Here's a great resource to learn how Project 2025 will affect you or those you love and care about on specific issues such as health care, food assistance, education, etc. The site is called "25 and Me" and is a collaboration between Rajat Paharia and Google Gemini. — Read the rest
The post "25 and Me" is your guide to the horrors of Project 2025 appeared first on Boing Boing.
In this video, I went over an email update from Citadel Studios. Legends of Aria Classic is coming back and will launch in Steam with a brand new payment model. (Get Rid of Game Lag » https://www.wtfast.com/?fpr=kabalyero37)
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Sword of Convallaria features a code redemption system similar to other Gacha video games that players can use to earn rewards for free. The rewards can offer anything from various resources to unique furniture items for Home Decoration. However, most of these codes are limited and stay active for a couple of weeks whereas, others last for a much longer period.
It is easy to miss out on the codes and lose several precious rewards for free. This guide includes all currently active codes in Sword of Convallaria that all players can redeem.
There are 10 currently active codes available for redemption in Sword of Convallaria four of which are added recently. You will find all the active codes listed with their corresponding rewards below.
NOTE: All the codes in Sword of Convallaria are case-sensitive. Enter the codes in ALL CAPS for a successful redemption.
To redeem the codes and obtain the rewards in Sword of Convallaria, follow the steps mentioned below.
The post All Active Codes in Sword of Convallaria – August 2024 appeared first on Nintendo Smash: Video Games News, Reviews & Guides.
Sword of Convallaria is a Tactical RPG where you take the role of a leader of the mercenary group and strategize to save the nation of Iria. As players begin the adventure, they will get to select the appearance of Voyager (the main protagonist) either male or female. However, the game doesn’t keep players from changing the appearance of Voyager as it can be changed once players have reached Elysium.
This guide will help you change your appearance in Sword of Convallaria.
The appearance options for the main protagonist (Voyager) are only two including male or female. At the start of the adventure, once you have made the primary appearance choice, you will get to play as it is for the tutorial (The Rift). Upon completing the tutorial and reaching the hub Elysium, you will be free to change the appearance but it will cost you 50 Hope Luxites.
To change the appearance of Voyager, select the profile icon on the top left corner of the screen, and then navigate to edit by selecting the icon with a pencil in it. It will give you the options for changing Nickname, Décor, and Appearance. Select the appearance option and then select the male or female appearance for the character. Once done, confirm the change for 50 Hope Luxites and your Voyager appearance will be changed.
Moreover, you can change the appearance at any time but each time it will cost you 50 Hope Luxites which aren’t easy to come by. Hope Luxites is a currency used for various other purposes with Secret Fates being the top ones as they are used for summoning heroes and weapons.
Changing the appearance of Voyager doesn’t change anything in terms of gameplay, abilities, skills, etc. As Voyager only comes to certain main story missions, you will only witness the physical appearance change while playing with it or at the Elysium. Other than that, it is useless to change the appearance and it is best to save as many Hope Luxites as possible because the end-game missions require a party of stronger heroes which can only be formulated by rolling legendary heroes.
The post Sword of Convallaria: How to Change Your Appearance appeared first on Nintendo Smash: Video Games News, Reviews & Guides.
Strengthening your characters/heroes is essential for completing the higher-level missions in Sword of Convallaria, but it cannot be simply achieved by increasing your Voyager Level and leveling up the characters/heroes individually. Additionally, you will be required to get your characters some powerful weapons and gear items to grant a significant boost to their stats.
This guide will help you get the weapons and gear items for the characters in Sword of Convallaria.
Players will obtain weapons and gear items (trinkets and tarot whispers) by progressing in the main story. Upon completing certain main missions in the story, the first-time rewards will offer various gear items. However, most of the awarded gear from the story is only viable until mid-game as the most effective weapons and gear items are attained from rolling the Mighty Weapons banner in the Summon Menu.
To get your hands on the legendary weapons and gear items, roll the Mighty Weapons banner using the Secret Fate currency. It is the same currency used for pulling the Heroes/Characters. Head over to the Summon Menu and scroll down to find the Mighty Weapons event. Select it and summon it to have a chance of getting a legendary weapon/gear item.
However, similar to the heroes, the probability of higher-grade weapons and gear items is lower.
Moreover, you will also see a guaranteed summon after a few roles revealing that you can get a legendary weapon/gear item on a certain amount of summons. In such case, you must have a required amount of Secret Fates as each summons costs 1 Secret Fate.
The post Sword of Convallaria: How to Get Weapons & Gear Items appeared first on Nintendo Smash: Video Games News, Reviews & Guides.
