Artificial Intelligence Post Number 29

February 6, 2016

A 2/4/2016 article by Cade Metz of Cade Metz Business describes how Artificial Intelligence (AI) is transforming Google Search. Here are some relevant quotes from this article that supports how AI is proceeding on its ability to discover “deep neural networks, networks of hardware and software that approximate the web of neurons in the human brain. By analyzing vast amounts of digital data, these neural nets can learn all sorts of useful tasks, like identifying photos, recognizing commands spoken into a smartphone, and, as it turns out, responding to Internet search queries. In some cases, they can learn a task so well that they outperform humans. They can do it better. They can do it faster. And they can do it at a much larger scale.”

“The truth is that even the experts don’t completely understand how neural nets work. But they do work. If you feed enough photos of a platypus into a neural net, it can learn to identify a platypus. If you show it enough computer malware code, it can learn to recognize a virus. If you give it enough raw language—words or phrases that people might type into a search engine—it can learn to understand search queries and help respond to them. In some cases, it can handle queries better than algorithmic rules hand-coded by human engineers.”

“At one point, Google ran a test that pitted its search engineers against Rank Brain,” a deep learning system. “Both were asked to look at various web pages and predict which would rank highest on a Google search results page. RankBrain was right 80 percent of the time. The engineers were right 70 percent of the time.”

“Increasingly, we’re discovering that if we can learn things rather than writing code, we can scale these things much better.”

Those of you who have read my book Artificial Intelligence Newborn – It is 2025 , and I am Here! will see that AI seems to be progressing even faster than in my fiction novel. Perhaps the title should have been “It is 2020, and I am Here!

Update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that don’t believe it! The current S&P is 1880, flat since EW’s prediction early this year.

Artificial Intelligence Post Number 28

January 30, 2016

It has been a while since I have mentioned the breakthrough computer chip IBM’s TrueNorth. But it is alive and well. There is a start up using this chip on a new designed “Pattern Computer.” This is a desktop supercomputer. It “is highly efficient, extensible, scalable, and unbelievably fast.” It is designed to discover patterns in big data – “where we might otherwise not see them.” “It could be in physics, in climate change, or in anything.” “This could be the advent of an entirely new computer age, and a revolutionary change in human and computer interaction.”

See whole article at http://www.geekwire.com/2015/new-startup-building-desktop-supercomputer-seeking-big-breakthroughs-using-chips-that-work-like-the-human-brain/

Update on Elliot Wave’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that don’t believe it! The current S&P is 1940, up 3.2% since EW’’s prediction.

Artificial Intelligence Post Number 27

January 25, 2016

I have said all along that I believe that the real breakout of AI will come from people working to beat the stock market. Following is a site that, to a very small degree, is trying to do that. Their self-learning algorithms are still too narrow in scope, and 15 years of data is not enough. But they have the right idea. The people that are far ahead are not publishing their algorithms.

http://iknowfirst.com/stock_market_forecast_chaos_theory_revealing_how_the_stock_market_works

Update on EW’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I said that don’t believe it! The current S&P is 1892, up 0.6% since EW’s prediction.

Artificial Intelligence Post Number 26

January 19, 2016

The U.S. Proposes Spending $4 Billion to Encourage Driverless Cars. The Obama administration aims to remove hurdles to making autonomous cars more widespread. Tesla’s head Elon Musk says that autonomous cars are ahead of schedule, and self-driving cars could be here in as little as a year. Estimates of 25,000 lives being saved once autonomous cars are fully implemented are “driving” this extensive technological effort which includes the development of self-learning algorithms, which are keys to AI.

But stodgy old IBM may be leading the pack on AI advancement. Here are excerpts from an interview on Forbes, done by Peter High, President of Metis Strategy. Peter interviewed IBM Watson Head Mike Rhodin On The Future Of Artificial Intelligence. You can find the whole article here: http://www.forbes.com/sites/peterhigh/2016/01/18/ibm-watson-head-mike-rhodin-on-the-future-of-artificial-intelligence/#4e7c0091306454c4fba23064.

Watson has been busy since winning on Jeopardy! Watson is now working in 17 different industries. The main industry has been healthcare, and Watson is working with many of the top hospitals and healthcare providers in the world. The mass amount of data alone requires computer help. “In 2015 alone, we will produce something around seven hundred thousand new reference documents in medicine. I am pretty sure most doctors do not have time to read all of them.” Many other professions like law have the same issue of too much data for human comprehension.

