My new short sci-fi story is now available on Amazon.
The Bite-Size Sci-Fi collection delivers short science fiction stories intended for a 30 to 45-minute read, perfect for a train or bus ride, lunch break, or short reading before bed. For longer trips, try several stories together! Fresh ideas, new characters, and unique challenges and conflicts will keep the reader captivated!
New to the collection, “Unstoppable” explores how a transhuman spy of the future operates in a hostile world where everyone is trying to kill her.
Twenty years ago, in 2001, I published my first book.
It was about computer data storage and took me over 6 months to write. My child was just one year old then but, with the full support of my wife, I used whatever available time I had to do research and write. It was an interesting experience that taught me a lot about book writing. And it was exciting to see my book in different libraries and online stores. Now, twenty years later, I am ready to publish another book. This time, however, it’s a collection of short science fiction stories about the future where AI and humans coexist, cooperate, help each other, betray each other. In other words, they do what two intelligent species normally do when they occupy the same space and time.
Twenty years ago, for my first book, I chose to use a real publisher––Prentice Hall. It was a time when the Internet was in its nascent state, Amazon.com was just starting its ascent, and ebooks were nothing but a promising concept. This time around, I am choosing Amazon.com as a place where you can buy the book.
I called my bookI, AI, which is an homage to the famous book I, Robot by Isaac Asimov. Below is the cover of the book, which was created by Ravven, a skillful and creative American artist living in the U.K.
The book is now available on Amazon at $0.99. I will keep it at this price for just a few days.
I hope you like it!
Ah, one more point: to keep my professional life and my other interests apart, I am using a pen name for my writing. So, don’t be too surprised.
It’s been a pleasure to speak twice at the Conference on World Affairs last week. Check out my second panel below. This time, we discuss “Fixing Spaceship Earth: Forecasting Our Future with Big Data and Tech.”
My wife is an expert wine-buyer and every good wine bottle she brings home has a little story attached to it. Even though, occasionally, I (secretly) don’t enjoy the taste of some of those wines, I know they are all considered to be of “high quality” and “very popular”. Some of them simply just don’t match my taste.
However, there have been plenty of wines I’ve tried in the past that were just plain bad. This made me think about the wine manufacturers – why do they even sell a particular (bad) wine? Can’t they just predict a customer’s response by tasting their own wine or by measuring objectively a few things about a wine’s chemistry and physics?
Wine-making is a big industry and there are already quite a few studies done and papers published in this field trying to answer this question. In fact, after a quick search online, I found a few data science studies trying to address it, and many of them were referring to the following paper:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
This particular publication came with a couple of datasets available for everyone to play around with, so I decided to use them for my next project. As an advisor to BigML, which is a leading analytics company, I wanted to analyze this wine quality data using their online platform and see 1) can I answer my question about predicting wine quality from some objective measurements and 2) how quickly could this be accomplished using the BigML online solution?
First, I downloaded the wine composition and quality assessment data from here: there are two datasets available with 1599 entries for red wine and 4898 entries for white. Even if I prefer red wine, I decided to go with the larger dataset for my study.
The dataset included 11 wine features such as residual sugar, density, pH, alcohol and few others (check the dataset if interested) and one numerical value for quality of each wine, which was expressed as a number between 0 (very bad) and 10 (excellent).
I felt that the regression analysis (having a numerical output in mind) will be too noisy and inaccurate, I decided to simply split the dataset into two classes: bad wine (0-6) and good wine (7-10).
I used ExcelⓇ to do these initial data manipulations and then imported the dataset into the BigML online portal (a simple drag and drop). Notice in the picture below how convenient it is to see all the distributions for each data column and their descriptive statistics.
The first post on this subject was focused on Cray supercomputers, which place beautiful images on the front to add an artistic touch to their technically-impressive machines.
In this (second) post, I will mostly address the “beauty through design” approach taken by Cray and a few other supercomputer makers.
Let’s start with the Thinking Machines Corporation. Founded in 1983, it has delivered some of the most advanced (for its time) and good-looking computers ever. A brief promotional video for its first models is available on YouTube.
