Harmonize

harmonize

AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage.
Escalate the case to a human representative if the chatbot cannot resolve the issue.
Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise.
Enable real-time data sharing between agents, preventing duplication and enhancing decision-making.
Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks.
Understand how decisions are being made.
Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun. AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. Wider Adoption: The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun.

Orchestrator Agents: The Next Evolution in Enterprise AI

Discovering the Opportunities and Threats of MiCA Regulations on Crypto Dark Pools in the EU 

As gamers, we are always chasing the next high when it pertains to immersiveness in our games. From the advent of VR to haptic vests and eye tracking, there are tools for every level of immersion short of a full-dive gaming rig. However, with this next piece of gear, we could be one step closer to just that. Allow me to introduce GameScent. GameScent aims to push immersion in gaming even further through its ability to release timed context-relevant scents when specific triggers appear in a game, movie, or TV show. By listening to audio queues through either a 3.5mm audio jack or anHDMI cable, GameScent’s AI can tell what is going on in whatever media you are playing. Then, the device can emit scents like explosions, rain, burning rubber, forest, and more into the air to further immerse you into your show or game. But does it really add to your immersion? Or is this just a gimmick? As someone who’s been testing the device for the past eight months, join me as I review GameScent, diving into its design, performance, and how it enhances the gaming experience with AI scent technology. Unboxing and First Impressions Upon opening the GameScent, I was surprised by how little actually comes in the box. The GameScent device is made up of three parts: an AI audio adapter, atomizer, and scent cartridges. GameScent Device Setup The setup was incredibly simple—the audio adapter plugs into your system just like a capture card would: HDMI in from your console or PC, then HDMI out to your monitor or TV. You can also use a 3.5mm jack to connect, but I haven’t needed to use it yet. The HDMI connection works perfectly for me. The atomizer is plug-and-play—just connect the device to power, and you’re all set. The kit I have came with six basic scents: Gunfire
Explosions
Rain
Burning rubber
Forest
Clean air The proprietary clean air scent neutralizes the others, so once you’re done gaming, the smell of gunfire doesn’t linger. Each scent is screwed into a cap and then added into the atomizer slot that corresponds with that scent number. GameScent’s AI will detect triggers in the game to release relevant scents during gameplay. GameScent App Setup Once your GameScent device is all set up, it’s time to install the app. GameScent has an app that lets you sync your devices to WiFi so they can talk to each other and allows you to control the devices. This is a great feature if you have nice company over and don’t want to have explosion smells going off while “watching” Netflix. Overall, this setup process was remarkably easier than other immersion devices I’ve used. Typically, a gimmicky device wouldn’t have this level of care and access for its backend software or apps. The device itself has a rather impressive build quality. For reference, my cat has knocked it off my desk a number of times in the last eight months, and it’s always booted right back up and worked perfectly. If the device isn’t used for a period of time, it may disconnect from WiFi. However, the app will recognize when this happens and prompt you to reconnect upon opening. On the whole, this device’s setup and build quality are fantastic. You can tell a lot of R&D went into developing not just hardware that will last but also an app that makes the setup process  simple and reliably easy. GameScent Review: Device Performance in Action You can build an exceptionally solid piece of hardware and a quality app to go with it;  however, if the core concept of the device doesn’t elevate your gaming experience, then what’s the point? Over my last eight months of testing and in writing this GameScent review, I’ve sought to answer the question: Does the GameScent device actually improve my gaming experience? To answer this, I’ll be looking at the GamesScent device through three lenses: PvP games, PvE games, and VR. GameScent and Player vs. Player Games My favorite game of all time is League of Legends. No, I’m not a masochist—I just love the game that much. I’ve played League for over 12 years at this point, maining characters like Brand, Fizz, Zed, and other mid-laners. In testing GameScent on League of Legends, I really wanted to see two things: Does the scent release distract me from the game?
Does the scent affect my mental state while playing? In the last eight months, I’ve played over 500 games of League of Legends. My impressions are as follows: I have not found GameScent to be distracting from competitive play. The scents are subtle enough that you can definitely tell they’re there but not so profound as to make me wretch or overwhelm my senses while playing.
