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) …
In this article, I've listed down the essential resources to master the basic and advanced version of data science using: Global Machine Learning Certifications – This list highlights the widely recognized & renowned certifications in machine learning which can add significant weight to your candidature, thereby increasing your chances to grab a data scientist job. This certification offers multiple courses such as algorithms for data science, probability and statistics, machine learning for data science, exploratory data analysis. It teaches aspiring data science candidates to learn data mining, machine learning, big data and data science projects and work with non-profits, federal agencies and local governments and make a social impact. It teaches real world, practical skills to become a data scientist / data engineer.
These are two excellent books on machine learning (AKA, statistical learning; AKA, model building). If we're talking about entry level data scientists to intermediate level data scientists, I'd estimate that they spend less than 5% of their time actually doing mathematics. Even if you use "off the shelf" tools like R's caret and Python's scikit-learn – tools that do much of the hard math for you – you won't be able to make these tools work without a solid understanding of exploratory data analysis and data visualization. While this figure is about data science in general, it also applies to machine learning specifically: when you're building machine learning models, 80% of your time will be spent getting data, exploring it, cleaning it, and analyzing results (using data visualization).
Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. In other words, the latest advances in software, big data, machine learning, biotech, and chemistry may be combining to quite possibly start a new industrial revolution. Lygos develops microbes to convert sugar into high-value specialty chemicals, focusing its flagship product on malonic acid (derived from petroleum), which is used in a diverse set of industries, including flavor and fragrance, electronic manufacturing, and coatings. And, though they will borrow tech from the titans of Silicon Valley (e.g., TensorFlow from Google), and cloud vendors like AWS will lower the bar for developers dipping their toes into machine learning, the biggest impact of big data will not go toward ad-clicking strategies.
Beyond the network of sensors & devices and base IT technologies partially listed in the last paragraph, what is unique and new in IOT is Data Science applications –Data Science applied with the focus on information extraction, insights generation and prescriptive decisions. When IoT is defined as "(Network of Sensors & Devices) IT (Engineering Data Science)", it seems to pervade ALL industries from my vantage point! I have partitioned applied Data Science into three: Industry, Business & Social Data Science. Specialization for each vertical notwithstanding, the three "types" of Data Science are best seen as a unified whole, which we are calling "Engineering Data Science or EDS".
The White House released a much-anticipated document entitled "Preparing for the Future of Artificial Intelligence." Sent from the Office of the President and the National Science and Technology Council Committee on Technology (or NSTC), the report is 58 pages of research, documentation, and recommendations on how the United States government plans to respond to artificial intelligence (AI) moving forward. The report was developed by the NSTC's Subcommittee on Machine Learning and Artificial Intelligence, "which was chartered in May 2016 to foster interagency coordination, to provide technical and policy advice on topics related to AI, and to monitor the development of AI technologies across industry, the research community, and the Federal Government," according to the report. The NSTC hosted five public workshops, as well as putting out a public Request for Information. The information drawn from those six sources informed the eventual recommendations of the committee.
Steve recognises the "disruptive and pervasive" impact AI is already having on business: "AI is enabling companies to achieve improved operational efficiency, develop new and improved products and services, and most significantly entirely new business models. Universities are particularly well suited for interdisciplinary approaches that include multiple technical disciplines as well as the liberal arts, humanities, arts, and social sciences. "Data sharing agreements with appropriate protections for sensitive confidential information enable university data science researchers to develop practical algorithms using real-world data. Municipal, state, and national governments are working to improve accessibility and the democratization of data.