Hope Luxites is a primary currency in Sword of Convallaria that is used for purchasing various items in the shop and recovering stamina for a longer playthrough for the day. However, despite it being the primary currency, you will often find it being low at your disposal. The main use of Hope Luxites is to exchange them for Secret Fates to pull heroes and weapons from the Summon Menu but each Secret Fate costs 150 Hope Luxites.
This guide will help you how to get Hope Luxites in Sword of Convallaria.
Hope Luxites currency is usually obtained by playing the game as several first-time rewards from completing the missions give Hope Luxites. Moreover, various features of the game offer Hope Luxites which can be overlooked by the new players. We have listed down all the methods from which you can obtain Hope Luxites in Sword of Convallaria.
NOTE: The redeemable code for Hope Luxites expires on August 16, 2024.
Moreover, new codes are added to Sword of Convallaria every month, so be on the lookout for any new code offering Hope Luxites before they expire.
The post Sword of Convallaria: How to Get Hope Luxites appeared first on Nintendo Smash: Video Games News, Reviews & Guides.
Secret Fate is an essential currency used for summoning the characters/heroes and weapons in Sword of Convallaria. As pulling your free heroes and weapons plays a crucial role in your early playthrough, getting your hands on as many Secret Fates as possible will help you greatly. Moreover, building a team of high-rarity heroes is recommended in the end-game missions so formulating a team early and leveling them up will make things a lot easier for you.
This guide will help you get Secret Fate and highlight the method of how to use it in Sword of Convallaria.
Players can earn Secret Fates through three different methods in Sword of Convallaria. It is a rare currency and awarded fairly in low amounts, but players who are comfortable with spending real money can get it at any time which is the first method of getting Secret Fates.
Secret Fate currency can be bought from the in-game shop using Hope Luxites. As Hope Luxites is more of a primary currency in Sword of Convallaria, you can exchange it in the ‘Exchange’ tab of the in-game shop for Secret Fate. 1 Secret Fate is equivalent to 150 Hope Luxites and you can purchase Hope Luxites in bundles from the shop.
However, apart from purchasing Hope Luxites, you can also achieve it through generic gameplay such as:
Now, for the free method, players can earn Secret Fates by participating and completing the Promise of Secret Fates event. Currently, this is the only event that is offering Secret Fates as a reward for each level milestone. The event contains three level milestones.
For each milestone, it gives 10x Secret Fate as a reward.
Lastly, another free method through which players can get Secret Fates is by checking the in-game mail. Developers usually send out various rewards to all the players and it is possible that you might receive a few Secret Fates in your mail.
Moreover, you can also redeem codes to get various rewards in your mail, but currently, there are no codes that contain Secret Fates as a reward.
The Secret Fates are used in the Summon Menu located at the bottom of the screen while at in Elysium. In the Summon Menu, you will see different events on the left side from where you will be pulling the heroes and weapons using the Secret Fates. Now, each event has a different probability of pulling a legendary hero or weapon so you will be spending quite a lot of Secret Fates before you get one.
Moreover, some of the events will also show you a guaranteed pull within a certain number of pulls. For instance, you will see Gloria’s guaranteed pull within 20 pulls. It means you have to spend 20 Secret Fates on the corresponding event to get a guaranteed Gloria pull.
The post Sword of Convallaria: How to Get & Use Secret Fate appeared first on Nintendo Smash: Video Games News, Reviews & Guides.
It seems like every time Konami shows off the upcoming Silent Hill 2 remake, something doesn’t sit right with fans. It started with the game’s announcement when it was revealed Layers of Fear and The Medium developer Bloober Team would be helming the reimaging of the seminal survival horror title, which did not…
Dina Genkina: Hi, I’m Dina Genkina for IEEE Spectrum‘s Fixing the Future. Before we start, I want to tell you that you can get the latest coverage from some of Spectrum‘s most important beats, including AI, climate change, and robotics, by signing up for one of our free newsletters. Just go to spectrum.ieee.org/newsletters to subscribe. And today our guest on the show is Suraj Bramhavar. Recently, Bramhavar left his job as a co-founder and CTO of Sync Computing to start a new chapter. The UK government has just founded the Advanced Research Invention Agency, or ARIA, modeled after the US’s own DARPA funding agency. Bramhavar is heading up ARIA’s first program, which officially launched on March 12th of this year. Bramhavar’s program aims to develop new technology to make AI computation 1,000 times more cost efficient than it is today. Siraj, welcome to the show.
