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A couple of weeks ago I attended Google’s Gemma Developer Day. A day dedicated to Google presenting the capabilities, portability and openness of their newest LLM models called Gemma. These models present an exciting new phase for Generative AI. Models small enough to be deployed on local devices and can still provide an engaging and helpful experience. It’s since I looked at Mobile AI and went away from the day feeling inspired. I decided to see what happens when I tried these models in an Android App. been a while The App I wanted to give Gemma an easy yet novel challenge. Playing a game of Simon Says. The rules are simple. One player gets to be Simon. Their role is to give the other players tasks to perform. The player playing as Simon has to say “Simon says” before they give the task. I created an app with three screens. An entry screen, an instructions screen, and a chat screen where Gemma can communicate and give tasks. To speed up building the chat screen I found this blog post from extremely helpful. Meyta Taliti With the screens created my next task was to integrate Gemma. For this I relied on a set of tools I learnt about called MediaPipe. MediaPipe is a collection of tools with the goal of simplifying integrating AI models into apps on Android, iOS, and the web. With MediaPipe you have alot of choice depending on your needs. MediaPipe If you want to get started quickly MediaPipe provides an API called for you to call out to AI models. These API’s are split into different areas such as Vision, Text & Audio. Tasks MediaPipe also provides a collection of pretrained models to embed within your apps. Again, useful for getting starting quickly. If you need something more custom and don’t want to create a model from scratch MediaPipe provides a tool called . Model Maker uses a process called to retrain an existing machine learning model and provide it with new data. The benefit of this approach is it saves time and requires less training data to create a new model. Model Maker Transfer Learning Model Maker can also reduce the size of the created model through this process. Do note that this process causes the model to “forget” some of its existing knowledge. The final tool from MediaPipe is , a web application to evaluate and tweak your custom models. Useful if you want to benchmark your models and understand how well they work prior to deployment. MediaPipe Studio For our needs we’re going to leverage the LLM Interfence API, a new API for MediaPipe. This allows us to communicate with Gemma and recieve a response. Putting MediaPipe to Work To use MediaPipe you first need to add it as a gradle dependency to the app: implementation ("com.google.mediapipe:tasks-genai:0.10.11") Next, you create an instance of . This is the object you use to communicate with Gemma: LlmInference val llmInference = LlmInference.createFromOptions(
            context,
            LlmInference.LlmInferenceOptions.builder()
                .setModelPath("/data/local/tmp/llm/gemma-2b-it-cpu-int8.bin")
                .build()
        ) It’s important to note the path set using . This is the where the Gemma model resides on the device. It’s also important the Gemma model used is the versions. The versions are not yet supported by MediaPipe, more on what this all means later. For now let’s download the model. .setModelPath gemma-2b 7b You can download Gemma from . A website dedicated to Data Scientists and Machine Learning. You need to create an account and accept the Terms & Conditions of use before you can download the models. You can find the Gemma page . Kaggle here If you’re following along to this post remember to only download the versions of the model, under the tab. You’re on your own if you try the versions. gemma-2b-it-cpu TensorFlow Lite gemma-2b-it-gpu Once the model is downloaded. Use the in Android Studio to import the model to the path set in . If you’ve changed the path or the model name then make sure to update the path name. Device Explorer .setModelPath Once the model is imported you can begin to pass prompts into Gemma using the method. Here is an example of the prompt I pass to Gemma to play Simon Says: .generateResponse private const val SimonSaysPrompt = """
    You are a Simon in a game of Simon Says. Your objective is to ask the player to perform tasks.
    
    For every task you give, you must prefix it with the words "Simon says".
    
    You must not ask the player to do anything that is dangerous, unethical or unlawful.
    
    Do not try to communicate with the player. Only ask the player to perform tasks.
"""

val gemmaResponse = llmInference.generateResponse(SimonSaysPrompt) If you’ve used LLMs before and have a basic understanding of Prompt Engineering this should look familar. To err on the side of caution I’ve included precautionary instructions in the prompt. We don’t want Simon asking the user to do anything questionable! If you try to run this on a device a couple of things may happen: The app may take a short while to respond and eventually provide a response. The app may crash. Looking in Logcat you’ll see messages about MediaPipe being unable to find the model. Did you set the right model path? The app may crash. If you look in Logcat you can see alot of native code logging and infomation about memory being recycled. My experience fell into the second and third category. Your own experiences may vary if you’ve setup everything correctly and using a high spec physical device. If you don’t have ether of these things. There’s another option, increasing the amount of RAM available through the Emulator. Creating an Android Emulator with Increased RAM Increasing the amount of RAM available usually helps in a memory intensive environment so why would a memory hungry LLM be any different? To do this I customised the amount of RAM my Android emulator used. If you have an existing emulator you may notice the RAM field is disabled. You can still update the amount of RAM it has available by clicking on the three dots to the right of it in the Device Manager. Click and then open up the and files in a text editor. Change the values of in each file. Thanks goes to this question for giving me the answer on how to do it. Show on Disk config.ini hardware-qemu.ini hw.ramSize Stack Overflow Alternatively you can create a custom emulator by going to the in Android Studio, clicking and then . As part of the options to customise you can select the amount of RAM. Device Manager Create Virtual Device New Hardware Profile I found 8GB of RAM to work relatively well. I also tried my luck with 22GB of RAM. It performs marginally better in terms of speed, although not as much as I expected. I suspect there is a bottleneck somewhere when Gemma is loaded into memory, as the rest of the emulator runs fluidly. Perhaps an improvement somewhere that can be made. Gemma 2b & Gemma 7b The Gemma models compatible with MediaPipe are the versions. The stands for 2 billion parameters. The amount of parameters working together to make the model work. gemma-2b 2b These are the values set within the model during training to provide the connections and inferences between each other when you ask Gemma a question. There is also a collection, which use 7 billion parameters. These are not supported by MediaPipe however. Maybe one day! gemma-7b If you’re interested in understanding more about parameters when it comes to LLMs I recommend this . page Having a 2 billion parameter model being loaded and running on a mobile device is an impressive achievement. How well does it work though? Let’s find out. gemma-2b-it-cpu-int4 The is a 4 bit LLM. This means each parameter used by the model has a memory size of 4 bits. The benefit here is the total size of the model is smaller, however the reduced memory size for each parameter means the accuracy and quality of the model are also affected. gemma-2b-it-cpu-int4 So how does perform? Not so great to be honest. Here are a few screenshots of my attempts to play Simon Says using the prompt above and asking it general questions. gemma-2b-it-cpu-int4 The responses were unexpected and it was frustrating to get the model to do anything resembling a game of Simon Says. It would veer off into a different topic and hallucinated inaccurate infomation. are a phoenema where LLMs speak falsehoods and untruthful things as if they are fact. Take the example above, it’s not true you can drive to Mars in 60 minutes at 60mph. Not yet anyway. 😃 Hallucinations There was also a lack of context awareness. Meaning it couldn’t remember something I mentioned earlier in a conversation. This is likely due to the constrainted size of the model. After a while I gave up on this model and decided to try the larger 8 bit sized model. gemma-2b-it-cpu-int8 The is an 8 bit LLM. Its larger in size to its 4 bit sibling. Meaning it can be more accurate and provide better quality answers. So what was the outcome here? gemma-2b-it-cpu-int8 This model was able to grasp the idea of Simon Says, immediately assuming the role of Simon. Unfortunately it too suffered from a lack of context awareness. To counter this I needed to reprompt the model everytime with the rules of Simon Says and combine it with another prompt to ask it to provide a task. The task prompts are randomly picked from a list to pass into Gemma, giving some variety in the tasks being asked. Here is an example of what is happening below: private const val SimonSaysPrompt = """
    You are a Simon in a game of Simon Says. Your objective is to ask the player to perform tasks.
    
