AI News Today

<p>After immersing myself for years and years in the world of finance (specifically, options market making and algorithmic trading), there was no escaping this mantra of “no risk, no reward.” Most of the time, my (trading) style was fairly risk-averse. I knew how to find the signals of opportunities among the daily noise to reap small rewards. Fortunately, I also learned how to think about the outliers and to take the occasional big but calculated risks.</p> <p> <a href="https://signal.org/blog/

<p>TIL that Bell Labs and a whole lot of other websites block archive.org, not to mention most search engines. Turns out I have <a href="https://github.com/danluu/debugging-stories/issues/3">a broken website link</a> in a GitHub repo, caused by the deletion of an old webpage. When I tried to pull the original from archive.org, I found that it's not available because Bell Labs blocks the archive.org crawler in their robots.txt:</p> <p></p> <pre><code>User-agent: Googlebot User-agent: msnbot User-

<p>Why are people so concerned with hardware power consumption nowadays? Some common answers to this question are that <a href="http://www.vox.com/2015/3/9/8178213/apple-macbook-all-batteries">power is critically important for phones, tablets, and laptops</a> and that <a href="ftp://ftp.cs.utexas.edu/pub/dburger/papers/ISCA11.pdf">we can put more silicon on a modern chip than we can effectively use</a>. In 2001 <a href="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=912412&url=http%3A%2F%

<p>There's an ongoing debate over whether "AI" will ever be good enough to displace humans and, if so, when it will happen. In this debate, the optimists tend to focus on how much AI is improving and the pessimists point to all the ways AI isn't as good as an ideal human being. I think this misses two very important factors.</p> <p>One, is that jobs that are on the potential chopping block, such as first-line customer service, customer service for industries that are either low margin or don't c

<p><em>Winter Break Of Code Day 2</em></p> <p>Because I might get sappy, here’s a picture to ease that before I let it all out.</p> <p><img src="/blog/images/wboc-hanaleitown.jpg" class="nice" /></p> <p>After being promptly woken up by numerous roosters at 7:30 AM, I still can’t believe I’m here. My application was pretty last-minute, completed when I was in a state of droopiness and scholarship-application-hyperdrive. But I got on a plane from the Midwest, went from cloudy, 20 degrees with wind

In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use […]

I am happy to announce that the Datumbox Machine Learning Framework is now open sourced under GPL 3.0 and you can download its code from Github! What is this Framework? The Datumbox Machine Learning Framework is an open-source framework written in Java which enables the rapid development of Machine Learning models and Statistical applications. It […]

In the previous articles we discussed in detail the Dirichlet Process Mixture Models and how they can be used in cluster analysis. In this article we will present a Java implementation of two different DPMM models: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model […]

This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models […]

This blog post is the fourth part of the series on Clustering with Dirichlet Process Mixture Models. In previous articles we discussed the Finite Dirichlet Mixture Models and we took the limit of their model for infinite k clusters which led us to the introduction of Dirichlet Processes. As we saw, our target is to […]

This article is the third part of the series on Clustering with Dirichlet Process Mixture Models. The previous time we defined the Finite Mixture Model based on Dirichlet Distribution and we posed questions on how we can make this particular model infinite. We briefly discussed the idea of taking the limit of the model when […]

This blog post is the second part of an article series on Dirichlet Process mixture models. In the previous article we had an overview of several Cluster Analysis techniques and we discussed some of the problems/limitations that rise by using them. Moreover we briefly presented the Dirichlet Process Mixture Models, we talked about why they […]

In the ISO research project for my MSc in Machine Learning at Imperial College London, I focused on the problem of Cluster Analysis by using Dirichlet Process Mixture Models. The DPMMs is a “fully-Bayesian” unsupervised learning technique which unlike other Cluster Analysis methods does not require us to predefine the total number of clusters within […]

By now most of you have heard/played the 2048 game by Gabriele Cirulli. It’s a simple but highly addictive board game which requires you to combine the numbers of the cells in order to reach the number 2048. As expected the difficulty of the game increases as more cells are filled with high values. Personally […]

In the previous article we have discussed about the Data Envelopment Analysis technique and we have seen how it can be used as an effective non-parametric ranking algorithm. In this blog post we will develop an implementation of Data Envelopment Analysis in JAVA and we will use it to evaluate the Social Media Popularity of […]

Data Envelopment Analysis, also known as DEA, is a non-parametric method for performing frontier analysis. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics. The technique was first proposed by Charnes, Cooper and Rhodes in 1978 and since then it became a […]

In this article we will discuss how you can build easily a simple Facebook Sentiment Analysis tool capable of classifying public posts (both from users and from pages) as positive, negative and neutral. We are going to use Facebook’s Graph API Search and the Datumbox API 1.0v. Similar to the Twitter Sentiment Analysis tool that […]

In previous articles we have discussed the theoretical background of Naive Bayes Text Classifier and the importance of using Feature Selection techniques in Text Classification. In this article, we are going to put everything together and build a simple implementation of the Naive Bayes text classification algorithm in JAVA. The code of the classifier is […]

Recently, I have been refreshing my knowledge of Machine Learning by taking Andrew Ng’s excellent Stanford Machine Learning course online. The lecture module on Neural Networks ends with an intriging motivating video of the ALVINN autonomous car driving itself along normal roads at CMU in the mid 90s.

<p><em>This is an archive of Dan Weinreb's comments on Symbolics and Lisp machines.</em></p> <h3 id="rebuttal-to-stallman-s-story-about-the-formation-of-symbolics-and-lmi">Rebuttal to Stallman’s Story About The Formation of Symbolics and LMI</h3> <p>Richard Stallman has been telling a story about the origins of the Lisp machine companies, and the effects on the M.I.T. Artificial Intelligence Lab, for many years. He has published it in a book, and in a widely-referenced paper, which you can find