Tag Archives: August

For Sale – Corsair HS60 Surround headset, Carbon/white, boxed as new

Bought these late August 2019 from Amazon but don’t find them that comfortable as I wear glasses. Balance of 2 year warranty – happy to help out in any future claim, although I’ve no idea how practical that would be.

Corsair HS60 Stereo gaming headset with 7.1 surround sound USB dongle
Carbon/white
Perfect, as new working condition – under 10 hours use
Boxed with all accessories inc detachable mic (unused), USB dongle and manual/warranty booklets
Compatible with PC, Mac, PS4, XBOX, Switch and mobile devices
Precision-tuned 50mm audio drivers
Plush memory foam ear pads
CUE software compatible
Have been fully cleaned with anti bacterial wipes etc

Ideally you will collect but can post at additional cost

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For Sale – Macbook Pro 13 2015 8GB Ram 256GB Mint Condition Boxed

Macbook Pro 13″ 2015 is up for sale.

It was purchased in August 2016. Been used very occasionally. It is in amazing mint condition with no signs of use whatsoever. Screen replaced by Apple recently so it is brand new.

It only has a cycle count of 66 on it’s original battery.

The specs are as follows :

MacBook Pro Early 2015
13.3″ model with RETINA Display
i5 CPU with 2.7GHZ speed
8GB of RAM
256GB FLASH SSD

Will be reset and updated to latest Mojave build.
Would be perfect Christmas present for someone.

Will get it posted insured.

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For Sale – Ryzen 5 3600X, RTX 2060, 16GB RAM, 2.5TB Storage, Arctic Cooler & fans, warranty

For sale is my Gaming PC. Built early August this year with all new parts from Amazon and the like. Everything has warranty – from the balance of 1 year right up to 10 years for the fans. Have kept all the boxes, manuals, spare cabling etc. Happy to help with any warranty claims in the future, although how practical that would be I’m not sure but the offer is there. Will not split.

Simple reason for sale – I’ve caught the build bug and want to start from scratch again!

Case – NZXT H500 Mid tower ATX, Black with Blue bar, tempered glass side panel.
MOBO – MSI B450 PRO-VDH-PLUS M-ATX, running BIOS AGESA 1.0.0.4 (Beta). No WiFi/Bluetooth but there is a spare PCIe slot. I use a Powerline adapter.
CPU – AMD Ryzen 5 3600X (6 cores, 12 threads) plus unused stock cooler (Wraith Spire) – 3.8GHz/4.4GHz Boost.
Cooler – Arctic Freezer 34 eSports Duo, black with white fans, fitted using Arctic MX-4 thermal compound.
RAM – Corsair Vengeance LPX 16GB (2x8GB) DDR4 3200MHz C16 – Black.
PSU – Corsair TX550W 80+ Gold, semi-modular, black.
Storage (boot) – Crucial P1 512GB SSD NVMe PCIe M.2 SSD (located below GPU)
Storage (games) – Seagate Barracuda 3.5″ 2TB 7200rpm (located in the PSU shroud)
GPU – ZOTAC Geforce RTX 2060 – twin fans but not the “Twin fan” model if that makes sense, unless it is the same thing and I’ve misunderstood
Rear/Top exhaust fans (black/white) – 1 x 120mm Arctic F12 PWM PST + 1 x 120mm Arctic F12 PMW, replacing the stock 1x120mm NZXT exhaust fan (black), which I’ll also include.
Front intake fans (black/white) – 1 x 140mm Arctic F14 PWM PST + 1 x 140mm Arctic F14 PMW. Excellent air flow.
Lighting – 1 x non addressable RGB (12V) light strip magnetically fitted inside case roof. Use Mystic Light to change solid colour/effect – subtle and effective. Can easily be moved around the case or removed entirely. Subtle and effective – Im not into the RGB thing.
OS – Windows 10 Pro 64bit v1909. This is registered to me so I’d need to fully reset and install a clean, deactivated copy.

I have deliberately disabled (via BIOS) all boosting options – specifically MSI’s Game Boost and AMD’s Precision Boost 2/PBO, never overclocked.

Bearing this in mind and that I run at 1440p/144Hz at Very High/Ultra detail, I get well over 60FPS on anything eg. Doom 2016 @200FPS, Gears 5 @80-100FPS, BeamNG.drive @100+fps, latest COD MW @70-100FPS. Idles at <35dB and 35degrees, boots up in under 25 seconds. Absolutely no issues - would make a perfect PC for the gamer in your life, especially with Christmas approaching

Collection only – too much hassle/risk using a courier. Happy to demo but I’d then need time to fully reset.

Happy to answer any questions.

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Effectively implement Azure Ultra Disk Storage

In August 2019, Microsoft announced the general availability of a new Managed Disks tier: Ultra Disk Storage. The new offering represents a significant step up from the other Managed Disks tiers, offering unprecedented performance and sub-millisecond latency to support mission-critical workloads.

The Ultra Disk tier addresses organizations reluctant to move data-intensive workloads to the cloud because of throughput and latency requirements.

According to Microsoft, Azure Ultra Disk Storage makes it possible to support these workloads by delivering next-generation storage technologies geared toward performance and scalability, while providing you with the convenience of a managed cloud service.

Understanding Azure Ultra Disk

Managed Disks is an Azure feature that simplifies disk management for infrastructure-as-a-service storage. A managed disk is a virtual hard disk that works much like a physical disk, except that the storage is abstracted and virtualized. Azure stores the disks as page blobs, in the form of random I/O storage objects.

To use managed disks, you only have to provision the necessary storage resources and Azure does the rest, deploying and managing the drives.

Azure offers four Managed Disks tiers: Standard HDD, Standard SSD, Premium SSD and the new Ultra Disk Storage, which also builds on SSD technologies. Ultra Disk SSDs support enterprise-grade workloads driven by systems such as MongoDB, SQL Server, SAP HANA and high-performing, mission-critical applications. The latest storage tier comes with configurable performance attributes, making it possible to adjust IOPS and throughput to meet evolving performance requirements.

Azure Ultra Disk Storage implements a distributed block storage architecture that uses NVMe to support I/O-intensive workloads. NVMe is a host controller interface and storage protocol that accelerates data transfers between data center systems and SSDs over a computer’s high-speed PCIe bus.

Ultra Disk Storage makes it possible to utilize a VM’s maximum I/O limits using only a single ultra disk, without needing to stripe multiple disks.

Along with the new storage tier, Azure introduced the virtual disk client (VDC), a simplified client that runs on the compute host. The client has full knowledge of the virtual disk metadata mappings in the Azure Ultra Disk cluster. This knowledge enables the client to communicate directly with the storage servers, bypassing the load balancers and front-end servers often used to establish initial disk connections.

With earlier Managed Disk storage tiers, the route was much less direct. For example, Azure Premium SSD storage is dependent on the Azure Blob storage cache. As a result, the compute host runs the Azure Blob Cache Driver, rather than the VDC. The driver communicates with a storage front end, which, in turn, communicates with partition servers. The partition servers then talk to the stream servers, which connect to the storage devices.

The VDC, on the other hand, supports a more direct connection, minimizing the number of layers that read and write operations traverse, reducing latency and increasing performance.

Deploying Ultra Disk Storage

Azure Ultra Disk Storage lets you configure capacity, IOPS and throughput independently, providing the flexibility necessary to meet specific performance requirements. For capacity, you can choose a disk size ranging from 4 GiB to 64 TiB, and you can provision the disks with up to 300 IOPS per GiB, to a maximum of 160,000 IOPS per disk. For throughput, Azure supports up to 2,000 MB per second, per disk.

