Data Disinformation: the Next Big Problem

Automatic code generation LLMs like ChatGPT are capable of producing SQL snippets. Regardless of quality, those are capable of retrieving data (from prepared datasets) based on user prompts.
That data may, however, be garbage. This will lead to garbage decisions by lowly literate stakeholders.
Like with network neutrality and pii/psi ownership, we must act now to avoid yet another calamity.

Imagine a scenario where a middle-manager level illiterate barks some prompts to the corporate AI and it writes and runs an SQL query in company databases.
The AI outputs some interactive charts that show that the average worker spends 92.4 minutes on lunch daily.
The middle manager gets furious and enacts an Orwellian policy of facial recognition punch clock in the office.
Two months and millions of dollars in contractors later, and the middle manager checks the same prompt again... and the average lunch time is now 107.2 minutes!
Finally the middle manager gets a literate person to check the data... and the piece of shit SQL behind the number is sourcing from the "off-site scheduled meetings" database.
Why? because the dataset that does have the data for lunch breaks is labeled "labour board compliance 3", and the LLM thought that the metadata for the wrong dataset better matched the user's prompt.

This, given the very real world scenario of mislabeled data and LLMs' inability to understand what they are saying or accessing, and the average manager's complete data illiteracy, we might have to wrangle some actions to prepare for this type of tomfoolery.

I don't think that access restriction will save our souls here, decision-flumberers usually have the authority to overrule RACI/ACL restrictions anyway.
Making "data analysis" an AI-GMO-Free zone is laughable, that is simply not how the tech market works. Auto tools are coming to make our jobs harder and less productive, tech people!
I thought about detecting new automation-enhanced data access and visualization, and enacting awareness policies. But it would be of poor help, after a shithead middle manager gets hooked on a surreal indicator value it is nigh impossible to yank them out of it.

Gotta get this snowball rolling, we must have some idea of future AI housetraining best practices if we are to avoid a complete social-media style meltdown of data-driven processes.
Someone cares to pitch in?

IT jobs explained with a broken light bulb 😂

nothing like building a lead generation site in the advertising sector and then running a test through the site with an addblocker turned on.

The next step for improving large language models (if not diffusion) is hot-encoding.

The idea is pretty straightforward:
Generate many prompts, or take many prompts as a training and validation set. Do partial inference, and find the intersection of best overall performance with least computation.

Then save the state of the network during partial inference, and use that for all subsequent inferences. Sort of like LoRa, but for inference, instead of fine-tuning.

Inference, after-all, is what matters. And there has to be some subset of prompt-based initializations of a network, that perform, regardless of the prompt, (generally) as well as a full inference step.

Likewise with diffusion, there likely exists some priors (based on the training data) that speed up reconstruction or lower the network loss, allowing us to substitute a 'snapshot' that has the correct distribution, without necessarily performing a full generation.

Another idea I had was 'semantic centering' instead of regional image labelling. The idea is to find some patch of an object within an image, and ask, for all such patches that belong to an object, what best describes the object? if it were a dog, what patch of the image is "most dog-like" etc. I could see it as being much closer to how the human brain quickly identifies objects by short-cuts. The size of such patches could be adjusted to minimize the cross-entropy of classification relative to the tested size of each patch (pixel-sized patches for example might lead to too high a training loss). Of course it might allow us to do a scattershot 'at a glance' type lookup of potential image contents, even if you get multiple categories for a single pixel, it greatly narrows the total span of categories you need to do subsequent searches for.

In other news I'm starting a new ML blackbook for various ideas. Old one is mostly outdated now, and I think I scanned it (and since buried it somewhere amongst my ten thousand other files like a digital hoarder) and lost it.

I have some other 'low-hanging fruit' type ideas for improving existing and emerging models but I'll save those for another time.

I am having an introspective moment as a junior dev.

I am working in my 3rd company now and have spent the avg amount of time i would spent in a company ( 1- 1.5 years)

I find myself in similar problems and trajectories:

1. The companies i worked for were startups of various scales : an edtech platform, an insurance company (branch of an mnc) and a b2b analytics company

2. These people hire developers based on domain knowledge and not innovative thinking , and expect them to build anything that the PMs deem as growth/engagement worthy ( For eg, i am bad at those memory time optimising programming/ ds/algo, but i can make any kind of android screen/component, so me and people like me get hired here)

3. These people hire new PMs based on expertise in revenue generation and again , not on the basis of innovative thinking, coz most of the time these folks make tickets to experiment with buttons and text colors to increase engagement/growth

4. The system goes into chaos mode soon since their are so many cross operating teams and the PMs running around trying to boss every dev , qa and designer to add their changes in the app.

5. meanwhile due to multiple different teams working on different aspects, their is no common data center with up to date info of all flows, products and features. the product soon becomes a Frankenstein monster.

6. Thus these companies require more and more devs and QAs which are cogs in the system then innovative thinkers . the cogs in the system will simply come, dimwittingly add whatever feature is needed and goto home.

7. the cogs in system which also start taking the pain of tracking the changes and learning about the product itself becomes "load bearing cogs" : i.e the devs with so much knowledge of the product that they can be helpful in every aspect of feature lifecycle .

8. such devs find themselves in no need for proving themselves , in no need for doing innovative work and are simply promoted based on their domain knowledge and impact.

My question is simply this : are we as a dev just destined to be load bearing cogs?

we are doing the work which ideally a manager should be doing, ie maintaining confluence docs with end to end technical as well as business logic info of every feature/flow.

