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Security Tokens are the latest hot topic in the craziness of the cryptocurrency ecosystem. Not only in the press, but also among investors who are savvy to find “the next killer application on the blockchain”. At TenX ( ), we recently announced as a mix of a traditional and innovative investment vehicle. While defining this token both from a legal and regulatory perspective however, we did find several challenges that might make a scalable breakthrough for security tokens not as easy as hoped. So, let’s step into Alice’s crypto shoes and climb down the rabbit hole. www.tenx.tech our TenX reward token Photo credit: http://www.disneystoryoriginspodcast.com/07a-alice-in-wonderland-part-1/ During events all around the world where I speak on the status quo of TenX, the future of blockchain and how to become #cryptofit, I ask the question, whether the audience knows what this new overly hyped thing called a crypto security token might be. Mostly, less than 10% in the room do. With that in mind let’s first get an overview, of what we are actually talking about here: What is a crypto security token? According to Wikipedia, a token is “a thing serving as a visible or tangible representation of a fact, quality, feeling, etc.” ( ). In crypto terms we mostly call things tokens when such a representation is not its entire own blockchain, such as we have in a cryptocurrency like Bitcoin, but rather when it is part of an ownership on top of another blockchain. The most famous example are tokens on the Ethereum blockchain defined by the ERC20 standard ( ). Here, any creator does not have to establish their own blockchain, but can use the Ethereum infrastructure by defining six simple functions to generate a new token within minutes. https://en.wikipedia.org/wiki/Token https://theethereum.wiki/w/index.php/ERC20_Token_Standard https://github.com/ethereum/EIPs/issues/20 The next question is, what such a token does or represents. To date, the large majority of all ERC20 tokens created are utility tokens, meaning, these tokens are used for some kind of service. One such example is a loyalty program, which is what we at TenX are using our PAY token for right now ( ), but they can take other forms like a fictional key to a lock or other claims. https://coinmarketcap.com/currencies/tenx/ How do tokens get created? Mostly this happens during Initial Coin Offerings (ICOs), where a token issuer (the company) creates these tokens at almost no cost and sells them to the public. This method has allowed token issuers to earn billions of dollars to date. Since most companies create these tokens to raise funds, it begs the question on why these tokens are created with utility functions and not, instead like during an IPO ( ), with security like properties. A security is a tradable financial asset where its value is derived from a contractual claim, such as bank deposits, bonds, and stocks and are usually more liquid than other tangible assets, such as commodities or real estate, and may be traded on financial markets ( ). Going for an IPO is mostly too expensive for a small startup, but especially over the past two years more and more voices came up asking why not create a crypto token that does represent some kind of ownership, right or claim against the company. This is what the crypto community has coined as crypto security tokens. https://www.investopedia.com/terms/i/ipo.asp https://en.wikipedia.org/wiki/Financial_asset The major problem with this idea? Just like 90% in a room full of crypto enthusiasts don’t know what a crypto security is, neither do most regulators. The concept of mixing the best out of both worlds, namely the low cost and ease of regulation of the crypto markets on one hand and the possibility of creating a financial security with all its claims on the other hand, does not exist in any legal system. It is an idea that we entrepreneurs came up with in order to drive innovation. While I am personally pro “less regulation” in general, I do see the point on why in this case, some rules are necessary, especially to protect investors’ hard earned money. I am part of many regulatory blockchain workgroups all around the world, and no regulator I have ever raised this to, has an answer to what such a hybrid of non-security and security should look like — yet. For almost two years “word on the crypto street” was, that this problem will get solved. But today, at the end of November 2018, we are not much further in legally defining this idea, than we were over a year ago. Some companies have been attempting to create security tokens, so why not “just do it”? Because we have to answer the following question first: “How does a secondary market work with security tokens?” The primary market is during the STO (Security Token Offering), when the company sells the tokens to the public. Nevertheless, anyone who buys such a token, wants to have an exit strategy — a plan on how to actually benefit from the token. Something that is completely illiquid is not worth much as anyone whose frozen assets worth millions of dollars might confirm to you when he cannot cash out the funds. We need a secondary market to do so, and for this we have to solve some questions first — five, to be exact: Question 1: What are the rules to transfer a token from Person A to Person B? In order to send bitcoins from one person to another, one just needs to send it to the other person’s address. The same applies to utility tokens. When it comes to securities however, we need to consider KYC/KYB (Know your Customer/Business), age, residency etc. For example, a twelve-year old is not allowed to buy securities in most jurisdictions. How would such limits of transfer be enforced in a system that is open-source, borderless and censorship resistant by design? If I wanted to transfer Apple shares to another person it gets registered in a central database — this gets completely flipped on top of its head when this database is decentralized. But, there is light at the end of the tunnel (hopefully not an oncoming train though…): The ERC token standard 1400 and its sister 1462, which is also the standard we are using for the TenX token would allow whitelisting of addresses for transfers and could solve this problem to stay compliant — maybe even within 2019. ERC 1400: The transfer of security tokens can fail for various reasons in contrast to utility tokens, where in general a transaction only fails because of insufficient account balance. https://github.com/ethereum/EIPs/issues/1411 Question 2: How would exchanges handle deposits & withdrawals? Trading security tokens on an exchange is quite straight forward from a KYC/KYB perspective, since they could require any user to get registered and verified upfront, similar to what most crypto exchanges are doing already anyways. However, how do tokens get onto an exchange and how could a user withdraw them. At the end it is: NYKNYM — Not Your Keys, Not Your Money — and the crypto ecosystem participants like to stay in control of their own private keys, which means storage of tokens on an exchange where one does not control the private key is not a desired solution. So, how can I trade security tokens on an exchange but then withdraw and/or deposit them? Here, lots of uncertainties pop up in regards to “source of funds”. How did you get the tokens in the first place? What if there was no proper whitelisting done with your address? How can you prove ownership of a token? Only few of these questions have answers today. Furthermore, we at TenX contacted 23 crypto security token exchanges, that claimed that they are either live already or close to live, to see what their requirements for listing a security token is. The answer? The majority replied, that they are not ready (yet) — but would be soon. The same answer I have received a year ago, when we contacted 9 crypto security exchanges to get further details. My prediction: Even in 2019 it will be very difficult to see progress here. Some sandboxes might pop up, like it is envisioned in Singapore, but not the all-access crypto-security-token exchanges that we would like to see. Still, we are quite optimistic in that regard that we can trailblaze as a positive example in partnerships with exchanges. Question 3: As a token holder, who do I bring a claim against? The next challenge to tackle is to find an answer to the problem who to go to, when you are unhappy with your token. If you bought your token during the STO from the company, this answer is quite easy, since there is only you and the company. What to do however, when you buy the token on the open secondary market? If a trade doesn’t go as planned, is it the company’s fault? Probably not, rather the exchange or in an Over the Counter (OTC) trade, the other party. What if you are of the belief that the seller has overcharged you for the token? What if the token performance is not as planned? In unregulated utility token markets, which comprise 100% of all traded tokens as of now, the answers are quite clear: There are not many rules, hence the term unregulated. These tokens are not expected to bring any returns but rather serve a utility function, so there is not much of a claim against the issuing company. There is no insider trading and if something goes wrong with a trade, the only party to go to is the exchange — who may or may not help you out. Then we have the exact opposite in regulated security markets like a stock exchange. Here we have clear rules on when to trade, when not to, what is allowed and what is not. Responsibilities are clearly defined. How could this work in a security token example? This is a heck of a problem because while we want as many freedoms as possible we also want as many certainties. Both stand at odds. For example, how is insider trading regulated? If I bought a token on the secondary market and I am unhappy with the token, who do I go to? These and many questions have yet to be answered. The hardest part here is, that the answers are very subjective and not as objectively solvable as the questions before. There is no right or wrong about who is responsible. Yes, while the exchange might be responsible, it could also be the seller or the company. It will be interesting to see if this gets solved in 2019 at all and if it does, how — will a standard get established among various exchanges? Part of the answers will only be possible if the following two questions get answered. Question 4: As a company issuing a Security Token, what do I have to report? This question is targeted towards the company performing a STO and what reporting measures it has to perform afterwards. In most stock markets these reports happen on a quarterly basis and are completely standardized. In the crypto ecosystem so far they are pretty much nonexistent, since they are not required for issuing a utility token and no clear rules are established (yet) for security tokens. Even though we were not required to do so, we at TenX published a transparency report one year after our tokensale in order to inform the public about various topics like financials, further plans and challenges: To date only a handful of companies have followed suit, even though they have received billions of dollars in exchange for mostly close to worthless tokens. While I completely agree that strict reporting arrangements such as for mature publicly traded companies would be way too stringent and short-sighted for startups, some oversight is definitely necessary to provide transparency for investors. Yet, the devil lies in the details: Need such reports get audited? How detailed would they have to be? Is it fair to measure a startup that might take a decade to disrupt an industry on a quarterly or annual basis? These and many more questions are yet to be answered. https://blog.tenx.tech/tenx-transparency-report-q2-2018-98bb05dd98b8 TenX Transparency Report — Q2, 2018: https://blog.tenx.tech/tenx-transparency-report-q2-2018-98bb05dd98b8 TenX Transparency Report — Q2, 2018: https://blog.tenx.tech/tenx-transparency-report-q2-2018-98bb05dd98b8 Question 5: What is the legal connection between token and underlying asset? The fifth and final question surrounds the legal connection between controlling a token and having a valid claim against the underlying asset. A simple question on what ownership actually is, is not as easy to answer. With gold, ownership is owning the physical piece. With shares, it is having them registered to someone’s name. With real estate it is about having the title in your name. But on a blockchain? On these decentralized databases you cannot even own anything other than a private key that lets you add entries into the database. If you can prove ownership of the key, you can cryptographically prove ownership to the digital token. But this is a completely new concept and it has yet to be shown, if and how this would hold in court. Would a regulator accept a lawsuit based on a mathematical formula that shows, that yes, you do hold some security token to some company even though these tokens are not registered in anyone’s name? What if two or more people can prove ownership because they both know the private key? Liechtenstein seems to be the first country in the world to start tackling these difficult questions in a new blockchain law, that hopefully goes into effect in 2019: https://www.liechtenstein.li/en/news-detail/article/liechtenstein-preparing-blockchain-act/ It would lay the foundation on how a legal connection between ownership of a token and its underlying asset is truly established. “Consultation launched on Blockchain Act” published by the Government Principality of Liechtenstein from 29th August, 2018 Yay or Nay? So, what does all that mean for security tokens as the next killer app on the blockchain? A famous quote that is rightly/wrongly attributed to Y-combinator Founder Paul Graham says: “If you want to know what tech play to invest into next, look at what kids and software developers are playing with!” Following this quote, you would have been right in 2011, when kids were playing with bitcoins to buy in-game items and you would have been right with Ethereum in 2015. However, I have yet to hear of a kid or software engineer that is playing with security tokens. Still, I do believe there is massive merit in crypto security tokens, especially in comparison to the utility token model, where investors don’t have many rights but lots of risks. How do you see these challenges around security tokens? Will they get solved in 2019? If yes, how and if no, why not? Let me know in the comments below and I am looking forward to a fruitful discussion on this exciting topic in the future. Follow me on Twitter ( ) for more updates on this and many other topics. www.twitter.com/julianhosp — — — Dr. Julian Hosp is the co-founder and president of the Singapore based company TenX ( ), a leading blockchain company that makes cryptocurrencies spendable anytime, anywhere in everyday life. To date, TenX has received over 100M USD in funding. www.tenx.tech Prior to TenX, Julian was a 10-year professional athlete and a medical doctor. He co-authored COMIT ( ), an open-source protocol that allows interoperability between blockchains, connecting them to an agnostic network. www.comit.network He is the author of several bestsellers such as „Cryptocurrencies simply explained” and “Blockchain 2.0 — more than just Bitcoin”, which have been translated into 15 languages and has sold over 100,000 copies. He was named one of the world’s top Blockchain and Cryptocurrency experts and also works in blockchain groups with the European Union on topics such as regulation, social impact and economics. As a Speaker of the Washington Speakers Bureau, he is frequently invited to global tech and entrepreneur events as well as government summits around the world and he is also a regular commentator in the media on current blockchain trends, the future of cryptocurrency and best practices.