Ultra Disk Storage makes it possible to utilize a VM’s maximum I/O limits using only a single ultra disk, without needing to stripe multiple disks. You can also configure disk IOPS or throughput without detaching the disk from the VM or restarting the VM. Azure automatically implements the new performance settings in less than an hour.

To deploy Ultra Disk Storage, you can use the Azure Resource Manager, Azure CLI or PowerShell. Ultra Disk Storage is currently available in three Azure regions: East US 2, North Europe and Southeast Asia. Microsoft plans to extend to other regions, but the company has not provided specific timelines. In addition, Ultra Disk Storage supports only the ESv3 and DSv3 Azure VMs.

Azure Ultra Disk handles data durability behind the scenes. The service is built on Azure’s locally redundant storage (LRS), which maintains three copies of the data within the same availability zone. If an application writes data to the storage service, Azure will acknowledge the operation only after the LRS system has replicated the data.

When implementing Ultra Disk Storage, you must consider the throttling limits Azure places on resources. For example, you could configure your VM with a 16-GiB ultra disk at 4,800 IOPS. However, if you’re working with a Standard_D2s_v3 VM, you won’t be able to take full advantage of the storage because the VM gets throttled to 3,200 IOPS as a result of its limitations. To realize the full benefits available to Ultra Disk Storage, you need hardware that can support its capabilities.

Where Ultra Disk fits in the Managed Disk lineup

Azure Managed Disks simplify disk management by handling deployment and management details behind the scenes. Currently, Azure provides the following four storage options for accommodating different workloads.

The Standard HDD tier is the most basic tier, providing a reliable, low-cost option that supports workloads in which IOPS, throughput and latency are not critical to application delivery. For this reason, the Standard HDD tier is well suited to backup and other non-critical workloads. The maximum disk size for this tier is 32,767 GiB, the maximum IOPS is 2,000 and the maximum throughput is 500 MiB per second.

The Standard solid-state drive tier offers a step up from the Standard HDD tier to support workloads that require better consistency, availability, reliability and latency. The Standard SSD tier is well suited to web servers and lightly used applications, as well as development and testing environments. The maximum disk size for this tier is 32,767 GiB, the maximum IOPS is 6,000 and the maximum throughput is 750 MiB per second.

Prior to the release of the Ultra Disks tier, the Premium SSD tier was the top offering in the Managed Disks stack. The Premium tier is geared toward production and performance-sensitive workloads that require greater performance than the lower tiers. This tier can benefit mission-critical applications that support I/O-intensive workloads. The maximum disk size for this tier is 32,767 GiB, the maximum IOPS is 20,000 and the maximum throughput is 900 MiB per second.

The Ultra Disks tier is the newest Managed Disks service available to customers. The new tier takes performance to the next level, delivering high IOPS and throughput, with consistently low latency. Customers can dynamically change performance settings without restarting their VMs. The Ultra Disks tier targets data-intensive applications such as SAP HANA, Oracle Database and other transaction-heavy workloads. The maximum disk size for this tier is 65,536 GiB, the maximum IOPS is 160,000 and the maximum throughput is 2,000 MiB per second.

Because Ultra Disk Storage is a new Azure service, it comes with several limitations. The service is available in only a few regions and works with only a couple types of VMs. Additionally, you cannot attach an ultra disk to a VM running in an availability set. The service also does not support snapshots, VM scale sets, Azure disk encryption, Azure Backup or Azure Site Recovery. You can’t convert an existing disk to an ultra disk, but you can migrate the data from an existing disk to an ultra disk.

Despite these limitations, Azure Ultra Disk Storage could prove to be an asset to organizations that plan to move their data-intensive applications to the cloud. No doubt Microsoft will continue to improve the service, extending their reach to other regions and addressing the lack of support for other Azure data services, but that hasn’t happened yet, and some IT teams might insist that these issues be resolved before they consider migrating their workloads. In the meantime, Ultra Disk Storage promises to be a service worth watching, especially for organizations already committed to the Azure ecosystem.

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Machine reading comprehension with Dr. T.J. Hazen

Dr. TJ Hazen

Episode 86, August 21, 2019

The ability to read and understand unstructured text, and then answer questions about it, is a common skill among literate humans. But for machines? Not so much. At least not yet! And not if Dr. T.J. Hazen, Senior Principal Research Manager in the Engineering and Applied Research group at MSR Montreal, has a say. He’s spent much of his career working on machine speech and language understanding, and particularly, of late, machine reading comprehension, or MRC.

On today’s podcast, Dr. Hazen talks about why reading comprehension is so hard for machines, gives us an inside look at the technical approaches applied researchers and their engineering colleagues are using to tackle the problem, and shares the story of how an a-ha moment with a Rubik’s Cube inspired a career in computer science and a quest to teach computers to answer complex, text-based questions in the real world.

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Transcript

T.J. Hazen: Most of the questions are fact-based questions like, who did something, or when did something happen? And most of the answers are fairly easy to find. So, you know, doing as well as a human on a task is fantastic, but it only gets you part of the way there. What happened is, after this was announced that Microsoft had this great achievement in machine reading comprehension, lots of customers started coming to Microsoft saying, how can we have that for our company? And this is where we’re focused right now. How can we make this technology work for real problems that our enterprise customers are bringing in?

Host: You’re listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. I’m your host, Gretchen Huizinga.

Host: The ability to read and understand unstructured text, and then answer questions about it, is a common skill among literate humans. But for machines? Not so much. At least not yet! And not if Dr. T.J. Hazen, Senior Principal Research Manager in the Engineering and Applied Research group at MSR Montreal, has a say. He’s spent much of his career working on machine speech and language understanding, and particularly, of late, machine reading comprehension, or MRC.

On today’s podcast, Dr. Hazen talks about why reading comprehension is so hard for machines, gives us an inside look at the technical approaches applied researchers and their engineering colleagues are using to tackle the problem, and shares the story of how an a-ha moment with a Rubik’s Cube inspired a career in computer science and a quest to teach computers to answer complex, text-based questions in the real world. That and much more on this episode of the Microsoft Research Podcast.

(music plays)

Host: T.J. Hazen, welcome to the podcast!

T.J. Hazen: Thanks for having me.

Host: Researchers like to situate their research, and I like to situate my researchers so let’s get you situated. You are a Senior Principal Research Manager in the Engineering and Applied Research group at Microsoft Research in Montreal. Tell us what you do there. What are the big questions you’re asking, what are the big problems you’re trying to solve, what gets you up in the morning?

T.J. Hazen: Well, I’ve spent my whole career working in speech and language understanding, and I think the primary goal of everything I do is to try to be able to answer questions. So, people have questions and we’d like the computer to be able to provide answers. So that’s sort of the high-level goal, how do we go about answering questions? Now, answers can come from many places.

Host: Right.

T.J. Hazen: A lot of the systems that you’re probably aware of like Siri for example, or Cortana or Bing or Google, any of them…

Host: Right.

T.J. Hazen: …the answers typically come from structured places, databases that contain information, and for years these models have been built in a very domain-specific way. If you want to know the weather, somebody built a system to tell you about the weather.

Host: Right.

