Fairness in machine learning has received considerable attention. However, most studies on fair learning focus on either supervised learning or unsupervised learning. Very few consider semi-supervised settings. Yet, in reality, most machine learning tasks rely on large datasets that contain both labeled and unlabeled data. One of key issues with fair learning is the balance between fairness and accuracy. Previous studies arguing that increasing the size of the training set can have a better trade-off. We believe that increasing the training set with unlabeled data may achieve the similar result. Hence, we develop a framework for fair semi-supervised learning, which is formulated as an optimization problem. This includes classifier loss to optimize accuracy, label propagation loss to optimize unlabled data prediction, and fairness constraints over labeled and unlabeled data to optimize the fairness level. The framework is conducted in logistic regression and support vector machines under the fairness metrics of disparate impact and disparate mistreatment. We theoretically analyze the source of discrimination in semi-supervised learning via bias, variance and noise decomposition. Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.

To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test set. Many researchers and engineers are developing methods that enable training or designing a model more efficiently. Developing an efficient model includes several strategies such as network architecture search, pruning, quantization, knowledge distillation, utilizing cheap convolution, regularization, and also includes any craft that leads to a better performance-resource trade-off. When combining these technologies together, it would be ideal if one source of performance improvement does not conflict with others. We call this property as the orthogonality in model efficiency. In this paper, we focus on knowledge distillation and demonstrate that knowledge distillation methods are orthogonal to other efficiency-enhancing methods both analytically and empirically. Analytically, we claim that knowledge distillation functions analogous to a ensemble method, bootstrap aggregating. This analytical explanation is provided from the perspective of implicit data augmentation property of knowledge distillation. Empirically, we verify knowledge distillation as a powerful apparatus for practical deployment of efficient neural network, and also introduce ways to integrate it with other methods effectively.

With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.

The United Arab Emirates' Mohamed Bin Zayed University of Artificial Intelligence and Israel's Weizmann Institute of Science have agreed to work together, UAE state news agency WAM said on Sunday. The memorandum of understanding follows the UAE's decision a month ago to normalize relations with Israel. Both countries have said they hope normalized ties will bring economic and technological benefits. The MoU is the first signed between Israeli and UAE higher education bodies, WAM said, intending to "advance the development and use of artificial intelligence as a tool for progress. Spheres of possible collaboration include academic exchanges, conferences, sharing computing resources and the establishment of a joint virtual institute for artificial intelligence, WAM said.

One of the major concerns around machine learning is deploying it. Running a large number of deployment tools and environments, and migrating a model to a production environment can be extremely challenging. There are countless independent tools from data preparation to model training, and software tools that cover every stage of the machine learning life cycle. Machine learning developers need to use and deploy dozens of libraries while in a production environment. There is no standard way to migrate models from any library to any of these tools, so that every time a new deployment is made, new risks are created.

Everyday, with the world harnessing new information, the students are feeling the challenge to retain it. With the vast amount of information and subjects that demands understanding often feel getting pushed by education. While some of the students can overcome this challenge, many others need assistance to counter it. But it becomes difficult for a teacher, to selectively aid student in learning, especially when they also feel the burden of collective performance. To address this issue, an artificial intelligence is designed by the researchers from North Carolina State University, from predicting the position of educational games in inducing learning amongst students.

DUBAI (Reuters) - The United Arab Emirates' Mohamed Bin Zayed University of Artificial Intelligence and Israel's Weizmann Institute of Science have agreed to work together, UAE state news agency WAM said on Sunday. The memorandum of understanding follows the UAE's decision a month ago to normalize relations with Israel. Both countries have said they hope normalised ties will bring economic and technological benefits. The MoU is the first signed between Israeli and UAE higher education bodies, WAM said, intending to "advance the development and use of artificial intelligence as a tool for progress". Spheres of possible collaboration include academic exchanges, conferences, sharing computing resources and the establishment of a joint virtual institute for artificial intelligence, WAM said.

We are moving ahead for our future and new career avenues are getting more demands in the age of science and technology. One of the areas where we may focus more is artificial intelligence. Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

Machine Learning is providing solutions for many existing problems in any domain. It is also helping to increase the profit in that specific domain. Machine Learning is going to play a significant role in all areas in the future. Many of us have heard that Machine Learning plays a fantastic role in business. But many of us do not know the role of machine learning in business and marketing.

Business intelligence (BI) brings a varied collection of strategies that uncover the hidden insights beneath the data sources and convert raw data into intelligent information for business decision making. To stay competitive, businesses must rediscover and use the data they have generated, this makes BI so important. Business intelligence, lets organisations to extract insights from a pool of accessible data to deliver exact, significant, and nearly real-time inputs for decision making. This specialization is offered in collaboration with Tableau, and is aimed for newcomers to data visualization with no prior experience using Tableau. In this course, you will view examples from real world business cases and journalistic examples from leading media companies.