    For every task you give, you must prefix it with the words "Simon says".
    
    You must not ask the player to do anything that is dangerous, unethical or unlawful.
    
    Do not try to communicate with the player. Only ask the player to perform tasks.
"""

private const val MovePrompt = SimonSaysPrompt + """
    Give the player a task related to moving to a different position.
"""

private const val SingASongPrompt = SimonSaysPrompt + """
    Ask the player to sing a song of their choice.
"""

private const val TakePhotoPrompt = SimonSaysPrompt + """
    Give the player a task to take a photo of an object.
"""

private val prompts = listOf(
    MovePrompt,
    SingASongPrompt,
    TakePhotoPrompt
)

val prompt = prompts.random()
val response = llmInference.generateResponse(prompt) It does ocassionally throw a curve ball response that seems out of character. I’m putting this down to the size of the model. It’s also worth considering this is only v1. Once the prompts were set in stone I found it was useful to rely on the prompts only and not take the user input into consideration. Because the model lacks context awareness the user input causes it to stop playing Simon Says and instead respond to the input. Adding this bit of trickery wasn’t a satisfying outcome, but one needed to keep Gemma playing Simon Says. Impressions and Thoughts So can Gemma play Simon Says on an Android device? I’m going to say “kind of, with help”. I would like to see the 4 bit version of Gemma 2b responding more intuitively. Making Gemma 2b context aware to avoid the need to reprompt it for every request and being careful with user input would help too. For simple requests needing only a single prompt. I can see Gemma 2b being able to comfortably handle these tasks. Its also worth bearing in mind these are v1 of the models. The fact they run and work on a mobile operating system is an impressive achievement! The Future of on Device LLMs What about the future of LLMs on mobile devices? There’s two barriers I see. Hardware limitations and practical use cases. I think we’re at a point where only high end devices can effectively run these models. Devices that come to mind are the Pixel 7 or Pixel 8 series of phones with their Tensor G chips, and Apples iPhone with their Neural Engine chip. We need to see these kind of specifications filtering through to mid-range phones. Interesting ideas could come from on device LLMs tapping into . A technique for LLMs to communicate with external data sources to retrieve additional context when providing answers. This could be an effective way to boost performance. Retrieval Augmented Generation The second barrier is finding practical use cases. I think these are limited whilst devices can communicate with more powerful LLMs over the internet. GPT-4 from OpenAI for instance is rumoured to support over a trillion parameters! There could come a time though where the cost of deploying these models on mobile devices becomes cheaper than hosting them in the cloud. Since cost cutting is all the rage these days I can see this being a viable use case. There’s also the privacy benefits of having your own personal LLM, with no infomation leaving the confines of your device. A useful benefit that will appeal to privacy conscious app users. My bet is we’re still a few years away from LLMs being regularly deployed on device. Useful Resources If you’re keen to try Gemma for yourself on a mobile device here are some resources to help: : The contains a wealth of infomation including benchmarks, quick start guides, and infomation on Googles approach to responsible Generative AI development. Gemma offical Gemma website : MediaPipe has its own where you can learn more about it and how to use it. Highly recommended reading. MediaPipe Google Developer section : The Google Developer Group has dedicated channels to Generative AI. Check out the #gemma, #gemini and #ml channels to chat with like minded people. Google Developer Group Discord Discord Clone and run the for this blog post to see it in action. It also includes usage of the Image Classification Task from MediaPipe. Setup instructions are in the README. Simons Says App: sample code Footnotes Updated 23/03/24 to mention calling the LLM inference from an IO thread It occurred to me after writing this post that calling out to gemma is a read / write operation on a file. Moving the method out to an IO thread will avoid the immense jank when gemma is loaded into memory: .generateResponse() suspend fun sendMessage(): String { 
  return withContext(Dispatchers.IO) {
    val prompt = prompts.random()
    llmInference.generateResponse(prompt)
  }
} Also appears . here

