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Creating a city of inclusion for our country’s Special Olympics athletes – Microsoft on the Issues

Special Olympics athlete Virginia Wade with her mother
Special Olympics athlete Virginia Wade, left, who is from Seattle, with her mother. Virginia was one of 22 skiers chosen to represent the U.S. women’s team in the 2017 Special Olympics World Winter Games.

On July 1, 4,000 athletes and coaches from across the country will arrive in Seattle to compete in the 2018 Special Olympics USA Games. Microsoft is proud to be the presenting sponsor of these games. It will be a special week for all of us – the athletes, the city of Seattle and our region, including Microsoft’s employees. As the honorary chairman of this year’s USA Games, I’m delighted to welcome athletes from near and far who are coming not just to compete, but to celebrate their well-earned achievements.

What began as a backyard summer camp in the 1960s by Eunice Kennedy Shriver, the sister of former President John F. Kennedy, Special Olympics has grown into the world’s largest sports organization for people with intellectual disabilities. Shriver recognized how sports brought communities together, encouraged teamwork, built social skills and instilled confidence. She also believed that everyone, no matter their ability, deserved an opportunity to grow, learn and experience joy through sports.

Fifty years after the first games, the Special Olympics boasts more than 5.7 million athletes in 172 countries and more than 1 million volunteers around the world. And while the organization has played a transformative role in the lives of athletes with intellectual disabilities, it also became a global movement of acceptance and inclusion. Through sports, health, school and youth engagement, the organization brings people around the world together, with and without intellectual disabilities, to teach tolerance, unity and respect.

Special Olympics USA Games 2018 logo

For Microsoft, it’s an honor to sponsor this year’s milestone event that celebrates diversity and inclusion in a way no other organization has. “Diversity and inclusion” is a key component to how we understand and work toward our mission every day: To empower every person and every organization on the planet to achieve more. That’s why we’ve partnered with Special Olympics since 2014, using cloud-based data management to transform how the games are run and how athletes are cared for, while supporting the group’s efforts to build a more inclusive global community.

This year, athletes challenge Seattle to “Rise with Us” and make the 2018 games the most inclusive Special Olympics to date. Already, the games are well on that path with 39 percent of competitors participating in Unified Sports (teams of athletes with and without intellectual disabilities), youth-led leadership initiatives, thousands of volunteer opportunities, and the special events designed for the broader community to participate. And this summer’s games will be one of the largest sporting events ever to come to our city, with an expected 50,000 spectators.

As the Pacific Northwest prepares for the arrival of the 2018 Special Olympics USA Games, state and local leaders, businesses, organizations and individuals have an important role to play in creating a region that welcomes everyone. This summer’s games are about much more than sports. They’re about creating a city of inclusion where everyone is welcome and can contribute their talents and skills.

One of the best ways you can show the world this spirit of inclusion is to support the games. Attend the opening ceremonies, to be held on July 1 at the University of Washington’s Husky Stadium. Cheer on athletes who will be competing in 14 sports at events across the region. Volunteer for one of the 10,000 positions needed to make these games a success.

This summer’s games offer the opportunity for each of us to ask important questions, challenge our biases, learn together and act collectively to create more inclusive communities. In other words, this year’s USA Games will require each and every one of us to rise to the occasion and show the world what the Special Olympics – and Seattle – stand for.

Tags: 2018 Special Olympics USA Games, Brad Smith, inclusion, Special Olympics

Researchers use AI to improve accuracy of gene editing with CRISPR

From left, Nicolo Fusi, a researcher at Microsoft, Jennifer Listgarten, who recently joined the faculty at UC Berkeley, and John Doench, an associate director at the Broad Institute, collaborated on a method of using AI to improve gene editing results. Photo by Dana J. Quigley.

A collaboration between computer scientists and biologists from research institutions across the United States is yielding a set of computational tools that increase efficiency and accuracy when deploying CRISPR, a gene-editing technology that is transforming industries from healthcare to agriculture.

CRISPR is a nano-sized sewing kit that can be designed to cut and alter DNA at a specific point in a specific gene.

The technology, for example, may lead to breakthrough applications such as modifying cells to combat cancer or produce high-yielding drought-tolerant crops such as wheat and corn.

Elevation, the newest tool released by the team, uses a branch of artificial intelligence known as machine learning to predict so-called off-target effects when editing genes with the CRISPR system.

