What do our representations of AI say about gender and sexuality? Recent films, like Her and Ex Machina, portray specifically female AI. Her presents Samantha as a questing mind with emotional needs similar to those of humans. Samantha has true consciousness in her ability to love Theo. Yet, because she is non-corporeal, she is not quite human.

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive documentation and wiki helps fill in the details.

In two previous posts, I covered the emerging industry of cloud-based machine learning solutions. First, I covered Microsoft's Azure Machine Learning and IBM's Watson Analytics. Microsoft's Azure ML provides a graphical drag-and-drop interface for connecting preprogrammed components of a data science pipeline together. The service is similar to KNIME and seemed targeted for users who knew just enough to know what to do, but not so much that they would want to code up fresh algorithms. One value added for Microsoft's product is a smooth integration for companies which already have their data stored in Microsoft's Azure compute cloud.

Machine Learning is nothing but building a'machine' which'learns' from its experience. And, becomes better with experience – just like humans. We also learn from our experiences. Companies like Google, Facebook, Microsoft are using machine learning techniques at a larger scale. However, one common mis-conception people have is that they need to learn coding to start machine learning.

I read somewhere on this forum that last years winner had only 18 misclassification's. That made me suspicious way my submitted model is only in 126 place on a leader-board( my current result is 42 True predictions and 14 False ones). It seems that if I would had made my predictions more aggressive, something like: if pred 0.5: pred 0.95 else: pred 0.05 My log_loss would be 0.34, instead of 0.53. I'm curious is this common case to manually edit your probabilities or other classifiers like neural networks ( never tried it) just predicting with higher confidence?

I read somewhere on this forum that last years winner had only 18 misclassification's. That made me suspicious way my submitted model is only in 126 place on a leader-board( my current result is 42 True predictions and 14 False ones). It seems that if I would had made my predictions more aggressive, something like: if pred 0.5: pred 0.95 else: pred 0.05 My log_loss would be 0.34, instead of 0.53. I'm curious is this common case to manually edit your probabilities or other classifiers like neural networks ( never tried it) just predicting with higher confidence?

PatternEx takes'security' far beyond current WFD, UBA & SIEM solutions by combining machine learning algorithms with the human intuition of InfoSec analysts.

Artificial intelligence or AI is the future of the legal profession. The good news for anyone worried by that statement is people have been making it for several decades. The first international conference on law and artificial intelligence was held in Boston in 1987, before the invention – let alone the mass use of – the worldwide web. Despite the early enthusiasm the concept of computers taking over legal reasoning tasks from human lawyers has yet to become reality. Partly this is because artificial intelligence developed more slowly everywhere than the enthusiasts predicted.

In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning. Several of these approaches have well-known divergence issues, and I will present simple methods for addressing these instabilities. The talk will include a case study of recent successes in the Atari 2600 domain, where a single agent can learn to play many different games directly from raw pixel input.

In machine learning, the problem of reinforcement learning is concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioral decisions. This tutorial will introduce the fundamental concepts and vocabulary that underlie this field of study. It will also review recent advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology.