HoloLens developers create Pokémon Go mockup for Microsoft’s augmented reality headset

When Pokémon Go launched last week, we theorized that Microsoft’s HoloLens could be a huge platform for the game. While HoloLens’ high price makes any kind of official port extremely unlikely, that hasn’t stopped HoloLens developers from creating mock-ups of how the game might play when paired with Microsoft’s headset.

First up, there’s CapitolaVR and a video created by David Robustelli. This build was created in Unity with the HoloLens SDK. It shows Pokémon being randomly generated in the environment before being captured by Pokéballs.

“For now the Pokémon are randomly generated within a mapped environment,” Robustelli told UploadVR. “We are now focusing on how to use different gestures for specific interactions. For example, like opening your inventory or activating your map and zooming in and tilting your map. The thing is there is a huge amount of possibilities that are unexplored and which could work for games like these. A more challenging thing is how to access the Google Maps API and enabling it within a running app. But I’m sure that in the upcoming years when more and more people are developing for this hardware also more things will be standard to use in tools and apps.”

The other prototype video was put together by Koder, a “developers-as-a-service” coding business. It includes shots of a potential UI — along with Pokémon that dance along to the included soundtrack — plus real-world Pokéballs. The idea of an incorporated AR toy that the user had to interact with is somewhat interesting, but the practical implementation would undoubtedly be problematic (imagine hundreds of people throwing Pokéballs around in the same relatively small area).

Both videos illustrate the potential ways that HoloLens or its eventual successor could be used to improve AR games in the future, and the smash success of Pokémon Go. At the same time, after watching both videos, I’m left wondering — what the heck is playing this game supposed to accomplish?
I mean, seriously. It’s really cool, for the first few minutes, to see little Pokémon superimposed over the real world and I like the concept of a real-world Pokéball, even if the practical implementation at scale would never work for an open-world social game. The actual gameplay mechanisms seem to be lacking, however. There’s no robust battle system beyond challenging other people to control gyms. The fighting mechanics at the heart of other Pokémon titles seem absent here and reviewers that focus on Pokémon Go’s actual gameplay rather than its novelty seem to agree that there’s not a lot of depth to this title.

Polygon’s review goes into some detail on this topic and I think it’s an understandable issue — and in some respects, even a desirable one. It’s easy to forget that the games we enjoy today evolved over time and with no small amount of trial and error. The ability to store larger amounts of data on floppy disks allowed developers to create text adventure games, while the ability to display graphics on the Apple II led to the creation of the first graphical adventure game, Mystery House. The FPS genre was kickstarted by games like Ultima Underworld, which ran on an engine significantly more powerful than the one powering the first FPS title most people have heard of — Wolfenstein 3D.

With VR and AR both still in their infancy, we’re at the very beginning of what will eventually be done with both mediums. In the long run, even popular games like Pokémon Go will look quaint — the same way that Wolfenstein 3D and Quake do today.

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Nvidia’s GTX 1060 GPU: Fast, cool, and potent competition for AMD’s RX 480

• By Joel Hruska on July 19, 2016 at 3:43 pm
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When AMD announced its RX 480 would launch in late June, it was virtually inevitable that Nvidia would follow suit with a midrange challenger of its own. Until now, the two companies have pursued different strategies with their 14nm refreshes — Nvidia chose to do a standard top-down refresh cycle, while AMD rolled out a midrange competitor first. The RX 480 proved to be potent competition for Nvidia’s previous-generation Maxwell products with competitive power consumption, more VRAM, and better overall performance.

Nvidia obviously wasn’t willing to risk losing sales in the larger mass market, even if its GTX 1080 and 1070 have locked down the high-end GPU space. As a result, we have the GTX 1060 — a 6GB Pascal GPU with 1,280 CUDA cores, 80 texture units, and 48 ROPS. Total memory bandwidth is 192GB/s, courtesy of a 192-bit memory bus.



Sample allocation on the GTX 1060 launch was extremely tight, which means our inbound Gigabyte sample hasn’t quite arrived yet. We’ve rounded up a number of GTX 1060 reviews from across the web, including TechSpot, TechPowerUp, THG, Hot Hardware, and Forbes. The major question on everyone’s mind: How does the GTX 1060 compare with the RX 480?

