lunes, 30 de octubre de 2017

The Sentient Enterprise

I just have read the innovative book "The Sentient Enterprise", written by Oliver Ratzesberger and Mohanbir Sawhney.

This book is a "must read" for all of us involved in the improvement of the decision processes.

To clarify why, it is worthy to mention only two fundamental IT problems that are analyzed inside the book:
  • Why the enterprises are wasting human resources, time, efforts, their computing power, and money by unnecessary duplicating data?
  • Why the IT analytic solutions are usually delayed, and many times become obsolete when they are finally finished?
In order to help solving these issues, Ratzerberger and Sawhney introduce a model with five stages:
  1. The Agile Data Platform
  2. The Behavioral Data Platform
  3. The Collaborative Ideation Platform
  4. The Analytical Application Platform
  5. The Autonomous Decisioning Platform
This approach decomposes the huge problem: better decisions, and allows to develop specific solutions for each stage; thus making the whole process affordable.

I specifically recommend reading in detail chapter 7 "The Autonomous Decisioning Platform", and chapter 8 "Implementing Your Course to Sentience".

These chapters expose important guidelines, and introduce a perspective that matches most of the research topics behind the Autonomous Intelligent Systems (AIS)

lunes, 25 de septiembre de 2017

How useful are Recommendation Systems?

Typically, recommendation engines and systems enhance the user experience, because they assist us in finding information, reduce search and navigation time, and increase our satisfaction. 

However, I am still receiving recommendations about options to buy vacation packs, books, movies, music, etc.; that I had reviewed more than one year ago. Even worse, I receive friend suggestions because they are friends of someone that I know… Why? Really, most of the time I am not interested in receiving those kinds of recommendations… 

Therefore, I asked myself: How many people accept and follow these recommendations? 

Unfortunately, I don’t have access to all the required data in order to evaluate this; but according to Symeonidis and Zioupos (“Matrix and Tensor Factorization Techniques for Recommender Systems” ISBN 978-3-319-41356-3):
  • Amazon – 35% of product sales come from recommendations in Amazon.com  
  • Netflix – 66% of movies rented in Netflix.com are recommended  
  • Google – 38% more click-throughs are generated from recommendations in Google news
In my opinion, the main reason of this behavior can be traced back to the origin of the algorithms that have been used to create recommendations: clustering, ranking, scoring, pattern matching, etc. This means “the History”, usually understood as Big Data, OLAP, OLTP, etc. But it is clear that this "historical approach" demands many resources: storage, computing power, and time. 

But: What happen if we’re looking for recommendations about the outcome of a “random” process? 

The situation becomes harder if we don’t have enough information about the process itself. Let’s put it on an easy way: We need recommendations that could not be related to the previous history of the process. 

Example: A recommendation with probability 0.7 is not a winning one in gambling. Unfortunately, the number of alternatives experiences an exponential growth in order to achieve a greater probability. It is difficult to create such recommendations using "brute force" algorithms, and the task will demand the most powerful computers. Even worse: There is not heuristics to "prune" the decision tree.

This means that the inputs to the recommendation’s process are not well suited because the data could be either too poor or too much, the representation of the recommendation’s knowledge has not been identified as it should be, the inference rules could not be useful because there is not a previous experience about the behavior of the process, and the explanation about how the recommendations were created does not allow to identify the whole reasoning process: the domain is not well formalized. 

Then, I'm asking myself: How to predict the future behavior of a system whose previous history might not be relevant to the predictive process?

I believe that a new approach is needed to the recommendation’s process. This approach should redefine how to analyze the inputs, how to build new models of knowledge’s representation, how to propose different inference mechanisms that might not be well suited and computed by Turing’s Machines, many alternatives of solution, and explanations about how these recommendations were reasoned that may not match the common sense.

As a conclusion, I think that research in Artificial Intelligence should include more than machine learning, deep learning, and neural networks; because the focus of the problem should consider not only the data -the history-, but also the mechanisms to extract real knowledge of them: models, representations, inference rules, and explanations.

This path will lead us to “skilled intelligent systems”: solutions that can be transferred from one domain to other domains, and recommendations that will be really useful.   

viernes, 15 de septiembre de 2017

Comentario - Comment

Estimados lectores:

Cuando oficialmente dejé de trabajar como Profesor Titular en el año 2010, publiqué mis clases en Español para dominio público y sin fines de lucro en OneDrive < https://1drv.ms/f/s!ApPTAVJ07A-CgRQ2NbUjw6U1nDnB > .

A lo largo de varios años he visto dichos materiales referenciados y reproducidos por diversos sitios como SlideShare también sin fines de lucro. (Ejemplo en SlideShare: < https://www.slideshare.net/fuvylvp/almacenes-de-datos-olap-y-minera-de-datos?qid=2634f8ee-fb1d-4c57-aec0-a0add8ce77e6&v=&b=&from_search=1 > )

Sin embargo, hoy encontré un sitio que se declara "sin fines de lucro", pero que pide "apoyo al compartir y/o descargar" uno de mis documentos: "Aspectos Avanzados de la Tecnología de Objetos".

Personalmente considero que esta información debe ser conocida por todos.

Agradezco de antemano cualquier comentario ó sugerencia sobre acciones futuras al respecto.

