Lawsuit Against Nintendo Switch Claims Infringement, Seeks To Halt Sales

Gamevice, the company behind a variety of controller solutions for iPhones, iPads, and other mobile devices, is attempting to sue Nintendo over the Nintendo Switch for a second time.

According to GoNintendo, Gamevice cites similarities between the Nintendo Switch and its own mobile controllers, many of which are also designed around the idea of split controller that connect to your phone or tablet on either side. The Switch’s design is similar, and Gamevice claims it infringes on their own patents in the US. It’s seeking a legal order to prevent the import of Switch consoles from Japan.

This is familiar territory for Nintendo. Gamevice has previously attempted to sue the company for a similar cause, citing infringement on 19 of its patents. The Patent Trial and Appeal Board ruled in favor of Nintendo in that case (a ruling which Gamevice is still appealing), but will now need to preside over a separate case with similar circumstances. If successful, Gamevice could prevent the sale of Nintendo Switch consoles in the US.

That would be disastrous for Nintendo, considering how popular the console is in the West. Right now, it’s still difficult to find Switch stock anywhere (although we can help with that) given how popular its latest exclusive, Animal Crossing: New Horizons, is. It might be a while before anything comes of this legal action, if ever, so don’t panic purchase a Switch just yet.

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Google’s AI teaches robots how to move by watching dogs

Google researchers developed an AI system that learns from the motions of animals to give robots greater agility, reveals a preprint paper and blog post published this week. The coauthors believe their approach could bolster the development of robots that can complete tasks in the real world, for instance transporting materials between multilevel warehouses and fulfillment centers.

The teams’ framework takes a motion capture clip of an animal — a dog, in this case — and uses reinforcement learning, a training technique that spurs software agents to complete goals via rewards, to train a control policy. Providing the system with different reference motions enabled the researchers to “teach” a four-legged Unitree Laikago robot to perform a range of behaviors, they say, from fast walking (at a speed of up to 2.6 miles per hour) to hops and turns.

To validate their approach, the researchers first compiled a data set of real dogs performing various skills. (Training largely took place in a physics simulation so that the pose of the reference motions could be closely tracked.) Then, by using the different motions in the reward function (which describes how agents out to behave), the researchers trained with about 200 million samples a simulated robot to imitate motion skills.

Google robot simulation

But simulators generally provide only a coarse approximation of the real world. To address this, the researchers employed an adaptation technique that randomized the dynamics in the simulation, for example varying physical quantities like the robot’s mass and friction. These values were mapped using an encoder to a numerical representation — i.e., an encoding — which was passed as an input to the robot control policy. When deploying the policy to a real robot, the researchers removed the encoder and searched directly for a set of variables that allowed the robot to successfully execute skills.

The team says that they were able to adapt a policy to the real world using less than 8 minutes of real-world data across approximately 50 trials. Moreover, they demonstrated that the real-world robot learned to imitate various motions from a dog, including pacing and trotting, as well as artist-animated keyframe motions like a dynamic hop-turn.

“We show that by leveraging reference motion data, a single learning-based approach is able to automatically synthesize controllers for a diverse repertoire [of] behaviors for legged robots,” wrote the coauthors in the paper. “By incorporating sample efficient domain adaptation techniques into the training process, our system is able to learn adaptive policies in simulation that can then be quickly adapted for real-world deployment.”

Google robot simulation

The control policy wasn’t perfect — owing to algorithmic and hardware limitations, it couldn’t learn highly dynamic behaviors like large jumps and runs and it wasn’t as stable as the best manually-designed controllers. (In 5 episodes for a total of 15 trials per method, the real-world robot fell on average after 6 seconds while pacing; after 5 seconds while backward trotting; 9 seconds while spinning; and 10 seconds while hop-turning.) The researchers leave to future work improving the robustness of the controller and developing frameworks that can learn from other sources of motion data, such as video clips.

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The ‘least-privilege’ model protects digital assets and user productivity

Presented by Nutanix

The way we work has morphed into a diverse and complex model. It’s one that opens up new cybersecurity challenges, particularly in the area of user identity.

