Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

Amazon's cloud services platform Amazon Web Services recently announced three AI services it said will make it easy for developers to build apps that can understand natural language, turn text into speech, have conversations using voice or text, analyze images and recognize faces, objects and scenes. This, in turn, underscores the increasing importance of AI to consumers, brands and marketers, but also raises some questions about how it will – and should – be developed. Building apps with AI capabilities has been challenging to date because doing so requires access to vast amounts of data and specialized expertise in machine learning and neural networks, Amazon said in a press release. "The combination of better algorithms and broad access to massive amounts of data and cost-effective computing power provided by the cloud is making AI a reality for application developers," added Raju Gulabani, vice president of databases, analytics and AI at AWS, in a statement. "Thousands of machine learning and deep learning experts across Amazon have been developing AI technologies for years to predict what customers might like to read, to drive efficiencies in our fulfillment centers through robotics and computer vision technologies and to give customers our AI-powered virtual assistant, Alexa. Now, we are making the technology underlying these innovations available to any developer…we are excited to see how customers use Amazon Lex, Amazon Polly and Amazon Rekognition to build a new generation of apps that have human-like intelligence and can see, hear, speak and interact with people and their environments."

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Today, if you ask the Google search engine on your desktop a question like "How big is the Milky Way," you'll no longer just get a list of links where you could find the answer -- you'll get the answer: "100,000 light years." While this question/answer tech may seem simple enough, it's actually a complex development rooted in Google's powerful deep neural networks. These networks are a form of artificial intelligence that aims to mimic how human brains work, relating together bits of information to comprehend data and predict patterns. Google's new search feature's deep neural network uses sentence compression algorithms to extract relevant information from big bulks of text. Essentially, the system learned how to answer questions by repeatedly watching humans do it -- more specifically, 100 PhD linguists from across the world -- a process called supervised learning.

We explore the relation between memcomputing, namely computing with and in memory, and swarm intelligence algorithms. In particular, we show that one can design memristive networks to solve short-path optimization problems that can also be solved by ant-colony algorithms. By employing appropriate memristive elements one can demonstrate an almost one-to-one correspondence between memcomputing and ant colony optimization approaches. However, the memristive network has the capability of finding the solution in one deterministic step, compared to the stochastic multi-step ant colony optimization. This result paves the way for nanoscale hardware implementations of several swarm intelligence algorithms that are presently explored, from scheduling problems to robotics.

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Artificial Intelligence (AI) was once considered a fanciful idea found only in science fiction, but we're now starting to see it enter the mainstream market. Google boasts a collection of AI offering to aid searches, while Apple's introduction of Siri has meant that AI is now in the back-pockets of millions of smartphone users around the world. An increasing number of businesses are turning to AI to streamline processes, increase efficiency and limit errors. The recruitment industry has the opportunity to take advantage of machines to carry out time-consuming, repetitive tasks, which could see the industry undergo a complete transformation, benefitting those on both sides of the table. What could artificial intelligence do for recruitment?

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

Up until today, users looking to manipulate their Motion AI bots outside of our platform configured their own web servers to listen to and respond to webhooks. Module, this is no longer necessary. Whether you want to interact with an external database, connect with a third-party API, or do virtually anything else -- there is no need to leave our platform. Each Node.js function created through Motion AI is passed a payload object that contains metadata based on an end-user's response to the bot. This data can be acted upon within the Node.js

Artificial intelligence (AI) shows a lot of promise yet some of the most recent news seems a bit alarming. Two AI agents were programed to communicate privately and they created their own cryptography. AI is now improving its capabilities by dreaming. And AI predicted correctly that Trump would win the presidency. Naturally, these events are causing people to ask a lot of questions about AI.