Saturday, October 13, 2018

Overview of AI

The gist of this post is a recap, somewhat, that is being done briefly. I have been working in Quora, for the most part, but intend to get back to this blog.

The recent CACM of the ACM published an overview of AI by the head of CompSci at UCLA. His view resonated real strongly with me. So, I want to provide a quick look at some of the key items.

First, the article: Human-level intelligence or animal-like abilities?  Author: Adnan Darwiche.

Adnan starts by summarizing some of the recent successes (if you can call them that) that got so much attention in the press. The computer (AI driven) winning in Go. The computer learning how to play a game and beating expert humans. The list goes on. And, the basis is the neural net approach that is not new. Marvin Minsky wrote of these one-half a century ago.

But, too, people got upset. Some are believing in the coming singularity. I say, worry about the current crapularity. Oh, the robots are coming. Just like there was a warning 200 years ago about the invasion of the Brits who wanted to spank the free Americans.

Adnan mentions some of the mainstream areas of AI that have been ignored in the rush after the numeric paradise that is being given us by machine learning. He mentions "knowledge representation, symbolic reasoning, and planning" in short. Adnan was there when the AI winter came about from the mania of the '80s. He was student.

I would tell him that knowledge based engineering came out of that thrust and went fine for decades doing great things. I can (and will) point to writings about this. However, let's continue with Adnan's look from his lofty position.

He reminds us that AI had a reasoning approach, from the beginning. The recent work? No, it's numeric processing, pure and simple. Now, he mentions that particular neural nets (NNs, of which there are many variety) are doing some type of representation of a model. Given that, we can understand these supposed black-boxes with the proper approach. He uses two concepts: model-based and function-based. The former was (is) a huge part of AI, in general. You know, the latter was seen in engineering, too.

So, what we are talking is that the tried-and-true of science and engineering somehow got lost in the rush after the search for the magic function (oh yes, there ain't none). The main argument for the NNs is the advancements that we see in "sophisticated statistical and optimization techniques" that do seem to have amazing results.

But, as Adnan said, it seems to be that we're chasing what animals already do well. And, not doing as well as they. The article provides sufficient examples. But, consider this: no animal can make a computer.

Too, there seems to be a huge divide between the true believers here (chasing after money, power, fame, what have you) and those more scientifically oriented. Besides, the whole of the atmosphere has been polluted with out-of-control hype. Adnan uses 'bullied by success' (let's talk some type of innoculation, shall we?).

What is real here? Well, Adnan has the answer. He says that we're dealing with 'function engineering' where one goal is to approximate cognitive functions. There are more. In fact, Adnan's position is strongly influenced by Judea Pearl who recently wrote on the subject (The Book of Why: The New Science of Cause and Effect).

To me, Adnan is talking the old middle-out technique that has been the reality of complicated systems all along. We could think of the NNs as supporting bottom up. But, be aware that there are more areas of technology awaiting attention. The top-down would be somewhat assisted by the traditional approaches. Albeit, we are really talking the human expert rising to the challenge. Per usual.

However, NNs might just be universal as some have claimed from the beginning (not Marvin as he was a critic to his death) in some simulation sense. Guess what? Physics and reality might be closely mimicked by our computationalist's approaches, however being is there irregardless. The 'crap' from extensive computer work (and there are whole bunches to denote) grows. Unfortunately, that whole bit of accumulating residue gets no (or little) attention.

Throughout the paper, Adnan quotes anonymous sources. There is good reason to not get into the cross-hairs at this time. What is really at stake? Well, truth engineering has been dealing with these issues from its inception (my work of the past two decades). So, our pace has (might have) been that of a snail, yet it's tied into other than gaming. Oh yes, we have a whole lot of work to do in reviewing the just past decade and one-half.

Remarks: Modified: 10/16/2018

10/16/2018 -- As Adnan suggested, since there is little scientific thinking going on, I see that we have raging imagination causing the 'running amok' atmosphere. And, we forget the past. This little study has major importance: Carnegie Mellon is Saving Old Software from Oblivion. Those who know have been concerned that experiments (heavily reliant upon computers) cannot be repeated due to technology due to several factors. One is the flash push (wow factor) versus sustainable science. 