Gone are the days when employees convened each morning in a common facility that housed all the equipment, supplies, and data necessary to do their jobs. And they no longer routinely turn off the light some eight hours later and head for home, with no need to access those data resources again until the next workday.

Instead, modern employees may be located in any number of highly distributed branch offices that cross geographies, times zones, and cultures. They might be working while on the road visiting customers or attending a conference. And as we have all become acutely aware, a disaster or pandemic may suddenly mandate prolonged remote work from a home office.

Regardless of where they are and the circumstances, workers require access to the data and applications fundamental to performing their jobs. Some even need it 24/7. It’s IT’s job to provide that access, while at the same time making sure that people can only get to the data they really need, so as to curtail growing threats associated with overprivileged access.

Diverse users, data, devices pose new challenges

Controlling access is getting trickier by the day. There’s an increasing cast of characters to support, moving beyond traditional employees to include contractors, suppliers, and business partners, each with their own set of access requirements and restrictions. These folks no longer conveniently share a single, contained local network in a common location that can be physically locked down.

Not only are today’s users now highly distributed, so are corporate data and applications, which may run across on-prem infrastructure, private-managed clouds, and public cloud services. Even the client devices are far from standard: Users are requesting access from different makes of tablets, smartphones, laptops, desktops, and workstations.

Given all these variables, the issue of identity has come under fresh scrutiny. It once made sense to group users with similar roles and provide a set of network access rights to the whole group, such as with virtual LANs (VLANs). Now, many organizations are further restricting access, down to the individual employee, contractor, supplier, or partner. And those rights might depend on what device or what access network the employee is using at the time, as some are more secure than others.

One reason for these changes is to help prevent the internal misuse of data: 34% of data beaches in 2019 involved an internal user, according to the Verizon 2019 Data Breach Investigations Report. In addition, companies don’t want overprivileged users to become targets for hackers seeking to piggyback on their access credentials to gain entry into the corporate network: Nearly a third of data breaches in 2019 (29%) involved stolen user credentials, according to Verizon.

In addition to moving to stronger user authentication methods, which might include secure cards or biometrics as well as passwords, companies are starting to embrace a “zero trust” security model. This uses the “least privilege” principle, which narrowly defines user access rights.

Embrace least privilege controls

Simply put, least privilege controls restrict access rights to the minimum each user needs to perform their job. That means no more liberally doling out Domain Admins rights in Active Directory, root-level access to operating systems, and administrator-level access to the corporate virtualization infrastructure, among other changes.

Still, achieving least-privilege access control is not as simple as you might think. It’s fairly common, for instance, for employees to move in and out of different roles within an organization. It’s critical that their access privileges adjust accordingly with each change, which can be onerous for lean or overworked IT shops.

Access privileges should be revoked and reassigned each time—and rescinded permanently when workers leave the company. If not, privileges could accumulate to the point where an individual has far greater access than appropriate. That opens the door to employee misuse, and can make users targets of hackers seeking extensive access into your corporate data.

Ways to implement

There are several ways to implement least privilege, which is really more about your internal policies than any one particular technology. First and foremost, it’s time to move away from the mindset of “keeping out the bad guys” at the network perimeter, which no longer physically exists. From there, you need to identify the most important data to protect against theft, misuse, destruction, or any combination. Once you make these decisions, you can build an architecture to set and enforce the granular least privilege policies needed to protect these assets.

Firewalls and VPNs. One approach to executing least privilege security is to put the entire corporate network outside the firewall, forcing all users to connect through a virtual private network (VPN). Using this method specifies grant/deny permissions very narrowly for any remotely accessible applications and services.

Virtual Desktop infrastructure (VDI). Another way to enforce least privilege is by using virtual desktop infrastructure, a proven technology. With VDI, data and applications reside centrally, where they’re more easily safeguarded. Remote users log in over a network using web browsers or thin clients. The desktop feels local to the user, but is actually managed and safeguarded by IT and security teams. Based on a user’s identity, desktop security controls and network policy can be configured to ensure that users can only access resources that they’re entitled to use.

