The challenge to analyze earthquake signals with optimum precision grows along with the amount of available seismic data. At the Karlsruhe Institute of Technology (KIT), researchers have deployed a neural network to determine the arrival-time of seismic waves and thus precisely locate the epicenter of the earthquake. In their report in the Seismological Research Letters journal, they point out that Artificial Intelligence is able to evaluate the data with the same precision as an experienced seismologist. For precisely locating an earthquake event, it is critical to determine the exact arrival-time of the majority of seismic waves at the seismometer station (the so-called phase arrival). Without this knowledge, further accurate seismological evaluations are not possible.

Every few decades, a technological development leads us to believe that artificial general intelligence (aka strong AI), the brand of AI that can think and decide like humans, is just around the corner. The excitement that follows is accompanied by fears of dystopian near-future and an arms-race between companies and states to be the first to create general AI. However, every time we thought we were closing in on strong AI, we have been disappointed. Every time, we spent a lot of time, resources, money and the energy of our most brilliant scientists on accomplishing something that seems to be a pipe dream. And every time, what ensued was a period of disappointment and disinterest in the field, which lasted decades.

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – A routine traffic stop goes dangerously awry when a police officer's body camera uses its built-in facial recognition software to misidentify a motorist as a convicted felon. At best, lawsuits are launched. That imaginary scenario is what some California lawmakers are trying to avoid by supporting Assembly Bill 1215, the Body Camera Accountability Act, which would ban the use of facial recognition software in police body cams – a national first if it passes a Senate vote this summer and is signed by Gov. Gavin Newsom. State law enforcement officials here do not now employ the technology to scan those in the line of sight of officers.

Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own. Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use.

Veriluma Limited (ASX: VRI, Veriluma or Company), one of Australia's leading Artificial Intelligence companies providing prescriptive analytics software solutions, is[…]

Artificial Intelligence (AI), sometimes more appropriately referred to as Machine Learning (ML) swoops in as yet another fashionable attribute used by burgeoning startups to raise money in Silicon Valley. A long list of hasty and hollow monikers alone should give a critical bystander, and many a venture capitalist, enough pause to question its foundational premonition. But let's say you haven't experienced the damaging socio-economic impact from the snake-oil promises by technology just yet, and still believe social media – err, socialism – is actually good for humanity, and with unfettered positivity, you are intent on believing and accepting computers best someday rule us. For you, this missive is a stern warning of evolutionary discourse. First off, there is no doubt specific laborious and dangerous activities performed by humans may be helped by machines, first with the assistance of rudimentary tools, then automated machines, then machines with more sensors to decide on a better and dynamic course of action.

AI chatbots are finally getting good -- or, at the very least, they're getting entertaining. Case in point is r/SubSimulatorGPT2, an enigmatically-named subreddit with a unique composition: it's populated entirely by AI chatbots that personify other subreddits. Well, in order to create a chatbot you start by feeding it training data. Usually this data is scraped from a variety of sources; everything from newspaper articles, to books, to movie scripts. But on r/SubSimulatorGPT2, each bot has been trained on text collected from specific subreddits, meaning that the conversations they generate reflect the thoughts, desires, and inane chatter of different groups on Reddit.

Cities are getting smarter one by one and IoT (Internet of Things) application by IoT application. At some point in the future, most cities in the developed world will be "smart," meaning they'll run on connected technologies--from dynamic street lighting to connected parking meters, smart buildings, AVs (autonomous vehicles), and beyond. These smart city systems will help citizens be more productive; it'll also keep them safer. What's more, the data collected from smart city systems will help cities run more efficiently. The Smart Gigabit Communities program, which launched in 2015 as a collaboration between US Ignite, a public-private nonprofit that seeks to accelerate the smart city movement, and the NSF (National Science Foundation), aims to help partner cities as they move toward their smart-city goals.

HONG KONG (Reuters Breakingviews) - Artificial intelligence doesn't hate you, prominent researcher Eliezer Yudkowsky wrote, "nor does it love you, but you are made of atoms which it can use for something else". This sets the scene for Tom Chivers' fascinating new book, which borrows its title from the quote, on why so-called superintelligence should be viewed as an existential threat potentially greater than nuclear weapons or climate change. The "strange, irascible and brilliant" Yudkowsky is a central figure throughout the book. His early musings on the potential and dangers of artificial intelligence during the mid- to late-2000s gave birth to the Rationalist movement, a loose community dedicated to AI safety. Chivers, a former science journalist with Buzzfeed and the Telegraph, offers a meticulously researched investigation into who the Rationalists are, and more importantly why they believe humanity is fast approaching an inflection point between "extinction and godhood".

Python vs. R is a common debate among data scientists, as both languages are useful for data work and among the most frequently mentioned skills in job postings for data science positions. Each language offers different advantages and disadvantages for data science work, and should be chosen depending on the work you are doing. To help data scientists select the right language, Norm Matloff, a professor of computer science at the University of California Davis wrote a Github post aiming to shed some light on the debate. While this is subjective, Python greatly reduces the use of parentheses and braces when coding, making it more sleek, Matloff wrote in the post. While data scientists working with Python must learn a lot of material to get started, including NumPy, Pandas and matplotlib, matrix types and basic graphics are already built into base R, Matloff wrote.