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Gender Bias Inside the Digital Revolution: Digital Human Rights
Search engines and the algorithms that drive them (along with the rest of our online lives) make things more convenient and easy to find, but they also often bake in long-standing societal biases that adversely impact historically disenfranchised groups. This has raised questions about how best to address such biases and make technologies function with more respect to basic human rights, and what role public and private sector actors should play in the process: "Research also demonstrates that public opinion and what we see online are mutually-reinforcing. In a recent report, UN special rapporteur on the promotion and protection of the right to freedom of opinion and expression David Kaye warns that because artificial intelligence (AI) can personalize what users see online, AI “may reinforce biases and incentivize the promotion and recommendation of inflammatory content or disinformation in order to sustain users’ online engagement.” In other words, search engine content can be shaped by public biases and may reinforce biases by rebounding them as search results that 73 percent of users believe to be accurate and trustworthy." - Catherine Powell and Abigail Van Buren, Council on Foreign Relations
gender  bias  ai  algorithms  HumanRights  foe 
1 hour ago by dmcdev
Awful AI

Artificial intelligence in its current state is unfair, easily susceptible to attacks and notoriously difficult to control. Nevertheless, more and more concerning uses of AI technology are appearing in the wild. This list aims to track all of them. We hope that Awful AI can be a platform to spur discussion for the development of possible contestational technology (to fight back!).
ai  algorithms  ethics  technology  machine-learning 
6 hours ago by jm
A Tour of The Top 10 Algorithms for Machine Learning Newbies
In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one algorithm works best for every problem, and it’s especially relevant for supervised…
machinelearning  AI  algorithms  datascience  ml  machine-learning  Unread  via:popular  tutorial  IFTTT 
8 hours ago by tranqy
Computer vision: how Israel’s secret soldiers drive its tech success
November 20, 2018 | Financial Times | Mehul Srivastava in Tel Aviv.
.... those experiences that have helped such a tiny country become a leader in one of the most promising frontiers in the technology world: computer vision. Despite the unwieldy name it is an area that has come of age in the past few years, covering applications across dozens of industries that have one thing in common: the need for computers to figure out what their cameras are seeing, and for those computers to tell them what to do next.........Computer vision has become the connecting thread between some of Israel’s most valuable and promising tech companies. And unlike Israel’s traditional strengths— cyber security and mapping — computer vision slides into a broad range of different civilian industries, spawning companies in agriculture, medicine, sports, self-driving cars, the diamond industry and even shopping. 

In Israel, this lucrative field has benefited from a large pool of engineers and entrepreneurs trained for that very task in an elite, little-known group in the military — Unit 9900 — where they fine-tuned computer algorithms to digest millions of surveillance photos and sift out actionable intelligence. .........The full name for Unit 9900 — the Terrain Analysis, Accurate Mapping, Visual Collection and Interpretation Agency — hints at how it has created a critical mass of engineers indispensable for the future of this industry. The secretive unit has only recently allowed limited discussion of its work. But with an estimated 25,000 graduates, it has created a deep pool of talent that the tech sector has snapped up. 

Soldiers in Unit 9900 are assigned to strip out nuggets of intelligence from the images provided by Israel’s drones and satellites — from surveilling the crowded, chaotic streets of the Gaza Strip to the unending swaths of desert in Syria and the Sinai. 

With so much data to pour over, Unit 9900 came up with solutions, including recruiting Israelis on the autistic spectrum for their analytical and visual skills. In recent years, says Shir Agassi, who served in Unit 9900 for more than seven years, it learned to automate much of the process, teaching algorithms to spot nuances, slight variations in landscapes and how their targets moved and behaved.....“We had to take all these photos, all this film, all this geospatial evidence and break it down: how do you know what you’re seeing, what’s behind it, how will it impact your intelligence decisions?” .....“You’re asking yourself — if you were the enemy, where would you hide? Where are the tall buildings, where’s the element of surprise? Can you drive there, what will be the impact of weather on all this analysis?”

Computer vision was essential to this task....Teaching computers to look for variations allowed the unit to quickly scan thousands of kilometres of background to find actionable intelligence. “You have to find ways not just to make yourself more efficient, but also to find things that the regular eye can’t,” she says. “You need computer vision to answer these questions.”.....The development of massive databases — from close-ups of farm insects to medical scans to traffic data — has given Israeli companies a valuable headstart over rivals. And in an industry where every new image teaches the algorithm something useful, that has made catching up difficult.......“Computer vision is absolutely the thread that ties us to other Israeli companies,” he says. “I need people with the same unique DNA — smart PhDs in mathematics, neural network analysis — to tell a player in the NBA how to improve his jump shot.”
Israel  cyber_security  hackers  cyber_warfare  Israeli  security_&_intelligence  IDF  PhDs  computer_vision  machine_learning  Unit_9900  start_ups  gene_pool  imagery  algorithms  actionable_information  geospatial  mapping  internal_systems 
20 hours ago by jerryking
Statistical rule of three
The rule of three gives a quick and dirty way to estimate these kinds of probabilities. It says that if you’ve tested N cases and haven’t found what you’re looking for, a reasonable estimate is that the probability is less than 3/N.
algorithms  datascience  math  statistics 
22 hours ago by whip_lash
Creating a QR Code
Awesome step by step demo of how QR codes are generated. Love interactive explanations, it's remarkable how much better they make the experience by allowing play and experimentation.
linklist  algorithms  programming  goodread  guide 
yesterday by seanclynch

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