Striking the right balance

It can be challenging for network administrators to determine how to create policies that don’t hinder worker productivity but still maximize protections against unauthorized access.

The most important first step is deciding what to protect, using network- and user-based access controls. The technology used to create the rules, likely some combination of Active Directory, VDI, VPNs, and firewalls, is secondary to making those decisions.

Finally, organizations have to be vigilant about enforcement. Automation combined with identity-based policy can help streamline operations and tasks like employee onboarding, role/job shifts, and other events that require user permissions to be altered. Nonetheless, it’s a best practice to avoid a “set and forget” mindset. By strictly limiting who can access critical systems and revisiting this plan regularly, you reduce the risk of unintentional or malicious data misuse and theft.

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A Fantastic RTX 2070 Graphics Card Is Discounted Right Now

With all of the visually stunning games on the way this year, such as Resident Evil 3 and Cyberpunk 2077, upgrading your PC can sound rather enticing, especially now that most of us are stuck inside. Over the past couple of weeks, Newegg has had a number of great deals that help with that, and their latest is one of the best. The Gigabyte RTX 2070 8GB is currently $380 with promo code 4NFJSPC54.

The RTX 2070 is great for 1440p gaming and shines at 1080p, especially if you’re looking for frame rates that exceed 60 FPS. I currently have a comparable RTX 2070 and run the vast majority of games at High to Ultra settings at 1440p with frame rates ranging from 60-144 FPS. Of course, performance will vary based on the rest of your PC’s hardware. Gigabyte’s RTX 2070 features three DisplayPort outputs and a fourth for HDMI, so make sure you have the appropriate cables if you’re looking to hook up multiple monitors.

Gigabyte RTX 2070 8GB

$380 with promo code 4NFJSPC54

Newegg has a number of other good deals as well. There’s also a great deal on an AMD Ryzen 7 3700X processor for $299, which comes with three months of Xbox Game Pass for PC. Be sure to check out Newegg for the rest of the great PC hardware deals.

If you’re looking for some games to play on your PC, you should check out all of the free games you can claim right now. Several developers have made their games free to help support those staying at home during the COVID-19 pandemic.

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New High-End Gaming Laptops Revealed By MSI

MSI has revealed its upcoming slate of gaming laptops, which all come equipped with 10th-gen Intel Core processors and Nvidia RTX Super graphics cards. The new laptops include the GS66 Stealth, GE66 Raider, and Creator 17. The company states that these laptops were made with gamers and content creators in mind, with specs that can easily handle tasks like gaming and rendering high-quality video.

The GS66 Stealth is available now for pre-order, while the GE66 Raider and Creator 17 laptops will launch online on April 15. Each laptop comes with a TN display with the option to switch out for an IPS-level panel, which provides brighter and better colours.

GS66 Stealth gaming laptop

Starting at $1,599; Available for pre-order now

The GS66 Stealth has a discrete look with an all-black chassis, making it more low-key than most gaming laptops. It can boast up to an i9-10980HK processor with an RTX Super graphics card. Whichever specs you choose, you’ll get a 15.6-inch, 1080p display with a 300Hz refresh rate. Pairing an i9-10980HK with an RTX 2080 Super should produce some impressive results on that 300Hz display. It also comes with two NVMe SSD slots and a per-key RGB-lit gaming keyboard by SteelSeries as well as a Cooler Boost Trinity+ cooling system and a 99.9Whr battery.

GE66 Raider gaming laptop

Starting at $1,799 on April 15

The GE66 Raider looks more like a gaming laptop than the GS66 Stealth, though it doesn’t stand out as particularly garish. It features the same range of specs as the GS66 Stealth as well, including up to an i9-10980HK processor and RTX 2080 Super as well as the 15.6-inch, 1080p display with the 300Hz refresh rate. It’s all supported by the same 99.9Whr battery and same RGB-lit keyboard. It also boasts an RGB light display on the front of the laptop’s base.