T.J. Hazen: And somebody else might build a system to tell you about the age of your favorite celebrity and somebody else might have written a system to tell you about the sports scores, and each of them can be built to handle that very specific case. But that limits the range of questions you can ask because you have to curate all this data, you have to put it into structured form. And right now, what we’re worried about is, how can you answer questions more generally, about anything? And the internet is a wealth of information. The internet has got tons and tons of documents on every topic, you know, in addition to the obvious ones like Wikipedia. If you go into any enterprise domain, you’ve got manuals about how their operation works. You’ve got policy documents. You’ve got financial reports. And it’s not typical that all this information is going to be curated by somebody. It’s just sitting there in text. So how can we answer any question about anything that’s sitting in text? We don’t have a million or five million or ten million librarians doing this for us…

Host: Right.

T.J. Hazen: …uhm, but the information is there, and we need a way to get at it.

Host: Is that what you are working on?

T.J. Hazen: Yes, that’s exactly what we’re working on. I think one of the difficulties with today’s systems is, they seem really smart…

Host: Right?

T.J. Hazen: Sometimes. Sometimes they give you fantastically accurate answers. But then you can just ask a slightly different question and it can fall on its face.

Host: Right.

T.J. Hazen: That’s the real gap between what the models currently do, which is, you know, really good pattern matching some of the time, versus something that can actually understand what your question is and know when the answer that it’s giving you is correct.

Host: Let’s talk a bit about your group, which, out of Montreal, is Engineering and Applied Research. And that’s an interesting umbrella at Microsoft Research. You’re technically doing fundamental research, but your focus is a little different from some of your pure research peers. How would you differentiate what you do from others in your field?

T.J. Hazen: Well, I think there’s two aspects to this. The first is that the lab up in Montreal was created as an offshoot of an acquisition. Microsoft bought Maluuba, which was a startup that was doing really incredible deep learning research, but at the same time they were a startup and they needed to make money. So, they also had this very talented engineering team in place to be able to take the research that they were doing in deep learning and apply it to problems where it could go into products for customers.

Host: Right.

T.J. Hazen: When you think about that need that they had to actually build something, you could see why they had a strong engineering team.

Host: Yeah.

T.J. Hazen: Now, when I joined, I wasn’t with them when they were a startup, I actually joined them from Azure where I was working with outside customers in the Azure Data Science Solution team, and I observed lots of problems that our customers have. And when I saw this new team that we had acquired and we had turned into a research lab in Montreal, I said I really want to be involved because they have exactly the type of technology that can solve customer problems and they have this engineering team in place that can actually deliver on turning from a concept into something real.

Host: Right.

T.J. Hazen: So, I joined, and I had this agreement with my manager that we would focus on real problems. They were now part of the research environment at Microsoft, but I said that doesn’t restrict us on thinking about blue sky, far-afield research. We can go and talk to product teams and say what are the real problems that are hindering your products, you know, what are the difficulties you have in actually making something real? And we could focus our research to try to solve those difficult problems. And if we’re successful, then we have an immediate product that could be beneficial.

Host: Well in any case, you’re swimming someplace in a “we could do this immediately” but you have permission to take longer, or is there a mandate, as you live in this engineering and applied research group?

T.J. Hazen: I think there’s a mandate to solve hard problems. I think that’s the mandate of research. If it wasn’t a hard problem, then somebody…

Host: …would already have a product.

T.J. Hazen: …in the product team would already have a solution, right? So, we do want to tackle hard problems. But we also want to tackle real problems. That’s, at least, our focus of our team. And there’s plenty of people doing blue sky research and that’s an absolute need as well. You know, we can’t just be thinking one or two years ahead. Research should be also be thinking five, ten, fifteen years ahead.

Host: So, there’s a whole spectrum there.

T.J. Hazen: So, there’s a spectrum. But there is a real need, I think, to fill that gap between taking an idea that works well in a lab and turning it into something that works well in practice for a real problem. And that’s the key. And many of the problems that have been solved by Microsoft have not just been blue sky ideas, but they’ve come from this problem space where a real product says, ahh, we’re struggling with this. So, it could be anything. It can be, like, how does Bing efficiently rank documents over billions of documents? You don’t just solve that problem by thinking about it, you have to get dirty with the data, you have to understand what the real issues are. So, many of these research problems that we’re focusing on, and we’re focusing on, how do you answer questions out of documents when the questions could be arbitrary, and on any topic? And you’ve probably experienced this, if you are going into a search site for your company, that company typically doesn’t have the advantage of having a big Bing infrastructure behind it that’s collecting all this data and doing sophisticated machine learning. Sometimes it’s really hard to find an answer to your question. And, you know, the tricks that people use can be creative and inventive but oftentimes, trying to figure out what the right keywords are to get you to an answer is not the right thing.

Host: You work closely with engineers on the path from research to product. So how does your daily proximity to the people that reify your ideas as a researcher impact the way you view, and do, your work as a researcher?

T.J. Hazen: Well, I think when you’re working in this applied research and engineering space, as opposed to a pure research space, it really forces you to think about the practical implications of what you’re building. How easy is it going to be for somebody else to use this? Is it efficient? Is it going to run at scale? All of these problems are problems that engineers care a lot about. And sometimes researchers just say, let me solve the problem first and everything else is just engineering. If you say that to an engineer, they’ll be very frustrated because you don’t want to bring something to an engineer that works ten times slower than needs to be, uses ten times more memory. So, when you’re in close proximity to engineers, you’re thinking about these problems as you are developing your methods.

Host: Interesting, because those two things, I mean, you could come up with a great idea that would do it and you pay a performance penalty in spades, right?

T.J. Hazen: Yeah, yeah. So, sometimes it’s necessary. Sometimes you don’t know how to do it and you just say let me find a solution that works and then you spend ten years actually trying to figure out how to make it work in a real product.

Host: Right.

T.J. Hazen: And I’d rather not spend that time. I’d rather think about, you know, how can I solve something and have it be effective as soon as possible?

(music plays)

Host: Let’s talk about human language technologies. They’ve been referred to by some of your colleagues as “the crown jewel of AI.” Speech and language comprehension is still a really hard problem. Give us a lay of the land, both in the field in general and at Microsoft Research specifically. What’s hope and what’s hype, and what are the common misconceptions that run alongside the remarkable strides you actually are making?

T.J. Hazen: I think that word we mentioned already: understand. That’s really the key of it. Or comprehend is another way to say it. What we’ve developed doesn’t really understand, at least when we’re talking about general purpose AI. So, the deep learning mechanisms that people are working on right now that can learn really sophisticated things from examples. They do an incredible job of learning specific tasks, but they really don’t understand what they’re learning.

Host: Right.