Friday, February 16, 2018

Re-organization

We have a web-site associated with this work, albeit we have not really used it much: ajswtlk.com. It will be re-organized. Also, we will be working with the Thomas Gardner Society, Inc. in areas that overlap. To be brief, as one looks at the discussions about truth and related around the world, there is a growing influence from social media. Quora is an example.

But, discussions are all over the map. AI, especially ML/DL, is a sidetrack with robotics seen as a new life form. So much is going on that ignores the importance of people to the issue of reality and truth. Yes, people are part of the process in so many ways.

However, too, the U. S. stands as a symbol, for a whole lot of things. That will be discussed. But, one fact is that the history of the U.S. needs to be re-examined. History is told by the victor, it is said. I say, history is told by people who like to talk and write.

When you look at history, we have many contributors whose story is told by others, and not very well. We have chance to look at that with Thomas Gardner. We, basically, know his from his children and the descendants thereof. Yet, Thomas was right there from the beginning (1623) of the U.S. through the establishment of Massachusetts which, by the way, subsumed the Plymouth colony.

All through the history of the U.S., there are threads from people from those early arrivals who carried on the activity related to the establishment and evolution of the country. Generation by generation, they were there for the good times and the heart ache. Yet, history glosses over that.

Truth, though, pertains to people more than to anything. Arguable, perhaps, but, then not.

Remarks: Modified: 02/16/2018

02/16/2018 --

Wednesday, November 29, 2017

Modern ways

Not posting here does not mean that there are not things going on. For the past few months, I have been writing on Quora, with the intent of getting back here. In fact, the whole aspect of truth engineering has been brought out there. The intent is to update this blog in the near future.

For one thing, I ran into an interest in Common Lisp on the part of the younger set. There are several manifestations in use. One called clojure has been used for both front-end and back-end work. What is suggest to me is that we can start to talk a workbench approach.

Aside: There is a lot to discuss, but Lisp has been involved with AI from the beginning. Here is some information related to why it is so good: What-did-Alan-Kay-mean-by-Lisp-is-the-greatest-single-programming-language-ever-designed

If one listens to some AI/DS/ML/DL (that is, Artificial intelligence, data science, machine learning, deep learning) practitioners, one sense can an out-of-control situation. Gobs of time, money, space get gobbled running against suspect data, one might say. As, this information is wide and not very deep. So, who is deep? In other words, where is the science?

Too, there is a general malaise with regard to not understanding. That is, the computer comes up with something, and the humans cannot provide anything that allows comprehension to look at it and make a judgment. No, the reaction is to think the the computer is out of our league.

That might be true in several senses, but it is not in terms of truth (hence, we need to engineer this).

Remarks: Modified: 11/29/2017

11/29/2017 --

Wednesday, July 26, 2017

On computing

On Quora, there is a lot of discussion of AI. This whole framework is warped as it does not consider several important issues related to humans  and computing. It is as if the guys of SV (I say silly) can run amok (boys being boys, see USA Today, 7/25/17), and we are just to marvel at their talent.

Think again. A long tradition is being ignored. Why? Engineers do not care for humanities nor do they give a mote for philosophy. Psychology? Nada.

Well, how can we address that? Truth engineering was proposed as a way to discuss the matters that pertain to the issues. Last post, I mentioned hermeneutics (see starting list). Since then, I ran into Crapularity heumeneutics (think the huge interest now in the Singularity). I agree with Florian but see a whole lot of other ways that his concept can be used. All in good time goes the adage.

Later, I ran into sociology of  knowledge (paper by Inanna Hamati-Ataya). So, I am collecting an initial viewpoint with which to launch discussions and work.

 Recently, someone asked this: What is the difference between Lacan's and Jung's literary criticism? Well, I had already considered this topic from the Jungian sense. Mostly, it would have been done by some later follower of Jung, and archetypes would play a huge role.

