THE UK police are monitoring hundreds of thousands of Twitter posts containing hate speech every day. It is part of a pilot project to predict spikes in hate crimes in the run up to 31 October, when the UK is due to leave the European Union. The Online Hate Speech Dashboard is being used by analysts at the National Police Chiefs' Council's online hate crime hub, which was established by the Home Office in 2017 to "tackle the emerging threat of online hate crime". It gathers Twitter posts from across the UK and uses artificially intelligent algorithms to detect speech that is, for example, Islamophobic, anti-Semitic or directed against people from certain countries or with disabilities or from LGBT groups. The police chiefs' council tasked Matthew Williams at Cardiff University, UK, and his colleagues with developing the dashboard so that government organisations could monitor hate speech.

In 2018, there were 281 climate-related and geophysical events recorded in the International Disaster Database (EM-DAT). It is estimated that these events caused the deaths of 10,733 people across the world, impacting some 61 million people. Last year saw a rise in seismic activity in Indonesia, in particular. Add to that a string of disasters in Japan and India, including flooding and increased volcanic activity – culminating in more deaths than the previous 18 years combined from this activity alone. The World Bank is researching how it can use data and machine learning algorithms to predict certain types of disasters, and improve recovery in their aftermath, says Vivien Deparday, Disaster Risk Management Specialist at the Bank's Global Facility for Disaster Risk Reduction.

AI continues to transform industries across the globe, and business decision makers of all kinds are taking notice. But there's a problem: although 80% of today's enterprises recognize that AI is critical to their future, only 14% succeed in harnessing it (source). In other words, a gap remains between the potential of AI and its ease of deployment. At Google Cloud AI, closing this gap is perhaps my most important responsibility. After years of breakthroughs, AI has stabilized with the emergence of sophisticated tools, best practices, and a rapidly growing community of builders.

This week more than 700 machine learning researchers from over 30 African countries will come together at Kenyatta University in Nairobi, Kenya for the third annual Deep Learning Indaba. On paper it looks a lot like other artificial intelligence (AI) convenings around the world. There are keynotes from luminaries, talks from up-and-coming researchers, and trainings on new methods. But as we've worked with the Indaba organizers to sponsor the event over the last few months, we've learned that the Indaba represents so much more: First, the Indaba is a hub and catalyst for Africa's growing AI community. At The Rockefeller Foundation, we know that supporting communities of dedicated engineers can change the world.

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you'd start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you'd carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains. Now there's a bot that can do all that. New AI technology being developed at Microsoft Research AI can understand a natural language description, sketch a layout of the image, synthesize the image, and then refine details based on the layout and the individual words provided.

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you'd start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you'd carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains. Now there's a bot that can do all that. New AI technology being developed at Microsoft Research AI can understand a natural language description, sketch a layout of the image, synthesize the image, and then refine details based on the layout and the individual words provided.

BASF and Technische Universität Berlin (TU Berlin) have signed an agreement to cooperate closely in the area of machine learning. The aim of the Berlin-based Joint Lab for Machine Learning (BASLEARN) is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example from process or quantum chemistry. Both partners are jointly committed to this aim in the coming years. As an essential part of the cooperation, BASF will support the research work of Dr. Klaus Robert Müller, professor of machine learning and spokesperson for the Berlin Center for Machine Learning at TU Berlin, with a total of over €2.5 million over the coming five years. Machine learning is a key pillar of artificial intelligence. The objective is to analyze large volumes of data to recognize patterns and relationships which can be used to develop prediction models that optimize themselves based on their results.

It's difficult to track birds flying across the sky in the dark of night, but every fall and spring, millions of birds migrate through the night. Weather radar can offer a spotty view of the phenomenon, but to track nighttime migrations with greater accuracy and reliability, a group of researchers at the University of Massachusetts at Amherst turned to artificial intelligence. Scientists designed a machine-learning algorithm to analyze weather radar images and differentiate migrating birds from precipitation. The algorithm replicates the power of neural networks to analyze and classify radar images. Researchers used the new artificial intelligence program to survey decades-long radar data sets, revealing seasonal and continent-wide migration patterns.

The government is now working on policies to develop electronic components manufacturing base in the country and encourage exports, secretary in the Ministry of Electronics and IT Ajay Prakash Sawhney said. "From near assembly, we are right now moving in that direction seriously with policies to bringing sub-assemblies..., component manufacturing in India," Sawhney said at the Digital Governance Tech Summit. Once printed circuit board (PCB or the motherboard) assembly takes off in India, it will not only cater to the mobile phone segment, but also other major electronic devices, he added. The last five years, the MeiTY secretary said, witnessed a resurgence of manufacturing in India, starting with assembling of mobile phones. "From around six crore mobile phones that were being assembled in 2014-15, we closed the last financial year with assembly of 29 crore mobile phones within the country. Our entire consumption for the country is about 33-34 crore a year," Sawhney said.

Double Robotics' newest robot has a mind of its own. The previous iterations of the Double 3 had been more like "FaceTime on wheels," co-founders Marc DeVidts and David Cann said in a recent phone call about the telepresence robot on self-balancing wheels. But starting Tuesday, the screen on the roving bot will come with better built-in camera options -- and it can get around with just a few clicks. Called "click-to-drive," the human controlling the bot from a computer back at home can control the robot with their face beaming in from a built-in screen. Earlier versions required an iPad to work with the wheel-based robot.