mainly is a tool to enhance our physical or cognitive capacities. But what if we find real partners in machines? As a matter of fact, machines and humans are a perfect match because they are complementary, and we are here to decide which computer traits we need to develop and use.
Copyright by Sciforce
Sci-Fi writers, futurologists and IT researchers and practitioners sometimes conceptualize ‘human-level ’ as the Holy Grail of research. The idea of symbiosis between humans and machines is also settled in mass conscience creating new hopes and new phobias. Will we have a war with machines and end up as their slaves — a slowly-thinking race unable to predict the future and make decisions properly? Or will we live as masters with an army of robotic helpers?
In 2014, a Japanese venture capital company, Knowledge Ventures, elected an system to its board of directors. Is it an example of the closest human-machine symbiosis or is it a sign that we are losing our battle with ?
Since Alan Turing’s times, the major driving force behind research has been machine’s competition with human cognition. If we think of such examples as beating humans in chess or simply passing the Turing test — it is either machines proving themselves better than humans or humans outdoing computers in some areas.
This competition is prompted by the fact that the only model we have of anything close to general is the human brain. Researchers are inspired by the way our brain is built and how each neuron has thousands of synapses — we can see how it is mimicked in neural networks.
This approach in itself shows the limitations of that cannot (and probably will never be able to) fully reconstruct brain functioning. Just as an example, the human brain is very flexible; for instance, it filters information very effectively so that we learn without gigantic amounts of data, whilst systems are notorious for their hunger for data.
It goes without saying that Artificial intelligence differs from our brain:
- Artificial intelligence is so far shallow and has limited capacity for transfer.
- It has no natural way to deal with hierarchical structure.
- cannot inherently distinguish causation from correlation.
Besides, the whole our world is shaped towards human cognition. We are smart because we are small modules in a big world; we feel part of the society and draw our intelligence and our understanding of the contextual environment from it. , on the contrary,
- has not been well integrated with prior knowledge,
- cannot draw open-ended inferences based on real world knowledge, and.
- presumes a largely stable world.
From the psychological side, an essential part of human interaction is empathy and contextual awareness, and we are born with a great intuition for both. It is basically intuition that we are trying to introduce to . Currently, instead of brute-forcing its way through the dataset, successful AIs learn to do things by stapling multiple algorithms together. However, machines still fail to generalize much beyond already known data, such as a new pronunciation of a word or an unconventional image, and have trouble dealing with limited amounts of data.
At this point, the most important difference comes into the spotlight: humans have consciousness:
But are so irreparably different? Consciousness is a structure of thoughts, or, at a deeper level, it is just neurons. Consciousness is not binary; it’s a matter of degree. Humans and other animals have different levels of consciousness, and so do adults and children and even different adults. If we stop thinking of machines as continuation of humanity, we can benefit from cooperation with them without feeling threatened by the ghosts of misanthropic robots.
Lacking consciousness, computers remain task-driven, meaning that they do nothing unless they have a set goal. Humans are those who give the goal and meaning to what does for us and with us.
The root idea of collaboration between humans and machines is to enhance each other’s strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter.
This collaboration envisages that every participant has their role, be it a domain specialist getting meaning out of scattered raw data or the selected algorithms.
Role of Humans
In many cases, algorithms are trained with human supervision. Domain specialists collect huge datasets to be fed into algorithms from any field of human knowledge from idioms in multiple languages and disease courses to cultivation of different sorts of apples. Moreover, systems undergo training on how to interact with humans to develop just the right personality: confident, caring, and helpful but not bossy. For example, Apple’s Siri was created with the help of human trainers to simulate certain human-like traits. […]
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