The “PreCheck” program is billed as a convenient service to allow U.S. travelers to “speed through security” at airports. However, the latest proposal released by the Transportation Security Administration (TSA) reveals the Department of Homeland Security’s greater underlying plan to collect face images and iris scans on a nationwide scale. DHS’s programs will become a massive violation of privacy that could serve as a gateway to the collection of biometric data to identify and track every traveler at every airport and border crossing in the country.
Cameras concealed within the screen will track the make, model and colour of passing cars to deliver more targeted adverts. Brands can even pre-program triggers so that specific adverts are played when a certain model of car passes the screen, according to Landsec, the company the owns the screens.
With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.
With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry.
The biggest headache in machine learning? Cleaning dirty data off the spreadsheets. Data Science is what I try to do while my SQL queries crash.
If you imagine the life of a machine learning researcher, you might think it’s quite glamorous. You’ll program self-driving cars, work for the biggest names in tech, and your software could even lead to the downfall of humanity. So cool! But, as a new survey of data scientists and machine learners shows, those expectations need adjusting, because the biggest challenge in these professions is something quite mundane: cleaning dirty data.
This comes from a survey conducted by data science community Kaggle (which was acquired by Google earlier this year). Some 16,700 of the site’s 1.3 million members responded to the questionnaire, and when asked about the biggest barriers faced at work, the most common answer was “dirty data,” followed by a lack of talent in the field.
As a young programmer, Joshua Browder built a chatbot to act as a kind of AI lawyer that would help people dispute parking tickets. Not only did it work, but it was hugely popular, which led Browder to expand the program to help anyone harmed by the Equifax scandal sue the company in small claims court. Now his company, DoNotPay, is aiming even higher: by the end of this year, Browder plans to launch an addition to the platform that will you let you sue anyone.
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