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Slave to the algorithm

by Adrian Pennington on Mar 16, 2017

Artificial Intelligence is popularly the subject of dystopian visions from Bladerunner to Terminator. Even starting this article with the phrase ‘AI has begun to enter mainstream consciousness’ is loaded with extra-curricula meaning. The technology has emerged from theory into the sunlight impacting everything from robotic bank tellers to self-driving cars. Media is no exception.

Data, specifically metadata, has been the currency of media organisations for some time and essentially all AI does is to take this to another level. Recent extraordinary advances have been possible thanks to technical and intellectual advances that have allowed the development of very large-size Artificial Neural Networks (ANN - computational models inspired by how the brain works) coupled with the availability of a huge quantity of data to train them.

To illustrate the scale of the progress, the performance of object recognition algorithms on the benchmark ImageNet database went from an error rate of 28% in 2010 to less than 3% in 2016, lower than the human error rate on the same data.

Equity funding of AI-focused start-ups reached a record high in the second quarter of 2016 of more than $1 billion, according to researcher CB Insights.

Most R&D is being driven by computing and web giants Microsoft, Google, Facebook and Amazon who are best positioned to hoover consumer data on everything from buying habits to exercise regimens. Banks of their machines can be fed vast amounts of multimedia for processing and organising by algorithms for object, voice and facial recognition, emotion detection, speech to text or any programme we want to through at it.

Google CEO Sundar Pichai has said the company’s shift to AI is as fundamental “as the invention of the web or the smartphone.” He went so far as to suggest that we are evolving from a mobile-first to an AI-first world.

In reality, most AI apps are productised machine learning (ML) applications for which the term AI is misleading.  ML can be understood as ‘learning machines’ as distinguished from AI, or ‘machines that think’. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines ML as “the science of getting computers to act without being explicitly programmed”.

You need robust ML before you get to AI of course and currently there are few true mainstream AI applications outside of autonomous cars.

IBM prefers to talk about augmented intelligence.  “It’s an approach which asks how AI supports decision making and demands a societal change in how we look at technology,” says Carrie Lomas, IBM’s cognitive solutions and IoT executive. “Through personal devices like tablets to all manner of items with sensors, the industry as a whole is taking in lots of data and combining it with different types of information to enable a genuinely new understanding of the world.”

IBM’s cognitive computer system Watson is a set of APIs or building blocks which can be combined for different software applications by third parties.

For example, IBM has combined i Alchemy Language APIs with a speech to text platform, to create a tool for video owners to analyse video – forming IBM Cloud Video. It is able to scan news and social media in real time to understand how people are talking about a company; understand important topics and how people feel about them.

Some 75% of Netflix’ usage is driven by recommended content that was itself also developed with data – reducing the risk of producing content that people won’t watch and proposing content that consumers are eager for. This ground-breaking use of big data and basic cognitive science in the content industry has shown others its potential.


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