publishedmedium:machine learning

  • Style-based GANs – Generating and Tuning Realistic Artificial Faces | Lyrn.AI

    Donc on crée des gens qui n’existent pas, de manière tellement réaliste qu’on ne les distingue pas de la réalité → voilà qui va poser la question de la photographie comme preuve.
    Une vidéo bien parlante pour illustrer le propos.

    Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces. While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face.

    A new paper by NVIDIA, A Style-Based Generator Architecture for GANs (StyleGAN), presents a novel model which addresses this challenge. StyleGAN generates the artificial image gradually, starting from a very low resolution and continuing to a high resolution (1024×1024). By modifying the input of each level separately, it controls the visual features that are expressed in that level, from coarse features (pose, face shape) to fine details (hair color), without affecting other levels.

    This technique not only allows for a better understanding of the generated output, but also produces state-of-the-art results – high-res images that look more authentic than previously generated images.

  • How to Hire a Python Developer With Right Skill Set?

    Bram Cohen has beautifully crafted Python language in a nutshell, as “simple, clean syntax, object encapsulation, good library support and optional named parameters”.Hence hiring a Python developer is the best approach for any company where it has a huge potential to grow any business to a great extent. Some of the pioneers in the technology industry like YouTube, Reddit, NASA, PayPal, Spotify, Quora etc are the popular projects that are built using Python language. Hire a python developer to get benefited from the compelling features of the Python program.Why Python is a preferable language among the companies?In the era of Artificial Intelligence and Machine Learning certain programming languages always have a standard demand in the market irrespective of the evolution of other niche (...)

    #hire-python-developers #python-programming #python-web-development #hire-python-programmers #python-web-developer

  • Big Data and Machine Learning with Nick Caldwell

    Episode 39 of the Hacker Noon Podcast: An interview with Nick Caldwell CPO at Looker and former VP of #engineering at Reddit.Listen to the interview on iTunes, or Google Podcast, or watch on YouTube.In this episode Trent Lapinski interviews Nick Caldwell from Looker, you get to learn about big data, machine learning and AI.“Modern data stores are extremely powerful. You can put tons and tons of data into them. You can query them without losing speed. And in some cases, you can even do analytics in the database. We’re just seeing this trend where the data layer is becoming more and more powerful, and Looker is riding that trend.”“My favorite learning, again, was just what’s going on in the data engineering space. The BigQuery to me, at that time, was just mind blowing. You dump 4–5 petabytes of (...)

    #artificial-intelligence #machine-learning #big-data

  • #ai for #fun: Awesome Apps You Can Test Right Now

    Artificial Intelligence and Machine Learning. What is it? Just another hype, modern trend to name the technology, or maybe, it is a potential superpower to kill humans?! Today we hear those buzzwords and what is more serious, wrong assumptions about them from everywhere, but only a few understand what it really is and how it works. And if you are not among the latter one, maybe it’s time to change this situation? If your answer is yes, I offer you learning this technology through the uncomplicated and entertaining approach. It’s unnecessary to spend a couple of hours on boring research, just try AI by yourself and explore its capabilities on practice.AI in Your Browser: Talk to Books, Draw like an Artist and More1. Semantris : When was the last time you played Tetris?It’s a fun and (...)

    #funai #ai-for-fun #artificial-intelligence

  • How Will Machine Learning Impact Mobile Apps?

    You might have heard about the term “Machine Learning”. It is basically an application of Artificial Intelligence which enables computers and software to learn and envision outcomes automatically without the interference of human being.Machine learning has already served in various fields & today is the time to talk about how it is serving to mobile application development.It is true that whenever the latest technology comes, people find it difficult to handle. But when Mobile applications adopt every technology providing them in various apps, it becomes familiar with the people easily as people are very used to the mobiles. So, it could be said that gradually people become familiar with technologies.As every new technology ends with easy to handle and use. In this regard Corrado, a (...)