Creator 17 gaming laptop

Starts at $1,799 on April 15

MSI’s Creator 17 laptop is more focused on being a great workstation machine for video editors and producers–though it’s also capable of gaming. You can pair it with up to an i7-10875H processor and an RTX Super series graphics card. It features a 17.3-inch, 4K display that’s MiniLED-lit and HDR 1000-capable. It also comes with a white backlit keyboard.

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“Human-layer” cybersecurity and AI thwart data breaches (VB Live)

Presented by Tessian, Inc.

Data breaches are at an all-time high, because traditional cybersecurity methods just can’t predict human behavior. Learn how stateful machine learning delivers a robust, people-centric approach to cybersecurity when you access this free VB Live event!

Access on demand here!

Over the past decade companies have been boosting their cybersecurity budgets and investments, yet data breaches are still on the rise. The reason? Human error. From breached customer and client records to phishing attacks that lead to compromised systems or direct wire fraud, human-layer issues are the number-one cause of data breaches. In fact, 88% of data breaches reported to the UK’s information commissioner’s office were caused by human factors.

“Organizations are really only as secure as the gatekeepers to these digital systems and data,” says Ed Bishop, co-founder and chief technology officer at Tessian. “No matter the industry or sector you might be in, if you have people controlling systems and data within your organization, you have human-layer vulnerabilities.”

These human layer attacks impact companies both financially and from a reputtion standpoint. For example, publicly traded companies suffer an average of a 7.5% drop in their share price after a data breach. It’s an instant reflection of loss of confidence, loss of reputation, and has an impact on business continuity going forward.

Why are employees your biggest vulnerability?

These breaches are increasing as the business world goes digital, employees are increasingly distributed, and email remains the main artery of communication for the human layer. It’s where some of the most sensitive information in an organization is shared, and yet there are still very few security checks in place. Ease of access to emails has only increased, with employees shooting out messages from laptops, smartphones, tablets, and now even watches. With the volume and speed of information transactions increasing, workers are simply more prone to making errors.

“Just think about how easy it is to misdirect an email when you’re in a rush, or how easy it is to click on a link from a sender that seems legit at a quick glance,” Bishop says. “Ultimately people are human and everyone makes mistakes, but until people start embracing that concept, this problem will just keep growing.”

Companies are simply too focused on protecting the machine layer, when it’s people that make up a company’s most important security layer. The only solution is to build technology that can protect company data by identifying and preventing attacks aimed at employees.

“The conversation has to move beyond blaming employees for accidental data loss or being prey to phishing attempts, to how can technology empower users to feel safe in their environment,” he explains.

Where traditional cybersecurity methods fall short

Protecting company data requires a layered approach, in four parts: removing access to data and systems, adding security policies, boosting training and awareness, and adding a technological solution aimed at detecting and preventing human error.

Traditional cybersecurity methods rely broadly on rule-based technologies. This is great for capturing threats that can essentially be codified into if-this-then-that logic. For example, if the email says “internal only” in the subject and it’s getting sent externally, an algorithm can detect the breach and warn the user.

However rule-based approaches aren’t intelligent, can flag too often, and create too much noise. They’ll ultimately end up affecting the productivity and effectiveness of the employees that they’re trying to protect. And most importantly, they’re just not able to capture the kind of intricacies of human layer security problems.

The new human-layer security bridges the gap

Machine layer solutions are still essential. But human layer security is the natural next evolution for companies that are trying to innovate in the security space and expand their security protection.

“The reason traditional machine learning models can detect malware is because of the simple fact that malware is always malicious,” Bishop says. “However, with human layer security problems, this is no longer true.”

Everything with humans is dynamic and in flux, he explains. Relationships are formed during the duration of a project, and then they fall away. You worked with a counterpart a lot a year ago, but now it would be highly unusual for that counterpart to email you asking for an invoice to be paid. Traditional machine learning methods are ineffective at solving these human layer security problems, just because they don’t understand how relationships and scenarios change over time. of this concept that they need to understand time. To be effective, a machine learning solution needs to be able to say, at this exact moment in time, for this person and their relationships, does this behavior look unusual? That’s what stateful machine learning can do.

