Deep Learning has made several breakthroughs in recent years. Compared to traditional computation platforms, it has become more sophisticated and advanced than ever. Smart homes, intelligent personal assistant, etc. are some of the major breakthroughs in the present era. In this article, we list down 8 platforms which can be used to build mobile deep learning solutions. Facebook's open-source deep learning framework, Caffe2 is a lightweight, modular, and scalable framework which provides an easy way to experiment with deep learning models and algorithms. The framework comes with native Python and C APIs that work interchangeably and integrates with Android Studio, Microsoft Visual Studio, or XCode for mobile development.

Here are five things in technology that happened this past week and how they affect your business. Picnic--a Seattle startup, --announced this past week that it has created an assembly platform that can make 300, 12-inch pizzas per hour (and 180 18-inch pizzas per hour), making this robot a first-of-its-kind. When a pizza is ordered, the order goes into a digital queue, prompting the robot to begin making the pie the moment that the dough is placed in the appropriate spot in the machine. Data is then sent back to Picnic through the internet in order for developers to help the robot to improve upon any errors made. The A.I. driven pizza platform is currently being used at 3 different establishments in Seattle.

Artificial Intelligence (AI), is this term supposed to make us feel good about our future, or should we be worried? Let's get into the depths of it and find out for ourselves. Let's start with talking about how often we hear the term AI in our day to day lives. Smartphone manufacturers have made it a habit to boast about how their smartphones are so smart and come equipped with AI and all the things AI can do for us. As fascinating as this is, AI is meant to be much more than just suggesting what type of picture we are clicking on our smartphone and tweaking the settings to produce the best result.

House Democrats leading the Trump impeachment inquiry are "cherry-picking what to leak," House Foreign Affairs committee member Congressman Lee Zeldin, R-N.Y., claimed Saturday. Appearing on "Fox & Friends: Weekend" with host Ed Henry, Zeldin said Democrats aren't being upfront with the American public. They're lying about other claims and the American public gets completely deceived as a result of it," he said. At a fiery rally in Louisiana on Friday, the president hit back at Democrats' "witch hunt." This is one of the great con jobs ever. We must never let it happen to another president. This should never be allowed to happen again," he told his crowd of supporters.

There is a tremendous amount of active research in making deep learning models interpretable (e.g., LIME and Layer wise Relevance Propagation). In summary, a high accuracy data science component by itself may not mean much even if it solves a pressing business need. On one extreme, it could be that the data science solution achieves high accuracy at the cost of high compute power or high turnaround time, neither of which are acceptable by the business. On the other extreme, it could be that the component that the end-user interacts with has minimal sensitivity to the errors of the data science component and thus a relatively simpler model would have sufficed the business needs. A good understanding of how the data science component fits into the overall end-to-end solution will undoubtedly help make the right design and implementation decisions.

MOUNTAIN VIEW, CALIFORNIA -- In a move that is completely unsurprising to many, Google's AI research division has announced that they are issuing PhD degrees to select employees. Industry research organizations like Google Brain, DeepMind, and FAIR are well known as heavy hitters in the artificial intelligence research community, publishing as many papers (if not more) as academic institutions like Stanford, Berkeley, and MIT. Many top professors from academia have migrated over to industry research labs as well, sacrificing the security of academic tenure for fat stacks of money. Although Google has previously experimented with research residencies, this is the first time that they have issued postgraduate degrees. According to a representative, the tech giant decided to issue PhDs in order to attract scarce AI talent.

One of the most immediate ways that organizations are seeing value in artificial intelligence is in the use of chatbots and conversational interfaces, one of the seven fundamental patterns of AI. Chatbots have been in use for decades, but only recently have they had sufficient intelligence to handle conversations with a wide range of vocabulary, accents, and conversational styles. Now we have chatbots that can be developed to engage in very diverse interactions and handle many different conversational patterns. Chatbots have proven to be very valuable in many use cases ranging from customer support to conversational commerce. As a result, companies and organizations of all types are investing in chatbots and conversational systems.

Matthew Michela, president and CEO of Newton, Mass.-based Life Image, says there are three questions healthcare executives need to ask when assessing the value of an artificial intelligence product for radiology. Life Image provides access to points-of-care and curated clinical and imaging data. According to the company, it delivers large-scale, heterogenous, de-identified imaging sets that are linkable to other longitudinal data. What follows are the three questions Michela says healthcare leaders must ask about artificial intelligence products for radiology. Question 1: Does the Product Solve a Relevant Clinical Problem?

"AI can only solve'toy' versions of real-life issues" However, researchers were not able to deliver on the lofty promises associated with AI. In 1973, the British parliament commissioned a thorough investigation of the state of research in AI. The resulting Lighthill report stated that AI was not able to achieve anything that could not also be achieved in other sciences. The report concluded by proclaiming that most successful AI algorithms would grind to a halt on real world problems and they were only suitable for solving'toy' versions of real-life issues. The UK subsequently significantly scaled back government funded AI research projects.

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