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.

Learning algorithms are the seeds, data is the soil, and the learned programs are the grown plants. The machine learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way. Machine learning algorithms are different: they are called learners, they input data and output other algorithms. The algorithms produced by learners are of several types, but the most common ones are called classifiers. They are used to assign a class, or label, to an object having certain numeric or categorical features.

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"When I was just a little girl, I asked my mother what will I be? Here's what she said to me? Que Sera Sera. Whatever will be, will be. The future's not ours to see, Que Sera Sera." When I was a very young child, my Grampy (Grandfather) used to sing this beautiful, upbeat and whimsical song to us, and for some reason, it stayed with me throughout my life.