T.J. Hazen: So, they can discover complex patterns that can associate things. So in the vision domain, you know, if you’re trying to identify objects, and then you go in and see what the deep learning algorithm has learned, it might have learned features that are like, uh, you know, if you’re trying to identify a dog, it learns features that would say, oh, this is part of a leg, or this is part of an ear, or this is part of the nose, or this is the tail. It doesn’t know what these things are, but it knows they all go together. And the combination of them will make a dog. And it doesn’t know what a dog is either. But the idea that you could just feed data in and you give it some labels, and it figures everything else out about how to associate that label with that, that’s really impressive learning, okay? But it’s not understanding. It’s just really sophisticated pattern-matching. And the same is true in language. We’ve gotten to the point where we can answer general-purpose questions and it can go and find the answer out of a piece of text, and it can do it really well in some cases, and like, some of the examples we’ll give it, we’ll give it “who” questions and it learns that “who” questions should contain proper names or names of organizations. And “when” questions should express concepts of time. It doesn’t know anything about what time is, but it’s figured out the patterns about, how can I relate a question like “when” to an answer that contains time expression? And that’s all done automatically. There’s no features that somebody sits down and says, oh, this is a month and a month means this, and this is a year, and a year means this. And a month is a part of a year. Expert AI systems of the past would do this. They would create ontologies and they would describe things about how things are related to each other and they would write rules. And within limited domains, they would work really, really well if you stayed within a nice, tightly constrained part of that domain. But as soon as you went out and asked something else, it would fall on its face. And so, we can’t really generalize that way efficiently. If we want computers to be able to learn arbitrarily, we can’t have a human behind the scene creating an ontology for everything. That’s the difference between understanding and crafting relationships and hierarchies versus learning from scratch. We’ve gotten to the point now where the algorithms can learn all these sophisticated things, but they really don’t understand the relationships the way that humans understand it.

Host: Go back to the, sort of, the lay of the land, and how I sharpened that by saying, what’s hope and what’s hype? Could you give us a “TBH” answer?

T.J. Hazen: Well, what’s hope is that we can actually find reasonable answers to an extremely wide range of questions. What’s hype is that the computer will actually understand, at some deep and meaningful level, what this answer actually means. I do think that we’re going to grow our understanding of algorithms and we’re going to figure out ways that we can build algorithms that could learn more about relationships and learn more about reasoning, learn more about common sense, but right now, they’re just not at that level of sophistication yet.

Host: All right. Well let’s do the podcast version of your NERD Lunch and Learn. Tell us what you are working on in machine reading comprehension, or MRC, and what contributions you are making to the field right now.

T.J. Hazen: You know, NERD is short for New England Research and Development Center

Host: I did not!

T.J. Hazen: …which is where I physically work.

Host: Okay…

T.J. Hazen: Even though I work closely and am affiliated with the Montreal lab, I work out of the lab in Cambridge, Massachusetts, and NERD has a weekly Lunch and Learn where people present the work they’re doing, or the research that they’re working on, and at one of these Lunch and Learns, I gave this talk on machine reading comprehension. Machine reading comprehension, in its simplest version, is being able to take a question and then being able to find the answer anywhere in some collection of text. As we’ve already mentioned, it’s not really “comprehending” at this point, it’s more just very sophisticated pattern-matching. But it works really well in many circumstances. And even on tasks like the Stanford Question Answering Dataset, it’s a common competition that people have competed in, question answering, by computer, has achieved a human level of parity on that task.

Host: Mm-hmm.

T.J. Hazen: Okay. But that task itself is somewhat simple because most of the questions are fact-based questions like, who did something or when did something happen? And most of the answers are fairly easy to find. So, you know, doing as well as a human on a task is fantastic, but it only gets you part of the way there. What happened is, after this was announced that Microsoft had this great achievement in machine reading comprehension, lots of customers started coming to Microsoft saying, how can we have that for our company? And this is where we’re focused right now. Like, how can we make this technology work for real problems that our enterprise customers are bringing in? So, we have customers coming in saying, I want to be able to answer any question in our financial policies, or our auditing guidelines, or our operations manual. And people don’t ask “who” or “when” questions of their operations manual. They ask questions like, how do I do something? Or explain some process to me. And those answers are completely different. They tend to be longer and more complex and you don’t always, necessarily, find a short, simple answer that’s well situated in some context.

Host: Right.

T.J. Hazen: So, our focus at MSR Montreal is to take this machine reading comprehension technology and apply it into these new areas where our customers are really expressing that there’s a need.

Host: Well, let’s go a little deeper, technically, on what it takes to enable or teach machines to answer questions, and this is key, with limited data. That’s part of your equation, right?

T.J. Hazen: Right, right. So, when we go to a new task, uh, so if a company comes to us and says, oh, here’s our operations manual, they often have this expectation, because we’ve achieved human parity on some dataset, that we can answer any question out of that manual. But when we test the general-purpose models that have been trained on these other tasks on these manuals, they don’t generally work well. And these models have been trained on hundreds of thousands, if not millions, of examples, depending on what datasets you’ve been using. And it’s not reasonable to ask a company to collect that level of data in order to be able to answer questions about their operations manual. But we need something. We need some examples of what are the types of questions, because we have to understand what types of questions they ask, we need to understand the vocabulary. We’ll try to learn what we can from the manual itself. But without some examples, we don’t really understand how to answer questions in these new domains. But what we discovered through some of the techniques that are available, transfer learning is what we refer to as sort of our model adaptation, how do you learn from data in some new domain and take an existing model and make it adapt to that domain? We call that transfer learning. We can actually use transfer learning to do really well in a new domain without requiring a ton of data. So, our goal is to have it be examples like hundreds of examples, not tens of thousands of examples.

Host: How’s that working now?

T.J. Hazen: It works surprisingly well. I’m always amazed at how well these machine learning algorithms work with all the techniques that are available now. These models are very complex. When we’re talking about our question answering model, it has hundreds of millions of parameters and what you’re talking about is trying to adjust a model that is hundreds of millions of parameters with only hundreds of examples and, through a variety of different techniques where we can avoid what we call overfitting, we can allow the generalizations that are learned from all this other data to stay in place while still adapting it so it does well in this specific domain. So, yeah, I think we’re doing quite well. We’re still exploring, you know, what are the limits?

Host: Right.

T.J. Hazen: And we’re still trying to figure out how to make it work so that an outside company can easily create the dataset, put the dataset into a system, push a button. The engineering for that and the research for that is still ongoing, but I think we’re pretty close to being able to, you know, provide a solution for this type of problem.

Host: All right. Well I’m going to push in technically because to me, it seems like that would be super hard for a machine. We keep referring to these techniques… Do we have to sign an NDA, as listeners?

T.J. Hazen: No, no. I can explain stuff that’s out…

Host: Yeah, do!

T.J. Hazen: … in the public domain. So, there are two common underlying technical components that make this work. One is called word embeddings and the other is called attention. Word embeddings are a mechanism where it learns how to take words or phrases and express them in what we call vector space.

Host: Okay.

T.J. Hazen: So, it turns them into a collection of numbers. And it does this by figuring out what types of words are similar to each other based on the context that they appear in, and then placing them together in this vector space, so they’re nearby each other. So, we would learn, that let’s say, city names are all similar because they appear in similar contexts. And so, therefore, Boston and New York and Montreal, they should all be close together in this vector space.

Host: Right.

T.J. Hazen: And blue and red and yellow should be close together. And then advances were made to figure this out in context. So that was the next step, because some words have multiple meanings.

Host: Right.

T.J. Hazen: So, you know, if you have a word like apple, sometimes it refers to a fruit and it should be near orange and banana, but sometimes it refers to the company and it should be near Microsoft and Google. So, we’ve developed context dependent ones, so that says, based on the context, I’ll place this word into this vector space so it’s close to the types of things that it really represents in that context.

Host: Right.

T.J. Hazen: That’s the first part. And you can learn these word embeddings from massive amounts of data. So, we start off with a model that’s learned on far more data than we actually have question and answer data for. The second part is called attention and that’s how you associate things together. And it’s the attention mechanisms that learn things like a word like “who” has to attend to words like person names or company names. And a word like “when” has to attend to…

Host: Time.

T.J. Hazen: …time. And those associations are learned through this attention mechanism. And again, we can actually learn on a lot of associations between things just from looking at raw text without actually having it annotated.