So is that the only definition of a Software Engineer in a technical product?

then how come innovations happen in companies like meta Microsoft google open ai etc?

if i have to guess as a far observer, i would say their diversity in different fields helps them mix and match stuff and lead to innovative stuff.

For eg, the android os team in google has helped add many innovative things in google cloud product and vice versa.

same is with azure and windows . windows is now optomissed to run in cloud machines when at one point it was just a horrible memory hogging and slow pc OS

for small companies, 1 ideology/product/domain is their hero ideology/product/domain .

an insurance company tries to experiment with stuff related to insurances,health,vehicles,and the best innovations they come up with is "lets give user a discount in premium if they do 5000 steps a day for an year".

edtech would say "lets do live streaming for children apart from static videos"

but Android team at google said , "since ai team is doing so well, lets include ai in various system apps and support device level models" ~ a much larger innovation as 2 domains combined to make a product

The small companies are not aiming to be an innovative product, they are just aiming to be a monopoly product. and this is kinda sad

Somehow I found Rousseau's the social contract.

I'm early into it since you know fucking chomo faggots with no balls keep screwing the world up trying to steal real peoles personalities and make them queer which eventually will lead to a generation that murders them being bred.

Anyway I found a love phrase.

Slaves loose everything in their chains even the desire of escaping them.

He continues.

Force made the first slaves, cowardice perpetuated the condition.

In short
The world being full of cocksucking perverse house niggers that love the taste of table scraps is the problem of the free man whose life is being devoured by scum like tosensei

Hello all, recently I have been doing alot of front end work in web forms and lead generation. I would love to learn more about marketing and how it can be applied as a dev.

Does anyone recommend any good books atall?

Thanks!

In a country, a long time ago there was a programmer by the name of Alex. He was a programming genius and apart from a few hours of sleep, he was busy developing unique programs for new generation technology firms. Alex was a bachelor and he happily and proudly lived the way he wanted to. He did not have duties, authority over him, bosses to report to, children to take care of, and distractions. He could sit and code for the entire day without getting any break or feeling a bit tired. However, he had no idea that everything in his life was soon going to turn around. Before Marriage: The Bachelor’s Life Alex was the epitome of a modern ‘Play Boy ‘ or every man’s dream. He was fairly dressed, had a classy house, a snazzy car, and a good-paying job. He was in the habit of spending his mornings drinking coffee while browsing through the different coding topics. He comes in the afternoon and spends the evening part of the day with his friends. Life has never been this good. Alex was able to work hard and the more he was innovative, he enjoyed it. It illustrates how a young person would sit for many hours coding at night and not bother about other people around him. He was alone as a bird and as per him, that’s what he wanted to be. He had no peer to tell the truth to, no wife to prepare meals for, no maids to babysit his mess. A man could chow down a pizza for breakfast, lunch, and supper with not even a raised eyebrow from onlookers. He was profiting from living the best life he possibly could. After Marriage: Married Life: Alex & Sarah The climax for Alex is when he marries Sarah on a sunny morning on a fine day. Young people met, and after becoming enamored, started a family and got married to find a new home. Sarah was friendly with people and it was very easy for her to make friends; however, she had little knowledge of technology. Alex had it in his mind that marriage does not change the life you lead and how wrong he was. It was a fairy-tale to have such a perfect life for several days after the marriage. Their nights would be spent in front of the television set with their arms wrapped around each other, eating takeout. Despite this, when the number of days stretched into weeks, and the weeks into months, Alex felt the beginning of a shift in his behavior. The Coding Cave That Transformed into A Home Office Due to the pandemic the coding cave Alex used to have became a home office. Sarah had made up her mind to open her business from home, therefore, she required a home office. Thus, she moved inside the cubicle that Alex had created as his coding cave and left him with no space to code. He now had to code in the living room, because Sarah would incessantly request him to either lower the auditory input of the keys he was typing or to switch off the LCD screen. The Once-Clean Apartment Turns into a Mess Alex was a neat freak, and he adored tidiness, especially in his apartment. But after marriage, his once clean and neat-looking apartment was changed into a dirty one. Although Sarah was not very neat, she used to litter her things anywhere she felt like without being conscious of it. Alex was a programmer and his coding notes were mixed with Sarah's business papers, it irritated him so much. Alex’s to-do list before marriage The to-do list before marriage only comprised coding-related tasks. At marriage, however, he seemed to have developed a longer list of things to do than ever before. Instead of just going to the grocery store to buy some food, Alex seemed to have endless tasks to do mostly around the house. He had to cook for himself, sweep the house, and wash the dishes among other things. This was a new world as far as he was concerned. The Pizza Days Are Over Gone there is no more time for Alex could eat pizza in the morning, afternoon as well and evening. Sarah was very conscious of what she took as food or what her family took as food and therefore ensured that Alex took healthy home-cooked foods. He could not have the pizza anymore but the meals prepared by Sarah were really tasty. Conclusion Therefore from a life before marriage to the life after marriage, it was evident that Alex led two different lives. He went from a playful man with not much responsibility to a man with more responsibilities as a husband and a father. Still, he wouldn’t have it any other way, despite these changes. Later he cherished Sarah and the life they had, and nothing in this world could make him exchange what he had now. Essentially, it was a tricky business being married, but a blessing, and an addition of love, company, and much hilarity too. Therefore, if you are a bachelor reading this, embrace your coding cave and your pizza days because once you utter the words ‘I do,’ all those will be things of the past.But trust me, it's all worth it.