#Data Validation Companies

[apeirosolutions321]

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[magellansolutions]

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[godigitalblog]

Understanding the Importance of Database Validation

Database validation plays a crucial role in maintaining the accuracy and reliability of data. It involves verifying the integrity of data and ensuring that it meets certain predefined criteria. By validating the database, organizations can prevent errors, inconsistencies, and inaccuracies that could lead to serious consequences.

One of the key reasons why database validation is important is because it helps in maintaining data quality. Data quality refers to the accuracy, completeness, consistency, and reliability of data. Without proper validation, data quality can be compromised, leading to incorrect insights, poor decision-making and even financial losses.

Another important aspect of database validation is data security. Validating the database helps in identifying and mitigating security risks such as unauthorized access, data breaches, and data corruption. By implementing proper validation measures, organizations can ensure the confidentiality, integrity, and availability of their data.

In addition, database validation is essential for regulatory compliance. Many industries have strict regulations and standards regarding data management. By validating the database, organizations can ensure that they comply with these regulations and avoid legal penalties or reputational damage.

Overall, understanding the importance of database validation is crucial for organizations that rely on data for their operations. It helps in maintaining data quality, ensuring data security, and complying with regulatory requirements.

Implementing Data Integrity Checks

Implementing data integrity checks is an important step in database validation. Data integrity refers to the accuracy, consistency, and reliability of data. By implementing data integrity checks, organizations can identify and correct any inconsistencies or errors in the database.

There are different types of data integrity checks that can be implemented. One common approach is to use referential integrity, which ensures that relationships between tables are maintained. This involves setting up constraints and rules that prevent the creation of invalid relationships or the deletion of data that is referenced by other tables.

Another approach is to use data validation rules, which define the acceptable values and formats for specific fields. These rules can be applied during data entry or through batch processes to ensure that the data meets the required criteria. For example, a validation rule can be used to check that a phone number field contains only numeric characters and has a specific length.

Data integrity checks can also include data consistency checks, which verify that the data is consistent across different tables or systems. This can involve comparing data values, performing calculations, or checking for duplicates or missing data.

By implementing data integrity checks, organizations can ensure that the database remains accurate, consistent, and reliable. This not only improves data quality but also helps in preventing data corruption, data loss, and other potential issues.

Leveraging Automated Validation Tools

Leveraging automated validation tools can greatly simplify the process of database validation. These tools are designed to automate various aspects of the validation process, reducing the time and effort required.

One of the key benefits of automated validation tools is that they can perform comprehensive checks on large datasets quickly and accurately. These tools can analyze the data, identify errors or inconsistencies, and generate detailed reports. This allows organizations to quickly identify and resolve any issues, ensuring the accuracy and reliability of the database.

Automated validation tools also provide the advantage of repeatability and consistency. Once a validation process is defined, it can be easily repeated on a regular basis or whenever new data is added to the database. This ensures that the validation is consistently applied and reduces the risk of human errors or oversights.

Furthermore, automated validation tools often come with built-in validation rules and algorithms that can be customized to meet specific requirements. Organizations can define their own validation criteria and rules, ensuring that the tool aligns with their unique needs and data standards.

In summary, leveraging automated validation tools can streamline the database validation process, improve efficiency, and enhance the overall quality of the database.

Establishing Clear Validation Criteria

Establishing clear validation criteria is essential for effective database validation. Validation criteria define the specific requirements that data must meet to be considered valid. By establishing clear criteria, organizations can ensure consistency and accuracy in the validation process.

When establishing validation criteria, it is important to consider both the technical and business requirements. Technical requirements include data formats, data types, field lengths, and referential integrity rules. These requirements ensure that the data is structured correctly and can be processed and analyzed effectively.

Business requirements, on the other hand, define the specific rules and constraints that are relevant to the organization's operations. These requirements can vary depending on the industry, regulatory standards, and internal policies. For example, a financial institution may have specific validation criteria for customer account numbers or transaction amounts.

