First ever footage of the underside of the'doomsday' Thwaites glacier has been sent back by a robotic yellow submarine dubbed Icefin. Glaciologists have likened the groundbreaking images and video to the first steps on the moon taken by Neil Armstrong in 1969. Early analysis reveals that turbulent warm waters underneath the ice sheet, which is the same size as Britain, are causing an'unstoppable retreat'. Experts have previously predicted that if Thwaites was to melt completely, it would lead to a significant increase in worldwide sea levels of around two feet (65cm). The impact on coastal communities around the world would be catastrophic.

WASHINGTON – It may sound more like science fiction than science fact, but researchers have created bionic jellyfish by embedding microelectronics into these ubiquitous marine invertebrates with hopes to deploy them to monitor and explore the world's oceans. A small prosthetic enabled the jellyfish to swim three times faster and more efficiently without causing any apparent stress to the animals, which have no brain, central nervous system or pain receptors, the researchers said. The next steps will be to test ways to control where the jellyfish go and develop tiny sensors that could perform long-term measurements of ocean conditions such as temperature, salinity, acidity, oxygen levels, nutrients and microbial communities. They even envision installing miniscule cameras. "It's very sci-fi futuristic," said Stanford University bioengineer Nicole Xu, co-author of the research published this week in the journal Science Advances.

Adrian Schwaiger, research engineer at the Fraunhofer Institute for Cognitive Systems IKS, will be presenting his new paper »Benchmarking Uncertainty Estimation Methods for Deep Learning With Safety-Related Metrics« at the SafeAI workshop. The workshop belongs to the AAAI-20 Conference in New York City. In this paper, Adrian Schwaiger and his colleagues Maximilian Henne, Karsten Roscher and Gereon Weiss compare current models of measuring uncertainty in deep neural networks in order to optimally combine the safety and performance of cognitive systems. Deep neural networks generally perform very well in giving accurate predictions, but they often fail in recognizing when these predictions may be wrong. This absence of awareness regarding the reliability of given outputs is a big obstacle in deploying such models in safety-critical applications for example in autonomous vehicles or in medical technology.

It is not often that one witnesses a transformational advance in medicine. But the application of artificial intelligence (AI) to improve the early detection of disease is exactly that. I was a co-author of the paper recently published in Nature showing that an AI system developed by Google was better at spotting breast tumors than doctors. Now, researchers in the US have reported that AI-supported laser scanners are faster than doctors at detecting brain tumors. These are very exciting developments that will, ultimately, have a big impact on the accuracy, logistics, and speed of diagnosis.

IIT Delhi deep-tech startup CYRAN AI Solutions has introduced a one of its kind do-it-yourself (DIY) artificial intelligence (AI) kit for school students in order to help them learn and build smart products using the technology. The product goes by the name BUDDHI Kit, which stands for Build Understand Design Deploy Human-Like Intelligence. The Kit will help students in experiencing artificial intelligence, learning AI basics and building AI-based projects in a fun way, says IIT Delhi. The product is the brainchild of Professor Manan Suri of IIT Delhi, who was recognized by the Massachusetts Institute of Technology (MIT), USA as one of the world's top 35 innovators under the age of 35. The artificial intelligence kit was launched earlier this week by Professor V Ramgopal Rao, Director, IIT Delhi in the presence of representatives from education, skills NGOs, industry, schools, teachers and students.

In this part 2 of the series, we will examine some key considerations and hurdles in patenting machine learning-based biotech or synthetic biology inventions. In this series, we are focusing on artificial intelligence inventions, but as Alan Turing aptly pointed out, that neologism is a "suitcase" term because you can stuff a lot of intelligence classifications and different types of technologies into it. Many of the ground-breaking AI developments in biotech are in the AI subfield of Machine Learning. First, we will briefly discuss what is meant by Machine Learning and discuss some relevant terms. Second, we will review some real world challenges in patenting AI inventions.

A drug molecule "invented" by artificial intelligence (AI) will be used in human trials in a world first for machine learning in medicine. It was created by British start-up Exscientia and Japanese pharmaceutical firm Sumitomo Dainippon Pharma. The drug will be used to treat patients who have obsessive-compulsive disorder (OCD). Typically, drug development takes about five years to get to trial, but the AI drug took just 12 months. Exscienta chief executive Prof Andrew Hopkins described it as a "key milestone in drug discovery".

This article comes from Joon Im, a student in Business Science University. Joon has completed both the 201 (Advanced Machine Learning with H2O) and 102 (Shiny Web Applications) courses. Joon shows off his progress in this Web Scraping Tutorial with rvest. I recently completed the Part 2 of the Shiny Web Applications Course, DS4B 102-R and decided to make my own price prediction app. The app works by predicting prices on potential new bike models based on current existing data.

The field of artificial intelligence and machine learning, particularly the subcategory of deep learning, has experienced massive growth in recent years, with applications ranging from speech recognition to material inspection, healthcare to gaming, to name but a few. One area that's being transformed by machine learning is tomographic imaging – in which a series of data projections (such as X-ray radiographs, for example) are reconstructed into a three-dimensional image. A newly published book, Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging. The book arose from discussions among four colleagues with a long-standing interest in advanced medical image reconstruction: Ge Wang from Rensselaer Polytechnic Institute, Yi Zhang of Sichuan University, Xiaojing Ye from Georgia State University and Xuanqin Mou from Xi'an Jiaotong University. "Deep tomographic reconstruction is a new area, and the development of this area has been rapid over the past years," explains Wang.

Artificial Intelligence (AI) is not just a futuristic concept that might evolutionize the world as we know it, but rather an already-existing technology that strongly impacts various industries. Although we are still far from stretching AI to the fullest extent, smart systems are already changing the way we live and do business in many different areas. Experts define AI as the simulation of human intelligence processes by machines, especially computer systems. So, what can intelligent machines do for us? We prepared a list of seven incredible examples of AI in action.