Scientists have developed a new software that can analyse the handwriting of any individual and accurately replicate it, an advance that may spark the comeback of the handwritten word in a world dominated by the QWERTY keyboard. Researchers at University College London (UCL) created'My Text in Your Handwriting', a programme which examines a sample of a person's handwriting and generates new text saying whatever the user wishes, as if the author had handwritten it themselves. "Our software has lots of valuable applications," said Tom Haines, from UCL. "Stroke victims, for example, may be able to formulate letters without the concern of illegibility, or someone sending flowers as a gift could include a handwritten note without even going into the florist," Haines said. "It could also be used in comic books where a piece of handwritten text can be translated into different languages without losing the author's original style," he said. The machine learning algorithm is built around glyphs – a specific instance of a character.

WEF names robotics as one of the areas of technology that could revolutionise finance in the next few decades. Biometrics, cloud computing, cognitive computing, blockchain technology, machine learning, quantum computing, and robotics are set to transform financial services over the next few decades, according to the World Economic Forum (WEF). WEF made the prediction in its "Future of Financial Infrastructure" report looking at blockchain technology, which was published on Friday. The influential non-profit rates the transformative potential of these new technologies alongside past innovations such as the personal computer, the internet, and smartphones. WEF's report focuses on blockchain technology -- referred to as distributed ledger technology in the above graphic -- so it does not actually give any concrete examples of how technologies other than blockchain might be applied to finance.

The robots are coming, and they're getting smaller, smarter and cheaper. While today, businesses may own just a single drone, one day, large parts of whole industries could be overseen by a new generation of robots. But, how will groups of these robots perform useful tasks like collecting crop data or organizing around a common goal? That's one role for the blockchain proposed in a new white paper from MIT Media Lab research affiliate Eduardo Castello Ferrer. The eight-year robotics veteran believes that by using the blockchain to distribute information, whole armies of robots could solve problems and accomplish tasks more efficiently. The idea is that in robotic swarms, each robot follows basic rules, drawing inspiration from creatures like ants and fish that often clump together.

Below is a compilation of APIs that have benefited from Machine Learning in one way or another, we truly are living in the future so strap into your rocketship and prepare for blastoff. To check out all this information, click here.

Chipmaker Intel today announced that it has acquired Nervana, a startup that has been developing artificial intelligence software and hardware. Terms of the deal weren't disclosed, but a source familiar with the matter told VentureBeat Intel paid more than 350 million. "Nervana's Engine and silicon expertise will advance Intel's AI portfolio and enhance the deep learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors," Diane Bryant, executive vice president and general manager of Intel's Data Center Group, said in a blog post. While Intel has a running business in high-performance computing (HPC), it has taken a back seat to Nvidia, another HPC supplier, when it comes to creating chips for deep learning, a trendy type of artificial intelligence that involves training artificial neural networks on lots of data and then getting them to make inferences on new data. Google has deployed competing chips named tensor processing units (TPUs) that can handle Google's TensorFlow open source deep learning framework.

Unsupervised nearest neighbors is the foundation of many other learning methods, notably manifold learning and spectral clustering. Supervised neighbors-based learning comes in two flavors: classification for data with discrete labels, and regression for data with continuous labels. The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. The number of samples can be a user-defined constant (k-nearest neighbor learning), or vary based on the local density of points (radius-based neighbor learning). The distance can, in general, be any metric measure: standard Euclidean distance is the most common choice. Neighbors-based methods are known as non-generalizing machine learning methods, since they simply "remember" all of its training data (possibly transformed into a fast indexing structure such as a Ball Tree or KD Tree.). Despite its simplicity, nearest neighbors has been successful in a large number of classification and regression problems, including handwritten digits or satellite image scenes.

This notebook was written by Dr. Randal S. Olson from GitHub. In this notebook, Randal is going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like. In addition to providing code examples, he also hopes to imbue in you a sense of good practices so you can be a more effective -- and more collaborative -- data scientist. Randal will be following along with the data analysis checklist from The Elements of Data Analytic Style, which he strongly recommends reading as a free and quick guidebook to performing outstanding data analysis. In the time it took you to read this sentence, terabytes of data have been collectively generated across the world -- more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields -- statistics, physics, computer science, design, and many more -- the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.

Machine learning is a fascinating study, but how do you actually use it on your own problems? You may be confused as to how best prepare your data for machine learning, which algorithms to use or how to choose one model over another. In this post you will discover a 14-part crash course into applied machine learning using the Weka platform without a single mathematical equation or line of programming code. Applied Machine Learning With Weka Mini-Course Photo by Leon Yaakov, some rights reserved. Before we get started, let's make sure you are in the right place.

Private sector lender Kotak Mahindra Bank has started an'Innovation Lab' in Bengaluru to tap into the best technology that impacts its operations, and is open to investing in startups, a senior company official has said. "We started an'Innovation Lab' recently which is right now a dedicated space from where we are partnering with startups in the fintech space to test concepts and launch them into commercial products," Kotak Bank's Chief Digital Officer Deepak Sharma told PTI. The fourth largest private sector bank has put together a seven-member core team, which is working with an equal number of startups in the artificial intelligence, analytics, biometric or iris scanning and machine learning space. A bulk of the startups are Bengaluru-based, while two are from the US and Australia, Sharma said. Domestic lenders, especially those in the private sector, have been increasing their engagement with the fledgling fintech startup community to tap into the technologies of the future which will help them stay relevant.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.