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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?

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

Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e__ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead Introduction: Ever since the buzz about GenAI and ChatGPT, I have been trying to understand the space and leveraging it into my day-to-day activities as a Data Engineer. No doubt it has been useful for debugging with respective programming and documentation. Personally, it played a significant role in helping me plan my Dubai itinerary. It even contributed to naming my friend’s daughter. However, it works pretty well if you give appropriate and precise requirements. There is a lot of anxiety among people that AI is going to take our jobs as they feel almost everything will be automated through AI. Is LLM as good as everyone claims? In this article, we will look into the specifics of Gen AI’s role in data engineering and see where it flourishes, where it requires enhancement, and where human expertise remains irreplaceable. Areas of Strength: Basic Data Querying: 100% the human necessity is going to get disrupted, but the severity based on the complexity is high. Even the SQL practitioners won’t feel much difference here, and self-serve analytics will catch fire in this area which is something phenomenal! Again, to quote positively, data engineers won’t need to guide the stakeholders nearly as much! Troubleshooting Pipeline Failures: LLMs coupled with agents will slash on-call load massively. LLMs processing stack traces/quality failures can recommend the built-in remedies and nudge the ball down the field to address the problem, presumably even over Slack. And that is a fantastic percentage of data engineering hours saved! To be clear, some hard failures require manual troubleshooting. Anomaly Detection: Regardless of the structure, format, source, complexity, timeliness, or even natural play of data, large language models can identify anomalies. LLMs are well-suited to anomaly identification because they can work across a variety of data sources, identify subtle patterns, and distinguish when data isn’t behaving as expected. This will help data engineers to identify and fix data quality problems in advance. Areas for Enhancement: Data Cleansing: Our results suggest that Language Models (LLMs) are highly effective in data cleansing due to their high performance in unstructured data management and semantic error identification and correction, as well as high scalability. Furthermore, our review of the current state-of-the-art suggests that even more substantial improvements are possible, including fine-tuning of LLMs to specific domains or even organization data. In addition, various interactive mechanisms as well as coupling with data quality frameworks can significantly increase the efficiency of LLMs. Overall, LLMs allow for organizing and improving the whole process of data cleansing, helping to obtain high-quality datasets, and, consequently, valuable insights. Visualization and Report Generation: Language Models (LLMs) excel in visualization and documentation tasks by automating the creation of reports, summarizing data insights, and generating narrative-driven reports. Enhancements needed include improving document organization and categorization, data-driven storytelling capabilities integrating with version control systems and visualization tools for seamless output generation, and enhancing interpretability features to ensure clarity and coherence in reports. Writing Pipeline Code in SQL, Spark, or Flink LLM can be a helpful starting point, and for our work, it is a good boilerplate. Still, the generated material is hard to debug — or often not, worth the trouble. Prompt-driven content is still far from being able to turn out a nice, optimized, and absolutely accurate data pipeline. Optimization By providing suggestions on efficient query structures and detecting problems that slow performance, LLMs are especially powerful when it comes to SQL optimization. They can even make recommendations for optimizations on the basis of historical query data. Things for improvement include integrating database-specific optimization techniques into the training of LLM, bridging with query execution plans in order to give context-oriented advice and feedback mechanisms so that one can modify strategies for optimization according to what users prefer as well as performance metrics. Physical Data Model As soon as the logical model is finalized, LLM will be efficient in moving and upgrading this logical (or conceptual) data model into the targeted architecture. The problem that LLM encounters with all the examples is that it’s hard for most people to understand the pipeline, table, and its relationship. Areas Where Human Expertise Remains Irreplaceable: Logical Data Model Data modeling is a very soft skills-focused task. You have to communicate with different stakeholders to understand the business context in detail. Understanding what data is needed, the relationship between entities, and considering different scenarios are human-oriented tasks that LLMs must continue to struggle with. Building Pipeline and Framework Once they reach a certain level of complexity, LLMs begin to give up. Imagine if someone asked ChatGPT, “Can you provide me the source code of Android’s next version?” While LLMs can help with routine data engineering tasks, they do not have the insight or inventiveness unique to humans that allows for new solutions to complex challenges, unlocking creativity in technical terms. Domain Expertise and Intuition: Human data engineers bring a unique blend of domain expertise and intuition to their work that often surpasses what conventional software isn’t able to achieve through automation. This intuition allows them to find subtle patterns in data that no fully rigorous automated system can. Based on their knowledge of this field and their experiences in it, they can predict future trends. They can also make decisions in light of a broader context, including corporate or government needs, ethical considerations, and potential future scenarios. This holistic approach to data engineering surpasses what LLMs have achieved. Although LLMs have great power, they are typically restricted to the patterns and relationships they have been trained to recognize. Conclusion: On the whole, Gen AI can certainly automate trivial procedures such as looking for information, launching programs that don’t work properly, repeatedly correcting faults, and anomaly detection. My gut tells me that Gen AI will win out and excel at tasks such as Data Cleansing, Optimization, and Writing Pipelines — once the team understands gaps and missing patterns. But it can not learn purely human soft skills that are important for building logical data models or a framework. I believe that these skills, which cannot be automated, are also essential for stakeholders to ask for innovation in the first place. After all, no one ever asked Steve Jobs if they needed anything like an iPhone; he just thought up what would become its invention. References: https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b https://barrmoses.medium.com/will-genai-replace-data-engineers-no-and-heres-why-708b0a27da6b ​​__ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e __ https://www.linkedin.com/pulse/navigating-data-governance-challenges-era-large-fnq5e https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.dataversity.net/navigating-the-risks-of-llm-ai-tools-for-data-governance/ https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://www.skyflow.com/post/why-ai-governance-requires-data-governance https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://coreykeyser.medium.com/you-cant-govern-ai-without-governing-data-27bb88d8f9ea https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://www.ankursnewsletter.com/p/gpt-4-gpt-3-and-gpt-35-turbo-a-review https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://towardsdatascience.com/from-chaos-to-clarity-streamlining-data-cleansing-using-large-language-models-a539fa0b2d90 https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead https://medium.com/@wangshally11/data-cleaning-warm-up-before-training-large-language-models-74ed1edceead

