In the not-too-distant future, we'll have plenty of reasons to want protect ourselves from facial detection software. Even now, companies from Facebook to the NFL and Pornhub already use this technology to identify people, sometimes without their consent. But as fast as this technology develops, machine learning researchers are working on ways to foil it. As described in a new study, researchers at Carnegie Mellon University and the University of North Carolina at Chapel Hill developed a robust, scalable, and inconspicuous way to fool facial recognition algorithms into not recognizing a person. This paper builds on the same group's work from 2016, only this time, it's more robust and inconspicuous.

Editor's note: This is a guest post by Natalie Fratto, VP of Early Stage Practice at Silicon Valley bank. On the walk back from her high school, Max drops by the corner bodega to pick up a NeuroStim pill -- a prescription neuro-plasticity stimulator. Neurostim will accelerate her brain's ability to create new synaptic pathways, helping her quickly learn new behaviors and spot new connections when exposed to rapidly changing stimuli. The AEI is a standardized test, implemented 10 years ago in place of the SAT. It has become a globally accepted metric for aptitude and projected performance in the modern workplace.

Software development is always evolving. And Software engineers continue to evolve with it. Before DataEng became a thing, we had DBA's and ETL folks. Software Engineers became more involved with the work and created the DataEng field. Before DevOps, we had Operations/Systems Administrators.

UC Berkeley researchers have developed a robotic learning technology that enables robots to imagine the future of their actions so they can figure out how to manipulate objects they have never encountered before. In the future, this technology could help self-driving cars anticipate future events on the road and produce more intelligent robotic assistants in homes, but the initial prototype focuses on learning simple manual skills entirely from autonomous play. Using this technology, called visual foresight, the robots can predict what their cameras will see if they perform a particular sequence of movements. These robotic imaginations are still relatively simple for now – predictions made only several seconds into the future – but they are enough for the robot to figure out how to move objects around on a table without disturbing obstacles. Crucially, the robot can learn to perform these tasks without any help from humans or prior knowledge about physics, its environment or what the objects are.

University of Pennsylvania workshop addresses potential biases in the predictive technique. Stephen Hawking once warned that advances in artificial intelligence might eventually "spell the end of the human race." And yet decision-makers from financial corporations to government agencies have begun to embrace machine learning's enhanced power to predict--a power that commentators say "will transform how we live, work, and think." During the first of a series of seven Optimizing Government workshops held at the University of Pennsylvania Law School last year, Aaron Roth, Associate Professor of Computer and Information Science at the University of Pennsylvania, demystified machine learning, breaking down its functionality, its possibilities and limitations, and its potential for unfair outcomes. Chairman of the Penn Department of Criminology Richard Berk offers commentary. Machine learning, in short, enables users to predict outcomes using past data sets, Roth said.

Jeanne Pepper Bernstein has been searching for her 19-year-old son since he went missing in Lake Forest last Tuesday. On Sunday afternoon, she had a message for him. "If there's any way you can come home, whatever has happened, wherever you've been, whoever you've talked to -- it doesn't matter," she said in an interview with The Times. "We love you so much that we would give up everything we have to have you back." As she offered her wrenching plea, family and friends used drones to canvass the Foothill Ranch area of Lake Forest, where authorities believe Blaze Bernstein was last seen by a friend in Borrego Park.

These are two of the greatest challenges people face when deploying deep learning solutions. Fact is, while highly accurate, deep learning algorithms are complex and require more computation than other approaches. The analysis of massive data sets can lead to high power and heat dissipation in data centers which limits processing speeds; always-on applications can quickly drain power and memory resources in portable devices, such as smartphones and wearables. That limits real-world applications, particularly on mobile and handheld devices. One of the greatest limitations of progress in deep learning is the amount of computation available.

Artificial intelligence (AI) is the perfect example of how something new could be used to change every aspect of our lives when we change the lens. And education is an area that has unlimited potential to utilize innovation. The ability to tap into new technologies to enhance and accelerate the learning process can streamline everything from admissions and grading to student access to vital resources. One of the simplest but impactful things AI can do for the educational space is to speed up the administrative process both for institutions and educators. The tedious process of grading homework, evaluating essays and measuring student responses can require valuable time from lecturers and teachers who would prefer to focus on their lesson planning and one-on-one time with students.

Today, it is no longer a question of adopting AI or not. Instead, ask yourself if you and your sales team are ready for the inevitable. Artificial intelligence for business is a reality. If your goal is to forge ahead and lead in your field, then you need to adapt to a workplace where AI plays a crucial role. As J.J. Kardwell, founder, and CEO of predictive marketing software company EverString put it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales."

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.