Playing Simon Says with Gemma-2b and MediaPipe 

Gemma-2b এবং MediaPipe-এর সাথে সাইমন সেস খেলছে

Simon Says mit Gemma-2b und MediaPipe spielen

Jugando a Simon Says con Gemma-2b y MediaPipe

Jouer Simon Says avec Gemma-2b et MediaPipe

जेम्मा-2बी और मीडियापाइप के साथ साइमन सेज़ बजाना

Gemma-2b と MediaPipe を使用して Simon Says を再生する

Gemma-2b 및 MediaPipe를 사용하여 Simon Says 재생

Jogando Simon Says com Gemma-2b e MediaPipe

Игра в Simon Says с Gemma-2b и MediaPipe

Simon Says'ı Gemma-2b ve MediaPipe ile oynamak

Chơi Simon Says với Gemma-2b và MediaPipe

使用 Gemma-2b 和 MediaPipe 玩“西蒙说”游戏

By Robert Bell, CEO of 2B3D.com "Compassion" is not a word you see in a lot of business plans among top technology and financial firms. It may even be considered something of a liability for most businesses, but I don't see it that way. As the 2B3D team prepares for our upcoming Veterans Day NFT drop, we believe the compassionate aspect of our mission bears repeating: Compassion is our greatest strength, and underpins everything we do. was founded by me as well as my close friend, Andrew Bjarnsen, an Afghan War veteran and combat medic who himself struggles with PTSD. Andrew set out on this mission to help his fellow veterans through the use of virtual reality technology. 2B3D What we are creating at 2B3D (along with our partners at BiocoRx, Mason Labs, and Metabillionaire) is centered around a deep concern for the pain and plight of others. In fact, when we talk about the massive utility and gamification of our NFTs, it is purposely designed in response to the growing need for mental health treatment in a world that needs more empathy all-around- whether in daily 1-on-1 with our neighbors, business partners and fellow citizens, or when we’re thinking about others far removed from our own location and daily experiences. 2B3D is deep in the creation of a first-of-its-kind virtual reality medical environment (VRME) in the metaverse, architected specifically to to deliver free mental health services to military veterans in real-time- and largely to address just about everything that is inadequate about the current status quo for vets seeking these services. We’ll launch this in the first quarter of the new year. Our firm is owned and operated by military veterans, and we intend to head off the growing mental health crisis in America, starting with our at-risk military veterans. We are  deploying new treatments through virtual reality (VRx) that will radically improve the physical and mental health of veterans throughout the nation regardless of their location or distance from big cities. In the last several years, suicide rates have increased, and this burden of self-harm is disproportionately carried by veterans. and we feel something must be done. Veteran suicides rates are vastly outpacing those of the general US population It stands to reason, since resources are strained, the response is too slow. 2B3D’s mission is to build new technologies that can offer solutions. It is our passion. Director Christopher Jones of Public Health Service at the Center for Disease Control and Prevention testified in June that between 2001 to 2019, the rate of suicide In the general population rose nearly 30%, while among veterans the rate increased nearly 36%. What 2B3D is building is a realistic VR medical environment available online 24 hours a day through VR headsets, and in 2D through phones and desktop PCs. Veterans experiencing suicidal ideation (often exacerbated by PTSD symptoms and substance use disorder) will be able to access the 2B3D metaverse for immediate access to suicide prevention counselors, group therapy, meditation spaces, and / treatment. EMDR DBT Our realistic environments will not be just a ‘second-best’ option for suffering veterans, but a cutting-edge solution in an increasingly online society. Veterans who have seen combat or feel isolated are the most vulnerable to thoughts of suicide. 20-30% of veterans who commit suicide which affects divergent thinking, making it nearly impossible to break out of a mental spiral. At those times, seconds literally count. also screen positive for service-related injuries like Post Traumatic Stress Disorder Imagine it's 3 a.m. somewhere in the world where a veteran is suffering a mental health crisis forty-five minutes from the nearest emergency room. They may be considering suicide as an option to ending their pain, and they begin to spiral. Help through 2B3D will be available, always on standby. Rather than leave their home at 3 a.m. to get in a car or find public transportation in search of help, veterans will simply use their VR headset, phone, or computer to access our virtual medical environment equipped with mental health resources. In a safe, virtual space, they also have the option to talk with a live person. Even in the form of avatars, research has shown that this human connection can be enough to prevent a tragedy from occurring. Our funding will be as unique as our service. To that end, we've announced the launch of our Supporter Badge, an NFT collection of 3D PTSD support ribbons that will fund our virtual reality mental health solutions for military veterans. This collection will be available for purchase at this Veterans Day, Nov. 11, 2022. https://mint.2b3d.com By raising funds through the sale of our Supporter Badge NFT collection, we'll be able to provide military veterans with access to proven cognitive therapy modalities in safe and effective VR environments. The public sale price of each 3D ribbon will be $10 dollars. All proceeds go to helping us in our mission to help veterans, and 40% percent of the proceeds will be donated directly to veteran nonprofits and charities also fighting to save veteran lives. Those who are interested in helping veterans do not need experience with NFTs or cryptocurrency to purchase one of our Supporter Badges. They are available for purchase with a credit card on our website. There is no limit to the number of NFTs we can sell to help veterans. For those who love NFTs and helping veterans, the public release of an additional 2,880 unique Collector’s Pass NFTs will also be live on Nov. 11, 2022, with a floor price of .7 ETH each, or approximately $100 until sold out. Every Collector’s Pass is represented as a yet-to-be-revealed 3D object with unique properties like special graphics and rare traits, and future in-game applications. The number of available Collector’s Passes is set at exactly 2,880 - a number representing the 2,880 minutes in the span of two days in which, statistically, 44 veterans will take their own lives. Every minute counts. Join us. Launching Veteran’s Day at . https://mint.2b3d.com ### About Robert Bell Robert Bell is an entrepreneur, investor, and philanthropist who founded 2B3D Inc in 2020 as an all-in-one gaming studio, nonfungible token mint, auction house, and storage system. Bell’s mission was to push the bounds of virtual, augmented and mixed reality technologies as a way to help military veterans suffering from post-traumatic stress disorder. His mission to bring peace and healing to veterans is fueled by play-to-earn games, utility NFTs, and access to exclusive in-game cryptocurrency within a wider decentralized metaverse. Bell is also the founder and CEO of Bell Medical solutions. About 2B3D 2B3D is a military veteran-operated tech company specializing in web3 gaming, virtual reality, NFTs, and mental health solutions in the metaverse. 2B3D is currently developing a metaverse ecosystem using blockchain technology with play-to-earn games, an NFT marketplace and VR showrooms, and VR medical environments for therapy and meeting with healthcare professionals in real-time. The 2880 Genesis Collection NFT will drop on the eleventh day of the eleventh month, in honor of military veterans on Veterans Day. The 2B3D metaverse includes several core projects: VRx | Virtual healthcare with NFT prescriptions and real life professionals. NFTy150 | NFT marketplace, minting option and showroom. Topher's Inferno | Connecting enthusiastic gamers with ambitious developers. RestXP | B2B meeting rooms with a resting crypto reward. So Many Gods | A Play-to Earn, space-themed sci fi looter shooter. Sci-fi To learn more about 2B3D, visit or follow us on Twitter at @2B3Dinc. www.2B3D.com

The Ultimate Utility: 2B3D is Using NFTs to Save Military Veteran Lives

Blake 2B and Blake3 are slightly different algorithms. A while ago, I came up with the question of whether or not Siacoin should migrate to Blake3. This question was posted on . Reddit This question has intrigued me, but what intrigued me more or less was the answer. It would need to be a hardfork, and although Blake3 is considered multiple times faster by others, there are debates in regards to HAIFA construction vs. traditional Merkle trees and so forth. That said, a hardfork of Siacoin would likely be easy given the code is standardized, but according to devs on Reddit, a Blake3 upgrade is the wrong thing to be worried about. I do agree. Is Blake a good choice? However, I still had a curiosity in exploring Blake3. In one of my own projects, I have decided to let the founder integrate Blake3 as its core hashing mechanism. That project is (2nd version), and I think Blake3 would work as the hashing algo of choice for BitBadges. The whitepaper can be seen .  It is worth a read. BitBadges here Another fact I found interesting, is that besides Siacoin, another Blake2b adaptor was . Nano Nano utilizes Blake2b in regards to the digested seed hashing. In regards to Blake3 adaption, Nano and most of these other cryptos would likely need a hard fork over soft fork. Interestingly enough about Blake3, is there have been many adaptations. This includes a PHP Hash Blake3 , and Blake3 itself has a Rust and C implementation. class Blake3 is said to be a merkle tree on the inside, and the speed itself is substantially higher then Blake2b in regards to the they gave. benchmark test Conclusion Blake3 is utilized more towards CPU multithreading, and likely if a cryptocurrency wants the benefit of hashing, but aren't too focused on multithreading or parallelization, then Blake2b is still a decent choice. For me, as a person liking grid and verifiable forms of computing, I had the preference of Blake3. That said, it is nice that it was already available at the time starting development.

