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A data science expert, mind science practitioner and advertising strategist walk into a bar...

When that trio actually comprises Shavani Naidoo of Primedia, Anne Thistleton of Light Consultancy and 'everyone's favourite ad commentator' Andy Rice, it must be the bar at IMM Graduate School's second annual 'marketing the future' event...

The Natural Roots of Artificial Intelligence

At the core of each instance of AI there is an algorithm — a set of instructions delivered to a computer to generate an output from an input.

Or if categorizing AI based on caliber, there is artificial narrow intelligence (ANI) that specializes in one area, artificial general intelligence (AGI) that can apply intelligence to any problem, and a hypothetical artificial superintelligence (ASI) that surpasses human intelligence.

By starting with a set of inputs along with their corresponding outputs, then passing the computer a learning algorithm, it writes a program that can produce the right outputs from a new set of inputs.

While speculative in nature, its advocates posit that within the next century we will be able to scan a brain in spacial and chemical resolution, model the signal processing functions of individual brain cells, and create a cell-by-cell executable model of the brain in artificial hardware.

While knowledge engineering takes this approach by manually encoding knowledge, symbolist machine learning avoids this “knowledge bottleneck” by employing a learning algorithm that empirically constructs knowledge on its own.

Taking inspiration from how our brains’ biological neural networks give rise to learning by reinforcing connections between neurons, connectionists model this through artificial neural networks.

This is done by creating layers of artificial neurons that leverage a learning algorithm called backpropagation, which successively changes the connections in each layer to bring the program’s output in line with what is expected.

Evolutionaries emulate this through genetic algorithms, where the fulfilment of a program’s purpose is substituted for ‘survival’ as the measure of fitness, through which programs are evolved over generations with random mutations and/or breeding.

Programs go through a cyclical process of starting with a prior probability — a belief before seeing evidence — that gets turned into a posterior probability that incorporates new evidence.

It is important to note that each type of machine learning requires some degree of oversight from programmers, who provide training data to generate a program, and make tweaks to its parameters to improve the model.

This oversight can come in the form of supervised learning where the programmer provides the correct output for each input, reinforcement learning where the programmer defines rewards for actions taken by the learning algorithm, and unsupervised learning where the learning algorithm operates on the input data without the guidance of known outputs or rewards.

Here’s How Artificial Intelligence Is Fueling Climate Change

You can think of artificial intelligence (AI) in the same way you think about cloud computing, if you think about either of them through an environmental lens: an enormous and growing source of carbon emissions, with the very real potential to choke out humans’ ability to breathe clean air long before a sentient and ornery AI goes all Skynet on us.

At the moment, data centers—the enormous rooms full of stacks and stacks of servers that juggle dank memes, fire tweets, your vitally important Google docs and all the other data that is stored somewhere other than on your phone and in your home computer—use about 2% of the world’s electricity.

Of that, servers that run AI—processing all the data and making the decisions and computations that a machine mimicking a human brain must handle in order to achieve “deep learning”—use about 0.1% of the world’s electricity, according to a recent MIT Technology Review article.

According to The MIT Technology Review, Dickerson recently told a conference audience in San Francisco that—unless super-efficient semiconductors are innovated in the next five years—data centers handling AI demands could account for 10% of the world’s electricity use by 2025, a hundred-fold increase in a half-decade.

That number is likely to remain stable or grow, as the U.S. continues to export natural gas and as China builds coal-fired power plants around the world as part of its “belt-and-road” soft-power initiative and to fulfill domestic demand as its economy begins to grow.

The History of AI (What Is Artificial Intelligence)

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