Computer Vision is the interdisciplinary field of artificial intelligence and computer science, is basically the transition of data from either a still or a video camera into an accurate representation. Just like human vision, a computer vision also works on validating the computers to visualise, recognise and process images. One of the most buzzing fields under artificial intelligence, computer vision has found plenty of use cases in the industry. There are many resources available to come up to speed with computer vision. In this article, we list down 5 best free resources that will come handy in learning computer vision. The list is in no particular order.

Why do 97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation? Thought leaders in PPC, social and mobile marketing explain in this in-depth survey. Are disciplines such as search engine marketing, social and mobile marketing all trending towards a fully automated world where artificial intelligence (AI) robots take over our jobs? In a survey of top influencers in online marketing with expertise in paid search, social and mobile, 97% of respondents suggested that the future of marketing will actually be smart marketers working hand-in-hand with machine learning-based automation solutions. To help us understand the growing role of machine learning in marketing, we spoke with some of the top influencers in the space, including Michael Brenner (@brennermichael) of Marketing Insider Group, Serena Ehrlich (@serena) of BusinessWire, Adelyn Zhou (@adelynzhou) of TOPBOTS and Chris Messina (@chrismessina), creator of the hashtag - among others. Selected responses to the survey are below. Interested in learning more about how new technology and academic disciplines such as machine learning and data science are changing digital forever?

If walking to a regular vending machine seems too inconvenient, what if the vending machine came to you? PepsiCo is doing just that at the University of Pacific campus in Stockton, California with robots called "snackbots." Using a smartphone app, students can order quasi-healthy snacks like Baked Lays, Sunchips or a Starbucks Cold Brew (from PepsiCo's "Hello Goodness" vending platform), and have it delivered between 9 AM and 5 PM to one of 50 locations around the 175 acre campus. The autonomous snackbots, built by Y-Combinator startup Robby Technologies, can travel 20 miles on a charge, and are equipped with a camera, headlights and all-wheel drive to handle rough or wet terrain. Once it arrives, you simply release the lid, grab your snacks and close it to complete the sale. The app presumably takes care of the security and dispensing end of things.

We've all seen the stark headlines: "Being Rich and Successful Is in Your DNA" (Guardian, July 12); "A New Genetic Test Could Help Determine Children's Success" (Newsweek, July 10); "Our Fortunetelling Genes" make us (Wall Street Journal, Nov. 16); and so on. The problem is, many of these headlines are not discussing real genes at all, but a crude statistical model of them, involving dozens of unlikely assumptions. Now, slowly but surely, that whole conceptual model of the gene is being challenged. We have reached peak gene, and passed it. It is, of course, an impressive story. Today, most people know about Gregor Mendel's breeding experiments with pea plants in the 1850s.

Jaana Partanen is not your typical AI programming geek. Until a year ago, the 59-year-old dentist from the Finnish town of Mikkeli had no idea what to make of terms like "machine learning" or "neural networks." Now, Partanen spends her evenings learning the basics of coding and she is thinking about how to apply artificial intelligence to her job, either to help write up medical summaries or perform orthodontics. "I can see it [artificial intelligence] is already here, and it serves us -- very much actually," she said, adding that following the latest developments in the field has become a hobby. She's one of tens of thousands of non-technology experts who are taking part in a grand experiment aimed at repurposing the country's economy toward high-end applications of artificial intelligence. The idea has a simple, Nordic ring to it: Start by teaching 1 percent of the country's population, or about 55,000 people, the basic concepts at the root of artificial technology, and gradually build on the number over the next few years.

Creating the AKS cluster is a one time process for your workspace. Once created, you can reuse this cluster for multiple deployments. If you delete the cluster or the resource group that contains it, then you must create a new cluster the next time you need to deploy. For provisioning_configuration(), if you pick custom values for agent_count and vm_size, then you need to make sure agent_count multiplied by vm_size is greater than or equal to 12 virtual CPUs. For example, if you use a vm_size of "Standard_D3_v2", which has 4 virtual CPUs, then you should pick an agent_count of 3 or greater.

Artificial Intelligence has with time became an essential part of our daily life since technology is used in a wide area of day to day services. The younger generations are the one to give a shape to the AI technique. Hence, it is significant for schools to introduce the concept to their curriculum. While the debate regarding the teaching methodology rages among educators, psychologists and parents, it is this emerging technology that is required to be noticed as well. In a bid to mould the education system, the Central Board of Secondary Education (CBSE) decided to include AI in their syllabus as an elective subject for students.

Ever since its commencement, machine learning has been on a quest to transform the programming industry, in the most fundamental ways. It has been dubbed as the'part of AI that works'. According to PayScale, the average pay for a data scientist, IT with Machine Learning skills is INR 855,503 per year. James Scott, Senior Fellow, Institute for Critical Infrastructure Technology, had written, "Signature-based malware detection is dead. Machine learning based Artificial Intelligence is the most potent defense to the next gen adversary and the mutating hash."

Data science became the highest-paid IT profession in 2018, and the field is set for further growth in 2019 as the tools and techniques become more accessible and AI moves from hype to practical use cases. A recent Deloitte survey estimated 57 percent of businesses are increasing spending in AI as organisations start to wake up to the potential business benefits. "We are just at the beginning of the enterprise machine learning transformation. In 2019, we'll see a new step in maturity, as companies advance from PoCs to production capabilities," says Stephen Line, VP of EMEA at Cloudera. "Enterprise machine learning adoption will continue as businesses look to automate pattern detection, prediction and decision making to drive transformational efficiency improvement, competitive differentiation and growth. As early adopters advance from proof-of-concepts to production deployment of multiple use-cases, we'll continue to see an emergence of technologies and best practices aimed at helping operationalise, scale and ultimately industrialise these capabilities to achieve full transformational value," he predicts.

Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, like well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably under-represented in real-world problems. They mainly stem from the easier construction of such functions and result in improbable properties such as separability, optima located exactly at the boundary constraints, and the existence of variables that solely control the distance between a solution and the Pareto front. Here, we propose an alternative way to constructing multiobjective problems-by combining existing single-objective problems from the literature. We describe in particular the bbob-biobj test suite with 55 bi-objective functions in continuous domain, and its extended version with 92 bi-objective functions (bbob-biobj-ext). Both test suites have been implemented in the COCO platform for black-box optimization benchmarking. Finally, we recommend a general procedure for creating test suites for an arbitrary number of objectives. Besides providing the formal function definitions and presenting their (known) properties, this paper also aims at giving the rationale behind our approach in terms of groups of functions with similar properties, objective space normalization, and problem instances. The latter allows us to easily compare the performance of deterministic and stochastic solvers, which is an often overlooked issue in benchmarking.