WWDC16 just ended and Apple left us with new amazing innovative APIs. This year speech recognition, proactive applications, machine learning, user intents, and neural networks have been the most frequent terms used during the conference. So, besides a new rich version 3 of Swift, almost every new addition to iOS, tvOS and macOS is related with artificial intelligence. For example, Metal and Accelerate in iOS 10 provide an implementation of convolutional neural networks (CNNs) for the GPU and CPU respectively. During the keynote, Craig Federighi (Apple's SVP Software Engineering) showed how the Photos app on iOS organizes our photos according to different smart criteria. He highlighted that Photos app uses deep learning to provide such functionality. Also, Federighi showed how Siri, now available to developers, can suggest what we need.

Data scientists are no longer needed to mine data for business value, and organisations should instead look to machine learning platforms.

Customer service is an important business operation which keeps your company closer to your customers by getting in touch with them. Small sized businesses can take advantage of chatbots in serving their customers by giving them replies and resolving their queries quickly. The chatbot can automate low-value tasks focusing on those that really add value to the company. Chatbots can provide rich contents like images and videos helping the customers better. In any case, if the bot is not able to resolve the issues entirely, then they should hand over to a human advisor in a seamless way.

Artificial Intelligence start-up Arya.ai announced on Monday the global launch of'Braid, an open Source tool to build intelligence quickly into systems. "Open sourcing key tools in AI, will help discover newer, interesting and more impactful use cases and applications for AI that we may not have even thought of," said Vinay Kumar Sankarapu, CEO and founder of Arya.ai. Technology companies and start-ups trying to create products that use Artificial Intelligence are racing to build neural networks. By their very nature however, neural networks are complex and call for Deep Learning. Building neural networks, which are not unlike actual human brains with their complex layers, is a resource-intensive, expensive and time consuming process.

In case you haven't noticed, there's yet another major shift in technology. This one has to do with the A.I. and chatbots that help us in our daily lives and at work. They offer many advantages, but adjusting to them has been no easy task. Despite some of the massive advancements we've made in tech, it's clear we're still in an "adjustment period" -- with A.I., in particular. Natural selection is taking its course, as individuals and entire industries are forced to get smart or fall behind.

Academic and technology experts have joined Stanford University's report on robotics development entitled, "One Hundred Year Study on Artificial Intelligence." Not only will the investigative inquiry focuses on the advancement of artificial forms, but it will also involve issues associated ethical challenges. "Artificial Intelligence and Life in 2030," a research paper consisting of 28,000 words, will tackle impacts in sectors affiliated with employment, healthcare, security, entertainment, education, service robots, transportation and poor communities. Foreseeing how smart technologies will affect urban life will also be included. With the release of the AI100 report, researchers and scientists hope that by thinking and discussing ahead what AI might actually bring, preparations to address both the coming benefits and challenges must be instituted.

Artificial Intelligence (AI) is one of the hottest areas of technology research. Companies like IBM, Google, Microsoft, Facebook and Amazon are investing heavily in their own R&D, as well as buying up startups that have made progress in areas like machine learning, neural networks, natural language and image processing. Given the level of interest, it should come as no surprise that a recent artificial intelligence report from experts at Stanford University concluded that "increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030." In a recent article, we provided an overview of 45 AI projects that seem particularly promising or interesting.

There's no denying that artificial intelligence is lightyears ahead of what it was just a few years ago. The technology continues to advance at an ever-increasing rate. But the ultimate goal of artificial intelligence researchers is to replicate human intelligence. So how do artificial intelligence and human intelligence measure up? The so-called "deep learning" that artificial intelligence is capable of isn't really the type of profound learning like humans are capable of.

The segment got a shot in the arm when Facebook Messenger opened up to bots in April. WhatsApp has also announced plans to open up for businesses. "Messenger has 1 billion users. The benefit of integrating a bot on Messenger is that it is a hassle free experience, requiring no registration," said Nitin Babel, co-founder of Niki.ai, a startup in the chatbot space. Arihant Jain of Joe Hukum, a bot building startup, adds that with large companies opening to bots, a community of sorts is beginning to develop.

Although there has been a rapid and greatly publicized growth of data analytics and machine learning methodologies across many applications, and in virtually every industry, these developments seem to have almost completely been missed in the semiconductor integrated circuit (IC) space. With the 14nm process technology node currently in production, and both 10 and 7nm nodes at different stages of development, the IC'ecosystem' is being restructured and consolidated across its four traditional components (i.e., fabless design companies, electronic design automation and intellectual property suppliers, process and metrology tools suppliers, and silicon foundries). There are, however, intrinsic technology factors (e.g., the continual deceleration of geometric scaling and the delayed introduction of key patterning technologies) that are primary sources of disruption to this restructuring. There are also critical hidden gaps and bottlenecks in the design-to-manufacturing data information pipeline. The deployment of carefully selected data analytics techniques (with/without machine learning algorithms) therefore represents a strategic opportunity to enable a 2 year/node ('more-Moore') cycle at 10nm and below in the semiconductor industry.