The biological system is a natural form of technology. A simple examination of the nanobiology of the macromolecular system of any cell will attest to this – enzymes and structural proteins are veritable nanomachines, linked to the information processing network of DNA and plasma membranes. Far from being a primordial or rudimentary organic technology – we are discovering more and more the level of complexity and paragon technological sophistication of living systems, which as is being discovered, even includes non-trivial quantum mechanical phenomena once thought to only be possible in the highly specialized and controlled environment of the laboratory. Reciprocally, soon our technologies will become living systems – particularly through nanotechnology (which is being accomplished through reverse engineering and hybridization with biomolecules, particularly DNA) and general artificial intelligence – machine sentience. Following this parallelization of biology with technology, we can examine how humanity as a technological supraorganism is undergoing a period of punctuated speciation – an evolutionary transformation of both our inner and outer world.

Ford Motor Company heightens its efforts to pave the way for autonomous vehicles by making a five-year, US$ 1-billion-dollar investment in startup artificial intelligence company Argo AI. This strategic move makes the Detroit automaker the majority stakeholder of Argo AI while also putting them a step ahead in the competition to develop and produce self-driving vehicles by 2021. Seeking ways to make transportation safer by combining advancements in computer science, robotics and artificial intelligence, Argo AI was founded by former Google and Uber leaders and is composed of experienced roboticists and engineers led by Argo AI founders Bryan Salesky, company CEO, and Peter Rander, company COO who are both alumni of Carnegie Mellon National Robotics Engineering Center. The team from Argo AI will support the machine-learning software developed of Ford's autonomous vehicle development team by providing the robotics talent and expertise in order to achieve a fully functioning self-driving vehicle. Ford will spearhead the development of the autonomous vehicle hardware platform, systems integration, manufacturing, exterior and interior design, and regulatory policy management while Argo AI develops and deploys the latest advancements in artificial intelligence, machine learning and computer vision to help build safe and efficient self-driving vehicles.

On a post I wrote last year, I talked about the fact that Deep Neural Network could not label a changed image correctly (e.g. Recently, a related result is shown by researchers from University of Wyoming and Cornell University. They produced images completely unrecognizable to human eyes (as shown in the right picture) while DNN will still label them to be familiar objects (such as cheetah/peacock/baseball/…) with 99.99% confidence. Researchers used one of the best Deep Neural Networks, the "AlexNet" trained on the 1.3-million-image ILSVRC 2012 ImageNet dataset, to achieve state-of-the-art performance, and "LeNet" model trained on the MNIST dataset to test if the result holds for other DNN architectures. "AlexNet" and "LeNet" are both provided by the Caffe Software package.

Elizabeth Nieves is looking for a job. And, so is her chatbot. On Facebook, you can send a message to the Hire Eli bot and learn about Nieves' skills and experience. There's a photo, and the bot can tell a joke. You can click a button to find out where she has worked and learn about her education.

As a distinguished and globally recognized thought leader and innovator, Bruce Morton has over three decades of experience within the human capital and workforce management industry. Throughout his career he has...

New research from DeepMind, Alphabet Inc.'s London-based artificial intelligence unit could ultimately shed light on this fundamental question. They have been investigating the conditions in which reward-optimizing beings, whether human or robot, would chose to cooperate, rather than compete. The answer could have implications for how computer intelligence may eventually be deployed to manage complex systems such as an economy, city traffic flows, or environmental policy. Joel Leibo, the lead author of a paper DeepMind published online Thursday, said in an email that his team's research indicates that whether agents learn to cooperate or compete depends strongly on the environment in which they operate. While the research has no immediate real-world application, it would help DeepMind design artificial intelligence agents that can work together in environments with imperfect information.

NEW YORK Wells Fargo & Co has created a team to develop artificial intelligence-based technology and appointed a lead for its newly combined payments businesses, as part of an ongoing push to strengthen its digital offerings. Wells Fargo's AI team will work on creating technology that can help the bank provide more personalized customer service through its bankers and online, the bank said on Friday. It will be led by Steve Ellis, head of Wells Fargo's innovation group. Well Fargo's AI focus comes as banks and other large financial institutions increase their investment in the emerging technology which seeks to train computers to perform tasks that would normally require human intelligence. Projects range from systems that can spot payments fraud or misconduct by employees, to technology that can make more personal recommendations on financial products to clients.

Given the sheer number of backpropagation tutorials on the internet, is there really need for another? One of us (Sanjeev) recently taught backpropagation in undergrad AI and couldn't find any account he was happy with. So here's our exposition, together with some history and context, as well as a few advanced notions at the end. This article assumes the reader knows the definitions of gradients and neural networks. It is the basic algorithm in training neural nets, apparently independently rediscovered several times in the 1970-80's (e.g., see Werbos' Ph.D. thesis and book, and Rumelhart et al.).

PubNub BLOCKS enables you to process your data mid-stream, to execute functions on your data in motion. This is huge, because you no longer need to spin up and manage new servers to run a simple function. It's all done in the network. That's why we say that PubNub is a programmable network. IBM Watson is a powerful technology that brings cognition to applications, with the ability to understand, reason, learn, and interact. I like to say it gives your application a brain, extending the capabilities of your application beyond simply interacting through a set of rules.

Like all hardware device makers eager to meet the newest market opportunity, Intel is placing multiple bets on the future of machine learning hardware. The chipmaker has already cast its Xeon Phi and future integrated Nervana Systems chips into the deep learning pool while touting regular Xeons to do the heavy lifting on the inference side. However, a recent conversation we had with Intel turned up a surprising new addition to the machine learning conversation--an emphasis on neuromorphic devices and what Intel is openly calling "cognitive computing" (a term used primarily--and heavily--for IBM's Watson-driven AI technologies). This is the first time to date we've heard the company make any definitive claims about where neuromorphic chips might fit into a strategy to capture machine learning, and marks a bold grab for the term "cognitive computing" which has been an umbrella term for Big Blue's AI business. Intel has been developing neuromorphic devices for some time, with one of the first prototypes that was well known in 2012.