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Monday, June 27, 2016

attempting to understand Bob Dylan (just like all those big firm's Natural Language Processing programs claim they can do)







Suddenly, natural language processing (NLP) is back in the news. (Oddly this is a term I made up around 1970 because I didn’t like the previous term: computational linguistics.) I should be very happy that a field in which I spent a lot of time, having a resurgence, but I am not. People say they are working on NLP but they seem to universally misunderstand the problem. To explain the problem I will discuss the meaning of some Bob Dylan lyrics. (I chose these because IBM Watson ads chose Bob Dylan to be in their commercials and Watson summarized his work as “love fades.”)

I have selected a verse from a few of what I consider to be some of his most popular songs:


Blowin' In The Wind (1963)

Yes, and how many times must a man look up
Before he can see the sky?
Yes, and how many ears must one man have
Before he can hear people cry?
Yes, and how many deaths will it take 'til he knows
That too many people have died?


What do those lyrics mean? To me, this is a song about people’s insensitivity to the plight of others. It was written when the Viet Nam War was just beginning, and Civil Rights protestors were getting killed.

What would modern day natural language programs be able to get out of this verse? That he says “yes” a lot? That some people need more ears?

Let’s look at another verse from another song:


A Hard Rain's A-Gonna Fall (1963)

Oh, what did you meet my blue-eyed son ?
Who did you meet, my darling young one?
I met a young child beside a dead pony
I met a white man who walked a black dog
I met a young woman whose body was burning
I met a young girl, she gave me a rainbow
I met one man who was wounded in love
I met another man who was wounded in hatred
And it's a hard, it's a hard, it's a hard, it's a hard
And it's a hard rain's a-gonna fall.

What is this about? To me it seems to be about the hard knocks of life and is making the prediction that things will be getting even worse. Current NLP programs would see this as being about people, I assume, and maybe rain. Would any modern NLP program be able to understand the metaphor about hard rain or giving a gift of a rainbow? I doubt it. Yet, understanding metaphor, is critical to NLP since metaphor is everywhere. (This food tastes like crap.)

Stanford offers an NLP course (via Coursera.) This is what they say about it:

This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.

So, using a lot math you can figure out that a gift of a rainbow is about helping someone appreciate the beauty around them? I guess a Hidden Markov Model would do that for you.

Here are more lyrics from another song:

The Times They Are A-Changin’ (1964)

Come writers and critics
Who prophesize with your pen
And keep your eyes wide
The chance won't come again
And don't speak too soon
For the wheel's still in spin
And there's no tellin' who
That it's namin'
For the loser now
Will be later to win
For the times they are a-changin’.

Was Dylan speaking out against the Viet Nam War here? It seems to me he was asking the media to stop reporting on the war as a wonderful glory for the U.S. and start speaking up about its horrors. How did I figure that out? I read it, thought about it, and recalled its context. Nothing miraculous.(But, imagine any of these NLP program doing that!)  To understand you need to be thinking about what something means. Would your typical modern day NLP program think this was about prophesy, or losing?


Maggie's Farm (1965)

I ain't gonna work for Maggie's pa no more
No, I ain't gonna work for Maggie's pa no more
Well, he puts his cigar
Out in your face just for kicks
His bedroom window
It is made out of bricks
The National Guard stands around his door
Ah, I ain't gonna work for Maggie's pa no more.

This is a hard one to understand, even for a person. I saw it as a song about dropping out of the system. Here is what Wikipedia says about it:

The song, essentially a protest song against protest folk, represents Dylan's transition from a folk singer who sought authenticity in traditional song-forms and activist politics to an innovative stylist whose self-exploration made him a cultural muse for a generation.

On the other hand, this biographical context provides only one of many lenses through which to interpret the text. While some may see "Maggie's Farm" as a repudiation of the protest-song tradition associated with folk music, it can also (ironically) be seen as itself a deeply political protest song. We are told, for example, that the "National Guard" stands around the farm door, and that Maggie's mother talks of "Man and God and Law." The "farm" that Dylan sings of can in this case easily represent racism, state oppression and capitalist exploitation.

How would Microsoft’s NLP group get their programs to understand this? Here is what they say about themselves:

The Redmond-based Natural Language Processing group is focused on developing efficient algorithms to process texts and to make their information accessible to computer applications. Since text can contain information at many different granularities, from simple word or token-based representations, to rich hierarchical syntactic representations, to high-level logical representations across document collections, the group seeks to work at the right level of analysis for the application concerned.