Clear validation criteria should also include error handling and exception handling procedures. These procedures define how the system should handle data that does not meet the validation criteria. It can involve rejecting the data, triggering notifications or alerts, or performing automatic data corrections.

By establishing clear validation criteria, organizations can ensure that the database is validated consistently and accurately. This helps in maintaining data quality, data integrity, and overall data reliability.

Regular Monitoring and Maintenance

Regular monitoring and maintenance are crucial for effective database validation. Database validation is not a one-time process but an ongoing effort to ensure the accuracy and reliability of the database.

Monitoring the database involves regularly checking for errors, inconsistencies, and security risks. This can be done through automated monitoring tools that generate alerts or notifications when issues are detected. It can also involve manual checks and reviews by database administrators or data analysts.

Maintenance activities include data cleansing, data updates, and system optimization. Data cleansing involves identifying and correcting any errors or inconsistencies in the data. This can include removing duplicate records, standardizing data formats, or resolving data conflicts.

Data updates are necessary to ensure that the database reflects the most up-to-date information. This can involve regular data imports or integrations with external systems. It is important to validate the updated data to ensure its accuracy and consistency.

System optimization involves fine-tuning the database performance and configuration. This can include optimizing queries, indexing tables, or allocating sufficient resources for the database server. Regular performance monitoring and tuning help in maintaining the efficiency and responsiveness of the database.

By regularly monitoring and maintaining the database, organizations can proactively identify and resolve any issues, ensuring the accuracy, reliability, and security of the data.

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Source: https://seoe2zblogs.medium.com/understanding-the-importance-of-database-validation-b24fdddd006e

[itesservices]

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[andrewleousa]

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[haivoai]

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[mostlysignssomeportents]

Are the means of computation even seizable?

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in PITTSBURGH in TOMORROW (May 15) at WHITE WHALE BOOKS, and in PDX on Jun 20 at BARNES AND NOBLE with BUNNIE HUANG. More tour dates (London, Manchester) here.

Something's very different in tech. Once upon a time, every bad choice by tech companies – taking away features, locking out mods or plugins, nerfing the API – was countered, nearly instantaneously, by someone writing a program that overrode that choice.

Bad clients would be muscled aside by third-party clients. Locked bootloaders would be hacked and replaced. Code that confirmed you were using OEM parts, consumables or adapters would be found and nuked from orbit. Weak APIs would be replaced with muscular, unofficial APIs built out of unstoppable scrapers running on headless machines in some data-center. Every time some tech company erected a 10-foot enshittifying fence, someone would show up with an 11-foot disenshittifying ladder.

Those 11-foot ladders represented the power of interoperability, the inescapable bounty of the Turing-complete, universal von Neumann machine, which, by definition, is capable of running every valid program. Specifically, they represented the power of adversarial interoperability – when someone modifies a technology against its manufacturer's wishes. Adversarial interoperability is the origin story of today's tech giants, from Microsoft to Apple to Google:

https://www.eff.org/deeplinks/2019/10/adversarial-interoperability

But adversarial interop has been in steady decline for the past quarter-century. These big companies moved fast and broke things, but no one is returning the favor. If you ask the companies what changed, they'll just smirk and say that they're better at security than the incumbents they disrupted. The reason no one's hacked up a third-party iOS App Store is that Apple's security team is just so fucking 1337 that no one can break their shit.

I think this is nonsense. I think that what's really going on is that we've made it possible for companies to design their technologies in such a way that any attempt at adversarial interop is illegal.

"Anticircumvention" laws like Section 1201 of the 1998 Digital Millennium Copyright Act make bypassing any kind of digital lock (AKA "Digital Rights Management" or "DRM") very illegal. Under DMCA, just talking about how to remove a digital lock can land you in prison for 5 years. I tell the story of this law's passage in "Understood: Who Broke the Internet," my new podcast series for the CBC:

https://pluralistic.net/2025/05/08/who-broke-the-internet/#bruce-lehman

For a quarter century, tech companies have aggressively lobbied and litigated to expand the scope of anticircumvention laws. At the same time, companies have come up with a million ways to wrap their products in digital locks that are a crime to break.

