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) …
The Data Science Trends for 2018 are largely a continuation of some of the biggest trends of 2017 including Big Data, Artificial Intelligence (AI), Machine Learning (ML), along with some newer technologies like Blockchain, Edge Computing, Serverless Computing, Digital Twins, and others that employ various practices and techniques within the Data Science industry. The Dataconomy article titled The Future of Big Data Is Open Source aptly captures the industry buzz that dominated the last part of 2016 and the entire 2017. Then, Big Data and Data Science were the biggest industry buzzwords, but a lot has changed since then. In 2016, and 2017, Big Data was a market differentiator for businesses, and continues to be. Now, as we enter 2018, there are numerous new technologies that will coordinate within the greater foundations of Data Science and Big Data, and expand such industries into new spaces that are only beginning to be understood and appreciated.
To comprehensively study, understand and inform policy around these complex systems, the next generation of researchers in the physical, social and biological sciences will need fluency with data analysis methods that transverse traditional academic boundaries. A new interdisciplinary curriculum will train graduate students from geosciences, economics, computer science, public policy and other programs in computational and data science techniques critical for modern science. The program will build upon successful UChicago training initiatives such as the Executive Program in Applied Data Analytics, the Computational Analysis and Public Policy curriculum at the Harris School of Public Policy and the Data Science for Social Good Summer Fellowship. Instruction and mentorship will be provided by several UChicago research groups, including the Center for Robust Decision-Making on Climate and Energy Policy (climate and agricultural modeling), Knowledge Lab (text mining), the Energy Policy Institute at UChicago (environmental and energy economics), the Center for Data Science and Public Policy (data analytics and project management) and the Center for Spatial Data Science (spatial analysis).
Data Science is an exciting field to work in, combining advanced statistical and quantitative skills with real-world programming ability. Java is an extremely popular, general purpose language which runs on the (JVM) Java Virtual Machine. MATLAB's widespread use in a range of quantitative and numerical fields throughout industry and academia makes it a serious option for data science. Verdict -- "a useful general purpose scripting language, yet it offers no real advantages for your data science CV" Ruby is another general purpose, dynamically typed interpreted language.
This post presents a collection of data science related key terms with concise, no-nonsense definitions, organized into 12 distinct topics. Enjoying a surge in research and industry, due mainly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies - that is, neural network architectures with multiple hidden layers - to solve problems. Deep learning is a process, like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms. This post presents 16 key database concepts and their corresponding concise, straightforward definitions.
This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. From there, we will work through signing up for a free Salesforce Einstein account (that you can keep for life) that has the new Salesforce Einstein AI engine enabled. There is no coding required - you can build AI enabled apps with clicks instead of code, thanks to being able to leverage the Salesforce Einstein AI framework.
Although AI has been all over the press lately, it is not news… It is rather a 30-year old corpus of work, aimed at creating intelligent machines, by combining three building blocks: machine learning, human learning and data science. Just like children get their foundational learnings from their parents, teachers and by the school books they read, machine learning is based on known properties, and the machine learns from the data. As mentioned before the computing advancements have enabled a fast acceleration of three technologies which underpin the maturation of artificial intelligence: object recognition, natural language processing and speech. Capitalizing on the progress of machine learning around object recognition, natural language processing and speech, we have seen our expectations towards AI graduate from the most basic to much more advanced outcomes.
At events, in meetings and in general conversation with people, it's struck me that many seem to use data science, machine learning and artificial intelligence interchangeably. Data science is the process of extracting information, understanding and learning from raw data to inform decision making in a proactive and systematic fashion that can be generalized. Popular machine learning algorithms include Linear Regression, Logistic Regression, Decision Tree, Support Vector Machines (SVM), Naive Bayes, K- Nearest Neighbor, K-Means, Random Forest, Principal Component Analysis (PCA), and Gradient Boost & Adaboost, Neural Networks and Deep Learning Neural Networks ("Deep Learning"). Many of these advancements in AI fall under the category of Applied or Narrow AI and often utilize deep learning neural nets to build reinforcement learning models (see above).
I treat data science problems as complex systems involving comprehensive system complexities, or X-complexities, in terms of data (characteristics), behavior, domain, social factors, environment (context), learning (process and system), and deliverables. Data complexity is reflected in terms of sophisticated data circumstances and characteristics, including large scale, high dimensionality, extreme imbalance, online and real-time interaction and processing, cross-media applications, mixed sources, strong dynamics, high frequency, uncertainty, noise mixed with data, unclear structures, unclear hierarchy, heterogeneous or unclear distribution, strong sparsity, and unclear availability of specific sometimes critical data. It may be embodied in such aspects of business problems as social networking, community emergence, social dynamics, impact evolution, social conventions, social contexts, social cognition, social intelligence, social media, group formation and evolution, group interaction and collaboration, economic and cultural factors, social norms, emotion, sentiment and opinion influence processes, and social issues, including security, privacy, trust, risk, and accountability in social contexts. Environment complexity is another important factor in understanding complex data and business problems, as reflected in environmental (contextual) factors, contexts of problems and data, context dynamics, adaptive engagement of contexts, complex contextual interactions between the business environment and data systems, significant changes in business environment and their effect on data systems, and variations and uncertainty in interactions between business data and the business environment.
Newsweek and International Business Times are to host an Artificial Intelligence and Data Science in Capital Markets event, taking place on 1 and 2 March 2017 at the Barbican in the City of London. Professor Nick Jennings CB, FREng, Vice-Provost (Research) Imperial College, said: "Imperial College London is at the forefront of getting technology and economics/business expertise working together to create real impact at the forefront of the fintech revolution. Professor David Hand, chief scientific advisor, Winton Capital Management, added: "The world is changing: big data, streaming data, open data, high frequency data, and administrative data are just some of the creatures inhabiting this new world of data. "Data science -- the technology of extracting information from data - has become critically important to commercial success and public understanding.
Five years ago, universities like MIT and Stanford first opened up free online courses to the public. Today, more than 700 schools around the world have created thousands of free online courses. I've compiled this list of over 438 such free online courses that you can start this month. You can find complete lists of the technology-related courses starting later in 2017 on our Computer Science and Programming subject pages.