Five Unsolved Challenges of Crypto Security Tokens

can do it, e can do it, a lot of others can do it too: interruption-free service migrations. Amazon Googl So why are so many enterprise businesses, banks, and others not able to do it? Why do I still get a notification by my bank that there will be service interruptions between Saturday 4 p.m. and Sunday 10 a.m. Why is there still that senior manager asking the question upon a change request: "will that change be disruptive?" 20 years ago, that might have been a reasonable question, but when was the last time you went to and got a “service down due to maintenance”? google.com I do not know why we are still talking about this. However, I do know how to do interruption-free service migrations. It’s very simple: Just watch and learn from the cashiers at your local supermarket Let’s say they have 4 counters. #1, 2, and 3 are open and have a cashier behind them. Now the cashier behind counter #2 is done for the day and needs a replacement. stop everything, take the cash drawer out, wait for her replacement, who has to put in his cash drawer, all while the whole queue of customers on that counter is waiting. Here’s what the cashier won’t do: A fourth cashier will open counter #4 and start serving customers. Next, the cashier at counter #2 will put up a sign to avoid getting more customers in the queue. She will still serve the remaining customers in her queue. Then she will close it. In fact, this is what will happen: closed Notice how this is a 4-step process: Open a new service Route traffic to the new service Serve all remaining traffic in the old service Close the old service Also, notice how this leaves all possibilities open: The cashier at counter #2 could suddenly decide to re-open, or not close at all, while the cashier at counter #4 still serves. Or the cashier at counter #4 could decide that it wasn’t a good idea, put a sign and leave after the remaining queue was served. It’s all seamless. closed Production Systems So why do we still have these nail-biting upgrade processes in production systems? These all-or-nothing-high-risk-no-rollback upgrade processes, potentially with service interruption, when those cashiers provide us with the perfect blueprint of how to do it? Okay — I do realize that production systems are a bit more complex than what I described above. But as always, the way to deal with complexity, is to break it down into small, digestible chunks. This is the hard part. Once that is done, any service upgrade can be done gradually with low-risk and rollback at any point in time, just like the cashiers could do it above. Let me try to use some examples in the following to make my point clear. Examples Changing Certificates Almost any non-trivial system needs to deal with certificates. The problem is always, how do you make sure your clients your servers get updated simultaneously? and The answer is, they don’t. Instead, the side verifying the validity of a certificate should allow a new one an old one for the transition period. and Here are the steps: add new certificate to the the list of accepted certificates on the client (assuming this is the verifying side) On server, replace old certificate with new one Wait for all requests using the old certificate to finish on client remove old certificate from client Update DB Schema Let’s say you have 1000 machines accessing an SQL database to feed a web app. A new version of the web app uses a renamed column in the database. So you need to rename that column in the database and simultaneously upgrade all your 1000 machines to the new web app version. How can you do that? You can’t. Instead, your new version must be able to understand a schema which contains both the new column name and the old column name. It must always try to use the new one first, and falling back to the old one when necessary. When writing, it should always write using the new and old column name. Here’s how you deploy: Add the new column to the database. (Open counter #4) Deploy your new web app version to your machines in staggered manner, potentially with a canary (Reroute traffic to counter #4, put up a ) closed sign Have a background job that copies all old column values to the new one. (Serve all remaining customers at counter #2) Remove the fallback mechanism in the web app and deploy again (again staggered). (Close counter #2) Optionally: Remove the old column. (Tear down counter #2 :-)) A New Storage Service Backend A very similar one as the previous. Let’s say you used S3 so far, but want to migrate to another S3 compatible storage service from another vendor. Your service may consist of thousands of machines. Again, to do this, you introduce a code change: writes now go to the new old storage service. Reads go to the new, but fall back to the old when a resource is not found. Deletes also go to both. and The migration plan goes like this: Set up the new data store. (Open counter #4) Deploy your updated system to your machines (Reroute traffic to counter #4, put up a ) closed sign Have a background that copies all your data from the old storage service to the new one (Serve all remaining customers at counter #2) Remove the code change, configure the new data store. (Close counter #2) only Note that you might need to increase the number of machines for your service temporarily, because of the more expensive data storage operations. Implications Looking at the examples, specifically the last one, one thing has become clear by now: to do migrations like these, you need to own the code. If you’re operating a system that you cannot change, you won’t be able to make changes that help interruption-free migrations. It’s for that reason probably, that newer forms of organizations, e.g. like Amazon’s two-pizza teams that follow a “You build it, you run it” culture are better suited for such interruption-free service migrations than the more classic ones where there is a strict separation between development and operations. It’s also obvious that these kinds of migration plans require teams to be able to do frequent deployments with small changes. At any point in time during such migration you must be able to stop or rollback. That’s difficult when your update contains other unrelated changes and when your deployment process is manual and slow. It also helps to have a mechanism to deploy to so-called canaries: single machines that get the update first. Only if they prove working correctly will the update be rolled out on to more machines. If you wonder what can help you do this kind of deployments, use . It probably cannot compete in scale with Amazon’s internal deployment system, . But it’s one of the most powerful you can currently get, especially being free and open source. BOSH Apollo Conclusion Cashiers at your local supermarket can give you the blueprint for doing interruption-free service migrations. Using this blueprint, you can break down your (potentially huge) migration into small changes that provide seamless, interruption-free migrations. Embracing an organizational structure that merges developers and operators helps to implement such small changes that aid the actual migration. Finally, use a powerful deployment system like to deploy the changes in an automated way. BOSH