Although CRISPR shows great promise in a number of fields, one challenge is that lots of genomic regions are similar, which means the nano-sized sewing kit can accidentally go to work on the wrong gene and cause unintended consequences – the so-called off-target effects.

“Off-target effects are something that you really want to avoid,” said Nicolo Fusi, a researcher at Microsoft’s research lab in Cambridge, Massachusetts. “You want to make sure that your experiment doesn’t mess up something else.”

Fusi and former Microsoft colleague Jennifer Listgarten, together with collaborators at the Broad Institute of MIT and Harvard, University of California Los Angeles, Massachusetts General Hospital and Harvard Medical School, describe Elevation in a paper published Jan. 10 in the journal Nature Biomedical Engineering.

Elevation and a complementary tool for predicting on-target effects called Azimuth are publicly available for free as a cloud-based end-to-end guide-design service running on Microsoft Azure as well as via open-source code.

Using the computational tools, researchers can input the name of the gene they want to modify and the cloud-based search engine will return a list of guides that researchers can sort by predicted on-target or off-target effects.

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Nature as engineer

The CRISPR gene-editing system is adapted from a natural virus-fighting mechanism. Scientists discovered it in the DNA of bacteria in the late 1980s and figured out how it works over the course of the next several decades.

“The CRISPR system was not designed, it evolved,” said John Doench, an associate director at the Broad Institute who leads the biological portions of the research collaboration with Microsoft.

CRISPR stands for “clustered regularly interspaced short palindromic repeats,” which describes a pattern of repeating DNA sequences in the genomes of bacteria separated by short, non-repeating spacer DNA sequences.

The non-repeating spacers are copies of DNA from invading viruses, which molecular messengers known as RNA use as a template to recognize subsequent viral invasions. When an invader is detected, the RNA guides the CRISPR complex to the virus and dispatches CRISPR-associated (Cas) proteins to snip and disable the viral gene.

Modern adaptations

In 2012, molecular biologists figured out how to adapt the bacterial virus-fighting system to edit genes in organisms ranging from plants to mice and humans. The result is the CRISPR-Cas9 gene editing technique.

The basic system works like this: Scientists design synthetic guide RNA to match a DNA sequence in the gene they want to cut or edit and set it loose in a cell with the CRISPR-associated protein scissors, Cas9.

Today, the technique is widely used as an efficient and precise way to understand the role of individual genes in everything from people to poplar trees as well as how to change genes to do everything from fight diseases to grow more food.

“If you want to understand how gene dysfunction leads to disease, for example, you need to know how the gene normally functions,” said Doench. “CRISPR has been a complete game changer for that.”

An overarching challenge for researchers is to decide what guide RNA to choose for a given experiment. Each guide is roughly 20 nucleotides; hundreds of potential guides exist for each target gene in a knockout experiment.

In general, each guide has a different on-target efficiency and a different degree of off-target activity.

The collaboration between the computer scientists and biologists is focused on building tools that help researchers search through the guide choices and find the best one for their experiments.

Several research teams have designed rules to determine where off-targets are for any given gene-editing experiment and how to avoid them. “The rules are very hand-made and very hand-tailored,” said Fusi. “We decided to tackle this problem with machine learning.”

Training models

To tackle the problem, Fusi and Listgarten trained a so-called first-layer machine-learning model on data generated by Doench and colleagues. These data reported on the activity for all possible target regions with just one nucleotide mismatch with the guide.

Then, using publicly available data that was previously generated by the team’s Harvard Medical School and Massachusetts General Hospital collaborators, the machine-learning experts trained a second-layer model that refines and generalizes the first-layer model to cases where there is more than one mismatched nucleotide.

The second-layer model is important because off-target activity can occur with far more than just one mismatch between guide and target, noted Listgarten, who joined the faculty at the University of California at Berkeley on Jan. 1.

Finally, the team validated their two-layer model on several other publicly available datasets as well as a new dataset generated by collaborators affiliated with Harvard Medical School and Massachusetts General Hospital.

Some model features are intuitive, such as a mismatch between the guide and nucleotide sequence, noted Listgarten. Others reflect unknown properties encoded in DNA that are discovered through machine learning.

“Part of the beauty of machine learning is if you give it enough things it can latch onto, it can tease these things out,” she said.

Off target scores

Elevation provides researchers with two kinds of off-target scores for every guide: individual scores for one target region and a single overall summary score for that guide.

