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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 […]
Using Artificial Intelligence to solve the 2048 Game (JAVA code) Read More »
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
Developing a Naive Bayes Text Classifier in JAVA Read More »
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
How to build your own Facebook Sentiment Analysis Tool Read More »
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
Measuring the Social Media Popularity of Pages with DEA in JAVA Read More »
With the popularity of AI coding tools rising among some software developers, their adoption has begun to touch every aspect of the process, including human developers using the tools to improve existing AI coding tools. We’re not talking about runaway self-improvement here; just people using tools to improve the tools themselves. In interviews with Ars
OpenAI built an AI coding agent and uses it to improve the agent itself Read More »
Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. Their new approach breaks
MIT researchers propose a new model for legible, modular software Read More »