Digital locks let Chamberlain, a garage-door opener monopolist block all third-party garage-door apps. Then, Chamberlain stuck ads in its app, so you have to watch an ad to open your garage-door:

https://pluralistic.net/2023/11/09/lead-me-not-into-temptation/#chamberlain

Digital locks let John Deere block third-party repair of its tractors:

https://pluralistic.net/2022/05/08/about-those-kill-switched-ukrainian-tractors/

And they let Apple block third-party repair of iPhones:

https://pluralistic.net/2022/05/22/apples-cement-overshoes/

These companies built 11-foot ladders to get over their competitors' 10-foot walls, and then they kicked the ladder away. Once they were secure atop their walls, they committed enshittifying sins their fallen adversaries could only dream of.

I've been campaigning to abolish anticircumvention laws for the past quarter-century, and I've noticed a curious pattern. Whenever these companies stand to lose their legal protections, they freak out and spend vast fortunes to keep those protections intact. That's weird, because it strongly implies that their locks don't work. A lock that works works, whether or not it's illegal to break that lock. The reason Signal encryption works is that it's working encryption. The legal status of breaking Signal's encryption has nothing to do with whether it works. If Signal's encryption was full of technical flaws but it was illegal to point those flaws out, you'd be crazy to trust Signal.

Signal does get involved in legal fights, of course, but the fights it gets into are ones that require Signal to introduce defects in its encryption – not fights over whether it is legal to disclose flaws in Signal or exploit them:

https://pluralistic.net/2023/03/05/theyre-still-trying-to-ban-cryptography/

But tech companies that rely on digital locks manifestly act like their locks don't work and they know it. When the tech and content giants bullied the W3C into building DRM into 2 billion users' browsers, they categorically rejected any proposal to limit their ability to destroy the lives of people who broke that DRM, even if it was only to add accessibility or privacy to video:

https://www.eff.org/deeplinks/2017/09/open-letter-w3c-director-ceo-team-and-membership

The thing is, if the lock works, you don't need the legal right to destroy the lives of people who find its flaws, because it works.

Do digital locks work? Can they work? I think the answer to both questions is a resounding no. The design theory of a digital lock is that I can provide you with an encrypted file that your computer has the keys to. Your computer will access those keys to decrypt or sign a file, but only under the circumstances that I have specified. Like, you can install an app when it comes from my app store, but not when it comes from a third party. Or you can play back a video in one kind of browser window, but not in another one. For this to work, your computer has to hide a cryptographic key from you, inside a device you own and control. As I pointed out more than a decade ago, this is a fool's errand:

https://memex.craphound.com/2012/01/10/lockdown-the-coming-war-on-general-purpose-computing/

After all, you or I might not have the knowledge and resources to uncover the keys' hiding place, but someone does. Maybe that someone is a person looking to go into business selling your customers the disenshittifying plugin that unfucks the thing you deliberately broke. Maybe it's a hacker-tinkerer, pursuing an intellectual challenge. Maybe it's a bored grad student with a free weekend, an electron-tunneling microscope, and a seminar full of undergrads looking for a project.

The point is that hiding secrets in devices that belong to your adversaries is very bad security practice. No matter how good a bank safe is, the bank keeps it in its vault – not in the bank-robber's basement workshop.

For a hiding-secrets-in-your-adversaries'-device plan to work, the manufacturer has to make zero mistakes. The adversary – a competitor, a tinkerer, a grad student – only has to find one mistake and exploit it. This is a bedrock of security theory: attackers have an inescapable advantage.

So I think that DRM doesn't work. I think DRM is a legal construct, not a technical one. I think DRM is a kind of magic Saran Wrap that manufacturers can wrap around their products, and, in so doing, make it a literal jailable offense to use those products in otherwise legal ways that their shareholders don't like. As Jay Freeman put it, using DRM creates a new law called "Felony Contempt of Business Model." It's a law that has never been passed by any legislature, but is nevertheless enforceable.