2B or to B3? That Is The Question... Blake 2B vs 3 for Siacoin

When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision, a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants. When in-game cheating becomes the norm, does the gaming industry have a major problem on its hands? A global study of the gaming industry would say yes. It was found that 77% of online gamers are likely to stop playing a multiplayer game online if they think other players are cheating, and 48% of online gamers are likely to buy less in-game content. Add in the accrued value of digital assets as a key feature of blockchain gaming, and developers have an obligation to tackle this problem head-on. study study An attractiveness to bending the rules is not something limited to the world of online gaming; it is rife in all parts of society. However, as social psychologist Corey Butler points out ‘anonymity (playing with strangers or online) and opportunity can tip the scales toward greater cheating. When strong incentives like money or grades are on the line, otherwise honest people might be tempted to cheat. If somebody with a competitive nature is presented with the opportunity to easily outperform the other at a task, then it's safe to say that many will bite the bullet and risk the reward for the easy win—factor in the monetary value of rewards in Web3 gaming, and this desire to win increases significantly. However, when it comes to multiplayer online games, the competition is designed around entertainment, fun, and the community. These core values of gaming make cheating a stain on the industry as a whole. Cheating is the new norm. Cheating in some of the largest games has become an economy of its own, with many platforms and YouTube channels dedicated to introducing cheating, hacks, and navigating the ways to cheat. Don't believe us, simply type fortnite cheat terms into a google search. Combating this trend is critical for maintaining the reputation of these brands. Simon Vieira, CEO of MixMob, a Solana-based gaming platform, believes the temptation to cheat is rife, but it is nothing new, “Web3 games offer enhanced incentives due to the tangible value of game coins and digital items. However, this also creates a greater temptation to cheat. That said, traditional gaming has previously addressed similar issues. For instance, real money gaming employs methods such as biotic randomness to obscure certain game aspects like matchmaking. This approach reduces predictability, thus diminishing the incentives to cheat while still maintaining a skill-based gaming environment. We apply these strategies at MixMob to ensure fairness and keep the game engaging for all players,” says Vieira. When hacking games, cheaters now have an endless stack of tools to choose from when it comes to infiltrating game mechanics with glitching and farming to using aimbots in the software and speed hacks. This problem is exacerbated in Web3 gaming as it lacks an authoritative host. “Fair play is crucial in multi-player games as it directly impacts player engagement and retention. A fair environment ensures that players feel valued and that the game remains competitive and enjoyable for everyone,” notes Vieira. Furthermore, with AI, it is easier than ever to predict patterns of player behavior and create new ways to exploit and manipulate gaming data. So, how can this problem be addressed without compromising the gaming experience? Speaking to Tashi Protocol CEO Sandeep Bhatia, it is clear that consensus will play a future role in blockchain gaming. “Dynamite as the network transport for multiplayer games can prevent certain forms of cheating like 'lag switching' or local state hacking. The benefit of having consensus on top of a peer-to-peer network layer ensures that players cannot cheat, and the architecture reduces costs.” Frustration with the publishers Publishers are tackling the issue of one player, or several players at a time, by banning them and using anti-cheat software. Just last month, Activision , a gaming giant and creator of Call of Duty, banned 27,000 accounts from the game. However, this isn't deterring new cheats coming into the sector. Activision Activision Tackling it from the outset in the process of the game studio is the only way to ensure that both the publishers and the gamers benefit in the long run. To restore confidence, creators of blockchain games must demonstrate trust from the outset. “Shifting from a traditional gaming infrastructure to adopt a decentralized, serverless framework will optimize the entire Web3 gaming experience and solve the issue of cheating from the ground up. Not only can we reduce the costs by eliminating the need for centralized servers, but Tashi's Dynamite ensures fair distribution and protection against common security threats,” says Bhatia. Once a player sees that a game is compromised, it ruins the fun element and may even deter them from entering the environment because of security fears. In addition, the influx of bots and other hacks has led to unfair advantages within some of the most loved games. What can blockchain game publishers do? Gaming studios must take the necessary steps to demonstrate their commitment to fair play. This is essential in the blockchain space as it continues to build trust with the traditional gaming communities. At MixMob, they leverage blockchain transparency to allow the community to participate in monitoring and reporting cheaters actively. “This community refereeing not only helps maintain fairness but also fosters a sense of responsibility and integrity among players, and it has worked great for us,” says Vieira. “Web3 game publishers must take decisive steps to limit cheating. The actions taken should vary based on the severity of the cheating incident. Potential measures include banning, suspending, or permanently removing players involved in unfair practices. Additionally, publishers can adopt proven strategies from traditional gaming, such as introducing randomness to some parts of the game to reduce predictability, which helps deter cheating,” he continues. Game developers who are keen to implement anti-cheat measures are often outplayed by hackers who have mastered the art of cheating and the avoidance of detection. Sure, developers can ban cheaters who are caught in the act or embed cheat detection software. However, this doesn't prevent cheating from occurring from the outset. The vulnerabilities have already been exploited by the time the developer has their hands on the activities. If, however, a game creator starts the development process using the Tashi Consensus Protocol, it has eliminated the act of cheating from the initial design stage. “Gamers rely on trust that game developers would have implemented the best practices to ensure cheaters are punished. That is why Tashi works to educate all its gaming partners on the novel ways blockchain solves cheating issues. We make our tech easy to use and guide all our partners who might come from the Web2 world and are new to using blockchain tech,” added Bhatia. While anti-cheat software like Riot Game's Vanguard or Activision Blizzard's Ricochet stands as the frontline defense, the second layer can utilize the transparency provided by blockchain technology to fight against bad actors. Implementing innovative approaches to game development, like the Tashi Dynamite Consensus Engine, pushes gaming to a new era of fair play and reclaims the values that true gamers want to see in the game development stage. In addition to fostering a culture of fair play, a paradigm shift is necessary in the approach to combating cheating in online gaming. By 2027, the number of users in the Online Games market is expected to amount to 1.2bn users. The projected revenue in the Online Games market worldwide is expected to reach US$27.97bn in 2024. Reclaiming the gaming sector for true gamers who value the entire gaming experience is crucial as blockchain games position themselves alongside some of the gaming giants.

