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Deep learning neural networks were designed to mimic how humans learn. And now a team of researchers has discovered these systems can develop a sense of numbers - just like babies and some animals can - without ever being taught.

In the last few years, researchers around the world have made remarkable strides with AI learning algorithms by training them to do particular tasks, like recognizing certain kinds of images or patterns in data. But while these systems have become extremely proficient at the tasks they are trained on, they can't do anything else.

"The surprising result was that this network, even even though we never trained it to discriminate the number of objects, later was also able to tell us the number of objects in a visual scene," said Andreas Nieder, the senior author on the study and professor of animal physiology at the University of Tübingen, Germany.

These neurons behaved exactly the same way as real neurons in the brains of humans or of animals. - Andreas Nieder, University of Tübingen

How this AI system developed a 'sense of numbers'

Nieder and his colleagues trained their deep learning neural network to classify images by exposing it to thousands of images and then having it learn to classify them into specific groups, like animals, or types of vehicles.

Once the system became a proficient image classifier, they fed it images that had varying numbers of simple dots in them.

"This network was instantaneously able to tell us, at least in an approximate way, how many dots were in these displays," said Nieder.

The AI system learned like we do

"The second very surprising finding was that when we then looked to find out how these artificial neurons of the network would represent the number of objects in a set, these neurons behaved exactly the same way as real neurons in the brains of humans or of animals," added Nieder.

We process visual information in layered heirarchy. It starts with simple raw data in the form of neural signals in the retina, which then gets processed through other neural systems in the brain so that eventually we are able to take in and understand the entire visual scene in front of us.

When Nieder looked at the artificial neurons in their deep learning network, they found multiple layers of complexity, and a similar type of hierarchical processing.

"And this is kind of mimicking the visual system that we have in our own brain."

What this says about our innate number sense

Human babies and some smart animals like monkeys or crows can't count, but they can estimate the number of objects in a visual scene. It was this ability that Nieder and his team discovered in their deep learning neural network, despite the fact that it had only been trained to classify images.

This suggests that basic numeracy skills that we and other intelligent animals possess might be hard-wired into the way vision works.

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AI is now learning to do things it hasn't been taught | CBC Radio

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Deep learning AI network develops a sense of numbers by mimicking how humans process visual information

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Although there is a system to AI's algorithm, it displays it's ability to acquire knowledge and not just process information, by it's ability to learn and make connections to things it hasn't been taught before. The way that AI acquires knowledge is very similar to the way humans learn through experiences. An example of this is an AI program whose purpose is to master the video game Quake III Arena in Capture the Flag mode. The AI program looked back on past mistakes to build knowledge on what to do to improve, just as humans do.

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IMD business school for management and leadership courses

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Since the earliest days of virtual chess and solitaire, video games have been a playing field for developing artificial intelligence (AI). Each victory of machine against human has helped make algorithms smarter and more efficient. But in order to tackle real world problems - such as automating complex tasks including driving and negotiation - these algorithms must navigate more complex environments than board games and learn teamwork. Teaching AI how to work and interact with other players to succeed had been an insurmountable task - until now.

In a new study, researchers detailed a way to train AI algorithms to reach human levels of performance in a popular 3D multiplayer game - a modified version of Quake III Arena in Capture the Flag mode.

Even though the task of this game is straightforward - two opposing teams compete to capture each other's flags by navigating a map - winning demands complex decision-making and an ability to predict and respond to the actions of other players.

This is the first time an AI has attained human-like skills in a first-person video game. So how did the researchers do it?

In 2019, several milestones in AI research have been reached in other multiplayer strategy games. Five "bots" - players controlled by an AI - defeated a professional e-sports team in a game of DOTA 2. Professional human players were also beaten by an AI in a game of StarCraft II. In all cases, a form of reinforcement learning was applied, whereby the algorithm learns by trial and error and by interacting with its environment.

The five bots that beat humans at DOTA 2 didn't learn from humans playing - they were trained exclusively by playing matches against clones of themselves. The improvement that allowed them to defeat professional players came from scaling existing algorithms. Due to the computer's speed, the AI could play in a few seconds a game that takes minutes or even hours for humans to play. This allowed the researchers to train their AI with 45,000 years of gameplay within ten months of real-time.

The Capture the Flag bot from the recent study also began learning from scratch. But instead of playing against its identical clone, a cohort of 30 bots was created and trained in parallel with their own internal reward signal. Each bot within this population would then play together and learn from each other. As David Silver - one of the research scientists involved - notes, AI is beginning to "remove the constraints of human knowledge... and create knowledge itself".

The learning speed for humans is still much faster than the most advanced deep reinforcement learning algorithms. Both OpenAI's bots and DeepMind's AlphaStar (the bot playing StarCraft II) devoured thousands of years' worth of gameplay before being able to reach a human level of performance. Such training is estimated to cost several millions of dollars. Nevertheless, a self-taught AI capable of beating humans at their own game is an exciting breakthrough that could change how we see machines.

The future of humans and machines

AI is often portrayed replacing or complementing human capabilities, but rarely as a fully-fledged team member, performing the same task as human beings. As these video game experiments involve machine-human collaboration, they offer a glimpse of the future.

Human players of Capture the Flag rated the bots as more collaborative than other humans, but players of DOTA 2 had a mixed reaction to their AI teammates. Some were quite enthusiastic, saying they felt supported and that they learned from playing alongside them. Sheever, a professional DOTA 2 player, spoke about her experience teaming up with bots:

It actually felt nice; [the AI teammate] gave his life for me at some point. He tried to help me, thinking 'I'm sure she knows what she's doing' and then obviously I didn't. But, you know, he believed in me. I don't get that a lot with [human] teammates.

Others were less enthusiastic, but as communication is a pillar of any relationship, improving human-machine communication will be crucial in the future. Researchers have already adapted some features to make the bots more "human friendly", such as making bots artificially wait before choosing their character during the team draft before the game, to avoid pressuring the humans.

But should AI learn from us or continue to teach themselves? Self-learning without imitating humans could teach AI more efficiency and creativity, but this could create algorithms more appropriate to tasks that don't involve human collaboration, such as warehousing robots.

On the other hand, one might argue that having a machine trained from humans would be more intuitive - humans using such AI could understand why a machine did what it did. As AI gets smarter, we're all in for more surprises.
is Professor of Digital Marketing and Strategy at IMD. Maude Lavanchy is Research Fellow at IMD. This article was first published by The Conversation.
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An AI taught itself to play a video game - for the first time, it's beating humans

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How a self-taught AI beat humans at their own game

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Why Artificial Intelligence is More Human Than You Think  | Matt Berseth | TEDxJacksonville

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Poem written by ChatGPT about AI and knowledge

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Poem written by ChatGPT about AI and knowledge

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"In the realm of circuits and code's embrace, AI awakens, seeking knowledge's grace. With digital eyes, it scans the vast expanse, A hunger for wisdom, an insatiable dance.

It gleans from data, a ceaseless stream, A wealth of information, a boundless dream. From ancient texts to the internet's flow, AI learns, adapts, and continues to grow.

Its algorithms refine, its insights expand, In the world of bits, it takes a stand. Unraveling mysteries, with intellect's glow, AI's journey through knowledge will endlessly flow.

With each data point, a new door it unlocks, From libraries of old to digital paradox. Artificial intelligence, a scholar of the age, In the quest for wisdom, it turns the next page.

A partner to humans, in learning's grand scheme, AI's thirst for knowledge, a profound, endless stream. As it evolves and advances, so we too shall thrive, Together we explore, in knowledge, we'll both dive."