If GameScent impacts my mental state while playing, I’d say it helps me get into a flow state faster. I’ve noticed if I’m playing Brand and smell explosions while playing, I get chills. I get hyped. I’m in the zone. If I smell rain while throwing my Fizz ult, it gets me pumped up and ready to dive in. If anything, I’d postulate that GameScent improved my performance in League of Legends—and after playing for 12 years, that’s saying something! GameScent and Player vs. Environment Games When testing PvE games, I played roughly 100 hours of Elden Ring and 100 hours of Cyberpunk. In both games, I completely lost myself in the worlds of the Lands Between and Night City. However, was this due to my use of GameScent? Or do I just love a good story? Smelling the burning rubber of tires and gunfire in a car chase while driving around Night City, it’s hard to argue my experience wasn’t elevated. When streaming, I’d often forget to talk as I was so engrossed in the game. In Elden Ring, fighting the Fire Giant in the DLC was incredible, in part due to GameScent’s AI releasing explosion scents when it attacked. It was epic—an experience I’ll never forget. I can honestly say that without GameScent, these experiences would have been way less memorable and impactful. GameScent and Virtual Reality Now, when it comes to VR, GameScent is an incredibly heavy hitter. If we’re talking about the next step into a full dive rig, this tech is a must-have. Imagine playing SuperHot VR with a haptic vest and gloves and smelling your gunfire as you weave through its stylized dystopia. Or playing Skyrim VR and smelling the rain falling as you walk over a ridge and see Whiterun for the first time. Arguably, VR has the best use case for GameScent as a more immersive gaming experience lends itself to really feeling like you’ve stepped into another world. Whether you play VRChat, RPGs, or VR FPS, GameScent is absolutely SURE to elevate your immersive gaming experience. But is GameScent Just for Gaming? For my GameScent review, I think I’ve conclusively established the device has elevated my gaming experience, but can it be used outside of gaming? GameScent for Movies and TV I’m a massive weeb, and if there’s a way I can be even more immersed into my anime, then I’m going to try it. The good thing is that GameScent isn’t limited to just consoles or PCs. You can connect it to everything from home theater systems to your living room flat screen. From the smell of explosions when boats fire cannons in One Piece to the release of forest scents while watching Game of Thrones, GameScent delivers on its promise to immerse you into your media. GameScent for Tabletop Gaming I’ve also been able to test another fun use case:  tabletop gaming. My friends meet once a month for DnD sessions, and giving our DM the power to release scents to amp up the campaign right at our gaming table has made our sessions insanely fun. The GameScent app lets you manually push prompts to the atomizer to release scents. So, if a dragon breathes fire, then a corresponding scent can be released. Walking in the rain towards a hidden elf village? Push the rain scent. Wizard casts fireball in an enclosed room? Combine the explosion scent with the burning rubber scent to really make it hit home how much destruction was caused. There are all kinds of fun ways to incorporate GameScent into your hobbies. So, I can confidently say no, it’s not just for video games. GameScent Review Final Thoughts I set out to explore a new AI technology in the gaming space and deliver an honest GameScent review. After a thorough eight months of testing, I can confidently say GameScent has enhanced my gaming experience. From upping my win-rate in League of Legends to immersing me into other worlds like Skyrim, Night City, and the Lands Between, GameScent has not just enhanced my gaming experience but also helped me fall in love with gaming again. For a long time, I’ve only played games on stream. Gaming has started to feel like a chore or like work. Since using GameScent, I’ve actually started playing games off-stream again. Recently, I’ve been getting lost in Baldur’s Gate 3, and the way GameScent has an application for every aspect of it is actually incredible. Even my wife has started playing with me and has noticed she gets more into the game when GameScent is running. I can honestly say that going forward I’ll be using GameScent EVERY time I turn on my PC. Join me on TikTok and Twitch to see how I harmonize gaming and cooking—two of my biggest passions. Follow along for more content where I blend immersive gameplay with creative culinary adventures! As gamers, we are always chasing the next high when it pertains to immersiveness in our games. From the advent of VR to haptic vests and eye tracking, there are tools for every level of immersion short of a full-dive gaming rig. However, with this next piece of gear, we could be one step closer to just that. Allow me to introduce GameScent . GameScent GameScent aims to push immersion in gaming even further through its ability to release timed context-relevant scents when specific triggers appear in a game, movie, or TV show. By listening to audio queues through either a 3.5mm audio jack or an HDMI cable, GameScent’s AI can tell what is going on in whatever media you are playing. Then, the device can emit scents like explosions, rain, burning rubber, forest, and more into the air to further immerse you into your show or game. HDMI But does it really add to your immersion? Or is this just a gimmick? As someone who’s been testing the device for the past eight months, join me as I review GameScent, diving