In other words, since this isn’t a document, it is unlikely that Microsoft could do anything with “Maggie’s Farm” at all. Or, maybe my own ability to process language is off and they would get that the “farm” referred to the state’s exploitation of its own people.

Let’s try another:

Rainy Day Woman # 12 & 35 (1966)

Well, they'll stone ya when you're trying to be so good
They'll stone ya just a-like they said they would
They'll stone ya when you're tryin' to go home
Then they'll stone ya when you're there all alone
But I would not feel so all alone
Everybody must get stoned.


I have always liked this song because it says two different things at the same time. To me, it says that if you try do anything at all, someone will always be trying to stop you. It also says drugs are a good solution to dealing with all this.

Maybe Google knows how to deal with this kind of thing. Here is what Google says about their NLP work:

Natural Language Processing (NLP) research at Google focuses on algorithms that apply at scale, across languages, and across domains. Our systems are used in numerous ways across Google, impacting user experience in search, mobile, apps, ads, translate and more.
Our work spans the range of traditional NLP tasks, with general-purpose syntax and semantic algorithms underpinning more specialized systems. We are particularly interested in algorithms that scale well and can be run efficiently in a highly distributed environment.

Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number. They also label relationships between words, such as subject, object, modification, and others. We focus on efficient algorithms that leverage large amounts of unlabeled data, and recently have incorporated neural net technology.

On the semantic side, we identify entities in free text, label them with types (such as person, location, or organization), cluster mentions of those entities within and across documents (coreference resolution), and resolve the entities to the Knowledge Graph.

Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level.

So, they would probably get the second stoned reference, but the idea that people will try to prevent anything you might do for no good reason would be lost on Google.

Finally, one more song to contemplate:

The Boxer (1970)

  I'm just a poor boy
Though my story's seldom told
I have squandered my resistance
For a pocketful of numbles
Such are promises, all lies and jest
Still a man hears what he wants to hear
And disregards the rest.


I have always liked this song a great deal. But, I cannot tell you what it is about from looking at these lyrics. Here is the rest of it:

When I left my home and family
I was no more than a boy
In the company of strangers
In the quiet of the railway station
Running scared, laying low
Seeking out the poorer quarters
Where the ragged people go
Looking for the places only they would know.

Asking only workman's wages
I come looking for a job
But I get no offers
Just a come-on from the whores on Seventh Avenue
I do declare
There were times when I was so lonesome
I took some comfort there.

Then I'm laying out my winter clothes
And wishing I was gone, going home
Where the New York City winters aren't bleeding me
Leading me
Going home.

In the clearing stands a boxer
And a fighter by his trade
And he carries the reminders
Of every glove that laid him down
And cut him till he cried out
In his anger and his shame
"I am leaving, I am leaving"
But the fighter still remains.

Seeing the entire song makes it seem to me like a song about hope. But when you Google it you find out Dylan was very interested in boxing and that Paul Simon wrote this song  as a “dig against Dylan”.

Well, who knows? I don’t really care what these songs mean. But, oddly I can’t listen to them without taking meaning from them. A song resonates because you get something out of it that stays with you. It may not teach you anything. You may not learn anything from it. But you understand it as best you can nevertheless. To understand means to figure out what words mean in a context and what ideas they are trying to convey. Notice that “ideas” are never mentioned in the write ups I have quoted above. Google is not trying to figure out what ideas are being expressed but they do expect humans and computers to “merge” sometime soon (which mean people were suddenly a lot dumber.)

The hype about NLP these days is about Siri or other imitators that haven’t a clue what you just said but can respond with some words that may or may not be relevant to you.

It would be nice if all these research firms with piles of money to spend would work on the real NLP problem, which is figuring out how humans understand what is said to them and then automatically alter their memories accordingly. When we listen to someone talk, we attempt to discern what ideas they are trying to convey and then we grow in some small way from having participated in the conversation. To put this another way, NLP is really about learning and memory, as I said 35 years ago. Too bad that nowadays we only care about selling better ads to people or answering questions about where they can find a restaurant.

The times they are a changing.

