There is a huge amount of pressure on healthcare services today with much written about the resulting impact on patient care. You only need to reflect on the recent election campaign to see what an emotive issue it remains throughout the UK. What's also true is that advanced technology is taking a bigger role in healthcare than ever before, looking to alleviate some of the burdens on our health systems and provide greater support for our wellbeing. One clear theme underpinning this shift is applications. Whether being used by organisations to adapt and streamline processes to improve data access and patient care or by patients themselves looking at ways to better understand and manage their health, the sector is set to be transformed by the evolution of apps.

Every day we hear about Machine Learning and Big Data Analytics... 'United Parcel Service saves 39 million gallons of fuel after using Big Data Analytics to optimise fleet operations'; 'PayPal uses Machine Learning on Customer, Financial and Network data to combat fraud'; 'Amazon uses Machine Learning to discover'lowest price' for over 20 million products'... Machine learning, a subset of Artificial Intelligence (AI) is a method of data analysis that uses algorithms to iteratively learn from data and derive insights without being explicitly programmed. We can find examples of how Machine Learning is already a part of our daily lives -- like Google Maps, using location data from smartphones, analyses the speed of movement of traffic at any given time. Or like Amazon makes recommendations for products -- "customers who bought this item also bought". Behind all these lie complex algorithms that are continuously learning new data and refining outcomes. In banking for example, using client's financial data, risk preferences and desired target return, 'Robo-Advisers' provide personalised, algorithm driven portfolio management services without human supervision.

Messenging app'Messenger' launched a range of new artificial intelligence (AI) features in Australia on Wednesday. The AI, called'M', works almost like a prompting service, where it recognises words and phrases used in a conversation and then suggests relevant content and actions based on the chat between the two users. For example, if you're speaking to someone on their birthday, 'M' will recognise either through a phrase used or their Messenger profile when their birthday is and then prompt you to send a birthday message. Similarly, if you are chatting about making plans or struggling to come to a group decision about something, the AI will suggest you make a plan or start a group poll respectively. If you are chatting in a one-on-one conversation, and one person rises the idea of making a call, 'M' will prompt you to start a video or voice chat.

About this course: How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data.

The world was atwitter last week as President Trump crudely took North Korea to task over its nuclear program, a threat that has slowly but surely eclipsed the distractions of our imperialist wars in the Middle East. But according to tech mogul Elon Musk, humans face a far graver threat from something they use in their everyday lives and could be holding in their hands as they read these words: algorithmic artificial intelligence. Specifically, algorithmic AI that evolves into malevolent super-intelligent entities and seeks to end their meatbag parent species -- us. Late last week, Musk tweeted that AI is far more dangerous than North Korea, adding that he believes regulation will be necessary to contain the burgeoning technology. If you're not concerned about AI safety, you should be.

Militants firing from bombed-out buildings had ambushed a U.S.-backed militia on a rubble-strewn street in Raqqah, Islamic State's self-declared capital and one of its last urban strongholds. The militia was pinned down and their commander wanted the drone to take out the gunmen. The pilot studied the surveillance video streaming onto his screen. A captain, he instructed the staff sergeant at his side to set the drone's target sights and powered up a Hellfire missile under its wing. "Rifle," the pilot said and the missile soared away.

It's not news that the evolution of education technologies and models of transformed learning are moving at the speed of sound. "Machine Learning", (The Economist, July 22-28, 2017) jolted me into the certainty that--regardless of educators' embracing and meaningfully engaging technologies--the ecosystem will successfully evolve without them. This is about using new software to alter how learners and teachers best use their time. It is not about infusing technology into the usual way that schools do business. Artificial intelligence (AI) is helping design new ways of education: specifically, having machines learn about the student by analyzing data unleashed in the learning process.

For quick reminders and messages, it is a lot faster and more convenient to simply talk to your phone, rather than pull up an app and type on a keyboard. That's part of the reason Google has been updating Gboard for Android with voice support for more international languages, working with native speakers to train machine learning models. Today, Google announced that it's supporting an additional 20 languages and also adding English dialects for four African countries. This means language support for a total of 30 new international locations, mostly centered on the Indian subcontinent and Africa. The full list of languages is: Amharic, Armenian, Azerbaijani, Bengali, English (Ghana, Kenya, Nigeria, Tanzania), Georgian, Gujurati, Javanese, Kannada, Khmer, Lao, Latvian, Malayalam, Marathi, Nepali, Sinhala, Sundanese, Swahili, Tamil, Telugu and Urdu.

Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft. The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane.

Digital technology innovations and advancements, and our adoption of them, have changed us. We are different consumers, employers and employees. We have become mobile, impatient and demanding. We demand immediate, accurate and real-time responses. We use our technology not just for reading historic events and news, but also for predicting our future turn while navigating at 60 MPH.