Target scores are machine-learning based probabilities provided for every single region on the genome that something bad could happen. For every guide, Elevation returns hundreds to thousands of these off-target scores.

For researchers trying to determine which of potentially hundreds of guides to use for a given experiment, these individual off-target scores alone can be cumbersome, noted Listgarten.

The summary score is a single number that lumps the off-target scores together to provide an overview of how likely the guide is to disrupt the cell over all its potential off-targets.

“Instead of a probability for each point in the genome, it is what’s the probability I am going to mess up this cell because of all of the off-target activities of the guide?” said Listgarten.

End-to-end guide design

Writing in Nature Biomedical Engineering, the collaborators describe how Elevation works in concert with a tool they released in 2016 called Azimuth that predicts on-target effects.

The complementary tools provide researchers with an end-to-end system for designing experiments with the CRISPR-Cas9 system – helping researchers select a guide that achieves the intended effect – disabling a gene, for example – and reduce mistakes such as cutting the wrong gene.

“Our job,” said Fusi, “is to get people who work in molecular biology the best tools that we can.”

In addition to Listgarten, Fusi and Doench, project collaborators include Michael Weinstein from the University of California Los Angeles, Benjamin Kleinstiver, Keith Joung and Alexander A. Sousa from Harvard Medical School and Massachusetts General Hospital, and Melih Elibol, Luong Hoang, Jake Crawford and Kevin Gao from Microsoft Research.

Related:

John Roach writes about Microsoft research and innovation. Follow him on Twitter.

Tags: CRISPR, healthcare

The future is quantum: Microsoft releases free preview of Quantum Development Kit – The AI Blog

From left, Charles Marcus, Krysta Svore, Leo Kouwenhoven and Michael Freedman are leading Microsoft’s quantum computing efforts. Photo by Brian Smale.

So you want to learn how to program a quantum computer. Now, there’s a toolkit for that.

Microsoft is releasing a free preview version of its Quantum Development Kit, which includes the Q# programming language, a quantum computing simulator and other resources for people who want to start writing applications for a quantum computer. The Q# programming language was built from the ground up specifically for quantum computing.

The Quantum Development Kit, which Microsoft first announced at its Ignite conference in September, is designed for developers who are eager to learn how to program on quantum computers whether or not they are experts in the field of quantum physics.

It’s deeply integrated into Visual Studio, Microsoft’s suite of developer tools, so aspects of it will be familiar to people who are already developing applications in other programming languages. And it’s designed to work with a local quantum simulator, also released as part of the kit, that can simulate around 30 logical qubits of quantum computing power using a typical laptop computer. That will allow developers to debug quantum code and test programs on small instances right on their own computers.

For larger-scale quantum challenges, Microsoft also is offering an Azure-based simulator that can simulate more than 40 logical qubits of computing power.

Along with the kit, Microsoft also is making a comprehensive suite of documentation, libraries and sample programs available. That will give people the background they need to start playing around with aspects of computing that are unique to quantum systems, such as quantum teleportation.

That’s a method of securely sharing information across quantum computing bits, or qubits, that are connected by a quantum state called entanglement.

“The hope is that you play with something like teleportation and you get intrigued,” said Krysta Svore, a principal researcher at Microsoft who has led the development of the quantum software and simulator.

The kit will let people create applications that can run right now on the quantum simulator, and those same apps also will eventually work on a topological quantum computer, which Microsoft is in the process of developing for general purpose quantum computing.

“The beauty of it is that this code won’t need to change when we plug it into the quantum hardware,” Svore said.

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From artificial intelligence to climate change

Experts believe quantum computers could allow scientists to address some of the world’s toughest challenges, such as world hunger or the dangerous effects of climate change. That’s partly because quantum computers will be able to do calculations in hours or even minutes that would take the lifetime of the universe for even the most advanced classical computers in use today.

Quantum computers also are expected to help spur major advances in fields such as artificial intelligence.

For example, many of the current breakthroughs in AI are based in part on machine learning, in which a system is given a set of data and learns from that data to recognize things like words, sounds or objects.

With a quantum computing simulator, Svore said computer scientists are already seeing how they could create quantum algorithms for that type of AI research. In early testing on the simulator, they are seeing how these quantum algorithms could more quickly find more nuanced patterns in data, which could spur major advances in fields such as speech, vision or language recognition.

“It seems like there’s a huge amount of potential there, and we’re just scratching the surface,” Svore said.