“We think there’s a real opportunity to empower employees with a technology that helps protect them, and spots the most advanced threats,” Bishop says. “Stateful machine learning technology can ultimately give companies the opportunity to build trust with their workers as well as improve security.”

For an in-depth look at what stateful machine learning is and how it works, how to make it the backbone of your people-centric cybersecurity strategy, and more, don’t miss this VB Live event.

Access is free on demand.

Attendees will learn:

  • How stateful machine learning can accurately predict behaviors and detect possible human-made threats before they do damage
  • How technology can prevent data breaches caused by people making mistakes, breaking rules, or being hacked
  • How to empower employees to correct damaging mistakes before they make them


  • Ed Bishop, Co-founder and Chief Technology Officer, Tessian
  • Joe Maglitta, Senior Contributor/Analyst, VentureBeat

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Microsoft’s Mahjong-winning AI could lead to sophisticated finance market prediction systems

Last August, Microsoft Research Asia detailed an AI system dubbed Super Phoenix (Suphx for short) that could defeat Mahjong players after learning from only 5,000 matches. A revised preprint paper out this week delves a bit deeper, revealing that Suphx — whose performance improved with additional training — is now rated above 99.99% of all ranked human players on Tenhou, a Japan-based global online Mahjong competition platform with over 350,000 members.

Building superhuman programs for games is a longstanding goal of the AI research community — and not without good reason. Games are an analog of the real world, with a measurable objective, and they can be played an infinite amount of times across hundreds (or thousands) of powerful machines. Moreover, its researchers assert that the learnings are applicable to other domains, like the enterprise, where mundane but cognitively demanding tasks impact workers’ productivity.

“Most real-world problems such as finance market predication and logistic optimization share the same characteristics with Mahjong — i.e., complex operation/reward rules, imperfect information,” wrote the paper’s coauthors. “We believe our techniques designed in Suphx for Mahjong, including global reward prediction, oracle guiding, and … policy adaptation have great potential to benefit for a wide range of real-world applications.”

Tackling Mahjong

The paper’s coauthors note that Mahjong is an imperfect information game with complicated scoring rules. The loss of one round doesn’t mean a player played poorly; they might tactically lose to ensure they secure the top rank. Plus, Mahjong has a huge number of possible winning hands, and different winning hands result in different winning scores for each round. Taking into account the up to 13 game tiles in each person’s hand, the 14 tiles in the “dead” wall visible throughout the game, and the 70 tiles in the “live” wall that becomes visible once the tiles are drawn and discarded, on average there are more than 1048 hidden states, indistinguishable to players, at any one time.

For these reasons, it’s hard for a Mahjong player — let alone a machine learning model — to decide which moves to make based on private tiles alone. Cognizant of this, the team built Suphx to tackle 4-player Japanese Mahjong (Riichi Mahjong), which has one of the largest Mahjong communities in the world.

Suphx comprises a family of convolutional neural networks, a type of AI model commonly applied to computer vision, and it learns five models to handle different scenarios: the discard, Riichi, Chow, Pong, and Kong models. Based on these, Suphx employs another rule-based model to decide whether to declare a winning hand and take the round, checking whether a winning hand can be formed from a tile discarded by other players or drawn from the wall.

The researchers had to design a set of features to encode game information into channels that could be “digested” by the models, including one for each of the 34 tiles in Japanese Mahjong and four for private player tiles. They also hand-crafted over 100 look-ahead features to indicate the probability and round score of a winning hand if a specific tile was discarded and then a tile from the wall was drawn.

Suphx had a three-step training process. First, all five of its models were trained using the logs of top human players collected from Tenhou’s platform. Then, they were fine-tuned via self-play reinforcement learning, using self-play workers containing a set of CPU-based Mahjong simulators and trajectory-generating GPU-based inference engines. Finally, during online play, run-time policy adaptation is used to leverage observations on the current round to make the system perform even better.

