Did you know that nearly 80% of the data in the world is unstructured data? Unstructured data presents challenges for those who are in charge of data management and require enterprise systems that can control the data and access it as needed. Simply put, unstructured data is data which cannot be quantified, it cannot be counted in numbers. For example: text files, powerpoints, word documents, instant messages (tweets, likes, posts), emails, web pages, jpeg images, audio files and videos. It is hard to categorize and store unstructured data vis-à-vis structured data which can be easily quantified and stored in databases.

Think of the rise of Big Data as the "Big Bang" of digital marketing. Before it, digital marketing and its analog counterpart were the sums of gut feelings, psychology textbooks, and only a broad understanding of "what happened before." Then, BANG! Online commerce arose…and there was more customer data than we could ever interpret. Of course, it seems obvious now. Despite the ghost stories being told about AI, entrepreneurs and data scientists recognize AI as a quick, useful way to process data at a rate that no team ever could.

This meetup is meant for Developers and Enterprises who are working in the space of AI and interested to learn more about Deep Learning, Neural Networks and other AI applications. Key focus will be Artificial Intelligence, Deep Learning, NLP, ML, neural networks. We look forward to enthusiasts who can share their knowledge and alongside learn with us.

An artificial intelligence platform that has previously predicted the winners of the Oscars and the Super Bowl has now won the "holy grail" of gambling: the Kentucky Derby Superfecta. The human-based artificial intelligence--called UNU--predicted the first, second, third and fourth horses in the Derby at 540-1 odds, earning the technology's inventor Louis Rosenberg 10,842 from a 20 dollar bet. "When I saw the horses cross the line, I knew I was witnessing a milestone in the predictive abilities of AI, as well as a harbinger to future changes in how the world views sports gambling," Rosenberg tells Newsweek. UNU uses a unique form of artificial intelligence called swarm intelligence that aims to amplify rather than replace human intelligence. It works like this: a group of people login to an UNU online forum through their smartphones or computers.

Bespoke software development consultancy Scott Logic has successfully launched a newly-created game at its latest undergraduate Hackathon. The artificial intelligence (AI) challenge, which was built from scratch by some of the firm's software developers, was unveiled at yesterday's Durham University Hack event, which was organised in partnership with student volunteers from Compsoc, the institution's computing society, for the first time. The experience-driven afternoon involved an intense period of collaborative coding, in which competitors battled it out in teams of up to six for some sought-after prizes, before chatting with Scott Logic's developers over refreshments. These Hack events, which are held at universities across the country, have been part of Scott Logic's recruitment strategy since 2013. They give students the chance to find out more about the company and ask questions of its staff in an informal setting, while having fun at the same time.

Schools need to place more focus on teaching young people digital skills, according to former Education Secretary Lord Kenneth Baker. Baker, who is Chairman of education charity the Edge Foundation, warned that millions of jobs are under threat from automation and that jobs in professions that were once considered careers for life are being lost. According to Baker, many professional jobs are under threat from artificial intelligence, and it could have an impact on sectors such as the law, transport and construction. He has produced the Digital Revolution report urging the education system to tackle the way in which technological changes will affect the jobs market. The report provides an in-depth look at the digital revolution and how it will affect employment and education. Baker stated that more emphasis should be placed on digital skills, which he said are "almost entirely absent from the core curriculum in mainstream schools."

So, since messages are ephemeral we should constantly keep the user updated on what's going, while also avoiding overwhelming the user with a wall of information. How do we satisfy both these imperatives? Here's what I propose: The system should allow the user to request information about what is going on, through appropriate feedback within reasonable time. By putting the onus on the user to request system status as necessary, we avoid overwhelming the user with potentially redundant information. Also, appropriate feedback within reasonable time should go without saying.

Interestingly, Nest (now a part of the same company) shied away from the idea of an Echo like device, citing privacy concerns about talking to Google, its search engine, algorithms and other internet magicks. Recode's sources suggest voice search and intelligent responses from your Google devices will be the centerpiece of Google's showcase, alongside virtual reality developments. Show us what you've got.

At the end of last month, Sundar Pichai wrote his first annual shareholder letter as CEO of Google. Eight months prior, Larry Page and Sergey Brin led a restructure of the company they founded, separating its core internet business from subsidiaries focusing on new areas of innovation, such as self-driving cars, drones, augmented reality, biotech and life sciences. Page and Brin now lead umbrella company Alphabet as CEO and president respectively, while Pichai heads up Google. It was the first time the annual shareholder letter had been written by anyone other than Page and Brin. Pichai kept it simple, outlining the key areas Google will focus on across its product lines – but he was bold enough to say they will all be driven by a long-term investment in machine learning and artificial intelligence.

Machine learning is the process of building analytical models to automatically discover previously unknown patterns from data that indicate associations, sequences, anomalies (outliers), classifications, and clusters and segments. These patterns reveal hidden rules as to why an event happened--for example, rules that predict likely customer churn. The widely used Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is used to develop predictive analytical models. CRISP-DM includes six phases: business understanding, data understanding, data preparation, model development using supervised and unsupervised learning, model evaluation and model deployment. The business understanding phase involves defining the business problem or use case, the business objectives and the business questions that need to be answered.