Essay

On May 22, 2019, the OECD’s 36 member countries including the US, formally adopted the first set of intergovernmental policy guidelines on Artificial Intelligence (AI), agreeing to uphold international standards to ensure that AI systems are designed to be robust, safe, fair and trustworthy.

Degrading humanity towards manipulated, brain-hacked servants as the historian and philosopher Yuval Noah Harari is concerned about, strongly differs from the goals defined by the OECD principles.

Human-Centric AI enhances and empowers humans rather than replacing and controlling them. Hence the five OECD principles are fully aligned to the goals of Human-Centric AI.

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By 2025 corporate knowledge-silos will no longer exist. Modelling corporate activities with a digital ‘sandbox’ enables rapid prototyping for testing new business models and business opportunities.

Adding AI common-sense reasoning to information that defines a corporation’s identity and values, gives corporate leaders the opportunity to reflect on future strategies or invoke decision-making with a much higher level of confidence.

Just as emotional intelligence became a leadership-asset about ten years ago, we now see a rising need for leaders to apply and support collective intelligence with an appreciation of interdisciplinary thinking.

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Many consider the division between analytic and intuitive thinking as opposites. However, a 2015 meta-analysis – an investigation where the impact of a group of studies is measured – has shown that analytic and intuitive thinking are typically not correlated and could happen at the same time.

Infants learn through interacting with the real world, which appears to be training various intuitive engines or simulators inside the brain without the need for massive training data.

We need a much better understanding of humans, both in respect to intelligence as well as intuition. This clearly stipulates that neuroscience and behavioral science are required to advance AI further.

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Human knowledge is being digitized at an exponential rate. Our biologically constrained cognitive abilities make it impossible to keep pace.

Hence, as some see it, it is essential that we develop a secure real-time interface between the human brain and the data storage and processing systems that reside in the cloud. To others, numerous ethical implications clearly prohibit interfacing the brain with an intelligent machine connected to the cloud. 

Neural-nanorobots are expected to provide a non-invasive, secure and virtually autonomous real-time interface between the brain and the cloud. They can be applied via injection into the vasculature system to monitor the information passing between synapses and neurons.

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In our decision-making process, we seek external information to guide us. This includes the advice and opinions from people we know, people with expertise in the field or people we can trust.

To foster consciousness is vital if we are to maintain our mental independence in a digital world where fake information and malicious influencing are threatening the very foundation of our democratic society.

Augmenting humans by applying interactive Generative Adversarial Networks (GANs) can improve conscious experience as a means of making sound decisions against the destructive use of AI. Individuals who consistently make good decisions may not be the ones who are the smartest or the luckiest. They are the ones who thoroughly understand themselves.

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Neuromorphic systems offer two major promises: First, because they are pulse-driven, potentially asynchronous, and highly parallel, they could be a gateway to an entirely new way of computing at high performance and very low energy. Second, they could be the best vehicle to support unsupervised learning—a goal that may prove necessary for key applications such as autonomous vehicle navigation in unchartered areas or natural-language comprehension.

What neuromorphic chips can do is to provide self-learning without requiring large datasets as in convolutional artificial networks. The system learns similar to the way humans learn.

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In theory, a civilization might be less vulnerable to collapse if new technologies can mitigate against pressures such as climate change. Our technological capabilities may have the potential to delay collapse.

In the past, collapse was confined to regions – it was a temporary setback, and people often could return to agrarian lifestyles following the collapse. However, the world is now deeply interconnected and interdependent. Additionally, new instruments of violence, such as lethal autonomous weapons, will be available soon.

The most dangerous threat, however, comes from the exponentially rising complexity induced by AI in combination with the rise of inequality and oligarchy by tech-giants such as Facebook, Google and Amazon.

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Following an inquiry launched in 2017 as concern grew about the influence of false information and its ability to be spread unscrutinised on social media, a UK parliamentary committee has published its findings on February 18, 2019 after an 18-month investigation into disinformation and fake news.

It could be vital that IEEE’s proposed standard ‘P7008 for Ethically Driven Nudging by Robotic, Intelligent and Autonomous Systems’ in combination with strong government supported regulations against misuse can restore trust into internet applications and social media. Fighting this attempt, as perceived by the UK’s Parliamentary Committee when investigating Facebook, is likely to provoke the ‘Digital Gangster’ image.

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Back in 2017 Denis Hassabis of Alphabet’s Deep Mind suggested intensifying the cooperation with Neuroscience to advance AI towards Artificial General Intelligence (AGI). Now, two years later, Artificial Neural Networks (ANNs) have made significant progress both in respect to diversity as well as quality.

Humans have a powerful “physical intelligence” to infer physical properties of objects and predict future states in complex, dynamic scenes. This “abstract system of knowledge” is based on physics (for example forces or masses) and psychology (for example desires or beliefs).

As new computational methods continue to be provided by research, intelligent machines are likely to match humans in learning, possibly within the next ten years.

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To advance AI and Deep-learning further, three issues need to be addressed:
Time:  It can take weeks to train deep-learning networks, engaging high-salaried individuals.
Cost:  Computer time on hundreds of Graphic Processing Units (GPUs) for weeks is expensive.
Data:  In many cases the lack of enough labelled data simply makes it impossible to carry out the project.

To handle the raising complexity to solve these problems requires increased hardware performance.
A three-way race for future AI-applications is on the way, employing the following hardware technologies:

• High-Performance Computing (HPC)
• Neuromorphic Computing (NC)
• Quantum Computing (QC)

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