Emperor Penguin Linus Torvalds announced the release on Sunday evening, US time.
What’s new this time around? Support for GPUs seem the headline item, with plenty of new drivers and hooks for AMD kit. Perhaps most notable is the adoption of the Virgil 3D project which makes it possible to parcel up virtual GPUs. With virtual Linux desktops now on offer from Citrix and VMware, those who want to deliver virtual desktops with workstation-esque graphics capabilities have their on-ramp to Penguin heaven.
Raspberry Pi owners also have better graphics to look forward to, thanks to a new Pi KMS driver that will be updated with acceleration code in future releases.
There’s also better 64-bit ARM support and fixes for memory leaks on Intel’s Skylake CPUs.
Torvalds also says the new release caught a recent problem, by “unbreaking the x86-32 ‘sysenter’ ABI, when somebody (*cough*android-x86*cough*) misused it by not using the vdso and instead using the instruction directly.”
It will, of course, be months before the new kernel pops up in a majority of production Linux rigs. But it’s out there for those who want it. And Torvalds is of course letting world+dog know he’s about to start work on version 4.5. ®
Sourced through Scoop.it from: www.theregister.co.uk
How to Protect Yourselves this Shopping Season From External Infiltration
it’s recommended that you gear up for the season, not with protection from the cold, but protection from viruses and malware. Whether you are a business or a consumer, you can use the following tips to help secure yourself and your computers from malware and viruses this winter.
Dangers Lurking out there Today
With big chains like Target having data breaches during Black Friday, it only makes sense that small businesses and consumers go the extra mile to safeguard their data. To date, the businesses that have been hacked and attacked have done a great job of offering its customers with identity theft protection foras long as oneyear. This monitoring allows consumers to know when unauthorized use of their credit cards have taken place and can quickly take action against the culprit.
Business Security 101
If you run a business, you know that the security of your systems should be up to par. With that said, it’s important that you ensure that all business transactions are completed over network connections that are secure. You can have a VPN and network firewall installed for communications and data transfers that are handled remotely, rather than relying on Wi-Fi and data network security. Whatever protection you are using for your computer systems today whether AVG or Immunet Antivirus, it’s important that you update antivirus software regularly. Times are always changing, which means that you need to keeps your antivirus software up-to-date with new and emerging threats. All of the equipment you use, such as servers, desktops, laptops and mobile devices should have encryption implemented for further security. Then just in case, you should have any critical data backed up and have a procedure ready that can be used for a secure restoration of your systems.
Security Tips for Consumers
From natural language processing to image recognition, there are a variety of technologies, each suited to different purposes, comprising today’s smart machines landscape, CIO Journal Journalist Thomas H. Davenport writes. Maybe some time we will have a system to recommend the best smart machine technology for the desired application. Until then “smart humans” have a role to play.
ut what specific technologies are we dealing with here, and what the heck do we call them? “Artificial intelligence” has been bandied about for a while, and it’s become an umbrella term for a lot of different tools. Perhaps its only problem is that it’s old, and we have heard so many times of its rise (and fall) that many have grown weary and skeptical of the term.
The newest umbrella term is “cognitive computing,” suggesting that we are finally developing computers that can mimic the human brain. The only problem with this term comes when you have an extended conversation with a neuroscientist, and you realize that we still know very little about how the brain works. There are articles attesting to our ignorance here and here. So to refer to these smart machines as examples of cognitive computing is not really very smart.
The truth is that there are a variety of technologies that comprise the current landscape of smart machines (the overview term that I find least problematic). Each is suited to a different set of purposes, and it’s pretty rare that more than one is integrated in a particular application. Unlike the human brain—which can perform a variety of cognitive tasks, a particular computer system can only do one type of task. Here’s an incomplete alphabetical list of the systems that can undertake cognitive tasks:
Analytics—Tools that do mathematical or statistical analysis on structured data—typically numbers. These have grown in power and sophistication over recent years, and can now be used to drive a variety of decision types. They are also increasingly embedded in other systems and business processes.
Complex event processing—This type of system takes as inputs a variety of real-time data sources and types about events, and then combines them to determine their significance or to take action. It takes in data, transforms it as necessary, analyzes it to detect trends and patterns, and takes necessary actions. CEP systems are widely used in algorithmic stock trading, in which a system might monitor a variety of economic and social indicators, and then determine that a stock trade would be economically beneficial. CEP is also used to detect credit card fraud—ideally before it is successfully committed.
Image recognition—Early systems for recognizing images were quite limited. But now that computers have become a lot more powerful, they can identify more complex images, including specific faces and types of animals. Google was able, for example, to build a complex image recognition that could identify cats in videos.
Machine learning/neural networks—These are somewhat automated approaches to analytics. They create models to fit data and improve as they learn. There are various forms of machine learning approaches, including neural networks, Bayesian classifiers, decision trees, support vector machines, and so forth. The differences between these are generally perceptible only to specialists.
Natural language processing—These tools take text or speech as input, and increasingly can “read” or extract meaning from it. IBM Corp.’s original Watson falls into this category, as does Apple Inc.’s Siri. Since much of human experience is represented in language, this is a powerful category, and the one most likely to be described as “cognitive computing.”
Rules and business rules–Rules express logic in a structured language—typically an “if/then” structure. They were the primary programming approach for so-called “expert systems,” a branch of artificial intelligence. Business rules express operational approaches to business in this structure. A business rule might specify how customers are to be treated (e.g., a customer returning an item doesn’t have to go through a credit check), or when certain quantitative thresholds are reached (e.g., a mortgage loan can be given if the loan-to-value ratio for the house in question is less than 20%).
These and other categories of smart machines can now address almost any topic on which there is data or recorded expertise. They’re all useful, but they’re not universally useful. Since each tool is suited only to a particular purpose, managers increasingly need to be familiar with the tools and how they fit particular applications. If you’ve got a problem that can be structured in a set of rules, you don’t want to hire Watson for that job. If you have a bunch of data in rows and columns, a rules engine won’t help you much.
One thing that human brains—at least some of them—are good at is seeing the big picture. That can include looking over the variety of technology alternatives for cognitively-oriented situations, and selecting the right one. We could probably have a system—I am envisioning a set of rules—that asks the business user a set of questions about the desired application, and then recommends a particular technology. But we don’t have that yet. So in the short run we will have to rely not on smart machines, but on smart humans, for this purpose.
Thomas H. Davenport is a Distinguished Professor at Babson College, a Research Fellow at the Center for Digital Business, Director of Research at the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.