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Pál Kerékfy
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A large share of countries around the world are now using Chinese AI surveillance technology, including facial recognition technology, in full or in part. This is according to a report by Carnegie Endowment for International Peace. Many countries are combining Chinese tech with U.S.-made surveillance tech, among them the U.S. and China themselves, but also India, Australia, Brazil and several European countries. Many countries in Latin America, South-East Asia, Africa and the Middle East are relying on Chinese technology alone after participating in the Belt and Road initiative, as are Japan, Italy and the Netherlands.
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“a robot swarm may be made up of tiny, identical devices -- each equipped with a sensor. When information collected by one robot agent is shared with the other devices in the group, it allows the individual devices to function as a united group. Robot swarm tends to be simple and agents are often equipped with sonar, radar or cameras to collect information about the environment around them.”
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A projektmenedzsment szakma hagyományt teremtő és folytató, témáiban megújuló PM Fórumra idén már a 22. alkalommal kerül sor. A Fórum idén is kiváló alkalom a kapcsolatépítésre, az ötletek cseréjére, a szakmai beszélgetésekre.
A 22. Fórum a projektmenedzsment emberi tényezőit helyezi a középpontba, külön is kiemelve a felgyorsult versenyben az agilitás és a komplexitás projekt megoldásait, a hagyományostól eltérő emberi képességek és attitűdök fejlesztését és hogy az ezeket támogató szervezeti-működési rendre van szükség.
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There’s software used across the country to predict future criminals. And it’s biased against blacks. ON A SPRING AFTERNOON IN 2014, Brisha Borden was running late to pick up her god-sister from school when she spotted an unlocked kid’s blue Huffy bicycle and a silver Razor scooter. Borden and a friend grabbed the bike and scooter and tried to ride them down the street in the Fort Lauderdale suburb of Coral Springs. Just as the 18-year-old girls were realizing they were too big for the tiny conveyances — which belonged to a 6-year-old boy — a woman came running after them saying, “That’s my kid’s stuff.” Borden and her friend immediately dropped the bike and scooter and walked away. But it was too late — a neighbor who witnessed the heist had already called the police. Borden and her friend were arrested and charged with burglary and petty theft for the items, which were valued at a total of $80.
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Pál Kerékfy
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The definition of artificial intelligence is constantly evolving, and the term often gets mangled, so we are here to help. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can.
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Pál Kerékfy
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Techniques used to analyse data are producing misleading and often wrong results, critics say. A growing amount of scientific research involves using machine learning software to analyse data that has already been collected. This happens across many subject areas ranging from biomedical research to astronomy. The data sets are very large and expensive. But, according to Dr Allen, the answers they come up with are likely to be inaccurate or wrong because the software is identifying patterns that exist only in that data set and not the real world.
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Quayside, a developing “smart city” on the southern edge of Toronto’s downtown, has been forced into an ethical debate as to how privatisation could pose a threat to individual privacy rights. Sidewalk Labs, an infrastructure division owned by Google’s parent company Alphabet, lost one of the project’s lead policy experts and privacy consultants after she was informed that third parties would be able to access the personal data of Quayside residents without a deletion or anonymisation process. “I imagined us creating a Smart City of Privacy, as opposed to a Smart City of Surveillance,” Cavoukian wrote in her resignation letter. “That’s just not on. Your personal information, your privacy is critical. It is not just a fundamental human right. It forms the foundation of our freedom.”
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Enjoy the best moments of our inaugural Hackathon showing some of the most unexpected collaborations and innovative performances, beautifully accompanied by PREM’s composition '2030'. The world-famous Abbey Road Studios, where many of The Beatles’ most popular songs were recorded, is looking to support music innovation through technology, and recently held its first hackathon event. The studio runs a musictech incubation programme called Abbey Road Red. The hackathon in November 2018 gathered 100 participants to explore new ways of using technology to create and consume music. The participants – comprised of programmers, technologists, developers and music producers – were given a number of questions to guide their creations, including “How will artists create music in 2030?” and “Can you play or create music using emotions to trigger different sounds, samples, parameters or effects?”.
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Depending on how paranoid you are, this research from Stanford and Google will be either terrifying or fascinating. A machine learning agent intended to transform aerial images into street maps and back was found to be cheating by hiding information it would need later in "a nearly imperceptible, h…
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The researchers started with 140,000 hours of YouTube videos of people talking in diverse situations. Then, they designed a program that created clips a few seconds long with the mouth movement for each phoneme, or word sound, annotated. The program filtered out non-English speech, nonspeaking faces, low-quality video, and video that wasn’t shot straight ahead. Then, they cropped the videos around the mouth. That yielded nearly 4000 hours of footage, including more than 127,000 English words.
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Artificial intelligence is getting its teeth into lip reading. A project by Google’s DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programmes to create a lip-reading system that leaves professionals in the dust.
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A new paper from the University of Oxford (with funding from Alphabet’s DeepMind) details an artificial intelligence system, called LipNet, that watches video of a person speaking and matches text to the movement of their mouth with 93.4% accuracy. Even professional lip-readers can figure out only 20% to 60% of what a person is saying.
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In the recent five years, we are seeing that Chinese AI is getting to be almost as good as Silicon Valley AI. And I think Silicon Valley is not quite aware of it yet. China's advantage is in the amount of data it collects. The more data, the better the AI.
