Essay

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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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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