    #ai-in-mobile-app #artficial-intelligence #machine-learning-in-app #machine-learning #mobile-app-development

  • Quantum Computing Explained — It’s Rocket #science

    Quantum Computing Explained — It’s Rocket ScienceBut rocket science is easier than you think ?A couple of years ago, I thought Quantum Mechanics was a bunch of theoretical science with no real world applications: Not anymoreIn recent years, we’ve seen the emergence of Quantum Computing; spearheaded by companies such as D-Wave, Rigetti Computing, and IBM. In a nutshell (Kurzgesagt ?), Quantum Computing will revolutionize computing, allowing us to solve complex computational problems that would have been impossible on classical computers.Today, with classical computers:Drug Discovery takes over 10 years to get to market, costing billions for each drugFeature spaces in Machine Learning are limited to computational constraintsToday, quantum computers:Exponentially increase speeds in Drug Discovery, (...)

    #technology #hackernoon-top-story #quantum-computing #innovation

  • ML.NET: Machine Learning framework by Microsoft for .NET developers

    ML.NET: Machine Learning framework by Microsoft for .NET developersWhenever you think of data science and machine learning, the only two programming languages that pop up on your mind are Python and R. But, the question arises, what if the developer has knowledge of other languages than these?We have a solution in the form of Microsoft’s recently introduced build 2018 of its own version of the machine learning framework especially for .NET and C# developers. The framework is open source and cross-platform and can also run on Windows, Linux, and macOS.The developers always wanted to have a NuGet package which they can plug in with a. Net application for creating machine learning applications. After the release of the first version,ML.NET is still a baby but it is already showing the (...)

    #dotnet #dotnet-developer #mldotnet #microsoft-framework #machine-learning

  • 7 Successful Applications of #ai & Machine Learning in the #travel #industry

    Owing to our increased dependency on gadgets, people today are more likely to plan trips via smart apps. They can actually spend many hours glued to the screen — finding the best place, best price, and the best itinerary. This is where Artificial Intelligence & Machine Learning come into play. This can generate super-personalized suggestions to prospective travelers, by analyzing large datasets.It is apparent that outside of chatbots, the field of AI and machine learning in the travel and tourism industry is still in its infancy. Much of the impact of artificial intelligence on the travel and tourism industry focuses on customer service and engagement.Compared to sectors such as banking, healthcare, and e-commerce, it’s clear that the travel and tourism industry does not have a very (...)

    #artificial-intelligence #machine-learning

  • What is the Future of Machine Learning?

    How this advanced technology will affect software development servicesThe applications of machine learning have had a massive implication across all sectors and industries across the world. The most basic example of machine learning can be seen in the form of tailored recommendations in your favorite music streaming app or when you shop online on websites such as Amazon.But how would you define machine learning? In a nutshell, machine learning can be defined as an amazing subset and application of artificial intelligence, which enables programs to modify their encoded algorithms automatically without a need for human intervention.So, how does it work? While it may seem to be complicated on the surface but the concept of machine learning can be explained easily. Let me put it this way: (...)

    #machine-learning-ai #machine-learning-future #ml-future #machine-learning-recipe #artificial-intelligence

  • Why is Python Used for Machine Learning?

    You might have heard about the python which is the topmost programming language of the computers. It is a high-level programming language which is having dynamic semantics. As it, language is very easy and readable, so it reduces the cost of program maintenance.As Python is considered to be the simplest programming language of all, its usage is also ranked in the topmost place. In regard to this, a few seconds are needed to spend at the below-given graph:One of the major advantages of its being easy is that it is very easy to interface with other languages as well particularly with C and C++. So let’s spend our little time to know what is the impact of using Python for machine learning.Have a look below: python holds an (...)

    #python-for-ml #machine-learning-python #python-development #python-ml #python-and-ml

  • 16 Best Resources to Learn AI & Machine Learning in 2019

    While making GeekForge — a daily listing of interesting coding tasks — we researched several sources where you can learn AI and ML, and we thought it would be a good idea to share this list with you.Two years have already passed since Mark Cuban said that if you don’t understand artificial intelligence, deep learning, and machine learning “you’re going to be a dinosaur within three years.” If you still didn’t dig yourself into that knowledge, especially if you’re a developer, then you’ve got about a year left to see whether he was right or not.But luckily for you, if you are in fact interested in keeping your skills up to date, I hand-picked the best resources that are relevant today, regardless if you’re a beginner in the field or if you’ve already got your feet wet a long time ago. From video (...)