LLMs: An Assessment From a Data Engineer

Table of Links Abstract and I. Introduction II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References V. APPROACH III: LIGHT NODE - EXTERNAL QUERY & LOCAL VERIFICATION With the cost of running a full node continuously rising, the Ethereum community is seeking to develop a solution that can ensure data integrity and reduce system requirements. The result is the light node [23]: A light node is a server that only synchronizes limited information (such as block headers) with the blockchain network instead of block contents. When a light node encounters an RPC query, it pulls related contents from the blockchain network, verifies the contents with the synchronized information, and sends the result back to the client (see Fig. 4). Traditionally, a light node synchronizes all block headers with the network and pulls related blocks to answer the query when it receives one. However, the recent innovation of ultralight nodes, such as Flyclient [10], allows a light node to synchronize less data with the blockchain but still be able to verify data related to the query, provided that some node in the blockchain network is willing to compute a piece of proof for the correctness of the data. When the required data on the blockchain is very specific and limited, like when the users only care about one address that they own, specific light nodes, in the form of wallet software, are also available to the users. For zero-knowledgebased blockchains (such as Zcash [21] and Monero [14]), the wallet software can synchronize with the blockchains by downloading and analyzing blocks one by one to extract all transactions related to the address, without actually storing the blocks. These specific light nodes can be much more efficient for new blockchain users because they only care about blocks that come after the creation of the address, so they do not have to analyze any data before the creation time. There are also many studies on universal light nodes in the research area, but few have been deployed to real-world blockchains [12]. Some notable examples in active development include Flyclient [10] and IBC [30]. A light node is expected to ensure data integrity because it can check the block contents with the node’s synchronized headers. However, a recent study shows that the security guarantee is weakened when the adversary controls the network [44]. A. The Case of Ethereum We notice that support for light nodes is minimal as of 2022 for Ethereum, one of the most popular blockchains [24]: there is only one execution client and no production-ready light clients on the consensus layer. The proper functioning of light nodes depends on their ability to pull blocks from full nodes in the network, but there is no incentive for full nodes to provide such service, and as a result, light nodes often fail to find peers. Future development on light nodes focuses on allowing light nodes to relay information among themselves [22]: it is planned not to provide financial incentives for nodes, and the network is designed to work like BitTorrent and IPFS. B. Open Questions While significant engineering effort is still necessary before light clients become universal, there is still room for innovations to improve the client’s performance. A recent SoK paper has suggested privacy-preserving, efficient resynchronization after offline, and cross-chain interoperability as potential future research directions [12]. Based on the premise that proving some statement to be true is more difficult than proving it to be false, some recent study also focuses on fraud proof [1]. The idea is that the light node does not need to verify the information it receives to be true, but can simply ask a few full nodes to make sure that it is not false. We also notice that no matter the design of the light client, if it needs a full node to serve as a proofer for additional efforts beyond simply broadcasting a block, then incentives might be necessary to ensure that the full node renders the service. Toward that end, we think that the light node mechanism may merge with the node-as-a-service providers, with the providers serving the light nodes in a way that ensures integrity. In this sense, light nodes may evolve as a way to a verifiable RPC protocol. Authors:
(1) Zhongtang Luo, Purdue University (luo401@purdue.edu);
(2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com);
(3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Expectations of Blockchain Accessibility II. Expectations of Blockchain Accessibility III. Approach I: Maintain a Ledger Locally - Run a Full Node III. Approach I: Maintain a Ledger Locally - Run a Full Node IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) IV. Approach II: Query a Third-Party Ledger-Node-As-A-Service (NAAS) V. Approach III: Light Node - External Query & Local Verification V. Approach III: Light Node - External Query & Local Verification VI. Concluding Remark and References VI. Concluding Remark and References V. APPROACH III: LIGHT NODE - EXTERNAL QUERY & LOCAL VERIFICATION With the cost of running a full node continuously rising, the Ethereum community is seeking to develop a solution that can ensure data integrity and reduce system requirements. The result is the light node [23]: A light node is a server that only synchronizes limited information (such as block headers) with the blockchain network instead of block contents. When a light node encounters an RPC query, it pulls related contents from the blockchain network, verifies the contents with the synchronized information, and sends the result back to the client (see Fig. 4). Traditionally, a light node synchronizes all block headers with the network and pulls related blocks to answer the query when it receives one. However, the recent innovation of ultralight nodes, such as Flyclient [10], allows a light node to synchronize less data with the blockchain but still be able to verify data related to the query, provided that some node in the blockchain network is willing to compute a piece of proof for the correctness of the data. When the required data on the blockchain is very specific and limited, like when the users only care about one address that they own, specific light nodes, in the form of wallet software, are also available to the users. For zero-knowledgebased blockchains (such as Zcash [21] and Monero [14]), the wallet software can synchronize with the blockchains by downloading and analyzing blocks one by one to extract all transactions related to the address, without actually storing the blocks. These specific light nodes can be much more efficient for new blockchain users because they only care about blocks that come after the creation of the address, so they do not have to analyze any data before the creation time. There are also many studies on universal light nodes in the research area, but few have been deployed to real-world blockchains [12]. Some notable examples in active development include Flyclient [10] and IBC [30]. A light node is expected to ensure data integrity because it can check the block contents with the node’s synchronized headers. However, a recent study shows that the security guarantee is weakened when the adversary controls the network [44]. A. The Case of Ethereum A. The Case of Ethereum We notice that support for light nodes is minimal as of 2022 for Ethereum, one of the most popular blockchains [24]: there is only one execution client and no production-ready light clients on the consensus layer. The proper functioning of light nodes depends on their ability to pull blocks from full nodes in the network, but there is no incentive for full nodes to provide such service, and as a result, light nodes often fail to find peers. Future development on light nodes focuses on allowing light nodes to relay information among themselves [22]: it is planned not to provide financial incentives for nodes, and the network is designed to work like BitTorrent and IPFS. B. Open Questions B. Open Questions While significant engineering effort is still necessary before light clients become universal, there is still room for innovations to improve the client’s performance. A recent SoK paper has suggested privacy-preserving, efficient resynchronization after offline, and cross-chain interoperability as potential future research directions [12]. Based on the premise that proving some statement to be true is more difficult than proving it to be false, some recent study also focuses on fraud proof [1]. The idea is that the light node does not need to verify the information it receives to be true, but can simply ask a few full nodes to make sure that it is not false. We also notice that no matter the design of the light client, if it needs a full node to serve as a proofer for additional efforts beyond simply broadcasting a block, then incentives might be necessary to ensure that the full node renders the service. Toward that end, we think that the light node mechanism may merge with the node-as-a-service providers, with the providers serving the light nodes in a way that ensures integrity. In this sense, light nodes may evolve as a way to a verifiable RPC protocol. Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). Authors: Authors: (1) Zhongtang Luo, Purdue University (luo401@purdue.edu); (2) Rohan Murukutla, Supra (r.murukutla@supraoracles.com); (3) Aniket Kate, Purdue University / Supra (aniket@purdue.edu). 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