Host: Mm-hmm.

T.J. Hazen: Once we’ve learned all that, we have a base, and that base tells us a lot about how language works. And then we just have to have it focus on the task, okay? So, depending on the task, we might have a small amount of data and we feed in examples in that small amount, but it takes advantage of all the stuff that it’s learned about language from all these, you know, rich data that’s out there on the web. And so that’s how it can learn these associations even if you don’t give it examples in your domain, but it’s learned a lot of these associations from all the raw data.

Host: Right.

T.J. Hazen: And so, that’s the base, right? You’ve got this base of all this raw data and then you train a task-specific thing, like a question answering system, but even then, what we find is that, if we train a question answering system on basic facts, it doesn’t always work well when you go to operation manuals or other things. So, then we have to have it adapt.

Host: Sure.

T.J. Hazen: But, like I said, that base is very helpful because it’s already learned a lot of characteristics of language just by observing massive amounts of text.

(music plays)

Host: I’d like you to predict the future. No pressure. What’s on the horizon for machine reading comprehension research? What are the big challenges that lie ahead? I mean, we’ve sort of laid the land out on what we’re doing now. What next?

T.J. Hazen: Yeah. Well certainly, more complex questions. What we’ve been talking about so far is still fairly simple in the sense that you have a question, and we try to find passages of text that answer that question. But sometimes a question actually requires that you get multiple pieces of evidence from multiple places and you somehow synthesize them together. So, a simple example we call the multi-hop example. If I ask a question like, you know, where was Barack Obama’s wife born? I have to figure out first, who is Barack Obama’s wife? And then I have to figure out where she was born. And those pieces of information might be in two different places.

Host: Right.

T.J. Hazen: So that’s what we call a multi-hop question. And then, sometimes, we have to do some operation on the data. So, you could say, you know like, what players, you know, from one Super Bowl team also played on another Super Bowl team? Well there, what you have to do is, you have to get the list of all the players from both teams and then you have to do an intersection between them to figure out which ones are the same on both. So that’s an operation on the data…

Host: Right.

T.J. Hazen: …and you can imagine that there’s lots of questions like that where the information is there, but it’s not enough to just show the person where the information is. You also would like to go a step further and actually do the computation for that. That’s a step that we haven’t done, like, how do you actually go from mapping text to text, and saying these two things are associated, to mapping text to some sequence of operations that will actually give you an exact answer. And, you know, it can be quite difficult. I can give you a very simple example. Like, just answering a question, yes or no, out of text, is not a solved problem. Let’s say I have a question where someone says, I’m going to fly to London next week. Am I allowed to fly business class according to my policies from my company, right? We can have a system that would be really good at finding the section of the policy that says, you know, if you are a VP-level or higher and you are flying overseas, you can fly business class, otherwise, no. Okay? But, you know, if we actually want the system to answer yes or no, we have to actually figure out all the details, like okay, who’s asking the question? Are they a VP? Where are they located? Oh, they’re in New York. What does flying overseas mean??

Host: Right. They’re are layers.

T.J. Hazen: Right. So that type of comprehension, you know, we’re not quite there yet for all types of questions. Usually these things have to be crafted by hand for specific domains. So, all of these things about how can you answer complex questions, and even simple things like common sense, like, things that we all know… Um. And so, my manager, Andrew McNamara, he was supposed to be here with us, one of his favorite examples is this concept of coffee being black. But if you spill coffee on your shirt, do you have a black stain on your shirt? No, you’ve got a brown stain on your shirt. And that’s just common knowledge. That is, you know, a common-sense thing that computers may not understand.

Host: You’re working on research, and ultimately products or product features, that make people think they can talk to their machines and that their machines can understand and talk back to them. So, is there anything you find disturbing about this? Anything that keeps you up at night? And if so, how are you dealing with it?

T.J. Hazen: Well, I’m certainly not worried about the fact that people can ask questions of the computer and the computer can give them answers. What I’m trying to get at is something that’s helpful and can help you solve tasks. In terms of the work that we do, yeah, there are actually issues that concern me. So, one of the big ones is, even if a computer can say, oh, I found a good answer for you, here’s the answer, it doesn’t know anything about whether that answer is true. If you go and ask your computer, was the Holocaust real? and it finds an article on the web that says no, the Holocaust was a hoax, do I want my computer to show that answer? No, I don’t. But…

Host: Or the moon landing…!

T.J. Hazen: …if all you are doing is teaching the computer about word associations, it might think that’s a perfectly reasonable answer without actually knowing that this is a horrible answer to be showing. So yeah, the moon landing, vaccinations… The easy way that people can defame people on the internet, you know, even if you ask a question that might seem like a fact-based question, you can get vast differences of opinion on this and you can get extremely biased and untrue answers. And how does a computer actually understand that some of these things are not things that we should represent as truth, right? Especially if your goal is to find a truthful answer to a question.

Host: All right. So, then what do we do about that? And by we, I mean you!

T.J. Hazen: Well, I have been working on this problem a little bit with the Bing team. And one of the things that we discovered is that if you can determine that a question is phrased in a derogatory way, that usually means the search results that you’re going to get back are probably going to be phrased in a derogatory way. So, even if we don’t understand the answer, we can just be very careful about what types of questions we actually want to answer.

Host: Well, what does the world look like if you are wildly successful?

T.J. Hazen: I want the systems that we build to just make life easier for people. If you have an information task, the world is successful if you get that piece of information and you don’t have to work too hard to get it. We call it task completion. If you have to struggle to find an answer, then we’re not successful. But if you can ask a question, and we can get you the answer, and you go, yeah, that’s the answer, that’s success to me. And we’ll be wildly successful if the types of things where that happens become more and more complex. You know, where if someone can start asking questions where you are synthesizing data and computing answers from multiple pieces of information, for me, that’s the wildly successful part. And we’re not there yet with what we’re going to deliver into product, but it’s on the research horizon. It will be incremental. It’s not going to happen all at once. But I can see it coming, and hopefully by the time I retire, I can see significant progress in that direction.

Host: Off script a little… will I be talking to my computer, my phone, a HoloLens? Who am I asking? Where am I asking? What device? Is that so “out there” as well?

T.J. Hazen: Uh, yeah, I don’t know how to think about where devices are going. You know, when I was a kid, I watched the original Star Trek, you know, and everything on there, it seemed like a wildly futuristic thing, you know? And then fifteen, twenty years later, everybody’s got their own little “communicator.”

Host: Oh my gosh.

T.J. Hazen: And so, uh, you know, the fact that we’re now beyond where Star Trek predicted we would be, you know, that itself, is impressive to me. So, I don’t want to speculate where the devices are going. But I do think that this ability to answer questions, it’s going to get better and better. We’re going to be more interconnected. We’re going to have more access to data. The range of things that computers will be able to answer is going to continue to expand. And I’m not quite sure exactly what it looks like in the future, to be honest, but, you know, I know it’s going to get better and easier to get information. I’m a little less worried about, you know, what the form factor is going to be. I’m more worried about how I’m going to actually answer questions reliably.

Host: Well it’s story time. Tell us a little bit about yourself, your life, your path to MSR. How did you get interested in computer science research and how did you land where you are now working from Microsoft Research in New England for Montreal?