Monday, June 20, 2016

I don't care about Odysseus Mr Kelly and neither did Jimmy Cagney

I like old movies. The other day I was watching a Jimmy Cagney movie, when my mind went to one my fixations, education. What is the connection between Cagney and education? Something personal.

I attended Stuyvesant High School, which was, (and is,) a school for smart science-oriented kids for which one needs to pass a test in order to get in. I should have liked Stuyvesant I suppose, but I am sorry to say I didn’t. I was reminded of one of the reasons I didn’t by watching Jimmy Cagney. Jimmy Cagney and I had the same English teacher. (Oh come Roger, you are not that old.)

His name was Mr Kelly and he had taught at Stuyvesant High School all his life. Jimmy Cagney was born in 1899, so let’s assume he went to high school in 1917. I started Stuyvesant in 1962. So Mr Kelly had to have been there for 45 years, I suppose, and indeed he was. Was Jimmy a science superstar? No. Stuyvesant was a local school for the lower east side in New York back in those days.  Mr. Kelly used to brag about what he told Jimmy or what Jimmy had said. He was his most famous student (this, of course, included many of New York’s best and brightest for many years.)

I remember this about Mr Kelly, in part, because he tended to say it a lot. What else do I remember about Mr. Kelly’s English class? I remember he used to sit in the back of the room and in a booming voice say  “Why did Odysseus…” followed by whatever the action was. When I typed Why did Odysseus into Google, these questions came up:

Why did Odysseus leave Ithaca?
Why did Odysseus go to fight in Troy?

Now, as an adult I have spent a great deal of time in Greece. I have been to Ithaca and Troy. And, I can tell you that I simply have no idea why Odysseus did anything or why Mr Kelly, or more accurately the New York City school system, wanted me to know. And, moreover I don’t care.

Now, I realize that intellectuals like to claim that knowledge about the Ancient Greeks is important to know. I am, at least in theory, an intellectual, and I still don’t care,

Now imagine how many of our students care.

Why do we insist on teaching things that kids don’t care about and have no reason to care about?

Is this a very clever way to behave? How do students who don’t care manage to get by? Is their future made more difficult by not caring about such stuff? 

I argue that it is. I got by despite not caring about this kind of thing. Most of the school population does not get by with this attitude and so, although there is no reason to know anything about Odysseus, most kids are punished severely for not knowing because they can’t pass tests and get good grades and get into college. It is time to re-think what we do in high school. Some kids can survive it. many cannot.

I am sure that someone somewhere now wants to lecture me on what I missed out on and why I should care about Odysseus. But I care about other thing, like computers and Artificial Intelligence and how the mind works, none of which were taught at Stuyvesant High School at the time, and managed to get by just fine.


Can we please let kids choose to learn what it interests them to learn? 

Monday, June 6, 2016

A little IBM Watson irony

Last year, IBM asked me if they could produce an “art visual” with a quote of mine on it. In light of IBM’s complete disregard of the implications of the quote that they selected with respect to their claims for Watson, I thought it would be fun to show the visual here:



since the print is kind of small, here is what it says:


"number crunching can only get you so far. Intelligence, artificial or otherwise, requires knowing why things happen, what emotions they stir up, and being able to predict possible consequences of actions"

Tuesday, May 31, 2016

Is IBM trying to kill off AI research by misusing the word "cognitive?"

Welcome to the Cognitive Era, says IBM’s advertising. I have been trying to figure out what that could mean. If you look inside IBM’s site you find they are proud of Cognitive Health and Cognitive Cooking to take two examples of what the any claims they make. (I was wondering what Cognitive Elder Care. might be) I have trouble knowing what these terms mean because I know what the word cognitive means, and therefore I am finding what IBM is saying incomprehensible.

Let’s start with a brief history of the word cognitive. The field of Cognitive Psychology began in the late 60’s. Until that time, oddly enough, “how the mind works” was not a subject studied in psychology. A journal with that name started about then and I published an article in that journal in 1972 in that journal’s 3rd volume.

In 1977, I helped start the field of Cognitive Science in an attempt to join together people from disciplines other than psychology, all of whom cared about how the mind works. “Cognitive” meant: human thinking. When I started a company (1981) and called it Cognitive Systems. I was trying to say that the programs we built were modelled on human thinking. Around that time, John Searle visited my lab for a week, and wrote a somewhat nasty article featuring the Chinese Room problem that I assume was meant to be an attack on me. He was attacking what was referred to then as the strong AI hypothesis that said that if a computer could do smart things, then it was thinking. This was never my position, but Searle talked more to my students during that week then he did to me, so I guess he thought I believed in the Strong AI hypothesis. I do not. 