Light Nodes: Balancing Cost and Data Integrity in Blockchain Accessibility 

Your Guide For Overcoming Procrastination and Getting Down to Work

Buckle up, folks, because it looks like the web’s latest scandal is about to get your blood boiling. If you’ve been getting more spam than usual lately, it might just be time to thank the shady operators at Soft2Share.com. The operator of the website has been caught red-handed in an outrageous spam scheme attempting to sell unauthorized email lists. Hold on to your keyboards—things are about to get ugly. In what can only be described as a brazen attempt to cash in on email lists, Soft2Share.com has been caught spamming thousands of unsuspecting websites. And how did they do it? By using their own domain email address and Gmail accounts. Let’s dive into the details. Soft2Share.com was caught red-handed attempting to peddle email lists that contain hundreds, if not thousands, of email addresses. And who are these unfortunate email list victims? Private website owners, bloggers, news publications—you name it. If you’ve got an email address and a website, chances are, Soft2Share.com had you on its hit list. Spam Email Subject Line - 10 Million B2B Email Leads From contact@soft2share.com | Clarissa Turner Hi there, I hope you're having a great day. I am a digital marketer and work at Brand Visibility. Just Wondering, are you interested in buying 10 Million verified B2B email leads from the Apollo, Lusha, Cognism, Uplead, Linkedin and ZoomInfo databases?Features: EMEA, NAM, and APAC coverage, validated emails, high accuracy and GDPR compliance. Selling Price: $150 Let me know if you are interested.Regards. - The spammer. One user reported receiving the unsolicited email 17 times (After requesting to stop each time) - After the 17th a response was provided to the stop request - “We use AI for email marketing” - “We:”??? - What a joke! It’s just a freelancer spammer with no shame! Now, you might be wondering why this is such a big deal. After all, spam has been around forever, right? True, but this isn’t just your run-of-the-mill junk email. This is a calculated, aggressive scheme to exploit the personal information of countless individuals and organizations for profit. And it’s not just about annoying emails; it's about privacy violations and a blatant disregard for digital ethics. For bloggers and journalists who rely on email to connect with their audiences, this isn’t just a nuisance—it’s a potential threat to their livelihood. And for private website owners, it’s a breach of security that could open the door to further harassment. What makes this situation even worse is the fact that Soft2Share.com didn’t stop at their own domain. Oh no, they took it a step further by utilizing Gmail accounts to extend their reach. It’s as if they wanted to make sure no inbox was left untouched by their spammy grasp. And let’s not forget the larger implications. When spam emails proliferate, they don’t just annoy—they can lead to serious security risks, including phishing attempts and malware distribution. By selling these lists, Soft2Share.com isn’t just playing with fire; they’re throwing gasoline on it. So, what’s the takeaway from all this? First off, be vigilant. If you’re receiving an uptick in spam, especially if it seems oddly specific or targeted, your email might have ended up on one of these dubious lists. Secondly, it’s time we call out and hold accountable those who profit from our personal data and email addresses. Soft2Share.com may have thought it is operating under the radar, but its actions have lit up the digital world like a neon sign. Hold onto your inboxes, folks, because the Soft2Share.com spam scandal has taken a turn for the absurd. As if flooding inboxes with unwanted emails wasn’t bad enough, it seems the spammer behind Soft2Share.com has now perfected the art of ignoring pleas to stop. Picture this: you've been relentlessly bombarded with spam from Soft2Share.com, and your inbox is overflowing with offers to buy email lists that include thousands of addresses. Frustrated and fed up, you decide to fight back by firing off a polite but firm request to stop the spamming. But what happens? Silence. Crickets. Your emails are swallowed by the void, and the spam continues to pour in, unabated. But wait, there’s more! One particularly fed-up recipient decided to up the ante. They threatened to report Soft2Share.com to the hosting provider, the CAN-SPAM Act administrator, and various spam reporting databases. Surely, such a threat would get a reaction, right? Well, you’d think so. Instead of a professional or even halfway serious response, this beleaguered spam victim received a reply that can only be described as laughably inadequate. The spammer claimed to be from a “large firm”—argued that her actions were perfectly “fine and ethical marketing.” Seriously? This is the spin we’re getting? The response continued in a bewildering fashion, asserting that there was absolutely no need for opt-in permissions or agreements—because who needs pesky legalities when you're making a quick buck off spam, right? And just when you thought it couldn’t get any more absurd, the spammer threw in a veiled threat of legal action. As if any self-respecting legal team would back a freelancing spammer trying to make a few dollars from dodgy email lists. So, let’s dissect this debacle. The so-called “large firm” is actually just a freelancer with questionable ethics and a severely misguided understanding of marketing practices. The email lists being peddled are clearly harvested and then distributed without consent, and the defenses being offered are as flimsy as wet tissue paper. It’s comical and infuriating all at once. On the one hand, we have a spammer who thinks they can just sweep aside legal obligations and ethical standards with a dismissive wave. On the other, we have an industry that’s clearly in need of stricter regulations and more robust enforcement to tackle these digital parasites. Ladies and gentlemen, if you’re looking for a poster child for the digital parasites dragging our online world into the muck, look no further than Soft2Share.com. This slimy operation is a textbook case of everything that’s wrong with the darker corners of the internet. Their modus operandi? Selling email lists stuffed with thousands of addresses so they can spam even more unsuspecting victims. The audacity of these spammers knows no bounds. Despite repeated cease-and-desist requests from frustrated recipients, Soft2Share.com continues to ignore pleas for sanity. Instead of addressing their unethical behavior, they’ve opted to brush off complaints with laughable justifications. So, next time you encounter spam, remember that it’s not just a nuisance—it’s a symptom of a much larger problem. And let’s be clear: operators like Soft2Share.com are at the heart of this problem. They’re the parasites feeding off the health of our digital world, and it’s up to all of us to push back. Note - None of the email addresses contained in the email lists attempting to be sold via spam emails authorized the list provider from soft2share to be mounted in the documents. The contacts in the lists were