The short answer is: Pretty well — but it’s not the knockout blow that Nvidia wanted.
The GTX 1060 is generally faster than the RX 480, but the gap between the two cards shifts dramatically depending on the benchmark. Of the 23 distinct titles benchmarked by the various websites above, the GTX 1060 wins 14 of them, while the RX 480 takes nine. Pascal is extremely well-positioned against Polaris, with lower power consumption and superior overclocking. THG calculates its overall performance advantage over RX 480 at 13.5% while TechPowerUp reports a 7% difference in overall performance at 1080p.

The GTX 1060’s first problem is its cost. The GPU debuting today is the Founder’s Edition, priced at $299. Partner cards starting at $250 are already on the way, but the $300 version of the GPU is 25.5% more expensive than the RX 480 while offering 7 – 13.5% better performance. That gives the 8GB edition of the RX 480 a clear value advantage. The $250 version of the GTX 1060 will be far more competitive, but AMD has the 4GB RX 480 sitting at the $200 price point. Given that the performance difference between the 4GB and 8GB cards is minimal, a $200 4GB RX 480’s performance-per-dollar will compare extremely well against the GTX 1060.

Second, there’s the matter of which games the RX 480 wins. Four of its nine wins are in games that use new, low-latency APIs. Specifically, the RX 480 wins against the GTX 1060 in Hitman, Ashes of the Singularity, Doom, and even Gears of War: Ultimate Edition (according to Forbes the game is much improved). The GTX 1060 wins Rise of the Tomb Raider and the new DX12 version of the Total War: Warhammer title.

If we segregate the list into DX11 versus DX12, AMD wins 21.7% of the DirectX 11 comparisons and 67% of the DirectX 12 / Vulkan comparisons. That’s a non-trivial difference, and it could speak to the long-term strengths of GCN versus Pascal.

In the past I’ve cautioned readers against assuming that early DirectX 12 performance figures would be valid over the long term. We’re just shy of Windows 10’s one-year anniversary, and I have to say that the titles we’ve seen thus far have tended to favor AMD. There are a variety of explanations for this, including the argument that the reason Nvidia’s performance is flat in DX12 is because it did a far better job than AMD at maximizing performance under DirectX 11. Regardless of the reason, AMD is building a potent narrative about its own DirectX 12 performance — and Nvidia has yet to offer much in the way of an API counter-argument. The 1060 also gets a few dings for lacking SLI — the RX 480 supports Crossfire and comes in at a lower price target to boot.

Conclusions

The general opinion on the GTX 1060 is that it’s a very good GPU with lots of overclocking headroom and excellent power consumption characteristics. Reviewers are split on whether or not it qualifies as “better” than the RX 480. TechSpot declares AMD the overall winner thanks to superior performance-per-dollar and ongoing availability concerns about the GTX 1060, while TechPowerUp thinks the GTX 1060 will be the superior solution in the long term. Hot Hardware gave the overall nod to the GTX 1060 as well, as well as an Editor’s Choice award.

Critically, no one seems to think the Founder’s Edition of the card is a good buy; THG calls the $50 premium on the 1060 a “killer in any discussion of value.” If you care about maximum power efficiency and minimal noise, the GTX 1060 is a clear winner over the RX 480. If you’re more concerned with performance in DX12/Vulkan or care about maximizing your game performance per dollar, the situation is murkier and the  RX 480 is still quite compelling. Multiple reviewers recommend waiting for less expensive partner boards and evaluating the card on their characteristics before making a final decision.

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Neuroscientists just isolated the part of the brain that controls free will

Free will might have been the province of philosophers until now, but we’ve cracked the problem with an fMRI. Neuroscientists from Johns Hopkins report in the journal Attention, Perception, & Psychophysics that they were able to see both what happens in a human brain the moment a free choice is made, and what happens during the lead-up to that decision — how activity in the brain changes during the deliberation over whether to act.

“How do we peek into people’s brains and find out how we make choices entirely on our own?” asked Susan Courtney, a professor of psychological and brain sciences and coauthor of the report. “What parts of the brain are involved in free choice?”

The team devised a novel way to track a participant’s focus without using cues or commands, avoiding a Schrodinger’s-like dilemma of altering the process of choice by calling attention to it. Participants took positions in MRI scanners, and then were left alone to watch a split screen as rapid streams of colorful numbers and letters scrolled past on both sides. They were asked just to pay attention to one side for a while, then to the other side. When to switch sides, and for how long to look, was entirely up to them. Over the duration of the experiment, the participants glanced back and forth, switching sides dozens of times.

