI to the point where artificial cognitive intelligence will become a commodity, providing intelligence as a service to everyone.

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In his most recent book, “Human Compatible”, Stuart Russell takes on the challenges of AI to issues such as human purpose, authority and basic wellbeing. The major challenge ahead is to move AI from a data-centric to a human-centred approach, also referred to the creation of a General-Purpose AI (GAI).

Leveraging data and people’s expertise in new ways offers a path forward for smarter decisions, more innovative policymaking, and more accountability in governance.

Collective Intelligence, augmented by AI, is likely to enhance the development of human wisdom, enriching our individual life vis-à-vis the ever-growing complexity of scientific discovery and its impact on our society.

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The dot.com crash starting in the year 2000 was the result of greed and unrealistic profit expectations.

Recovering from the losses, private equity investment in AI has accelerated. It is estimated that more than USD 50 billion was invested in AI start-ups during the period 2011 through to mid-2018. However, in 2019 growth has come to a halt.

Are we overpromising and underdelivering on what AI is capable of? If the downturn in AI investments continuous, we might experience a crash in late 2020 or early 2021 as venture capital’s expectations are more and more confronted with the limitations of ‘Narrow AI’, such as the handling of common sense or the interpretation and understanding of content.

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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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