But, Lacan? Well, on looking him up, I found that he liked Freud's approach. Too, he gave Chomsky grief. There is something to like about that. Hence, there is a thread from Freud to Jung to Lacan that needs some attention. Chomsky is in there to pull things to the modern times.

I pulled this out of a book on Lacan.

Note that Lacan talked what I might call peripatetic leanings. As he does raise a question. There is a bias for the brain. Look around, it's everywhere. AI (and SV) have taken that to an extreme. Oh, they say, replicate the brain and end up with intelligence that is artificial (and supposedly better than human). 

How did we get that bias? When were those other types of intelligences (say, Gardner's; he of Harvard) thrown out?

Lots to discuss. The benefit? A more full view so that we can dampen both the mania and the hysteria. Perhaps, then, Hawkins could sleep peacefully. 

Remarks: Modified: 07/26/2017

07/26/2017 --

Tuesday, June 27, 2017

Hermeneutics, at last

Reading links on two subjects.
An associated bit of scholarship goes under the title of Sociology of Knowledge. We will be looking at this, among other things, in our review of advanced computing (and its huge set of issues). 

Remarks: Modified: 07/26/2017

06/27/2017 --

Saturday, May 13, 2017

Intelligence

Quora has loads of discussion about IQ. Too, people are stressing EI (some use EQ).

Truth engineering deals with the necessity for a human-in-the-loop in terms of resolving hard problems. The computer cannot overcome vertigo, otherwise.

But, what type of person is required for this? All sorts of other questions pertain to the discussion.

So, let's start to look at intelligence among other things: The future belongs to the stupid (the blogger is a Professor of Psychology). I ran across this post in a Quora discussion. Seems like a good place to start (too, there is work being done - example: The Unz Review: An Alternative Media Selection).

I am proposing that, like Gardner of Harvard suggests, intelligence is multifaceted. The IQ test looks at a small portion. There may be 'smart ways' (perhaps, modes) that are not seen by the high-IQ. Or, rather, the higher IQ would require additional training.

And, not being controversial, broader scope for 'best and brightest' might be a wise choice on the part of society. What would that scope entail?

Remarks: Modified: 05/13/2017

05/13/2017 --

Thursday, May 11, 2017

Ten years

Ten years ago, I started this blog. The first post was "Truth, can it be engineered?" Overall, there have been 284 posts. The topics have followed the times.

Early on, there were posts about engineering. The key topics were 'earned value' and 'middle out' which are constant issues. Then, the topics changed to finance due to the downturn, its consequences and the long road. We still are in jeopardy.

Prior to starting the blog, I was doing Wikipedia edits. I pulled this little graph that shows edits by year. It can be broken down further, but the timeline shows interrelationships.


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Truth engineering started under the auspices of working in a knowledge-based engineering (KBE) environment. With all of the emphasis lately on AI (due to machine learning and deep diving of data), one wonders what is the basis for all of this. Well, KB, and its offshoot of KBE, were there from the beginning. And, they will continue. We will address this more here as we go along.

In the meantime, on a related page in Wikipedia, there was a request for real examples. I briefly sketched two recently. See . Talk:ICAD (software) for the examples.

Also, in terms of getting a plane to fly, it is more arduous than making a little smart phone. And, it demonstrates going up against nature big time. Nature is the chief guide. We have to conform and do so smartly. Nothing new there, as engineering has been around from the beginning.

What is new is the computer? What? And, social media and fake news. Don't blame the media, rather we need to look at this stuff from a new angle. Hence, truth engineering, for one thing.

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Post note: There was no reference in this blog to KBE over the years. Why? There were two in the related blog: Out on a limb and Here we go again, III (only a cursory mention). I suppose that I was looking at truth beyond the computer. Guess what? Jobs and his mobile gift has changed the landscape (and his cohorts with their various clouds, too). And so, can we go forward without knowing that 'truth' is computational, albeit with natural or artificial resources?

Remarks: Modified: 05/11/2017

05/11/2017 --