    #software-development #education #artificial-intelligence #learning-to-code #machine-learning

  • A Quick Introduction to Artificial Intelligence, Machine Learning, Deep Learning and #tensorflow

    What used to be just a pipe dream in the realms of science fiction, artificial intelligence (AI) is now mainstream technology in our everyday lives with applications in image and voice recognition, language translations, chatbots, and predictive data analysis.In this article, we’ll introduce AI along with its related terms machine learning and deep learning. By the end of the article you should understand these terms, how things generally work and be more familiar with terms like Inception and YOLO (and no, we’re not talking about the Leonardo DiCaprio movie or some internet meme).Artificial intelligence (AI) is the simulation of human intelligence by computers. Machine learning is a branch of AI where algorithms are used to learn from data to make future decisions or predictions. Deep (...)

    #open-source #artificial-intelligence #deep-learning #machine-learning

  • How to Understand Machine Learning with simple Code Examples

    Understanding machine learning using simple code examples.“Machine Learning, Artificial Intelligence, Deep Learning, Data Science, Neural Networks”You must’ve surely read somewhere about how these things are gonna take away future jobs, overthrow us as dominant species on earth and how we’d have to find Arnold Schwarzenegger and John Connor to save humanity.With current hype, there is no surprise you might have.But, what is Machine Learning and what is Artificial Intelligence?Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.And what is Neural Network?Artificial neural (...)

    #deep-learning #artificial-intelligence #neural-networks #machine-learning #javascript

  • Why AI & ML Will Shake Software Testing up in 2019

    On the overwhelming wave of tech progress, there is presumably nobody who hasn’t heard about Artificial Intelligence and Machine Learning. As the hottest buzzwords of our age, they restore a strong faith in a more advanced future for almost every realm. And when it comes to the Software Industry, we know how many flaws could be fixed with automation in the #qa processes.AI and ML power have hit somewhat of an apex and now we have enough reasons for reckoning that they are perfect tools, but does it mean the machine will replace the human? What part will AI play in Functional Test Automation? Here is a comprehensive display.Breaking, Rebuilding, Regressing: The Challenge of Traditional Testing ApproachEvery day, QA Engineers stumble upon a plethora of difficulties and waste a lot of time (...)

    #machine-learning #software-testing #software-development #artificial-intelligence

  • Idiot’s Guide to Precision, Recall and Confusion Matrix

    Evaluation metrics for classification modelsBuilding Machine Learning models is fun, making sure we build the best ones is what makes a difference!Evaluating ML modelsRegression modelsRMSE is a good measure to evaluate how a machine learning model is performing.If RMSE is significantly higher in test set than training-set — There is a good chance model is overfitting.(Make sure train and test set are from same/similar distribution)What about Classification models?Guess what, evaluating a Classification model is not that simpleBut why?You must be wondering ‘Can’t we just use accuracy of the model as the holy grail metric?’Accuracy is very important, but it might not be the best metric all the time. Let’s look at why with an example -:Let’s say we are building a model which predicts if a bank (...)

    #artificial-intelligence #machine-learning #ai #deep-learning #technology

  • The Truth Nobody Wants to Tell You About #ai for Trading

    Holy grail or poisoned chalice?Are you positive about your backtest results?TL;DR Nobody has cracked it. Period.Machine Learning has always fueled the fantasies of Wall Street. After all, AI detects faces, drives cars, beats the World Champions at Chess, Go, and now Starcraft 2. Its application to trading seems natural, doesn’t it?Totally! What if we could throw a treasure trove of data at an AI and let it predict future market prices?Don’t bother with that…Prices cannot be predicted, they are mostly random. They are not predictable on average, only on occasions but nobody knows when.The era of the magic ‘black box’ automating alpha hasn’t come yet.Nobody has cracked automated trading using Machine Learning-based predictions.That’s right. Sorry to disappoint. Nobody knows whether their bot will (...)

