BLISS Team Educational Services, Bred Hub, has called on the Federal Government to introduce Artificial Intelligence in school curriculum as part of measures to prepare this generation for the future. Speaking ahead of train-the-trainer programme in Lagos, the General Manager, Bliss Team Educational Services, Christian Chime, said that government must champion the initiative that would introduce children and youths to the world of innovation through the teaching of Robotics, Artificial Intelligence, Coding and Science, Technology, Engineering and Mathematics (STEM) education. Chime noted that in the nearest future, the world would expect AI/Robotics to be a way of life and would play great roles in human existence on earth; adding: "Whoever leads in Artificial intelligence in 2030 will rule the world until 2100. We want this skill to be part of school curriculum, we want every student to be able to build and programme robotics. Irrespective of what a child wants to become, he or she needs to understand how to use technology because it is taking over every industry it makes process faster in whatever industry they are going to find themselves. "Bred Hub in partnership with UBTECH is organizing an interactive Artificial Intelligence and Robotics training for teachers and educators so that they can educate their students.

In this blog, Microsoft talks about ways to address the top challenges to AI adoption through empowering inclusion, expanding access to accessible and affordable technology, supporting faculty and staff with skills, training, and resources, and partnering on long-terms AI strategies. Artificial intelligence is transforming higher education, according to a new study released today by IDC and commissioned by Microsoft. The report details the expected opportunity with AI in higher education and the challenges institutions must overcome to realize results. The study covered 509 higher education institutions in the US, and found that nearly all respondents--99.4 Fifteen percent called AI a "game-changer," and 54 percent of higher education institutions in the US have started to experiment with AI, while 38 percent have adopted AI as a core part of their business strategy.

Beidi Chen and Tharun Medini, graduate students in computer science at Rice University, helped develop SLIDE, an algorithm for training deep neural networks without graphics processing units. Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like GPUs. Computer scientists from Rice, supported by collaborators from Intel, will present their results today at the Austin Convention Center as a part of the machine learning systems conference MLSys. Many companies are investing heavily in GPUs and other specialized hardware to implement deep learning, a powerful form of artificial intelligence that's behind digital assistants like Alexa and Siri, facial recognition, product recommendation systems and other technologies. For example, Nvidia, the maker of the industry's gold-standard Tesla V100 Tensor Core GPUs, recently reported a 41% increase in its fourth quarter revenues compared with the previous year.

Measuring the quality of a generated sequence against a set of references is a central problem in many learning frameworks, be it to compute a score, to assign a reward, or to perform discrimination. Despite great advances in model architectures, metrics that scale independently of the number of references are still based on n-gram estimates. We show that the underlying operations, counting words and comparing counts, can be lifted to embedding words and comparing embeddings. An in-depth analysis of BERT embeddings shows empirically that contextual embeddings can be employed to capture the required dependencies while maintaining the necessary scalability through appropriate pruning and smoothing techniques. We cast unconditional generation as a reinforcement learning problem and show that our reward function indeed provides a more effective learning signal than n-gram reward in this challenging setting.

Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. It operates feature learning on the graph structure, through aggregating the features of the neighbor nodes to obtain the embedding of each target node. Owing to the strong representation power, recent research shows that GCN achieves state-of-the-art performance on several tasks such as recommendation and linked document classification. Despite its effectiveness, we argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. Although neural network can approximate any continuous function, including the multiplication operator for modeling feature crosses, it can be rather inefficient to do so (i.e., wasting many parameters at the risk of overfitting) if there is no explicit design. To this end, we design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size. We term our proposed architecture as Cross-GCN, and conduct experiments on three graphs to validate its effectiveness. Extensive analysis validates the utility of explicitly modeling cross features in GCN, especially for feature learning at lower layers.