Topological quantum computing

The Quantum Development Kit is part of Microsoft’s plan to build a robust, full-fledged quantum computing system, which includes everything from the quantum computing hardware to the full software stack. The company’s researchers also are working on projects focused on cryptography and security in a quantum computing world.

Microsoft’s approach is centered on the development of a topological qubit, a more robust type of qubit that Microsoft’s experts believe will provide a better basis for practical, scalable quantum computing.

One big challenge about quantum computing is that qubits are extremely finicky. They need to be stored at very low temperatures, for example, or they might be disturbed and destroyed.

Because they are so finicky, most approaches to building qubits require massive amounts of error correction, or techniques to ensure information is delivered reliably. With a topological qubit, error correction is built right into the physics of the qubit itself. That makes it easier to scale up and deliver reliable results, and to do computations that are orders of magnitude larger than is possible on a classical computer, with fewer qubits than other quantum systems.

It goes without saying that quantum physics is extremely complex, and even some of the smartest people in the world confess that quantum computing is hard for them to understand.

Todd Holmdahl, the corporate vice president in charge of Microsoft’s quantum effort, noted that it’s up to Microsoft to figure out the quantum physics – and then to deliver tools like the Quantum Development Kit that people without a quantum physics degree can use. The hope is that these tools will make the power of quantum computing accessible to many more people.

“What you’re going to see as a developer is the opportunity to tie into tools that you already know well, services you already know well,” Holmdahl said. “There will be a twist with quantum computing, but it’s our job to make it as easy as possible for the developers who know and love us to be able to use these new tools that could potentially do some things exponentially faster – which means going from a billion years on a classical computer to a couple hours on a quantum computer.”

Related:

Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

Tags: Quantum, Quantum computing

4-H youth leader rocks the Hour of Code, plans to continue the movement in her community – Microsoft on the Issues

4H youth leader and Seattle Seahawk player with student in Microsoft Store in Bellevue, Washington
4-H youth leader and TEALS student Nora Medina, left, and Seattle Seahawks player Luke Willson participate in an Hour of Code with a student Dec. 5 at the Microsoft Store at Bellevue Square Mall in Bellevue, Washington.

Earlier this fall, Microsoft and National 4-H Council announced a partnership to support young people to be digital leaders, equipping them with the digital skills and other resources to help them make an even bigger, positive impact on their communities. Youth leaders are working with educators, community members and others to identify challenges their communities face, and to use technology to address those challenges.

Nora Medina, from Quincy High School in central Washington state, is working to inspire kids to learn to code, and help adults build digital skills to close the digital divide in her community. We caught up with Nora during Computer Science Education Week when she visited the Microsoft Store in Bellevue, Washington, alongside Seattle Seahawk Luke Willson, to coach elementary school students through their first Hour of Code. Nora and Luke used the new Minecraft tutorial for Hour of Code, called Hero’s Journey, which introduces kids to coding in a fun and engaging way. While our partnership with 4-H is wide-ranging, going beyond digital skills, computer science was the focus of this conversation with Nora:

4H youth leader and TEALS student Nora Medina with Seattle Seahawk Luke Willson
4-H youth leader and TEALS student Nora Medina with Seattle Seahawk Luke Willson.

How did you discover computer science?

I was introduced to coding and Code.org in middle school in an afterschool club. I started by playing with Minecraft and JavaScript. After that I got involved in Digital Tools class, which opened up more classes at my high school, where I learned web design. I realized you can do so much with your imagination and your creativity. Nothing limits you!

Why do you think learning to code is important for kids today?

Coding is everywhere! If you know coding, companies will be more inclined to hire you. You’ll have more skills to offer.

What can you tell us about your involvement with 4-H?

We’re starting a service project where the main focus is teaching adults digital skills. There’s a gap between students and parents. If we teach adults about digital skills, and why we’re on our phones so much, that can bring us closer as a community, and opens up more opportunities for parents and adults!

Microsoft is a leading supporter of Computer Science Education Week because the lack of access to computer science education threatens to widen the income gap between those who have the skills to succeed in the 21st century and those who do not, impeding students’ ability to eventually thrive in their future careers. We’re inspired by young people like Nora Medina who are stepping forward to help us, and others, address the problem.