    #algorithmic-trading #crypto-trading #ai-for-trading #ai-crypto-trading

  • Deep Learning vs. Machine Learning: A Simple Explanation

    Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be.So if you’ll stick with me for some time, I’ll try to explain what really is the difference between deep learning vs machine learning, and how can you leverage these two subsets of AI for new and exciting business opportunities.Deep learning vs Machine learningBefore I start, I hope you would be familiar with a basic understanding of what both the terms deep learning and machine learning mean. If you don’t, here are a couple of simple definitions of deep learning and machine learning for dummies:Machine Learning for dummies:A subset of (...)

    #dl-vs-ml #deep-learning #ml-algorithm #artificial-neural-network #machine-learning

  • How to make a simple Machine Learning #website from scratch

    Learn to make a simple machine learning website which adapts text colour ? according to changeable background contrast.Are you enthusiastic about machine learning? Are you trying to implement a simple machine learning webpage from scratch?Do you want to make something cool with HTML/CSS and #javascript?If yes, then you’re at the perfect place.Machine Learning, AIHere we will be making a simple webpage that changes the text colour according to the background color, using a simple machine learning library brain.js.Let’s kick off our excitement with the result itself.Here is the link to the webpage.Go to the above link and try changing the RGB values of the slider, you’d notice the auto changing text colour with respect to the background contrast.This is what we would be building here.What? You (...)

    #artificial-intelligence #web-development #machine-learning

  • Understanding How Artificial Intelligence Can Make #blockchain Safer and Smarter

    In the AI field, you can build smart Machine Learning algorithms or impressive Neural Networks, but this powerful technology could be trustable or could generate intelligent responses based on the data you use when you train it.As I wrote in my article: Understanding The Gold Rush of Scalable and Validated Data powered by Blockchain and Decentralized AI for Hackernoon:The best results in the AI field are in closed and well-defined ecosystems, such as video games, where AI algorithms have beaten every world champions, even in DOTA 2, considered one of the most complex video game in the industry… …In open environments like social media or big data, AI’s algorithms have performed less, or sometimes AI’s results are dangerously wrong.Wait but why?In scripted environments like video games, you (...)

    #smart-contracts #artificial-intelligence #blockchain-ai

  • 2018 Stealth Capital Report: Tracking Hidden Billions

    It should come as no surprise that investment in tech startups is booming. Early stage startups are on-pace to reach over $8 billion dollars of funding this year in 2018, while late stage startups are receiving over $70 billion.But what does this mean for startups in stealth? If you haven’t heard of a startup, does it even really exist? Are credible startups with real venture dollars remaining in stealth for longer as they build their technology?What we’ve seen at Brex is that the stealth startup economy is thriving. Brex has identified over $1.43B in stealth capital outstanding, with the vast majority raised in the last two years. These stealth startups make up 3–5% of the overall startup market. And some of the most high profile startups in Machine Learning, Autonomous Driving, and (...)

    #stealth-capital #stealth-vc #venture-capital #stealth-capital-report #tracking-hidden-billions

  • New report exposes global reach of powerful governments who equip, finance and train other countries to spy on their populations

    Privacy International has today released a report that looks at how powerful governments are financing, training and equipping countries — including authoritarian regimes — with surveillance capabilities. The report warns that rather than increasing security, this is entrenching authoritarianism.

    Countries with powerful security agencies are spending literally billions to equip, finance, and train security and surveillance agencies around the world — including authoritarian regimes. This is resulting in entrenched authoritarianism, further facilitation of abuse against people, and diversion of resources from long-term development programmes.

    The report, titled ‘Teach ’em to Phish: State Sponsors of Surveillance’ is available to download here.

    Examples from the report include:

    In 2001, the US spent $5.7 billion in security aid. In 2017 it spent over $20 billion [1]. In 2015, military and non-military security assistance in the US amounted to an estimated 35% of its entire foreign aid expenditure [2]. The report provides examples of how US Departments of State, Defense, and Justice all facilitate foreign countries’ surveillance capabilities, as well as an overview of how large arms companies have embedded themselves into such programmes, including at surveillance training bases in the US. Examples provided include how these agencies have provided communications intercept and other surveillance technology, how they fund wiretapping programmes, and how they train foreign spy agencies in surveillance techniques around the world.