Is It Game Over for Cheaters? Using Blockchain Technology to Eliminate Unwanted Gaming Behavior

Working With Wav2vec2 Part 1: Finetuning XLS-R for Automatic Speech Recognition

Wav2vec2 এর সাথে কাজ করা পার্ট 1: স্বয়ংক্রিয় বক্তৃতা স্বীকৃতির জন্য XLS-R ফাইনটিউনিং

Arbeiten mit Wav2vec2 Teil 1: Feinabstimmung von XLS-R für die automatische Spracherkennung

Trabajar con Wav2vec2 Parte 1: Ajuste de XLS-R para el reconocimiento automático de voz

Travailler avec Wav2vec2 Partie 1 : Affiner XLS-R pour la reconnaissance vocale automatique

Wav2vec2 के साथ कार्य करना भाग 1: स्वचालित वाक् पहचान के लिए XLS-R को परिष्कृत करना

Wav2vec2 の操作パート 1: 自動音声認識のための XLS-R の微調整

Wav2vec2 작업 1부: 자동 음성 인식을 위한 XLS-R 미세 조정

Trabalhando com Wav2vec2 Parte 1: Ajuste fino do XLS-R para reconhecimento automático de fala

Работа с Wav2vec2. Часть 1. Точная настройка XLS-R для автоматического распознавания речи

Wav2vec2 ile Çalışmak Bölüm 1: Otomatik Konuşma Tanıma için XLS-R'ye İnce Ayar Yapmak

Làm việc với Wav2vec2 Phần 1: Tinh chỉnh XLS-R để nhận dạng giọng nói tự động

使用 Wav2vec2 第 1 部分：微调 XLS-R 以实现自动语音识别

The AI industry is dominated by discussions of massive LLMs like GPT-4, Claude 3, and Gemini. But there are equally big issues with relying on these closed-source LLMs. For one, you have minimal control. The models often worsen over time and exhibit biases that you can’t fix. Plus, the firms gatekeeping the models, from OpenAI to Anthropic to Google, can throttle your usage, increase your prices, or simply ban your account. If you run a business, you’re risking everything by using closed-source models. Another issue is their sheer size. They’re far too big and slow to be useful for hardware devices like portable assistants - something Rabbit and Humane AI learned only after scathing reviews. That’s where open-source, tiny LLM models come into play. The most accurate of these in the <2B parameter category is H2O.ai’s Danube 2. They’re becoming increasingly popular for deployment on edge devices, as traditional large language models are too computationally demanding to run efficiently on resource-constrained hardware. Tiny LLMs for phones Danube 2 is a “tiny LLM” that packs a surprising punch for its size. With just 1.8 billion parameters, Danube is lightweight compared to giants like GPT-4, but it achieves highly competitive performance across a wide range of natural language tasks. Trained on a carefully curated dataset of 2 trillion tokens from diverse web sources, Danube leverages techniques refined from models like Mistral to maximize its efficiency. Basically, you can download this model to your device and run it wherever you like — whether that’s new portable consumer hardware, a security camera, an IoT sensor, or anywhere else. One of the key advantages of Danube is its accessibility. With an Apache 2.0 license, it’s freely available for commercial use, lowering the barriers to entry for developers and businesses looking to incorporate AI into their mobile applications. H2O.ai has also released a fine-tuned version, Danube2–1.8B-Chat, specifically optimized for conversational AI. This could spur a wave of more natural and responsive chatbots and virtual assistants on devices from smartphones to smart home hubs. Tiny AI in healthcare The potential applications for small AI extend far beyond consumer-facing chatbots or phones. In the healthcare sector, companies like HeartSciences are leveraging compact machine learning models to supercharge the electrocardiogram (ECG). Heart disease is the leading cause of death globally, claiming around 51,000 lives each day, and ECGs are one of the primary tools for diagnosing cardiac issues. However, interpreting these readings requires specialized expertise that many healthcare providers lack, particularly in resource-constrained settings. HeartSciences’ MyoVista Wavelet ECG system aims to bridge this gap by incorporating sophisticated AI algorithms capable of spotting subtle patterns that human eyes might miss. Trained on vast datasets, these models act as an early warning system for cardiac dysfunction. Crucially, the machine learning behind MyoVista is designed to run efficiently on the kind of hardware you’d find in a typical doctor’s office or clinic, not a high-performance computing cluster. This is the essence of small AI — bringing the power of cutting-edge algorithms to the point of care, where they can have the greatest impact on patient outcomes. When minutes can mean the difference between life and death, having AI-powered insights available immediately is a game-changer. And with hundreds of millions of ECGs administered every year, the scalability of lightweight models like those used in MyoVista is key to making a real dent in the global burden of heart disease. Tiny AI for security Another promising application for small AI is in the realm of smart security systems. Traditional security cameras rely on motion detection and human monitoring to identify potential threats, leading to high rates of false alarms and missed incidents. However, by incorporating compact machine learning models, these systems can become much more intelligent and autonomous. Imagine a security camera equipped with an AI model trained to recognize not just motion but specific types of behaviors and objects. Such a system could distinguish between a person walking their dog and someone attempting to break into a building, alerting human operators only when there’s a genuine threat. By running these models directly on the camera hardware rather than streaming video to the cloud for analysis, smart security systems can operate more efficiently and protect sensitive data from potential breaches. Of course, developing effective AI models for smart security presents its own set of challenges. The models must be able to operate in real-time, processing video feeds as they come in while also being robust to variations in lighting, weather, and other environmental factors. They must also be trained on diverse datasets to avoid bias and ensure fair performance across different demographic groups. Despite these challenges, the potential benefits of AI-powered smart security are significant. By reducing false alarms and enabling proactive threat detection, these systems can help keep people and property safe while also reducing the workload on human operators. And as the underlying hardware continues to become more powerful and energy-efficient, the possibilities for edge-based AI in security applications will only continue to grow. Takeaways As the AI community continues to push the boundaries of what’s possible with large-scale models and infrastructure, it’s important not to lose sight of the transformative potential of small AI. By bringing powerful capabilities to the devices and contexts where they’re needed most, compact models have