"Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed" Arthur Smauel. We can think of machine learning as approach to automate tasks like predictions or modelling. For example, consider an email spam filter system, instead of having programmers manually looking at the emails and coming up with spam rules. We can use a machine learning algorithm and feed it input data (emails) and it will automatically discover rules that are powerful enough to distinguish spam emails. Machine learning is used in many application nowadays like spam detection in emails or movie recommendation systems that tells you movies that you might like based on your viewing history.

Artificial intelligence is changing the world. At least, the White House thinks so. Last week, the Obama administration released a 60-page report titled Preparing for the Future of Artificial Intelligence. It paints with a broad stroke the current state of A.I. in several different fields -- health, education, the environment -- and proposes ways in which industry and government can work together to advance the public good. It's a remarkable document, if only for the fact that it's being issued by an outgoing administration in its final months in office.

John Dyer, Executive Director of Communications and Educational Technology at DTS, talks about the advancements of artificial intelligence and the theological and ethical implications of future development in this science.

Researchers in the Robot Learning Lab at Cornell University developed a robot that can prepare a cup of latte without ever having seen the machine before – the robot does this by visually observing the machine and by reading online instruction manuals, similar to how humans learn. The team used CUDA and TITAN X GPUs to train their deep learning models and then also uses the GPU during testing where the robot is trying to infer which human manipulation motion is best suited for the object it has never seen before. "We use a deep learning neural network that can tell the robot which action in a database is the closest to the one it has to perform," said researcher Jaeyong Sung. The researchers turned to the Amazon Mechanical Turk crowdsourcing service to collect a large library of actions -- they invited hundreds of visitors to guide a robot through the motions to perform various tasks described in a set of printed instructions. To make crowdsourcing possible, the researchers created a web interface that lets the user guide an imaginary robot arm, almost like playing a video game.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. Don't know where to start? If you are looking for something more, you could look here for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason. Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in.

A Tokyo-based nonprofit organization will begin offering online Japanese-language classes this month to children from abroad who need help to keep up in class at Japanese elementary and junior high schools. Youth Support Center's YSC Global School in Fussa, western Tokyo, is set to offer instruction provided by language education experts via personal computers or tablets to young foreign nationals living anywhere in Japan. The NPO will cooperate with municipalities and schools without sufficient resources to teach Japanese to such children. Yuran Nakajima, 16, watched a lecture on a PC monitor at YSC's office in Fussa during a trial session in September. Three other students sat in the classroom elsewhere in the city, where the lesson was being taught.

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. We estimate the learning channel capacity associated with several algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost, even in recurrent networks. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far.

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it's as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition. Deep learning is the next step to machine learning with a more advanced implementation. Currently, it's not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data.

Today, Udacity announced partnerships with an additional ten companies to help graduates of its new self-driving car nanodegree program find jobs. The program, launched on the stage of TechCrunch Disrupt last month, aims to bring together a large community of students interested in learning, and eventually contributing, to the front lines of autonomous car development. As one of Udacity's nanodegree initiatives, it was designed in conjunction with large corporations with hiring in mind. Previously Udacity had built partnerships with Mercedes-Benz, Nvidia, Otto, and Didi Chuxing. Today however, it is adding BMW,HCL, AutonomouStuff, Elektrobit, HERE, NextEv, Local Motors, McLaren Applied Technologies, Polysync and LeEco to its rosters.

From alarm's snooze in the morning to watching television at night, and all the activities in between – human life is increasingly influenced by machines. Today, machine learning has transformed devices with static pre-programmed software into smart machines driven by Artificial Intelligence (AI), giving them the ability to learn by themselves based on gathered data. Machine learning focuses on developing computer programs that can teach themselves to grow and update the program embedded in a device by analyzing incoming data. Defined as the science of getting computers to act without being explicitly programmed, Machine learning is aiding human life by paving the path towards smart technologies such as self-driving cars, practical speech recognition, and effective web search. It has also improved the concept of understanding human genome in the field of biotechnology.