In terms of connectivity in the brain, the actual process of switching attention from one side to the other was tightly linked with activity in the parietal lobe, which is sort of the top back quadrant of the brain. Activity during the period of deliberation before a choice took place in the frontal cortex, which engages in reasoning and plans movement. Deliberation also lit up the basal ganglia, important parts of the deep brain that handle motor control, including the initiation of motion. The basal ganglia has also been an important target for research on dopaminergic diseases like Huntington’s and Parkinson’s, but the area has also been implicated in OCD, which has a lot to do with attention and volition.

Connectivity isn’t the only important factor here, though. Participants’ frontal-lobe activity began earlier than it would have if participants had been cued to shift attention, which demonstrates that the brain was planning a voluntary action rather than merely following an order. Following commands or running through practiced actions, like the way you can sometimes drive home on autopilot, don’t need the same lead time. Timing is crucial.

It’s worth pointing out, too, that this is an entirely novel research tool. It should be examined and held up to criticism and comparison, because it stands to revolutionize how we study not just the brain but the mind. Now that scientists have a way to follow the execution of free will, they can use the technique to watch what’s happening in the brain as people navigate more complex decisions, such as weighing short-term rewards against long-term rewards — and perhaps even pinpoint the tipping point between them.

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Nvidia’s GTX 1080 redefines high-end gaming performance

Nvidia formally announced the GTX 1080 in Austin just over a week ago, but it held back on the GPU deep dive at the initial presentation. While the GTX 1080 isn’t scheduled to launch until May 27, Nvidia has lifted the curtain on the GPUs performance and technical advances. We’ve covered some of the card’s improvements in VR and overall positioning, plus new technologies like Ansel that give gamers more artistic freedom, so we’ll be focusing on areas where Nvidia hadn’t disclosed as much information. Unfortunately, Nvidia was unable to supply us with a GTX 1080 in time for launch, so we’ll have to defer benchmarks and performance comparisons for another day.

Pascal is an evolutionary step forward from Maxwell and many of the technologies that debuted in the GeForce 9xx family have been refined, improved, and enhanced for the GTX 1080. The base GPU packs 2560 cores with 20 SM blocks and 128 cores per block. There are 160 texture units and 64 ROPS, which is interesting — one common theory was that AMD and Nvidia would both ship substantially more ROPs this year to ensure that they didn’t become fill-rate limited in VR or at higher resolutions. Nvidia chose to deal with this another way, which we’ll explore shortly.

The GTX 1080 packs 25% more cores and 25% more texture units than the GTX 980 it replaces, along with a much higher base clock (1.61GHz vs. 1.1GHz for Maxwell) and significantly faster RAM (320GB/s of memory bandwidth, compared to 224GB/s for Maxwell). On paper, the 1080 looks much more like the 980 Ti. In practice, it often outperforms that card.

Memory bandwidth, memory compression
Pascal uses GDDR5X to increase its memory bandwidth, but Nvidia chose to stick with a 256-bit memory bus rather than the 384-bit bus that the GTX 980 Ti and GTX Titan X use. The secret sauce in Nvidia’s recipe? A more advanced version of the same delta color compression techniques that Maxwell used.

When Nvidia launched Maxwell, it claimed that Maxwell reduced its memory bandwidth needs by 19-30% over Kepler, depending on the game in question. Pascal delivers a further 11-28% improvement over Maxwell, again depending on the game in question. Considering that the GTX 1080 already offers 43% more bandwidth than the GTX 980, the added color compression is icing on the cake — or a smart way to ensure the card can scale to 4K or even beyond, depending on your point of view.

The outrageously pink image above shows the difference between Maxwell and Pascal’s color compression. You can see that Maxwell already does a pretty good job of compressing the frame, but there are still a significant number of areas where Maxwell couldn’t compress the data.

Here’s Pascal’s version of the same frame. If you’re thinking “Wow, that’s really pink,” you’re on the right track. What this means is that Pascal can extract memory compression savings from a much larger percentage of the frame than Maxwell could. In the long run there’s an inevitable diminishing marginal return to saving bandwidth via compression, but the feature worked extremely well in Maxwell and should give Pascal an additional edge in 4K gaming.

