Today, Mark takes on that ongoing tension of artificial intelligence and jobs, looking at how things are looking for jobs in Government administration, healthcare, and education. Public sector jobs could be under threat by robots in the next 15 years, according to a new report by The Reform think-tank. As technology is advancing at an unprecedented rate, the report explains, there is a growing opportunity to improve efficiency to the tune of billions of pounds by engaging robots to undertake certain tasks. The tasks that these bots can undertake, however, may have ramifications for civil servants' jobs. The co-author of the report, Alexander Hitchcock, has put this caveat on The Reform's think-tank: "Such a rapid advance in the use of technology may seem controversial, and any job losses must be handled sensitively. But the result will be public services that are better, safer, smarter, and more affordable."

Jupyter notebook is a tool that helps you create readable ML code and results, as you can keep code, images, comments, formulae and plots together. It helps you keep the code, comments(in markdown) and results(as graphs/plots) together in a very presentable way. It also provides line by line code execution like scala/nodejs repls do. And autocompletion is thrown into the goodness mix as well. The presentability and ease of use of notebooks make them an ideal environment for learning a new language as well as Machine learning concepts.

Writing in the Economist newspaper on 8 October 2016, US President Barack Obama called out artificial intelligence (AI) as one of several areas where'in recent years we have seen incredible technological advances'.2 Long a backroom area of computer science, AI has captured the popular imagination over the last two years as the range and impact of practical AI applications have expanded at a dizzying pace: a quick search on ft.com for'artificial intelligence and robotics' returned 4 stories from September and October 2014, 16 for the same period in 2015 and 54 in 2016. AI is one of several areas of digital innovation that are all both developing increasingly rapidly and interacting with each other in ways whose consequences are challenging to foresee. A useful portmanteau for these changes is the'fourth industrial revolution'. After steam, electricity and computing, this is the term coined3 by Davos founder Klaus Schwab for the deep digital transformation now upon us.

Every year some 35,000 people from around 140 countries working in the education sector gather to experience and observe ideas, practices and technologies that allow educators and learners to fulfil their potential. This year, as to be expected, it did not disappoint, with some very exciting talks and lots of great new products and technologies. Where else can you listen to Heston Blumenthal discuss how food and cooking can unleash creativity in the classroom, and see Sir Tony Robinson share stories about his love of history and his personal quest for learning, not before enjoying a talk by Sir Ken Robinson about his views on the necessity for new approaches in the education system. Where else can you listen to Heston Blumenthal discuss how food and cooking can unleash creativity in the classroom? At this year's event, Microsoft vice-president of worldwide education said: 'We've got to make technology available, but to bring it all together we have to raise the bar for how we can drive innovation and transformation', a statement that we at Jisc fully support.

Artificial General Intelligence (AGI) is an emerging field aiming at the building of "thinking machines"; that is, general-purpose systems with intelligence comparable to that of the human mind. What is currently labeled'artificial intelligence' is largely narrow automated knowledge work, lacking the flexibility and adaptability seen in animal intelligence. The pursuit of AGI begins at a foundational level, asking fundamental questions about models of cognition, knowledge acquisition, making choices through reason, thinking and conceiving the world in adaptive and intuitive ways. You emphasize the importance and value of "artificial understanding" of human language. What are the current "natural language processing" systems (Siri, Alexa, chat-bots, etc.) doing and how does this differ from what AGI is striving for w/regards to working with language? None of the language understanding systems go beyond identifying words correctly in context; this is a major step forward, but not enough.

Don't let the fun, casual name mislead you. Chatbots--software that you can "chat with"--have serious implications for the business world. Though many businesses have already considered their use for customer service purposes, a chatbot's internal applications could be invaluable on a larger scale. For instance, chatbots could help employees break down siloes and provide targeted data to fuel every department. This digital transformation is happening, even in organizational structures that face challenges with other formats of real-time communication.

Understanding how customers interact with their favorite apps is crucial for predicting engagement -- and with prediction comes personalization. With this intelligence, mobile-savvy brands can communicate more effectively with users. In 2016, my mobile marketing company analyzed millions of mobile interactions, and our data showed that personalized content inside push notifications boosts engagement four times. What's more, personalized send times lifted retention seven times. The modern app is smarter than its predecessors, able to personalize content down to the individual user rather than broad segments.

Did your voice give it away? US start-up Canary Speech is developing deep-learning algorithms to detect if people have neurological conditions like Parkinson's or Alzheimer's disease just by listening to the sound of their voice. And it's found a controversial source of audio data to train its algorithms on: phone calls to a health insurer. The health insurer – which Canary Speech would not name but says is "a very large American healthcare and insurance provider" – has provided the company with hundreds of millions of phone calls that have been collected over the past 15 years and are labelled with information about the speaker's medical history and demographic background. Using this data, the company says its algorithms could pick up on vocal cues that distinguish someone with a particular condition from someone without that condition.

In the past human work was preferred over a machine's work because a human was more accurate than a machine. After all a human could look at all angles and make an informed decision, and a machine could not. But enter machine learning today, and a machine might be more useful than a human in shaping customer experiences. Today machine learning can help brands scale their engagement operations and provide increasingly relevant experiences. And the good news is now you don't have to be a software expert to use machine learning.

Something is wrong in the way ML is being taught to developers. Most ML teachers like to explain how different learning algorithms work and spend tons of time on that. For a beginner who wants to start using ML, being able to choose an algorithm and set parameters looks like the #1 barrier to entry, and knowing how the different techniques work seems to be a key requirement to remove that barrier. Many practitioners argue however that you only need one technique to get started: random forests. Other techniques may sometimes outperform them, but in general, random forests are the most likely to perform best on a variety of problems (see Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?), which makes them more than enough for a developer just getting started with ML.