into its design, performance, and how it enhances the gaming experience with AI scent technology. Unboxing and First Impressions Upon opening the GameScent, I was surprised by how little actually comes in the box. The GameScent device is made up of three parts: an AI audio adapter, atomizer, and scent cartridges. GameScent Device Setup The setup was incredibly simple—the audio adapter plugs into your system just like a capture card would: HDMI in from your console or PC, then HDMI out to your monitor or TV. You can also use a 3.5mm jack to connect, but I haven’t needed to use it yet. The HDMI connection works perfectly for me. The atomizer is plug-and-play—just connect the device to power, and you’re all set. The kit I have came with six basic scents: Gunfire Explosions Rain Burning rubber Forest Clean air Gunfire Explosions Rain Burning rubber Forest Clean air The proprietary clean air scent neutralizes the others, so once you’re done gaming, the smell of gunfire doesn’t linger. Each scent is screwed into a cap and then added into the atomizer slot that corresponds with that scent number. GameScent’s AI will detect triggers in the game to release relevant scents during gameplay. GameScent App Setup Once your GameScent device is all set up, it’s time to install the app. GameScent has an app that lets you sync your devices to WiFi so they can talk to each other and allows you to control the devices. This is a great feature if you have nice company over and don’t want to have explosion smells going off while “watching” Netflix. Overall, this setup process was remarkably easier than other immersion devices I’ve used. Typically, a gimmicky device wouldn’t have this level of care and access for its backend software or apps. The device itself has a rather impressive build quality. For reference, my cat has knocked it off my desk a number of times in the last eight months, and it’s always booted right back up and worked perfectly. If the device isn’t used for a period of time, it may disconnect from WiFi. However, the app will recognize when this happens and prompt you to reconnect upon opening. On the whole, this device’s setup and build quality are fantastic. You can tell a lot of R&D went into developing not just hardware that will last but also an app that makes the setup process  simple and reliably easy. GameScent Review: Device Performance in Action You can build an exceptionally solid piece of hardware and a quality app to go with it;  however, if the core concept of the device doesn’t elevate your gaming experience, then what’s the point? Over my last eight months of testing and in writing this GameScent review, I’ve sought to answer the question: Does the GameScent device actually improve my gaming experience? To answer this, I’ll be looking at the GamesScent device through three lenses: PvP games, PvE games, and VR. GameScent and Player vs. Player Games My favorite game of all time is League of Legends. No, I’m not a masochist—I just love the game that much. I’ve played League for over 12 years at this point, maining characters like Brand, Fizz, Zed, and other mid-laners. In testing GameScent on League of Legends, I really wanted to see two things: Does the scent release distract me from the game? Does the scent affect my mental state while playing? Does the scent release distract me from the game? Does the scent affect my mental state while playing? In the last eight months, I’ve played over 500 games of League of Legends. My impressions are as follows: I have not found GameScent to be distracting from competitive play. The scents are subtle enough that you can definitely tell they’re there but not so profound as to make me wretch or overwhelm my senses while playing. If GameScent impacts my mental state while playing, I’d say it helps me get into a flow state faster. I’ve noticed if I’m playing Brand and smell explosions while playing, I get chills. I get hyped. I’m in the zone. If I smell rain while throwing my Fizz ult, it gets me pumped up and ready to dive in. I have not found GameScent to be distracting from competitive play. The scents are subtle enough that you can definitely tell they’re there but not so profound as to make me wretch or overwhelm my senses while playing. If GameScent impacts my mental state while playing, I’d say it helps me get into a flow state faster. I’ve noticed if I’m playing Brand and smell explosions while playing, I get chills. I get hyped. I’m in the zone. If I smell rain while throwing my Fizz ult, it gets me pumped up and ready to dive in. If anything, I’d postulate that GameScent improved my performance in League of Legends—and after playing for 12 years, that’s saying something! GameScent and Player vs. Environment Games When testing PvE games, I played roughly 100 hours of Elden Ring and 100 hours of Cyberpunk. In both games, I completely lost myself in the worlds of the Lands Between and Night City. However, was this due to my use of GameScent? Or do I just love a good story? Smelling the burning rubber of tires and gunfire in a car chase while driving around Night City, it’s hard to argue my experience wasn’t elevated. When streaming, I’d often forget to talk as I was so engrossed in the game. In Elden Ring, fighting the Fire Giant in the DLC was incredible, in part due to GameScent’s AI releasing explosion scents when it attacked. It was epic—an experience I’ll never forget. I can honestly say that without GameScent, these experiences would have been way less memorable and impactful. GameScent and Virtual Reality Now, when it comes to VR, GameScent is an incredibly heavy hitter. If we’re talking