    The EU and individual European countries are sponsoring surveillance globally. The EU is already spending billions developing border control and surveillance capabilities in foreign countries to deter migration to Europe. For example, the EU is supporting Sudan’s leader with tens of millions of Euros aimed at capacity building for border management. The EU is now looking to massively increase its expenditure aimed at building border control and surveillance capabilities globally under the forthcoming Multiannual Financial Framework, which will determine its budget for 2021–2027. Other EU projects include developing the surveillance capabilities of security agencies in Tunisia, Burkina Faso, Somalia, Iraq and elsewhere. European countries such as France, Germany, and the UK are sponsoring surveillance worldwide, for example, providing training and equipment to “Cyber Police Officers” in Ukraine, as well as to agencies in Saudi Arabia, and across Africa.

    Surveillance capabilities are also being supported by China’s government under the ‘Belt and Road Initiative’ and other efforts to expand into international markets. Chinese companies have reportedly supplied surveillance capabilities to Bolivia, Venezuela, and Ecuador [3]. In Ecuador, China Electronics Corporation supplied a network of cameras — including some fitted with facial recognition capabilities — to the country’s 24 provinces, as well as a system to locate and identify mobile phones.

    Edin Omanovic, Privacy International’s Surveillance Programme Lead, said

    “The global rush to make sure that surveillance is as universal and pervasive as possible is as astonishing as it is disturbing. The breadth of institutions, countries, agencies, and arms companies that are involved shows how there is no real long-term policy or strategic thinking driving any of this. It’s a free-for-all, where capabilities developed by some of the world’s most powerful spy agencies are being thrown at anyone willing to serve their interests, including dictators and killers whose only goal is to cling to power.

    “If these ‘benefactor’ countries truly want to assist other countries to be secure and stable, they should build schools, hospitals, and other infrastructure, and promote democracy and human rights. This is what communities need for safety, security, and prosperity. What we don’t need is powerful and wealthy countries giving money to arms companies to build border control and surveillance infrastructure. This only serves the interests of those powerful, wealthy countries. As our report shows, instead of putting resources into long-term development solutions, such programmes further entrench authoritarianism and spur abuses around the world — the very things which cause insecurity in the first place.”

    #surveillance #surveillance_de_masse #rapport

    Pour télécharger le rapport “Teach ’em to Phish: State Sponsors of Surveillance”:

    ping @fil

    • China Uses DNA to Track Its People, With the Help of American Expertise

      The Chinese authorities turned to a Massachusetts company and a prominent Yale researcher as they built an enormous system of surveillance and control.

      The authorities called it a free health check. Tahir Imin had his doubts.

      They drew blood from the 38-year-old Muslim, scanned his face, recorded his voice and took his fingerprints. They didn’t bother to check his heart or kidneys, and they rebuffed his request to see the results.

      “They said, ‘You don’t have the right to ask about this,’” Mr. Imin said. “‘If you want to ask more,’ they said, ‘you can go to the police.’”

      Mr. Imin was one of millions of people caught up in a vast Chinese campaign of surveillance and oppression. To give it teeth, the Chinese authorities are collecting DNA — and they got unlikely corporate and academic help from the United States to do it.

      China wants to make the country’s Uighurs, a predominantly Muslim ethnic group, more subservient to the Communist Party. It has detained up to a million people in what China calls “re-education” camps, drawing condemnation from human rights groups and a threat of sanctions from the Trump administration.

      Collecting genetic material is a key part of China’s campaign, according to human rights groups and Uighur activists. They say a comprehensive DNA database could be used to chase down any Uighurs who resist conforming to the campaign.

      Police forces in the United States and elsewhere use genetic material from family members to find suspects and solve crimes. Chinese officials, who are building a broad nationwide database of DNA samples, have cited the crime-fighting benefits of China’s own genetic studies.

      To bolster their DNA capabilities, scientists affiliated with China’s police used equipment made by Thermo Fisher, a Massachusetts company. For comparison with Uighur DNA, they also relied on genetic material from people around the world that was provided by #Kenneth_Kidd, a prominent #Yale_University geneticist.

      On Wednesday, #Thermo_Fisher said it would no longer sell its equipment in Xinjiang, the part of China where the campaign to track Uighurs is mostly taking place. The company said separately in an earlier statement to The New York Times that it was working with American officials to figure out how its technology was being used.

