Algorithms Aren’t Always The Answer

On November 17 in his weekly Monday Note, Jean-Louis Gassée wrote: “App Store Curation: An Open Letter To Tim Cook“. He summed up his own letter best when he said:

With one million titles and no human guides, the Apple App Store has become incomprehensible for mere mortals. A simple solution exists: curation by humans instead of algorithms.

Is he right?

Where Have We Heard This Before?

When I read Monsieur Gassée’s article, I was immediately reminded of Beats. When Apple acquired Beats, Jimmy Iovine also opined on the importance of human curation, this time in regards to music.

There is a sea of music, an ocean of music and absolutely no curation for it. Your friends can’t curate for you.

(P)eople need navigation through all this music and somebody to help curate what song comes next.

Right now, somebody’s giving you 12 million songs, and you give them your credit card, and they tell you ‘good luck.’ … I’m going to offer you a guide … it’s going to be a trusted voice, and it’s going to be really good. ~ Jimmy Iovine

Algorithms Aren’t Always The Answer

What’s going on here? I thought this was the age of algorithms. Google was going to allow us to search the world’s information and give us driverless cars. Pandora was going to use the Music Genome Project to give us the music we loved. And eHarmony was going to match us with our soul mate. Yet now we’re retreating to human curation? What’s gone wrong?

Stereotypes And Subjectivity

A cowboy and a biker are on death row and are to be executed on the same day. The day comes, and they are brought to the gas chamber.
 The warden asks the cowboy if he has a last request, to which the cowboy replies:

“Ah shore do, warden. Ah’d be mighty grateful if ’n yoo’d play ‘Achy Breaky Heart’ fur me bahfore ah hafta go.”

“Sure enough, cowboy, we can do that,” says the warden. He turns to the biker, “And you, biker, what’s your last request?”

“Kill me first.”

Funny right? Only it’s a stereotype, not a reliable rule. In reality, The Biker may have liked ‘Achy Breaky Heart’ and The Cowboy may have preferred the gas chamber to having to hear that song even one more time. Machine Learning is great at learning rules. But human beings don’t use algorithms. We use common sense. And there’s nothing harder to replicate than common sense.

common sense

Machine Learning

Turns out we need to distinguish between Machine Learning and Common Sense. In his book “Everything Is Obvious,” Duncan J. Watts explains why computers use Machine Learning instead of common sense:

(Machine learning) is a statistical model of data rather than thought processes. This approach…was far less intuitive than the original cognitive approach, but it has proved to be much more productive, leading to all kinds of impressive breakthroughs, from the almost magical ability of search engines to complete queries as you type them to building autonomous robot cars, and even a computer that can play Jeopardy. ((Excerpt From: Duncan J. Watts. “Everything Is Obvious.” iBooks. https://itun.es/us/jw4lz.l))

Common Sense

Machine Learning is great. It makes search engines like Google work and it may someday give us driverless cars. But Machine Learning can’t curate App Stores, Music Stores and dating sites because it measures things differently than we do. Which reminds me of another joke:

An attorney, an accountant and a statistician went deer hunting. The attorney loosed his arrow at the deer but it landed five feet beyond the deer. The accountant loosed his arrow at the deer but it landed five feet short. The statistician then began to wildly celebrate yelling: “We hit it! We hit it!”

The point? The statistician was obviously using the wrong method to determine what constituted hitting the deer. Computer algorithms use the wrong method too — not because they’re stupid but because they’re smart and because the rules we use to guide our preferences are not subject to smart, logical constructs.

(V)irtually every everyday task is difficult for essentially the same reason—that the list of potentially relevant facts and rules is staggeringly long. Nor does it help that most of this list can be safely ignored most of the time—because it’s generally impossible to know in advance which things can be ignored and which cannot. So in practice, the researchers found that they had to wildly overprogram their creations in order to perform even the most trivial tasks.

(C)ommonsense knowledge has proven so hard to replicate in computers—because, in contrast with theoretical knowledge, it requires a relatively large number of rules to deal with even a small number of special cases.