scraped while the spammer states they are GDPR compliant. Buckle up, folks, because it looks like the web’s latest scandal is about to get your blood boiling. If you’ve been getting more spam than usual lately, it might just be time to thank the shady operators at Soft2Share.com. The operator of the website has been caught red-handed in an outrageous spam scheme attempting to sell unauthorized email lists. Hold on to your keyboards—things are about to get ugly. spam scheme In what can only be described as a brazen attempt to cash in on email lists, Soft2Share.com has been caught spamming thousands of unsuspecting websites. And how did they do it? By using their own domain email address and Gmail accounts. Let’s dive into the details. Soft2Share.com was caught red-handed attempting to peddle email lists that contain hundreds, if not thousands, of email addresses. And who are these unfortunate email list victims? Private website owners, bloggers, news publications—you name it. If you’ve got an email address and a website, chances are, Soft2Share.com had you on its hit list. Spam Email Subject Line - 10 Million B2B Email Leads From contact@soft2share.com | Clarissa Turner Hi there, I hope you're having a great day. I am a digital marketer and work at Brand Visibility. Just Wondering, are you interested in buying 10 Million verified B2B email leads from the Apollo, Lusha, Cognism, Uplead, Linkedin and ZoomInfo databases? Features : EMEA, NAM, and APAC coverage, validated emails, high accuracy and GDPR compliance. Features Selling Price: $150 Let me know if you are interested. Regards. - The spammer. One user reported receiving the unsolicited email 17 times (After requesting to stop each time) - After the 17th a response was provided to the stop request - “We use AI for email marketing” - “We:”??? - What a joke! It’s just a freelancer spammer with no shame! Now, you might be wondering why this is such a big deal. After all, spam has been around forever, right? True, but this isn’t just your run-of-the-mill junk email. This is a calculated, aggressive scheme to exploit the personal information of countless individuals and organizations for profit. And it’s not just about annoying emails; it's about privacy violations and a blatant disregard for digital ethics. For bloggers and journalists who rely on email to connect with their audiences, this isn’t just a nuisance—it’s a potential threat to their livelihood. And for private website owners, it’s a breach of security that could open the door to further harassment. What makes this situation even worse is the fact that Soft2Share.com didn’t stop at their own domain. Oh no, they took it a step further by utilizing Gmail accounts to extend their reach. It’s as if they wanted to make sure no inbox was left untouched by their spammy grasp. And let’s not forget the larger implications. When spam emails proliferate, they don’t just annoy—they can lead to serious security risks, including phishing attempts and malware distribution. By selling these lists, Soft2Share.com isn’t just playing with fire; they’re throwing gasoline on it. So, what’s the takeaway from all this? First off, be vigilant. If you’re receiving an uptick in spam, especially if it seems oddly specific or targeted, your email might have ended up on one of these dubious lists. Secondly, it’s time we call out and hold accountable those who profit from our personal data and email addresses. Soft2Share.com may have thought it is operating under the radar, but its actions have lit up the digital world like a neon sign. Hold onto your inboxes, folks, because the Soft2Share.com spam scandal has taken a turn for the absurd. As if flooding inboxes with unwanted emails wasn’t bad enough, it seems the spammer behind Soft2Share.com has now perfected the art of ignoring pleas to stop. Picture this: you've been relentlessly bombarded with spam from Soft2Share.com, and your inbox is overflowing with offers to buy email lists that include thousands of addresses. Frustrated and fed up, you decide to fight back by firing off a polite but firm request to stop the spamming. But what happens? Silence. Crickets. Your emails are swallowed by the void, and the spam continues to pour in, unabated. But wait, there’s more! One particularly fed-up recipient decided to up the ante. They threatened to report Soft2Share.com to the hosting provider, the CAN-SPAM Act administrator, and various spam reporting databases. Surely, such a threat would get a reaction, right? Well, you’d think so. Instead of a professional or even halfway serious response, this beleaguered spam victim received a reply that can only be described as laughably inadequate. The spammer claimed to be from a “large firm”—argued that her actions were perfectly “fine and ethical marketing.” Seriously? This is the spin we’re getting? The response continued in a bewildering fashion, asserting that there was absolutely no need for opt-in permissions or agreements—because who needs pesky legalities when you're making a quick buck off spam, right? And just when you thought it couldn’t get any more absurd, the spammer threw in a veiled threat of legal action. As if any self-respecting legal team would back a freelancing spammer trying to make a few dollars from dodgy email lists. So, let’s dissect this debacle. The so-called “large firm” is actually just a freelancer with questionable ethics and a severely misguided understanding of marketing practices. The email lists being peddled are clearly harvested and then distributed without consent, and the defenses being offered are as flimsy as wet tissue paper. It’s comical and infuriating all at once. On the one hand, we have a spammer who thinks they can just sweep aside legal obligations and ethical standards with a dismissive wave. On the other, we have an industry that’s clearly in need of stricter regulations and more robust enforcement to tackle these digital parasites. Ladies and gentlemen, if you’re looking for a poster child for the digital parasites dragging our online world into the muck, look no further than Soft2Share.com. This slimy operation is a textbook case of everything that’s wrong with the darker corners of the internet. Their modus operandi? Selling email lists stuffed with thousands of addresses so they can spam even more unsuspecting victims. The audacity of these spammers knows no bounds. Despite repeated cease-and-desist requests from frustrated recipients, Soft2Share.com continues to ignore pleas for sanity. Instead of addressing their unethical behavior, they’ve opted to brush off complaints with laughable justifications. So, next time you encounter spam, remember that it’s not just a nuisance—it’s a symptom of a much larger problem. And let’s be clear: operators like Soft2Share.com are at the heart of this problem. They’re the parasites feeding off the health of our digital world, and it’s up to all of us to push back. Note - None of the email addresses contained in the email lists attempting to be sold via spam emails authorized the list provider from soft2share to be mounted in the documents. The contacts in the lists were scraped while the spammer states they are GDPR compliant.