I think that the human mind does many things and I want to know how it does them and I want to build computer programs that operate in the same way. I am interested more in people than in machines but I think that if we copied people on a computer we could have some machines that behave intelligently. I don’t actually think the machines themselves would know what they were doing or actually would be intelligent. I used to my AI classes that I was a “fleshist.” If a person said something I would think that that person was thinking, but if a machine said the same thing, I wouldn’t think that. Others disagree with me on this, but I have never been an advocate of the strong AI hypothesis.

Why am I saying all this now? I am trying to understand what IBM could possibly mean when it uses the word cognitive and announces that we are now in the “cognitive era”. Do they think they Watson is actually thinking? I certainly hope not.

Do they think that Watson is imitating how people think in some way? I can’t believe that they think that either. No one has ever proposed that machines that can search millions of pages of text are smart. Matching key words, no matter how well you do it, is not even a human capability much less one that underlies the human ability to think. 


When AI started, they were some major people associated with it, whom, of course I knew well. Marvin Minsky as interested in people first, machines second. Allan Newell was interested in people first and machines second, Herb Simon wanted to copy chess grand masters,   rather than build chess playing machines that won by being fast at search. Even John McCarthy, with whom I never agreed about anything, was trying to copy how the mind worked. I once asked him “how can you believe that the mind happens to work using a logic system invented in the 19th century?” (McCarthy thought all knowledge representation could be done using Predicate Calculus.)

That phrase, knowledge representation, is the right thing to think about. It is the cornerstone of what AI was always all about. We need to represent knowledge in some way before we can effectively use it in a computer program. AI people have always worried about knowledge representation.

But this idea seems have disappeared in recent AI work and does not exist at all in Watson. Now AI people worry about how many pages of text they can search and how match key words and phrases. (Take a look at what IBM says that Watson does in natural language processing and you will only hear about phrase matching.)

Back to Cognitive Health. I am very interested in getting computers to be able to be helpful in health care. Do I think that they can be helpful by searching millions of pages of text? Probably. 

But there are real questions about what can be done to help people using AI. I, for one, have many questions I would like to ask about drugs and health issues, as I age, and I find that asking a doctor isn't always helpful because not all doctors the answers, and asking a computer is sometimes helpful if it can match what you asked to some text that it happens to have. As I write this I have a question about a drug I am taking that no text I can find can actually  answer. I have been able to find an expert at a major hospital to ask this question and he told me that his my father was taking it, so he certainly thought it was safe. But my questions was more subtle than that, in part because it is a new drug and often little is really known about new (and highly promoted) drugs.  I really have no one to ask


Would I like a computer to be able to answer these questions? Of course. That is what AI was supposed to be all about. We always wanted to get computers to be really helpful using everyday English backed by a great deal of knowledge of a given domain.  But if IBM keeps claiming it has solved Cognitive Health, I am wondering how many people who might want to think up about new ways to represent  knowledge about how the body works and how drugs work, might stop working on what they care about and simply assume that IBM owns the turf and that there is no reason to try and compete with them. IBM is not trying to solve the problem I care about, which is getting access to knowledge that is easily comprehensible about problems everyday people actually have. A lot of that knowledge isn’t in any computer in the first place or is in academic journals, so all the key word search in  the world really will not help the average person much.

As for Cognitive Cooking, one of my PhD students  in the 80’s wrote a program called CHEF that reasoned from prior cases in order to invent new recipes using on the ingredients you happened to have on hand.  I am sure CHEF was better than the program that IBM is selling because it was based on case-based reasoning and not on matching key words.


IBM really has to stop saying Cognitive about  everything it is trying to sell. It is hurting our future because it is very likely to serve as a deterrent to more research on knowledge representation, real natural language processing and case based reasoning. These are important problems. They have not been solved and IBM needs to stop asserting that they are by claiming Watson to be “cognitive" when it actually does no thinking at all.

Sunday, May 15, 2016

should we fear AI or just fear the people who write about AI?