Suraj Bramhavar: Thanks for having me.
Genkina: So your program wants to reduce AI training costs by a factor of 1,000, which is pretty ambitious. Why did you choose to focus on this problem?
Bramhavar: So there’s a couple of reasons why. The first one is economical. I mean, AI is basically to become the primary economic driver of the entire computing industry. And to train a modern large-scale AI model costs somewhere between 10 million to 100 million pounds now. And AI is really unique in the sense that the capabilities grow with more computing power thrown at the problem. So there’s kind of no sign of those costs coming down anytime in the future. And so this has a number of knock-on effects. If I’m a world-class AI researcher, I basically have to choose whether I go work for a very large tech company that has the compute resources available for me to do my work or go raise 100 million pounds from some investor to be able to do cutting edge research. And this has a variety of effects. It dictates, first off, who gets to do the work and also what types of problems get addressed. So that’s the economic problem. And then separately, there’s a technological one, which is that all of this stuff that we call AI is built upon a very, very narrow set of algorithms and an even narrower set of hardware. And this has scaled phenomenally well. And we can probably continue to scale along kind of the known trajectories that we have. But it’s starting to show signs of strain. Like I just mentioned, there’s an economic strain, there’s an energy cost to all this. There’s logistical supply chain constraints. And we’re seeing this now with kind of the GPU crunch that you read about in the news.
And in some ways, the strength of the existing paradigm has kind of forced us to overlook a lot of possible alternative mechanisms that we could use to kind of perform similar computations. And this program is designed to kind of shine a light on those alternatives.
Genkina: Yeah, cool. So you seem to think that there’s potential for pretty impactful alternatives that are orders of magnitude better than what we have. So maybe we can dive into some specific ideas of what those are. And you talk about in your thesis that you wrote up for the start of this program, you talk about natural computing systems. So computing systems that take some inspiration from nature. So can you explain a little bit what you mean by that and what some of the examples of that are?
Bramhavar: Yeah. So when I say natural-based or nature-based computing, what I really mean is any computing system that either takes inspiration from nature to perform the computation or utilizes physics in a new and exciting way to perform computation. So you can think about kind of people have heard about neuromorphic computing. Neuromorphic computing fits into this category, right? It takes inspiration from nature and usually performs a computation in most cases using digital logic. But that represents a really small slice of the overall breadth of technologies that incorporate nature. And part of what we want to do is highlight some of those other possible technologies. So what do I mean when I say nature-based computing? I think we have a solicitation call out right now, which calls out a few things that we’re interested in. Things like new types of in-memory computing architectures, rethinking AI models from an energy context. And we also call out a couple of technologies that are pivotal for the overall system to function, but are not necessarily so eye-catching, like how you interconnect chips together, and how you simulate a large-scale system of any novel technology outside of the digital landscape. I think these are critical pieces to realizing the overall program goals. And we want to put some funding towards kind of boosting that workup as well.
Genkina: Okay, so you mentioned neuromorphic computing is a small part of the landscape that you’re aiming to explore here. But maybe let’s start with that. People may have heard of neuromorphic computing, but might not know exactly what it is. So can you give us the elevator pitch of neuromorphic computing?
Bramhavar: Yeah, my translation of neuromorphic computing— and this may differ from person to person, but my translation of it is when you kind of encode the information in a neural network via spikes rather than kind of discrete values. And that modality has shown to work pretty well in certain situations. So if I have some camera and I need a neural network next to that camera that can recognize an image with very, very low power or very, very high speed, neuromorphic systems have shown to work remarkably well. And they’ve worked in a variety of other applications as well. One of the things that I haven’t seen, or maybe one of the drawbacks of that technology that I think I would love to see someone solve for is being able to use that modality to train large-scale neural networks. So if people have ideas on how to use neuromorphic systems to train models at commercially relevant scales, we would love to hear about them and that they should submit to this program call, which is out.
Genkina: Is there a reason to expect that these kinds of— that neuromorphic computing might be a platform that promises these orders of magnitude cost improvements?
Bramhavar: I don’t know. I mean, I don’t know actually if neuromorphic computing is the right technological direction to realize that these types of orders of magnitude cost improvements. It might be, but I think we’ve intentionally kind of designed the program to encompass more than just that particular technological slice of the pie, in part because it’s entirely possible that that is not the right direction to go. And there are other more fruitful directions to put funding towards. Part of what we’re thinking about when we’re designing these programs is we don’t really want to be prescriptive about a specific technology, be it neuromorphic computing or probabilistic computing or any particular thing that has a name that you can attach to it. Part of what we tried to do is set a very specific goal or a problem that we want to solve. Put out a funding call and let the community kind of tell us which technologies they think can best meet that goal. And that’s the way we’ve been trying to operate with this program specifically. So there are particular technologies we’re kind of intrigued by, but I don’t think we have any one of them selected as like kind of this is the path forward.
