Scientists repurpose hoverfly vision to detect drones by sound After some careful study, it turns out the brain of an insect is pretty good at separating signal from noise. Researchers from the University of South Australia, Flinders University, and Australian defense company Midspar Systems found that to be the case when they teamed up to reverse-engineer the visual systems of hoverflies …
Given that there is always a government somewhere that is funding the terrorists, in the broad view you are right.
However, there's a fair chance that it is not your govenment that is funding terrorists, so you are also wrong.
Unless you're in Russia, in which case you do not say such things because, if you do, you're the terrorist.
Sophisticated pattern matching is all we do. We just do it on several levels at once. Google might not do the clock thing but they're not interested in doing that - they only want your money and giving the right answer to slightly complicate questions doesnt help them do that. People are starting to pull apart animals brains - someone has taken the honey bee brain and got the navigations system and redone that in AI and it works far far faster and uses several orders of magnitude less nodes than any trained AI equivalent. As we find out and emulate more and more bits of brains things will come together quite quickly - currently we're more or less just bashing numbers against each other and seeing what falls out but when we learn how our own heads actually work - and there are already full brain models to work from - then the fun starts.
Heh. I wanted to know the German name for the small garden bird Great Tit. So I searched 'German Great Tit'.
The image search produced, as expected, a number of these birds. And a much larger number of young ladies serving beer at Oktoberfest...
Very ingenious but I can see numerous problems with this technology, the most obvious being how can you tell which one is the threat among many and how can you tell the difference between a distant drone and an audio system sending out sound that emulates a drone? At the very least I can think that bad actors will have the incentive to develop all sorts of acoustic countermeasures. Then there's always the fun factor of bringing an airport to a halt just by making a drone sound.
"bad actors will have the incentive to develop all sorts of acoustic countermeasures. "
That's how offensive and defensive weapons technology has worked since Ug first realised he could throw a rock. It's all about developing a lead in one area, keeping said lead for as long as possible, all the while working on the next advance because that lead will be "countermeasured" at some stage.
What's said to be a Ukrainian-made long-range anti-drone rifle is one of the latest weapons to emerge from Russia's ongoing invasion of its neighbor.
The Antidron KVS G-6 is manufactured by Kvertus Technology, in the western Ukraine region of Ivano-Frankivsk, whose capital of the same name has twice been subjected to Russian bombings during the war. Like other drone-dropping equipment, we're told it uses radio signals to interrupt control, remotely disabling them, and it reportedly has an impressive 3.5 km (2.17 miles) range.
"We are not damaging the drone. With communication lost, it just loses coordination and doesn't know where to go. The drone lands where it is jammed, or can be carried away by the wind because it's uncontrollable," Kvertus' director of technology Yaroslav Filimonov said. Because the downed drones are unharmed, they give Ukrainian soldiers recovering them a wealth of potential intelligence, he added.
In brief US hardware startup Cerebras claims to have trained the largest AI model on a single device powered by the world's largest Wafer Scale Engine 2 chip the size of a plate.
"Using the Cerebras Software Platform (CSoft), our customers can easily train state-of-the-art GPT language models (such as GPT-3 and GPT-J) with up to 20 billion parameters on a single CS-2 system," the company claimed this week. "Running on a single CS-2, these models take minutes to set up and users can quickly move between models with just a few keystrokes."
The CS-2 packs a whopping 850,000 cores, and has 40GB of on-chip memory capable of reaching 20 PB/sec memory bandwidth. The specs on other types of AI accelerators and GPUs pale in comparison, meaning machine learning engineers have to train huge AI models with billions of parameters across more servers.
Comment Facebook parent Meta has reportedly said it needs to increase its fleet of datacenter GPUs fivefold to help it compete against short-form video app and perennial security concern TikTok.
The oft-controversial tech giant needs these hardware accelerators in its servers by the end of the year to power its so-called discovery engine that will become the center of future social media efforts, according to an internal memo seen by Reuters that was written by Meta Chief Product Officer Chris Cox.