Ashok Goel, a professor at Georgia Tech, made the news this week with the revelation that one of the TA’s that he used in his AI course was actually an ”AI.”  Now, I have no reason to believe that Goel was trying to do something wrongheaded. I think he was just playing around. But the media love AI stories these days and have yet again led the public on a very wrong headed journey about what AI is and what it can be.

Could a chatbot be an effective TA? It could certainly beat a rather disinterested TA. There are many real TA’s who are not really very good or very interested in what they are being made to do. But that is not an important question. The right question is whether we could make a real AI program that could perform this function as well as a very good person does? The news media want us to believe we are on our way to that. I have a different point of view. It is not that we can’t do it. It is that we aren’t actually trying to do that yet. To explain this, I need to provide some history.

The first of what are now called chatbots was called ELIZA and was written by Joseph Weizenbaum. This is well known. What I will now tell you is not so well known. Weizenbaum was a friend of mine. Ken Colby was a friend of mine as well. I know some things about all this that others may not know.

Colby and Weizenbaum were friends, at least they started out as friends. Colby was a psychiatrist who became interested in whether computers could function as therapists. I was hired by Colby (in 1968) to help him at the point where he was building PARRY. PARRY was supposed to be a paranoid patient. Colby was interested in showing that he could fool therapists who would not know if they were talking with a real person or a computer. PARRY supposedly lived in a locked ward of a hospital and it was Colby’s idea to build an effective model of paranoia and prove it was effective by fooling therapists. In essence, he was trying to pass the Turing test with PARRY.

He and Weizenbaum talked about these issues for years and at some point had a falling out. Weizenbaum wanted to show up Colby by building program that could fool people at MIT into thinking it was a Rogerian therapist. He chose Rogerian  therapists, in part, to make fun of Colby, but also because Rogerians don’t usually say that much in response to what their patients tell them.

An example of ELIZA that I found on the web is:

How do you do. Please state your problem.
>> I spend too much time with computers.
Why do you mention computers ?
>> You are one.
Does it please you to believe I am one ?
>> Yes.
You seem to be quite positive.
>> I suppose I am.
How long have you been ?


Weizenbaum found that people wound up pouring their hearts out to ELIZA and he was very upset by this. He wrote a book attacking AI, called Computer Power and Human Reason, to explain why he thought AI would never work. The irony is, of course, that Goel’s program did no more than what ELIZA did in the 60’s (possibly even less), but it is now worthy of articles in the Wall St Journal and the Washington Post. Key word analysis that enables responses previously written by people to be found and printed out, is not AI. Weizenbaum didn’t think he was building a Rogerian therapist (or doing AI) really. He was having some fun. Colby was trying to model a paranoid because he was interested in whether he could do it. He did not think he was building a real (AI) paranoid. And, I assume Goel does not think he is building a real AI TA. But the press thinks that, and the general public will soon think that, if it keeps publishing articles about things like this.

This technology is over 50 years old folks. Google uses key words, as does Facebook, as does every chatbot. There is nothing new going on. But we all laughed at ELIZA. Now this same stuff is being taken seriously.

What is the real problem? People do behave in any way that is anything remotely like what these “AI’s” do. If you tell me about a personal problem you have, I do not respond by finding a sentence in my memory that matches something you said and then saying that without knowing what it means. I think about your problem. I think about whether I have any reasonable advice to give you. Or, I ask you more questions in order to better advise you. None of this depends upon key word and canned sentences. When I do speak, I create a sentence that is very likely a sentence I have never uttered before. I am having new ideas and expressing them to you. You say your views back to me, and a conversation begins. What we are doing is exchanging thoughts, hypotheses and solutions. We are not doing key word matching.

It may be that you can make a computer that seems paranoid. Colby had a theory of paranoia which revolved around “flare” concepts like mafia, or gambling, or horses. (See his book Artificial Paranoia.) He was trying to understand both psychiatry and paranoia using an AI modeling perspective.

The artificial TA is not an attempt to understand TA’s, I assume. But, let’s think about the idea that we might actually like to build an AI TA. What would we have to do in order to build one? We would first want to see what good teachers do when presented with problem students are having. The Georgia Tech program apparently was focused on answering student questions about due dates or assignments. That probably is what TA’s actually do which makes the AI TA question a very uninteresting question. Of course, a TA can be simulated if the TA’s job is basically robotic in the first place.

