Sea lampreys--invasive, leechlike creatures that once nearly destroyed the Great Lakes' fishing economy--are kept in check by a small U.S.-Canadian program. Will it survive Trump's slash-and-burn campaign? Ally Porter walked ahead of me as we sidestepped down a steep, loamy embankment. Our path lit only by headlamps, a waning sliver of moon, and what seemed to be thousands of stars, we made our way to a mucky riverbank about twenty feet below. At one point, I lost my footing and ended up wedged against a tree trunk. Porter, who had two tight braids that landed just below her shoulders, kept going. She moved with ease through several inches of sludge, toward a yellow glow stick tied to a tree at the water's edge.

As working from home moves from a temporary solution to the new normal, companies need new ways to secure data and protect internal networks . Banks are most likely to use voiceprints to authenticate users but more companies are considering this approach. Nuance Communications uses a voiceprint algorithm powered by a deep neural network to analyze 1,000 parameters of an individual's voice, including tone, pitch, pacing and fluctuations in the sound. The engine determines which parameters are most relevant for each individual and weights the appropriate elements accordingly. Simon Marchand, chief fraud prevention officer at Nuance, worked in fraud prevention for 10 years in the financial and telecom industries.

Raspberry Pi is a capable little machine, but if you're interested in developing your own embedded machine-learning applications, training custom models on the platform has historically been tricky due to the Pi's limited processing power. But things have just taken a big step forward. Yesterday, Edge Impulse, the cloud-based development platform for machine learning on edge devices, announced its foray into embedded Linux with full, official support for the Raspberry Pi 4. As a result, users can now upload data and train their own custom machine-learning algorithms in the cloud, and then deploy them back to their Raspberry Pi. SEE: C programming language: How it became the foundation for everything, and what's next (free PDF) (TechRepublic) Four new machine-learning software development kits (SDKs) for Raspberry Pi are available week, including C, Go, Node.js and Python, allowing users to program their own custom applications for inferencing. Support for object detection has also been added, meaning Raspberry Pi owners can use camera data captured on their device to train their own custom object detection algorithms, instead of having to rely on'stock' classification models.

Chances are you've already encountered, more than a few times, truly frightening predictions about artificial intelligence and its implications for the future of humankind. The machines are coming and they want your job, at a minimum. Scary stories are easy to find in all the erudite places where the tech visionaries of Silicon Valley and Seattle, the cosmopolitan elite of New York City, and the policy wonks of Washington, DC, converge--TED talks, Davos, ideas festivals, Vanity Fair, the New Yorker, The New York Times, Hollywood films, South by Southwest, Burning Man. The brilliant innovator Elon Musk and the genius theoretical physicist Stephen Hawking have been two of the most quotable and influential purveyors of these AI predictions. AI poses "an existential threat" to civilization, Elon Musk warned a gathering of governors in Rhode Island one summer's day.

Artificial Intelligence (AI) is the buzzword these days, and for good reason. Businesses around the world are taking up AI technologies to try and reduce operational costs, increase efficiency, grow revenue and improve customer experience. Businesses are also looking at putting a full range of smart technologies such as machine learning, natural language processing and more, into their processes and products. However can businesses that are new to AI, reap major rewards? When it comes to artificial intelligence, most people are well aware of the tropes from popular entertainment: the malevolent computer, the android gone rogue.

There could be a time in the not too distant future whereby AI has replaced many of our jobs, but rather than mean we are all out of work, such a move would allow us to focus on doing more meaningful jobs that don't need to have an impact on the economy for the world to still operate. "You always see jobs opening up in previous industrial revolutions, but I think at some point AI is going to be capable of doing pretty much anything," explains Dr Alex Allan, the CTO and founder of AI London startup Kortical to Pocket-lint in an interview for the Pocket-lint podcast. "The potential advantage of that is that the cost of living could go to virtually nothing for everyone. Imagine if you have an AI doctor in your phone that could be just as good as a GP? It means anyone in the world who has got a smartphone, which is more than 50 per cent of people, would have access to healthcare. "There will be challenges of how we transition from a society where everyone has to have a job, to one where potentially you might not need that many jobs that directly influence the economy.

Implementing AI can be costly for smaller businesses; Kortical aims to bring this technology to smaller organisations Artificial intelligence or AI is big news in business, but AI is still a growing technology and there are not enough talented data scientists to deliver all the projects that currently exist, never mind develop the projects of the future. Tech talent in this area tends to go to the businesses that have deep enough pockets to attract the best, but does that mean that other organisations have to miss out? "AI is not a bubble, it's a technology that is delivering real value today" Dr Alex Allan, co-founder, Kortical Not according to Andy Gray and Dr Alex Allan, co-founders of Kortical, a business that helps bring AI to the masses. "We saw the opportunity to create a platform that could help people that were not AI experts to be able to create AI models as well as let expert data scientists take on more projects with a tool that would increase their productivity," says Mr Gray. AI's applications are many and varied, whether in simple chatbots or complex systems managing robotics. From basic automation, to free time for busy employees to powerful intelligence that can perform tasks faster than teams of people, businesses around the world are experimenting with how AI can help them do more, faster, and cheaper, than ever before.

Victoria has sanctioned a trial of driverless cars on rural roads in a bid to improve the dramatically more dangerous conditions outside urban areas. People are five times more likely to be killed on a Victorian country road than in the city. The automated vehicle technology is being developed by Bosch as part of a $2.3m state government grant and will be tested on high-speed rural roads later this year. "This trial is an exciting step towards driverless vehicles hitting the road," the acting premier, Jacinta Allan, said. Bosch has been granted the state's first permit to allow automated vehicles for on-road testing, with other successful applicants to be announced soon.

AFTER graduating from the pages of Sci-Fi novels to the screens of computer scientists in mere decades, Artificial Intelligence (AI) has morphed from concept to reality quicker than you can say "buzzword". Despite protestations, we have moved swiftly past the point of AI's place as a buzzword in the corporate lexicon. Artificial Intelligence brings with it a confluence of fear and excitement; of expectations and radical change. But questions about its capacity to solve real-world problems linger. We brought together a panel of AI, robotics, and customer experience (CX) experts to deliberate the current state of AI––the challenges for brands and consumers–– and what the future holds.

We propose a new zero-shot Event-Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) semantic embedding of concepts definitions, and (c) retrieve videos by free text event query (e.g., "changing a vehicle tire") based on their content. We first embed the video into the multi-modal semantic space and then measure the similarity between videos with the event query in free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-the-art that uses big descriptions from 12.6\% to 13.5\% with MAP metric and from 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.