      Dr. Kidd said he had been unaware of how his material and know-how were being used. He said he believed Chinese scientists were acting within scientific norms that require informed consent by DNA donors.

      China’s campaign poses a direct challenge to the scientific community and the way it makes cutting-edge knowledge publicly available. The campaign relies in part on public DNA databases and commercial technology, much of it made or managed in the United States. In turn, Chinese scientists have contributed Uighur DNA samples to a global database, potentially violating scientific norms of consent.

      Cooperation from the global scientific community “legitimizes this type of genetic surveillance,” said Mark Munsterhjelm, an assistant professor at the University of Windsor in Ontario who has closely tracked the use of American technology in Xinjiang.

      Swabbing Millions

      In Xinjiang, in northwestern China, the program was known as “#Physicals_for_All.”

      From 2016 to 2017, nearly 36 million people took part in it, according to Xinhua, China’s official news agency. The authorities collected DNA samples, images of irises and other personal data, according to Uighurs and human rights groups. It is unclear whether some residents participated more than once — Xinjiang has a population of about 24.5 million.

      In a statement, the Xinjiang government denied that it collects DNA samples as part of the free medical checkups. It said the DNA machines that were bought by the Xinjiang authorities were for “internal use.”

      China has for decades maintained an iron grip in Xinjiang. In recent years, it has blamed Uighurs for a series of terrorist attacks in Xinjiang and elsewhere in China, including a 2013 incident in which a driver struck two people in Tiananmen Square in Beijing.

      In late 2016, the Communist Party embarked on a campaign to turn the Uighurs and other largely Muslim minority groups into loyal supporters. The government locked up hundreds of thousands of them in what it called job training camps, touted as a way to escape poverty, backwardness and radical Islam. It also began to take DNA samples.

      In at least some of the cases, people didn’t give up their genetic material voluntarily. To mobilize Uighurs for the free medical checkups, police and local cadres called or sent them text messages, telling them the checkups were required, according to Uighurs interviewed by The Times.

      “There was a pretty strong coercive element to it,” said Darren Byler, an anthropologist at the University of Washington who studies the plight of the Uighurs. “They had no choice.”

      Calling Dr. Kidd

      Kenneth Kidd first visited China in 1981 and remained curious about the country. So when he received an invitation in 2010 for an expenses-paid trip to visit Beijing, he said yes.

      Dr. Kidd is a major figure in the genetics field. The 77-year-old Yale professor has helped to make DNA evidence more acceptable in American courts.

      His Chinese hosts had their own background in law enforcement. They were scientists from the Ministry of Public Security — essentially, China’s police.

      During that trip, Dr. Kidd met Li Caixia, the chief forensic physician of the ministry’s Institute of Forensic Science. The relationship deepened. In December 2014, Dr. Li arrived at Dr. Kidd’s lab for an 11-month stint. She took some DNA samples back to China.

      “I had thought we were sharing samples for collaborative research,” said Dr. Kidd.

      Dr. Kidd is not the only prominent foreign geneticist to have worked with the Chinese authorities. Bruce Budowle, a professor at the University of North Texas, says in his online biography that he “has served or is serving” as a member of an academic committee at the ministry’s Institute of Forensic Science.

      Jeff Carlton, a university spokesman, said in a statement that Professor Budowle’s role with the ministry was “only symbolic in nature” and that he had “done no work on its behalf.”

      “Dr. Budowle and his team abhor the use of DNA technology to persecute ethnic or religious groups,” Mr. Carlton said in the statement. “Their work focuses on criminal investigations and combating human trafficking to serve humanity.”

      Dr. Kidd’s data became part of China’s DNA drive.

      In 2014, ministry researchers published a paper describing a way for scientists to tell one ethnic group from another. It cited, as an example, the ability to distinguish Uighurs from Indians. The authors said they used 40 DNA samples taken from Uighurs in China and samples from other ethnic groups from Dr. Kidd’s Yale lab.

