Outside of the pure technology reasons, APIs have gained traction due to the inherent focus on simple, practical deployment. This, in turn, made it easier for technology leaders to convince their bosses that it was worth the investment, simply because it was easy to deliver tangible results very quickly. The API deployment model, that is, where and how APIs are deployed, executed, and accessed by consumers, is often referred to as “microservices” – decomposing the business workflow into a set of extremely fine grained services, each of which only does one thing and does it well. A microservice is typically not bigger than 100-1000 lines of code, outside of which it is time to split it into two separate services.
“AI is making the biggest advances in things like speech recognition, computer vision problems and processing millions of images very fast,” Baveja said. “A lot of it’s driven by much faster processing, much cheaper processing and having much more data.” Within a year, the team hopes to have an early version of the tool that students can use to receive a customized list of classes they should take based on their unique circumstances. Human advisers will remain essential, Baveja said, but humans suffer from constraints such as limited time and availability. And while human advisers are good at recognizing contextual information like a student’s emotional state, even the most experienced adviser doesn’t have in mind a statistical overview of all student and class data enriched by concomitant patterns and trends.
Facebook AI Research (FAIR), which had already released to open source its deep-learning modules for the open source development environment Torch in Jan. 2015, last month announced another move. This time Facebook said it would release its server hardware design that's been optimized for machine learning to open source. Facebook has submitted the GPU-based system design materials to the Open Compute Project. The company said that the system is designed for greater energy and heat efficiency, as well as ease of maintenance. Digital tech giants such as Facebook, Google, and Amazon that have large data center operations have long designed their own hardware rather than use the designs from others, such as HP Enterprise and Dell. So, why the big wave of open source releases for machine learning-related development by these big companies?
Developers often have a knee-jerk response and encrypt everything everywhere. While that sounds like the safest route, there are trade-offs you should consider first. For instance, if you implement encryption in flight, you'll have to encrypt and decrypt data before you place it on the network for remote consumption, and before any of your applications can consume it. That requires processing time and imposes a performance penalty that, in the cloud, can add up to a major cost. Make sure you have clear requirements for encryption in flight and that the level of risk and potential loss, calculated in dollars, is worth the cost of using encryption in flight. Encryption at rest is the most practical to apply, because you're ensuring that the data can't be read as it sits on a cloud-based storage system.
Add the very obvious talent shortage, and you have a recipe for spiraling costs. If I learned one thing in 2015, it was that surrounding myself with good people who want to work hard is paramount, yet I ended up contracting outside the U.K. based on recommendations. If there’s one reason London is awash with so many one- and two-man bands, it’s that the cost of finding and hiring a team is so high. Sure, you can go to Lithuania or Pakistan or Timbuktu and find someone who can get the job done, but that’s no replacement for in-house talent — and without a good team, you’re basically nowhere. The government’s plan to loosen up visa requirements for suitably qualified candidates should help in the short and medium term, but only a greater emphasis on training will sort out the problem in the long term.
For too long have smaller companies adopted the attitude that they are too small or too low value to be targeted, and for too long has cyber-security taken a back seat. As this research shows, the outsourced approach is increasingly a viable alternative to the "go at it alone" status quo. It opens the door to a world of experienced MSPs, the best of which offer comprehensive, lightweight security solutions that are affordable, easy-to-install and provide real-time protection against modern threats. These small businesses are often targeted by advanced and persistent threats because of their partnerships with bigger fish. Without addressing these security capabilities SMBs will find it increasingly difficult to work with larger enterprises.
The daily lives that are being impacted most by these changes are those of the people responsible for running data centers and delivering those analytics services. The role of IT professional has switched from a proactive annual planning set of responsibilities, to a reactive “how do I find compute and storage resources really quickly” list of requirements. It’s impossible to predict capacity needs, and speed is required to respond to opportunities and risks as they happen. In addition, the ability to leverage other resources on the cloud to ease the risk of predicting capacity improperly has resulted in a whole host of governance challenges. This huge amount of change has even lead to some referring to 2015 as “one of the most radically disruptive and transformative periods in IT industry history.”
The data scientist of 2016 has been described as “part analyst, part artist.” She combines an analytical mind with the ability to interpret data to spot trends in business that are otherwise unseen. This skill requires an innate sense of creativity and thinking outside of the box. A solid foundation in math, analytics, computer science, and applications, as well as computer programming, are some of the skills needed to succeed in a career in data science. The all-star hybrid data scientist jobs that are often advertised online are something to take with a pinch of salt. As with most careers in tech, it is not likely that you have had extensive experience in all areas that are required on the job spec. A recent article on the topic states that it is important to look beyond the definition of the “unicorn” data scientist.
The digitally-enabled platform economy is often cited as the greatest opportunity for growth, but one that is off limits to companies outside Silicon Valley. It is true that ‘born digital’ companies dominate platform business models today, offering new value by bringing millions of consumers and service providers together. They can act fast because their model doesn’t rely on owning assets or producing goods. However, we now see classic manufacturing companies exploiting the value of data and digital platforms to build similar ecosystems of partners and customers on top of their asset heavy business models. In healthcare, engineering, even agricultural equipment, long established brands are embracing digital disruption by creating entirely new services and streams of revenue.
The market is flooded with stolen credit card details, he said, so “healthcare records attract the premium now”. Investigators do not believe information from the Anthem breach has been sold on black markets. However, other hackers have targeted victims of the Anthem attack with fake emails that appear to be from Anthem or offer credit protection. Those emails aim to steal data that could be sold to criminals, people familiar with the case say. Anthem plans to spend $130m over two years to better protect its networks from breaches. The company has assured regulators that it has strengthened its system, taking steps such as changing administrator passwords every 10 hours and hiring 55 cyber security experts.
Quote for the day:
"MindfulLeaders cultivate the ability to think clearly & to focus on the most important opportunities" -- @Bill_George