FACEBOOK IS A COMPANY known primarily for its social feed of emotional statuses, endless emojis, pictures of 'hols with the ladz', and, of course, a big blue thumbs-up.
Normally associated with tech giants like IBM, Google and Apple, or some disruptive Tech City startup, artificial intelligence (AI) is not the first thing to spring to mind when pondering Zuckerberg's 1.5 billion-strong social network.
Yet alongside solar-powered drones, virtual reality headsets and a wealth of coding tech, Facebook is also building its own deep learning and cognitive computing AI.
"The core goal here is to build systems that can better understand and perceive the world the way we as people do, so it can help us manage that world," said Mike Schroepfer (pictured) Facebook's chief technical officer.
This may sound like Facebook is just making another virtual assistant with a few smart moves and dry witticisms. But the social networking giant actually appears to be pushing the boundaries of AI tech that could leave Siri and Cortana scratching their digital heads.
Memory Networks, the moniker Facebook has given its AI technology, is what Schroepfer sees as the key to unlocking the door that separates deep learning machines which need to be taught, from intelligent systems that learn by themselves.
"[Memory] is in my opinion a fundamentally missing component of AI; there's no way we could view AI systems that can do the sorts of things we want without the capability to learn and retain new facts that they've never seen before," he said.
"One of the challenges with AI systems is many of the existing systems are dumb pattern matchers; you ask it a question, it gives you an answer, it doesn't learn as it goes.
"So one of the challenges with Memory Networks is can we take a neural net, this thing that you train, and can we attach a short-term memory to it so that it can take in data and answer questions based on that data."
Schroepfer described standard deep learning systems as just ways to create a black box of data for pattern matching after lengthy training.
But where Memory Networks differs is the ability to ingest new data and use machine learning to effectively get incrementally smart over time, rather than rely on being taught by a human.
In practice, this works by having a deep learning neural network to act as a ‘reasoning' system, which uses logical techniques like deduction, applied to data in a separate memory to turn it into knowledge that can be used to answer questions.
Through a form of associative memory - the ability to learn and remember the relationship between unrelated items such as the name of a place and its appearance - Memory Networks can then store and retrieve internal answers, observations and knowledge, thus getting smarter.
Schroepfer used the example of feeding Memory Networks the basic script of a film. Through natural language comprehension, Memory Networks can reason the movie's events and timeline to answer general questions without being specifically taught to answer exact queries.
In effect, the neural network is applying logic and reasoning to its memories, learning from knowledge and experience, not unlike our own fleshy human brains.
Applying Memory Networks to natural text-based language is only one half of Facebook's AI research.
Schroepfer explained that adding image recognition into the mix is the way to help Memory Networks better perceive the world, or most likely pictures uploaded onto social media.
Facebook's image recognition tech analyses photos at a pixel level, and has been trained to recognise patterns among them to better distinguish separate different objects in a photo even if they overlap. This process of segmentation then allows the AI tech to identify each object in the picture.
Schroepfer noted Facebook's image recognition system can do this 30 percent faster than most other systems and through using 10 times less training data.
But the magic happens when image recognition is combined with Memory Networks. This produces Visual Q&A, an AI system that answers questions posed to it via manual or voice inputs by people with impaired vison who want to know what a picture is composed of and what is happening in it.
Think smart, look sharp
Schroepfer highlighted how the company's AI research was exploring how it can use image recognition to teach neural networks to perceive whether something is going to happen from observation, rather than have an innate understanding of the situation. This is similar to how children work out when something is going to fall without understanding the physics behind it.
"That's how people learn; they learn by messing around with the world and seeing what happens. And we have computer systems now that are brilliant on a lot of things but don't understand basic physics and don't understand operations of the world because they haven't been able to observe it," he explained.
"So one of the other things we're trying to do is to teach computers some basic common sense about the world, and one way we are doing this is by stacking blocks together and showing an image of that to a computer and asking it to determine is this stack of blocks going to fall in this case or stand up."
According to Schroepfer, Facebook's techs were able to build a classifier that is over 90 percent accurate at identifying when said block was going to fall, and can in fact beat most humans at the task.
"It's one of many different ways we're trying to help systems understand what's going to happen in the future and help us think about not just reacting to what's happened but helping me plan things in the future," Schroepfer added. He noted how the elements of this AI research were being added into the M assistant to make it more capable or understanding complex requests.
Beating the human
You could question why Facebook is effectively looking to create an AI with 'common sense' rather than relying on strict logic systems, which can often work their way through all possible outcomes to come up with the right answer.
As ever, Schroepfer provided a compelling reason, backed up by an example; in this case, pitting a computer against a human in the Chinese board game Go, a game people consistently win over computers.
This is because unlike chess, where a computer can trump humans by working through all the possibilities of board configurations, Go has significantly more moves. For example, after the first two moves in chess there are 400 possible next moves; with Go there are 130,000. This is too much information for AI to crunch without bursting into a silicon sweat.
So Facebook looked to combine traditional AI tech designed to apply deep learning methods with Go and connect this with image recognition.
Schroepfer said that this approach gave the AI the ability to work out from patterns on the board what's a good move, instead of crunching thousands upon thousands of potential moves. In short, Facebook created a Go AI that has intuition.
"We've built up some of the image recognition technology and connected that together to some deep learning systems about possible good moves, and basically in a short number of months we've built a Go AI that can beat some of the AIs that were designed specifically for the purpose of playing Go, and it's as good as a very good amateur player," said Schroepfer, without sounding smug.
While the idea of beating humans may send a chill up some people's spines and send others running deep into the internet-free zones of Wales while screaming ‘Skynet', an army of Facebook-branded remembering, reasoning and predicating AIs is some way off; 10 years or more according to Schroepfer.
But the social media company, which is now very much a major technology industry player, and its AI research is a good indication of how neural networks and smart systems in the near future will be developed.
"The lesson really here is that by combining the different technologies, you could very rapidly build something that was better than the thing that people have been working on for many, many years, and I think this will be one of the many ways we will see advances in AI in the future," said Schroepfer.
He concluded with Facebook's ultimate AI destiny: "When these AI systems get good enough, we can afford to scale it to the entire planet; it's a super power we can give to every person on the planet."
We only hope everyone remembers that with great power comes great responsibility. µ
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