“Watson mirrors the human chain of events when we make decisions: it observes, it interprets, it evaluates, and it makes decisions.” “The key breakthrough was that we were able to feed the system natural language text: reference material, the internet, Wikipedia, web pages.” “The basis of the system is that you read it, you train it, and then you continue to learn through use– much like we do. It is mirroring the human learning process.”

Watson Analytics, Watson Discovery Advisor, and Watson Explorer are three service offerings. Watson Analytics learns over time how to identify the correct data sources that you want to apply to a problem and how to make recommendations on how to improve the data to get a better outcome. Watson Discovery Advisor creates a framework for how you can interact with the computer, whether it is asking questions or having a dialogue back and forth. Watson Explorer helps us work within an enterprise to pull together lots of different types of information from existing enterprise data sources, but then take that information and connect it up to cloud-based Watson systems.

“Not only does Watson have the ability to read information, we have both voice-to-text, text-to-voice capabilities, and computer vision capabilities added in. We started teaching Watson to speak new languages. We started with Spanish and Japanese, we have added Brazilian Portuguese; we are working on Arabic right now. French and Italian are right behind it, and German will be in the wings.”

“You see…the third technological revolution getting started here…the information revolution. This is something that is going to be with us for decades.” In my opinion, you are seeing the start of the AI revolution!

Update on EW’s prediction that the S&P 500 will drop 41.6% this year from 1880 to 1100. I don’t believe it! The current S&P is 1881, up 0.05%.

Artificial Intelligence Post Number 25

December 14, 2015

What is Elon Musk, the CEO of Tesla and Space X, up to? Many months ago he invested in a company called DeepMind, with the intent of monitoring the advance of AI; to make sure it didn’t go in evil directions that could hurt mankind. Then Google bought DeepMind. Now Elon and several others have “financed” over a billion dollars in a non-profit called OpenAI. It will be co-chaired by Elon and Sam Altman, the CEO of Y Combinator. They already have eight skilled researchers and are in the process of hiring more.

Per the company’s name, they plan to make everything they do public and open-sourced. They are not just going to be monitoring AI’s progress; they will be pushing the AI envelope themselves. Their goal is that once true AI happens, it will not only be in the hands of a few who could use it for evil purposes. Similar to the NRA saying that only a good guy with a gun can stop a bad guy with a gun, OpenAI hopes to have many good guys with thinking AI computers that can minimize or eliminate the power of an evil person, country, or company with AI that is potentially smarter than a million people. Amazon Web Services is donating much computer power and infrastructure. Elon has stated that much information will be shared from Tesla and Space X.

It is my guess that even Elon has been surprised at the progress of self-driving cars and the intelligence of the self-learning systems Tesla has developed. In fact Tesla has recently advertised for more programmers to work on these systems, and Elon has stated that he will interview the candidates himself and the group will be answering directly to him.

I am surprised that I am not getting more comments on this blog. Am I the only one that believes that we may be seeing disruptive gains in AI that exceed the most ambitious timelines “experts” have projected?

Artificial Intelligence Post Number 24

December 6, 2015

I like to read recent books related to AI. “Artificial Intelligence: The God Killer,” by Zed Marston was published on October 3, 2015, and despite it being a short book, it has a 5 star rating on Amazon. It is an easy read.

I believe that this this book, as many books on AI, makes a serious mistake in that it assumes that once computers have abilities exceeding that of humans, they will get almost god-like knowledge and wisdom. But how will this happen? The computers will have to build on the same knowledge base that humans use. They will just do it faster and with more accuracy. No unique knowledge on questions such as “why are we here?” or “why is there anything?” or even “does the theory of evolution really explain everything?” will suddenly appear for the computers! The computers will do a better job of defining the inconsistencies in the traditional religious texts, but people already ignore those inconsistencies. Sure, AI may help us build better telescopes or advanced rocketry for space exploration; but until that data are available it is unlikely that we are going to make breakthroughs or have a better understanding of basic philosophical questions that often drive people to religion or a belief in some creator. Until humans (and maybe even computers) have a better explanation for their existence and the meaning of life, the need for humans to believe in an overall creator will likely continue. In fact, AI may even increase this belief mode because few people will want to accept computers as superior in any way. They will continue to believe that man was created in God’s image, and that we are special.

