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Artificial intelligence (AI) has been around since the 1950s. The original pioneers dreamed of a computer that could perform tasks like humans, such as playing chess or translating languages. But the plans didn't come to fruition, and AI soon fell out of favour.
AI technology continued to improve exponentially over the next few decades. Many organisations now embrace AI as a core element of their business.
Deep learning is just one kind of AI, where AI is loosely inspired by the way our brains work.
Some AIs use advanced statistics to help computers make predictions; others use logic to make deductions about their environment, while others simulate evolution or even swarms of bees to find solutions to difficult problems.
The more advanced AIs can recognise features in images better than us, give expert opinions more reliably than us, and play many games better than us.
But, AI can still not be made with the flexibility and learning capacity of the human brain. Neither do we understand how intelligence arises in humans, making it hard to match AIs with human intelligence.
When you unlock your phone by looking at it, an AI has recognised your face. AIs check for fraud every time you buy something online, monitor your online shopping behaviour, suggest news stories that you are more likely to be interested in, and answer questions in online help desks.
AIs can compose music, design buildings, and paint artworks.
AI is created to help us and should not be feared. Like all future technologies, we need to ensure that it is used responsibly.
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Qualia relates to the raw sensations of experience, like colours, smells, sounds.
It is through our actual experience that we know what something tastes, looks or smells like, and it is not some information already drilled inside our brains on birth. We have to experience sensations to understand them.
Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algor...
Traditionally, evolutionary algorithms are used to solve specific problems. For instance, the ability to control a two-legged robot. Solutions that perform the best on some metrics are selected to produce offspring.
In spite of successes, these algorithms are more computationally intensive than approaches such as "deep learning."
It goes beyond traditional evolutionary approaches. It explains innovation. Instead of optimizing for a specific goal, it embraces the creative exploration of a diverse population of solutions.
The steppingstone’s potential can be seen by analogy with biological evolution: feathers likely evolved for insulation and only later became handy for flight.