Google's Deepmind Building An AI To Create Humanity In More Agreement

"An unprecedented 80 percent of Americans, according to a recent Gallup poll, think the country is deeply divided over its most important values ahead of the November elections. The general public’s polarization now encompasses issues like immigration, health care, identity politics, transgender rights, or whether we should support Ukraine. Fly across the Atlantic and you’ll see the same thing happening in the European Union and the UK.

To try to reverse this trend, Google’s DeepMind built an AI system designed to aid people in resolving conflicts. It’s called the Habermas Machine after Jürgen Habermas, a German philosopher who argued that an agreement in a public sphere can always be reached when rational people engage in discussions as equals, with mutual respect and perfect communication.

But is DeepMind’s Nobel Prize-winning ingenuity really enough to solve our political conflicts the same way they solved chess or StarCraft or predicting protein structures? Is it even the right tool? Philosopher in the machine

One of the cornerstone ideas in Habermas’ philosophy is that the reason why people can’t agree with each other is fundamentally procedural and does not lie in the problem under discussion itself. There are no irreconcilable issues—it’s just the mechanisms we use for discussion are flawed. If we could create an ideal communication system, Habermas argued, we could work every problem out.

“Now, of course, Habermas has been dramatically criticized for this being a very exotic view of the world. But our Habermas Machine is an attempt to do exactly that. We tried to rethink how people might deliberate and use modern technology to facilitate it,” says Christopher Summerfield, a professor of cognitive science at Oxford University and DeepMind’s staff scientist who worked on the Habermas Machine.

The Habermas Machine relies on what’s called the caucus mediation principle. This is where a mediator, in this case the AI, sits through private meetings with all the discussion participants individually, takes their statements on the issue at hand, and then gets back to them with a group statement, trying to get everyone to agree with it. DeepMind’s mediating AI plays into one of the strengths of LLMs, which is the ability to briefly summarize a long body of text in a very short time. The difference here is that instead of summarizing one piece of text provided by one user, the Habermas Machine summarizes multiple texts provided by multiple users, trying to extract the shared ideas and find common ground in all of them.

But it has more tricks up its sleeve than simply processing text. At a technical level, the Habermas Machine is a system of two large language models. The first is the generative model based on the slightly fine-tuned Chinchilla, a somewhat dated LLM introduced by DeepMind back in 2022. Its job is to generate multiple candidates for a group statement based on statements submitted by the discussion participants. The second component in the Habermas Machine is a reward model that analyzes individual participants’ statements and uses them to predict how likely each individual is to agree with the candidate group statements proposed by the generative model.

Once that’s done, the candidate group statement with the highest predicted acceptance score is presented to the participants. Then, the participants write their critiques of this group statement, feed those critiques back into the system which generates updated group's statements and repeats the process. The cycle goes on till the group statement is acceptable to everyone."

Noble cause - helping people reach common ground.

Or maybe, (eventually) takeover humanity, hive mind style.