How Machine Learning Is Changing The Face Of Financial Services Information Security Buzz

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Few industries have as much historical and structured data than the financial services industry, making it the perfect playing field for machine learning technologies. Investment banks were pioneers of AI technologies, using machine learning since as early as the 1980s. Nowadays, traders and fund managers rely on AI-driven market analysis to make investment decisions that are paving the way for fintech companies to develop new digital solutions for financial trading. AI-driven solutions such as stock-ranking based on pattern matching and deep learning for formulating investment strategies are just some of the innovations available on the market today.


Operationalizing AI

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When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?


Operationalizing AI

#artificialintelligence

When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?


Iran is still willing to negotiate with U.S., foreign minister says

The Japan Times

DUBAI, UNITED ARAB EMIRATES – Iran is not ruling out negotiations with the United States even after an American drone strike that killed a top Iranian general, the country's foreign minister said in an interview released Saturday. Mohammed Javad Zarif told Germany's Der Spiegel magazine that he would "never rule out the possibility that people will change their approach and recognize the realities," in an interview conducted Friday in Tehran. There has been growing tension between Washington and Tehran since in 2018, when President Donald Trump pulled the United States out of the nuclear deal with Iran. The U.S. has since reimposed tough sanctions that have crippled Iran's economy. But Zarif suggested Iran was still willing to talk, though reiterated his country's previous demand that first the U.S. would have to lift sanctions.


AI helping Japan railway companies to combat problems with snow

The Japan Times

Japanese railway companies are turning to artificial intelligence to help tackle potential problems for their shinkansen bullet trains caused by accumulations of snow. West Japan Railway Co. is developing an AI system to gauge the amount of snow attached to Hokuriku Shinkansen trains that cut through Niigata, Toyama and Ishikawa prefectures adjacent to the Sea of Japan. The railway operator currently decides how many personnel to deploy for snow clearance a day beforehand, based on information from meteorological data providers and past experience, but it is often not very accurate. AI will gather data from images of trains that have accumulated snow while traveling, study weather conditions and predict the number of personnel necessary for clearance work. Test operations have proved positive so far and the system is set for full introduction next winter.


Google DeepMind's 'Sideways' takes a page from computer architecture ZDNet

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Increasingly, machine learning forms of artificial intelligence are contending with the limits of computing hardware, and it's causing scientists to rethink how they design neural networks. That was clear in last week's research offering from Google, called Reformer, which aimed to stuff a natural language program into a single graphics processing chip instead of eight. And this week brought another offering from Google focused on efficiency, something called Sideways. With this invention, scientists have borrowed a page from computer architecture, creating a pipeline that gets more work done at every moment. Most machine learning neural nets during their training phase use a forward pass, a transmission of a signal through layers of the network, followed by backpropagation, a backward pass through the same layers, only in reverse, to gradually modify the weights of a neural network till they're just right.


AI can help find illegal opioid sellers online. And wildlife traffickers. And counterfeits.

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An estimated 130 people die from opioid-related drug overdoses each day in the United States, and 2 million people had an opioid use disorder in 2018. This public health crisis has left officials scrambling for ways to cut down on illegal sales of these controlled substances, including online sales. Now the National Institute on Drug Abuse, which is part of the US Department of Health and Human Services, is investing in an artificial intelligence-based tool to track how "digital drug dealers" and illegal internet pharmacies market and sell opioids (though online transactions are likely not a large share of overall illegal sales). New AI-based approaches to clamping down on illegal opioid sales demonstrate how publicly available social media and internet data -- even the stuff you post -- can be used to find illegal transactions initiated online. It could also be used to track just about anything else, too: The researcher commissioned by NIDA to build this tool, UC San Diego professor Timothy Mackey, told Recode the same approach could be used to find online transactions associated with illegal wildlife traffickers, vaping products, counterfeit luxury products, and gun sales.


Angular Image Classification App Made Simple With Google Teachable Machine

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AI is a general field that encompasses machine learning and deep learning. The history of artificial intelligence in its modern sense begins in the 1950s, with the works of Alan Turing and the Dartmouth workshop, which brought together the first enthusiasts of this field and in which the basic principles of the science of AI were formulated. Further, this industry experienced several cycles of a surge of interest and subsequent recessions (the so-called "AI winters"), in order to become one of the key areas of world science today. However, there are several examples and applications of artificial intelligence in use today, a large community of developers is still wondering how or from where to start developing AI-driven applications. So this article may be a kick start for those who are eager to start developing AI or ML-driven applications.


London Cops Will Use Facial Recognition to Hunt Suspects

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There will soon be a new bobby on the beat in London: artificial intelligence. London's Metropolitan Police said Friday that it will deploy facial recognition technology to find wanted criminals and missing persons. It said the technology will be deployed at "specific locations," each with a "bespoke watch list" of wanted persons, mostly violent offenders. However, a spokesperson was unable to specify how many facial recognition systems will be used, where, or how frequently. The Met said use of the technology would be publicized beforehand and marked by signs on site.


Building a Lie Detector for Images

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The Internet is full of fun fake images -- from flying sharks and cows on cars to a dizzying variety of celebrity mashups. Hyperrealistic image and video fakes generated by convolutional neural networks (CNNs) however are no laughing matter -- in fact they can be downright dangerous. Deepfake porn reared its ugly head in 2018, fake political speeches by world leaders have cast doubt on news sources, and during the recent Australian bushfires manipulated images mislead people regarding the location and size of fires. Fake images and videos are giving AI a black eye -- but how can the machine learning community fight back? A new paper from UC Berkeley and Adobe researchers declares war on fake images.