Essay

AI has made rapid progress by leveraging enormous amounts of human expertise and data. However, for some problems this human knowledge may be too unreliable or simply unavailable. As a result, a long-standing ambition of AI is to create algorithms capable of achieving superhuman performance with no human input.

Ever since the mathematician/physicist Sir Roger Penrose published his best-selling book “The Emperor’s New Mind”, there is an intense discussion going on about the effect quantum theory could have for AI. Penrose essentially believes that current day computers and hence AI can never reach the highest levels of human intelligence because human understanding is non-computational and hence exceeds the capabilities of machines.

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Recent developments in artificial intelligence (AI) and machine-learning in combination with large data-libraries significantly improve the quality and cost generating predictions.

Prediction is one of the possible objectives of mathematical modelling in fields such as healthcare, economics and finance. Decision-making is the process of identifying and choosing alternatives based on the values, preferences and beliefs of the decision-maker.

To get started with AI, the challenge is to identify the key decisions where the outcome is tied to uncertainty. While experts are mostly in agreement about the benefits of AI in healthcare, some doctors and academics are wary we could be headed in the direction of data-driven medical practices too fast.

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Common-sense reasoning (CSR) is a major stepping-stone in reaching Artificial General Intelligence (AGI). Many experts from the AI-community, like Geoffrey Hinton, considered the ‘grandfather’ of neural networks, or Demis Hassabis from Google DeepMind agree, that we need new concepts that go beyond Deep Learning and back-propagation.

At a glance, humans can perceive whether a stack of dishes will topple, a branch will support a child’s weight … or if a tool is firmly attached to a table or free to be lifted. Such rapid physical inferences are central to how people interact with the world and with each other, yet their computational underpinnings are poorly understood.

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Asking questions has always played a role in leadership, but it’s more vital than ever in today’s fast-changing digital landscape, says Hal Gregersen, the executive director of the MIT Leadership Center and lecturer at MIT Sloan School of Management.

Consequently, the drive to provide business leaders with conversational AI systems to add support to their decision making is gaining momentum.

Microsoft has become a major driving force in conversational AI. Forming the ‘Microsoft AI & Research Group’, bringing together more than 5,000 computer scientists and engineers to focus on the companies AI products and services, underlines the intensity of this strategic shift to develop machines that can think, reason and communicate like humans.

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In a bold move that could redefine the delivery of advice and the future of work itself, UBS has created a digital clone of its Swiss chief economist, Mr Kalt. The event marks the first time that a global bank has created a CEA by cloning a human staff member to be presented to clients to answer their questions.

A CEA’s level of expertise will eventually be superior to the expertise a human can comprehend. However, the human’s ability to think across multiple layers of knowledge will remain unique. Once true Artificial General Intelligence (AGI) becomes available, this scenario might change as CEAs will be able to cover a broader range of expertise. So far however, we have a long way to go to reach AGI.

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Our ability to understand and relate to other people is, in part, a function of an empathetic process. According to Wikipedia, empathy is the capacity to understand or feel what another person is experiencing from within their frame of reference.

Researchers and developers are creating algorithms that try to determine the emotional state of a human being, based on input such as gestures, facial expressions, text, and tone of voice.

As AI rapidly advances its communication skills, persuasion and empathy are no longer an exclusive human asset. Artificial empathy is about to overthrow the idea that to communicate expertise and advice is best performed by humans.

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Even though AI systems are no substitute for interactions with a real human, they could have the potential to improve our quality of life and enhance our emotional intelligence.

The ‘happimeter’ app is a new player on the block of personal analytic service providers. New is the combination of a smart watch with a smart phone and the focus on happiness with an algorithm that executes input data provided by the sensors of the smart watch combined with environmental information from the smart phone.

The ‘happimeter ‘and its analytical capability evokes the question if AI’s current development path is approaching a brick wall.

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Both human as well as machine learning generate knowledge, one residing in the brain the other residing in the machine. This fact raises the question how we apply what kind of knowledge and how we balance these knowledge resources for optimal results.

The application of so-called neural network software, mimicking functions of the human brain, coupled with the availability of low-cost powerful computational hardware resources, provides opportunities to solve problems which in the past have relied on human brain-power.

With machine learning a new tool has emerged which is likely to raise our capacity to learn if applied in an ethically responsible way. The consequences are far- reaching as continuous brain research will advance machine learning over the years to come.

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According to Wikipedia, art is defined as the expression or application of human creative skill and imagination, producing works to be appreciated primarily for their beauty or emotional power.

In June 2016, Google launched the Magenta project, which directly addresses the question, “Can machines be creative?”

Researchers at Rutgers University’s Art and Artificial Intelligence Laboratory recently generated entirely new artworks, using a new computational system that plays the role of an artist, attempting to demonstrate creativity without any need for a human mind.

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The issue of artificial consciousness and the possible behavior of conscious, intelligent machines will fuel the ongoing debate about the future of humanity.

To achieve Artificial General Intelligence (AGI), consciousness has to be part of the equation. If we want to move AI towards AGI, we will need our computers to become more like our brains.

To crack the neural code of intelligence and consciousness, neuroscience research needs to uncover the functionality of a neurons behaviour coupled to its synaptic network.

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