In the United States alone, there are over 500,000 open computing jobs, however last year, less than 43,000 computer science students graduated into the U.S. workforce.  Learning to code is one of the most important steps students can take to prepare themselves to fully participate in, and benefit from, our digital economy. That’s why Microsoft Philanthropies is working to help young people and adults become creators of technology, advance their careers and grow their local economies by making computer science education and digital skills available to everyone.

Learn more, and find resources to start learning to code, or to teach others, by visiting your local Microsoft Store or https://www.microsoft.com/en-us/digital-skills/hour-of-code.

Tags: Computer Science Education Week, Hour of Code, Microsoft Philanthropies, Microsoft Store

AI’s sharing economy: Microsoft creates publicly available datasets

From left, Adam Atkinson of Microsoft Research Maluuba, Yoshua Bengio of University of Montreal and Samira Ebrahimi Kahou of Microsoft Research Maluuba are among the AI experts who worked on the FigureQA dataset. Photo courtesy of Microsoft Research Maluuba.

Samira Ebrahimi Kahou and her colleagues at Microsoft Research Maluuba recently set out to solve an interesting research problem: How could they use artificial intelligence to correctly reason about information found in graphs and pie charts?

One big obstacle, they discovered, was that the research area was so new that there weren’t any existing datasets available for them to test their hypotheses.

So, they made one.

The FigureQA dataset, which the team released publicly earlier this fall, is one of a number of datasets, metrics and other tools for testing AI systems that Microsoft researchers and engineers have created and shared in recent years. Researchers all over the world use them to see how well their AI systems do at everything from translating conversational speech to predicting the next word a person may want to type.

The teams say these tools provide a codified way for everyone from academic researchers to industry experts to test their systems, compare their work and learn from each other.

“It clarifies our goals, and then others in the research community can say, ‘OK, I see where you’re going,’” said Rangan Majumder, a partner group program manager within Microsoft’s Bing division who also leads development of the MS MARCO machine reading comprehension dataset. The year-old dataset is getting an update in the next few months.

For people used to the technology industry’s more traditional way of doing things, that kind of information sharing can seem surprising. But in the field of AI, where academics and industry players are very much intertwined, researchers say this type of openness is becoming more common.

“Traditionally, companies have kept their research in-house. Now, we’re really seeing an industrywide impact where almost every company is publishing papers and trying to move the state of the art forward, rather than moving it into a walled garden,” said Rahul Mehrotra, a program manager at Microsoft’s Montreal-based Maluuba lab, which also has released two other datasets, NewsQA and Frames, in the past year.

Many AI experts say that this more collaborative culture is crucial to advancing the field of AI. They note that many of the early breakthroughs in the field were the result of researchers from competing institutions sharing knowledge and building on each other’s work.

“We can’t have all the ideas on the planet, so if someone else has a great idea and wants to try it out, we can give them a dataset to do that,” said Christian Federmann, a senior program manager with the Microsoft Translator team.

Federmann’s team developed the Microsoft Speech Language Translation Corpus so they and others could test bilingual conversational speech translation systems such as the Microsoft Translator live feature and Skype Translator. The corpus was recently updated with additional language pairs.

Federmann also notes that Microsoft is one of the few big players that has the budget and resources to create high-quality tools and datasets that allow the industry to compare its work.

That’s key to creating the kind of benchmarks that people can use to credibly showcase their achievements. For example, the recent milestones in conversational speech recognition are based on results of the Switchboard corpus.

Rangan Majumder, a partner group program manager within Microsoft’s Bing division, leads development of the MS MARCO machine reading comprehension dataset

Paying it forward

Many of the teams that are developing datasets and other metrics say they are, in a sense, paying it forward because they also rely on datasets that others have created.

When they were a small startup, Mehrotra said Maluuba relied heavily on a Microsoft dataset called MCTest. Now, as part of Microsoft, they’ve been pleased to see that the datasets they are creating are being used by others in the field.

Devi Parikh, an assistant professor at Georgia Tech and research scientist at Facebook AI Research, said the FigureQA dataset Maluuba recently released is helpful because it allows researchers like herself to work on problems that require the use of multiple types of AI. To accurately read a graphic and answer a question about it requires both computer vision and natural language processing.

“From a research perspective, I think there’s more and more interest in working on problems that are at the intersection of subfields of AI,” she said.

Still, researchers and engineers working in the AI field say that while some information sharing is valuable, there are also times when competing researchers want to be able to compare their systems without revealing all the information about the data they are using.