I have read the Old Testament, the New Testament, and the Qur’an. My personal beliefs are that each is flawed with huge inconsistencies and biases. But others have read one or more of these books and become (or remained) believers. It isn’t that the readers don’t see the inconsistencies. It just is that in most cases they give these religious sources huge latitude in that the books were written by people, not their God, and often written many years after the events described. These believers also don’t see any better explanations. Would not a computer give religion the same latitude, especially given that there seems to be no real explanation of the existence of anything that can be scientifically tested and explained with real confidence? Again, computers may not recognize religion as a strong explanation for anything; but they are unlikely to rule out all religious beliefs without having alternative testable explanations for the basic existence questions.

In my book “Artificial Intelligence Newborn – It is 2025, and I Am Here,” I include the effect of religion. Although the book is fiction, it is an attempt to present a very viable possible future once computers get the ability to think.

Artificial Intelligence Post Number 23

November 15, 2015

In earlier updates I emphasized how I believe that true artificial intelligence will come from an accumulation of various targeted self-learning algorithms that go after specific areas. I said that that such algorithms may already exist for the stock market, but these algorithms are not viewable because people or companies are using them to make money in the market, and they are aware that the disclosure of these algorithms may make them useless or cause laws to be passed against their usage.

In my last blog I went into some detail on how Tesla’s AutoPilot is a self-learning system, and how similar systems are likely to grow into true autonomous cars.

Another area I have noted as being ripe for AI is medical. In a 10/15/2015 article in ScienceDaily (http://www.sciencedaily.com/releases/2015/10/151007084259.htm), they discuss “a new diagnostic technology based on advanced self-learning computer algorithms which — on the basis of a biopsy from a metastasis — can with 85 per cent certainty identify the source of the disease and thus target treatment and, ultimately, improve the prognosis for the patient… The newly developed method, which researchers are calling TumorTracer, are based on analyses of DNA mutations in cancer tissue samples from patients with metastasized cancer, i.e. cancer which has spread.”

Even more exciting is that “Researchers expect that, in the long term, the method can also be used to identify the source of free cancer cells from a blood sample, and thus also as an effective and easy way of monitoring people who are at risk of developing cancer.”

AI is coming!

Artificial Intelligence Post Number 22

November 6, 2015

I have been reading about every book that comes out about AI, the most recent being “Surviving AI: The promise and peril of artificial Intelligence,” by Calum Chase. The one thing that is obvious from most of the books I have read on AI is that the authors believe that if you accept evolution, you must believe in the inevitability of Artificial Intelligence.

If the function of the brain could have evolved by the randomness of natural selection acting on the genetic variation among individuals, the equivalent ability can certainly be obtained in a powerful computer using the scientific method, without all the trial and error. Of course, this ability may take a totally different form, just like planes don’t fly like birds and submarines don’t swim like fish. And the exact timing of when this will happen is obviously not predictable. Per most authors, only people believing in a creator that made humans special such that no other entity can “think” should doubt the inevitability of AI.

Although most books acknowledge the possibility that AI will not come until we have a full understanding of the human brain, most suggest that AI will evolve from a different approach, either from a master algorithm leading directly into AI or the cumulative result of many isolated self-learning AI algorithms that are eventually harmonized into an overall thinking system.

I am watching Tesla’s “Autopilot” (note I own some Tesla stock) with great interest. Mobileye makes some of the technology, with chips that interpret what car cameras are seeing. The chips use “deep learning” methods to interpret data coming from the car’s sensors. Deep learning uses multiple simplified algorithms to approximate complex functions coming from the various vision systems and other sensors. These simplified algorithms enable quick but sufficient decision making by the car’s computer systems to control safety systems and driver assists.

The Tesla system is self-learning and will improve on its own based on when and how the driver and the systems react to individual road situations. It also will give feedback on the roads themselves, such that over time Tesla will have road maps far more detailed than anything currently available from the likes of GPS or Google Maps. These maps will be continuously updated based on the feedback coming from each Tesla vehicle. Even at his early stage, over 1 million miles are being driven daily by Tesla cars on Autopilot. The detail being gathered will enable the next step in self-driving vehicles.

Is this AI? The program is self-learning and is probably already quite different than what the human programmers initially entered. And certainly within a few years the decision making going on in each vehicle will be impressive. In fact, Elon Musk, the CEO of Tesla, believes that within five years his vehicles will be capable of self-driving.