T.J. Hazen: Right. Well, I’ve never been one to long-term plan for things. I’ve always gone from what I find interesting to the next thing I find interesting. I never had a really serious, long-term goal. I didn’t wake up some morning when I was seven and say, oh, I want to be a Principal Research Manager at Microsoft in my future! I didn’t even know what Microsoft was when I was seven. I went to college and I just knew I wanted to study computers. I didn’t know really what that meant at the time, it just seemed really cool.

Host: Yeah.

T.J. Hazen: I had an Apple II when I was a kid and I learned how to do some basic programming. And then I, you know, was going through my course work. I was, in my junior year, I was taking a course in audio signal processing and in the course of that class, we got into a discussion about speech recognition, which to me was, again, it was Star Trek. It was something I saw on TV. Of course, now it was Next Generation….!

Host: Right!

T.J. Hazen: But you know, you watch the next generation of Star Trek and they’re talking to the computer and the computer is giving them answers and here somebody is telling me you know there’s this guy over in the lab for computer science, Victor Zue, and he’s building systems that recognize speech and give answers to questions! And to me, that was science-fiction. So, I went over and asked the guy, you know, I heard you’re building a system, and can I do my bachelor’s thesis on this? And he gave me a demo of the system – it was called Voyager – and he asked a question, I don’t remember the exact question, but it was probably something like, show me a map of Harvard Square. And the system starts chugging along and it’s showing results on the screen as it’s going. And it literally took about two minutes for it to process the whole thing. It was long enough that he actually explained to me how the entire system worked while it was processing. But then it came back, and it popped up a map of Harvard Square on the screen. And I was like, ohhh my gosh, this is so cool, I have to do this! So, I did my bachelor’s thesis with him and then I stayed on for graduate school. And by seven years later, we had a system that was running in real time. We had a publicly available system in 1997 that you could call up on a toll-free number and you could ask for weather reports and weather information for anywhere in the United States. And so, the idea that it went from something that was “Star Trek” to something that I could pick up my phone, call a number and, you know, show my parents, this is what I’m working on, it was astonishing how fast that developed! I stayed on in that field with that research group. I was at MIT for another fifteen years after I graduated. At some point, a lot of the things that we were doing, they moved from the research lab to actually being real.

Host: Right.

T.J. Hazen: So, like twenty years after I went and asked to do my bachelor’s thesis, Siri comes out, okay? And so that was our goal. They were like, twenty years ago, we should be able to have a device where you can talk to it and it gives you answers and twenty years later there it was. So, that, for me, that was a queue that maybe it’s time to go where the action is, which was in companies that were building these things. Once you have a large company like Microsoft or Google throwing their resources behind these hard problems, then you can’t compete when you’re in academia for that space. You know, you have to move on to something harder and more far out. But I still really enjoyed it. So, I joined Microsoft to work on Cortana…

Host: Okay…

T.J. Hazen: …when we were building the first version of Cortana. And I spent a few years working on that. I’ve worked on some Bing products. I then spent some time in Azure trying to transfer these things so that companies that had the similar types of problems could solve their problems on Azure with our technology.

Host: And then we come full circle to…

T.J. Hazen: Then full circle, yeah. You know, once I realized that some of the stuff that customers were asking for wasn’t quite ready yet, I said, let me go back to research and see if I can improve that. It’s fantastic to see something through all the way to product, but once you’re successful and you have something in a product, it’s nice to then say, okay, what’s the next hard problem? And then start over and work on the next hard problem.

Host: Before we wrap up, tell us one interesting thing about yourself, maybe it’s a trait, a characteristic, a life event, a side quest, whatever… that people might not know, or be able to find on a basic web search, that’s influenced your career as a researcher?

T.J. Hazen: Okay. You know, when I was a kid, maybe about eleven years old, the Rubik’s Cube came out. And I got fascinated with it. And I wanted to learn how to solve it. And a kid down the street from my cousin had taught himself from a book how to solve it. And he taught me. His name was Jonathan Cheyer. And he was actually in the first national speed Rubik’s Cube solving competition. It was on this TV show, That’s Incredible. I don’t know if you remember that TV show.

Host: I do.

T.J. Hazen: It turned out what he did was, he had learned what is now known as the simple solution. And I learned it from him. And I didn’t realize it until many years later, but what I learned was an algorithm. I learned, you know, a sequence of steps to solve a problem. And once I got into computer science, I discovered all that problem-solving I was doing with the Rubik’s Cube and figuring out what are the steps to solve a problem, that’s essentially what things like machine learning are doing. What are the steps to figure out, what are the features of something, what are the steps I have to do to solve the problem? I didn’t realize that at the time, but the idea of being able to break down a hard problem like solving a Rubik’s Cube, and figuring out what are the stages to get you there, is interesting. Now, here’s the interesting fact. So, Jonathan Cheyer, his older brother is Adam Cheyer. Adam Cheyer is one of the co-founders of Siri.

Host: Oh my gosh. Are you kidding me?

T.J. Hazen: So, I met the kid when I was young, and we didn’t really stay in touch. I discovered, you know, many years later that Adam Cheyer was actually the older brother of this kid who taught me the Rubik’s Cube years and years earlier, and Jonathan ended up at Siri also. So, it’s an interesting coincidence that we ended up working in the same field after all those years from this Rubik’s Cube connection!

Host: You see, this is my favorite question now because I’m getting the broadest spectrum of little things that influenced and triggered something…!

Host: At the end of every podcast, I give my guests a chance for the proverbial last word. Here’s your chance to say anything you want to would-be researchers, both applied and other otherwise, who might be interested in working on machine reading comprehension for real-world applications.

T.J. Hazen: Well, I could say all the things that you would expect me to say, like you should learn about deep learning algorithms and you should possibly learn Python because that’s what everybody is using these days, but I think the single most important thing that I could tell anybody who wants to get into a field like this is that you need to explore it and you need to figure out how it works and do something in depth. Don’t just get some instruction set or some high-level overview on the internet, run it on your computer and then say, oh, I think I understand this. Like get into the nitty-gritty of it. Become an expert. And the other thing I could say is, of all the people I’ve met who are extremely successful, the thing that sets them apart isn’t so much, you know, what they learned, it’s the initiative that they took. So, if you see a problem, try to fix it. If you see a problem, try to find a solution for it. And I say this to people who work for me. If you really want to have an impact, don’t just do what I tell you to do, but explore, think outside the box. Try different things. OK? I’m not going to have the answer to everything, so therefore, if I don’t have the answer to everything, then if you’re only doing what I’m telling you to do, then we both, together, aren’t going to have the answer. But if you explore things on your own and take the initiative and try to figure out something, that’s the best way to really be successful.

Host: T.J. Hazen, thanks for coming in today, all the way from the east coast to talk to us. It’s been delightful.

T.J. Hazen: Thank you. It’s been a pleasure.

(music plays)

To learn more about Dr. T.J. Hazen and how researchers and engineers are teaching machines to answer complicated questions, visit Microsoft.com/research

Go to Original Article
Author: Microsoft News Center

Microsoft Investigator Fellowship seeks PhD faculty submissions

August 1, 2019 | By Jamie Harper, Vice-President, US Education

Microsoft is expanding its support for academic researchers through the new Microsoft Investigator Fellowship. This fellowship is designed to empower researchers of all disciplines who plan to make an impact with research and teaching using the Microsoft Azure cloud computing platform.