the potential to democratize access to AI’s benefits and touch billions of lives in meaningful ways. From the smartphones in our pockets to the medical devices in our clinics to the security cameras watching over our streets, small AI is poised to make a big impact. As we work to overcome the challenges around data, privacy, and bias, we must also recognize and celebrate the quiet revolution that’s happening at the edge. The future of AI may be small, but its potential is enormous. The AI industry is dominated by discussions of massive LLMs like GPT-4, Claude 3, and Gemini. But there are equally big issues with relying on these closed-source LLMs. For one, you have minimal control. The models often worsen over time and exhibit biases that you can’t fix. worsen over time exhibit biases Plus, the firms gatekeeping the models, from OpenAI to Anthropic to Google, can throttle your usage, increase your prices, or simply ban your account. If you run a business, you’re risking everything by using closed-source models. Another issue is their sheer size. They’re far too big and slow to be useful for hardware devices like portable assistants - something Rabbit and Humane AI learned only after scathing reviews . scathing reviews That’s where open-source, tiny LLM models come into play. The most accurate of these in the <2B parameter category is H2O.ai’s Danube 2 . They’re becoming increasingly popular for deployment on edge devices, as traditional large language models are too computationally demanding to run efficiently on resource-constrained hardware. H2O.ai’s Danube 2 Tiny LLMs for phones Danube 2 is a “tiny LLM” that packs a surprising punch for its size. With just 1.8 billion parameters, Danube is lightweight compared to giants like GPT-4, but it achieves highly competitive performance across a wide range of natural language tasks. Trained on a carefully curated dataset of 2 trillion tokens from diverse web sources, Danube leverages techniques refined from models like Mistral to maximize its efficiency. Basically, you can download this model to your device and run it wherever you like — whether that’s new portable consumer hardware, a security camera, an IoT sensor, or anywhere else. One of the key advantages of Danube is its accessibility. With an Apache 2.0 license, it’s freely available for commercial use, lowering the barriers to entry for developers and businesses looking to incorporate AI into their mobile applications. H2O.ai has also released a fine-tuned version, Danube2–1.8B-Chat , specifically optimized for conversational AI. This could spur a wave of more natural and responsive chatbots and virtual assistants on devices from smartphones to smart home hubs. Danube2–1.8B-Chat Tiny AI in healthcare The potential applications for small AI extend far beyond consumer-facing chatbots or phones. In the healthcare sector, companies like HeartSciences are leveraging compact machine learning models to supercharge the electrocardiogram (ECG). HeartSciences Heart disease is the leading cause of death globally, claiming around 51,000 lives each day, and ECGs are one of the primary tools for diagnosing cardiac issues. However, interpreting these readings requires specialized expertise that many healthcare providers lack, particularly in resource-constrained settings. HeartSciences’ MyoVista Wavelet ECG system aims to bridge this gap by incorporating sophisticated AI algorithms capable of spotting subtle patterns that human eyes might miss. Trained on vast datasets, these models act as an early warning system for cardiac dysfunction. Crucially, the machine learning behind MyoVista is designed to run efficiently on the kind of hardware you’d find in a typical doctor’s office or clinic, not a high-performance computing cluster. This is the essence of small AI — bringing the power of cutting-edge algorithms to the point of care, where they can have the greatest impact on patient outcomes. When minutes can mean the difference between life and death, having AI-powered insights available immediately is a game-changer. And with hundreds of millions of ECGs administered every year, the scalability of lightweight models like those used in MyoVista is key to making a real dent in the global burden of heart disease. Tiny AI for security Another promising application for small AI is in the realm of smart security systems . Traditional security cameras rely on motion detection and human monitoring to identify potential threats, leading to high rates of false alarms and missed incidents. However, by incorporating compact machine learning models, these systems can become much more intelligent and autonomous. smart security systems Imagine a security camera equipped with an AI model trained to recognize not just motion but specific types of behaviors and objects. Such a system could distinguish between a person walking their dog and someone attempting to break into a building, alerting human operators only when there’s a genuine threat. By running these models directly on the camera hardware rather than streaming video to the cloud for analysis, smart security systems can operate more efficiently and protect sensitive data from potential breaches. Of course, developing effective AI models for smart security presents its own set of challenges. The models must be able to operate in real-time, processing video feeds as they come in while also being robust to variations in lighting, weather, and other environmental factors. They must also be trained on diverse datasets to avoid bias and ensure fair performance across different demographic groups. Despite these challenges, the potential benefits of AI-powered smart security are significant. By reducing false alarms and enabling proactive threat detection, these systems can help keep people and property safe while also reducing the workload on human operators. And as the underlying hardware continues to become more powerful and energy-efficient, the possibilities for edge-based AI in security applications will only continue to grow. Takeaways As the AI community continues to push the boundaries of what’s possible with large-scale models and infrastructure, it’s important not to lose sight of the transformative potential of small AI. By bringing powerful capabilities to the devices and contexts where they’re needed most, compact models have the potential to democratize access to AI’s benefits and touch billions of lives in meaningful ways. From the smartphones in our pockets to the medical devices in our clinics to the security cameras watching over our streets, small AI is poised to make a big impact. As we work to overcome the challenges around data, privacy, and bias, we must also recognize and celebrate the quiet revolution that’s happening at the edge. The future of AI may be small, but its potential is enormous.

