Machine learning has transformed the 21st century with its ability to record and process massive amounts of data. It provides a way to automatically find patterns and reasons about data. A report from inside BIGDATA read 'If Data is the New Oil, we're about to be busy'. It says that when enterprise execs and AI experts say data is the new oil, they mean its fuel for our information economy; the single largest driver of innovation.
Young Enterprise Northern Ireland or YENI are a leading enterprise education charity in NI. This charity is committed to providing every young person with the opportunity to develop and explore entrepreneurial skills for the 21st century and prepare them for the modern economy.
Kaggle is an online community of data scientists and machine learners, owned by Google LLC. It offers an environment where users can find and publish datasets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitors to solve data science challenges.
The term 'machine learning' is everywhere; in the media, education and local businesses. The whole field of machine learning can seem pretty overwhelming but you'll be surprised at how much we're surrounded by it.
The term Artificial Neural Networks (ANN) refers to a set of algorithms that try to mimic the brain. It is constructed of an input layer, the data you input, a number of hidden layers, which help to group unlabelled data according to similarities, and then the output layer which is the hypothesis. Neural networks can fall into a range of categories, this blog will focus on classification.
It's hard to go a day without hearing the word 'data'. We rely on data on a daily basis in all industries; media, education, technology, retail etc. We are all getting excited about the possibility of what data can do. We know the buzzwords; Analytics, Big Data, Data Visualisation and Data Science! But at the root of these buzzwords is the need for visible, authoritative, consistent data that we can rely on to gain insights to support missions and goals.
It's hard to miss the images of plastic waste in the media, with some of the statistics and pictures being both frightening and sickening. In 2015, the annual production of plastics increased to nearly 381 million tonnes, this is roughly equivalent to the mass of two-thirds of the world population. From a study conducted in 2010, it was estimated that 8 million tonnes of plastic entered the oceans with roughly 10,000 to 100,000s tonnes of plastic in surface waters (Our World in Data).
It was announced this week that the Gov.UK Verify had fallen short of its target of 25 million users by 2020, only securing 3.6 million so far. With a current spend of £154m on Verify, the government is due to stop funding it from April 2020, handing operations over to the private sector. So what is Verify and how did it fail?
There are few (if any) industries that can be transformed by artificial intelligence to the degree that healthcare can. As initiatives around the world seek to digitize healthcare data, there are huge opportunities for game-changing tools and platforms. However, with potentially a yottabyte (1024 gigabytes) of healthcare data in the United States alone, we are no closer to this utopian, data-driven world of healthcare. Data in existence is not standardized, highly fragmented and is stored in incompatible legacy platforms. The technology exists, so why is healthcare so far behind other sectors in utilizing existing technology and what needs to be done to catch up?