In the 25 years I've been fighting anticircumvention laws, I've spoken to many government officials from all over the world about the opportunity that repealing their anticircumvention laws represents. After all, Apple makes $100b/year by gouging app makers for 30 cents on ever dollar. Allow your domestic tech sector to sell the tools to jailbreak iPhones and install third party app stores, and you can convert Apple's $100b/year to a $100m/year business for one of your own companies, and the other $999,900,000,000 will be returned to the world's iPhone owners as a consumer surplus.

But every time I pitched this, I got the same answer: "The US Trade Representative forced us to pass this law, and threatened us with tariffs if we didn't pass it." Happy Liberation Day, people – every country in the world is now liberated from the only reason to keep this stupid-ass law on their books:

https://pluralistic.net/2025/01/15/beauty-eh/#its-the-only-war-the-yankees-lost-except-for-vietnam-and-also-the-alamo-and-the-bay-of-ham

In light of the Trump tariffs, I've been making the global rounds again, making the case for an anticircumvention repeal:

https://www.ft.com/content/b882f3a7-f8c9-4247-9662-3494eb37c30b

One of the questions I've been getting repeatedly from policy wonks, activists and officials is, "Is it even possible to jailbreak modern devices?" They want to know if companies like Apple, Tesla, Google, Microsoft, and John Deere have created unbreakable digital locks. Obviously, this is an important question, because if these locks are impregnable, then getting rid of the law won't deliver the promised benefits.

It's true that there aren't as many jailbreaks as we used to see. When a big project like Nextcloud – which is staffed up with extremely accomplished and skilled engineers – gets screwed over by Google's app store, they issue a press-release, not a patch:

https://arstechnica.com/gadgets/2025/05/nextcloud-accuses-google-of-big-tech-gatekeeping-over-android-app-permissions/

Perhaps that's because the tech staff at Nextcloud are no match for Google, not even with the attacker's advantage on their side.

But I don't think so. Here's why: we do still get jailbreaks and mods, but these almost exclusively come from anonymous tinkerers and hobbyists:

https://consumerrights.wiki/Mazda_DMCA_takedown_of_Open_Source_Home_Assistant_App

Or from pissed off teenagers:

https://www.theverge.com/2022/9/29/23378541/the-og-app-instagram-clone-pulled-from-app-store

These hacks are incredibly ambitious! How ambitious? How about a class break for every version of iOS as well as an unpatchable hardware attack on 8 years' worth of Apple bootloaders?

https://pluralistic.net/2020/05/25/mafia-logic/#sosumi

Now, maybe it's the case at all the world's best hackers are posting free code under pseudonyms. Maybe all the code wizards working for venture backed tech companies that stand to make millions through clever reverse engineering are just not as mad skilled as teenagers who want an ad-free Insta and that's why they've never replicated the feat.

Or maybe it's because teenagers and anonymous hackers are just about the only people willing to risk a $500,000 fine and 5-year prison sentence. In other words, maybe the thing that protects DRM is law, not code. After all, when Polish security researchers revealed the existence of secret digital locks that the train manufacturer Newag used to rip off train operators for millions of euros, Newag dragged them into court:

https://fsfe.org/news/2025/news-20250407-01.en.html

Tech companies are the most self-mythologizing industry on the planet, beating out even the pharma sector in boasting about their prowess and good corporate citizenship. They swear that they've made a functional digital lock…but they sure act like the only thing those locks do is let them sue people who reveal their workings.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/05/14/pregnable/#checkm8

[pandemic-info]

"Any chance we're wrong about Covid?"

It's a valid question many people earnestly think about — even the very cautious.

'it becomes important to ask: "what does the data actually say?"'