But, what about creating a real AI mentor? How would we build such a thing? We would first need to study what kinds of help students seek. Then, we would have to understand how to conduct a conversation. This is not unlike the therapeutic conversation where we try to find out what the student’s actual problem was. What was the student failing to understand? When we try to help the student we would have to have a model of how effective our help was being. Does the student seem to understand something that he or she didn't get a minute ago?   A real mentor would be thinking about a better way to express his advice. More simply? More technically? A real mentor would be trying to understand if simply telling answers to the student made the best sense or whether a more Socratic dialogue made better sense. And a real TA (who cared) would be able to conduct that Socratic dialogue and improve over time. Any good AI TA would not be trying to fake a Rogerian dialogue but would be thinking how to figure out what the student was trying to learn and thinking about better ways to explain or to counsel the student.

Is this possible? Sure. We stopped working on this kind of  thing because of the AI winter than followed from the exaggerated claims being made about what expert systems could do in1984. 

We are in danger of AI disappearing again from overblown publicity about simplistic programs.

To put this all in better perspective, I want to examine a little of what Weizenbaum was writing in 1976:

He attacked me (but started off nicely anyhow):


Roger C. Schank, an exceptionally brilliant young representative of the modern school, bases his theory on the central idea that every natural language utterance is a manifestation, an encoding of an underlying conceptual structure. Understanding an utterance means encoding it into one’s own conceptual structure.

So far so good, he said nice things and represented me accurately. But then….

Schank does not believe that an individual’s entire base of conceptions can be explicitly extricated from him. He believes only that there exists such a belief structure within each of us and that if it could be explicated, it could in principle be respond by his formalism….

There are two questions that must ultimately be confronted. First, are the conceptual bases that under linguistic understanding entirely formalizable even in principle as Schank suggests and as most workers in AI believe? Second, are there ideas that, as I suggested, “no machines will ever understand because they relate to objectives that are inappropriate for machines?” ……

It may be possible, following Schank’s procedures to construct a conceptual structure that corresponds to the meaning of the sentence, “will you come to dinner with me this evening?” But it is hard to see — and I know this is not an impossibility argument, how Schank-like schemes could possibly understand that same sentence to mean a shy young mans’ desperate longing for love. 

I quoted parts of what Weizenbaum had to say because these were the kinds of questions people were thinking about in 1976 in AI. Weizenbaum eventually became anti-AI, but I always like his “dinner” question. It is very right-headed and it is the least we can ask of any AI-based TA or mentor. Can we build a program that understands what the student is feeling and what the student’s real needs are, so that we can give good advice? Good teachers do that. Why should future online teaching be worse than what good teaching is like today without computers or AI?

Do we actually have to do all this in order to build AI?

Could we simply build an automated TA/ mentor that did not do all that but still performed well enough to be useful?

These are important questions. Maybe Goel’s program did perform well enough to consider using it in MOOCs where there are thousands of students. I am not fundamentally interested in that question however.

Here is what I am interested in. Can we stop causing people to so misunderstand AI that every ELIZA-like program makes headlines and causes people to believe that the problems we were discussing in the 70’s have been solved?

The fundamental AI problems have not been solved because the money to work on them dried up in mid 80s. There are businesses and venture capitalists today who think they are investing in AI but really they are investing in something else.  They are investing in superficial programs that really are ELIZA on steroids. Would it be too much to ask people to think about what people do when they engage in a  conversation and build computer programs that could function  as an effective model of human behavior? I hope we can get people with money to start investing in the real AI problem again. Until we do, I will be finding myself on the side of Weizenbaum when when he was being critical of his user’s  reactions to ELIZA (for good reason.) We should start working on real AI or stop saying that we that are. There is nothing to be afraid of about AI, since hardly anyone is really working on it any more. Most “AI people” are just playing around with ELIZA again. It is sad really.

Weizenbaum and Colby were brilliant men. They were both asking fundamental questions about the nature of mind and the nature of what we can and cannot replicate on a computer. These are important questions. But, today, with IBM promoting something that is not that much more than ELIZA people are believing every word of it. We are in a situation where machine learning is not about learning at all, but about massive matching capabilities to produce canned responses.  The real questions are the same as ever. What does it mean to have a mind? How does intelligent behavior work? What is involved in constructing an answer to a question? What is involved in comprehending a sentence? How does human memory work? How can we produce a memory on a computer that changes what it thinks with every interaction and gets reminded of something it wants to think more about? How can we get a  computer to do what I am doing now — thinking, wondering, remembering, and composing?