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There's a lot of hype about artificial intelligence, and it's important to understand this is not general intelligence like that of a human. This system can read faces and grade papers but it has no idea why these children are in this room or what the goal of education is. A typical AI system can do one thing well, but can't adapt what it knows to any other task. So for now, it may be that calling this "intelligence," isn't very smart.
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Kai-Fu Lee, a venture capitalist who used to develop AI for Google and Microsoft, predicts that AI will automate 40 percent of the world's jobs in 15 years.
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AI is the most empowering of all technologies because it effectively makes anyone who uses it smarter. It increases the productivity of anyone who can apply it to their job.
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Our machines speak a different language now, one that even the best coders can't fully understand. If in the old view programmers were like gods, authoring the laws that govern computer systems, now they're like parents or dog trainers. And as any parent or dog owner can tell you, that is a much more mysterious relationship to find yourself in.
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With machine learning, programmers don't encode computers with instructions. They train them. If you want to teach a neural network to recognize a cat, for instance, you don't tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually it works things out. If it keeps misclassifying foxes as cats, you don't rewrite the code. You just keep coaching it.
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Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as: - How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
- How can we grow our prosperity through automation while maintaining people’s resources and purpose?
- How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
- What set of values should AI be aligned with, and what legal and ethical status should it have?
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“What may set IBM apart is the sheer number of IoT playing fields it engages in addition to AI and analytics, including cloud, development tool set, services, device management, storage and security. Add to that its longstanding reach into enterprise and industrial IT.
"Where public clouds [such as Microsoft IoT Hub and AWS] have APIs, Watson has whole solutions," said Bret Greenstein, IBM's global vice president of Watson IoT offerings, which is not to say that IBM insists on provisioning end-to-end IoT deployments; certainly, as part of the IBM Cloud, the Watson IoT platform is all about cross-vendor integration.” Everyone plays with everyone else. “The pattern in IoT is the same as the pattern in cloud; we're containerizing everything.”
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From ransomware to botnets, malware takes seemingly endless forms, and it’s forever proliferating. Try as we might, the humans who would defend our computers from it are drowning in the onslaught, so they are turning to AI for help. Cybersecurity firm Endgame released a large, open-source data set called EMBER (for “Endgame Malware Benchmark for Research”). EMBER is a collection of more than a million representations of benign and malicious Windows-portable executable files, a format where malware often hides. A team at the company also released AI software that can be trained on the data set. The idea is that if AI is to become a potent weapon in the fight against malware, it needs to know what to look for.
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Most enterprises today know to avoid obvious sources of bias when building AI tools, but relatively few are aware of the risk of unconscious algorithmic bias. To fix bias, you have to be aware that it's arising from the data, and you have to correct for the bias that's in the data.
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Changing the pattern on an object can fool an image recognition system into thinking it is looking at something else entirely – raising big concerns about face ID and driverless cars. A hacker could make a hospital look like a target to a military drone, or a person of interest look like an innocent stranger to a face-recognition security system,” says Jeff Clune at the University of Wyoming.
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Minor changes to street sign graphics can fool machine learning algorithms into thinking the signs say something completely different. The upshot here is that slight alterations to an image that are invisible to humans can result in wildly different (and sometimes bizarre) interpretations from a machine learning algorithm.
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Food and drinks giant is using artificial intelligence software, known as Robot Vera, to phone and interview candidates to fill vacancies for factory, driving and sales representative roles in Russia. Vera frees HR staff from routine, non-interesting work. “They can spend their time to better support hiring managers, working with databases, do better assessments and provide more training.”
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Curated by Pál Kerékfy
collaboration expert, CIO, mathematician, university teacher
I share my experience in collaboration, IT service management, risk & security with students and others.
My blog:
https://kerekfypal.blog
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Érdekes, sőt meglepő! Mely országok hagyatkoznak teljesen kínai technológiára a polgáraik mesterséges intelligenciával (pl.: arcfelismerő rendszerrel) való megfigyelésében? Nem mind meglepetés, Latin-Amerika, Délkelet-Ázsia, Afrika és a Közel-Kelet szinte várható volt. De Hollandia, Japán és Olaszország?
Az, hogy az USA a sajátja mellett kínait is használ, és Kína a sajátja mellett amerikait is, kevésbé lep meg.
Az a statisztika is érdekes kérdéseket vet fel, hogy a hatalomgyakorlás módja és a nagyközönség szisztematikus megfigyelése között milyen a kapcsolat. A liberális demokráciák vezetnek. (Persze, ennek simán lehet annyi az oka, hogy több pénzük van effélére.)
Annak fényében, amivel állandóan vádolják a kínai technológiai cégeket és az ország vezetését (és valószínűleg nem alaptalanul), meglep, hogy a fejlett nyugati demokráciák kínai technológiára támaszkodnak egy ennyire kritikus területen.
Mi lehet ennek az oka? Nincs valódi alternatíva? A cikk szerint a három nagy amerikai cég (IBM, Palantir és Cisco) csak kullog a sokat bírált Huawei mögött. Ami azt illeti, a Palantirnak sem kell a szomszédba mennie egy kis erkölcstelen módszerért: http://collaboration.kerekfypal.blog/p/4095150120/2018/03/07/evek-ota-titokban-figyelik-new-orleans-lakoit-a-palantir-technologiajaval
#China #USA #Huawei #Palantir #IBM #Cisco #Kína #Hollandia #Netherlands #Italy #Olaszország #Japan #AI #surveillance #facial