From predicting traffic jams to advancing the Internet of Things, Azure has continued to evolve with the times, and this fellowship aims to keep Azure at the forefront of new ideas in the cloud computing space. Similarly evolving, Microsoft fellowships have a long history of supporting researchers, seeking to promote diversity and promising academic research in the field of computing. This fellowship is an addition to this legacy that highlights the significance of Azure in education, both now and into the future.

Full-time faculty at degree-granting colleges or universities in the United States who hold PhDs are eligible to apply. This fellowship supports faculty who are currently conducting research, advising graduate students, teaching in a classroom, and plan to or currently use Microsoft Azure in research, teaching, or both.

Fellows will receive $100,000 annually for two years to support their research. Fellows will also be invited to attend multiple events during this time, where they will make connections with other faculty from leading universities and Microsoft. They will have the opportunity to participate in the greater academic community as well. Members of the cohort will also be offered various training and certification opportunities.

When reviewing the submissions, Microsoft will evaluate the proposed future research and teaching impact of Azure. This will include consideration of how the Microsoft Azure cloud computing platform will be leveraged in size, scope, or unique ways for research, teaching, or both.

Candidates should submit their proposals directly on the fellowship website by August 16, 2019. Recipients will be announced in September 2019.

We encourage you to submit your proposal! For more information on the Microsoft Investigator Fellowship, please check out the fellowship website.

Go to Original Article
Author: Microsoft News Center

For Sale – MSI GTX 1060 GAMING X 6GB

Upgraded to a whole new build, so I’m selling my 1060.
Almost 3 years old, warranty ends in August, can provide proof of purchase (Currys).

Never overclocked, non-smoking household, still works amazingly at 1080p and even tested at 1440p, some titles play incredibly well at various settings (Rocket League, Dota2, Destiny2, GTA V).

Postage not included. I have the original box.

Will update with pictures soon.

Price and currency: £120
Delivery: Delivery cost is not included
Payment method: PPG
Location: Manchester
Advertised elsewhere?: Not advertised elsewhere
Prefer goods collected?: I have no preference

______________________________________________________
This message is automatically inserted in all classifieds forum threads.
By replying to this thread you agree to abide by the trading rules detailed here.
Please be advised, all buyers and sellers should satisfy themselves that the other party is genuine by providing the following via private conversation to each other after negotiations are complete and prior to dispatching goods and making payment:

  • Landline telephone number. Make a call to check out the area code and number are correct, too
  • Name and address including postcode
  • Valid e-mail address

DO NOT proceed with a deal until you are completely satisfied with all details being correct. It’s in your best interest to check out these details yourself.

Go to Original Article
Author:

Microsoft shuts down zero-day exploit on September Patch Tuesday

Microsoft shut down a zero-day vulnerability launched by a Twitter user in August and a denial-of-service flaw on September Patch Tuesday.

A security researcher identified by the Twitter handle SandboxEscaper shared a zero-day exploit in the Windows task scheduler on Aug. 27. Microsoft issued an advisory after SandboxEscaper uploaded proof-of-concept code on GitHub. The company fixed the ALPC elevation of privilege vulnerability (CVE-2018-8440) with its September Patch Tuesday security updates. A malicious actor could use the exploit to gain elevated privileges in unpatched Windows systems.

“[The attacker] can run arbitrary code in the context of local system, which pretty much means they own the box … that one’s a particularly nasty one,” said Chris Goettl, director of product management at Ivanti, based in South Jordan, Utah.

The vulnerability requires local access to a system, but the public availability of the code increased the risk. An attacker used the code to send targeted spam that, if successful, implemented a two-stage backdoor on a system.

“Once enough public information gets out, it may only be a very short period of time before an attack could be created,” Goettl said. “Get the Windows OS updates deployed as quickly as possible on this one.”

Microsoft addresses three more public disclosures

Administrators should prioritize patching three more public disclosures highlighted in September Patch Tuesday.

Microsoft resolved a denial-of-service vulnerability (CVE-2018-8409) with ASP.NET Core applications. An attacker could cause a denial of service with a specially crafted request to the application. Microsoft fixed the framework’s web request handling abilities, but developers also must build the update into the vulnerable application in .NET Core and ASP.NET Core.

Chris Goettl of IvantiChris Goettl

A remote code execution vulnerability (CVE-2018-8457) in the Microsoft Scripting Engine opens the door to a phishing attack, where an attacker uses a specially crafted image file to compromise a system and execute arbitrary code. A user could also trigger the attack if they open a specially constructed Office document.

“Phishing is not a true barrier; it’s more of a statistical challenge,” Goettl said. “If I get enough people targeted, somebody’s going to open it.”

This exploit is rated critical for Windows desktop systems using Internet Explorer 11 or Microsoft Edge. Organizations that practice least privilege principles can mitigate the impact of this exploit.

Another critical remote code execution vulnerability in Windows (CVE-2018-8475) allows an attacker to send a specially crafted image file to a user, who would trigger the exploit if they open the file.

September Patch Tuesday issues 17 critical updates

September Patch Tuesday addressed more than 60 vulnerabilities, 17 rated critical, with a larger number focused on browser and scripting engine vulnerabilities.

“Compared to last month, it’s a pretty mild month. The OS and browser updates are definitely in need of attention,” Goettl said.

Microsoft closed two critical remote code execution flaws (CVE-2018-0965 and CVE-2018-8439) in Hyper-V and corrected how the Microsoft hypervisor validates guest operating system user input. On an unpatched system, an attacker could run a specially crafted application on a guest operating system to force the Hyper-V host to execute arbitrary code.

Microsoft also released an advisory (ADV180022) for administrators to protect Windows systems from a denial-of-service vulnerability named “FragmentSmack” (CVE-2018-5391). An attacker can use this exploit to target the IP stack with eight-byte IP fragments and withholding the last fragment to trigger full CPU utilization and force systems to become unresponsive.

Microsoft also released an update to a Microsoft Exchange 2010 remote code execution vulnerability (CVE-2018-8154) first addressed on May Patch Tuesday. The fix corrects the faulty update that could break functionality with Outlook on the web or the Exchange Control Panel. 

“This might catch people by surprise if they are not looking closely at all the CVEs this month,” Goettl said.

Xbox is at PAX West 2018 – Xbox Wire

Xbox is bringing games, gear, and more to downtown Seattle for PAX West August 31 – September 3. Whether you’re joining us in person or following along on social media and Mixer, here’s what you can expect:

Xbox Booth
North Hall, 4th Floor, Booths 403, 411, 417

Experience a few of the games that make up Xbox One’s diverse line-up at the Xbox booth. We’ll have playable demos of Forza Horizon 4, The Division 2, Shadow of the Tomb Raider, Devil May Cry 5, NBA 2k19, Metro Exodus, Kingdom Hearts III, Tunic, Ooblets, Kingdom Two Crowns, Generation Zero, Bendy and the Ink Machine, Supermarket Shriek, My Time Portia, and some new DLC from State of Decay 2. You will also have the opportunity to earn a Cuphead Pinny Arcade pin, participate in a Tomb Raider-themed scavenger hunt, guess the amount of Nuka Cola caps in a Fallout 76 experience, visit the Game Pass vending machine, and pick up exclusive Xbox Official Gear for the first time at PAX.  (PAX Badge required)

Mixer
North Hall, 4th Floor, Booth #425

Drop by booth #425 in the North Hall to meet up with some of your favorite broadcasters and Mixer Partners, and for a chance to win swag in the HypeZone LIVE! In addition to that, there’s also going to be a main stage at PAX featuring our Mixer Partners, developers, and so much more. Can’t make it to PAX West in person? No problem! Watch all the action happening at PAX West via Mixer.com/Mixer and Mixer.com/HypeZoneLIVE!

