artificial intelligence<\/a> that helps computers understand, interpret and manipulate human language.<\/p>\n\n\n\nA common example of NLP is when you type on your mobile and see word suggestions pop up based on what you\u2019re currently typing. That\u2019s natural language processing in action. It\u2019s a little thing that many of us have taken for granted for years, but its application in the business world is far-reaching.<\/p>\n\n\n\n
Take a company looking at advertising a new product. They can use google to find common search terms that their users type when they\u2019re looking for similar products. NLP allows for a quick compilation of the data into terms obviously related to their brand and those that they might not expect. Capitalizing on the uncommon terms can give the company the ability to advertise in new ways.<\/p>\n\n\n\n
How does it work?<\/em><\/p>\n\n\n\nThe first step in NLP depends on the application of the system. For voice-based systems like Alexa, special technology translates words into text. Next, the system breaks each word down into categories like nouns, verbs etc. This happens through a series of coded grammar rules that rely on algorithms that incorporate statistical machine learning to help determine the context of what you said. If we\u2019re not talking about speech-to-text NLP, the system just skips this step.<\/p>\n\n\n\n
The end result is the machine\u2019s ability to categorize what is said in many different ways. Depending on the underlying focus of the NLP software, the results get used in different ways. For instance, in our SEO example above, an application could use the decoded text to pull in keywords associated with a certain product.<\/p>\n\n\n\n
\n\n\n\n <\/figure>\n\n\n\nDeep learning<\/strong><\/h3>\n\n\n\nDeep learning is a subset of machine learning in artificial intelligence that takes mountains of unstructured data and attempts to make sense of it. It imitates the workings of the human brain in processing data and creating patterns for use in decision making.<\/p>\n\n\n\n
Deep learning vs. machine learning<\/em><\/p>\n\n\n\nA common AI technique used for processing big data is machine learning, which we covered in our last buzzword blog. Machine learning is a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data. A practical example of this is a company that wants to identify payments fraud within their business. By using machine learning tools, it can process huge levels of transaction data and pick out anomalies in the data, accurately predicting over time what is a legitimate or fraudulent payment.<\/p>\n\n\n\n
Deep learning is a means to do this in great depth. Where a standard program could analyse simple variables like overpayments, a deep learning function could analyse multiple data sets in a nonlinear way. The analysis could include time, geographic location, IP address, type of retailer and any other feature that is likely to point to fraudulent activity. Each data layer can be analysed simultaneously and results and patterns aggregated instantly. This allows unstructured data to be analysed and comprehended at scale and speeds impossible for humans or traditional analytical programs.<\/p>\n\n\n\n
\n\n\n\n <\/figure>\n\n\n\nInternet of things (IoT)<\/strong><\/h3>\n\n\n\nIoT is used to describe the process of connecting everyday devices to the Internet, in order to collect data from them, exchange data between devices or control them from a distance.<\/p>\n\n\n\n
These devices can be cars, home automation systems or your everyday toaster.<\/p>\n\n\n\n
Caroline Gorski, the head of IoT at Digital Catapult explains \u201cIt\u2019s about networks, it\u2019s about devices, and it\u2019s about data,\u201d. IoT allows devices on closed private internet connections to communicate with others and the Internet of Things brings those networks together. It gives the opportunity for devices to communicate not only within close silos but across different networking types and creates a much more connected world.<\/p>\n\n\n\n
What does this mean for business?<\/em><\/p>\n\n\n\nExperts predict that more than half of new businesses will run on the IoT by 2020. Within industrial applications, sensors on product lines can increase efficiency and cut down on waste. One study estimates 35 per cent of US manufacturers are using data from smart sensors within their set-ups already.<\/p>\n\n\n\n
But it\u2019s not all good news. Anyone who\u2019s purchased one of the myriad smart home products\u200a\u2014\u200afrom lightbulbs, switches, to motion sensors\u200a\u2014\u200awill attest to the fact IoT is in its infancy. Products don\u2019t always easily connect to each other and there are significant security issues that need to be addressed. A report from Samsung says the need to secure every connected device by 2020 is \u201ccritical\u201d. The firm\u2019s Open Economy document says \u201cthere is a very clear danger that technology is running ahead of the game\u201d. The firm said more than 7.3 billion devices will need to be made secure by their manufacturers before 2020.<\/p>\n\n\n\n
\n\n\n\nSo there we have it. Another round of data buzzwords taken head-on. If you\u2019re interested in finding out more about these technologies\u200a\u2014\u200aor have a data project that you\u2019re starting or struggling with\u200a\u2014\u200aplease do get in touch<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Digital business automation, Natural Language Processing, Deep Learning & Internet of Things Round up, round up! It\u2019s time to tackle more of the data buzzwords that are popping up in meeting rooms everywhere. Say goodbye to blank expressions and scratchy heads, this is your definitive guide to the latest technologies, explained in plain English. Digital […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[21,39,41,42,44,61],"acf":[],"yoast_head":"\n
Buzzword bonanza 2: Our plain English guide to data lingo -<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n