Soft2Share.com Caught Red-Handed In Large Spam Scheme

The Declining Critical Thinking Skills: From Artificial Intelligence to Average Intelligence

It is always the same. What is the typical pattern you imagine when someone says they are in an agile transformation? The usual scenario is the following: Depending on the amount of money an organisation has, it hires either one of the Big 3 or a “Boutique Agile” or not-so-popular consultancy company. They come, spend one to three months, and create a 200-page PowerPoint. The document is filled with eye-catching graphics, diagrams, scenarios, and key advantages of new approaches. It may also contain some suggestions on which tools to use, how to configure the first Jira board, which calendar cadence to adapt, and what the difference is between horizontal and vertical backlog slicing. It will most likely also include some sort of SAFe implementation somewhere between those slides—the organisation's senior management doesn’t want to lose control, does it? Then, the consultancy company departs. Project Managers are renamed into Scrum Master/Agile Coach/Product Owner, depending on the assessment of the level of maturity senior leaders decide their organisation have during the workshop on maturity level assessment and, maybe, personal preference of specific individual (even the sentence that describes it doesn’t make sense). Then, teams host Team Charter exercises, create Scrum Kanban Boards, refine two work sprints, and start their work. By this time, everyone is irritated already because: As consultants, during their initial assessment, didn’t have a chance/time/money (or all together) to understand that some teams are not working in the same environment and just can’t use a similar workflow to others;
Some teams have never had exposure to what Agile truly means, although they have been working in “new ways of working” for five years already;
Engineers now spend more time refining work than coding; everyone knows they hate it;
… Familiar, isn’t it? But is it the right approach? Back to school When children are first introduced to geometry in secondary school, it all starts with basics: What is a dot, line, and surface;
Why parallel lines never cross each other;
Why right angle is equal to 90 degrees; With this basic, it is easier to move forward. It's the same thing with Scrum. When you open Scrum Guide, it starts with the theory. It gently explains the idea of empiricism to the reader and introduces three Pillars - transparency, inspection, and adaptation. Whatever comes next - rules, events, artefacts, etc.- are all implementations of the basics and are impossible without following them. Same as geometry, isn’t it? If you open tasks for high-school students, you won’t be able to solve them without the basic knowledge provided to you in 5th grade. Try different Try to focus on these basics. You can’t touch or see them, and they are hard to measure and visualise on the dashboard. However, they are your values that truly uncover the beauty of Scrum and help to uncover “better ways of building software.” How can you promote and follow transparency? How can you ensure that you are actually taking some uncovered facts into account to improve your processes? Do you actually see all the unnecessary waste and explore ways of eliminating it? These questions are the quintessence of the Scrum Guide—“Easy to follow but hard to master.” Focus on understanding what potential you uncover through specific actions. If you know what's behind change/practice, you will find it easier for your team to understand and follow it. You will start supporting every action with the question, “Does it help us deliver better results? Do we support our actions by three fundamental pillars?” To make sure you are not making the same mistakes that many organisations who are stepping into the Agile Transformation territory have encountered before, try reading the Agile Manifesto and Scrum Guide again. While reading, think about how each item is presented in these documents, how they support each other, and how they build a common structure that makes sense. Use your imagination The transformation will only provide value when you think about the fundamental values every time the decision has to be made. If you have made a mistake, one of the key elements may need to be included. It is insanity to expect a different result without changing your actions. You need to change something to try again anyway - so why not try returning to school? Try to understand which exact element is missing. Find it, incorporate it into your process, and you will most likely succeed. Our main goal is to eliminate the habit that has been dominant on the market for the past twenty years—the habit of implementing tools and techniques without considering their value or how exactly they work. I highly recommend looking into Zombie Scrum — it's an amazing read and also the biggest library of problems that your team may encounter if they do not consider the purpose of every single item they implement from the framework. What is your secret to following what matters? Share in the comments. It is always the same. What is the typical pattern you imagine when someone says they are in an agile transformation? The usual scenario is the following: Depending on the amount of money an organisation has, it hires either one of the Big 3 or a “Boutique Agile” or not-so-popular consultancy company. They come, spend one to three months, and create a 200-page PowerPoint. The document is filled with eye-catching graphics, diagrams, scenarios, and key advantages of new approaches. It may also contain some suggestions