LANCASTER – Residents, volunteers and city workers will spruce up the neighborhood around Piute Middle School in the first of what city officials hope will be many neighborhood cleanup efforts instigated by citizens. Workers will replace graffiti-marred street signs, repaint faded address numbers on curbs, plant landscaping in parkways, bring in trash bins for residents to throw away junk, and paint and pull weeds at the school. “The city is happy to partner with the community,” city spokeswoman Anne Aldrich said. The cleanup starts at 8 a.m. Saturday in the neighborhood bordered by avenues H-8 and H-14 and by 5th Street East and 7th Street East. Volunteers are asked to meet at 8 a.m. at Piute Middle School, 425 E. Ave. H-11. The Piute neighborhood cleanup was organized as the first in this year’s series of Lookin’ Good Lancaster cleanups, city officials said. The campaign was an annual cleanup day for six years, but this year is being expanded to four events throughout the year, intended to let more citizens participate. “To expand the program, we are hoping for another level of community involvement this year,” City Manager Bob LaSala said. “If you are a service club, school or church group or neighborhood organization that has a community beautification project you would like to do, let the city know and we’ll see if we can help.” The city can provide tools, equipment, even trees and other landscaping material for planting trees along streets, picking up litter, removing graffiti or beautifying school campuses. To volunteer, or to propose a project for consideration, call (661) 723-5985. 160Want local news?Sign up for the Localist and stay informed Something went wrong. Please try again.subscribeCongratulations! You’re all set! AD Quality Auto 360p 720p 1080p Top articles1/5READ MORECasino Insider: Here’s a look at San Manuel’s new high limit rooms, Asian restaurant Large trash containers will be provided by Waste Management for spring cleaning. The neighborhood consists of tract homes approaching 50 years old, many of them now rentals. Around it are vacant lots and open fields that have become convenient dumping grounds for trash, though new homes are also going in. City officials said the Piute neighborhood effort is the initiative of resident Gary Burgess, a retired Edwards Air Force Base worker who decided to try to bring neighbors together to spruce up the area where he and his wife raised five children and have lived for 33 years. Burgess organized a block party last August on his street for neighbors to meet each other, then passed out fliers to invite people to a Neighborhood Watch meeting in October at the school. He approached city officials to seek their help. “It just got to the point that if I wasn’t going to move … I was going to try to make a difference here,” Burgess said. “There’s a lot of great people here … We all get lax on keeping our property up at times.”
It’s undeniable that machine learning has made enormous progress over the past few years: from amazing artificial intelligence accomplishments like defeating a top ranking player at the ancient and complex game of Go, to simple everyday uses like auto-tagging personal photo collections. At the core of the most advanced algorithms used to achieve these feats are artificial neural networks, which are technology’s way of mimicking the human brain. But just how smart are these neural networks? Since my not-quite-two-year-old son started paying attention to the world beyond his mom and started learning, I have been at awe by the way his brain learns by making associations; by comparison, I wonder how much further machine learning has to go.My toddler in an exciting moment of successful identification. He’s not always as cooperative as an artificial neural network, but he’s much cuter (Source: Gunn Salelanonda) Baby-X: The animated virtual infant Apparently, I’m not the first one to ask these questions. Researchers at the University of Auckland have been taking this comparison to the extreme by developing a highly realistic intelligent toddler simulation.The Laboratory for Animate Technologies webpage describes this simulation as an experimental vehicle incorporating computational models of basic neural systems involved in interactive behavior and learning. One of the intriguing features of this virtual baby is the way it is motivated to learn. For example, the software releases a simulated version of dopamine to make BabyX happy upon successfully identifying objects (because that’s what every virtual construct wants — virtual dopamine). This makes for a highly interactive experience, including getting the virtual infant’s attention and giving encouragement during learning sessions. In addition, the hyper-detailed and nuanced facial expressions make this psychobiological simulation eerily lifelike (and IMO more than slightly creepy).BabyX v3.0 Interactive Simulation by Laboratory for Animate Technologies (Click Here to see a video. Source: Laboratory for Animate Technologies) Comparing my toddler to the most advanced neural networks While the aforementioned simulation is an intriguing accomplishment, it’s still not even close to the real thing. When I teach my toddler new things (sometimes intentionally but often unintentionally), his reactions are much more varied. Sometimes he wants to show off a new skill or learned trait, while other times he makes it clear that he’s “not our trained monkey.”Maybe neural networks are better at identifying objects once they are trained, but the training part itself seems to be much more efficient with my son (when suitably incentivized — like with animal crackers). For example, I can show him a picture of an animal he’s never seen before and teach him the animal’s name and the sound it makes. Usually, after a couple of times of getting it wrong, he’ll recognize that animal. After seeing five or six other images of the same kind of animal and being told that it’s the same, he will, for the most part, be able to identify the entire category including all the variations (not just photographs and videos, but cartoon depictions, toys, and even stuffed caricatures).My toddler doing some object recognition and some showing off for the camera (Click Here to see a video. Source: Gunn Salelanonda) This is very different from the training of neural networks used to identify animals, which requires millions of images as input before they start to get it right.Another thing that infants have naturally — but machines have to strive for — is flexibility. Any hardware solution that is tailored to a specific algorithm or pre-trained network might be efficient, but lacks the ability to adapt to new circumstances. With neural networks constantly evolving, becoming deeper and including more layers, a