Interruption-free service migrations: what you can learn from the cashiers at your local…

Google recently announced a new logo redesign, citing that the platforms on which we interact with their products are now more diverse. Today we’re introducing a new logo and identity family that reflects this reality and shows you when the Google magic is working for you, even on the tiniest screens. Given the , this move to a sans-serif typeface makes sense for “tiny screens.” conflict that little pixels can sometimes have with subtle forms At first glance, the logo looks to be set in , a common go-to which was designed by Paul Renner in 1927. Futura But, if you look closely, you can see that it’s NOT exactly Futura. Futura is about as as a typeface gets. It gives it a mathematically precise look to the point that it can even be a little stale. “Geometric” But, Google made some subtle but powerful changes to the typeface to suit the image they were looking for. By examining these, you can gain an better understanding of the common logo-design practice of tweaking a typeface. How is Google’s new logo different from Futura? Below, you can see the subtle differences between Google’s new logo, and what that logo would look like if it were purely set in Futura. (The new logo is in bold colors, while the lighter colors are pure Futura.) The most important tweak is to the big “G,” which is now the basis of Google’s favicon, and will appear throughout their properties. Note the following changes: The crossbar in the G is slightly higher, and longer. This opens the counters in the letter, which makes it feel a bit more like a “grin.” This more open counter also will be more readable at smaller sizes. The “aperture” of the G is more open (note the top stroke ends sooner). Again this makes the forms more friendly-looking, and makes it read better at smaller sizes. Next, the small “e” has similar changes. The crossbar is slanted up, which is typical of most “Humanist” typefaces, and is a throwback to hand-scribed letterforms that predate printing. You’ll notice that the “e” also has a larger aperture. If you imagined them as faces, Futura would have a devious grin, while Google’s “e” is laughing hysterically. Finally, the lowercase “g” doesn’t descend as far below the baseline. This is probably to balance out the logo (you have that luxury when you know what word you’re typesetting), but also notice that terminal of the stroke in the descender slants out a bit. This, once again, makes it more readable at smaller size, while carrying the more “friendly” theme. The final difference you’ll notice is that the “l” is slightly shorter. This is, once again, likely a balance issue. Google’s New Logo is Based Upon Their Custom Font, Product Sans Many people are wondering “what font is the new Google logo?” To be perfectly accurate, Google’s new logo isn’t in any one particular font. It’s a logo, and usually logos (at least for a big company like Google) have some customizations to them. This is one of those rare cases where you know what letters will be displayed, so the designer can balance the letters how they see fit. exactly So, no, there is no one “Google logo font.” But, Google’s new logo is .” _based on_their new font, “Product Sans In case you’re wondering, no, you can’t use Product Sans yourself. As points out, the for Product Sans states: this thread license Google offers many fonts on open source terms. This is not one of them. Please see . www.google.com/fonts Fair enough. Google has made many fonts available, so they’re entitled to their own. Product Sans is clearly , which was designed by Paul Renner in 1927. heavily influenced by Futura The biggest difference you’ll see between Futura and Product Sans is the double-story “a,” which is a common feature of more Humanist typefaces. This choice was made to avoid “redundancy in product lockups,” which I assume means “too many damn circles.” SOURCE: GOOGLE’S PRODUCT SANS SPECIMEN PDF There is no “a” in “Google,” however, there is one glaring difference between Product Sans and Google’s logo: it’s that slanted “e” again. What Other Fonts Look Like Google’s Logo? There’s hardly such thing as a totally original font, so naturally, there are other fonts that resemble the Google logo. Here’s a comparison: Aside from Futura, Google’s logo , (although, Relish Pro has a notably smaller x-height). resembles Relish Pro Some have also said that Google’s new logos resembles the . But, I disagree. hated Comic Sans is also a , minus that funky descender on the lowecase “f.” Windlesham Pro close cousin This post was originally published on . the blog Design for Hackers

What Font is the New Google Logo?

