Essay

Surveillance capitalism is an economic system centred around the commodification of personal data with the core purpose of profit-making.

In her latest book ‘The Age of Surveillance Capitalism’ Shoshana Zuboff, Professor at Harvard, provides a detailed examination of the unprecedented power of surveillance capitalism and the quest by powerful corporations to predict and control our behaviour. In her view industrial capitalism exploited nature while surveillance capitalism now exploits human nature.

The stronger personal autonomy is, the more fulfilled and productive a human being may become in adapting to today’s socioeconomic demands. Hence, a mindset of personal autonomy is fundamental vis-à-vis the potential negative impacts of surveillance capitalism.

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To understand the complexity of achieving AGI it is worthwhile looking at some of the capabilities that AGI will need to master such as sensory perception, natural language understanding, creativity, and social and emotional engagement.

Brain processes underly not only simple motoric behaviours such as walking and eating but also complex cognitive acts and behaviour that we regard as basically human: thinking, speaking and creating works of art.

The focus of AGI research is to integrate expertise in Neuroscience with expertise in Neuropsychology and Neuroinformatics. With these disciplines combined, problem-solving will be applicable to a much wider spectrum than presently available with Narrow-AI.

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Generating ideas through discussions with employees, customers and other external parties represents one way in which collective intelligence is commonly used.

Collecting ideas and feedback from a greater number of diverse individuals generates large data sets for training artificial neural networks. The combination of collective internet-connectivity with knowledge will introduce new IT-platforms supporting AI-focused value generation.

Facing increasing complexity, AI-empowered human resource management will be key. The capacity of building and managing teams with diverse competencies and distinct behavioural profiles to optimize man-machine interactions will define a new competitive landscape for the ‘survival of the fittest’

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Quantum mechanics provides the calculation of probabilities of how physical systems can behave. It is typically applied in defining the behaviour of molecules, atoms and sub-atomic particles.

Unlike conventional computers, which have bits that can exist in either state of 0 or 1, with quantum computers the quantum bit (or qubit for short) can exist in additional states. It can exist as a discrete state (0 or 1) or as a superposition of both states, adding a third dimension for the processing of data.

Quantum states correspond to probabilities rather than to absolute values. This fact also implies that human analysis and interpretation is still required in problem-solving. The complexity of problems we can solve, however, far exceeds what conventional AI is capable of handling today. The future is Quantum-AI.

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Common-sense represents all the background knowledge we have about the physical and social world absorbed over our lives. It includes such things as our understanding of basic physics as well as our expectations about how humans behave.

Based on common-sense, a heuristic is defined as a mental shortcut that allows people to solve problems and make judgments quickly and efficiently. While heuristics can speed up the decision-making process, they can introduce errors.

Common-sense reasoning is considered the holy grail towards the goal of reaching human-level AI. While, so far, AI has not succeeded to overcome this barrier, there are indications that AI will eventually support various decision-making scenarios.

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The utility of science is knowledge of natural physical mechanisms and the generation of technology.

Efforts to understand complex systems, especially our brain, might well be the first barrier demonstrating the limits of science.

Albert Einstein argued that quantum theory was an incomplete description of the universe and that there must be hidden variables that we do not yet understand. Our cognitive and reasoning capabilities are too limited in dealing with this situation. 

Hence above human-level AI might well hold the key to unlocking the limits of science, opening a new scenario of knowledge generation.

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According to the WHO about 8.5% of the world’s population are currently infected or have died because of Covid-19, compared to 30% who were infected or died due to the Spanish Flu. The application of AI such as the rapid development of new vaccines have so far played a major role in preventing a global disaster.

Yet the Covid-19 pandemic has left serious scars both individually as well as economically. The massive rise of unemployment due to corporate closings severely threatens the foundation of our socioeconomic balance.

To heal the scars and to restore trust in our democratic culture we need a creative mindset of solving problems by fostering the cooperation between humans and intelligent machines.

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Manipulative messaging at an emotional level is Donald Trump’s approach to gain public support. The ‘Terror Management Theory’ assumes that existential terror and anxiety is always residing below the surface of one’s awareness. To manage this terror, humans adopt views, like religions, political ideologies, and national identities,  far from any rational arguments.

Moreover, studies conducted by the Pew Research Center in October 2020 show that Democrats and Republicans largely disagree on key values.

Hopefully, the upcoming US administration will be successful in reducing frustration and hate under the slogan of ‘Making Democracy Great Again’.

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Our minds have evolved to think so that we can better adapt to our environment and make smarter decisions on how to survive. At a biological level, our thoughts are millions of neurons firing off in our brains. These brain cells working together create concepts  and knowledge that arises in our consciousness.

In 1985, AI-Pioneer Marvin Minsky pointed out that the question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without emotions.

As neuroscience and related behavioural analysis are accelerating intelligence research, the functionality of the human brain is seen as guide in advancing AI towards human-like intelligence. Along this process the human capacity to think and to rethink has become an important design factor in AI development.

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The major challenge for building collective intelligence is to combine knowledge from different sources and to customize this information for the specific needs of individual users and local organisations.

Two types of assets are of increasing importance: data and people. Adding AI to the equation sets the stage for merging human and machine intelligence, taking advantage of the assets that both bring to the table.

A principal feature of innovative regions is their capacity to create environments favourable to turning knowledge into new products and services

An AI-Collaboration Platform is the prerequisite for forming and supporting regional intelligence with modules covering the entire innovation process.

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