The head of Google's Brain team has warned that a lack of diversity in artificial intelligence and computer science research is limiting the breadth of experience that can be brought to bear in devising'humanistic thinking'. Writing on Reddit Jeff Dean warned: "I am personally not worried about an AI apocalypse, as I consider that a completely made-up fear. I am concerned about the lack of diversity in the AI research community and in computer science more generally." This concern centres on the increasing specialisation which sees computer laboratories staffed solely by computer scientists and the fear that a single world view could take shape from this bias which can impede the development of new ways of thinking. In an effort to counteract this inbuilt skew Dean has overseen the creation of a Brain Residency programme with the intention of bringing in experts from a host of other disciplines such as physicists, mathematicians, biologists, neuroscientists and electrical engineers to create a more holistic research environment.

BENGALURU: Customer engagement plays an important part in traditional retail outlets which seems to be missing among the plethora of e-commerce portals. To resolve this, a bunch of young entrepreneurs have come up with an Artificial Intelligence-driven sales chatbot which engages customers with a human touch. And the good news is that it's live and will provide round-the-clock hands-on solutions and technical support. Navneet Gupta, Chief Technical Officer of Racetrack.ai We understand that customer engagement is of utmost priority for any business and we have designed it to not only intelligently engage with the customer but also make the conversations fruitful with every chat." He points out that the AI-driven platform can not only convert a simple query to a customer but can also attain a loyal customer for life by a unique customer engagement."

Forgery and forensic handwriting analysis are still almost entirely manual processes – but by taking the novel approach of viewing handwriting as texture-synthesis, we can use our software to characterise handwriting to quantify the odds that something was forged,

This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland. Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.

Flash memory is being drawn into the mainstream of enterprise storage, but its tendency to deteriorate with use remains an Achilles' heel. A paper released at the Aug. 9 start of the Flash Memory Summit in Santa Clara, Calif., finds that machine learning can counteract that deterioration and drastically extend its life cycle. The paper was written by Tom Coughlin, president of Coughlin Associates (PDF), a solid state consultant in Atascadero, Calif. He is also general chairman of the summit. The paper was sponsored by NVMdurance, a Limerick, Ireland, firm that is applying machine learning in the software it creates for managing solid state devices.

Unconventional emergencies generally lack experiences, and the development of the situation is always dynamic. So it's easy to pose a threat to the security and stability of the society. Byintroducing the standardization of domain knowledge in emergency decision system and giving an effective remedy for the emergency decision making method based on artificial intelligence, domain knowledge and ontology in the field of unconventional emergencies are helpful to solve the problem of the low degree of the pertinence and participation of the experts. It has some inspiration for the future emergency management.

This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference rule. Firing such a rule generates intrinsically new knowledge: rules are learned from training data, which consists of facts stored already in the knowledge base. This new knowledge may be used in the same inference chain to derive a decision.

One time when I was illustrating the code base and architecture of TensorFlow to my friends, they were quite surprised by how much more code was introduced since TensorFlow's first open-source release. They were only expecting several popular types of deep learning algorithms from the code base as heard from other people and social media. Yet, TensorFlow is not just for deep learning. It provides a great variety of building blocks for general numerical computation and machine learning. In this blog post, I will introduce the wide range of general machine learning algorithms and their building blocks provided by TensorFlow in tf.contrib.

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Experienced machine learning developer, Hugh Perkins, author of the popular open source OpenCL libraries DeepCL and cltorch, is an avid user of the Nimbix cloud. Mr. Perkins chose to work with Nimbix in addressing machine learning due to the powerful platform API, industry-leading selection of GPUs, superior-performance and economics. "Nimbix is a breath of fresh air," said Mr. Perkins. "The per-second billing, spin up times of seconds, and the availability of high end GPUs, make Nimbix an awesome choice for machine learning developers." The Nimbix cloud platform is democratized and developer-friendly, allowing users to monetize their trained neural networks in the application marketplace.