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".
MIT CSAIL Director Daniela Rus says the goal of the STL initiative is "to create a new generation of AI tools that are deeply rooted in systems." From self-driving cars to the Internet of Things, artificial intelligence (AI) has reached new levels of sophistication in recent years. With that in mind, this week MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) launched an industry collaboration focused on using machine learning to create functional human-like systems. Nearly 40 senior researchers will participate in the new "SystemsThatLearn@CSAIL" (STL) initiative alongside a range of organizations that include founding members BT, Microsoft, Nokia Bell Labs, Salesforce, and Schlumberger. Member companies will work with CSAIL scientists to suggest new lines of research and develop real-world applications.
In a rapidly changing world filled with too much information, Tom Lounibos, CEO of Soasta Inc., has a simple, down-to-earth, one-word solution for his customers looking to make a digital transformation: practice. A one-time college baseball star drafted by the major leagues, Lounibos admitted he often turns to sports metaphors when talking about business solutions. "If you're making the move to playing pro ball, it's the same game, but everything is just so much faster," he explained. "So how do you get there? You watch the films, you hit the batting cage ... in other words, you practice."
From self-driving cars to the internet of things, artificial intelligence (AI) has reached new levels of sophistication in recent years. With that in mind, this week MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) launched an industry collaboration focused on using machine learning to create functional human-like systems. Nearly 40 senior researchers will participate in the new "SystemsThatLearn@CSAIL" (STL) initiative alongside a range of organizations that include founding members BT, Microsoft, Nokia Bell Labs, Salesforce, and Schlumberger. Member companies will work with CSAIL scientists to suggest new lines of research and develop real-world applications. "Developing capabilities in AI and machine learning are key to the future of fields like finance, energy, manufacturing, and health care," says STL Executive Director Lori Glover.
The World Economic Forum expects automation and Artificial Intelligence (AI), to result in the loss of at least 5 million jobs globally by 2020. Yes, machines are going to steal your jobs. It may sound like a scene from Terminator in which a robot may push you out of your office chair, but the reality is scarier and invisible to an extent. The major threat to your job is from machine learning. Machine Learning is the ability to process huge volumes of data and complete tasks in a more efficient way than a human being can.
According to research conducted by Gartner, "Big data investments continue to rise but are showing signs of contracting." The company's most recent survey found that "48 percent of companies have invested in big data in 2016, up 3 percent from 2015. However, those who plan to invest in big data within the next two years fell from 31 to 25 percent in 2016." "The big issue is not so much big data itself, but rather how it is used. While organizations have understood that big data is not just about a specific technology, they need to avoid thinking about big data as a separate effort."
What meaningful careers exist in data science (stats/ML/optimization)? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. What meaningful careers exist in data science (stats/ML/optimization)? This is a great question, coming from academia myself, I can relate to it. It really depends on what you personally find meaningful, and what your goals are. Would you be happiest doing cutting edge machine learning that impacts millions of people?