Google's Pixel phone has basically been a critical success from the start. Even a year after its debut, the phone has remained a standard of comparison to which all other Android devices are held – and generally not to their benefit. That's mostly due to the Pixel's prowess in areas where other Android device-makers can't (or won't) compete. Significant as that is, though, ask an average non-tech-obsessed smartphone user what they think about the Pixel – and all you're likely to get in response is a glossy-eyed stare. Google may be positioning the Pixel as a mainstream device and even marketing it as such, to a degree, but it hasn't yet managed to break through that Samsung-scented wall and make its phone impactful in any broad and practical sense.
The scan would be a type of authentication known as biometric security. Smartphone fingerprint scanners have long led the way in this market as one of the most popular methods. However, the inclusion of facial scanning in the iPhone X and some Android phones has furthered the conversation on what physical characteristics can be used to secure a computing device. In addition to being used for smartphones, the heart scan technology could be used in airport security screenings, the release said. And, for those who may be worried about potential health effects of the scans, Xu mentioned in the release that the strength of the signal "is much less than Wi-Fi," and doesn't pose a health concern. "We are living in a Wi-Fi surrounding environment every day, and the new system is as safe as those Wi-Fi devices," Xu said in the release.
"The assumption that we previously held, which was that Social Security numbers and driver's license numbers are relatively private ... that's now gone," he said. "Beyond how Equifax changes credit scoring, there's a big question about how Equifax changes identity validation." This is a distinctly separate issue from fraud detection, Perret said. Bank accounts and card numbers can be shut down and reissued, but banks can't do the same for Social Security numbers and other identity factors. "On the fraud side, there's a ton of work we can do, including multifactor authentication," he said, but "the KYC requirements are pretty explicit ... so that needs to be updated." Indeed, a lot of the security practices being used today are done more out of tradition than out of effectiveness.
The success of Bitcoin, a popular cryptocurrency, may have encouraged the central bank to consider its own cryptocurrency since it is not comfortable with this non-fiat cryptocurrency, as stated by RBI executive director Sudarshan Sen a few days ago. Despite RBI's call for caution to people against the use of virtual currencies, a domestic Bitcoin exchange said it was adding over 2,500 users a day and had reached five lakh downloads. The company, launched in 2015, said the increasing downloads highlighted the "growing acceptance of Bitcoins as one of the most popular emerging asset class." A group of experts at RBI is examining the possibility of a fiat cryptocurrency which would become an alternative to the Indian rupee for digital transactions. According to a media report, RBI's own Bitcoin can be named Lakshmi, after the Hindu goddess of wealth.
A tongue-in-cheek definition is, a computer scientist who knows more statistics than his or her colleagues, or a statistician who knows more computer science than his or her colleagues. Time will tell if data science will become a new discipline, or if it will remain a cross-disciplinary field between these two (and perhaps other) fields. The statistician David Donoho published a paper in 2015 with the provocative title “Fifty Years of Data Science”. He was referencing the statistician John Tukey’s call, more than 50 years ago, for statistics to expand into what we now call data science. Donoho’s paper is well worth reading. I’ll list the six subfields of data science that he identifies and make several comments about each one ... A growing demand for people trained in data science has caused the shortage of these people to balloon. Moreover, only limited opportunities to obtain such training exist today
AI is changing the nuts and bolts of data management, alleviating data teams from a lot of tedious, manual dirty work so that they can focus their time on creating business outcomes and allowing data scientist to work at a speed and scale that is impossible today. The data scientist of tomorrow must be prepared to work with the AI revolution, optimizing processes without losing the human ability to think creatively and apply data-driven insights to real-world problems. The next generation of data scientists will be even more necessary for helping to apply models and algorithms to problems and processes across the enterprise. For data science students, it’s not only crucial to understand the data and the technology but it’s equally as valuable to learn how to function in teams, collaborate and teach.
The report dissects the salaries of more than 75 tech positions, including eight networking and telecommunications roles and two network-specific security roles. Among the 10 network and telecommunications roles, network architects will be paid the most in the coming year, Robert Half Technology says. ... By comparison, network architects in the 75th percentile can expect to see starting salaries of $160,750, the 50th percentile can expect $134,000, and the 25th percentile will earn $112,750, according to the guide. This is the first year that Robert Half Technology is breaking down compensation ranges by percentile in its annual salary guide. The categories are designed to help hiring managers weigh a candidate's skills, experience level, and the complexity of the role when making an offer.
Having made, lost and then re-made my fortune in and around the industry over the past 20-plus years, I cannot help but smile over the level of hype — and, as a certain US President would call it, “fake news” — surrounding the current world of IoT. In spite of what the media and investors would like to think, IoT is not new. I can recall building all sorts of systems including AMR (Automatic Meter Reading/ Smart Metering) networks covering whole cities, pharmaceutical storage monitoring, on-line pest/rodent trap systems, trucks and trailer tracking, foodstuff refrigeration monitoring and land subsidence monitoring, just to name a few examples. They all followed the same basic architecture as we see with today’s IoT offerings, but under the label M2M (Machine to Machine).
Although in the early years, hardware support was a serious issue and the command line was a requirement, the last five years have seen very rare occasions that I've come into a problem that couldn't be overcome. I cannot say the same thing about Windows. No matter the iteration, I've always managed to find troubling issues with the Microsoft platform. Generally speaking, those issues can be managed. The latest iteration of Windows is no exception. Coming from Windows 7, I skipped 8 and headed directly to 10. I've found going from Windows 7 to 10 akin to making the leap from GNOME 2.x to 3.x. The metaphor was quite different and took a bit of acclimation. Even though they go about it very differently, in the end, both platforms have the same goal—helping users get their work done.
Like every engineering project, setting wrong expectations and unrealistic goals will lead to poor outcome. It doesn’t have to be that way. Once you have a clear understanding of what problems you need to solve for stakeholders, you must define clear goals and requirements. For example, look at the existing pain points for your developers and how your private cloud solution will solve or mitigate those problems. Improving the developers experience ensure faster adoption and long-term success. Making the move to a private cloud require focus, perseverance, motivation, accountability, and strong communication. You must have a good understanding of your existing service costs by doing a thorough Total Cost of Ownership analysis. What does the day to day operations look like to support private infrastructure?
Quote for the day:
"If you only read the books that everyone else is reading, you can only think what everyone else is thinking." - Haruki Murakami