Quoting a few good answers from a thread:

"Covid left me disabled in 2020. I know with 100% certainty that I am not wrong about Covid. I live with the proof every minute of every day for the rest of my life."

"The insurance companies and government statisticians care, or rather they have taken an objective interest." > https://fred.stlouisfed.org/series/LNU01074597 > https://insurancenewsnet.com/innarticle/insurance-industry-coalition-forms-non-profit-to-study-excess-mortality

"There are parallels between how governments are responding to COVID-19 and how they responded to tobacco back in the day. “it would be a mistake to assume governments would automatically protect people from a public health threat in the face of more immediate economic considerations…there would be resistance to change that might be costly until evidence to justify it was overwhelming.”" > https://johnsnowproject.org/insights/merchants-of-doubt/

"I suspect most of us entertain this thought from time to time, especially when it’s this absurdly difficult and lonely to maintain a Covid Conscious lifestyle. But it’s important to remember that history is littered with people making terrible choices en masse: with handling past pandemics, the holocaust, slavery, witch burnings, etc. Hell pretty much everyone used to smoke and putting lead in everything was A-ok. Just because a lot of people believe something doesn’t mean they’re right. So it becomes important to ask what does the data actually say? The research and the statistical data on this subject paint an ugly but fairly quantifiable picture by which we can gauge our understanding of the situation and our choices in response to it. Read the science. Look at the data on things like Long Covid. There are also many of us who have already had our health absolutely ravaged by this virus or lost loved ones to it etc., and everyone in that position has first hand evidence for how dangerous this virus is. It’s tremendously difficult to swim against the current like we are and self-doubt is natural in those conditions, but that’s when seeking out factual information on the subject is the best course of action."

"But what it all comes back to for me is - say we're wrong, and covid is a big nothingburger and lockdowns are the root of all evil. Ok, well, what I'm doing is acting on the best information available to me at this time to protect my family. I can't regret that. I will always be able to look my kids in the eye and say "I did my best with what I had."" ... So if we're wrong - well, we wore masks, changed our social habits, reduced our consumerism and our contribution to the destruction of our planet, and reduced how often we got sick. None of those things are bad. If they're wrong, they and their kids are screwed. I'd rather err on the side of caution.

[apeirosolutions321]

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[directactionforhope]

KOSA Has Been Reintroduced - Take Action to Stop It!!!

KOSA won’t make kids more safe. Instead, it’ll put youth in danger by preventing them from accessing resources they need. Concern about young people’s harm from Big Tech are valid and real, but enabling censorship that harms the most marginalized kids is not the answer. Lawmakers concerned about online safety should reject KOSA and instead work to protect all internet users from abusive tech companies by passing a federal data privacy law and measures that do not threaten online communities that queer and trans youth depend on.

More on why KOSA needs to go here:

Fight for the Future, May 14, 2025

Contact your representatives here!!!

KOSA died in the House last time after getting 90% of the votes in the Senate. We CAN stop this again!!!

[mr-entj]

As a leader, are there any MBTI types you dislike leading or having on your team?

I dislike people who are incompetent, unreliable, and lack integrity regardless of MBTI type.

In general, though, all the types are pains in my ass in their own special ways:

High Te (ENTJ, ESTJ, INTJ, ISTJ)

Doesn't play well with others; I constantly need to fix the feelings they hurt and cushion their communication so they don't end up pariahs that no one wants to work with.

Bulldozes through people to get things done, requires a 'pause' button and calm hand because they get frustrated easily with things going slow. Sometimes you need to go slow to go far.

Struggles with 'team building' and 'that touchy feely shit'; highly transactional.

(ESTJ & ISTJ) "Mr./Mrs. No", too quick to shoot down ambitious but unproven ideas because it hasn't worked before or will take a lot of effort to accomplish.

(ENTJ & INTJ) Convinced their weird 'reads' into things are correct despite evidence to the contrary staring them right in the face. Not everything has a deeper and hidden meaning.