Machine learning is gaining popularity as a career choice among the young students of computer science and other quantitative fields. And of course, the study of machine learning does open up a few ways into the AI industry which is already quite big in spite of being at a nascent stage at best. It is time to gauge what the new year holds for us as far as these disruptive technologies are concerned. Machine learning adoption is at an all time high The concept of machine learning is pretty old. We can trace it back to the bloody days of the second world war where the legendary Alan Turing applied machine learning to break an impenetrable German code and ended up winning the war for England.

Lets discuss Large Scale Machine Learning. Nowadays python is the most emerging Language in the industry if we look at the chart below we can see the effective inclination in recent years. The main reason of its popularity is the vast use of it in machine learning and AI. There are many other languages but well known are C,C,R; python is taking the grounds because of its scalability and its usage on a vast scale and its compatibility on frameworks of Large Scale Machine Learning. Machine learning have compute complex algorithms which needs a language that have computation capability to perform linear algebra and calculus calculations.

Across the technologically mature economies, citizens, society, businesses, and governments alike are becoming aware of the emergence of powerful technologies and their potential to reshape every aspect of human activity. The names of rapidly advancing technologies are becoming part of our everyday experience, even if we don't fully understand their functionality, capabilities, long-term potential impacts, or implications. There is though, a growing understanding and expectation that our lives will be increasingly dependent on, and enhanced by, the coming together of a range of these technologies. The most impactful of these exponentially advancing technologies are likely to be 5G communications, smartphones, smart wearable and embedded devices such as watches, artificial intelligence (AI), machine learning (ML), big data, cloud computing, smart objects, smart speakers, home automation, blockchain, digital currencies, augmented reality (AR), virtual reality (VR), 3D printing, drones, robotics, sensors, the Internet of Things (IoT), and quantum computing. So, how might these technologies combine to create previously unimaginable changes in everything from lifestyles, relationships, and work, to our food, leisure, and travel experiences? To explore these possibilities, the Life in 2025: Say Hello Say Goodbye Scenarios were developed for Huawei Consumer Business Group. The scenarios preview what we could see emerge across ten different aspects of human activity by the year 2025: Dating, Leisure Time, Friends and Family, Food and Dining, Entertainment, Fashion and Beauty, Travel, Health and Wellness, The Workplace, and Communication. The next five years will see AI take the possibilities for dating to a new level. Dating apps could tap into information on our lives from across the web and social media, to enhance our composed profile.

ABU DHABI, 4th March, 2020 (WAM) -- A senior delegation from the Mohamed bin Zayed University of Artificial Intelligence, MBZUAI, the world's first graduate-level, research-based artificial intelligence university, recently discussed potential collaboration opportunities with Egypt's educational institutions during a recent visit to the country. The visit - organised by Egypt's Ministry of Higher Education and Scientific Research and the UAE Embassy in Cairo - touched upon the importance of the exchange of students and knowledge in the field of artificial intelligence, AI, to provide reciprocal benefits for the UAE and Egypt. Led by Professor Ling Shao, Executive Vice President and Provost, Assistant Professor Dr. Hang Dai, and Reem Al Orfali, Director of Student Affairs, the MBZUAI delegation met with representatives from the Supreme Council of Universities to demonstrate the breadth of the University's education and research facilities. The meeting emphasised the value of enabling both countries' plans to develop AI capacity for economic and societal empowerment. Discussions with the Supreme Council of Universities, as well as University deans, head of departments, and faculty members during the visit included joint research projects that would further the use of AI in healthcare and Arabic language processing amongst other fields, creating joint AI labs and a collaborative AI competition, exchanging professors, co-advising students, and the potential for summer and winter schools in AI, as well as exploring the scope for offering dual or joint degrees.

In the age of artificial intelligence, technology has given us many online tools, apps, and robots to help us educate our children. But, as parents know, nothing beats one-on-one interaction between a parent and a child. Our children learn most effectively by social means. With the influx of technology, there's one inherent problem in our current education system that seems to be amplified: flexibility. Our children never had the "flexibility" of education that can provide them with a tailored experience to fulfill their potential.