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) …
UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!
KNIME, a unified software platform for creating and productionizing data science, announced the availability of KNIME on AWS, its commercial offering for productionizing artificial intelligence (AI)/machine learning (ML) solutions on Amazon Web Services (AWS). KNIME on AWS is designed to allow customers to assemble and deploy ML solutions across the enterprise at scale and securely on AWS and to gain tangible value quickly. The offering is now featured in AWS Marketplace, including free trials. Many enterprises seek to create value by deploying ML and AI solutions but can lack the data scientists, data platform engineers, experience, money and time necessary to make a meaningful impact quickly. The result is that teams and individuals lacking this set of highly technical skills are left out of the innovation loop and are unable to realize the potential that their data offers.
MADS East 2019 was a two-day conference in December that gave attendees endless opportunities to expose themselves to new ideas in the space of data science for marketing. Some of this year's conference perks included: tables for one-on-one networking, a half-an-hour off the record roundtable with 7 industry leaders, two unique tracks per day, buffet-style lunches, breakfasts, snacks, a refreshing break for cocktails at the Opening Night Party, and NYC Times Square views. This article is my summary of the Day 1 presentations I was able to attend, including lessons and reminders from the speakers. Aside from staying up to date on industry trends, MADS East has also proven itself a valuable opportunity for data and marketing people who are looking to engage with professionals of varying career levels. I was expecting to be the only individual with little background in data or extended industry experience present, but to my surprise, there was a decent balance between early, mid and late-career attendees.
The Master DS&AI is intended for students interested in studying and combining advanced data analysis techniques with AI methods and techniques, in order to understand, use and develop intelligent systems to support and strengthen the human intellect. This Master's program is the first and only engineering program in the Netherlands in which advanced techniques and methods in the field of Data Science and Artificial Intelligence are combined. You currently cannot apply for this DS&AI program. Course information will become available as soon as possible, but is not expected before March 2020. Keep an eye on this internet page as new information will appear here.
Data science and AI are playing an increasingly important role healthcare, including the development of new drugs, therapies, and healthcare processes. Industry analyst, Michael Krigsman, speaks with a leader in data science for health care, Dr. Bülent Kiziltan, to learn more. Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA and MIT in close collaboration with pioneers of their respective fields. In the past 15 years he has led data driven efforts in R&D and built multifaceted strategies for the industry.
After carrying out a successful pilot at its Bagdad copper operation, Freeport McMoRan says it is rolling out a program across its North America and South America mines involving the use of data science, machine learning and integrated functional teams. The program, aimed at addressing bottlenecks, providing cost benefits and driving improved overall performance, was announced in its December quarter results this week. It said: "During 2019, FCX (Freeport) advanced initiatives in its North America and South America mining operations to enhance productivity, expand margins and reduce the capital intensity of the business through the utilisation of new technology applications in combination with a more interactive operating structure." It said the Bagdad mine (Arizona, USA) pilot program, initiated in late 2018, was "highly successful" in utilising these innovative technologies and it would build on this for the implementation across its other mines in North and South America. According to a report in the Financial Times, the system at Bagdad found that the mine was producing seven distinct types of ore and that the processing method, which involves flotation, could be adjusted to recover more copper by adjusting the PH level.
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You've spent months studying data science, now it's time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn's 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.
Cohort of 14 U.S. and international startups to relocate to Bentonville for 12 weeks PRESS RELEASE – The first-ever Arkansas-based artificial intelligence and machine learning accelerator will launch later this month, with the goal of helping a cohort of startups within these fields connect to regional enterprise partners. The Fuel Accelerator, in its second iteration, will provide regular, hands-on education and workshops to a cohort of 14 companies from across the United States, Europe and Asia. These 14 companies will make their way to Northwest Arkansas, at the foot of the Ozark Mountains, for a 12-week, enterprise-ready accelerator that will provide them with access to other startup founders, industry experts, institutions of higher education, and public policy officials. Fuel launched in late 2018 with eight startups participating in a supply chain-focused, 16-week program. The program helped its first cohort nurture relationships with key Fortune 500 companies through feedback sessions, training, pilots and demos.
In this blog post we propose a taxonomy of 6 levels of Auto ML, similar to the taxonomy used for self-driving cars. Machine Learning (ML) is currently one of the hottest and most hyped-up areas of science and technology. In terms of both theoretical discoveries and practical applications, ML seems to be going from success to success, with no slowing down in sight. It has become the dominant, and in some cases exclusive, approach to Artificial Intelligence (AI), which in turn has the promise to radically alter most aspects of our everyday lives. The connection between ML and AI is so strong that the two are used interchangeably, and have in many applications become synonymous. Another concept that is closely linked with ML is automation. Even though ML is frequently used for other purposes (predictive modeling being the best known), it's really the prospect of automating many operations and processes, which are now done manually, that best captures the excitement about ML and its core value proposition. Which begs the following question: how far can we go in automating ML itself?