During Microsoft's earnings call earlier this week, Microsoft CEO Satya Nadella said that Cortana digital assistant has its own strength when compared to other assistants like Alexa as Cortana has a lot of unique information sources to draw from, including Windows 10, Office 365, Xbox and more. It's good to to see Satya's defending for Cortana, but I would like to see his words in action. The rate at which Cortana is gaining capabilities is nowhere near to Alexa or Google Assistant. Similar to how Windows Phone was never able to catch up with 3rd apps ecosystem, Cortana will never be able to catch up with Alexa's 3rd party skills ecosystem. According my sources inside Microsoft, Microsoft is not even considering Cortana as a as true competitor to Alexa.

The company on Thursday announced the establishment of the Cortana Intelligence Institute, a collaboration with the Royal Melbourne Institute of Technology focused on broadening the capabilities of its virtual assistant. RMIT, as the university is most commonly known, is Australia's largest by enrollment. Researchers from the school will work with Microsoft personnel to apply AI to new tasks that currently can't be handled by neural networks.

At Search Party we are in the business of creating intelligent recruitment software. One of the problems we deal with is matching candidates and vacancies in order to create a recommendation engine. This usually requires parsing, interpreting and normalising messy, semi-/unstructured, textual data from résumés and vacancies, which is where the following come in: conditional random fields, bag-of-words, TF-IDFs, WordNet, statistical analysis, but also a lot of manual work done by linguists and domain experts for the creation of synonym lists, skill taxonomies, job title hierarchies, knowledge bases or ontologies. While these concepts are valuable for the problem we try to solve, they also require a certain amount of manual feature engineering and human expertise. This expertise is certainly a factor that makes these techniques valuable, but the question remains whether more automated approaches can be used to extract knowledge about the job space to complement these more traditional approaches.

Much like cloning, people are entertaining serious speculations in the acceptance of artificial intelligence. Many are of the opinion that such technology will eventually result in the elimination of the human race or in its suppression, just as seen in certain Sci-Fi movies. Others believe that the technology will be a considerable threat to jobs. Without jobs, there will be unrest, which would still, eventually, lead to the breakdown of the human race. But really, how accurate are these allegations or beliefs?

An interesting approach to analyzing and developing tools for neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a fully connected feedforward network with one hidden layer, a certain weight distribution, an activation function, and an infinite number of neurons is a mapping that can be viewed as a projection into a Hilbert space. We show that the equivalent kernel of an MLP with ReLU or Leaky ReLU activations for all rotationally-invariant weight distributions is the same, generalizing a previous result that required Gaussian weight distributions. We derive the equivalent kernel for these cases. In deep networks, the equivalent kernel approaches a pathological fixed point, which can be used to argue why training randomly initialized networks can be difficult. Our results also have implications for weight initialization and the level sets in neural network cost functions.

Brisbane, Australia-based M3DICINE is the latest device maker looking to distance physicians from their signature rubber-tubed stethoscopes. Today, the company announced the launch of Stethee, a wireless, artificial intelligence-boosted stethoscope that pairs with an iOS or Android app to quickly capture and analyze heart and lung data. "This most iconic and enduring symbol of healthcare is now reinvented, into an intelligent and sophisticated medical device -- a powerful diagnostic assistant that becomes more intelligent the more it is used," Dr. Nayyar Hussain, founder and CEO of M3DICINE, said in a statement. "The complementary Stethee Apps bring the power of machine learning and deep neural networks to health professionals worldwide who can use this data not only to monitor the progress and health of individual patients, but collectively to uncover new patterns and trends to help in the fight against heart and lung disease globally." Stethee, which was cleared by the FDA late last year, filters and amplifies the sounds of a patient's heart or lungs before sending the audio to a user's connected headphones via Bluetooth.

They are coming for our jobs, and worse, they're coming to get us. We are besieged by ever more dire warnings about artificial intelligence from increasingly loud voices. Elon Musk repeatedly warns that AI is a fundamental risk to the existence of human civilisation and we should beware the robots going down the street killing people. One frequently cited analysis by Oxford economists suggests that 47 per cent of workers in the US have jobs at high risk of automation over the next two decades. CSIRO has predicted a similar proportion in Australia.

One of the most stressful parts of traveling happens between heading to the airport and waiting to board your flight, as you start checking to see if your flight is on time. Flights already shows delays, and now we're sharing reasons for those delays and delay predictions too. Using historic flight status data, our machine learning algorithms can predict some delays even when this information isn't available from airlines yet--and delays are only flagged when we're at least 80% confident in the prediction. Simply search for your flight ("American 4442") or the airline and flight route ("Air New Zealand Auckland to Singapore"), and these details will appear right in your search results.

How will enterprises manage artificial intelligence deployments when most managers and executives don't understand the underlying models, data science, or technology? That question is almost haunting. Artificial intelligence (AI for short) is lumped together with big data, machine learning, and neural networks to create what equates to a technology buzzword orgy. And if you want another theme toss in cloud computing, which is the enabler for AI. Sure, we know AI is a bit hyped.

In this episode of Robots in Depth, Per Sjöborg speaks with Daniel Pizzata about how his passion for modular robotics led him to start Modbot, a company building robotics platforms. The goal of Modbot is to bring robots out of the research labs and make them accessible to everyone. Daniel started out in the defence sector in Australia using robotics to measure radio transmissions. He worked on many different projects, but felt that he wanted to work with technology that was more widely applicable in society. Daniel also talks about how he met his co-founder Adam Ellison and how they had the idea of a platform and a community that could widen the range of people that are able to develop robotics and automation solutions.