[pullquote]For computer to understand us, you would have to teach it everything about the world.[/pullquote]

Attempts to formalize common sense knowledge have all encountered versions of this problem—that, in order to teach a robot to imitate even a limited range of human behavior, you would have to, in a sense, teach it everything about the world.

Excerpts From: Duncan J. Watts. “Everything Is Obvious.” iBooks. https://itun.es/us/jw4lz.l

Conclusion

Robert A. Heinlein once said:

Don’t explain computers to laymen. Simpler to explain sex to a virgin.

It turns out, trying to explain humans to a computer is even more difficult. And not half as much fun.

For a computer to understand my music, my dating, or even my app preferences, it would need to know almost everything there is to know about me. Even then it wouldn’t be able to apply the same mishmash of rules to the problem as I would.

Human curation seems like a step back to me. But when it comes to providing humans with what they prefer, that step back may end up being a huge leap forward.

Real Intelligence

The age of intelligent machines is upon us. How did we get here? Who led the way?

You probably know that machines beat humans at chess, and that IBM’s Watson beat humans in the television game show Jeopardy. If you live in Silicon Valley, chances are that you’ve seen Google’s autonomous vehicles cruising local streets. But AI also plays a major role in routing commercial aircraft and truck fleets, in planning battles and supply routes in Iraq and Afghanistan, and in thousands of other applications that now touch our daily lives.

The IEEE Computer Society’s IEEE Intelligent Systems magazine just established the IEEE Intelligent Systems Hall of Fame, and named 10 inaugural members. In alphabetical order, they are:

Berners-Lee

Tim Berners-Lee, the 3Com Founders Professor of Engineering and head of the Decentralized Information Group at the Massachusetts Institute of Technology; a professor in the Electronics and Computer Science Department at the University of Southampton; director of the World Wide Web Consortium; and a founding director of the Web Science Trust;

Chomsky

• Noam Chomsky, linguist, philosopher, cognitive scientist, and Institute Professor and Professor (Emeritus) in MIT’s Linguistics and Philosophy Department; noted for his theory of generative grammar that revolutionized the scientific study of language;

Engelbart

• Douglas Engelbart, head of a Stanford Research Institute group that developed the first computer mouse, hypertext, networked computers, and precursors to GUIs;

Feigenbaum

• Edward Albert Feigenbaum, a Stanford University professor emeritus of computer science and cofounder of applied AI startup firms IntelliCorp, Teknowledge, and Design Power;

McCarthy

• John McCarthy, a Stanford and MIT professor who proposed Lisp, time-sharing computer systems, and program correctness proofs; credited with coining the term “AI”;

Minsky

• Marvin Minsky, Toshiba Professor of Media Arts and Sciences and Professor of Electrical Engineering and Computer Science at MIT, who developed the Society of Mind theory with Seymour Papert and many other advances in cognitive theory;

Nilsson

• Nils J. Nilsson, professor of engineering emeritus at Stanford, who while at SRI International developed statistical and neural-network approaches to pattern recognition;

Pearl

• Judea Pearl, a professor of computer science and statistics at University of California Los Angeles and director of its Cognitive Systems Laboratory; best known for introducing the probabilistic approach to AI and developing Bayesian networks as inference tools;

Reddy

• Raj Reddy, the Mozah Bint Nasser University Professor of Computer Science and Robotics in the School of Computer Science at Carnegie Mellon University; and

Zadeh

• Lotfi Zadeh, a University of California, Berkeley computer science professor known for his work on soft computing, fuzzy logic, and neural-net theory.

 

To put things in perspective for those of us with more pedestrian interests, here are the first 10 inductees of the Baseball Hall of Fame: Ty Cobb, Babe Ruth, Honus Wagner, Christy Mathewson, Walter Johnson, Nap Lajoie, Tris Speaker, Cy Young, Grover Cleveland Alexander, and George Sisler.

UPDATE, Aug. 29 — After watching this video of a conversation between two Cleverbot avatars, we may not be as close to the age of smart machines as I thought.