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) …
There are only a handful of machine learning conferences in the world that attract the top brains in this field. One such conference, which I am an avid follower of, is the International Conference on Machine Learning (ICML). Folks from top machine learning research companies, like Google AI, Facebook, Uber, etc. come together and present their latest research. It's a conference any data scientist would not want to miss. ICML 2019, held last week in Southern California, USA, saw records tumble in astounding fashion.
This repository is an end-to-end pipeline for the creation, intercomparison and evaluation of machine learning methods in climate science. The pipeline carries out a number of tasks to create a unified-data format for training and testing machine learning methods. The major entrypoints to the repository are through the notebooks directory, and the scripts. A blog post describing the goals and design of the pipeline can be found here. Anaconda running python 3.7 is used as the package manager.
Since "2001: A Space Odyssey," people have wondered: could machines like HAL 9000 eventually exist that can process information with human-like intelligence? Researchers at Michigan State University say that true, human-level intelligence remains a long way off, but their new paper published in The American Naturalist explores how computers could begin to evolve learning in the same way as natural organisms did--with implications for many fields, including artificial intelligence. "We know that all organisms are capable of some form of learning, we just weren't sure how those abilities first evolved. Now we can watch these major evolutionary events unfold before us in a virtual world," said Anselmo Pontes, MSU computer science researcher and lead author. "Understanding how learning behavior evolved helps us figure out how it works and provides insights to other fields such as neuroscience, education, psychology, animal behavior, and even AI. It also supplies clues to how our brains work and could even lead to robots that learn from experiences as effectively as humans do."
The increase of AI and machine learning in the workplace, while in many cases, just replacing repetitive human tasks, is also shaping new and engaging positions. Jobs requiring data science skills are in incredible demand. However, concerns surrounding AI are creating a market for jobs that didn't even exist two years ago, such as an AI ethicist. In a Deloitte survey last year, 10% of AI-aware executives ranked "Ethical Risks of AI" as their #1 AI-related concern, over other prevalent interests such as risks surrounding cybersecurity and poor decision making.
The fDi Benchmark comparison analysis confirms that Montréal is the best place in North America to invest in AI and explains the amazing growth it has experienced in the past few years. In the past two years, Montréal International has supported more than 30 artificial intelligence projects in the region, with investments totalling $500 million. Methodological note: fDi Benchmark developed two site comparison models. One evaluates the quality of the industry sector and the other calculates the operating costs. The focus is on the vibrancy of the ecosystem and the availability of a skilled workforce, which represent 35% and 30% respectively of the total score.
We are clearly shifting to an increasingly borderless workforce in the form of the networks of people who make a living that is dependent on a specific company but work without any formal employment agreement with said company. Every company's value chain consists not just of its own employees but millions of others including gig workers, contingent workers, partner employees and more. There is a greater need today than ever before to redefine an organization's systems to embrace this outer core. We are also dealing with increased human longevity which is creating new challenges of living and working that will require greater flexibility than ever before. Employees need the ability to go in and out of the traditional employee lifecycle, moving from the usual part-time and full-time arrangements to more fluid ones that allow them the flexibility of committing more sporadically while also making time for family, reskilling, the pursuit of a purpose or personal passion, and so on.
The role of a data analyst is evolving in the era of emerging artificial intelligence (AI). So much so, we might need to relabel this role as Analyst 2.0. Here's a look at what Analyst 2.0 means for the next-gen data guru helping organizations use data for better decision-making. We are all swamped with data. Analysts have a hard time keeping pace with demands for intel that can only be gleaned through analytics.
London (CNN Business)It takes a certain nimbleness to pick a strawberry or a salad. While crops like wheat and potatoes have been harvested mechanically for decades, many fruits and vegetables have proved resistant to automation. They are too easily bruised, or too hard for heavy farm machinery to locate.
Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science.
Artificial intelligence solutions are inundating the market right now: They're penetrating and transforming industries from transportation and agriculture to finance (paywall) and business development. And sales and marketing are no exception to this trend. Many tout AI's unique ability to recognize and track nuances that a human eye can't as the "golden ticket" that promises to skyrocket any company's attempts to understand their customers on a whole new level. I believe AI essentially presents companies with an opportunity to establish longer-lasting, deeper and more meaningful connections with their customers. How does this happen, exactly?