Law Enforcement & Public Safety


How AI can help reduce money laundering

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Money laundering is big criminal business worldwide. Banks are tasked by the regulators with reducing the volume and value of money laundering over their services, but that's easier said than done. In response, many are now starting to use artificial intelligence (AI) to tune results, finding small anomalies within a large amount of data. In the fight against money laundering, banks need both scale and granularity. In most countries, the regulatory requirements make it difficult to track the success of anti-money laundering (AML) projects, however.


A.I. Systems Echo Biases They're Fed, Putting Scientists on Guard

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"We are aware of the issue and are taking the necessary steps to address and resolve it," a Google spokesman said. "Mitigating bias from our systems is one of our A.I. principles, and is a top priority." Amazon, in a statement, said it "dedicates significant resources to ensuring our technology is highly accurate and reduces bias, including rigorous benchmarking, testing and investing in diverse training data." Researchers have long warned of bias in A.I. that learns from large amounts data, including the facial recognition systems that are used by police departments and other government agencies as well as popular internet services from tech giants like Google and Facebook. In 2015, for example, the Google Photos app was caught labeling African-Americans as "gorillas."


How to Take the Security Risk Out of Outsourcing Your Data Labeling

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If you're on an AI project team that has massive data that requires labeling for machine learning or deep learning, you're in a race to usable data. Outsourcing seems the easiest answer. But what happens when data labeling involves protected or private data? What are the security risks that come with outsourcing your data labeling? Here's the short answer: you'll need to take a close look at your data labeling service provider and ask some critical questions.


The Next-Generation Applications Of Artificial Intelligence And Machine Learning

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We have progressed from the development stage of artificial intelligence (AI) and machine learning (ML) technology into widespread implementation across industries. This advanced technology is everywhere, but most people don't know they interact with it daily. From virtual personal assistants like Siri and Alexa to the recommended shows that Netflix serves up based on viewing habits, ML powers these tools we've become so accustomed to using -- even if we don't stop to think about how it happens. What's the difference between AI and ML? AI is computer science that is focused on the capabilities of machines to imitate intelligent human behavior. AI allows computers to process large amounts of information and data and provide a computer-generated conclusion.


Ambient Intelligence Services

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What actions will you take to ensure you remain relevant and essential in the new age? The role of Data Scientist has been called the "sexiest job of the 21st Century." However, are you aware that you do not need to have a PhD or MSA degree to participate in Data Science? The Ambient Institute offers everything you need to guide you to a career in Data Science. Successful data science requires a wide array of skills spanning many different competencies.


Scammers target users of online dating apps using malicious AI 'bots' that aim to scam lonely hearts

Daily Mail - Science & tech

Have you ever matched with someone on a dating app that seems'too good to be true'? They may well be, according to an online dating consultant. Scammers have been letting'malicious bots' loose on dating apps in an attempt to convince people to part with their money, says online dating expert Steve Dean. He warned against'people' who open chats with'here's my phone number, you can call me here' with a link as soon as you swipe them on Tinder. Often these links take you to a scamming or live webcam site.


CENSIS helps launch pioneering AI system - CENSIS

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Ground-breaking AI technology being developed in Scotland will soon enhance Police Scotland's use of remotely piloted aircraft systems (RPAS) to find missing and vulnerable people. The technology, thought to be the first of its kind used by police forces in the UK, is a form of machine learning that provides real-time image analysis for identifying humans in rural areas. It has been developed by a consortium of partners – CENSIS, Thales UK, University of the West of Scotland and Police Scotland. With core AI development work complete and trials of the new system already underway, the project team expects the technology to be deployed in searches for missing and vulnerable people in Scotland in the near future. The technology identifies where a human being is located, rather than an individual.


Huge Data Leak Doxes Members of Notorious Neo-Nazi Forum

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A week ago today, hackers unleashed the first known attack using the vulnerability known as BlueKeep, a long-feared development that in practice turned out to be relatively benign. But don't worry, plenty of other things still went wrong. Like, say, the revelation that you can hack Alexa, Google Assistant, and Siri with lasers. Take the two former Twitter employees who allegedly used their insider access to spy on behalf of Saudi Arabia--a stark reminder of how ill prepared even the biggest companies are to protect consumer data from the people who work there. Or the spate of zombie text messages from February that hit people's phones Thursday with no explanation, the result of a third-party server that had failed on February 14 and was reactivated November 7. All the messages stranded in that queue finally got sent.


Huge Data Leak Doxes Members of Notorious Neo-Nazi Forum

#artificialintelligence

A week ago today, hackers unleashed the first known attack using the vulnerability known as BlueKeep, a long-feared development that in practice turned out to be relatively benign. But don't worry, plenty of other things still went wrong. Like, say, the revelation that you can hack Alexa, Google Assistant, and Siri with lasers. Take the two former Twitter employees who allegedly used their insider access to spy on behalf of Saudi Arabia--a stark reminder of how ill prepared even the biggest companies are to protect consumer data from the people who work there. Or the spate of zombie text messages from February that hit people's phones Thursday with no explanation, the result of a third-party server that had failed on February 14 and was reactivated November 7. All the messages stranded in that queue finally got sent.


7 Ways AI Reduces Mobile Fraud Just In Time For The Holidays

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On average, there are 82 new rogue applications submitted per day to any given AppExchange or application platform, all designed to defraud consumers. Mobile and digital commerce are cybercriminals' favorite attack surfaces because they are succeeding with a broad base of strategies for defrauding people and businesses. Phishing, malware, smishing, or the use of SMS texts rather than email to launch phishing attempts are succeeding in gaining access to victims' account credentials, credit card numbers, and personal information to launch identity theft breaches. The RSA is seeing an arms race between cybercriminals and mobile OS providers with criminals improving their malware to stay at parity or leapfrog new versions and security patches of mobile operating systems. Creating a series of rogue applications and successfully uploading them into an AppExchange or application store gives cybercriminals immediate access to global markets.