High Fe (ENFJ, ESFJ, INFJ, ISFJ)

Whines a lot, about everything, and requires a lot of time and energy listening to their feelings.

Needs external validation and requires regular praise/feedback or else they'll assume you're mad at them. The sulking and internal spiraling is a bit much.

Avoids confrontation, mirrors other people's opinions, and requires pushing and pushing to get them to reveal their true opinions and stand their ground. Uses stronger people around them to represent their views (human shields).

Tendency to gossip, can't stay out of people's business and spreads misinformation like wildfire.

A company isn't a family-- not everyone wants to attend your weekend team potluck.

High Ti (INTP, ISTP, ENTP, ESTP)

Always causes more chaos than clarity, requires a ton of work to fix the confusion bombs they drop into groups.

Debates endlessly, needs everything personally spoonfed and explained to them in exhausting detail until they're satisfied or else they can't accept the plan and they won't commit. This slows down everything for everyone.

Flaky, can't hit a deadline to save their lives. Requires check-ins and external pressure (group work) to hit milestones.

(ESTP & ISTP) Out of sight doesn't mean out of mind. There are things that can happen later in the future that we should plan for now.

(ENTP & INTP) Just because you read a book about some obscure topic once doesn't make you an expert. Books =/= reality. Don't be a backseat driver-- do it and prove it.

High Fi (INFP, ISFP, ENFP, ESFP)

Half asses everything they don't enjoy doing because this isn't their passion and their parents made them take this job when they really wanted to do art/music/acting something something.

Struggles to work with people who hold different political views than them; self-segregates.

Terrible at taking constructive criticism and feedback, gets defensive, shuts down, and takes everything personally.

(ENFP & INFP) Highly idealistic to the point they're blind to data that tells them the hard and cold truth, they struggle to manage practical things like resources, time, money.

(ESFP & ISFP) Out of sight doesn't mean out of mind. There are things that can happen later in the future that we should plan for now.

[naamahdarling]

I'm probably going to piss some people off with this, but.

The use of AI and machine learning for harmful purposes is absolutely unacceptable.

But that isn't an innate part of what it does.

Apps or sites using AI to generate playlists or reading lists or a list of recipes based on a prompt you enter: absolutely fantastic, super helpful, so many new things to enjoy, takes jobs from no-one.

Apps or sites that use a biased algorithm (which is AI) which is not controllable by users or able to be turned off by them, to push some content and suppress others to maximize engagement and create compulsive behavior in users: unethical, bad, capitalism issue, human issue.

People employing genAI to create images for personal, non-profit use and amusement who would not have paid someone for the same service: neutral, (potential copyright and ethics issue if used for profit, which would be a human issue).

People incorporating genAI as part of their artistic process, where the medium of genAI is itself is a deliberate part of the artist's technique: valid, interesting.

Companies employing genAI to do the work of a graphic designer, and websites using genAI to replace the cost of stock photos: bad, shitty, no, capitalist and ethical human issue.

People attacking small artists who use it with death threats and unbelievable vitriol: bad, don't do that.

AI used for spell check and grammar assistance: really great.

AI employed by eBay sellers to cut down on the time it takes to make listings: good, very helpful, but might be a bad idea as it does make mistakes and that can cost them money, which would be a technical issue.

AI used to generate fake product photos: deceptive, lazy, bad, human ethical issue.

AI used to identify plagiarism: neutral; could be really helpful but the parameters are defined by unrealistic standards and not interrogated by those who employ it. Human ethical issue.

AI used to analyze data and draw up complex models allowing detection of things like cancer cells: good; humans doing this work take much longer, this gives results much faster and allows faster intervention, saving lives.

AI used to audit medical or criminal records and gatekeep coverage or profile people: straight-up evil. Societal issue, human ethical issue.

AI used to organize and classify your photos so you don't have to spend all that time doing it: helpful, good.

AI used to profile people or surveil people: bad and wrong. Societal issue, human issue, ethical issue.