With little doubt, the same sort of progress is being made on stock market programs, medical diagnostic systems, legal research programs, teaching assists, military weapons, and other areas that will not be as visible to us until fully implemented. And will all these systems approach problems with the same “deep learning” methods used in Tesla’s Autopilot, such that a commonality exists that enables a master algorithm that will work much like the human mind?

Progress in these areas is so rapid that the next few years are going to be very exciting. This is NOT something that will take decades before our way of living is dramatically affected! And this disruption will be apparent far before an AI system becomes truly “thinking.”

Artificial Intelligence Post Number 21

October 29, 2015

I just finished reading “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World,” by Pedro Domingos. This is a very difficult read. The author covers certain areas in great (and confusing) detail, and is contradictory in other areas. For example, despite the concerns and warnings from many very smart people about AI, the author states that there is absolutely no risk of artificial intelligence computers taking over. But he then proceeds to list some ways that computers MAY take over, including The Master Algorithm getting into the hands of a bad person. What are the odds of THAT happening? He also says that there is a risk of people getting so confident of computers always being right that they begin to follow the AI computer like following a god? Again, what are the odds of THAT happening? Have people ever really done that?

The author believes that it is important that The Master Algorithm be discovered BEFORE individual algorithms get developed for specific narrow problems, because otherwise the detail of those individual algorithms will become too complex to incorporate into one Master Algorithm. Again, the author spends chapters talking about the progress already being made in these individual learning algorithms, including areas like self-driving cars, medical care, etc. He doesn’t say how we are supposed to stop this progress until someone identifies an overriding Master Algorithm. And he does say that the individual algorithms on specific subjects will be more all-encompassing. He just feels that required computer power will be overwhelming when someone tries to put everything together into one Master Algorithm package equivalent to a brain. He doesn’t mention breakthroughs like IBM’s TrueNorth computer chip.

The author then lists the competing philosophies being applied in the development of a Master Algorithm, which he calls “the five tribes of machine learning.” The five tribes are symbolists, connectionists, evolutionaries, Bayesians, and analogizers. I won’t even attempt to describe the details of each, which is the main content of the book.

I read this book because the author Pedro Domingos is a professor of computer science and is a winner of the SIGKDD Innovation Award. The book is very recent (published September 22, 2015) and is rated fairly well on Amazon. So I thought that I would be getting very up-to-date information. But I don’t feel it helped much in this blog’s quest to see the progress of AI in general, and when we should expect to see dramatic changes as the result of AI. I actually think that my fictional book “Artificial Intelligence Newborn” does a better job in laying out a possible AI future, especially given that Domingos gives a zero chance of AI taking over!

Artificial Intelligence Post Number 20

October 18, 2015

I would like to give a brief update of how much effort (money) is being put into artificial intelligence by hugely wealthy companies. First, let’s look at Apple. Just this month Apple purchased Percptio and VocalIQ. Perceptio makes image recognition technology for smartphones. VocalIQ is developing technology that helps computers understand everyday human speech. Does Apple want these companies’ expertise to help in the development of their rumored self-driving electric car that is supposed to come out in 2019?

Apple is not alone! In a 10/12/2015 event in San Francisco, IBM hosted a meeting to discuss AI. In this meeting IBM was blowing their own horn, emphasizing that they were working on every element of AI, with Watson and the TrueNorth chip offered as evidence. They also discussed how they can get insights from unstructured data. Earlier this year they noted that in the healthcare industry, much patient information is saved in text format and seldom used in analysis. Some estimates are that 80% of health information is unstructured (like in physician notes and patient surveys) and therefore not used. IBM Watson Content Analytics addresses this source of unused information to give a more complete view of patients’ needs and appropriate treatments.

Google recently invested in DFKI, a German AI lab. In 2014 they also paid $500 million to buy the UK company DeepMind. DeepMind’s Mission: “Solve intelligence!…We build powerful self-learning general purpose algorithms.”

Musk, Zuckerberg and Kutcher are investing in a company called Vicarious, which has raised over $100 million. Vicarious’ goal is to build a system that will have general intelligence matching a human.

Many of these companies would pursue AI even more aggressively if they could hire people with the required abilities. Google has publicly stated that their biggest challenge in AI is finding people with the right digital skills.

The last thing I want to emphasize is that despite all this effort by well-funded firms, I believe that there is at least an equal chance of AI breakthroughs coming from a quant playing with his computer in his basement and using the cloud for his required computer power. His motivation for developing self-learning AI programs will be the same that many hackers have of breaking into “secure” computer systems. It’s an ego thing!


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