In the reinforcement learning step, every Mahjong simulator randomly initialized a game with Suphx as a player and three other AI opponents. When any of the four players needed to take an action, the simulator sent the current state to the GPU inference engine, which then returned an action to the simulator. Meanwhile, the inference engines pulled the up-to-date policy to ensure that the self-play policy didn’t diverge from the latest policy.

A global reward predictor trained on player log data provided a reward signal by predicting the final game reward, given information about the current round and all previous rounds of the game. It was complemented by an “oracle” agent that sped up training during self-play by training on all perfect information about a state (including players’ private tiles and the tiles in the wall) and gradually discarding those features until it became a “normal” agent.

Suphx continually improves courtesy of an offline-trained policy, which randomly samples private tiles for three opponents and wall times from the pool of tiles (excluding the system’s own tiles) and then generates trajectories. Policy adaptation is performed for each round independently, and it restarts for each subsequent round.

Evaluating Suphx

The team evaluated Suphx on 20 Nvidia Tesla K80 GPUs, sampling 800,000 games from a data set of over a million games exactly 1,000 times. Prior to the experiments, they trained each model using 1.5 million games on 44 GPUs (4 Nvidia Titan XPs for the parameter server and 40 K80s for the self-play workers) over the course of two days.

After playing over 5,760 games against human players on Tenhou, Suphx achieved 10 dan in terms of record — something roughly only 180 players have ever done — and 8.74 dan in terms of stable rank (versus top human players’ 7.4). Anecdotally, the researchers report that Suphx is “very strong” at defense and has very low deal-in rate (10.06%), and that it developed its own playing styles that keep tiles safe and win with half-flushes.

“Looking forward, we will introduce more novel technologies to Suphx, and continue to push the frontier of Mahjong AI and imperfect-information game playing,” said the paper’s coauthors.

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Why Xbox Series X Went With Such A Big Design Change

Throughout gaming history, most consoles have kept to the same basic rectangular shape (with some notable exceptions). Now, Microsoft is throwing a new form factor in the game with the distinctly fridge-shaped Xbox Series X. In an interview with Eurogamer, key members of the design team revealed why the new Xbox is so different.

To give the shortest answer to this question–it’s all about performance, creating an over-powered console without it ending up the size of a regular PC. From the beginning the team knew it would require a different design mindset than any other console. “We knew it was going to be powerful and we knew it was going to require a totally different way of thinking about how to design a console,” principle designer Chris Kujawski said.

The goal for the new console was to double the system’s graphical performance, while keeping it just as quiet as the Xbox One. This technical challenge meant completely rethinking the structure of the machine.

“I like to think about our past generations as having a bit of an exoskeleton, so you have a mechanical structure with electrical shielding all on the outside then you have all the guts in the inside,” said Jim Wahl, Xbox’s director of mechanical engineering. “And so what we did in this generation is that we turned that completely inside out… and so this centre chassis essentially forms the spine, the foundation of this system and then we build things out from there.”

The inside of the console is quite densely packed, but the way the Xbox Series X is designed means this isn’t an impediment to airflow. “It creates what we call a parallel cooling architecture, so you get cool air in – and cool air streams through separate zones of the console,” Wahl said. “You have exhaust out the top and we have large venting holes, but the net effect of putting all of this together, having parallel paths, having this really powerful quiet fan at the top, is that we get 70 per cent more airflow through this console than the past generation and we get 20 per cent more airflow through our heatsink alone than in the past generation.”

Xbox Series X And Xbox One News

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The physical form of the console is then very much defined by the most vital parts within it, and how they fit together. “The ODD [optical disc drive] sets one dimension, the volume of the heat sink sets the other dimension, the height is set by airflow and throughout this kind of complex negotiation of figuring out how this stuff comes together, we landed on a square form factor which we love,” Kujawski explained.

Once this was all put together, it was sent out to focus testers to see how it worked with people’s TV setups, whether it would fit in their existing cabinets. Because the console isn’t quite as flat as previous versions, it can fit on smaller shelves despite being a good deal more chunky. While the console has been dramatically changed, some things still stay the same–like the controller’s reliance on AA batteries.