Want the full rundown? Make sure to get all the latest details from the official Mixer blog, right here: https://blog.mixer.com/2018/08/22/mixer-pax-west-2018/

Xbox PAX West Panels

Find out more about streaming and the Xbox Adaptive Controller at these panels featuring Xbox and Mixer members. (PAX West badge required.)

Building Your Streaming Community
Wyvern Theater, Saturday, September 1 from 3:00 p.m. – 4:00 p.m.

Building a community comes with many challenges and hurdles, good news is we’re here to help! We’ve gathered a council of content creators to discuss the ins-and-outs of building a great online community in your own livestreams. We’ll be smashing myths and sharing the facts about streaming to help you set a foundation for a positive and effective community.

The Xbox Adaptive Controller: Designed with the Community
Sasquatch Theater, Sunday, September 2 from 12:30pm-1:30pm

The Xbox Adaptive Controller is the newest controller by Xbox, created to help people with limited mobility play. Larry Hryb will lead a conversation with pivotal community experts and representatives of game accessibility organizations like AbleGamers and Stack Up, along with one of the controller’s creators.  We’ll describe the journey of designing the controller leveraging the input of gamers with disabilities from the start… and where we need to go next.

Xbox One Summer of PUBG Tour

The Xbox One Summer of PUBG tour will be making their final stop in Westlake Center during PAX West. No badge required to visit! Check out the PUBG bus and enter to win it or one of many other prizes. More information can be found here.

Xbox PAX West Sweepstakes

PAX West 2018 Sweepstakes Image

Enter for a chance to win one of 11 Xbox Design Lab controllers, influenced by some of our favorite games. Xbox Design Lab allows you to create your own personal controller from over a billion different color combinations, metallic finishes, and rubberized grips. Check it out and design your own controller at xboxdesignlab.xbox.com.

There are two ways to enter:

  • Take a photo of your favorite Xbox Design Lab controller in the Xbox booth and share via Twitter using #XboxPAX #XboxDesignLab #Sweepstakes. Don’t forget to follow @Xbox while you’re at it!
  • Follow @Xbox or @XboxCanada on Twitter and retweet one of their tweets mentioning the sweepstakes and including #XboxPAX #XboxDesignLab #Sweepstakes.

You have until September 3rd to enter. The contest is open to anyone from the US or Canada. Click through for the Official Rules.

See you at PAX West! For more Xbox news, follow @Xbox on Twitter, visit the Xbox PAX West website, and stay tuned to Xbox Wire.

August Patch Tuesday closes CPU bug, two zero-day exploits

Microsoft closed two zero-day vulnerabilities and released a fix for a new exploit for Intel processors on August Patch Tuesday.

Microsoft released an advisory (ADV-180018) on the latest speculative execution side channel vulnerability in Intel Core and Xeon processors called L1 Terminal Fault. Dubbed Foreshadow by security researchers, the vulnerability lets an attacker read data as it passes between a host and a virtual machine and a hypervisor.

The earlier Spectre and Meltdown variants allowed process-to-process interactions, but this latest hardware exploit allows a guest system to retrieve data from another guest system, said Brian Secrist, content manager at Ivanti, based in South Jordan, Utah.  

Once again, we have a bunch of hoops to jump through to get to full remediation… 2018 is keeping us real busy.
Brian Secristcontent manager, Ivanti

Full protection from Foreshadow (CVE-2018-3615, CVE-2018-3620 and CVE-2018-3646) on Windows requires a registry change, Microsoft patch and Intel firmware update to close the vulnerability.

“Once again, we have a bunch of hoops to jump through to get to full remediation,” Secrist said. “2018 is keeping us real busy.”

Microsoft addresses two zero-day exploits

Microsoft also closed a pair of zero-day remote code execution vulnerabilities. The first (CVE-2018-8373), in the Microsoft Scripting Engine with known exploits that affect all versions of Internet Explorer, allows an attacker to run arbitrary code on unpatched machines in the context of users who visit a specially crafted website. Depending on the user’s rights, the attacker could install programs or view and delete data. The patch changes how the scripting engine handles objects in memory. This CVE is critical for Windows desktop systems and important for server versions.

Rated important, the second zero-day (CVE-2018-8414) uses a Windows Shell bug in Windows 10 and Windows Server SAC Server Core for remote-code execution attacks. This vulnerability requires the user to run a malicious file either from email or a web site, after which an attacker can run code at the privilege level of the current user. The patch makes Windows Shell validate file paths properly.

August Patch Tuesday closes more than 60 vulnerabilities

More than half of the 60 vulnerabilities disclosed in August Patch Tuesday affect browsers or the scripting engine. Administrators should prioritize patching workstations and servers for a critical remote code execution vulnerability (CVE-2018-8345) that triggers when viewed by a user. Microsoft resolved this exploit by correcting the processing of shortcut .LNK references.

“Because the user doesn’t have to click on the malicious .LNK file to actually exploit the vulnerability, compared to browser vulnerability, it’s more likely for a server admin to be browsing through files. If they see this shortcut and the system renders it, then that’s when the exploit runs,” said Jimmy Graham, director of product management at Qualys, based in Foster City, Calif.

Jimmy Graham, QualysJimmy Graham, Qualys

Almost every major third-party vendor released patches and updates between the July and August Patch Tuesday, said Secrist. Adobe released four updates, including fixes for Adobe Flash and Acrobat. Google Chrome released version 68, and Firefox released updates for Thunderbird.

“We haven’t seen any increase in attacks or anything, just an example of better research and better coverage of vulnerabilities,” Secrist said.

July Patch Tuesday issues anger IT workers

After the July Patch Tuesday releases, Microsoft warned customers of potential SQL Server startup problems on Windows desktop (7 and 8.1) and server (2008 R2 and 2012 R2) versions on July 26. The company released several hotfixes and recommended uninstalling the July patches. Such rollbacks of faulty Microsoft updates have become a recurring headache for administrators.

Microsoft security updates for July also caused problems for the .NET Framework. On July 16, Microsoft posted a blog that “encouraged” Exchange customers to delay applying the July 10 updates to avoid disruptions with mail delivery. Hotfixes for affected systems — all supported versions of Windows Server — did not arrive until July 17. Up until that point, the only remedy was to uninstall the .NET Framework 4.7.2 update.

“Clearly there is a quality assurance issue of some kind,” Secrist said. “There’s another .NET release this month. Hopefully they spend more time on this one. We always strongly recommend you run [patches] through a test group and make sure they are stable before you push them out.”

Jeff Guillet, CEO of EXPTA Consulting in Pacifica, Calif., reached out to the Exchange product group for more information when the disruptions first occurred and said it was a two-fold problem of “really bad patches and bad communication.”

“Nobody even acknowledged that there was a problem and then all of a sudden they said, ‘Oh, by the way, we fixed this.’ [Administrators] had to troubleshoot it themselves because there was no communication from Microsoft saying this was a problem,” said Guillet.

While the intent of Patch Tuesday is to protect systems from vulnerabilities, the recent spate of patching issues concerns some IT administrators.

“Everybody’s kind of come to terms with [monthly patching], but the expectation was that a patch isn’t going to break stuff,” said Guillet. “So if it’s going to start breaking things, now I need to worry about testing it and I don’t have time because the next patches are coming up next Tuesday.”