NASA doesn't just want adults learning how to work with lunar soil. The space agency has launched a Lunabotics Junior contest that tasks K-12 students in the US with designing (but not manufacturing) a Moon-digging robot. They have to envision a compact (3.5ft by 2ft by 2ft) automaton that can dig and move lunar regolith while dealing with the stickiness of that soil. Kids have until January 25th, 2022 to submit both an image of the robot and a summary of how it's meant to operate. The two winners for the K-5 and 6-12 categories will each get a virtual classroom chat with Kennedy Space Center director Janet Petro, while four finalists in each group will have virtual sessions with a NASA expert.

Lily Xu is a PhD student at Harvard University, applying machine learning and game theory to wildlife conservation. She is particularly focused on the prevention of illegal wildlife poaching, and she told us about this interesting, and critically important, area of research. Green security is the challenge of environmental conservation under some unknown threat. The three domains that we focus on are illegal wildlife poaching, illegal logging and illegal fishing. Across all of these settings we have an environmental challenge, which is to preserve our natural ecosystems.

The machine learning field is quite interesting and is constantly evolving. In the modern world, you will find its application in every aspect of your lives starting from Facebook feed to Google Maps for navigation and so on. It is a subfield of artificial intelligence and involves learning computer algorithms that improve automatically through experience. Its demand is gradually rising because it can make high-value predictions to guide better decisions and smart actions in real-time without human intervention. So, to benefit our readers, we have created a comprehensive list of the best online machine learning courses and certifications from the leading educational platforms and renowned universities.

Data will control the twenty-first century. Every company, big or small, is attempting to use data to their advantage. Data-driven insights could aid businesses in transforming and targeting new markets, addressing customer pain points, increasing revenue, and more. As a result, a growing number of companies are concentrating on data collecting, interpretation, and application. of India sees significant digitisation of its industries and services, making it the second-largest data science hub. Analysts estimate that by 2026, the country will have around 11 million job openings.

The possibilities of artificial intelligence seem limitless. One of the most notable features of neural networks has been image manipulation. Restoring old photos or applying filters has been available for a long time. That's why artificial intelligence added to this process doesn't seem revolutionary. But creating images from scratch is a prime example of using technology.

Before we get on with the topic let me introduce myself!! Coz the reason you are here might be because you want to jump onto the AI bandwagon to make a carrier out it. My story, though incomplete, may encourage you to hop on or may persuade you to pursue some other stuff. I never was hard working, but always had interest in highly technical stuff. In my high-school or college I was interested in physics. I studied graduate level physics in high-school itself.

Youth are the most essential and promising segment of every country's population. India has a larger advantage over other countries in terms of becoming a global leader because we effectively tap into the youth's potential. Quality education is the only way to achieve this. With artificial intelligence's potential growth in India, now is the ideal time to incorporate AI in education to reap its benefits and prepare India's young for the future. Artificial intelligence has great potential in India.

Over the past year schools across the UK have implemented contactless technology to deliver hot school lunches safely and efficiently. But now, as schools return to normal operations, there's a new type of software powering contactless transactions: facial recognition. Pre-order, cashless payments and contactless are just some of the buzzwords we've heard over the past year, especially regarding school payments and the safe delivery of nutritious school meals. Now more than ever, schools are looking to technology for contactless solutions that increase efficiencies. In 2021, CRB Cunninghams, cashless catering, online payment, and identity management solutions provider to education establishments, launched facial recognition software to UK secondary schools.

Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

Breast cancer is the most common cancer among women worldwide. The human epidermal growth factor receptor 2(HER2) with immunohistochemical(IHC) is widely used for pathological evaluation to provide the appropriate therapy for patients with breast cancer. However, the deficiency of pathologists is extremely significant in the current society, and visual diagnosis of the HER2 overexpression is subjective and susceptible to inter-observer variation. Recently, with the rapid development of artificial intelligence(AI) in disease diagnosis, several automated HER2 scoring methods using traditional computer vision or machine learning methods indicate the improvement of the HER2 diagnostic accuracy, but the unreasonable interpretation in pathology, as well as the expensive and ethical issues for annotation, make these methods still have a long way to deploy in hospitals to ease pathologists' burden in real. In this paper, we propose a HER2 automated scoring system that strictly follows the HER2 scoring guidelines simulating the real workflow of HER2 scores diagnosis by pathologists. Unlike the previous work, our method takes the positive control of HER2 into account to make sure the assay performance for each slide, eliminating work for repeated comparison and checking for the current field of view(FOV) and positive control FOV, especially for the borderline cases. Besides, for each selected FOV under the microscope, our system provides real-time HER2 scores analysis and visualizations of the membrane staining intensity and completeness corresponding with the cell classification. Our rigorous workflow along with the flexible interactive adjustion in demand substantially assists pathologists to finish the HER2 diagnosis faster and improves the robustness and accuracy. The proposed system will be embedded in our Thorough Eye platform for deployment in hospitals.