The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves. The Cannon-Delivered Area Effects Munition (C-DAEM) system will use GPS to identify enemy tanks and armoured shells, which will be scanned in advance from the skies. According to sources, the Pentagon will invest vast sums into the AI-guided munitions, which could be ready by 2021. They will replace the Dual-Purpose Improved Conventional Munition (DPICM) artillery rounds, which were introduced in the 1980s. Cannon-Delivered Area Effects Munition system: The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves C-DAEM is a 155-millimeter artillery shell, and will be available for the M777 towed howitzer, the M109A6 Paladin self-propelled howitzer, and the new XM1299 self-propelled howitzer, which has a range of up to 43 miles.

Indian cab aggregator Ola on Tuesday said it had acquihired Bengaluru-based artificial intelligence startup Pikup.ai. As part of the deal, the team at Pikup.ai will join Ola. Speaking on the acqui-hiring, Inder Singh, Co-founder, Pikup.ai, said, By bringing deep domain expertise to Ola, this acquihire will also deliver innovations that continue to improve safety and transform customer experience. The cab aggregator also said it was increasing its focus on using advanced analytics and deep technology to build on its mobility solutions. With the availability of rich data, the application of machine learning and AI will enable the ride-hailing giant to identify deep insights that can lead to improved mobility outcomes.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

Reno, NV, July 30, 2019 (GLOBE NEWSWIRE) -- via NEWMEDIAWIRE -- Artificial Intelligence Technology Solutions, Inc., (AITX:OTCPK) is pleased to announce that Robotic Assistance Devices, Inc., (RAD) its wholly owned subsidiary, has received a new purchase order and subsequently deployed three SCOT units at a new multi-billion dollar real estate client. This follows the deployment of the SCOT mentioned in the February 5, 2019 press release, which was for a San Francisco Bay area commercial real estate customer. This client has indicated they are likely to expand their RAD system quickly. "We are gaining good momentum in the real estate vertical," said Steve Reinharz, President and CEO of RAD. "This is a new commercial real estate order and the first in the Northeast of the U.S. This is a large sector that has the potential to greatly increase sales for the company in the future."

A lot of startups in the San Francisco Bay Area claim that they're planning to transform the world. San-Francisco-based, Elon Musk-founded OpenAI has a stronger claim than most: It wants to build artificial general intelligence (AGI), an AI system that has, like humans, the capacity to reason across different domains and apply its skills to unfamiliar problems. Today, it announced a billion dollar partnership with Microsoft to fund its work -- the latest sign that AGI research is leaving the domain of science fiction and entering the realm of serious research. "We believe that the creation of beneficial AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity," Greg Brockman, chief technology officer of OpenAI, said in a press release today. Existing AI systems beat humans at lots of narrow tasks -- chess, Go, Starcraft, image generation -- and they're catching up to humans at others, like translation and news reporting.

As the world descends into geopolitical competition, other powers increasingly challenge European countries' ability to defend their interests and values. Russia is willing to weaponise energy supplies, cyber capabilities, and disinformation; China invests strategically and uses state capitalism to skew the market; Turkey instrumentalises migration; Saudi Arabia leverages its energy resources. And the Trump administration is willing to exploit European dependence on the transatlantic security alliance and the dollar to achieve short-term policy goals. What unites these disparate powers is their unwillingness to separate the functioning of the global economy from political and security competition. The EU has the market power, defence spending, and diplomatic heft to end this vulnerability and restore sovereignty to its member states.

Something stood out of the ordinary during a speech by China's president, Xi Jinping, in January 2018. Behind Xi, on a bookshelf, were two books on artificial intelligence (AI). Why were those books there? Similar to 2015, when Russia "accidentally" aired designs for a new weapon, the placement of the books may not have been an accident. Was China sending a message?

Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. However, most studies in this area use a constrained problem setup, comparing performance when speakers overlap almost completely, at artificially low sampling rates, and with no external background noise. In this paper, we strive to move the field towards more realistic and challenging scenarios. To that end, we created the WSJ0 Hipster Ambient Mixtures (WHAM!) dataset, consisting of two speaker mixtures from the wsj0-2mix dataset combined with real ambient noise samples. The samples were collected in coffee shops, restaurants, and bars in the San Francisco Bay Area, and are made publicly available. We benchmark various speech separation architectures and objective functions to evaluate their robustness to noise. While separation performance decreases as a result of noise, we still observe substantial gains relative to the noisy signals for most approaches.

Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length $L$, making modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only $O(L(\log L)^{2})$ memory cost, improving the time series forecasting in finer granularity under constrained memory budget. Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art.

Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.