Have a look at the full interview for more in-depth information on the technical aspects of the Xbox Series X’s construction.

Check out our roundup on everything we know so far about the Xbox Series X, including release date, games, hardware, and price.

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Element AI’s search tool surfaces curated coronavirus studies

Element AI today released a search tool that combs through the COVID-19 Open Research Dataset, a repository of over 44,000 scholarly articles about COVID-19 and related coronaviruses, for papers potentially of use to researchers. Users can search or query natural language terms, phrases, and keywords to surface articles that contain semantically similar content, or copy paragraphs of text or questions into the search bar to return articles with only the most important sentences highlighted.

A deluge of studies on the novel coronavirus, which is projected to sicken millions of people, have made their way to the web in the months since the outbreak began. (According to Reuters, at least 153 preprint studies about COVID-19 have been made publicly available as of March 24.) They promise insights into the virus’ spread, but many haven’t been peer-reviewed, making it difficult for stakeholders to sort the wheat from the chaff.

To this end, Element AI’s tool leverages tech from the company’s Knowledge Scout product, which uses AI to capture the relationships among different pieces of information, to learn and improve over time while building a repository of tacit knowledge. Element AI says that the platform will be progressively updated in the coming weeks with additional COVID-19 data sets, alongside features including open-domain question-answering capabilities, query-driven summarization, and topic discovery.

The launch of Element AI’s platform follows that of Vespa’s CORD-19 Search, which similarly trawls the COVID-19 Open Research Dataset for vetted research papers. For its part, Korea University’s DMIS Lab this month released Covidsearch, which provides real-time question-answering on 31,000 COVID-19-related articles with results that highlight relevant biomedical entities. And the Allen Institute for AI offers a no-frills platform that searches the full text of the COVID-19 Open Research Dataset.

The AI underpinning these and other COVID-19 search tools learn from signals (i.e., data derived from various inputs). Each signal informs the system’s predictions such that it learns how various resources are relevant (or not) to a search query. Natural language processing enables them to understand a piece of research in the context of a data set, while natural language search — a specialized application of AI that creates a “word mesh” from free-flowing text, akin to a knowledge graph — connects similar concepts that are related to larger ideas to return the same answer regardless of how a query is phrased.

The jury is out on how big of an impact semantic search tools might have on continuing COVID-19 research, but as alluded to earlier, they might tamp down on the more questionable research that has come to light. One recent paper suggests a link between the new coronavirus and HIV, while another claims it’s from outer space.

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The Best Switch Joy-Con Alternative For Handheld Mode Is On Sale Right Now

When it comes to playing the Nintendo Switch in handheld mode, it can be a bit difficult for those with big hands. Whether it’s cramps or my hands falling asleep, it’s not a very comfortable experience. Thankfully, there are a lot of great options, including Switch grips and one particularly great controller. The Hori Split Pad Pro was released alongside last year’s Daemon X Machina, but thankfully, it works great with almost every single game on the Switch–and right now, it’s discounted on Amazon.

The Hori Split Pad Pro is currently $40.54 on Amazon right now, down from its original price of $50. It comes with free Prime shipping, though depending on your location, it may take a while to arrive. It’s unclear how long this deal will last, so if you’re interested, it’s best to act sooner rather than later.

Hori Split Pad Pro

$40.54 ($50)

The Split Pad Pro is my preferred way of playing the Switch in handheld mode. Its ergonomic design makes it a more comfortable experience, and its analog sticks are more similar to a standard controller than what the Joy-Cons feature. It also boasts bigger face and shoulder buttons as well as an excellent D-pad and programmable back paddles–and to top it all off, the Switch still fits perfectly into its dock. It’s important to note that these controllers only work in handheld mode.

If you’re interested in more great pads, be sure to check out the best Nintendo Switch controllers for 2020. There are a bunch of excellent options, no matter what your budget or preferred genre is. If you game on the Switch, there’s an awesome controller that’s perfect for you.

More Tech Picks From GameSpot

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