Display support, media encode/decode, and SLI bridges
Nvidia has announced that the GTX 1080 will support a maximum resolution of 7680×4320 at 60Hz if two DisplayPort 1.3 connectors are used to drive the display. The GPU is only certified for DP 1.2 but is listed as DP 1.3 and 1.4 “ready.” HDMI 2.0b and HDCP 2.2 are both supported as well. Media standard support has a few new bells and whistles that previous Maxwell cards lacked. Pascal now supports full encode and decode in both H.265 and 10-bit H.265. 12-bit (decode-only) is also supported, as is hardware decode for Google’s VP-9 codec.

Those of you who are familiar with multi-GPU configurations are also aware that Nvidia has previously used SLI bridges to connect one GPU to another. AMD abandoned this approach back in 2013 when it launched Hawaii; AMD GPUs now connect directly over the  

PCI Express 3.0 bus. Nvidia is still sticking with bridges for Pascal and GP104, but this time it’s introducing a new, higher-bandwidth bridge standard for modern GPUs. Existing bridges should function well up to 2560×1440 @ 60Hz, but if you want >60Hz refresh rates or to run SLI in 4K or 5K mode, you’ll see top performance if you use newer bridges (Nvidia did note that its LED bridges are still rated for anything up to 5K). It’s not entirely clear if older “stiff” bridges are limited the same way as the older “floppy” bridges (cross-GPU bandwidth was lower on the flexible bridges than on their “stiff” counterparts.)

This slide shows the difference in Shadows of Mordor between the old and new bridge. It’s important to note that this dramatic difference was captured in 4K Surround mode, which means three 4K displays running the same game for a total resolution of 15360×2160. While the new bridges are much smoother than the old ones, the game itself doesn’t maintain a playable frame rate at these resolutions and detail settings. Lower resolutions and detail settings might not show the same gains.

Asynchronous compute
Asynchronous compute has been a hotly debated topic ever since Ashes of the Singularity debuted and showed AMD holding an advantage over Nvidia, ostensibly due to this particular capability. While that situation is rather more nuanced and game-specific, there are going to be a number of questions regarding how Pascal stacks up to the competition.

According to Nvidia, GP104 improves on Maxwell in some significant ways. Maxwell was only capable of performing draw-level preemption and could only switch to a different workload at a draw call boundary. What this meant practically was that there were significant penalties to running a mixed compute + graphics workload, and we saw that reflected in Maxwell’s performance when significant asynchronous workloads were running.

Unlike Maxwell, Pascal can perform much finer-grained preemption. In graphics workloads, it can preempt at the pixel level, flush the shader pipeline, and switch to compute. In compute workloads it can swap at the instruction level and return to doing graphics work. Nvidia claims that this takes 100 microseconds or less, and while the company didn’t offer competing figures for Maxwell, it should be significantly faster than what we saw last generation.

Asynchronous compute isn’t a feature most games rely on yet (Ashes of the Singularity is something of an exception), and we can’t deep dive into the question until we’ve got hardware. What I can say is that while Pascal significantly improves on Maxwell’s capabilities, it doesn’t offer the same set of compute capabilities that GCN does. The larger question is whether or not the difference between what the two companies support will have an impact on future DirectX 12 titles. The fact that Nvidia holds an estimated 75-80% of the gaming market is itself a powerful argument that developers should focus on building engines that cater to Nvidia’s architectures and GPU capabilities more so than AMD’s. At the same time, however, some developers have predicted that game engines may shift workloads towards compute engines no matter what — and that could potentially work in AMD’s favor in future DX12 titles.

I’ve always recommended evaluating GPUs based on the games you’re playing now, not the titles you might be playing in 12-24 months, and it’s difficult to predict how game engines might change in the next few years. At minimum, the changes Nvidia has made to Pascal should significantly reduce any asynchronous compute penalty relative to Maxwell. At best, we should see Pascal picking up some performance improvements from async compute, including in games where Maxwell took a performance hit.

The graph above shows how far the GTX 1080 has come relative to its predecessor, but there’s one caveat worth mentioning. According to Oxide, asynchronous compute is disabled on Nvidi cards by default, which means these test results may not tell us if Pascal actually benefits from async compute just yet. One final note: When Nvidia demoed its async compute capability at Austin, it did so using DirectX 11, not DX12. We weren’t able to discover more information on why it chose to demo using the older API, or what the performance ramifications were for that scenario.