Genkina: Cool. Yeah, so you’re kind of trying to see what architecture needs to happen to make computers as efficient as brains or closer to the brain’s efficiency.
Bramhavar: And you kind of see this happening in the AI algorithms world. As these models get bigger and bigger and grow their capabilities, they’re starting to introduce things that we see in nature all the time. I think probably the most relevant example is this stable diffusion, this neural network model where you can type in text and generate an image. It’s got diffusion in the name. Diffusion is a natural process. Noise is a core element of this algorithm. And so there’s lots of examples like this where they’ve kind of— that community is taking bits and pieces or inspiration from nature and implementing it into these artificial neural networks. But in doing that, they’re doing it incredibly inefficiently.
Genkina: Yeah. Okay, so great. So the idea is to take some of the efficiencies out in nature and kind of bring them into our technology. And I know you said you’re not prescribing any particular solution and you just want that general idea. But nevertheless, let’s talk about some particular solutions that have been worked on in the past because you’re not starting from zero and there are some ideas about how to do this. So I guess neuromorphic computing is one such idea. Another is this noise-based computing, something like probabilistic computing. Can you explain what that is?
Bramhavar: Noise is a very intriguing property? And there’s kind of two ways I’m thinking about noise. One is just how do we deal with it? When you’re designing a digital computer, you’re effectively designing noise out of your system, right? You’re trying to eliminate noise. And you go through great pains to do that. And as soon as you move away from digital logic into something a little bit more analog, you spend a lot of resources fighting noise. And in most cases, you eliminate any benefit that you get from your kind of newfangled technology because you have to fight this noise. But in the context of neural networks, what’s very interesting is that over time, we’ve kind of seen algorithms researchers discover that they actually didn’t need to be as precise as they thought they needed to be. You’re seeing the precision kind of come down over time. The precision requirements of these networks come down over time. And we really haven’t hit the limit there as far as I know. And so with that in mind, you start to ask the question, “Okay, how precise do we actually have to be with these types of computations to perform the computation effectively?” And if we don’t need to be as precise as we thought, can we rethink the types of hardware platforms that we use to perform the computations?
So that’s one angle is just how do we better handle noise? The other angle is how do we exploit noise? And so there’s kind of entire textbooks full of algorithms where randomness is a key feature. I’m not talking necessarily about neural networks only. I’m talking about all algorithms where randomness plays a key role. Neural networks are kind of one area where this is also important. I mean, the primary way we train neural networks is stochastic gradient descent. So noise is kind of baked in there. I talked about stable diffusion models like that where noise becomes a key central element. In almost all of these cases, all of these algorithms, noise is kind of implemented using some digital random number generator. And so there the thought process would be, “Is it possible to redesign our hardware to make better use of the noise, given that we’re using noisy hardware to start with?” Notionally, there should be some savings that come from that. That presumes that the interface between whatever novel hardware you have that is creating this noise, and the hardware you have that’s performing the computing doesn’t eat away all your gains, right? I think that’s kind of the big technological roadblock that I’d be keen to see solutions for, outside of the algorithmic piece, which is just how do you make efficient use of noise.
When you’re thinking about implementing it in hardware, it becomes very, very tricky to implement it in a way where whatever gains you think you had are actually realized at the full system level. And in some ways, we want the solutions to be very, very tricky. The agency is designed to fund very high risk, high reward type of activities. And so there in some ways shouldn’t be consensus around a specific technological approach. Otherwise, somebody else would have likely funded it.
Genkina: You’re already becoming British. You said you were keen on the solution.
Bramhavar: I’ve been here long enough.
Genkina: It’s showing. Great. Okay, so we talked a little bit about neuromorphic computing. We talked a little bit about noise. And you also mentioned some alternatives to backpropagation in your thesis. So maybe first, can you explain for those that might not be familiar what backpropagation is and why it might need to be changed?
