A machine can now not only beat you at chess, it can also outperform you in debate. Last week, in a public debate in San Francisco, a software program called Project Debater beat its human opponents, including Noa Ovadia, Israel’s former national debating champion.

Brilliant though it is, Project Debater has some weaknesses. It takes sentences from its library of documents and prebuilt arguments and strings them together. This can lead to the kinds of errors no human would make. Such wrinkles will no doubt be ironed out, yet they also point to a fundamental problem. As Kristian Hammond, professor of electrical engineering and computer science at Northwestern University, put it: “There’s never a stage at which the system knows what it’s talking about.”

What Hammond is referring to is the question of meaning, and meaning is central to what distinguishes the least intelligent of humans from the most intelligent of machines. A computer works with symbols. Its program specifies a set of rules to transform one string of symbols into another. But it does not specify what those symbols mean. Indeed, to a computer, meaning is irrelevant. Humans, in thinking, talking, reading and writing, also work with symbols. But for humans, meaning is everything. When we communicate, we communicate meaning. What matters is not just the outside of a string of symbols, but the inside too, not just how they are arranged but what they mean.

Meaning emerges through a process of social interaction, not of computation, interaction that shapes the content of the symbols in our heads. The rules that assign meaning lie not just inside our heads, but also outside, in society, in social memory, social conventions and social relations. It is this that distinguishes humans from machines. And that’s why, however astonishing Project Debater may seem, the tradition that began with Socrates and Confucius will not end with artificial intelligence.

When Elinor Lobel was 16, a “smart” insulin (胰岛素) pump was attached to her body. Powered by AI, it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is one of the new ways that data and AI can help improve lives.

Books that criticize the dark side of data are plentiful. They generally suggest there is much more to fear than fete in the algorithmic(算法的)age.

But the intellectual tide may be turning. One of the most persuasive supporters of a more balanced view is Elinor Lobel’s mother, Orly, a law professor. In The Equality Machine she acknowledges AI’s capacity to produce harmful results. But she shows how, in the right hands, it can also be used to fight inequality and discrimination.

A principle of privacy rules is “minimization”: collect and keep as little information as possible, especially in areas such as race and gender. Ms Lobel flips the script, showing how in hiring, pay and the legal system, knowing such characteristics leads to fairer outcomes.

Ms Lobel’s call to use more, not less, personal information challenges data-privacy orthodoxy(正统观念). But she insists that “tracking differences is key to detecting unfairness.” She advocates g loosening of privacy rules to provide more transparency(透明)over algorithmic decisions.

The problems with algorithmic formulae(公式) are tackled in depth in Escape from Model Land by Erica Thompson of the School of Economics. These statistical models are the backbone of big data and AL. Yet a perfect model will always be beyond reach. “All models are wrong,” runs a wise saying. “Some are useful.”

Ms Thompson focuses on a challenge she calls the Hawkmoth Effect. In the better known Butterfly Effect, a serviceable model, Vin the prediction of climate change, becomes less reliable over time because of the complexity of what it is simulating(模拟), or because of inaccuracies in the original data. In the Hawkmoth Effect, by contrast, the model itself is flawed; it might fail to take full account of the interplay between humidity, wind and temperature.

The author calls on data geeks to improve their solutions to real-world issues, not merely refine their formulae—in other words, to escape from model land. “We do not need to have the best possible answer,” she writes, “only a reasonable one.”

Both these books exhibit a healthy realism about data, algorithms and their limitations. Both recognize that making progress involves accepting limitations, whether in law or coding. As Ms Lobel puts it: “It’s always better to light a candle than to curse the darkness.”

Quantum ( 量子 ) computers have been on my mind a lot lately. A friend has been sending me articles on how quantum computers might help solve some of the biggest challenges we face as humans. I’ve also had exchanges with two quantum-computing experts. One is computer scientist Chris Johnson who I see as someone who helps keep the field honest. The other is physicist Philip Taylor.

For decades, quantum computing has been little more than a laboratory curiosity. Now, big tech companies have invested in quantum computing, as have many smaller ones. According to Business Weekly, quantum machines could help us “cure cancer, and even take steps to turn climate change in the opposite direction.” This is the sort of hype ( 炒作 ) that annoys Johnson. He worries that researchers are making promises they can’t keep. “What’s new,” Johnson wrote, “is that millions of dollars are now potentially available to quantum computing researchers.”

As quantum computing attracts more attention and funding, researchers may mislead investors, journalists, the public and, worst of all, themselves about their work’s potential. If researchers can’t keep their promises, excitement might give way to doubt, disappointment and anger, Johnson warns. Lots of other technologies have gone through stages of excitement. But something about quantum computing makes it especially prone to hype, Johnson suggests, perhaps because “‘quantum’ stands for something cool you shouldn’t be able to understand.” And that brings me back to Taylor, who suggested that I read his book Q for Quantum.

After I read the book, Taylor patiently answered my questions about it. He also answered my questions about PyQuantum, the firm he co-founded in 2016. Taylor shares Johnson’s concerns about hype, but he says those concerns do not apply to PyQuantum.

The company, he says, is closer than any other firm “by a very large margin ( 幅度 )” to building a “useful” quantum computer, one that “solves an impactful problem that we would not have been able to solve otherwise.” He adds, “People will naturally discount my opinions, but I have spent a lot of time quantitatively comparing what we are doing with others.”

Could PyQuantum really be leading all the competition “by a wide margin”, as Taylor claims? I don’t know. I’m certainly not going to advise my friend or anyone else to invest in quantum computers. But I trust Taylor, just as I trust Johnson.