about the next step into a full dive rig, this tech is a must-have. Imagine playing SuperHot VR with a haptic vest and gloves and smelling your gunfire as you weave through its stylized dystopia. Or playing Skyrim VR and smelling the rain falling as you walk over a ridge and see Whiterun for the first time. Arguably, VR has the best use case for GameScent as a more immersive gaming experience lends itself to really feeling like you’ve stepped into another world. Whether you play VRChat, RPGs, or VR FPS, GameScent is absolutely SURE to elevate your immersive gaming experience. But is GameScent Just for Gaming? For my GameScent review, I think I’ve conclusively established the device has elevated my gaming experience, but can it be used outside of gaming? GameScent for Movies and TV I’m a massive weeb, and if there’s a way I can be even more immersed into my anime, then I’m going to try it. The good thing is that GameScent isn’t limited to just consoles or PCs. You can connect it to everything from home theater systems to your living room flat screen. From the smell of explosions when boats fire cannons in One Piece to the release of forest scents while watching Game of Thrones, GameScent delivers on its promise to immerse you into your media. GameScent for Tabletop Gaming I’ve also been able to test another fun use case:  tabletop gaming. My friends meet once a month for DnD sessions, and giving our DM the power to release scents to amp up the campaign right at our gaming table has made our sessions insanely fun. The GameScent app lets you manually push prompts to the atomizer to release scents. So, if a dragon breathes fire, then a corresponding scent can be released. Walking in the rain towards a hidden elf village? Push the rain scent. Wizard casts fireball in an enclosed room? Combine the explosion scent with the burning rubber scent to really make it hit home how much destruction was caused. There are all kinds of fun ways to incorporate GameScent into your hobbies. So, I can confidently say no, it’s not just for video games. GameScent Review Final Thoughts I set out to explore a new AI technology in the gaming space and deliver an honest GameScent review. After a thorough eight months of testing, I can confidently say GameScent has enhanced my gaming experience. From upping my win-rate in League of Legends to immersing me into other worlds like Skyrim, Night City, and the Lands Between, GameScent has not just enhanced my gaming experience but also helped me fall in love with gaming again. For a long time, I’ve only played games on stream. Gaming has started to feel like a chore or like work. Since using GameScent, I’ve actually started playing games off-stream again. Recently, I’ve been getting lost in Baldur’s Gate 3, and the way GameScent has an application for every aspect of it is actually incredible. Even my wife has started playing with me and has noticed she gets more into the game when GameScent is running. I can honestly say that going forward I’ll be using GameScent EVERY time I turn on my PC. Join me on TikTok and Twitch to see how I harmonize gaming and cooking—two of my biggest passions. Follow along for more content where I blend immersive gameplay with creative culinary adventures! TikTok Twitch

GameScent Review: Enhancing Gaming with AI Technology

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Mapping CX/UX Research Competencies to Stay Human-Centered in Product Development

The Isekai Archetypes of Computer Programming

Authors:
(1) Seokil Ham, KAIST;
(2) Jungwuk Park, KAIST;
(3) Dong-Jun Han, Purdue University;
(4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 5 Conclusion In this paper, we proposed a new knowledge distillation based adversarial training strategy for robust multi-exit networks. Our solution, NEO-KD, reduces adversarial transferability in the network while guiding the output of the adversarial examples to closely follow the ensemble outputs of the neighbor exits of the clean data, significantly improving the overall adversarial test accuracy. Extensive experimental results on both anytime and budgeted prediction setups using various datasets confirmed the effectiveness of our method, compared to baselines relying on existing adversarial training or knowledge distillation techniques for multi-exit networks. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1I1A2A02061135), by the Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD230017TD), and by IITP funds from MSIT of Korea (No. 2020-0-00626). References [1] Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein. Square attack: a query-efficient black-box adversarial attack via random search. In Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII, pages 484–501. Springer, 2020. [2] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pages 39–57. Ieee, 2017. [3] Sihong Chen, Haojing Shen, Ran Wang, and Xizhao Wang. Towards improving fast adversarial training in multi-exit network. Neural Networks, 150:1–11, 2022. [4] Francesco Croce and Matthias Hein. Minimally distorted adversarial examples with a fast adaptive boundary attack. In International Conference on Machine Learning, pages 2196–2205. PMLR, 2020. [5] Francesco Croce and Matthias Hein. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning, pages 2206–2216. PMLR, 2020. [6] Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, and Xiaohua Xie. The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24678–24687, 2023. [7] Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606, 2021. [8] Micah Goldblum, Liam Fowl, Soheil Feizi, and Tom Goldstein. Adversarially robust distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3996–4003, 2020. [9] Dong-Jun Han, Jungwuk Park, Seokil Ham, Namjin Lee, and Jaekyun Moon. Improving lowlatency predictions in multi-exit neural networks via block-dependent losses. IEEE Transactions on Neural Networks and Learning Systems, 2023. [10] Mirazul Haque, Anki Chauhan, Cong Liu, and Wei Yang. Ilfo: Adversarial attack on adaptive neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14264–14273, 2020. [11] Sanghyun Hong, Yigitcan Kaya, Ionut,-Vlad Modoranu, and Tudor Dumitras. A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference. In International Conference on Learning Representations, 2020. [12] Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang. Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. In International Conference on Learning Representations, 2020. [13] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. arXiv preprint arXiv:1703.09844, 2017. [14] Hoki Kim. Torchattacks: A pytorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950, 2020. [15] Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane. Multi-exit semantic segmentation networks. In European Conference on Computer Vision, pages 330–349. Springer, 2022. [16] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [17] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015. [18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [19] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [20] Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, and Gao Huang. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1891–1900, 2019. [21] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. [22] Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning, pages 4970–4979. PMLR, 2019. [23] Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE symposium on security and privacy (SP), pages 582–597. IEEE, 2016. [24] Mary Phuong and Christoph H Lampert. Distillation-based training for multi-exit architectures. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1355– 1364, 2019. [25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252, 2015. [26] Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. Branchynet: Fast inference via early exiting from deep neural networks. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 2464–2469. IEEE, 2016. [27] Xinglu Wang and Yingming Li. Harmonized dense knowledge distillation training for multi-exit architectures. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10218–10226, 2021. [28] Qunliang Xing, Mai Xu, Tianyi Li, and Zhenyu Guan. Early exit or not: Resource-efficient blind quality enhancement for compressed images. In European Conference on Computer Vision, pages 275–292. Springer, 2020. [29] Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li. Dverge: diversifying vulnerabilities for enhanced robust generation of ensembles. Advances in Neural Information Processing Systems, 33:5505–5515, 2020. [30] Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, and Bo Li. Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness. Advances in Neural Information Processing Systems, 34, 2021. [31] Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning, pages 7472–7482. PMLR, 2019. [32] Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. Bert loses patience: Fast and robust inference with early exit. Advances in Neural Information Processing Systems, 33:18330–18341, 2020. [33] Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, and Hongxia Yang. Reliable adversarial distillation with unreliable teachers. arXiv preprint arXiv:2106.04928, 2021. [34] Bojia Zi, Shihao Zhao, Xingjun Ma, and Yu-Gang Jiang. Revisiting adversarial robustness distillation: Robust soft labels make student better. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16443–16452, 2021. This paper is available on arxiv under CC 4.0 license. Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Authors: Authors: (1) Seokil Ham, KAIST; (2) Jungwuk Park, KAIST; (3) Dong-Jun Han, Purdue University; (4) Jaekyun Moon, KAIST. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Works 2. Related Works 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3. Proposed NEO-KD Algorithm and 3.1 Problem Setup: Adversarial Training in Multi-Exit Networks 3.2 Algorithm Description 3.2 Algorithm Description 4. Experiments and 4.1 Experimental Setup 4. Experiments and 4.1 Experimental Setup 4.2. Main Experimental Results 4.2. Main Experimental Results 4.3. Ablation Studies and Discussions 4.3. Ablation Studies and Discussions 5. Conclusion, Acknowledgement and References 5. Conclusion, Acknowledgement and References A. Experiment Details A. Experiment Details B. Clean Test Accuracy and C. Adversarial Training via Average Attack B. Clean Test Accuracy and C. Adversarial Training via Average Attack D. Hyperparameter Tuning D. Hyperparameter Tuning E. Discussions on Performance Degradation at Later Exits E. Discussions on Performance Degradation at Later Exits F. Comparison with Recent Defense Methods for Single-Exit Networks F. Comparison with Recent Defense Methods for Single-Exit Networks G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms G. Comparison with SKD and ARD and H. Implementations of Stronger Attacker Algorithms 5 Conclusion In this paper, we proposed a new knowledge distillation based adversarial training strategy for robust multi-exit networks. Our solution, NEO-KD, reduces adversarial transferability in the network while guiding the output of the adversarial examples to closely follow the ensemble outputs of the neighbor exits of the clean data, significantly improving the overall adversarial test accuracy. Extensive experimental results on both anytime and budgeted prediction setups using various datasets confirmed the effectiveness of our method, compared to baselines relying on existing adversarial training or knowledge distillation techniques for multi-exit networks. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1I1A2A02061135), by the Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD230017TD), and by IITP funds from MSIT of Korea (No. 2020-0-00626). References [1] Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, and Matthias Hein. Square attack: a query-efficient black-box adversarial attack via random search. In Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII, pages 484–501. Springer, 2020. [2] Nicholas Carlini and David Wagner. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp), pages 39–57. Ieee, 2017. [3] Sihong Chen, Haojing Shen, Ran Wang, and Xizhao Wang. Towards improving fast adversarial training in multi-exit network. Neural Networks, 150:1–11, 2022. [4] Francesco Croce and Matthias Hein. Minimally distorted adversarial examples with a fast adaptive boundary attack. In International Conference on Machine Learning, pages 2196–2205. PMLR, 2020. [5] Francesco Croce and Matthias Hein. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning, pages 2206–2216. PMLR, 2020. [6] Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, and Xiaohua Xie. The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24678–24687, 2023. [7] Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606, 2021. [8] Micah Goldblum, Liam Fowl, Soheil Feizi, and Tom Goldstein. Adversarially robust distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3996–4003, 2020. [9] Dong-Jun Han, Jungwuk Park, Seokil Ham, Namjin Lee, and Jaekyun Moon. Improving lowlatency predictions in multi-exit neural networks via block-dependent losses. IEEE Transactions on Neural Networks and Learning Systems, 2023. [10] Mirazul Haque, Anki Chauhan, Cong Liu, and Wei Yang. Ilfo: Adversarial attack on adaptive neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14264–14273, 2020. [11] Sanghyun Hong, Yigitcan Kaya, Ionut,-Vlad Modoranu, and Tudor Dumitras. A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference. In International Conference on Learning Representations, 2020. [12] Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang. Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. In International Conference on Learning Representations, 2020. [13] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. arXiv preprint arXiv:1703.09844, 2017. [14] Hoki Kim. Torchattacks: A pytorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950, 2020. [15] Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane. Multi-exit semantic segmentation networks. In European Conference on Computer Vision, pages 330–349. Springer, 2022. [16] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. [17] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015. [18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [19] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [20] Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, and Gao Huang. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1891–1900, 2019. [21] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018. [22] Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning, pages 4970–4979. PMLR, 2019. [23] Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. Distillation as a defense to adversarial perturbations against deep neural networks. In 2016 IEEE symposium on security and privacy (SP), pages 582–597. IEEE, 2016. [24] Mary Phuong and Christoph H Lampert. Distillation-based training for multi-exit architectures. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1355– 1364, 2019. [25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252, 2015. [26] Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. Branchynet: Fast inference via early exiting from deep neural networks. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 2464–2469. IEEE, 2016. [27] Xinglu Wang and Yingming Li. Harmonized dense knowledge distillation training for multi-exit architectures. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 10218–10226, 2021. [28] Qunliang Xing, Mai Xu, Tianyi Li, and Zhenyu Guan. Early exit or not: Resource-efficient blind quality enhancement for compressed images. In European Conference on Computer Vision, pages 275–292. Springer, 2020. [29] Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li. Dverge: diversifying vulnerabilities for enhanced robust generation of ensembles. Advances in Neural Information Processing Systems, 33:5505–5515, 2020. [30] Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, and Bo Li. Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness. Advances in Neural Information Processing Systems, 34, 2021. [31] Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning, pages 7472–7482. PMLR, 2019. [32] Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. Bert loses patience: Fast and robust inference with early exit. Advances in Neural Information Processing Systems, 33:18330–18341, 2020. [33] Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, and Hongxia Yang. Reliable adversarial distillation with unreliable teachers. arXiv preprint arXiv:2106.04928, 2021. [34] Bojia Zi, Shihao Zhao, Xingjun Ma, and Yu-Gang Jiang. Revisiting adversarial robustness distillation: Robust soft labels make student better. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16443–16452, 2021. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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