11 Best AI Chat Tools for Developers in 2024

Author:
(1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie);
(2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk);
(3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie);
(4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie);
(5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction
HDR Standards
HDR Subjective Testing Workflow
Conclusion and References IV. CONCLUSION In this work, we proposed and verified various aspects of an HDR subjective testing workflow. This includes the evaluation of various factors such as brightness, colour, display, signal, viewing conditions, and HDR intermediate file conversions for encoding. We conducted experiments to investigate the behaviour of different display technologies in terms of sustained brightness and its variability across window sizes. We observed substantial differences in HDR performance among consumer-level TV models that can affect the presentation and perception of HDR content. However, accounting for all these factors in an experimental environment is accompanied by high costs and is a non-trivial process. REFERENCES [1] N. Nandhakumar, “Recent activities of the ultra hd forum,” SMPTE Motion Imaging Journal, vol. 131, no. 8, 2022. [2] T. Kunkel and P. Griffis, “2021 High-Dynamic Range (HDR) Progress Report,” SMPTE Motion Imaging Journal, vol. 130, no. 8, pp. 101–107, 2021. [3] International Telecommunication Union (ITU), “ITU-R BT.2390-10: High dynamic range television for production and international programme exchange,” BT Series Broadcasting service (television), 2021. [4] European Broadcasters Union (EBU), “Tech 3320: User Requirements for Video Monitors in Television Production,” TC, 2019. [5] European Broadcasters Union (EBU) , “TR 047: Testing HDR Picture Monitors,” TC, 2019. [6] European Broadcasters Union (EBU), , “Tech 3325 v2.0: Performance Measurement of Studio Monitors,” TC, 2022. [7] P. Hanhart, M. Reˇ ˇrabek, and T. Ebrahimi, “Subjective and objective eval- ´ uation of HDR video coding technologies,” in 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX). [8] S. Athar, T. Costa, K. Zeng, and Z. Wang, “Perceptual Quality Assessment of UHD-HDR-WCG Videos,” in 2019 IEEE International Conference on Image Processing (ICIP). [9] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in 2022 IEEE International Conference on Image Processing (ICIP). [10] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in 2023 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, January. [11] Recommendation ITU-R, “BT2100-2: image parameter values for high dynamic range television for use in production and international programme exchange,” 2018. [12] Recommendation of ITU-R, “BT.500-14: Methodologies for the subjective assessment of the quality of television images,” BT Series Broadcasting service (television), 2019. [13] Recommendation, of ITU-R, “Subjective video quality assessment methods for multimedia applications,” ITU-T P.910, 2022. [14] Blackmagic Design, https://blackmagicdesign.com/products/decklink/ techspecs/W-DLK-34, Online, April 2023. [15] FFmpeg Developers, https://ffmpeg.org/ffmpeg-devices.html#decklink, Online, April 2023. [16] Blackmagic Design, https://blackmagicdesign.com/products/ davinciresolve, Online, April 2023. [17] HDFury website, https://hdfury.com/, Online, April 2023. [18] Vibhoothi, Angeliki Katsenou, John Squires, Franc¸ois Pitie, Anil ´ Kokaram, https://gitlab.com/mindfreeze/qomex2023, Project Page, Online, May 2023. [19] 3GPP Technical Specification Group TSG SA, “5G Video Codec Characteristics,” TR 26.955, Release 17, 2022. [20] Netflix, “Netflix Open Content,” https://opencontent.netflix.com/, Online April 2023. [21] ITU-T and ISO/IEC, “HDRTools pacakge [Online],” 2015, available: https://gitlab.com/standards/HDRTools. [22] C. Clark, D. Reisner, J. Holben, W. Aylsworth, G. Ciaccio, T. Kang, J. Korosi, P. Renner, D. Hall, M. Goi et al., “American society of cinematographers motion imaging technology council progress report 2022,” SMPTE Motion Imaging Journal, vol. 131, no. 8, pp. 34–49, 2022. [23] Sveriges Television AB, “SVT OpenContent Video Test Suite 2022 – Natural Complexity,” https://www.svt.se/open/en/content/. [24] A. G. Rempel, W. Heidrich, H. Li, and R. Mantiuk, “Video viewing preferences for hdr displays under varying ambient illumination,” in Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization, 2009, pp. 45–52. