If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Data scientists have been called "unicorns" because finding the right person with the right set of skills -- including coding, statistics, machine learning, database management, visualization techniques, and industry-specific knowledge -- could be practically impossible. But machine learning and big data itself may be making those unicorns as obsolete as they are mythical. New machine learning algorithms can autonomously analyze data and identify patterns, even interpret the data and produce reports and data visualizations. While most people can see how certain information would be useful and what sort of insights might be derived from it, most lack the technical skills to perform the analytics. They might not have the computers that are able to carry out the large volume of calculations quickly enough to take action, but more often they lack the analytical skills to tell that computer what to do.
The global tech startup scene is a noisy, crowded space. And then there are the stories of those entrepreneurs whose ideas have endured into something truly transformational; Amazon, Apple, Google, you name it--many of the biggest and most influential companies in the world today were born of this heritage. "One thing we can all agree on: The key attribute of a startup is its ability to grow," wrote Forbes' Natalie Robehmed. And as my former CEO, Mark Jones, used to say, "All big companies were once small companies too. The only difference is that they grew up."
This article was written by Richard Downes. Richard is a Specialist Recruiter / Headhunter in the areas of Analytics, Data Science and Artificial Intelligence / Machine Learning and NLP (Natural Language Processing). His work within Analytics covers Predictive Analytics, Consumer Insight / Shopper Insight, and Loyalty right the way through to Credit and Risk.
The boss of a Manchester-based data analytics and artificial intelligence firm believes it has the potential to become a tech unicorn. Richard Potter is the chief executive of Peak, which secured £2.5 million in a Series A funding round led by London-based venture capital firm MMC Ventures in September. The firm has since unveiled a new website and brand identity which places a stronger focus on its AI and machine learning offering. In an interview with BusinessCloud, Potter said the company's ambition is to be a global technology champion in the AI and data analytics-as-a-service space. "Specifically for what we do, we think it's a nascent market with no other operators in this space because we're offering a service powered by our own technology platform," he said.
You may think that humans as a species are the most prolific colonizers on the planet but maybe you've never heard of the "Argentine ants of southern Europe". This massive colony is made up of ants from Argentina that were introduced to Europe 80 years ago, and needless to say they've wasted little time in dominating the landscape. While Europe is being invaded by immigrant ants, another much smaller infestation that's taking place is that of mythical unicorns. For those of you not in the know, a unicorn is a startup worth at least $1 billion, and there are 215 in total around the world. There are actually 16 unicorns grazing across Europe right now, and we got zee Germans out of the way after last week's top 8 unicorns by funding.
The position of Data Scientist is rapidly becoming a highly desired role as financial institutions consider how to implement Artificial Intelligence (AI) and Machine Learning (ML) projects within their organisations. Identifying the need for a Data Scientist is the easy part of the process, however, the real difficulty is in finding the right Data Scientist with the necessary skill set and knowledge needed to create real business benefits. The high value placed on Data Scientists is a direct result of the unique set of skills and expertise needed to implement and effective AI strategies, allowing them to have a huge influence on the nature and direction of projects. Based on the data that is given to them, it is they who make a judgement on the tools that are used and the characteristics of the investigation that will ultimately lead to the identification and delivery of the business value from AI and ML. Throughout the various stages of the project, Data Scientists have arguably the most important role working alongside the Developers, SME's and Data Engineers in realising the value.
In the light of recent growth concerning Artificial Intelligence (AI), we – marketers, are left both a little worried and excited when it comes to the future of our jobs. See now, SEO is crucial. A lot, and I mean very many SEO specialists try their best to optimize websites for SEO, write unique content, find guest post opportunities from highly authoritative websites and so on and so forth. Thanks to the buzzword of nowadays online world, I'm speaking about AI, performing SEO is getting easier day by day. But a question arises here, how much of that interference do we need and how do we keep the balance between what AI can do and what will be left for human specialists to work on?
Natural Language Processing (NLP) technologies can help to break down the barriers to widespread use of data analytics by making complex analytics possible to just about anyone, regardless of their technical ability. They might not want to take three or four years out to learn advanced computer science and statistics, and with the advances in cognitive computing that won't be necessary. In big data analytics, reporting the insights we've gleaned from analyzing large amounts of messy data sets is the crucial "last step" of the process – and it's often a step which causes us to stumble. But one program, called Quill, takes the trend a step further, producing text-based reports that explain the data clearly and concisely.