domingo, 7 de outubro de 2018

“The Big Risk of Artificial Intelligence Are Too Stupid Machines”



In the interview with the title “The Big Risk of Artificial Intelligence Are Too Stupid Machines”, that you can access here,  the author envisions a world in which computers may be able to learn everything.

Reading the interview, he done to Publico newspaper I remembered using Phyton to count words, classifying them, and thereby assessing the relevance of a document to study content.
I had read some articles with studies in that sense, using Phyton. There are software that have this purpose, to evaluate by the number of words most relevant, to verify how much the article, or interview, are a good starting point for a study on a theme.

I used the anaconda platform, with Jupyter Notebook and Phyton to analyze the up-and-coming words from the interview.










All work you can read in:
https://medium.com/@mrobalinho/about-the-big-risk-of-artificial-intelligence-are-too-stupid-machines-8f55e316a10f

terça-feira, 2 de outubro de 2018

House Prices: Advanced Regression Techniques


Development Kaggle Competition

Manuel Robalinho - Set 2018 
Work developed to my Master Degree.

Using Anaconda framework, Phyton and Jupyter Notebook to predict house prices.

All work here:


Start here if...

You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. 

Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Statistical about Autonomous Vehicles

Using Machine Learning to analise Statistical information about Autonomous Vehicles


By Manuel Robalinho on 2018/09

Work made for my Master Degree.

The state-of-the-art documentation of the implementation of autonomous vehicles has information that I thought was important I was creating an excel sheet with the projects in progress, partnerships, country where they occur, possible investment values.

I share the file I created with you: Search_VATs.xls
I took advantage of the information and using Python and Jupyter Notebook I created some charts with the excel information.

I had some difficulty in classifying projects funded by the European community, and I used the testing country, or the country where the company is involved in research and development of the project. Many projects are executed in partnerships, usually including software companies and automobile industry. In these cases the classification was obtained by the software developer.

Other cases being universities and companies of the automobile industry. No In this case they were classified by the origin of the University.
One unexpected case was finding 3 projects in Brazil, led by universities.
Another interesting example was to find China, Korea and Japan as a strong 3 countries in the investigation and implementation of autonomous vehicles. I believe that the high population of the Asian continent will lead the automotive industry to consider the implementation of these projects as an urgent matter.

The creation of many graphics was a way to made experiences about project the visual information, with many forms, using Phyton and pandas library, and matplotlib.
Using geographic information from libraries geopandas, and the coordinates available in geopandas datasets  ('naturalearth_lowres') I made the join the world map with the country of my excel information. I plot the result of this join:

The strongest colors represent countries with more projects.
Plotting the continent names and the bar graphics bellow:
Or plotting only a big world map with the most representative countries with autonomous vehicles projects.

In another reading i found some documentation from KPMG ‘KPMG-Autonomous-Vehicle-Readiness-Index’.  I liked the content and information presented, that describe the countries about openness and preparedness for autonomous vehicles.

I made an excel table with the information presented in the document, and 
used the same technique described above to make some graphics, that I present now:
For me the main novelty was to have Netherland as the most prepared and developed country for the implementation of autonomous vehicles. Perhaps because we heard a lot about the projects in the USA, Germany or Japan, I did not know all the preparation that this country has already developed in this goal. Another country that it’s a surprise for me it’s the good position of Singapore and United Arab Emirates. I don’t have knowledge about projects developed in these countries.
In the two graphics it’s notorious the bad position occupied by big countries like Brazil, India, Mexico and Russia.

In that graphics we can confirm the good technologic and innovation ranking occupied by United States and Germany. In the bad positions the same countries Brazil, India, Mexico and Russia.

In this chart is interesting the good position of Brazil and the bad positions of Japan or Spain.
This chart confirms the statistics and rankings for technological development and innovation. Countries where there are cells with projects in the area, are more technologically developed.
This graph shows the inversion of some countries in the ranking, but the values ​​of connectivity are also very similar among most countries. In the tail of the ranking we have the same countries that occupy it in practically all the graphs: Brazil, India, Mexico and Russia

Master graphic by country with the most important scores.
The darker colors represent the countries with the best scores.