Wrapping it all up
Pascal is a significant leap forward for Nvidia, thanks to a combination of higher clocks, increased core counts, and improved efficiency. The company is forecasting significant gains over and above GTX 980 in both traditional gaming and VR, with particularly impressive boosts arriving for VR titles. While we can’t speak to that specifically just yet, the on-paper gains are substantial.

One difference about this launch, however, is that AMD and Nvidia are taking very different approaches to the market. Nvidia has chosen to launch high-end parts first, with the GTX 1080 and 1070 taking over for the 980 Ti, 980, and GTX 970. AMD, in contrast, will launch an efficiency-focused GPU first, with Polaris 10 and 11 targeting the budget and mainstream segments in both mobile and desktop. This is the first time in a long while that the two companies have taken this approach, and it’ll be interesting to see how they compare in their respective brackets. It’s still not clear if Pascal’s VR performance gains will require substantial optimization or not, but VR enthusiasts who held off buying a new GPU when Oculus and Vive launched should be well-rewarded for their patience.

Current reviews show the GTX 1080 outperforming the GTX 980 by 25-35%, which is in-line with our expectations. This launch is going to put serious pressure on AMD to reduce the price of its Fury products — the non-Founders variant of the 1080 will sell for $500, which puts it head-to-head against Fury and Nano.

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That mighty thud was CERN dropping 300TB of raw collider data to the Internet

 

 

Most of what CERN does sounds like the rarefied heights of sci-fi, accessible only to physicists with badges and pocket protectors, or academics who use esoteric software — not to mere mortals like you and me. What even happens in an atom smasher? CERN is hunting answers to big questions like what dark matter is and why there’s so much of it, why the fundamental forces seem to merge into one at extreme temperatures, why gravity behaves as it does, and other big-topic questions that seem to have one foot firmly in the realm of philosophy.

The LHC has been generating huge amounts of data for release to the general public since its first successful run in 2010. Continuing this trend, CERN just put out another big chunk of data for public analysis, some 300TB of partially organized results from the LHC’s operations since 2014 (when they did their first big data dump). CERN hopes to engage the curiosity of physicists around the world, whether amateur, academic or professional, and get them learning about particle physics and doing hands-on data analysis from their experiments. Many of the LHC’s current experiments and projects have a crowd-sourced component, relying on distributed computing like folding@home or SETI@home do. There are several experimental collider datasets on the CERN open data site that anyone can download. The LHC@home springboard page provides an overview of the distributed computing projects the LHC is currently involved in, including the LHCb, ATLAS, and ALICE.

The ATLAS project is probing for fundamental particles like the Higgs Boson, as well as looking for information about dark matter and extra dimensions. It records the path, energy and identity of particles traveling through the collider and then performs offline event reconstruction based on the data banked from the ATLAS detectors. This turns the raw stream of numbers into recognizable things like photons and leptons so that they can be analyzed. (If you’re a little rusty on your quantum chromodynamics, CERN put out a PDF primer about the LHC that should help to get you up to speed.) Since the ATLAS detectors create petabytes of data during each experiment, the project needs a substantial amount of computational muscle to run reconstructions and, in their words, extract physics from the data. They specifically call out to grad students — for physics majors trying to come up with a master’s project, it might not hurt to get involved with ATLAS.

ALICE is a different ball of wax. Where the ATLAS experiment deals with colliding protons and tries to identify particles, the ALICE detector studies conditions like those found just after the Big Bang. For part of each operating year, the LHC fires lead ions instead of protons, creating conditions so extreme that protons and neutrons in the lead nuclei can “melt,” freeing constituent quarks and gluons from their mutual bond and creating a quark-gluon plasma. It is the ALICE project’s mission to explore what happens to particles under these extreme conditions, leading to insights on the nature of matter and the birth of the universe.

Prototyping is in process for the High-Luminosity upgrade to the LHC, which will create a sort of broadband production of data so that particle physics, which relies on statistics, can prod the Standard Model faster. This is a massive, profoundly collaborative project. The upgrade itself relies on innovations in several fields including magnetics, optics and superconductors. It’ll have a set of shiny quadrupole superconducting niobium-tin magnets, used to better focus the particle beam, and new high-temperature superconducting electrical lines capable of supporting currents of record intensities.

Once they fire up the HL-LHC, the volume of data produced will make 300TB look as small as the particles they’re accelerating. It’s expected to start operation about 2025, but we’ll be watching their updates the whole way.

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