Separately, CEO Mark Zuckerberg told Meta staff on Thursday in a weekly Q&A the biz had planned to hire 10,000 engineers this year, and this has now been cut to between 6,000 and 7,000 in the shadow of an economic downturn. He also said some open positions would be removed, and pressure will be placed on the performance of those staying at the corporation.
Comment More than 250 mass shootings have occurred in the US so far this year, and AI advocates think they have the solution. Not gun control, but better tech, unsurprisingly.
Machine-learning biz Kogniz announced on Tuesday it was adding a ready-to-deploy gun detection model to its computer-vision platform. The system, we're told, can detect guns seen by security cameras and send notifications to those at risk, notifying police, locking down buildings, and performing other security tasks.
In addition to spotting firearms, Kogniz uses its other computer-vision modules to notice unusual behavior, such as children sprinting down hallways or someone climbing in through a window, which could indicate an active shooter.
In the latest episode of Black Mirror, a vast megacorp sells AI software that learns to mimic the voice of a deceased woman whose husband sits weeping over a smart speaker, listening to her dulcet tones.
Only joking – it's Amazon, and this is real life. The experimental feature of the company's virtual assistant, Alexa, was announced at an Amazon conference in Las Vegas on Wednesday.
Rohit Prasad, head scientist for Alexa AI, described the tech as a means to build trust between human and machine, enabling Alexa to "make the memories last" when "so many of us have lost someone we love" during the pandemic.
Microsoft has pledged to clamp down on access to AI tools designed to predict emotions, gender, and age from images, and will restrict the usage of its facial recognition and generative audio models in Azure.
The Windows giant made the promise on Tuesday while also sharing its so-called Responsible AI Standard, a document [PDF] in which the US corporation vowed to minimize any harm inflicted by its machine-learning software. This pledge included assurances that the biz will assess the impact of its technologies, document models' data and capabilities, and enforce stricter use guidelines.
This is needed because – and let's just check the notes here – there are apparently not enough laws yet regulating machine-learning technology use. Thus, in the absence of this legislation, Microsoft will just have to force itself to do the right thing.
In Brief No, AI chatbots are not sentient.
Just as soon as the story on a Google engineer, who blew the whistle on what he claimed was a sentient language model, went viral, multiple publications stepped in to say he's wrong.
The debate on whether the company's LaMDA chatbot is conscious or has a soul or not isn't a very good one, just because it's too easy to shut down the side that believes it does. Like most large language models, LaMDA has billions of parameters and was trained on text scraped from the internet. The model learns the relationships between words, and which ones are more likely to appear next to each other.
Opinion The Turing test is about us, not the bots, and it has failed.
Fans of the slow burn mainstream media U-turn had a treat last week.
On Saturday, the news broke that Blake Lemoine, a Google engineer charged with monitoring a chatbot called LaMDA for nastiness, had been put on paid leave for revealing confidential information.
The latest drone headed to Ukraine's front lines isn't getting there by air. This one powers over rough terrain, armed with a 7.62mm tank machine gun.
The GNOM (pronounced gnome), designed and built by a company called Temerland, based in Zaporizhzhia, won't be going far either. Next week it's scheduled to begin combat trials in its home city, which sits in southeastern Ukraine and has faced periods of rocket attacks and more since the beginning of the war.
Measuring just under two feet in length, a couple inches less in width (57cm L х 60cm W x 38cm H), and weighing around 110lbs (50kg), GNOM is small like its namesake. It's also designed to operate quietly, with an all-electric motor that drives its 4x4 wheels. This particular model forgoes stealth in favor of a machine gun, but Temerland said it's quiet enough to "conduct covert surveillance using a circular survey camera on a telescopic mast."
GPUs are a powerful tool for machine-learning workloads, though they’re not necessarily the right tool for every AI job, according to Michael Bronstein, Twitter’s head of graph learning research.
His team recently showed Graphcore’s AI hardware offered an “order of magnitude speedup when comparing a single IPU processor to an Nvidia A100 GPU,” in temporal graph network (TGN) models.
“The choice of hardware for implementing Graph ML models is a crucial, yet often overlooked problem,” reads a joint article penned by Bronstein with Emanuele Rossi, an ML researcher at Twitter, and Daniel Justus, a researcher at Graphcore.
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