machine learning

Why next year could be a turning point for project management and AI


Artificial Intelligence hasn't quite arrived in the project management sphere yet, but it's on its way. Gartner forecasts that 80 per cent of project management roles will be eliminated by 2030 as AI takes on traditional project management functions such as data collection, tracking and reporting. The same report highlights that programme and portfolio management (PPM) software is behind the times, and AI-enabled PPM is only just beginning to surface in the market. However, while some tasks will inevitably be automated, it opens up other opportunities for project managers. It's important to know the difference between how AI-enabled automation can change project management and how AI-enabled insights from massive databases can make a difference.

Artificial Intelligence #3:kNN & Bayes Classification method


This can be thought of as the training set for the algorithm, though no explicit training step is Sobhan N. What you'll learn Use k Nearest Neighbor classification method to classify datasets. Write your own code to make k Nearest Neighbor classification method by yourself. Use k Nearest Neighbor classification method to classify IRIS dataset. Use Naive Bayes classification method to classify datasets.

Expert source on ethical AI in the workplace and hiring (Includes interview)


Britt argues that artificial intelligence – when used ethically – is positively impacting people when it comes to hiring and the employee experience. He expands further by saying that AI should be used to enhance human capability and help workers improve and grow – not to place a "big brother" monitoring system on them. Digital Journal spoke with CallMiner's Britt about examples of how AI has helped companies develop and retain top talent, simplify jobs, and drastically improve the employee experience. Digital Journal: How is artificial intelligence disrupting business? Britt: If by disruption we mean a radical change in a business, then the most disruptive aspects of AI is the need to rethink existing human-built processes to be AI processes, basically transforming a business from Human-to-Human to Machine-to-Human.

The Problem With Including AI In School Curriculum


One of the main reasons to integrate AI in the current school curriculum is to make the upcoming generation familiar with technology. The Government of India and the educational board have been pushing for more artificial intelligence to be integrated into the education system, not from the perspective of enhancing it, but also with the intention of making young minds more aware and skilled when it comes to artificial intelligence. Today, children are curious about the smart conversational devices and AI used in applications like Siri and Alexa; some of them even wonder how Netflix gives them precise recommendations. Gradually, they will grow curious and try to learn what algorithms are, what a neural network is, and how they work. The Government of India and the educational board have been taking measures to make the existing school curriculum more AI-centric with a firm belief that the students will learn about AI, have fun and also take India forward.

Accelerating data-driven discoveries


As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical "internet of things" devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data. But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That's where Paradigm4 comes in. The company, founded by Marilyn Matz SM '80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.

AI With Grove Zero and Codecraft (Scratch 3.0)


The neural network models used in the above application are all run locally in your browser, which has a few distinct advantages as compared to sending the data to the cloud for processing: smaller latency and better privacy. A number of neural networks are used in Cognitive services - Sound Classification for speech commands(, Face Landmark Detection, Face Expression Recognition and Age estimation. There are multiple ways you can build on these examples to make even more fun and exciting applications! If you decide to give it a try,be it with Grove Zero or just using Stage mode, do share in the comments below.

Royal Dutch Shell reskills workers in artificial intelligence as part of huge energy transition


Working at Royal Dutch Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

Machine Learning for JavaScript Developers TensorFlow.js


Machine Learning (ML) is a branch of Artificial Intelligence(AI) that gives machines capabilities to learn and improve without explicit programming or human interference, it uses data to learn itself. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. In simple terms, TensorFlow is a machine learning library made by Google used to design, build and train machine learning models. Google introduced TensorFlow in 2015 and was used with Python, though it has APIs in Java, C and Go.

Deep Learning, Knowledge Representation and Reasoning

Journal of Artificial Intelligence Research

The recent success of deep neural networks at tasks such as language modelling, computer vision, and speech recognition has attracted considerable interest from industry and academia. Achieving a better understanding and widespread use of such models involves the use of Knowledge Representation and Reasoning together with sound Machine Learning methodologies and systems. The goal of this special track, which closed in 2017, was to serve as a home for the publication of leading research in deep learning towards cognitive tasks, focusing on applications of neural computation to advanced AI tasks requiring knowledge representation and reasoning.

Columbia University DSI Alumni Use Machine Learning to Discover Coronavirus Treatments - insideBIGDATA


Two graduates of the Data Science Institute (DSI) at Columbia University are using computational design to quickly discover treatments for the coronavirus. Andrew Satz and Brett Averso are chief executive officer and chief technology officer, respectively, of EVQLV, a startup creating algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies. They apply their technology to discover treatments most likely to help those infected by the virus responsible for COVID-19.