Alpha's Manifesto

A black and white figure's thought-hive

Ethics in Artificial Intelligence

Review of a very interesting meetup by Dr McKillop

I recently participated in a meetup with the promising title “Ethics in AI”. Dr. Chris McKillop conducted the meetup, and she did not only has a lot of theoretical background under her arm, but also a great deal of experience with working on the field of Data Science and AI.

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Reducing size of Docker images

Making efficient final images

Heavy docker

Most blogs and manuals will recommend you the simpler approaches to reducing the image of your docker image. We’ll go a little further today but let’s reiterate them anyway:

  • Use the reduced version of base images (alpine usually recommended), avoid SDKs for final images
  • Use multistage build, do not copy over temporary files or sources
  • Take care of the .dockerignore, ignore as much as possible

Having said that, it is possible that you’ll still end up with a very huge docker image, and it’s difficult to understand what the next step from here.

This is where this post comes in.

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My sh*tpost generator: screaming-bot

Text generation with nltk, markovify, Tumblr, docker

(Image used without permission from Gunshow comic: Robot that screams.)

If the word offends you, below the fold I use it a lot, so you might not want to read this article. However, I think it’s the most appropriate term.

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Head First Data Analysis

Introductory solid book on Data Analysis

Head First Data Analysis Book Cover

I just finished reading “Head First Data Analysis “, by Michael Milton, and it was the first book I’ve ever read from the “Head First” series.

The book was very easily approachable, with concepts introduced only when they were necessary and to make a good, valid, practical point. The whole structure of the book revolves around practice and “real life” examples (although, greatly simplified) to prove how methodical logical steps can naturally lead to a good analysis mechanism.

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Preventing an AI arms race: open research

Secrecy is the halt of humanity's progress

I’ve noticed a trend in the AI research field, and one I feel pretty proud to talk about.

Given the latest advancements, even if there is or there is not a plateau in research in these next years, means that there is definitely a technological advance to nations or corporations that have the first step into new revolutionary AI. I am not talking about the new Skynet (although some people suggest it), but rather who gets the head start that creates the new vicious cycle. Those where our regulations are not enough to keep up, and we need to rethink what our laws are regarding economy, or military, or ethics.

We’ve all head the concerns on military self-driving attack drones. We’ve all heard the concerns about life-or-death decision taking self-driving cars. We’ve all heard and watched the theories, movies and games that revamp the Turing Test and want us to reconsider how deep we are in the rabbit hole.

And Musk’s OpenAI platform was a first response to that, with a clear strategy: make advancements available for everyone. The real strategy behind OpenAI is that when the time comes for that tipping point, such information will be available to everyone, enhancing competition and leveling up risks of a company/nation taking advantage. Of course, you might agree that this is a very complex problem and claiming that this is a simple solution that works is really oversimplifying the situation.

But it is, without a doubt, a clever one.

Very recently, researches raised up their voice against opacity in research journals, which is another example of this tendency. I share the thought that research and technological advance should be benefit for all of humanity. Not only because it will prevent unfairness, but also will benefit us all. Is there any good reason to willfully keep other organizations/nations behind in technological advance? Ethically speaking, none.

I very much like this new tendency, and as we’ve seen non-profit software foundations rise successfully in the past few decades, this might be the right time to make knowledge a concern for all of humanity.

Ciencia de datos ágil

Curso de Ernesto Mislej

7Puentes: Ciencia de datos ágil

(Post only in Spanish since I’m reviewing Spanish content. It’s a Udemy course talking about Data Science in an agile framework.)

Hace unos días terminé de participar del curso Ciencia de datos ágil, por Ernesto Mislej, co-fundador de 7puentes. Al comienzo me recomendaron el curso porque cubría un hueco que no estaba muy bien explicado en las fuentes online: cómo llevar adelante un proyecto de Data Science, fuera del típico proceso waterfall que siempre se describe. Además, Ernesto es una fuente de buena reputación, por lo que el curso me interesó.

El curso es relativamente corto, pero es algo condensado. El formato del curso es primero plantear algunos aspectos teóricos sobre el marco metodológico agile y la ciencia de datos. Luego, con esas bases, comienza un ejemplo práctico como si estuviéramos siendo parte de un proyecto. Tras eso, Ernesto nos lleva por lo que serían nuestras deducciones, idas, vueltas y conclusiones. También nos explica qué reuniones se “dieron” con nuestros clientes y todo lo que ocurre en un proyecto agile común y corriente.

Estos ejemplos son realmente esclarecedores: permiten ver cómo se llevaría adelante un proyecto como estos. Pero como ejemplo, por supuesto que tiene algunos aspectos simplificados. No mencionan de entregables, o procesos de testing, o casos de poca definición de negocios que también es el tipo de cosas que pasan en proyectos normales. No culpo al curso por esto: era claramente un curso introductorio a esta temática, pero por eso aún quedan algunas preguntas pendientes para quienes ya hayan participado de proyectos agile.

En definitiva, es una buena inversión de 2 horas y 15 dólares. (¡Muy accesible!) Definitivamente lo recomendaría.

Si les interesa, pueden visitarlo en Udemy: Ciencia de datos ágil.

How Google’s New AI Innovations Will Transform Retail

by Rae Steinbach, in cooperation with Y Media Labs

Google Logo

This is another great guest post, this time from Rae Steinbach. She  is a graduate of Tufts University with a combined International Relations and Chinese degree. After spending time living and working abroad in China, she returned to NYC to pursue her career and continue curating quality content. Rae is passionate about travel, food, and writing, of course.

Her post talks about the impact that Google’s AI vision will have on retail businesses. Thanks Rae; thank you very much!

During 2017’s Google I/O, where developers from all around the world explore emerging technologies together, the company announced several new elements for both Google Home and Google Assistant. For instance, Google Assistant, now equipped with AI, will be able to provide relevant information about your environment by “seeing” it through the phone’s camera. You could just point the lens at a business you pass on the street, suddenly receiving information about its services, customer reviews, and more.

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