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) …
How the features and benefits of data virtualization can make working with data easier and more efficient. Data lakes have become the principal data management architecture for data science. A data lake's primary role is to store raw structured and unstructured data in one central location, making it easy for data scientists and other investigative and exploratory users to analyze data. The data lake can store vast amounts of data affordably. It can potentially store all data of interest to data scientists in a single physical repository, making discovery easier.
There seems to be much confusion among the ranks of the untrained when it comes to an understanding of some basic IT concepts. This is especially true when looking at Artificial Intelligence (AI) and the misuse of nomenclature such as Machine Learning (ML), and Deep Learning (DL). In this article, I will try to brush away the mists of confusion, and present the case for each specific data related technologies. First of all, you need to understand that all three are related and sit within a specific hierarchy. To properly understand the differences between these three technologies, let's first look at how they stack together in terms of hierarchy.
GitHub is used by more than 30 million developers around the world and hosts repositories for some of the biggest ML-driven open source projects on the planet, but is perhaps less well known for the creation of AI-driven tools to help them do their jobs. VentureBeat sat down with GitHub senior data scientist Omoju Miller to talk about how one of the biggest homes for developers online is performing applied machine learning research to create more AI-driven services. At the GitHub Universe conference Tuesday, a number of major upgrades were made to GitHub and GitHub Enterprise services for businesses. Miller also spoke during the keynote address about Experiments, a new GitHub initiative to explore the use of AI and machine learning meant for developers. The first Experiments prototype named Semantic Code Search launched last month.
As companies increasingly turn to AI and machine learning, a clearer picture of what it takes to succeed with real-world AI is beginning to take shape. Beyond the small circle of tech giants and early adopters, a different set of skills and approaches is emerging as must-haves for enterprise AI teams. Not every organization can compete with the likes of Google and Facebook for top AI talent. And it's not just data science PhDs that companies are looking for. To meet their business needs, CIOs assembling AI teams are looking for subject matter expertise, software engineering skills, and the ability to translate learning algorithms into actual business value.
Developers want to learn the data sciences. They see machine learning and data science as the most important skill they need to learn in the year ahead. Accordingly, Python is becoming the language of choice for developers getting into the data science space. Those are some of the takeaways from a recent survey of more than 20,500 developers conducted by SlashData. The survey shows data science and machine learning to be the top skill to learn in 2019.
Artificial Intelligence is not a buzzword anymore. As of 2018, it is a well-developed branch of Big Data analytics with multiple applications and active projects. Here is a brief review of the topic. AI is the umbrella term for various approaches to big data analysis, like machine learning models and deep learning networks. We have recently demystified the terms of AI, ML and DL and the differences between them, so feel free to check this up.
Pattern recognition using machine learning methods is an area that exploded in recent years, given the increasing amount of available data. JAMIA has published an increased number of articles in this area in the past few years. Machine learning models to detect pulmonary nodules in CT scans are described by Grutzemacher (p. Developing new approaches to facilitate automation of clinical research is another area in which informatics has evolved considerably in the past few years. In particular, biomedical natural language processing and other methods to structure narrative text and voice recordings have motivated informatics research.
Text-based analytics, also known as text data mining, turns unstructured text into structured data that can be used in a multitude of ways by any business. Indian research firm MarketsandMarkets projects that the worldwide text-based analytics market will grow to 8.79 billion dollars by 2023, driven by major vendors like IBM and SAP. MarketsandMarkets continues that text analytics solutions empower users to perform quick data extraction and categorization with real-time insights from stored data and that the growing importance of insights generated from social media content to build effective marketing campaigns and enhance customer experience drives the market's growth. But while many companies are including text-based analytics to their roadmaps, the technology remains in the early stages of adoption. One reason for this is because companies are still struggling to master social media.