Emergency response data: RapidSOS, a startup whose software platform automatically transmits data from 911 callers to emergency responders, secured $25 million in new funding, led by the investment firm Energy Impact Partners in its Series B funding round. The company closed the round with $55 million in funding including $30 million collected last year. The funding accelerates RapidSOS's work partnering with thousands of public safety agencies, most major public safety software providers, and connected device companies to transform emergency response and disaster management Remote medical documentation: Tech-enabled remote documentation company Augmedix uses natural-language-processing technology and remote medical documentation experts to help reduce the burden of medical documentation for physicians. The San Francisco-based company landed $19 million in Series B funding that included investments from Redmile Group, McKesson Ventures, DCM Ventures, Wanxiang Healthcare Investments, and others. Augmedix provides clinicians with hardware, smartphones or Google Glass, to stream the clinic visit to a cloud-based platform.

By nature, I am an optimist. Despite geopolitical and economic challenges, I believe the world's best days remain ahead. Just look right here in India. In the last decade, more than 200 million people have been lifted out of extreme poverty. That is a staggering accomplishment.

The age of artificial intelligence is upon us. AI and machine learning have the potential to create $3.9 trillion in business value by 2022, according to research firm Gartner (NYSE:IT). Consulting firm McKinsey says that 82% of the businesses it surveyed are generating positive returns from their AI investments, including better customer satisfaction and productivity, better fraud detection at financial firms, and a host of other benefits. There are a lot of tech companies aiming to win pieces of this pie. These include cloud computing companies that own the vast data centers where information is stored and processed, the chipmakers producing the CPUs, GPUs, and FPGAs that can process the vast amounts of data involved, software companies that organize this data and produce AI programs, and IT consultancies that aid big businesses in implementing these systems.

Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning. Our three-day workshop stems on what we identify as the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning. We invite the leaders in these fields to bolster new collaborations and to look for new angles of attack on the mysteries of deep learning.

In this video from the UK HPC Conference, Peter Dueben from ECMWF presents: Progress and Challenges for the Use of Deep Learning to Improve Weather Forecasts. I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will then talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future. Peter Dueben is a Royal Society University Research Fellow at the European Centre for Medium-Range Weather Forecasts (ECMWF). He is contributing to the development and optimization of weather and climate models for modern supercomputers.

It's impossible to say precisely how artificial intelligence will disrupt the job market, so researchers at PwC have taken a birds eye view from the top down, and pointed to the results of sweeping economic changes. Their prediction, in a new report out Tuesday, is that it'll all balance out in the end. But the rise in robots and machine-learning software will make the country more productive over the next two decades, growing at a 2% annual clip, to put nearly the same number of jobs back in the system: 7.2 million, PwC estimates. To be clear those new jobs won't involve building robots or coding AI-powered software, which will only make up around 5% of employment, says John Hawksworth, PwC's chief economist. Instead around 1.5 million, or 22%, of the new jobs will be in health and social work.

To get ahead of such blazes, fire department officials in the Bay Area's Contra Costa County intentionally set four test fires of their own this summer. Each was surrounded by a set of field sensors capable of measuring temperature and humidity. When the initial ignition was detected, the sensors relayed the location of the blaze to a remote dashboard created by Zonehaven, a cloud-based analytics application that incorporate current weather conditions to devise a simulation of how quickly the fire would spread over the next five to 10 hours, and, if left unstopped, what nearby areas would be most at risk. The application is capable of sending out an immediate alert about an ignition situation to firefighters and local governments--anyone who needs instant information to start battling the blaze and plan evacuations. As you can see in the video below, the concept is being developed with the cooperation of the nearby Moraga-Orinda fire district, which battled California's deadly Rim and Camp fires in recent years.

While many federal agencies primarily think of artificial intelligence as an emerging technology to support their own missions, they also have a secondary role to play in fueling America's research, development and testing of AI by sharing their data, federal tech leaders said Wednesday. The development of innovative artificial intelligence applications relies on powerful underlying data, which many federal agencies hold via the services they provide to Americans. But both U.S. CIO Suzette Kent and Lynne Parker, assistant director of AI in the White House's Office of Science and Technology Policy, identified agencies' hesitancy to share their data with private and academic partners, as well as other agencies, as a leading challenge limiting the nation's development of meaningful AI solutions. Kent said one of her biggest concerns around AI is figuring out "how we make available the powerful data that are strategic assets of the federal government on behalf of its citizens." Agencies are responsible for handling the data properly, but much of it belongs to the public. "The agencies have a responsibility for the external components -- many of the things … around making data available, responding to request from industry, supporting research and development, whether that is in direct grants or specific topic areas or making data or facilities available to support those sets of activities," Kent said at a Bipartisan Policy Center event.

The deep learning predictive AI algorithm developed by I Know First has shown an accuracy of up to 95% in its predictions for Facebook (FB). The deep learning predictive AI algorithm developed by I Know First, a Fintech company that provides state of the art self-learning AI-based algorithmic stock market forecast solutions to uncover the best investment opportunities, has shown an accuracy of up to 95% in its predictions for Facebook (FB). That is according to aFacebook stock forecastevaluation report published by the company on August 25, 2019. The algorithm has demonstrated a higher accuracy rate for longer-term forecasts, as is often the case for predictive AI. "We provide AI-based forecasts for different time horizons. Machine learning algorithms do better on longer time horizons: the longer the time period, the greater the statistical significance and the greater the accuracy." said I Know First CEO Yaron Golgher. "The algorithm is able to identify the trend, and to filter out the background noise.

It is incumbent upon business leaders and Australian organisations to put diversity and the ethical implications of artificial intelligence (AI) at the heart of innovation if we're to ensure the world's third major disruptive force is harnessed for human good. That was the big call-out made by Dr Catriona Wallace, founder and executive director of the ASX-listed machine learning tech innovator, Flamingo AI, during this week's CeBIT conference in Sydney. Speaking on the rise of AI and the relationship between humans and machines, the entrepreneur highlighted several facts and figures on the extent of AI impact and innovation over the short and longer-term horizon, as well as the good and negative potential human consequences that come with it. As outlined by Dr Wallace, disruptive technologies, such as AI, are predicted to be the third of three major problems the world is facing that could detrimentally affect humanity. The other two are climate change, and nuclear war.