Those are AI questions, not questions. They are not questions about how we can fool people.

Monday, May 9, 2016

Boredom spurs creativity; are computers or mobile phone owners ever bored?

Boredom matters. We need it. But, two sets of supposedly thinking entities are never bored: “smart” (deep learning) computers, and people who are attached to their phones (which is beginning to look like nearly everybody.)

A friend’s teenage son (who was coming over for some advice) rang my doorbell the other day. In the time it took me to open the door, he was already looking at his phone. When I am on the elevator in my New York apartment I notice that literally everyone is look at their phones during the ride. Sherry Turkel has pointed out that this behavior is killing conversation and she is right. But it is also killing something even more important: creativity.

Creativity depends upon many things but a key one is boredom. When you are bored your mind wanders. You do this weird thing called “thinking.”

I have begun thinking more about AI in recent months because of the incessant nonsense being written about what computers can or might do. So. let me ask a simple question. Is Watson ever bored? Do these “deep learning” machines get bored? It seems obvious that they don’t. Why not? Because, in order to be bored you have to have something you like doing, a goal you are pursuing, a problem you are interested in, or wondering about and are in some way prevented from solving. 

Wittgenstein said that all creative thinking took place  in the “three B’s”: bed, bath and bus. What he meant was, that that was the only time time there was no one else talking or distracting him and with nothing much to do, his mind wandered and interesting thoughts occurred.

When could this possibly happen in the life of young people who cannot stop looking at their phones? What is there to be bored with or bored about? If you are bored with a facebook post you just go to the next one. If you are bored with whats on TV each change the channel. If you have nothing to do you surf the web. No one sits quietly and thinks any more.

I find this very scary for two reasons. Our educational system is in such bad shape in party because we don’t allow boredom which means we really do not encourage creativity. There are answers to be memorize, books to be read, and test to be  taken. We aren’t actually expected have original thoughts in high school ever. (Unless a kid happens to have a really good teacher and more freedom than is typically allowed. 

Now computers. The very idea that AI is progressing is patently absurd. What would it mean to have a smart computer that didn’t on occasion have an original idea about something? How could a computer be smart if it didn't worry about things from time to time. Americans are busy worrying about a Trump-Clinton election. We talk about it. We wonder about it. That worrying looks like thinking. What computer would worry about this? How could a computer possibly worry about this? Does Watson worry?

Now, of course that is the real AI question and the kind I used to work on when AI was funded by people who thought AI was something other than “deep learning.” I asked myself  and my students how we could get a computer to have creative thoughts. One answer is that a computer would have be trying to figure things out in some way, be considering hypotheses about whatever it is trying to explain, then imagining alternative explanations, and then trying to invent one’s own. This is what creativity looks like.

Could a computer do that? Of course it could in principle, but it wouldn’t be the so-called AI machines we have now which are very good at counting and matching and searching., That kind of AI depends on being annoyed by a state of affairs and thinking you should be able to come up with some better answer and then putting yourself in the equivalent of a bathtub or a bed or any place where it is quiet and there are no distractions so you can let your mind wander.

Computers will not become creative (or bored) any time soon unless those who fund AI change their perspective.


What really bothers me is that people won’t be creative  either. Young people’s first thought these days is to post a picture of what themselves or what they are are looking at, rather than to think about the world around them.

Tuesday, May 3, 2016

AI is nowhere near working; let's think about what people can do that AI can't

When I started working in AI in the 1960’s, it wasn’t really one field, just a set of people trying to get computers to do some interesting things that we knew people were capable of doing. These days, unfortunately, AI seems to mean “deep learning” whatever that is, and stuff IBM talks about that uses the word “cognitive.”  

I have recently been thinking about some of the aspects of AI that I did not work on. (I worked on natural language processing, memory, and learning.)  I think there are things worth discussing about the other areas of AI that might shed light on what is really going in today’s so-called AI.

Let’s start with Face Recognition. It is clear that face recognition technology is pretty good. Facebook can tell when your picture has been posted by someone and can add your name to it. I am sure there are all kinds of surveillance technologies that make use of face recognition as well.

