Every now and then I read a paper that makes a really strong connection with me, one where I can't stop thinking about the implications and I can't wait to share it with all of you. For me, this is one such paper. In the great see-saw of popularity for artificial intelligence techniques, symbolic reasoning and neural networks have taken turns, each having their dominant decade(s). The popular wisdom is that data-driven learning techniques (machine learning) won. Symbolic reasoning systems were just too hard and fragile to be successful at scale.

Sieving through resumes for a job seemed to be a universal problem for small and big companies alike. Gifted with technology, we now have access to many online job portals across the world claiming to find the right job for the right person. But how accurate is this service? It is from here that an idea originated in the brains of Arjun Pratap, founder and CEO of EdGE Networks. He believed that most people are in pursuit of passion, money and status and this, they find hard to capture as a package.

Earlier this week, the Obama administration discussed a new strategic plan aimed at fostering the development of AI-centered technologies in the United States. What's striking about it is, the Washington Post notes, although the United States was an early leader in deep-learning research (a subset of the overall branch of AI known as machine learning), China has effectively eclipsed it in terms of the number of papers published annually on the subject (Editor's note: the link could be paywalled; alternate source). From the report: The rate of increase is remarkably steep, reflecting how quickly China's research priorities have shifted. The quality of China's research is also striking. The chart narrows the research to include only those papers that were cited at least once by other researchers, an indication that the papers were influential in the field.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

I assume that you have a mixed dataset which has both numeric and non-numeric data types. In such cases, clustering based on a Euclidean distance measures will not be relevant. You could try conceptual clustering techniques which are based on concept hierarchy. The technique, called conceptual clustering, subdivides the data incrementally into subgroups based on a probabilistic measure known as "COHESION". A partition score is computed based on a category utility measure at each branch in concept hierarchy.

Everybody has had the experience of not recognising someone they know--changes in pose, illumination and expression all make the task tricky. So it's not surprising that computer vision systems have similar problems. Indeed, no computer vision system matches human performance despite years of work by computer scientists all over the world. That's not to say that face recognition systems are poor. The best systems can beat human performance in ideal conditions.

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Hybrid computing using a neural network with dynamic external memory by Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwi?ska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu and Demis Hassabis Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees.

The tech craze du jour is machine learning (ML). Billions of dollars of venture capital are being poured into it. All the big tech companies are deep into it. Every computer science student doing a PhD on it is assured of lucrative employment after graduation at his or her pick of technology companies. One of the most popular courses at Stanford is CS229: Machine Learning. ML is the magic sauce that enables Amazon to know what you might want to buy next, and Netflix to guess which films might interest you, given your recent viewing history.

Natural Language Processing Artificial intelligence Projects: Artificial intelligence is the study of intelligence exhibited by machines or software. Natural language processing is a field of computer science, artificial intelligence, and linguistics dealing with the interactions between computers and human languages. Essentially this is the area of human computer interaction. Natural language processing gives machines the ability to read and understand the languages that humans speak. The natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human written sources, such as news and other unstructured texts.