Thinking Machines’ CM-5 Supercomputer, also known as FROSTBURG , was installed at the US National Security Agency (NSA) in 1991 for code-breaking tasks, and was operational until 1997:
No decorations, no frills. However, this supercomputer still remains one of the most futuristic-looking supercomputers ever. Its flashing and constantly changing red light panels showed processing node usage, and were also used for its diagnostics. In fact, this old supercomputer looks so good it ended up in a Jurassic Park movie:
To me, the CM-5 design actually looks inspired by the WOPR computer from WarGames (1983), which wasn’t a real computer, of course, but a realistically-looking movie prop:
The main idea is to build an up-to-date library of modularized ‘common denominator’ analytics solutions for the engineering community to democratize data analytics, accelerate R&D process, reduce investment costs, increase efficiency and help engineers align products and services to what customers needs.
Another 402 games and I managed to improve my Rapid chess rating from 2000 (I mentioned it in my previous post) to 2100 on Lichess.org. Rapid (10 mins per player with no added time for each move) is what I mostly play these days.
This wasn’t easy at all since I have to play stronger and more unforgiving players now. But I am glad it happened eventually.
Now, I am going to close this account (Chesswhenhavetime-2) and open another one: Chesswhenhavetime-3. I hope to get to 2200 in Rapid one day.
And this is what it roughly means in terms of my relative strength:
Chess is a fascinating game that hasn’t lost its appeal even after the strongest human player of the time was finally defeated by a computer. I refer to the historical 1997 face-off between Garry Kasparov and IBM’s Deep Blue. In New York City, Deep Blue won by just a slight amount, 3½–2½. However, this small victory has taught us a lot about chess, computers, and ourselves.
We’ve learned that machines can consistently outperform humans in those competitions (games, conflict, planning activities, etc.) where all the rules are well-defined. No matter how complex these rules and competitions are, given enough time, machines’ superior computational abilities and nearly unlimited memory (storage, really) win against human creativity, intuition, and reasoning.
Since that victory in 1997, chess machines (or chess algorithms) have become stronger with every passing year. Nowadays, even the most brilliant player of our time, Magnus Carlsen, doesn’t have a chance against Stockfish or, even worse, AlfaZero, even if the World’s top ten super-grandmasters aid him.
Further, we also learned that this demonstration of a computer’s ability to imitate intellect in one field doesn’t mean it is getting closer to human-like intelligence. While Deep Blue and all the modern chess programs are brilliant at the one thing they are created for, winning chess games, they are pretty useless for everything else. And to become useful in any new areas, they have to be almost wholly modified.
In fact, this remains a confusing point for those who follow the field of AI by reading articles in popular magazines. These articles create the impression that with every step towards better image or speech recognition, successful Jeopardy performance, or successes in chess or Go, computers and algorithms truly acquire human cognitive abilities.
Modern chess algorithms effectively demonstrate that this is not the case. While being 1,000x (or even 1,000,000x) better than humans in chess and ‘thinking’ 50 or more moves forward during each game, these algorithms are less capable of solving most practical tasks than babies.
Finally, we discovered that despite being unable to compete with computers, we still find the ancient game of chess fascinating, want to play it, and want to succeed at it. Cars are faster than humans, but we still compete in running. Machines are stronger than us, but we still compete in weightlifting. Chess is no different––we will continue to play and compete.
Just a week ago, I finally reached the Lichess.com rating of 2,000. It took me 4.5 years and 11,818 (!) games to do it. Apparently, I have spent 45 days, 9 hours and 38 minutes playing non-stop to get to this point. And I only played when I had time for it.
I wish I had the luxury of playing chess online with anyone worldwide when I was just ten years old. Alas, it wasn’t possible then. To play chess, we needed a chessboard, a clock, and a willing partner, who was often difficult to find (even in Moscow). Some people even played by mail!
The result was that it was hard to play more than a few hundred games per year. Today, with the help of Lichess.com (or Chess.com or a similar platform), I can play on the order of 2,500 games per year without ever leaving my house.
Those who complain about the damage and dangers of technology often overlook simple examples like these. These instances of technology change our lives beyond recognition and make them better and more enjoyable.
Dr. Andrei Khurshudov is a Director of Advanced IoT Analytics at Caterpillar. He is managing several teams of data scientists and software developers working on advanced monitoring and analytics solutions for the company’s fleet of connected machines and IoT devices.
With over 20 years of experience as a researcher and an executive leader, Andrei has something to say about the hottest areas in business and technology.