The discovery by Tufts University biologists presents the first model of regeneration discovered by a non-human intelligence and the first comprehensive model of planarian regeneration, which had eluded human scientists for over 100 years. The work, published in the June 4, 2015, issue of PLOS Computational Biology, demonstrates how "robot science" can help human scientists in the future. In order to bioengineer complex organs, scientists need to understand the mechanisms by which those shapes are normally produced by the living organism. However, a significant knowledge gap persists between molecular genetic components identified as being necessary to produce a particular organism shape and understanding how and why that particular complex shape is generated in the correct size, shape and orientation, said the paper's senior author, Michael Levin, Ph.D., Vannevar Bush professor of biology and director of the Tufts Center for Regenerative and Developmental Biology. "Most regenerative models today derived from genetic experiments are arrow diagrams, showing which gene regulates which other gene. That's fine, but it doesn't tell you what the ultimate shape will be. You cannot tell if the outcome of many genetic pathway models will look like a tree, an octopus or a human," said Levin.

Each time analyst group Gartner unveils a new edition of its Hype Cycle chart, it inspires either schadenfreude or a sinking feeling. Your competitor has banked on a technology that's mired in the Trough of Disillusionment, and you were wise enough to cash out on the Slope of Enlightenment. The most curious detail about the 2016 edition of the Hype Cycle is not where any one technology shows up. It's how multiple incarnations of one underlying technology -- machine intelligence -- are spread out across several points on the infamous trough-and-plateau chart. Gartner's label for the rise of machine intelligence is "the perceptual smart machine age," and it predicts that such machines will be "the most disruptive class of technologies over the next 10 years." The benefits of what Gartner calls "radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks" are on the rise, but none has yet ripened to the point where it is boringly useful.

Ford outlined a few of the ways it's aiming to ship driverless cars by 2021, and part of the plan involves acquisitions. CEO Mark Fields revealed at a press event in Palo Alto today that the automaker acquired SAIPS, an Israeli company focusing on machine learning and computer vision. It's also partnering exclusively with Nirenberg Neuroscience, to bring more "humanlike intelligence" to machine learning components of driverless car systems. SAIPS' technology brings image and video processing algorithms, as well as deep learning tech focused on processing and classifying input signals, all key ingredients in the special sauce that makes up autonomous vehicle tech. This company's expertise should help with on-board interpretation of data captured by sensors on Ford's self-driving cars, and turning that data into usable info for the car's virtual driver system.

During the recent F8 summit, Mark Zuckerberg took to the stage to announce the integration of chatbot technology into Facebook Messenger. Explaining his desire for the consumer to "be able to message a business in the same way you message a friend", he proposed a new step forward for conversational commerce. But hold the iPhone, Zuckerberg. Before you get us all excited about something we didn't even know we needed... And with brands like Uber, Skyscanner and Amazon already getting in on the act, why does it spell such big news for marketers?

The machine was, and still is, my constant partner. I need her in order to translate the creative thoughts in my head into tangible ideas I can share with the world. Transitioning to design from a modern dance career in my twenties, I never thought a machine would be my accomplice for innovation. Machines have rapidly developed intelligence in this generation and their capabilities are changing the products we design. The process in which they are designed will also need to evolve.

When I began reading up on conversational commerce in late 2015, I had that same excited feeling that I had about iOS apps when the App Store first launched. Mike Nathanson, also an iOS Developer (and now my Co-Founder) and I started to brainstorm and work on a consumer-focused bot. We were both a little reluctant though to dive deeper into it because we anticipated that whatever we build would fail at conversation, a lot. We would develop, QA, release, measure, and repeat. With a conversational experience, it seemed unpredictable and with a high likelihood of conversational failure.

This week saw the placation of GST bill on logistics along with many more news such as evolvement of digital payments and artificial intelligence in E-commerce industry. The meeting of AI and e-commerce could not only transform the way jillions of online transactions are done, but also change the in-store purchase behaviors which are influenced by digital interactions. According to Sachin Bansal, CEO, Flipkart, artificial intelligence is a key differentiator in the fiercely competitive e-commerce business. Digital payments will grow 10 times to reach 500 billion by 2020 and contribute 15% of gross domestic product (GDP). Some of the key reasons of these acquisitions include privacy of customer's payment data, secured payment facility and use of payment data for big data analysis to the company.

Nasa launches million dollar contest to create'Valkyrie' AI robot that will accompany astronauts on ...

The latest news about Google's head of search, Amit Singhal, to leave the company he spent 15 years with, had the shocking effect on the SEO community. And what is more surprising - his successor, John Giannandrea, is the one who has worked on artificial intelligence at Google (including RankBrain - the part of search algorithm which uses AI to work with a queries search engine was not able to understand before). With this change of executives, we may be on the verge of a new era - the era of transition from the algorithm-based search to AI-based search. To power its artificial intelligence, Google uses deep learning (also known as neural networks) - one of machine learning methods, which uses a mathematical model to mimic the way as human brain neurons work. Deep learning is built on the concept of digital neurons, organized into layers.

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