Online education provider Udacity said today it's launching a nanodegree program to teach product managers how to create AI-powered products. The nontechnical course will also teach product managers how to identify business opportunities with AI or machine learning. Enrollment for the first program begins today and consists of 6 lessons and 3 projects, and lasts about 2 months. "Students will start off by learning the foundations of AI and machine learning, starting with the unsupervised and supervised models that are used in industry today," Udacity founder Sebastian Thrun told VentureBeat in an email. "As a next step, they will learn how to use Figure Eight's data annotation platform to develop a labeled dataset for supervised learning. Finally, students will develop a business proposal for an AI product of their choice, while learning strategies for continuously learning and updating a machine learning model."
SAP SE (NYSE: SAP) today said it will offer a new course on the openSAP platform that focuses on the ethical implications when developing and interacting with artificial intelligence (AI). Creating Trustworthy and Ethical Artificial Intelligence, offered June 25 through July 24, is geared toward all leaders, professionals, developers and general users of AI technology. "The potential for AI and machine learning is great, and the technology has already had significant impact in automating tasks and efficiently analyzing data sets," said Bernd Welz, chief knowledge officer, SAP. "As this technology continues to evolve and becomes further engrained in our society, it's important that we take the necessary steps to ensure that its development and continued application are carried out in an ethical way. Through this course, we're showing learners how they can keep ethics in the forefront when developing AI- and machine learning–enabled technologies."
If you want to keep up with the latest e-learning trends, then you must be aware that Artificial Intelligence has a great potential in the LMS world and can help to add never before seen value for the e-learning team. It is true that online training and learning needs a lot of management and proper system to deliver what the learners actually need and also in the form they need. So, the need for the moment is a new type of LMS that supports AI, which will help to put the learners in the center and help them better understand and also manage their courses. AI can bring a great change in the learning system and make it more effective and aligned with the needs. "Learning is a two-way process- A dialogue. With latest technology and social media, learners expect fast and quick response to their questions."
There's an endless supply of industries and applications machine learning can be applied to to make them more efficient and intelligent. Chat bots, spam filtering, ad serving, search engines, and fraud detection, are among just a few examples of how machine learning models underpin everyday life. Machine learning is what lets us find patterns and create mathematical models for things that would sometimes be impossible for humans to do. Unlike data science courses, which contain topics like exploratory data analysis, statistics, communication, and visualization techniques, machine learning courses focus on teaching only the machine learning algorithms, how they work mathematically, and how to utilize them in a programming language. Now, it's time to get started.
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience---how to adapt the system's behaviour---to optimize the learning of a collection of value functions. A simple answer is to compute an intrinsic reward based on the statistics of each auxiliary learner, and use reinforcement learning to maximize that intrinsic reward. Unfortunately, implementing this simple idea has proven difficult, and thus has been the focus of decades of study. It remains unclear which of the many possible measures of learning would work well in a parallel learning setting where environmental reward is extremely sparse or absent. In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed. We discuss the interaction between reward and prediction learners and highlight the importance of introspective prediction learners: those that increase their rate of learning when progress is possible, and decrease when it is not. We provide a comprehensive empirical comparison of 15 different rewards, including well-known ideas from reinforcement learning and active learning. Our results highlight a simple but seemingly powerful principle: intrinsic rewards based on the amount of learning can generate useful behaviour, if each individual learner is introspective.
The technology sector is set to benefit from a £18.5 million cash injection to drive up skills in AI and data science and support more adults to upskill and retrain to progress in their careers or find new employment. Up to 2,500 people will have the opportunity to retrain and become experts in data science and artificial intelligence (AI), thanks to a £13.5 million investment to fund new degree and Masters conversion courses and scholarships at UK academic institutions over the next three years. The ground-breaking Adult Learning Technology Innovation Fund, which will be launched in partnership with innovation foundation Nesta, will provide funding and expertise to incentivise tech firms to harness new technologies to develop bespoke, flexible, inclusive, and engaging online training opportunities to support more people into skilled employment. Companies across the tech sector already employ more than 2.1 million people, contribute £184 billion to the economy every year and inward investment to the UK AI sector stood at £1 billion for 2018, which is more than Germany, France, Netherlands, Sweden and Switzerland combined. To further strengthen the sector, Government is investing in data-driven technologies, such as artificial intelligence, through the modern Industrial Strategy, so tech businesses and people with the drive and talent can succeed.
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The majority of modern deep learning models are able to interpolate the data: the empirical loss can be driven near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning. Specifically, we use it to compute an adaptive learning-rate given a stochastic gradient direction. This results in the Adaptive Learning-rates for Interpolation with Gradients (ALI-G) algorithm. ALI-G retains the advantages of SGD, which are low computational cost and provable convergence in the convex setting. But unlike SGD, the learning-rate of ALI-G can be computed inexpensively in closed-form and does not require a manual schedule. We provide a detailed analysis of ALI-G in the stochastic convex setting with explicit convergence rates. In order to obtain good empirical performance in deep learning, we extend the algorithm to use a maximal learning-rate, which gives a single hyper-parameter to tune. We show that employing such a maximal learning-rate has an intuitive proximal interpretation and preserves all convergence guarantees. We provide experiments on a variety of architectures and tasks: (i) learning a differentiable neural computer; (ii) training a wide residual network on the SVHN data set; (iii) training a Bi-LSTM on the SNLI data set; and (iv) training wide residual networks and densely connected networks on the CIFAR data sets. We empirically show that ALI-G outperforms adaptive gradient methods such as Adam, and provides comparable performance with SGD, although SGD benefits from manual learning rate schedules. We release PyTorch and Tensorflow implementations of ALI-G as standalone optimizers that can be used as a drop-in replacement in existing code (code available at https://github.com/oval-group/ali-g ).
Machine Learning (ML) is a popular buzzword in the field of technology and recently it has entered the eLearning space as well. Machine learning enables computers or machines to make decisions that are data-driven, eliminating the need for explicit programming to execute a task. Machine learning makes use of algorithms that are designed to improve over time depending on the new data they'll be tracking. What if I tell that you've already experienced the benefits of ML without realizing that it's machine learning at work? For instance, if you have tried online food delivery platforms such as UberEATS, have you wondered how the app is able to predict an estimated time of delivery or display a list of popular restaurants near you?