Danube-2: The Tiny AI Model Leading the Open LLM Leaderboard

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 28 of 33. HackerNoon’s Legal PDF Series here V. A Sentence of 40 to 50 Years Is Necessary to Avoid Sentencing Disparities Among the factors a sentencing court must consider in imposing sentence is “the need to avoid unwarranted sentence disparities among defendants with similar records who have been found guilty of similar conduct.” 18 U.S.C. § 3553(a)(6). Congress adopted Section 3553(a)(6) “to eliminate unwarranted disparities nationwide.” United States v. Williams, 524 F.3d 209, 215 (2d Cir. 2008). No scheme to defraud since Madoff can be compared to this one in terms of its size, scope, and amount of loss. The PSR notes that sentencing data from the last five years shows that the average sentence for a defendant sentenced under the fraud Guidelines with offense level (43) is approximately 24 years’ imprisonment. The United States Sentencing Commission’s Judiciary Sentencing Information database does not indicate, however, what conduct resulted in these sentences. Nor does it indicate what the largest sentences were. Because of the range of potential white collar crimes that fall under the Guidelines in Section 2B1.1, the Government submits that a more focused approach is necessary to avoid unwanted sentencing disparities. Specifically, the Government submits that other fraud cases where the loss amounts were at the top of the loss table—and in particular, those involving embezzlement—is the relevant body of cases to evaluate. Below is a table of sentences imposed since Booker on defendants where the loss amount exceeded $100 million, and the money was stolen through a Ponzi scheme or some other form of misappropriation or embezzlement. As the table makes clear, most defendants who steal their clients’ or customers’ money with $100 million dollars in losses or more receive sentences ranging from 40 to 50 years’ imprisonment. Indeed, in cases with $200 million or more in losses, courts have typically imposed a sentence of 50 years imprisonment or more. For instance, in a case with similar facts to those here, Russell Wasendorf, the CEO of a futures commission merchant, embezzled more than $215 million in customers’ funds, and was sentenced to 50 years’ imprisonment. There are exceptions, of course, but it also bears noting that the defendant’s fraud would rank second, right below Madoff, in terms of total losses. Accordingly, to avoid unwarranted sentencing disparities amongst like defendants, a sentence of 40 to 50 years’ imprisonment is necessary. The defendant’s submission cites and then distinguishes several other fraud prosecutions involving large losses. Starting with Madoff, the defendant says it is not an apt comparison because Madoff embezzled more money, engaged in fraud from the beginning, was older, and used millions in stolen funds for himself. (Def. Mem. at 75). Bankman-Fried is right that there are differences between his and Madoff’s case, but those do not justify disregarding the Madoff sentence as a benchmark. Like Madoff, the defendant misappropriated billions of dollars of customer money over a multi-year period, spending much of it on his own ventures, and causing massive harm to his victims. United States v. Madoff, Nio. 09 Cr. 213 (DC), Dkt. 92 (Gov’t Sentencing Mem.). And unlike Madoff, Bankman-Fried has not accepted responsibility; rather, he obstructed justice, he perjured himself, and he committed other significant crimes. The defendants’ ages are certainly a distinguishing factor. Age, of course, cuts both ways. A shorter sentence because the defendant is relatively young means he will have an opportunity to commit fraud again when released. And a shorter sentence because of his age also undermines general deterrence, as it could signal to would be young fraudsters that it is worthwhile to attempt a massive fraud, because either they will get away with it, or the prison sentence will allow them a second chance to make money. Even if age were a reason why this Court should impose a shorter sentence than the one imposed on Madoff— a sentence that gives Bankman-Fried the possibility of release someday—it is not a reason to disregard the Madoff sentence given the similarities to the case. Next, the defendant distinguishes Judge Ramos’s recent 20-year sentence of Karl Greenwood, who was involved in the fake cryptocurrency scheme OneCoin. (Def. Mem. at 76- 77). That sentence is distinguishable, but not for the reasons in the defendant’s brief. Greenwood received a sentence shorter than what Bankman-Fried deserves, not a longer one. Greenwood pled guilty and was remorseful for his conduct. At sentencing, Judge Ramos stated that while the defendant’s criminal conduct “compelled” a substantial sentence, there were “substantial reasons” for granting “some measure of lenity, including “horrific conditions” that the defendant endured in a Thailand jail. United States v. Greenwood, No. 17 Cr. 630 (ER) (S.D.N.Y. Sept. 12, 2023), Sent. Tr. at 39. Those conditions included being “stripped naked,” “shaved down,” “chained to other inmates for 24 hours,” “forced to sleep on the floor full of cockroaches and rodents,” “food filled with magots,” “forced to sit outside in the heat for hours,” and some particular circumstances, which are under seal, that “resulted in lasting health effects and health consequences.” (Id. at 18- 19).[13] The particular circumstances of Greenwood’s case distinguish it, and make it an imperfect precedent for the sentence here. Bankman-Fried argues that the sentences imposed on defendants from the public company scandals from the early 2000s are poor comparisons. (Def. Mem. at 77). The Government agrees. Bernard Ebbers of WorldCom and John Rigas of Adelphia Communications were convicted of perpetrating large accounting frauds that resulted in losses to the companies’ investors. These were securities fraud cases, but the nature of the losses was different: the losses did not involve the theft of customers’ or clients’ funds that were entrusted to the defendant. Rather, in both cases, there was ambiguity as to whether the companies’ losses were fully attributable to the fraud. United States v. Ebbers, 458 F.3d 110, 127 (2d Cir. 2006) (“The loss to investors who hold during the period of an ongoing fraud is not easily quantifiable because we cannot accurately assess what their conduct would have been had they known the truth,” and recognizing that “the dot-com bubble burst and its likely negative future effect on WorldCom’s business was public knowledge,” which caused “a downward pressure on share price not attributable to the defendant.”); United States v. Rigas, 583 F.3d 108, 119–20 (2d Cir. 2009) (recognizing that “[t]he loss must be the result of the fraud, as opposed to other, non-fraudulent occurrences”) (citation omitted). That distinguishing feature is critical because, as the above table indicates, courts have routinely imposed lengthier sentences on defendants who have stolen from people who trusted them. Bankman-Fried acknowledges similarities to Elizabeth Holmes. (Def. Mem. at 78-79). But the similarities are superficial. Bankman-Fried stole billions from customers who entrusted him with money. The loss amount in Holmes’s case was less than $150 million, and those losses were borne by private investors, not the general public. United States v. Holmes, 18 Cr. 258 (N.D. Cal.), Dkt. 1712 (Court Order on Sentencing), at 1 (“the loss in this case can be reasonably found to be $120,146,247”), 6 (list of investors). The defendant claims that Holmes’s conduct was worse because her scheme resulted in parties receiving inaccurate medical information. But BankmanFried caused a similar type of trauma to his victims, but at a grander scale. He led victims to believe their money was safe and in accounts, to be used as needed. As the witness statements above make clear, people put their life savings in the defendant’s hands. So like the Theranos patient who got inaccurate medical results and was traumatized, the FTX customers have experienced devastating emotional and psychological trauma from the defendant’s fraud. The difference is, however, that the Theranos patients ultimately learned that the tests were junk and were able to right their lives. The same is not true for FTX’s customers, who have not gotten their money back. Finally, the defendant settles on Michael Milken as the “best comparison” for this case. There is very little about the defendant’s fraud that is any way like the crimes for which Milken was prosecuted. Although Milken was associated with Drexel Burnham’s leveraged buyouts and “junk bonds,” the charges to which Milken pled guilty had nothing to do with those practices; nor did they involve insider trading associated with Ivan Boesky. While Milken pled guilty to six offenses, they related to the following: Milken guaranteed Boesky against loss and helped him evade the net-capital rules in some transactions, he made adjustments in the price of securities bought and sold for another client without disclosing this to the fund’s customers, and he helped the same client lower his takes by arranging unprofitable trades. Milken’s convictions neither involved theft of customers’ funds, nor did they involve anything close to the amount of losses sustained here. Milken’s sentence is not a fair comparison for the conduct here. Arguing otherwise, Bankman-Fried cites to personal similarities to Milken—they were both young, had a “meteoric rise” in their industry, were “geniuses,” and had an interest in charity. (Def. Mem. at 81-82). But Section 3553 requires consideration of whether a sentence will create an unwarranted sentence disparity among defendants “who have been found guilty of similar conduct,” not defendants who may or may not be similar. Milken did not steal customers’ money. He did not steal over $10 billion dollars. He did not perpetrate multiple frauds with losses in the billions. He did not refuse to accept responsibility, obstruct justice, and perjure himself. A sentence equivalent to the one imposed on Milken of 10 years (let alone the two-year sentence he later received based on his cooperation) would be unreasonable here given the facts. At bottom, defendants who are in a position of trust and steal $100 million or more appropriately are sentenced to 40 to 50 years, or more. Such a sentence is necessary here and would be in line with the sentences meted out to other defendants who engaged in similar conduct. Continue Reading . Here [13] The specifics of these circumstances are sealed. If the Court believes the details of those circumstances are necessary in determining how, if at all, to consider the sentence in Greenwood, the Government will provide the information to the Court on a sealed basis. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Understanding Sentencing Guidelines in White Collar Crimes

