A remarkable feature of human beings is their capacity for creative behaviour, referring to their ability to react to problems in ways that are novel, surprising, and useful. Transformational creativity is a form of creativity where the creative behaviour is induced by a transformation of the actor's conceptual space, that is, the representational system with which the actor interprets its environment. In this report, we focus on ways of adapting systems of learned representations as they switch from performing one task to performing another. We describe an experimental comparison of multiple strategies for adaptation of learned features, and evaluate how effectively each of these strategies realizes the adaptation, in terms of the amount of training, and in terms of their ability to cope with restricted availability of training data. We show, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch.

Microsoft was one of the earliest companies to begin discussing and advocating for an ethical perspective on artificial intelligence. The issue began to take off at the company in 2016, when CEO Satya Nadella spoke at a developer conference about how the company viewed some of the ethical issues around AI, and later that year published an article about these issues. Nadella's primary focus was on Microsoft's orientation toward using AI to augment human capabilities and building trust into intelligent products. The next year, Microsoft's R&D head Eric Horvitz partnered with Microsoft's president and chief legal officer Brad Smith to form Aether, a cross-functional committee addressing AI and ethics in engineering and research. With these foundations laid, in 2018, Microsoft established a full-time position in AI policy and ethics.

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."

What goes into making plants taste good? For scientists in MIT's Media Lab, it takes a combination of botany, machine-learning algorithms, and some good old-fashioned chemistry. Using all of the above, researchers in the Media Lab's Open Agriculture Initiative report that they have created basil plants that are likely more delicious than any you have ever tasted. No genetic modification is involved: The researchers used computer algorithms to determine the optimal growing conditions to maximize the concentration of flavorful molecules known as volatile compounds. But that is just the beginning for the new field of "cyber agriculture," says Caleb Harper, a principal research scientist in MIT's Media Lab and director of the OpenAg group.

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."

Nisha Rataria remembers the moment that she understood the power of technology to significantly improve a child's learning and comprehension. As a teacher at the public Vidhya Nagar Primary School in Ahmedabad, Gujarat, India, Rataria teaches students from across the spectrum – bright, struggling, poor and middle class. A few years ago, her school implemented an artificial-intelligence based education program called EnglishHelper that provides a suite of tools to help children learn to speak, read and write English. Many of her students, who she says could not even recognize the alphabet, could now read English with some confidence. By the end of the 2019-2020 school year, EnglishHelper and ReadToMe could be used by nearly 20 million students worldwide.

It's in our nature to be addicted to things that distract us

From this academic year (2019-20), aspiring engineering candidates in Karnataka will have the opportunity to study the most in-demand courses - Artificial Intelligence (AI) and Machine Learning (ML). The Visvesvaraya Technological University (VTU) in its recent Executive Council meeting on May 30 resolved to introduce Bachelor of Engineering (BE) in Artificial Intelligence (AI) and Machine Learning with effect from the academic year 2019-20. The eligibility for admission to this course remains the same as BE and B.Tech programs in VTU. As of now, several colleges have modules in Machine Vision, Robot Programming and Artificial Intelligence in the third semester of the Instrumentation engineering course. But, offering it as degree course in itself is a first in VTU.

Automatic program correction is an active topic of research, which holds the potential of dramatically improving productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning and NLP have rekindled the hope to eventually fully automate the process of repairing programs. A key challenges is ambiguity, as multiple codes -- or fixes -- can implement the same functionality. In addition, dataset by nature fail to capture the variance introduced by such ambiguities. Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution of potential fixes. Our model is formulated as a deep conditional variational autoencoder that samples diverse fixes for the given erroneous programs. In order to account for ambiguity and inherent lack of representative datasets, we propose a novel regularizer to encourage the model to generate diverse fixes. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to $61\%$ of the mistakes.

An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations. However, due to the large number of network variants, such public-available trained models are often of different architectures, each of which being tailored for a specific task or dataset. In this paper, we study a deep-model reusing task, where we are given as input pre-trained networks of heterogeneous architectures specializing in distinct tasks, as teacher models. We aim to learn a multitalented and light-weight student model that is able to grasp the integrated knowledge from all such heterogeneous-structure teachers, again without accessing any human annotation. To this end, we propose a common feature learning scheme, in which the features of all teachers are transformed into a common space and the student is enforced to imitate them all so as to amalgamate the intact knowledge. We test the proposed approach on a list of benchmarks and demonstrate that the learned student is able to achieve very promising performance, superior to those of the teachers in their specialized tasks.