(task) AI Is Inventing Languages Humans Can’t Understand. Should We Stop It?

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AI, machine intelligence, language, complex adaptive systems, evolution of intelligence
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> https://www.fastcodesign.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it <https://www.fastcodesign.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it>
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> AI Is Inventing Languages Humans Can’t Understand. Should We Stop It?
> Researchers at Facebook realized their bots were chattering in a new language. Then they stopped it.
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> [Screenshot: courtesy Facebook]
> So should we let our software do the same thing? Should we allow AI to evolve its dialects for specific tasks that involve speaking to other AIs? To essentially gossip out of our earshot? Maybe; it offers us the possibility of a more interoperable world, a more perfect place where iPhones talk to refrigerators that talk to your car without a second thought.
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> The tradeoff is that we, as humanity, would have no clue what those machines were actually saying to one another.
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> We Teach Bots To Talk, But We’ll Never Learn Their Language
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> Facebook ultimately opted to require its negotiation bots to speak in plain old English. “Our interest was having bots who could talk to people,” says Mike Lewis, research scientist at FAIR. Facebook isn’t alone in that perspective. When I inquired to Microsoft about computer-to-computer languages, a spokesperson clarified that Microsoft was more interested in human-to-computer speech. Meanwhile, Google, Amazon, and Apple are all also focusing incredible energies on developing conversational personalities for human consumption. They’re the next wave of user interface, like the mouse and keyboard for the AI era.
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> The other issue, as Facebook admits, is that it has no way of truly understanding any divergent computer language. “It’s important to remember, there aren’t bilingual speakers of AI and human languages,” says Batra. We already don’t generally understand how complex AIs think <https://www.fastcompany.com/3064368/we-dont-always-know-what-ai-is-thinking-and-that-can-be-scary> because we can’t really see inside their thought process <https://www.fastcodesign.com/3032536/what-do-algorithms-look-like>. Adding AI-to-AI conversations to this scenario would only make that problem worse.
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> But at the same time, it feels shortsighted, doesn’t it? If we can build software that can speak to other software more efficiently, shouldn’t we use that? Couldn’t there be some benefit?
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> [Source Images: Nikiteev_Konstantin/iStock, Zozulinskyi/iStock]
> Because, again, we absolutely can lead machines to develop their own languages. Facebook has three published papers proving it. “It’s definitely possible, it’s possible that [language] can be compressed, not just to save characters, but compressed to a form that it could express a sophisticated thought,” says Batra. Machines can converse with any baseline building blocks they’re offered. That might start with human vocabulary, as with Facebook’s negotiation bots. Or it could start with numbers, or binary codes. But as machines develop meanings, these symbols become “tokens”–they’re imbued with rich meanings. As Dauphin points out, machines might not think as you or I do, but tokens allow them to exchange incredibly complex thoughts through the simplest of symbols. The way I think about it is with algebra: If A + B = C, the “A” could encapsulate almost anything. But to a computer, what “A” can mean is so much bigger than what that “A” can mean to a person, because computers have no outright limit on processing power.
> “It’s perfectly possible for a special token to mean a very complicated thought,” says Batra. “The reason why humans have this idea of decomposition, breaking ideas into simpler concepts, it’s because we have a limit to cognition.” Computers don’t need to simplify concepts. They have the raw horsepower to process them.
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> Why We Should Let Bots Gossip
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> But how could any of this technology actually benefit the world, beyond these theoretical discussions? Would our servers be able to operate more efficiently with bots speaking to one another in shorthand? Could microsecond processes, like algorithmic trading, see some reasonable increase? Chatting with Facebook, and various experts, I couldn’t get a firm answer.
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> However, as paradoxical as this might sound, we might see big gains in such software better understanding our intent. While two computers speaking their own language might be more opaque, an algorithm predisposed to learn new languages might chew through strange new data we feed it more effectively. For example, one researcher recently tried to teach a neural net to create <http://lewisandquark.tumblr.com/post/160985569682/paint-colors-designed-by-neural-network-part-2> new colors and name them. It was terrible at it, generating names like Sudden Pine and Clear Paste (that clear paste, by the way, was labeled on a light green). But then they made a simple change to the data they were feeding the machine to train it. They made everything lowercase–because lowercase and uppercase letters were confusing it. Suddenly, the color-creating AI was working, well, pretty well! And for whatever reason, it preferred, and performed better, with RGB values as opposed to other numerical color codes.
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> Why did these simple data changes matter? Basically, the researcher did a better job at speaking the computer’s language. As one coder put it to me, “Getting the data into a format that makes sense for machine learning is a huge undertaking right now and is more art than science. English is a very convoluted and complicated language and not at all amicable for machine learning.”
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> [Source Images: Nikiteev_Konstantin/iStock, Zozulinskyi/iStock]
> In other words, machines allowed to speak and generate machine languages could somewhat ironically allow us to communicate with (and even control) machines better, simply because they’d be predisposed to have a better understanding of the words we speak.
> As one insider at a major AI technology company told me: No, his company wasn’t actively interested in AIs that generated their own custom languages. But if it were, the greatest advantage he imagined was that it could conceivably allow software, apps, and services to learn to speak to each other without human intervention.