But, there is an aspect of face recognition that people naturally do, but computers cannot come close to doing today. I don’t mean to be political here, but my best example is recognizing Ted Cruz’s face. I can recognize him, but every time I see a man who is angry, mean, and just a little Satanic looking. Commentators say these things all the time, and I am not trying to comment on that; rather I am trying ask the question: what is it that we see when we look at someone and immediately distrust them and are slightly afraid of them?

To put this another way, when you are walking down the street and someone scary walks by, what is it that you see in his face that makes him seem scary?   I  have been running some experiments about how people react to talking head videos that we’ve captured of experts telling stories about their expertise. Every time someone looks at one of our videos they have an instant reaction to the human qualities of the person as well as to the story the person is telling. They like or dislike people in about ten seconds. What is it that they are seeing?

This is an interesting topic, but my point is about AI. AI is no where ready, no matter how well it does at face recognition, to tell us,  “I find this guy scary” or “distrustful" or “he seems to be lying”, even though people can do this all the time without conscious thought.

What does this tell us? It tells us that AI has a long way to go before it can do stuff that nearly any human can easily do.   Actually, any dog can do this. They too have instant reactions to a person. What are they seeing? This is the AI question. My guess is that Facebook even with its 100’s of AI people is not working on this problem and moreover, it doesn’t care. But it is a very important aspect of cognition. (Sorry, IBM, you don’t actually own that word.) Facebook is only working on the “you can count the pixels and pattern match” part of face recognition. When we feel attracted to someone, or we want to avoid them, we are using our innate ability to do a more subtle kind of face recognition.

I have this same problem with speech synthesis and speech recognition. I was riding in my wife’s car the other day and the navigation system  she was using told her to turn on “puggah” boulevard. We were in an area we know and both laughed out loud. The street is called PGA Blvd, which is the acronym for the Professional Golf Association. The program never heard about acronyms I guess. Later it told us to get on the ramp for W Palm Beach. Now a reader would think I am abbreviating west with the W, but I am not. The navigation system actually said “W.” My reaction was that this device is really stupid. How hard would it be to make an intelligent navigator with respect to speech synthesis? Well, apparently too hard for the company that made this one. (It would also be too hard for it to tell me about a new restaurant that I was passing and might want to check out, which is the kind of AI that I am interested in.) That is AI too, but it is not “deep learning,” so no one is funding it.

Which leads me to what I really wanted to talk about here, speech recognition. Someone said to me the other day that AI has made real strides in speech recognition. I laughed. Now, I realize people talk to Siri and other devices. And sometimes Siri “knows” what you are saying in the sense that it can find a response. As a way of pointing the real AI problem out to my friend, the next thing I said was:   “szeretlek nudunuca”. To which he responded “huh?” I said it again. He said he didn’t understand. I asked if he could tell me one word that I said. He said “no.” I said “can you even report a part of what I said?’ He said “no.” It all sounded like gibberish to him. Of course it did. I was talking Hungarian. When someone speaks an unfamiliar language you cannot hear where the word breaks are, and you cannot even decipher the sounds. This is because human speech recognition involves having heard everything before and understanding the context in which the spoken words said belong.  

It is difficult for people to understand a sentence that is out of context.  What is a normal response to a completely unexpected sentence? People generally have to be listening for something in order to understand it. Understanding involves guessing about what someone is likely to say. Those guesses are made on the basis our knowledge of each other and of the possible things we are or might talking about. To do that right in AI, we need to determine intentions and motivations and we need to have a model of the person we are talking to, including what they know and what their interests are.

The other day someone who I play softball with (who often asks me questions) asked me “what is Zion?” I had to ask him to explain what he was actually asking about. I heard the words, but had no idea what he was trying to find out.  After a bunch of sentences from him I got what the question was about. I didn't have any trouble with the words, but I had absolutely no idea what he wanted to learn from me. Siri and the others would not be able to have that conversation with him because there is no AI there. Apple, Google and the rest don’t care about that. It is the pretense of AI that seems to interest them.


We are very far from a computer that can do the things I’ve just discussed. AI will be hyped as much as IBM’s marketers and others choose to do it in an effort to make money. As for me, I would prefer that they actually worked on AI instead of trying to convince everyone that AI is already here.