on which tools to use, how to configure the first Jira board, which calendar cadence to adapt, and what the difference is between horizontal and vertical backlog slicing. It will most likely also include some sort of SAFe implementation somewhere between those slides—the organisation's senior management doesn’t want to lose control, does it? Then, the consultancy company departs. Project Managers are renamed into Scrum Master/Agile Coach/Product Owner, depending on the assessment of the level of maturity senior leaders decide their organisation have during the workshop on maturity level assessment and, maybe, personal preference of specific individual (even the sentence that describes it doesn’t make sense). Then, teams host Team Charter exercises, create Scrum Kanban Boards, refine two work sprints, and start their work. By this time, everyone is irritated already because: As consultants, during their initial assessment, didn’t have a chance/time/money (or all together) to understand that some teams are not working in the same environment and just can’t use a similar workflow to others; Some teams have never had exposure to what Agile truly means, although they have been working in “new ways of working” for five years already; Engineers now spend more time refining work than coding; everyone knows they hate it; … As consultants, during their initial assessment, didn’t have a chance/time/money (or all together) to understand that some teams are not working in the same environment and just can’t use a similar workflow to others; Some teams have never had exposure to what Agile truly means, although they have been working in “new ways of working” for five years already; Engineers now spend more time refining work than coding; everyone knows they hate it; … Familiar, isn’t it? But is it the right approach? Back to school When children are first introduced to geometry in secondary school, it all starts with basics: What is a dot, line, and surface; Why parallel lines never cross each other; Why right angle is equal to 90 degrees; What is a dot, line, and surface; Why parallel lines never cross each other; Why right angle is equal to 90 degrees; With this basic, it is easier to move forward. It's the same thing with Scrum. When you open Scrum Guide, it starts with the theory. It gently explains the idea of empiricism to the reader and introduces three Pillars - transparency, inspection, and adaptation. Whatever comes next - rules, events, artefacts, etc.- are all implementations of the basics and are impossible without following them. Same as geometry, isn’t it? If you open tasks for high-school students, you won’t be able to solve them without the basic knowledge provided to you in 5th grade. Try different Try to focus on these basics. You can’t touch or see them, and they are hard to measure and visualise on the dashboard. However, they are your values that truly uncover the beauty of Scrum and help to uncover “better ways of building software.” How can you promote and follow transparency? How can you ensure that you are actually taking some uncovered facts into account to improve your processes? Do you actually see all the unnecessary waste and explore ways of eliminating it? These questions are the quintessence of the Scrum Guide—“Easy to follow but hard to master.” Focus on understanding what potential you uncover through specific actions. If you know what's behind change/practice, you will find it easier for your team to understand and follow it. You will start supporting every action with the question, “Does it help us deliver better results? Do we support our actions by three fundamental pillars?” To make sure you are not making the same mistakes that many organisations who are stepping into the Agile Transformation territory have encountered before, try reading the Agile Manifesto and Scrum Guide again. While reading, think about how each item is presented in these documents, how they support each other, and how they build a common structure that makes sense. Use your imagination The transformation will only provide value when you think about the fundamental values every time the decision has to be made. If you have made a mistake, one of the key elements may need to be included. It is insanity to expect a different result without changing your actions. You need to change something to try again anyway - so why not try returning to school? Try to understand which exact element is missing. Find it, incorporate it into your process, and you will most likely succeed. Our main goal is to eliminate the habit that has been dominant on the market for the past twenty years—the habit of implementing tools and techniques without considering their value or how exactly they work. I highly recommend looking into Zombie Scrum — it's an amazing read and also the biggest library of problems that your team may encounter if they do not consider the purpose of every single item they implement from the framework. Zombie Scrum What is your secret to following what matters? Share in the comments.

Values First, or How to Start Transformation Correctly

মানগুলি প্রথমে, বা কীভাবে সঠিকভাবে রূপান্তর শুরু করবেন

Werte zuerst oder wie man die Transformation richtig startet

Los valores primero, o cómo iniciar la transformación correctamente

Les valeurs d’abord, ou comment démarrer correctement la transformation

मूल्य पहले, या परिवर्तन को सही ढंग से कैसे शुरू करें

価値観を第一に、あるいは変革を正しく始める方法

가치가 먼저, 또는 변환을 올바르게 시작하는 방법

Valores primeiro ou como iniciar a transformação corretamente

Ценности прежде всего, или Как правильно начать трансформацию

Önce Değerler veya Dönüşüme Doğru Şekilde Nasıl Başlanır?