Bramhavar: Yeah, so this algorithm is essentially the bedrock of all AI training currently you use today. Essentially, what you’re doing is you have this large neural network. The neural network is composed of— you can think about it as this long chain of knobs. And you really have to tune all the knobs just right in order to get this network to perform a specific task, like when you give it an image of a cat, it says that it is a cat. And so what backpropagation allows you to do is to tune those knobs in a very, very efficient way. Starting from the end of your network, you kind of tune the knob a little bit, see if your answer gets a little bit closer to what you’d expect it to be. Use that information to then tune the knobs in the previous layer of your network and keep on doing that iteratively. And if you do this over and over again, you can eventually find all the right positions of your knobs such that your network does whatever you’re trying to do. And so this is great. Now, the issue is every time you tune one of these knobs, you’re performing this massive mathematical computation. And you’re typically doing that across many, many GPUs. And you do that just to tweak the knob a little bit. And so you have to do it over and over and over and over again to get the knobs where you need to go.
There’s a whole bevy of algorithms. What you’re really doing is kind of minimizing error between what you want the network to do and what it’s actually doing. And if you think about it along those terms, there’s a whole bevy of algorithms in the literature that kind of minimize energy or error in that way. None of them work as well as backpropagation. In some ways, the algorithm is beautiful and extraordinarily simple. And most importantly, it’s very, very well suited to be parallelized on GPUs. And I think that is part of its success. But one of the things I think both algorithmic researchers and hardware researchers fall victim to is this chicken and egg problem, right? Algorithms researchers build algorithms that work well on the hardware platforms that they have available to them. And at the same time, hardware researchers develop hardware for the existing algorithms of the day. And so one of the things we want to try to do with this program is blend those worlds and allow algorithms researchers to think about what is the field of algorithms that I could explore if I could rethink some of the bottlenecks in the hardware that I have available to me. Similarly in the opposite direction.
Genkina: Imagine that you succeeded at your goal and the program and the wider community came up with a 1/1000s compute cost architecture, both hardware and software together. What does your gut say that that would look like? Just an example. I know you don’t know what’s going to come out of this, but give us a vision.
Bramhavar: Similarly, like I said, I don’t think I can prescribe a specific technology. What I can say is that— I can say with pretty high confidence, it’s not going to just be one particular technological kind of pinch point that gets unlocked. It’s going to be a systems level thing. So there may be individual technology at the chip level or the hardware level. Those technologies then also have to meld with things at the systems level as well and the algorithms level as well. And I think all of those are going to be necessary in order to reach these goals. I’m talking kind of generally, but what I really mean is like what I said before is we got to think about new types of hardware. We also have to think about, “Okay, if we’re going to scale these things and manufacture them in large volumes cost effectively, we’re going to have to build larger systems out of building blocks of these things. So we’re going to have to think about how to stitch them together in a way that makes sense and doesn’t eat away any of the benefits. We’re also going to have to think about how to simulate the behavior of these things before we build them.” I think part of the power of the digital electronics ecosystem comes from the fact that you have cadence and synopsis and these EDA platforms that allow you with very high accuracy to predict how your circuits are going to perform before you build them. And once you get out of that ecosystem, you don’t really have that.
So I think it’s going to take all of these things in order to actually reach these goals. And I think part of what this program is designed to do is kind of change the conversation around what is possible. So by the end of this, it’s a four-year program. We want to show that there is a viable path towards this end goal. And that viable path could incorporate kind of all of these aspects of what I just mentioned.
Genkina: Okay. So the program is four years, but you don’t necessarily expect like a finished product of a 1/1000s cost computer by the end of the four years, right? You kind of just expect to develop a path towards it.
Bramhavar: Yeah. I mean, ARIA was kind of set up with this kind of decadal time horizon. We want to push out-- we want to fund, as I mentioned, high-risk, high reward technologies. We have this kind of long time horizon to think about these things. I think the program is designed around four years in order to kind of shift the window of what the world thinks is possible in that timeframe. And in the hopes that we change the conversation. Other folks will pick up this work at the end of that four years, and it will have this kind of large-scale impact on a decadal.
Genkina: Great. Well, thank you so much for coming today. Today we spoke with Dr. Suraj Bramhavar, lead of the first program headed up by the UK’s newest funding agency, ARIA. He filled us in on his plans to reduce AI costs by a factor of 1,000, and we’ll have to check back with him in a few years to see what progress has been made towards this grand vision. For IEEE Spectrum, I’m Dina Genkina, and I hope you’ll join us next time on Fixing the Future.
After years of rumors and questions over what it would contain, we finally got to see what the Elden Ring: Shadow of the Erdtree DLC will entail. The three-minute-long trailer showed off a mix of gameplay and cutscene content, but of course, most of it spawned even more questions. Elden Ring’s lore is notoriously…
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