Data science has surged to the forefront of the data ecosystem, with demonstrable business value derived from the numerous expressions of Artificial Intelligence currently being adopted in the enterprise. It represents the nucleus of the power of predictive analytics, and the extension of data culture throughout modern organizations. Consequently, data science trends are more impactful than those in other data management domains, which is why its increasing consumerization (beyond the realm of data scientists) is perhaps the most meaningful vector throughout IT today. "You can't find the data scientist talent to build models? Well, how about if those models can be built by a business analyst with one mouse click and one API call?" asked Oliver Schabenberger, CTO and COO at SAS, in conversation with AI Business.

This is the second part of the neural network tutorial. The first part can be found here: https://link.medium.com/YCEAECVp0W Now that we have seen how a neural network is represented, we can go on to see how exactly it works. Since there are many layers having many neurons, there exists a complex set of weights to get an output from some input variables. Each weight in this network can be changed and hence there are countless configurations a neural network can have.

The Centers for Medicare and Medicare Services this week announced the 25 participants selected to move on to the next round of its Artificial Intelligence Health Outcomes Challenge. WHY IT MATTERS Launched this past March by the CMS Innovation Center, in collaboration with the American Academy of Family Physicians and the Laura and John Arnold Foundation, the AI Health Outcomes Challenge aims to give innovators a showcase for how they're developing AI and machine learning technologies, deep learning tools and neural networks. While the focus is on helping hospitals and health systems drive cost efficiencies for value based reimbursement, prevent adverse patient safety events and boost quality outcomes, CMS put out the call innovators from all sectors of the economy – not just from healthcare. More than 300 different organizations submitted proposals. They were evaluated by a group of data science experts, clinical informaticists and care providers. A CMS selection panel then chose 25 of the applicants to advance to Stage 1.

Technology is the future of customer experience. The companies best prepared to succeed in the future understand the importance of leveraging new technology to create personalized, convenient solutions and interactions. These statistics show the grow of new technology and how it impacts everything about the future of customer experience. More than 100 million consumers will use AR and VR technology to shop online and in store by 2020. By 2021, the AR and VR market in the U.S. is expected to be worth $215 billion.

How do OfSTED determine which schools to inspect? On Wednesday 11th April, I attended an NAHT meeting, a new commission on accountability, spanning every phase and sector of education. Over the next few months it will canvass the views of some of the foremost thinkers in this area of education policy with the aim to have interim findings before the summer term and to publish our full report in September 2018. This post captures a presentation delivered by an OfSTED representative and not the meeting itself. When will [XYZ school] be inspected?

Considering the immense challenges to building quantum computers, I'd say we are roughly where we were in around 1970 with classical computers. We have some quantum computers, but they are still pretty unreliable compared to today's standard. We call them NISQ devices - Noisy Intermediate-Scale Quantum devices. Noisy because they are pretty bad, and intermediate-scale because of their small qubit number. There are a few public quantum computers available for anyone to programme on.

Loves dancing, 80s music and meeting new people.

In the next five to ten years, your workplace will look fundamentally different. Thanks to technologies such as artificial intelligence, the internet of things and robotics work as we know it will drastically change. The future of work will come with great opportunities but also with plenty of challenges for organisations. It will require employees and management to adapt and work smarter. AI will augment your jobs, the Internet of Things will provide you with details insights and robotics will replace many jobs.

"Listen to your patient; they are telling you the diagnosis," an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients' stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care. The medical industry's fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.

Last February, OpenAI, an artificial intelligence research group based in San Francisco, announced that it has been training an AI language model called GPT-2, and that it now "generates coherent paragraphs of text, achieves state-of-the-art performance on many language-modelling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarisation – all without task-specific training". If true, this would be a big deal. But, said OpenAI, "due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper." Given that OpenAI describes itself as a research institute dedicated to "discovering and enacting the path to safe artificial general intelligence", this cautious approach to releasing a potentially powerful and disruptive tool into the wild seemed appropriate.