Doug Orr, a senior software engineering lead with SwiftKey, which Microsoft acquired last year, said his team wanted to create a standard way for measuring how good a job a system does at predicting what a person will type next. That’s a key component of SwiftKey’s systems, which offer personalized predictions based on a person’s communications style.

Instead of sharing a dataset, the team created a set of metrics that researchers can use with any dataset. The metrics, which are available on GitHub, allow researchers to have standardized benchmarks with which they can measure their own improvement and compare their results to others, without having to share proprietary data.

Orr said the metrics have benefited the team internally because they have a better sense of how much their systems are improving over time, and it allowed everyone in the field to be more transparent about how they are performing against each other.

Majumder, from the Bing team, says his team sees value in testing their systems with any and all available benchmarks, including internal data they don’t share publicly, datasets they build for public use and ones that others create, such as the SQuAD dataset.

When people join his team from other areas of the company, he says they often have to get used to the fact that they are entering a hybrid area where the team is developing products while also making AI research breakthroughs.

In the field of AI, he says, “what we have is somewhere in between engineering and science.”

Related:

Allison Linn is a senior writer at Microsoft. Follow her on Twitter.

Tags: AI, Big Data

For Sale – CPU & GPU combos

Selling what has been left over from recent upgrades.

CPU

  • G3258
    £30
  • i5 4430
    £60

GPU

  • MSI 670 GTX 2GB reference
    £60
  • MSI 970 GTX 4GB gaming edition
    £150

Selling as a combo only at the moment (CPU and GPU) as I may use the remaining parts for another build.

Price and currency: £90-210
Delivery: Delivery cost is included within my country
Payment method: PPG BT
Location: Bristol
Advertised elsewhere?: Advertised elsewhere
Prefer goods collected?: I have no preference

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For Sale – Intel G3258 and i5 4430

Selling two CPU’s that were left over from upgrades. Both LGA1150

  • G3258
    £30
  • i5 4430
    £60

Can include a stock cooler for postage.

Price and currency: 25
Delivery: Delivery cost is included within my country
Payment method: PPG BT
Location: Bristol
Advertised elsewhere?: Advertised elsewhere
Prefer goods collected?: I have no preference

______________________________________________________
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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:

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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.

For Sale – Asus GTX 660 2GB Graphics card £40 still available!

Hello. As they are of no use due to a laptop migration, I have the following left to sell:

Corsair HX750i PSU – (this one) Boxed with all the bits and pieces £65 – sale pending (external) – SOLD
NZXT S340 Case – (this one but in black)in great shape! £25 – SOLD
Asus GTX 660 2GB GPU (this one) £65

FullSizeRender 3.jpg

FullSizeRender 2.jpg

Cheers,
Mario

Price and currency: various
Delivery: Delivery cost is not included
Payment method: cash, BT, PayPal
Location: West London
Advertised elsewhere?: Not advertised elsewhere
Prefer goods collected?: I prefer the goods to be collected

______________________________________________________
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.

For Sale – Asus GTX 660 2GB Graphics card £40 still available!

Hello. As they are of no use due to a laptop migration, I have the following left to sell:

Corsair HX750i PSU – (this one) Boxed with all the bits and pieces £65 – sale pending (external) – SOLD
NZXT S340 Case – (this one but in black)in great shape! £25 – SOLD
Asus GTX 660 2GB GPU (this one) £65

FullSizeRender 3.jpg

FullSizeRender 2.jpg

Cheers,
Mario

Price and currency: various
Delivery: Delivery cost is not included
Payment method: cash, BT, PayPal
Location: West London
Advertised elsewhere?: Not advertised elsewhere
Prefer goods collected?: I prefer the goods to be collected

______________________________________________________
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.

For Sale – Asus GTX 660 2GB Graphics card £40 still available!

Hello. As they are of no use due to a laptop migration, I have the following left to sell:

Corsair HX750i PSU – (this one) Boxed with all the bits and pieces £65 – sale pending (external) – SOLD
NZXT S340 Case – (this one but in black)in great shape! £25 – SOLD
Asus GTX 660 2GB GPU (this one) £65

FullSizeRender 3.jpg

FullSizeRender 2.jpg

Cheers,
Mario

Price and currency: various
Delivery: Delivery cost is not included
Payment method: cash, BT, PayPal
Location: West London
Advertised elsewhere?: Not advertised elsewhere
Prefer goods collected?: I prefer the goods to be collected

______________________________________________________
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.