Dear readers:

When I officially quit working as a Professor in 2010, I published my lectures in Spanish for public domain and non-profit use in OneDrive <https://1drv.ms/f/s!ApPTAVJ07A-CgRQ2NbUjw6U1nDnB >.

Over the years I have seen such materials referenced and reproduced by various non-profit sites such as SlideShare. (Example in SlideShare: <https://www.slideshare.net/fuvylvp/almacenes-de-datos-olap-y-minera-de-datos?qid=2634f8ee-fb1d-4c57-aec0-a0add8ce77e6&v=&b=&from_search= 1>)

However, today I found a site that is declared "non-profit", but "needs support to share and download" one of my documents: "Aspectos Avanzados de la Tecnología de Objetos".

Personally I consider that this information should be known by everyone.

I appreciate in advance any comments or suggestions on future actions in this regard.

Dr. Juan Jose Aranda Aboy


lunes, 12 de junio de 2017

Building an intelligent Chef

Let's start by analyzing the scope of the job: Building an intelligent machine means that the system must pass Turing's Test. Therefore, we are introducing by default a "common sense" rule in our research.

An interesting problem appears if we want to create an intelligent machine that can act as a Chef.

Cooking is an open problem, and it introduces some challenges to the intelligent machine. We will consider only three:
1. Should it cook by replicating a recipe step by step? What about measures? Cooking time? Is Fuzzy Logic the tool that could help solving these problems?
2. Could it create its own recipes by using the available products only? Could it transfer some skills that it has learned before such as music composing or poetry? How? Would Evolutionary Algorithms help?
3. What results can be accepted as "tasty meals"?


We can continue writing challenges that our "Chef" should overcome, but the last one is the main problem: the expectations about the resulting meal vary for each person, and even worse: each culture redefines cooking according to its history, location, and standards.

Therefore, which one is the appropriate "output"? I'm afraid that there are many solutions, and all these "tasty meals" would pass Turing's Test, but some of them would not pass the people’s taste.

The next step should be to "model" our Chef. To do so, we will analyze the problem again by reviewing the required actions to build an intelligent machine transforming the inputs (i.e.: beef, salt, lemon, onion, garlic, margarine, etc.) in an output: the meal (Steak!).

First, the desired recipe should be selected. This can be done by surfing the Internet. Therefore, our Chef can solve this easily.

Second, it should be verified if there are all the required products in the recipe. Also, the intelligent machine must check if there is the required quantity of all those products. The cooperation of another intelligent system is needed to keep records of the existing products.

Third, preparation: the beef must be sprinkled each side with salt. Then it must be added lemon juice, garlic, and onion. So, the intelligent Chef will need some additional devices:
- Sprinkler,
- Squeezer, to extract the lemon juice
- Peel the garlic clove, and chop it
- Cut the onion 
Fortunately, we can assume that have been created a set of intelligent machines that can solve these problems.

Finally, the intelligent Chef should melt margarine in a large skillet over "medium-high heat", fry the steak on each side, and transfer to a hot serving plate. Two comments: 
A) Which one is the appropriate temperature of the skillet? What is the meaning of medium-high heat? 
B) How much time is needed to fry the steak on each side?


There are many other problems that the intelligent machine may ask itself. A few examples are:  
- What if there is "not enough" (less than the required) margarine, but there is enough vegetable oil and butter?
- What if the recipe must be cooked without garlic and onions, or less salt because of the consumer's requirements? 
- Can be used the same procedure to cook fry chicken?

This is only a preliminary exercise. Thus, I have written more questions than answers. However, I feel confident about the future, because Artificial Intelligence is still young, and there are many researchers contributing to the field, so my expectations are high.

miércoles, 5 de abril de 2017

Lend me the phone

This was the first request that my grandson made me when he came on vacation: my smartphone. By using this, he chatted, watched videos, and played different games... All in one!

After lending it to him, my memory began to remember what a telephone was like just a few years ago when I was his age.

I made a list of some applications, and therefore of things: devices, functions and media, which are incorporated in my current phone, occupying a volume of only 14.5x7x0.6 cubic centimeters:
  • Phone of course, including caller ID, answering machine, call waiting, and even FAX.
  • The classic agenda, that printed notebook having a calendar to remind important dates, our list of contacts, and the possibility of adding handwritten notes.
  • Typewriter - Teletypes.
  • Cameras, photo rolls development, video camera.  
  • Clock - Stopwatch - Alarm clock.
  • Calculator.
  • News printed in newspapers and magazines.
  • Radio.
  • Phonograph / Turntable record player.
  • Recorder / Player - Dictaphone 
  • Television - Remote control 
  • Cinema.
  • Mail: Letters / Telegrams / Cables.
  • Maps - Global Positioning Systems (GPS)
  • Photo albums, storage of discs, tapes, cassettes, etc ...
For comparison, I tried to remember what those devices were like: dimensions, weight, and other characteristics; as well as how were added the functions that are included in my "phone".

After thinking some time, I decided to tell my grandson some of my stories working with computers, and how they evolved to allow him to play, watch videos, send instant messages, and so on in such a short time. 

However, I stumbled upon a barrier: their ability to listen the stories.

Consequently, I decided to create "apps" and videos to tell these stories, so here we go ...