I'm not going to cover the astonishingly bad misinformation that has been thrown out there about genAI, or break down thought distortions, or go into the dark side of copyright law, or dive into exactly how it uses the data it is fed to produce a result, or explain how it does have many valid uses in the arts if you have any imagination and curiosity, and I'm not holding anyone's hand and trying to walk them out of all the ableism and regurgitated capitalist arguments and the glorification of labor and suffering.

I just want to point out: you use machine learning (AI) all the time, you benefit from it all the time. You could probably identify many more examples that you use every day. Knee-jerk panicked hate reflects ignorance, not sound principles.

You don't have beef with AI, you have beef with human beings, how they train it, and how they use it. You have beef with capitalism and thoughtlessness. And so do I. I will ruthlessly mock or decry misuse or bad use of it. But there is literally nothing inherently bad in the technology.

I am aware of and hate its misuse just as much as you do. Possibly more, considering that I am aware of some pretty heinous ways it's being used that a lot of people are not. (APPRISS, which is with zero competition for the title the most evil use of machine learning I have ever seen, and which is probably being used on you right now.)

You need to stop and actually think about why people do bad things with it instead of falling for the red herring and going after the technology (as well as the weakest human target you can find) every time you see those two letters together.

You cannot protect yourself and other people against its misuse if you cannot separate that misuse against its neutral or helpful uses, or if you cannot even identify what AI and machine learning are.

[itesservices]

Ensure credibility and reliability in clinical trials with robust data validation services. These services play a key role in enhancing accuracy, minimizing errors, and boosting the integrity of clinical results. By validating data at every phase, data validation ensures trustworthy outcomes, supporting better decision-making and regulatory compliance in the healthcare sector. 

[catalyswitch]

Was talking with wife recently about AI and the ways it's incredibly stupid and I am reminded of the time a few years ago the Execs at the place I worked previously wanted to incorporate AI into our workflow in order to help materials development. They wanted to make sure that the company was "utilizing the latest technology to make us more productive" so they partnered with a company that uses AI/ML to predict chemical structures in order to enhance performance based on our desired properties. My boss and I kinda thought this was stupid when it was first announced, but we were still unprepared for how bad it was really going to be.

The problem of course here is that what a computer thinks is good and will perform well does not often make sense according to the laws of physics. So more often than not the computer would spit out extremely specific and nonsensical structures that it believed would increase performance. These structures could range from completely impractical to sometimes downright impossible to actually make, so for every set of predictions we got back we had to first filter all the nonsense and then select a set from the ones that could be made and tested in a reasonable amount of time. In addition, they emphasized that the more data that they have the better the predictions would be, so the pressure was on to synthesize and validate as many molecules as possible as quickly as possible. This was a huge drain on time and energy because again some of these structures were nontrivial to make. Not that the computer people would be able to tell the difference. But still the executives were excited about it so we gave it a try anyway. The idea was that we would start by making a bunch of different materials and test the results and then feed those results back into the machine to predict better structures based on the ever growing data pool.

The funny part of the story, of course, is that with every iteration, the performance got worse. This was not surprising to me. The mechanisms that dictate performance in this field are not fully understood even now, and there are still many papers coming out every year adding more knowledge to the field. Additionally, the predictions weren't being made using some fundamental understanding of the mechanisms at play, but by training an algorithm using a pool of existing literature. You're just not going to get good results by "midjourneying" chemistry. We did around 3-4 iteration cycles with them over that year contract and every time the performance of the structures that it had predicted were worse than the last set, sometimes dramatically so. And they would tell us "no no, the data set isn't really big enough to give good results yet" and "once the model has tested enough structures it'll get better" but it didn't in that period. And it's possible that on a long enough timescale it might be possible? But, the reality was that we had a whole year of time and resources essentially wasted because our CEO thought that some tech guys in SV could use AI to do chemistry and didn't believe us when we said it was stupid.

And you know what? We figured out something that worked really well less than six months after dumping them and getting to do it our way again.

[haivoai]

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