Artificial intelligence is very interesting topic of research for many modern scientists. The concept of machine intelligence is really fascinating. It gives human a power to design something that can live on its own. The AI technology has become really advanced and its only matter of time when the machines will be able to learn almost anything. The machine learning algorithms are already very smart, however the processing power has been a challenge in last decade.

LONDON – 25 August 2016 – IP EXPO Europe, Europe's number one enterprise IT event, has today announced the addition of several influential industry speakers to this year's keynote and seminar programme. Attendees will have the opportunity to hear how key IT issues are affecting businesses and humanity alike, from Author & Founding Director of Oxford University's Future of Humanity Institute: Nick Bostrom; the'Father of Java': James Gosling; the creator of the'MySpace worm' and now an Independent Security Researcher: Samy Kamkar; and Independent Cyber Security Consultant Dr Jessica Barker. These additions to the 2016 IP EXPO Europe program are the latest in a list which already includes some of the world's most renowned technology innovators, from the likes of HPE, Microsoft, and Amazon Web Services. With his work influencing the likes of Bill Gates, Professor Stephen Hawking, and Elon Musk, keynote speaker Nick Bostrom, Author & Founding Director of Oxford University's Future of Humanity Institute, is one of the world's foremost authorities on Artificial Intelligence (AI). Opening Day One at this year's IP EXPO Europe, Bostrom will be discussing the impact that AI and intelligent machines will have on business and society, and sets out to answer the question: 'Will AI bring about the end of humanity?'

Say what you will, but IBM Watson is one resourceful supercomputer. We've previously seen the AI describe the contents of photos, predict the most popular toys during Christmas season and gauge your emotional state – all of that with an exceptional accuracy. Now IBM Watson has added yet another skill to its arsenal as it just learned how to make movie trailers. Earlier this week, 20th Century Fox trusted the supercomputer with the task to create the trailer for its upcoming sci-fi drama Morgan. With this accomplishment, IBM Watson becomes the first-ever AI to produce a movie trailer.

We revisit the elegant observation of T. Cover [Cov65] which, perhaps, is not as well-known to the broader community as it should be. The first goal of the tutorial is to explain--through the prism of this elementary result--how to solve certain sequence prediction problems by modeling sets of solutions rather than the unknown data-generating mechanism. We extend Cover's observation in several directions and focus on computational aspects of the proposed algorithms. The applicability of the methods is illustrated on several examples, including node classification in a network. The second aim of this tutorial is to demonstrate the following phenomenon: it is possible to predict as well as a combinatorial "benchmark" for which we have a certain multiplicative approximation algorithm, even if the exact computation of the benchmark given all the data is NPhard. The proposed prediction methods, therefore, circumvent some of the computational difficulties associated with finding the best model given the data. These difficulties arise rather quickly when one attempts to develop a probabilistic model for graph-based or other problems with a combinatorial structure.

I'm working my way through the Coursera course on Machine learning by Andrew Ng, and I'm really confused about the bias unit? In the programming assignment, we always set the parameter for the bias unit to 0, so how does it affect the neural network in any way? Since the bias unit will be multiplied by it's parameter/weight (which seems to always be zero), wouldn't it be completely insignificant?

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. You can read more about Prof. Rigollet's work and courses on his website.

A benefit of using ensembles of decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from a trained predictive model. In this post you will discover how you can estimate the importance of features for a predictive modeling problem using the XGBoost library in Python. Feature Importance and Feature Selection With XGBoost in Python Photo by Keith Roper, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. A benefit of using gradient boosting is that after the boosted trees are constructed, it is relatively straightforward to retrieve importance scores for each attribute.

Recently we shared an introduction to machine learning. While making machines learn from data is fun, the data from real-world scenarios often gets out of hand if you try to implement traditional machine-learning techniques on your computer. To actually use machine learning with big data, it's crucial to learn how to deal with data that is too big to store or compute on a single computing machine. Today we will discuss fundamental concepts for working with big data using distributed computing, then introduce the tools you need to build machine learning models. We'll start with some naive methods of solving problems, which are meant only as an example. As we move forward, we will make things more realistic. MapReduce is a technique that is used to distribute a data set in parts to different agents. An agent here means a single computer.

Getting into machine learning (ml) can seem like an unachievable task from the outside. However, after dedicating one week to learning the basics of the subject, I found it to be much more accessible than I anticipated. This article is intended to give others who're interested in getting into ml a roadmap of how to get started, drawing from the experiences I made in my intro week. Before my machine learning week, I had been reading about the subject for a while, and had gone through half of Andrew Ng's course on Coursera and a few other theoretical courses. So I had a tiny bit of conceptual understanding of ml, though I was completely unable to transfer any of my knowledge into code.

This is a list of resources I think would be useful for those who are just starting to explore the amazing Machine Learning domain of Computer Science and want to learn more about Neural Networks and their applications. The general idea behind putting these resources together and publishing this list is that when I just started I saw posts with hundreds of links without description and I simply didn't know which of them are worth spending time on. Focusing on most useful ones and giving short summaries instead is a good idea. I am not a Deep Learning expert and everything I wrote down is just my personal experience with these resources, very subjective opinion. In-depth Convolutional Neural Networks course highly recommended if one wants to learn about image recognition, Computer Vision-related problems and so on. The problemset is amazing; it has probably the best numpy tutorial I have ever seen and makes people implement algorithms they saw in lectures in pure Python numpy, which seems to be a great idea as it helps to get better understanding of how everything actually works.