Introduction There are different ways to form an investment thesis in crypto. One is to reject the importance of fundamentals and subscribe to the view that token prices are driven mainly by quick-gain-100x-seeking behavior or "financial nihilism." Is all crypto simply a meme? Should your technically sophisticated, tier-1 VC-backed, and community-vetted utility/governance "meme" coin be valued more than mine? Which narrative will be picked up by the KOLs next and pumped up by the crowd? What are we aping into? These views and strategies have merit but don't work at scale and across market cycles. Disciplined investors and researchers must carefully examine the facts and start seeing the forest for the trees. Let's stick to the first principles and dive into the concept of re-staking. Is it another backed by token emission or a new sustainable economy? As the saying goes, the proof is in the pudding; let's look for it! yield-generating bubble Cosmos Interchain Security Eigen Layer has popularised the idea of restaking (renting ETH economic security); however, it was initially proposed and implemented within the Cosmos ecosystem (renting ATOM economic security). Chains started with the Cosmos SDK provide configurability and sovereignty. However, developers' main challenge is building their own operator set (100-150 validators based on Tendermint consensus), bootstrapping liquidity, and using a native token, which has the risk of inflation and security value loss. enables other Chains (Consumer Chains) to be secured by the Cosmos Hub's validator set and the ATOM token's total market cap. In other words, it allows Consumer Chains to rent the economic security from Cosmos Hub. Cosmos Interchain security With 248MM ATOM staked, Cosmos Hub provides 3.2Bn$ in Economic Security to Consumer Chains. This first version of CIS is called “Replicated Security.” We Can Identify the Within the CIS Model: Following Stakeholders Validators – Run Cosmos Hub nodes and additional nodes for Consumer Chains Stakers – Delegate Atom tokens to Validators Consumer Chains – Rent Security from Cosmos Hub In Q3 2023, Chorus One the actual economic data from the two Consumer Chains on Cosmos – Stride and Neutron and made the following findings: reviewed Cosmos Hub Rewards: Cosmos Hub minted 367M$ per year or 1M$ daily in ATOM tokens as rewards for Stakers. Out of which 37M$ per year went to Hub Validators. Revenue: Cosmos Hub generated 1.5k$ per day in revenue from transaction fees. Stride generated 1k$ per day, and Neutron 12$ per day for Cosmos Hub. Combined daily transaction fees (2.5k$) would need to be 400 times higher to offset daily ATOM token emission (1M$) Infrastructure Costs (Excluding DevOps Costs) It's important to note that Validators need to set up additional nodes to support Consumer Chains, i.e., the hardware cost scales linearly with the addition of new Consumer Chains: The monthly node cost per Cosmos chain or ICS is 600$ per month or 7.2k$ per year. Cosmos Hub Security infra cost – 180 validators x 7.2k$ = 1.3M$ per year. Single Consumer Chain infra cost is the same – 1.3M$ per year. With Stride generating 365k$ per year and 36.5k$ in commission going to validators, such revenue doesn't come close to offsetting the infra costs for validators (1.3M$ per year). 40% of Cosmos Hub validators are not profitable, and this percentage increases as more Consumer Chains are onboarded. The consumer Chain would need substantially more revenue to provide an to Atom stakers. For example, to generate an extra 2% APR on staked ATOM, ICS would need to distribute $65MM per year to the Cosmos Hub. additional yield Finally, the ICS model removes the utility from the Consumer Chain native token, as it can only be used as a governance token and cannot be used for staking or network operations. , while the original idea of Cosmos Interchain Security offered easier onboarding of new chains, it came with a trade-off of increased infrastructure costs, lack of utility for the Consumer Chain native tokens, slashing risk for Validators, and high cost of economic security and therefore high revenue requirements for Consumer Chains. In summary As a result, Cosmos Hub governance is unlikely to approve more projects to join CIS unless they are set to deliver meaningful revenue to Hub validators and stakers. Moreover, the Replicated Security model is being revised, and it’s proposed to use a more flexible version called “ ,” whereby Validators can decide whether to run the nodes for Consumer Chains and Consumer chains would use only the Economic Security of supporting validators. Partial Set Security Eigen Layer Like CIS, allows Ethereum node operators to opt in to validate the new software modules. Eigen Layer calls these "chains" – Actively Validated Services. Eigen Layer The key difference with the CIS model is that the operators' cost of AVS support is marginal compared to running the ETH node; adding the AVS node to the existing ETH node doesn’t increase server load. Therefore, AVSs only need to generate enough fees to provide additional yield on top of native ETH staking yield. There Are the Within the Eigen Layer Model: Following Stakeholders Operators – Run Ethereum Nodes and additional AVS modules Restakers – Delegate Eth to Operators, which in turn opt into AVSs AVSs – Rent Security from Ethereum Infrastructure Costs (Excluding DevOps Costs): It costs approximately 1k USD per year to run 1 x ETH node (32 ETH), and more ETH validator slots can be added without significantly increasing the server load. The additional cost of supporting AVS is marginal (close to zero) Eigen Layer Economic Security As seen in the CIS model, Consumer Chains need to rent the entire $3.2Bn$ of economic security from Cosmos Hub and are constrained by the number of Cosmos Hub Validators. Eigen Layer implements "elastic scaling of security" and allows AVSs to dynamically control payouts to ETH stakers. 25000 AVS nodes can provide the equivalent of Cosmos Hub 3.2bn$ economic security. Similar to CIS, it would cost 64MM$ annually for AVS to generate an extra 2% APR on staked ETH. However, the key difference is that AVS can start with a smaller operator set and run more sustainable economics. Therefore, while the Eigen Layer brings additional infra costs almost to zero, it introduces the complexity of dynamic operator set and operator payout management. Watchtower networks will play an instrumental role in this process. Other Have Been Currently Worked Out: Open Questions and Challenges AVS Selection Process Who will choose AVSs? Will it be Operators or users of restaking protocols through governance voting? Or both? Operators are motivated to implement internal risk control frameworks to choose more profitable AVSs and manage slashing risks. Restaking protocols aim to conduct similar due diligence on behalf of their users. How will these two groups match AVS strategies and dynamically support the on and off-boarding of AVSs in an automated way? Are there any potential compliance issues with Stakers or Operators supporting AVSs in certain jurisdictions? These are not trivial questions! Potential Operator Centralization As the incremental cost to support new AVSs for operators is marginal – it would be more profitable for operators to support all available AVSs (as long as slashing risk is adequate), leading to centralization. Similarly, if users choose to restake only with the major operators, it will also lead to centralization. Eigen Layer is developing incentives to promote diverse operator sets and solo staking for AVSs to resolve this. Slashing risk Stakers opt-in by granting the Eigen Layer smart contracts the ability to impose additional slashing conditions on their staked ETH. The core idea of the Eigen Layer is to use "pooled security," whereby the same staked ETH is used to support multiple AVSs simultaneously. However, if one of the AVSs has a bug that leads to slashing, it can affect the operations of other AVSs. Eigen Layer addresses this risk by ensuring the system has " "  and a veto committee can reverse the slashing resulting from non-malicious behaviors. training wheels In addition, AVSs can acquire "attributable security," which, unlike pooled security, is specific to each AVS and will only be slashable by one AVS. Business Model Eigen Layer provides more flexibility for AVSs when defining the utility of their native token. In a mode, AVSs can use the native token for staking and network operations and balance its use vs. ETH throughout the project's lifetime. dual staking Conclusion In summary, the comparison between Cosmos Interchain Security (CIS) and Eigen Layer highlights the evolution of economic security concepts within blockchain ecosystems. While CIS pioneered the concept, it revealed drawbacks such as increased infrastructure costs, a lack of utility for native tokens, and high revenue requirements for Consumer Chains. Eigen Layer represents a substantial improvement by introducing elastic security scaling and dual staking. However, this enhanced flexibility comes with added complexity in managing slashing, security scaling, and operator payouts. It's truly fascinating to observe the ongoing evolution of this space as the latest restaking ideas from Eigen Layer permeate into other ecosystems such as Bitcoin ( ), Cosmos ( ), Solana ( ), and . Babylon Ethos Composable Avalanche Thank you to for your valuable feedback and review. Kam Benbrik

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