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Author: Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction HDR Standards HDR Subjective Testing Workflow Conclusion and References Abstract and Introduction Abstract and Introduction HDR Standards HDR Standards HDR Subjective Testing Workflow HDR Subjective Testing Workflow Conclusion and References Conclusion and References IV. CONCLUSION In this work, we proposed and verified various aspects of an HDR subjective testing workflow. This includes the evaluation of various factors such as brightness, colour, display, signal, viewing conditions, and HDR intermediate file conversions for encoding. We conducted experiments to investigate the behaviour of different display technologies in terms of sustained brightness and its variability across window sizes. We observed substantial differences in HDR performance among consumer-level TV models that can affect the presentation and perception of HDR content. However, accounting for all these factors in an experimental environment is accompanied by high costs and is a non-trivial process. REFERENCES [1] N. Nandhakumar, “Recent activities of the ultra hd forum,” SMPTE Motion Imaging Journal, vol. 131, no. 8, 2022. [2] T. Kunkel and P. Griffis, “2021 High-Dynamic Range (HDR) Progress Report,” SMPTE Motion Imaging Journal, vol. 130, no. 8, pp. 101–107, 2021. [3] International Telecommunication Union (ITU), “ITU-R BT.2390-10: High dynamic range television for production and international programme exchange,” BT Series Broadcasting service (television), 2021. [4] European Broadcasters Union (EBU), “Tech 3320: User Requirements for Video Monitors in Television Production,” TC, 2019. [5] European Broadcasters Union (EBU) , “TR 047: Testing HDR Picture Monitors,” TC, 2019. [6] European Broadcasters Union (EBU), , “Tech 3325 v2.0: Performance Measurement of Studio Monitors,” TC, 2022. [7] P. Hanhart, M. Reˇ ˇrabek, and T. Ebrahimi, “Subjective and objective eval- ´ uation of HDR video coding technologies,” in 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX). [8] S. Athar, T. Costa, K. Zeng, and Z. Wang, “Perceptual Quality Assessment of UHD-HDR-WCG Videos,” in 2019 IEEE International Conference on Image Processing (ICIP). [9] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in 2022 IEEE International Conference on Image Processing (ICIP). [10] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in 2023 Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, January. [11] Recommendation ITU-R, “BT2100-2: image parameter values for high dynamic range television for use in production and international programme exchange,” 2018. [12] Recommendation of ITU-R, “BT.500-14: Methodologies for the subjective assessment of the quality of television images,” BT Series Broadcasting service (television), 2019. [13] Recommendation, of ITU-R, “Subjective video quality assessment methods for multimedia applications,” ITU-T P.910, 2022. [14] Blackmagic Design, https://blackmagicdesign.com/products/decklink/ techspecs/W-DLK-34, Online, April 2023. [15] FFmpeg Developers, https://ffmpeg.org/ffmpeg-devices.html#decklink, Online, April 2023. [16] Blackmagic Design, https://blackmagicdesign.com/products/ davinciresolve, Online, April 2023. [17] HDFury website, https://hdfury.com/, Online, April 2023. [18] Vibhoothi, Angeliki Katsenou, John Squires, Franc¸ois Pitie, Anil ´ Kokaram, https://gitlab.com/mindfreeze/qomex2023, Project Page, Online, May 2023. [19] 3GPP Technical Specification Group TSG SA, “5G Video Codec Characteristics,” TR 26.955, Release 17, 2022. [20] Netflix, “Netflix Open Content,” https://opencontent.netflix.com/, Online April 2023. [21] ITU-T and ISO/IEC, “HDRTools pacakge [Online],” 2015, available: https://gitlab.com/standards/HDRTools. [22] C. Clark, D. Reisner, J. Holben, W. Aylsworth, G. Ciaccio, T. Kang, J. Korosi, P. Renner, D. Hall, M. Goi et al., “American society of cinematographers motion imaging technology council progress report 2022,” SMPTE Motion Imaging Journal, vol. 131, no. 8, pp. 34–49, 2022. [23] Sveriges Television AB, “SVT OpenContent Video Test Suite 2022 – Natural Complexity,” https://www.svt.se/open/en/content/. [24] A. G. Rempel, W. Heidrich, H. Li, and R. Mantiuk, “Video viewing preferences for hdr displays under varying ambient illumination,” in Proceedings of the 6th Symposium on Applied Perception in Graphics and Visualization, 2009, pp. 45–52. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Recommendations for Verifying HDR Subjective Testing Workflows: Conclusion

