Photographs of nearly half of all U.S. adults -- 117 million people -- are collected in police facial recognition databases across the country with little regulation over how the networks are searched and used, according to a new study. Along with a lack of regulation, critics question the accuracy of facial recognition algorithms. Meanwhile, state, city, and federal facial recognition databases include 48 percent of U.S. adults, said the report from the Center on Privacy & Technology at Georgetown Law. The search of facial recognition databases is largely unregulated, the report said. "A few agencies have instituted meaningful protections to prevent the misuse of the technology," its authors wrote.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Want to become a machine learning expert in supervised and unsupervised learning? MACHINE LEARNING (ML) is the construction of algorithms that one can learn from and respond to large data sets faster and make effective predictions. It instructs computers to find patterns in data without being explicitly programmed. As data grows in volume and becomes more complex, the applications of ML are becoming widespread and all pervasive. These courses are structured in a exciting way and at the same time they dive deep into Machine Learning.

Both infographics in one download – a part of the'Where Are the Opportunities in Artificial Intelligence?' series created in collaboration with Tractica, exclusively for the AI for Enterprise meeting in London.

Watson, can you grow into a multibillion-dollar business and become the engine of IBM's resurgence? IBM is betting its future that the answer is yes. Its campaign to commercialize Watson, the company's version of artificial intelligence technology, stands out, even during the current A.I. frenzy in the tech industry. IBM has invested billions of dollars in its Watson business unit, created at the start of 2014, which now employs an estimated 10,000 workers. Its big-ticket marketing push includes clever television ads that feature Watson trading quips with famous people like Serena Williams and Bob Dylan. And Watson, after a slow start, has shown its mettle by assisting in daunting tasks like diagnosing cancer.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

"If you want to know where to make money over the next two decades, look for companies that are finding ways to automate jobs that are currently being done by humans...that you wouldn't have thought previously could be done by a machine. Truck drivers are one thing and Google as well as Tesla have a great head-start in disrupting that market, but lawyers, doctors, teachers, customer service and sales reps – there are companies that are turning these professions into lines of code, and they're going to make a lot of money." Customer Relationship Management (CRM) software is a roughly 25bn a year market today and Gartner projects that it will be the fastest growing enterprise SaaS segment over the next few years, reaching over 40bn in annual spend in 2019. The importance of this market is being underscored by the all-out war between tech titans Microsoft, Salesforce, and Oracle who have already spent close to 40bn in the past two months on CRM-related acquisitions including LinkedIn ( 26.2bn cash), NetSuite ( 9.3bn cash), and Demandware ( 2.8bn stock). Companies today are striving to leverage what is rapidly approaching the zettabyte scale data loads that customers are uploading to the cloud every year, and most CEOs understand that getting a better customer 360 will be a key driver of their firms' success.

Tieto established its new data-driven businesses unit in July 2016 to help Nordic organisations to create innovative data-driven services and new business models. In support of this goal, Tieto is also investigating the opportunities AI can present to the new unit's leadership team. Alicia T. will become a full-fledged member of the management team and also possess the capacity to cast votes. "I am proud that we are the first Nordic company to invite AI to become a member of the leadership team of the new data-driven businesses unit. Our team is on a mission to make the future more intelligent. Our developers at the new data-driven unit want to test how AI – a technology which is increasingly popular in home electronics – could assist organisations in data-driven decision-making. I am confident that Alicia T. will help us in finding information and making data-based decisions that humans don't necessarily think of – and thus perhaps create something yet unforeseen," says Ari Järvelä, Head of Data-Driven Businesses.

SAN FRANCISCO -- New research from Google shows that black students are less likely to have computer science classes in school and are less likely to use computers at home even though they are 1.5 times more interested in studying computer science than their white peers. The findings are part a report released Tuesday by Google in partnership with Gallup that puts the spotlight on the racial and gender gap in K-12 computer science education. Google says its aim with the research, which surveyed thousands of students, parents, teachers, principals and superintendents, is to increase the numbers of women, blacks and Latinos in computer science. Computer science classes are popping up in K-12 schools around the country. The growing effort is coming from many quarters -- the National Science Foundation, the College Board, Freada Kapor's SMASH Academy, Black Girls Code, Girls Who Code, Code.org and major tech companies such as Google -- all searching for the best way to put computers and computer know-how in the hands of kids from all racial, ethnic and socioeconomic backgrounds.

Let's go through an example of Cancer Tissue Observations: Logistic regression is a popular method to predict a binary response. It is a special case of Generalized Linear models that predicts the probability of the outcome. Logistic regression measures the relationship between the Y "Label" and the X "Features" by estimating probabilities using a logistic function. The model predicts a probability which is used to predict the label class. Our data is from the Wisconsin Diagnostic Breast Cancer (WDBC) Data Set which categorizes breast tumor cases as either benign or malignant based on 9 features to predict the diagnosis.