Essay

We have the means to uncover the start of a pandemic like never possible before. Personal tracking data (GPS) coupled with body sensor information (body temperature, heart rate, brain activity etc.) provide potential disease-status-information in real-time which can be processed by powerful service centers.

The existing threat of machine viruses, personal data theft, extortion with ransomware or the distribution of fake-information is likely to pose a threat as serious as global pandemics.

While biotechnology stands a good chance to keep future pandemics in check, punishment and imprisonment of cyber-criminals is a must to assure future prosperity and human wellbeing.

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There are several theoretical approaches to brain functions, including one to the most mysterious thing the human brain can do – producing consciousness. But so far none of these theories are widely accepted, and none has yet passed the decisive test of experimental investigation.

The implications of machine intelligence, for the process of doing science and for the philosophy of science, can be immense. With predictions obtained by methods that no human can understand, can we deny that machines have better knowledge?

If prediction is in fact one of the primary goals of science, how should we modify the scientific method and the algorithms that for centuries have allowed us to identify errors and correct them?

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Creativity is a phenomenon whereby something new and somehow valuable is formed. The created item may be intangible (such as an idea, a scientific theory or a musical composition) or a physical object (such as a robot or a painting).

Most of the things we use every day are inventions that no single human being could ever design within her lifetime. Rather than the product of individual innovators, these inventions can be thought of as the product of our societies.

Ceding social authority to machines, we will be treating the achievements of mathematics as if they required no human understanding at all. We will be taking one of our highest forms of creativity and intelligence and reducing it to a single bit of information: yes or no.

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What happens when machines with human-level AI become reality? To answer this question, it becomes obvious that our values must be implemented in AI-technology to reach the objective of advancing humanity beyond the prevailing knowledge and communication culture.

Humanity has gone through cultural shifts before. Considering the timeline documented by this essay, it is obvious that the cultural shift towards human-level AI happens exponentially, raising many issues as to how individuals can be motivated to adapt.

Mastering this challenge will set free human creativity, drawing on the fact that humans are more than just containers of intelligence.

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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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