      In patent applications filed in China in 2013 and 2017, ministry researchers described ways to sort people by ethnicity by screening their genetic makeup. They took genetic material from Uighurs and compared it with DNA from other ethnic groups. In the 2017 filing, researchers explained that their system would help in “inferring the geographical origin from the DNA of suspects at crime scenes.”

      For outside comparisons, they used DNA samples provided by Dr. Kidd’s lab, the 2017 filing said. They also used samples from the 1000 Genomes Project, a public catalog of genes from around the world.

      Paul Flicek, member of the steering committee of the 1000 Genomes Project, said that its data was unrestricted and that “there is no obvious problem” if it was being used as a way to determine where a DNA sample came from.

      The data flow also went the other way.

      Chinese government researchers contributed the data of 2,143 Uighurs to the Allele Frequency Database, an online search platform run by Dr. Kidd that was partly funded by the United States Department of Justice until last year. The database, known as Alfred, contains DNA data from more than 700 populations around the world.

      This sharing of data could violate scientific norms of informed consent because it is not clear whether the Uighurs volunteered their DNA samples to the Chinese authorities, said Arthur Caplan, the founding head of the division of medical ethics at New York University’s School of Medicine. He said that “no one should be in a database without express consent.”

      “Honestly, there’s been a kind of naïveté on the part of American scientists presuming that other people will follow the same rules and standards wherever they come from,” Dr. Caplan said.

      Dr. Kidd said he was “not particularly happy” that the ministry had cited him in its patents, saying his data shouldn’t be used in ways that could allow people or institutions to potentially profit from it. If the Chinese authorities used data they got from their earlier collaborations with him, he added, there is little he can do to stop them.

      He said he was unaware of the filings until he was contacted by The Times.

      Dr. Kidd also said he considered his collaboration with the ministry to be no different from his work with police and forensics labs elsewhere. He said governments should have access to data about minorities, not just the dominant ethnic group, in order to have an accurate picture of the whole population.

      As for the consent issue, he said the burden of meeting that standard lay with the Chinese researchers, though he said reports about what Uighurs are subjected to in China raised some difficult questions.

      “I would assume they had appropriate informed consent on the samples,” he said, “though I must say what I’ve been hearing in the news recently about the treatment of the Uighurs raises concerns.”
      Machine Learning

      In 2015, Dr. Kidd and Dr. Budowle spoke at a genomics conference in the Chinese city of Xi’an. It was underwritten in part by Thermo Fisher, a company that has come under intense criticism for its equipment sales in China, and Illumina, a San Diego company that makes gene sequencing instruments. Illumina did not respond to requests for comment.

      China is ramping up spending on health care and research. The Chinese market for gene-sequencing equipment and other technologies was worth $1 billion in 2017 and could more than double in five years, according to CCID Consulting, a research firm. But the Chinese market is loosely regulated, and it isn’t always clear where the equipment goes or to what uses it is put.

      Thermo Fisher sells everything from lab instruments to forensic DNA testing kits to DNA mapping machines, which help scientists decipher a person’s ethnicity and identify diseases to which he or she is particularly vulnerable. China accounted for 10 percent of Thermo Fisher’s $20.9 billion in revenue, according to the company’s 2017 annual report, and it employs nearly 5,000 people there.

      “Our greatest success story in emerging markets continues to be China,” it said in the report.

      China used Thermo Fisher’s equipment to map the genes of its people, according to five Ministry of Public Security patent filings.

      The company has also sold equipment directly to the authorities in Xinjiang, where the campaign to control the Uighurs has been most intense. At least some of the equipment was intended for use by the police, according to procurement documents. The authorities there said in the documents that the machines were important for DNA inspections in criminal cases and had “no substitutes in China.”

      In February 2013, six ministry researchers credited Thermo Fisher’s Applied Biosystems brand, as well as other companies, with helping to analyze the DNA samples of Han, Uighur and Tibetan people in China, according to a patent filing. The researchers said understanding how to differentiate between such DNA samples was necessary for fighting terrorism “because these cases were becoming more difficult to crack.”

      The researchers said they had obtained 95 Uighur DNA samples, some of which were given to them by the police. Other samples were provided by Uighurs voluntarily, they said.

      Thermo Fisher was criticized by Senator Marco Rubio, Republican of Florida, and others who asked the Commerce Department to prohibit American companies from selling technology to China that could be used for purposes of surveillance and tracking.