flexible solution is a must. At the other extreme, though, a completely open solution that can do anything might be too wasteful in resources. That’s why a software solution implemented on an extremely efficient hardware architecture is, in my opinion, the best way to go. Like buying new shoes for little kids, a fine balance is required to find a comfortable fit while also leaving some room to grow.And then there’s the issue of power consumption. The brain’s capacity to identify images (like my kid and his uncanny ability to spot “tunnels” as well as anything that even looks like a “tunnel”), solve complex problems, and perform other tasks is unparalleled when it comes to utilization of energy. Comparisons between the human brain and machine intelligence, like this one, have stipulated that deep learning machines may use around 50,000 times more energy to perform the same task (literally 20 watts for the human compared to one megawatt for AlphaGo).Neural networks in embedded systems One of the biggest challenges today is to bring the power consumption down to make this technology feasible inside battery-operated devices. Extremely efficient embedded processors can already use artificial intelligence to achieve outstanding results. Applications of AI, like these new Google projects, are becoming commonplace in everyday life.Object recognition can already be performed on mobile devices with very high success rates. For example, in the video below you can see how the CEVA-XM4 vision processor runs a full working version of AlexNet, the large, pre-trained deep convolutional neural network.Using the CEVA Deep Neural Network (CDNN) on an FPGA development board, which runs at a tiny fraction of the speed of a production silicon SoC, the demo is able to identify an enormous variety of objects almost instantaneously. If it were running on silicon, the response time would be somewhere between twenty to thirty times faster! And all this is performed using extremely low power, thereby enabling it to run on even the smallest battery-powered handheld devices.CEVA CDNN Demo Running Full AlexNet on CEVA-XM4 Vision Processor (Click Here to see the video. Source: CEVA) Will future AI be as smart as a toddler? While today’s artificial neural networks are very useful for a wide variety of use cases (from smart surveillance to autonomous vehicles), there is still room to grow.What will the future be like when the learning phase can be implemented efficiently enough to also be performed on portable devices? When will portable AI applications also be able to learn new things quickly, from just a few instances, like my toddler can? That future could hold some scary scenarios as well as extremely exciting possibilities that may enable humankind to achieve new heights.Who knows, maybe by the time my son grows up, we’ll have the answers to some of these questions…Discover more Click Here to learn more about CEVA Deep Neural Network (CDNN) software framework, and Click Here to learn more about the CEVA-XM4 intelligent vision processor. Share this:TwitterFacebookLinkedInMoreRedditTumblrPinterestWhatsAppSkypePocketTelegram Leave a Reply Cancel reply You must Register or Login to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed. Continue Reading Previous Samsung’s burning batteries demonstrate the need for lithium ion alternativesNext CORNAMI’s sea-of-cores solution may defuse data explosion
OTTAWA – Justin Trudeau’s national security adviser is offering to give an unclassified briefing to MPs on a Commons committee about the prime minister’s trip to India, in addition to a more in-depth classified briefing to Conservative Leader Andrew Scheer.Scheer agreed Tuesday to the government’s offer of a classified briefing from Daniel Jean — but with strings attached. He said he wants journalists and Conservative MPs to be able to sit in on the non-secret portions of Jean’s briefing.After weeks of refusing to have Jean publicly talk about the trouble-plagued trip, the government announced late Tuesday that it has effectively agreed to Scheer’s conditions.Privy Council Office spokesman Paul Duchesne said a letter has been sent to Scheer “indicating that we will work with his office to co-ordinate the classified briefing for him on matters related to the national security of Canada as soon as possible.”Since Scheer has agreed to a secret briefing, Duchesne added that Jean has written to the chair of the public safety and national security committee “to offer an unclassified briefing at the earliest practical opportunity should that be the wish of the standing committee.”Conservatives last month tried twice to force Jean to appear before that committee to testify about his assertion that rogue factions within the Indian government helped to sabotage Trudeau’s trip. They were blocked both times by the Liberal majority.During the India trip, Jean told a background briefing with reporters travelling with the prime Minister that rogue forces within the Indian government were ultimately the ones responsible for the Jaspal Atwal affair. He gave similar briefings to reporters in Canada that same day.Jean gave the updates as an unnamed senior government official but was publicly identified by the Conservatives after the trip was over.Atwal was invited to a pair of receptions with Trudeau despite having been convicted three decades ago of trying to assassinate an Indian cabinet minister in the name of Sikh independence. After his appearance at the first of the receptions in Mumbai, Jean said the government was made aware of his identity and an invite to a second reception in Delhi was rescinded.The damage was done, however, as photos of Atwal posing at the first reception with Trudeau’s wife, Sophie Gregoire Trudeau, and Infrastructure Minister Amarjeet Sohi, were given to the media.Jean’s working theory was that Indian agents who fear the global rise of Sikh independence orchestrated the controversy in an attempt to embarrass Canada and undermine the entire trip.Jean suggested Atwal was closely connected to members of the Indian consulate in Vancouver, and said it was strange that the ban on Atwal travelling to India was lifted by Indian authorities after more than three decades.There was nothing in the way of actual evidence given to support the theory.The Conservatives, however, say they believe Jean disclosed classified details in those briefings to the media which is why the government refused to give the same briefing publicly to Conservative MPs.The government insists otherwise but, until Tuesday, had offered only a full briefing — complete with classified information — to Scheer, who is eligible to hear such details as a member of the Privy Council.Until now, Scheer has refused that offer, but he changed his mind Tuesday.