Recommendations for Verifying HDR Subjective Testing Workflows: HDR Subjective Testing Workflow

In the ever-evolving world of artificial intelligence (AI), one concept has gained prominence for its promise of customization and control: Bring Your Own Key (BYOK). While BYOK is often discussed in the context of cloud computing and security, this article delves into its applications in generative AI. Unlike traditional AI models, where developers dictate the algorithms to use, BYOK empowers users to select their preferred AI model. This offers a level of flexibility and personalization that has never been seen before. Note: This article is just for informational purposes, not to dissuade the use of BYOK in generative AI but to shed light on the nuances and potential pitfalls that developers and users may encounter. So, let’s embark on this journey into the heart of BYOK in generative AI, where customization meets responsibility. What's BYOK in Generative AI? Bring Your Own Key (BYOK) in generative AI refers to a model where users bring their pre-trained language models for use in an AI application or platform. In traditional generative AI applications, developers typically choose and implement the underlying models, determining how the AI behaves and responds. However, with BYOK, users can bring their preferred pre-trained models, allowing for a more personalized and adaptable experience. BYOK is often associated with the idea of customization and user empowerment. Users can choose the specific language model or generative AI algorithm that aligns with their needs, preferences, or the application's  requirements. This approach contrasts with the conventional model, where developers make decisions on behalf of the users regarding the algorithms that power the AI. Challenges With BYOK in Generative AI Although BYOK in generative AI offers users flexibility and customization, it also comes with several challenges and issues. If you’re a believer (either developer or user) of BYOK in generative AI, here are a few things you need to be on the lookout for when implementing or using BYOK. 1. Lack of Sufficient Knowledge The freedom to select whatever model you prefer to use with your AI research tool also comes with some level of responsibility. Selecting a suitable model for your specific use requires you to have sufficient knowledge of the different types of models available and how their performance will impact the result you get from your AI research assistant. The problem with most BYOK users is that they only see the freedom to customize and use whatever they can. However, they don’t have sufficient knowledge to make the right choice when it comes to picking the appropriate language model that fits their needs. 2. Cost Management and Overspending For those who understand the pricing model and monitoring mechanism, BYOK can be a great addition. But users who don’t know what to look for when selecting a model, might inadvertently select models that are too expensive. This could lead to unexpected costs and can result in them exceeding their spending budget. 3. Error Attribution Another issue with using BYOK in the generative AI space is users wrongly attributing errors to the AI application. When errors occur while using BYOK with an AI application, users could wrongly attribute the error to the application rather than recognize potential issues with the BYOK model they select. Moreover, debugging and troubleshooting errors become more complicated when the BYOK feature is implemented. With Traditional generative AI models, finding and solving issues only require you to look into the AI application. However, with the implementation of the BYOK feature in AI applications, developers need to carefully examine both the AI application and the user-provided models to find and fix the errors, which increases troubleshooting and debugging time. 4. Competing Model Selection In traditional generative AI models, developers already did the hard work of developing and testing the most appropriate foundation model for the AI research tool. Although you don’t have the freedom and flexibility to choose as you would in AI applications with the BYOK feature, you are not overwhelmed when using the application. On the other hand, when BYOK is implemented in your research AI tool, you have to select the perfect foundation language model for optimal performance. As a result, users might struggle with selecting the most appropriate model from the several hundreds, or even thousands, available. This could lead to decision paralysis or making suboptimal choices, which, in turn, could impact your model performance. If you have limited knowledge of foundation AI, for example, you might have decision paralysis if you want to use the BYOK feature via OpenRouter. Why? OpenRouter is an AI aggregating site with hundreds, if not thousands, of different pre-trained models. So, users with limited knowledge of the type of model they need will find it hard to make a correct choice. Addressing Challenges Associated with BYOK in Generative AI For every problem, there is always a solution. All you need to do is look inward. To address the challenges associated with the BYOK privileges in AI, as discussed above, here are solutions you can implement to enhance your experience, mitigate risks, and promote responsible usage of the AI application. 1. User Education and Well-Written Documentation One of the main challenges with BYOK in generative AI is the lack of sufficient knowledge. One significant way to mitigate the problem of overspending, cost management, and error attribution is through user education. Develop comprehensive training material and documentation to educate users on what they should expect when implementing BYOK in generative AI. Write guides and tutorial videos to educate users to choose the appropriate model, understand the pricing structure of the foundational models, and manage their budget effectively. 2. Recommend Appropriate Models Having the flexibility and freedom to choose the type of model you want to use also comes with its problems. Selection paralysis can occur when you have too many options to pick from. This could lead to selecting an inappropriate model for use with your AI research assistant. Recommending models to users can help mitigate this issue. So, even if you implement the BYOK feature, you should also inform them of the most appropriate model for optimal performance. 3. Implementing spending limits and Safeguards Finally, implementing spending limits and safeguards can prevent users from spending above what they intend. Setting up a mechanism to notify users when they approach or exceed their allocated budget to raise awareness will prevent the issue of overspending. With safeguards, you can implement continuous monitoring and analytical tools to track user behavior and identify issues with their behavior. With this, you can recommend safety measures and proactively address concerns related to BYOK usage. Conclusion In summary, BYOK in generative AI represents a shift toward user-centric customization, enabling individuals to bring their pre-trained models into applications and fostering a more tailored and adaptable AI experience. As we navigate the generative AI landscape, it becomes evident that BYOK is a double-edged sword. While offering users unprecedented flexibility never seen before, it also poses potential risks that need careful consideration.

BYOK (BringYourOwnKey) in Generative AI is a Double-edged Sword

Loneliness and the Biography of a Living Person

Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals, handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Transparency International, which publishes the Corruption Perceptions Index annually, confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy.


Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management.


Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals (SDGs), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole. Recently, my friend with a strong desire to make a difference in the world and deeply interested in the political future of society—asked me a question that sounded both paradoxical and provocative: “How can corruption be put to work for the state?” At first glance, the idea of using corruption as a tool for the state’s benefit seems controversial, but it raises an important question about the nature of governance and approaches to achieving public good in modern societies. These reflections, however, led me to another, more interesting idea: that the best path to building a prosperous society for everyone is through integrating economic growth and environmental protection as equal goals , handing these tasks over to technologies that will emerge very soon. I am convinced that only artificial general intelligence (AGI) can reliably and effectively manage the achievement of these two essential objectives in the future. as equal goals But let’s start with corruption, one of the most deeply rooted problems in various countries. Corruption can be found in both democratic and authoritarian systems, in nations with high and low levels of development. It functions as a parallel power structure, where personal interests replace public ones and distort democratic principles. In research Corruption and Government: Causes, Consequences, and Reform, Susan Rose-Ackerman and Bonnie J. Palifka emphasize that corruption infiltrates even those structures meant to uphold justice and transparency, undermining public trust in institutions—a phenomenon we can observe in many countries, such as Russia, China, and North Korea. Corruption and Government: Causes, Consequences, and Reform , Corruption and Government: Causes, Consequences, and Reform Corruption and Government: Causes, Consequences, and Reform Transparency International, which publishes the Corruption Perceptions Index annually , confirms that fighting corruption remains a pressing issue on a global scale. Despite the efforts of anti-corruption organizations and reformers, corruption often remains a persistent and seemingly unavoidable part of political systems. Corruption Perceptions Index Corruption Perceptions Index Corruption Perceptions Index annually annually It is worth noting that history has seen attempts at “managed” corruption, where instead of eradicating it completely, authorities aimed to minimize its harmful effects. For example, in some authoritarian regimes, corruption is tolerated as a way to maintain elite loyalty and system stability. Michael Johnston, in his book Syndromes of Corruption: Wealth, Power, and Democracy, explores the phenomenon of corruption and argues that such “managed” corruption may temporarily help sustain stability but ultimately deepens social inequality and increases public dissatisfaction in the long run. Syndromes of Corruption: Wealth, Power, and Democracy , Syndromes of Corruption: Wealth, Power, and Democracy Syndromes of Corruption: Wealth, Power, and Democracy In short, corruption cannot be turned into something beneficial, as the idea of "useful" corruption encounters several fundamental issues: Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Legalizing corruption or using it for state purposes undermines principles of justice and equality. Such a system becomes vulnerable and loses legitimacy. Ethical contradictions. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Corruption leads to resource waste and reduces the effectiveness of government management. Economic inefficiency. Social inequality. Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality . Corruption deepens social divides, as resources meant to improve life for all citizens are redirected to benefit a narrow group of individuals. Social inequality An interesting aspect of our discussion is that the ideal economic model for humanity should combine sustainable economic development with environmental protection. The United Nations, through its Sustainable Development Goals ( SDGs ), emphasizes that only balanced and sustainable economic growth can improve quality of life on the planet while minimizing environmental impact. SDGs SDGs SDGs Thus, a crucial aspect here is the integration of economic growth with environmental goals, which modern scientists and researchers already consider the only path to long-term well-being. Economist Jeffrey D. Sachs, in his book The Age of Sustainable Development, argues that a society’s happiness can be measured not only by GDP growth but also by its level of environmental sustainability, air and water quality, access to green spaces, and social equality. The combination of these factors helps create a society in which each individual feels part of a shared prosperity, while the environment remains viable for future generations. The Age of Sustainable Development , The Age of Sustainable Development The Age of Sustainable Development But what does AGI, technocracy, and the coming technological revolution have to do with this? In light of the above conclusions about corruption and the challenges of sustainable development, it’s clear that traditional management models, based on human decisions, are often unable to effectively address issues of sustainable growth. Sadly, people alone cannot fully help people. Modern technologies, especially artificial intelligence, have the potential to offer more flexible and precise solutions. I argue that AGI will be capable of managing the economy by striking a balance between economic growth and environmental sustainability. In book Life 3.0: Being Human in the Age of Artificial Intelligence, Max Tegmark notes that AGI will be able to process massive amounts of statistical data, which is essentially what the economy is built on—data analysis and interpretation. Works like The Second Machine Age by Erik Brynjolfsson and Andrew McAfee emphasize that AI has the potential to transform the management of social and economic processes, helping to reduce the impact of human weaknesses and biases. Life 3.0: Being Human in the Age of Artificial Intelligence , Life 3.0: Being Human in the Age of Artificial Intelligence Life 3.0: Being Human in the Age of Artificial Intelligence The Second Machine Age The Second Machine Age The Second Machine Age To me, this means that AGI could effectively allocate resources in the future to serve the public good, taking into account both current and long-term needs of society and our planet’s environment. I believe that the development of AGI will pave the way for a technocratic model of governance, where the economy becomes not just a tool for growth, but a system focused on creating conditions for societal happiness and well-being. In conclusion, I believe that humanity's best path forward lies not in attempts to adapt corruption for state needs, but in the pursuit of sustainable development grounded in advanced technologies. Corruption, even when "under control," undermines trust in institutions, slows progress, and deepens inequality. Instead of seeking "useful" corruption, humanity should focus on technologies that can provide long-term economic growth while also promoting environmental protection—key aspects upon which the well-being of future generations depends. The development of artificial general intelligence (AGI) could be the starting point for this progress. AGI has the potential to bring a new level of efficiency and transparency to governance, where traditional problems like corruption would cease to exist, as all processes would be focused on the public good. Furthermore, AGI could become a tool for balancing economic development with the preservation of natural resources. In a world where sustainable management is essential for the very possibility of life on the planet, such a model could be a solution and lifeline for future generations. In the face of global challenges such as climate change and the depletion of natural resources, it is clear that only the alignment of economic and environmental goals will create a harmonious future. Sustainable development, managed by AGI, has the potential to ensure long-term stability in which both public welfare and the environment are preserved for the benefit of future generations. Thus, AGI could offer humanity a completely new paradigm of governance, where corruption becomes obsolete, and steady economic growth along with environmental stewardship form the solid foundation for the happiness and prosperity of society as a whole.

Could Artificial General Intelligence Make Government Corruption Obsolete? 

Može li vještačka opća inteligencija učiniti korupciju vlade zastarjelom? 

¿Podría la Inteligencia Artificial General hacer obsoleta la corrupción gubernamental?

Inteligentzia Orokor Artifizialak Gobernuaren ustelkeria zaharkitu al dezake? 

Podería a intelixencia xeral artificial facer obsoleta a corrupción do goberno? 

क्या कृत्रिम सामान्य बुद्धिमत्ता सरकारी भ्रष्टाचार को ख़त्म कर सकती है?

A mesterséges általános intelligencia elavulttá teheti a kormányzati korrupciót? 

האם בינה כללית מלאכותית יכולה להפוך את השחיתות השלטונית למיושנת? 

汎用人工知能は政府の腐敗をなくすことができるか？

ປັນຍາທຽມທົ່ວໄປສາມາດເຮັດໃຫ້ການສໍ້ລາດບັງຫຼວງຂອງລັດຖະບານລ້າສະໄຫມໄດ້ບໍ? 

Может ли искусственный интеллект сделать коррупцию в правительстве ненужной?

Sirdoonka Guud ee macmalka ahi ma ka dhigi karaa musuqmaasuqa dawladda mid duugoobay? 

Kan artificiell allmän intelligens göra statlig korruption föråldrad? 

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.

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