Giá trị đầu tiên hoặc Cách bắt đầu chuyển đổi chính xác

价值观优先，或者如何正确开始转型

Solve for the Edges and Get The Middle for Free: Innovating for Inclusivity 

In May 2024, Google rolled out its AI Overview feature to enhance users’ experiences in the search engine. This latest feature was intended to streamline searching by summarizing information from multiple sources. The answers it provides are typically concisely written responses to queries that people enter in the search bar. With this new AI Overview, Google’s intentions were clear — to leverage advanced AI to make search faster and more reliable. However, the reality of the AI Overview’s performance has fallen short of expectations in the months since its release. Almost immediately after its launch, users began encountering some unexpected issues. Social media and community forums buzzed with examples of the AI Overview producing absurd recommendations. Some of its responses even provided dangerous advice to users. Among the most viral incidents was the suggestion to use ⅛ cup of nontoxic glue on pizza to help the cheese stick. People eventually discovered this recommendation was pulled from an old, satirical post on Reddit. Now, they are expressing their opinions and concerns on the internet about how Google was too quick to launch AI Overview. What Users Think of Google’s AI Overview Now, nearly two months after its release, user reactions to Google’s AI Overview still vary. Some people love it, while others express dissatisfaction. A HackerNoon poll found that 29% said they hated the feature and would prefer Google to get rid of it. Meanwhile, another 29% disliked it for now but believe Google could make some improvements. Reddit users have been vocal about AI Overview's shortcomings and poor advice. From eating a rock a day to incorrect cooking temperatures, the errors have led to widespread criticism of Google. A Reddit thread in the r/technology community discussed these issues extensively. Users expressed frustration over the feature, pointing out that it does not know the difference between humor and authentic content. Some people even mentioned that AI Overview was never a good idea in the first place. On the other hand, some people come to Google’s defense by explaining how LLMs work and the reasoning behind some of the inaccurate information they provide. Despite the backlash, some users see potential in the technology. Some believe it is valuable but needs some refinements. Google’s Thoughts on the Matter When people read about how poor some of the responses from the overviews are, they wonder whether Google truly considered how its audience would use its products. After all, 60% of online users consider trustworthiness a top priority when using a brand’s products — meaning providing trustworthy answers should have been the number one priority. However, it seems Google skipped over the testing part and launched its AI Overview without question. Google has admitted to the search feature’s errors. In a blog post, Google’s head of search, Liz Reid, stated that she saw AI Overview feature sarcastic and satirical content. While she finds public forums to be the best source of authentic information, Reid suggested that AI Overview can lead to some unhelpful advice. However, Google also claims some screenshots of AI Overviews with nonsensical responses may be fake. WIRED has even conducted its own testing to determine if this is true. It found that one screenshot with over 5 million views did not align with how AI Overview presents information to users. Still, Google realizes that AI Overviews prevent users from experiencing its full potential. That is why it has made over a dozen improvements, including enhancing the detection of queries that do not call for an AI Overview. Google also intends to keep a close eye on the tool and adjust its features according to user feedback. The Implications of the State of AI AI has proven to be a helpful tool for many, but some of the issues it presents clearly indicate that it still needs to improve. The overall takeaways about the state of AI involve a few things to keep in mind. 1. AI Relies Too Heavily on Biased Datasets AI systems are only as good as the data they are trained on, presenting a large problem for bias. When machine learning engineers feed these datasets to AI systems, they often contain bias from decades of content. As a result, LLMs will replicate these biases and open the possibility of perpetuating existing misinformation. For instance, if an AI system frequently sources data from platforms known for low-quality content, it can propagate false information. Addressing these biases requires a larger effort to curate diverse and high-quality datasets. Additionally, mechanisms should be established to detect and mitigate bias in AI training processes. 2. AI Lacks Contextual Awareness and Advanced Reasoning While AI can process data quickly, it often fails to understand nuances to provide deeper context. A great example is when Google’s AI Overview misinterpreted information from a travel blog post about slot canyons near Las Vegas. It confused them with normal canyons, not even knowing their differences. This mistake can confuse some people, showing AI’s limitations in understanding and reasoning contextually. Such errors diminish user trust and highlight the need for more sophisticated AI systems to accurately grasp and analyze context. 3. AI Acts as a People Pleaser One of the more troubling aspects of AI systems is their propensity to generate a response for every query. Even if they cannot provide a sensible answer, they will still create a response. This eagerness to please often leads to the machine fabricating information or presenting incorrect data confidently. As such, it provides misleading advice that can spread from one person to the next. AI Overview has had several cases where it offered advice based on satirical content or outdated sources simply because it was compelled to provide an answer. With this tendency comes the necessity to recognize AI’s limitations and refrain from asking it further questions when it cannot answer accurately. 4. AI Is Limited on Data Another clear issue is the constraints of the datasets on which they are trained. With a fixed cutoff date, AI can quickly become obsolete and provide responses that are no longer relevant or accurate. This is especially problematic in a rapidly changing world. As technology, medicine and global events evolve, AI’s outdated information can lead to significant errors in its answers. AI would need constant updates to keep information fresh. However, this is a large undertaking because the process must always start from scratch. Yet, AI's reliability diminishes without such updates, making it imperative to address this issue. Dealing With AI Weaknesses The recent launch of Google’s AI Overview provides insightful information on the current state of AI and its limitations. However, as AI evolves, users may begin to see it as a more trustworthy tool. Nonetheless, it is quite clear that these tools need serious updates to end people’s complaints. It may take some time before significant advancements are made, but it will be interesting to see what AI's future has in store. In May 2024, Google rolled out its AI Overview feature to enhance users’ experiences in the search engine. This latest feature was intended to streamline searching by summarizing information from multiple sources. The answers it provides are typically concisely written responses to queries that people enter in the search bar. With this new AI Overview, Google’s intentions were clear — to leverage advanced AI to make search faster and more reliable. However, the reality of the AI Overview’s performance has fallen short of expectations in the months since its release. Almost immediately after its launch, users began encountering some unexpected issues. Social media and community forums buzzed with examples of the AI Overview producing absurd recommendations. Some of its responses even provided dangerous advice to users. Among the most viral incidents was the suggestion to use ⅛ cup of nontoxic glue on pizza to help the cheese stick. People eventually discovered this recommendation was pulled from an old, satirical post on Reddit. Now, they are expressing their opinions and concerns on the internet about how Google was too quick to launch AI Overview. use ⅛ cup of nontoxic glue on pizza What Users Think of Google’s AI Overview Now, nearly two months after its release, user reactions to Google’s AI Overview still vary. Some people love it, while others express dissatisfaction. A HackerNoon poll found that 29% said they hated the feature and would prefer Google to get rid of it. Meanwhile, another 29% disliked it for now but believe Google could make some improvements. 29% said they hated the feature Reddit users have been vocal about AI Overview's shortcomings and poor advice. From eating a rock a day to incorrect cooking temperatures, the errors have led to widespread criticism of Google. Reddit A Reddit thread in the r/technology community discussed these issues extensively. Users expressed frustration over the feature, pointing out that it does not know the difference between humor and authentic content. Some people even mentioned that AI Overview was never a good idea in the first place. thread in the r/technology community On the other hand, some people come to Google’s defense by explaining how LLMs work and the reasoning behind some of the inaccurate information they provide. Despite the backlash, some users see potential in the technology. Some believe it is valuable but needs some refinements. Google’s Thoughts on the Matter When people read about how poor some of the responses from the overviews are, they wonder whether Google truly considered how its audience would use its products. After all, 60% of online users consider trustworthiness a top priority when using a brand’s products — meaning providing trustworthy answers should have been the number one priority. However, it seems Google skipped over the testing part and launched its AI Overview without question. 60% of online users consider trustworthiness Google has admitted to the search feature’s errors. In a blog post, Google’s head of search, Liz Reid, stated that she saw AI Overview feature sarcastic and satirical content. While she finds public forums to be the best source of authentic information, Reid suggested that AI Overview can lead to some unhelpful advice. However, Google also claims some screenshots of AI Overviews with nonsensical responses may be fake. WIRED has even conducted its own testing to determine if this is true. It found that one screenshot with over 5 million views did not align with how AI Overview presents information to users. one screenshot with over 5 million views Still, Google realizes that AI Overviews prevent users from experiencing its full potential. That is why it has made over a dozen improvements, including enhancing the detection of queries that do not call for an AI Overview. Google also intends to keep a close eye on the tool and adjust its features according to user feedback. The Implications of the State of AI AI has proven to be a helpful tool for many, but some of the issues it presents clearly indicate that it still needs to improve. The overall takeaways about the state of AI involve a few things to keep in mind. 1. AI Relies Too Heavily on Biased Datasets AI systems are only as good as the data they are trained on, presenting a large problem for bias. When machine learning engineers feed these datasets to AI systems, they often contain bias from decades of content. As a result, LLMs will replicate these biases and open the possibility of perpetuating existing misinformation. For instance, if an AI system frequently sources data from platforms known for low-quality content, it can propagate false information. Addressing these biases requires a larger effort to curate diverse and high-quality datasets. Additionally, mechanisms should be established to detect and mitigate bias in AI training processes. 2. AI Lacks Contextual Awareness and Advanced Reasoning While AI can process data quickly, it often fails to understand nuances to provide deeper context. A great example is when Google’s AI Overview misinterpreted information from a travel blog post about slot canyons near Las Vegas. It confused them with normal canyons, not even knowing their differences. misinterpreted information from a travel blog post This mistake can confuse some people, showing AI’s limitations in understanding and reasoning contextually. Such errors diminish user trust and highlight the need for more sophisticated AI systems to accurately grasp and analyze context. 3. AI Acts as a People Pleaser One of the more troubling aspects of AI systems is their propensity to generate a response for every query. Even if they cannot provide a sensible answer, they will still create a response. This eagerness to please often leads to the machine fabricating information or presenting incorrect data confidently. As such, it provides misleading advice that can spread from one person to the next. AI Overview has had several cases where it offered advice based on satirical content or outdated sources simply because it was compelled to provide an answer. With this tendency comes the necessity to recognize AI’s limitations and refrain from asking it further questions when it cannot answer accurately. 4. AI Is Limited on Data Another clear issue is the constraints of the datasets on which they are trained. With a fixed cutoff date, AI can quickly become obsolete and provide responses that are no longer relevant or accurate. This is especially problematic in a rapidly changing world. As technology, medicine and global events evolve, AI’s outdated information can lead to significant errors in its answers. AI would need constant updates to keep information fresh. However, this is a large undertaking because the process must always start from scratch. Yet, AI's reliability diminishes without such updates, making it imperative to address this issue. Dealing With AI Weaknesses The recent launch of Google’s AI Overview provides insightful information on the current state of AI and its limitations. However, as AI evolves, users may begin to see it as a more trustworthy tool. Nonetheless, it is quite clear that these tools need serious updates to end people’s complaints. It may take some time before significant advancements are made, but it will be interesting to see what AI's future has in store.

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