      On Wednesday, Thermo Fisher said it would stop selling its equipment in Xinjiang, a decision it said was “consistent with Thermo Fisher’s values, ethics code and policies.”

      “As the world leader in serving science, we recognize the importance of considering how our products and services are used — or may be used — by our customers,” it said.

      Human rights groups praised Thermo Fisher’s move. Still, they said, equipment and information flows into China should be better monitored, to make sure the authorities elsewhere don’t send them to Xinjiang.

      “It’s an important step, and one hopes that they apply the language in their own statement to commercial activity across China, and that other companies are assessing their sales and operations, especially in Xinjiang,” said Sophie Richardson, the China director of Human Rights Watch.

      American lawmakers and officials are taking a hard look at the situation in Xinjiang. The Trump administration is considering sanctions against Chinese officials and companies over China’s treatment of the Uighurs.

      China’s tracking campaign unnerved people like Tahir Hamut. In May 2017, the police in the city of Urumqi in Xinjiang drew the 49-year-old Uighur’s blood, took his fingerprints, recorded his voice and took a scan of his face. He was called back a month later for what he was told was a free health check at a local clinic.

      Mr. Hamut, a filmmaker who is now living in Virginia, said he saw between 20 to 40 Uighurs in line. He said it was absurd to think that such frightened people had consented to submit their DNA.

      “No one in this situation, not under this much pressure and facing such personal danger, would agree to give their blood samples for research,” Mr. Hamut said. “It’s just inconceivable.”
      #USA #Etats-Unis #ADN #DNA #Ouïghours #université #science #génétique #base_de_données

  • Mapping All of the Trees with Machine Learning – descarteslabs-team – Medium

    All this fuss is not without good reason. Trees are great! They make oxygen for breathing, suck up CO₂, provide shade, reduce noise pollution, and just look at them — they’re beautiful!
    8th Street in Park Slope, Brooklyn last May. Look at those beautiful trees!

    The thing is, though, that trees are pretty hard to map. The 124,795 trees in the San Francisco Urban Forest Map shown below, for example, were cataloged over a year of survey work by a team of certified arborists. The database they created is thorough, with information on tree species and size as well as environmental factors like the presence of power lines or broken pavement.

    But surveys like this are expensive to conduct, difficult to maintain, and provide an incomplete picture of the entire extent of the urban tree canopy. Both the San Francisco inventory below and the New York City TreesCount! do an impeccable job mapping the location, size and health of street trees, but exclude large chunks within the cities, like parks.

    #arbre #arbres #cartographie #machine_learning

  • #python #bootcamp For ML

    A simple effort from me to make a Python bootcamp 3 Days for beginners who are enthusiastic about Machine Learning.Introduction:Few days ago i think that i can make a bootcamp on python which most needed for machine learning enthusiastic or deep learning enthusiastic or data science enthusiastic.Then i was started this bootcamp. I hope that this bootcamp will be helpful for everyone who’s want to work in Data Science field or Machine learning field.Data Science.AI CommunityLet’s start learning with us!!Photo on UnsplashPython Basic(Day-01)Data Science.AI CommunityWe are going to get familiar with Python. In Day-01 we was covered the basic portion of Python which can be very useful for Machine Learning or Data Science.What is python?Python is developed by Guido van Rossum. Guido van Rossum (...)

    #hackernoon-top-story #data-science #machine-learning

  • 3 Ways #blockchain Could Unleash the Full Potential of Machine Learning

    We are currently living in the midst of a Artificial Intelligence revolution with new research and applications coming to fruition. Advancements in AI technology, like deep learning, have changed everything from the recommendations on your Netflix account to how doctors are treating patients before they become ill. It seems like every day there are new developments and new tasks being accomplished by machines previously done by humans. Not too long ago, A.I. seemed like something only part of a science fiction novel. Today, AI is all around us.Even with all of these current exciting applications of AI, many more will soon be realized in the near future. However, the advent of this new technology also brings unrealistically high expectations. The latest example, machine learning. It’s (...)

    #data-science #machine-learning #bitcoin #artificial-intelligence