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HOT TOPIC The AI Entrepreneurship thread. Code AI? Interested in AI? Data Science? Machine Learning?

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SamHalen

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So guys where do you see industry going in the upcoming years? Is it worth to dig deep into AI/Data Science if you will want to create something (considering you aren't a huge company like Google, Amazon etc.) and you're building from a ground up. Or is it better to go more into creating hardware/IOT stuff that will be needed for computation and also to gather data? If you would decide now to study one of them, which one you would go to? :)

The "frontiers" of AI are light years ahead of what you and I can access from online courses. Source: I manage an online course and it's taken me months just to create tutorials. I've heard of a few successful AI startups but they mostly get VC funding and have founders who went to Ivy league schools.

A more "fastlane" approach is to feed the crowd. What are the needs that all of these AI companies and people will need? All of these are unattractive, which means that there's less competition.

Skills

Sales and marketing of machine learning models
  • How can you prove that your model is worth $100/customer? Data scientists tend to come from technical backgrounds. I worked as a predictive modeler who would create the models and PowerPoint decks, but then we'd turn them over to our sales team who would sell them to clients.
  • My current course has about 30% of the curriculum just on communication. Basic things like writing, combining graphs/statistics into understandable language. The person who can explain their algorithm/AI wizardry in simpler language is worth more money than the person who struggles with communication.
Risks to fraud, data breaches, technical errors
  • Data cleaning, integrity, and reducing error. How can you know that your data is going to be consistent? "Garbage In; Garbage out." Data scientists need to know this, but it's not new! It's been around in Database language since SQL was invented in the 1980s... but people don't want to learn it because it's "boring".
  • Writing documentation.... Ugh. Is there anything that a data scientist hates more than doing this? Probably not. This task tends to get outsources to junior team members because it sucks. But the probme is that only the "expert" who created the model is knowleagable enough to write good documentation.
Hardware
  • How to use cloud services. There are basically three big competitors - Microsoft Azure, Google Cloud, and Amazon Web Services.
    • Companies concerned about data privacy still are hesitent to use the cloud, unless they are massive and have boat loads of money to spend on R&D.
    • Getting models into "Production". It's easy for someone to take their Python or R and make some AI tool, but it takes a lot longer to get it ready to be used in real-time. Most of the time this needs to be translated from R/Python into some other framework like JavaScript/Scale (?), Julia, etc. This is one of those tasks that data scientists hate.
    • Budgeting... The cloud offers "unlimited scalability" but with that comes unlimited costs. At my former company, we ran into major problems with cost overruns. We were using a machine learning model which was designed to run on about 100 GB of data, but then we had a client that had 100TB of data. The cloud computing costs went from about 20% of the costs (for normal clients) to 60% of the costs. This could have been avoided if we had knowledge of how to manage costs.
Hiring/Staffing/Recruiting
  • How do we hire the person with the right credentials?
    • Hire only Phd's
    • Hire Master's degree people and hope that their degree is actually useful
    • Put them through a rigourous hiring process involving technical projects, background checks with previous managers, reviews of their github/kaggle projects, and numerous on-site interviews and "white board" questions
  • How do we know if a "data scientist" from India who is selling themselves on Upwork for $500/hr is legit or not?
Education
  • Analytics managers have "continuing education" requirements like doctor's do. As algorithms get more complicated, it becomes more difficult to keep staff up to date. There's a growing need for corporate training. Remember all of that "Data Privacy" training that you were required to take? Imagine "AI Privacy" training in 3 years.
  • Learning platforms - My site is build using Moodle, which is good because it has a ton of open source documentation.. but there are newer platforms which have more features such as zoom integration, templated content (think Wordpress) for quickly making new courses, and so forth.
 

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I did not read the whole thread and I have a question.

I am a 31 year old simulation engineer. Basically I do technical simulations.

For example, if you have a connector for a pump and want to calculate the loads on the bolts at the connector, I can calculate it.

Good thing is, that I do not actually need to click around. I can sit down and program something, which actually builds that model and creates the code. It has been a while, but I can program in Pyhton, MATLAB and C.

Now recently, I have seen a video that shows the capabilities of this new GPT-3 code. It has read all Wikipedia and books that have been published since Earth existed or whatever and now it can compose its own poems.

Heck, it also writes the code if you say "write me a code that rolls a dice with 20 sides and records the first 10 throws". Or "make a mockup of a website that is a photo gallery where you can like or comment to each photo" (Instagram) and it makes a mockup of Instagram in minutes.

This brought me to the idea that I can apply this in my own field. I cost 100k€ to my company and there are probably like 100k-300k guys like me.

If I really can integrate AI to my own field, I would be spectacularly rich. You would not need then hours of work and 20 engineers in an office, but 2-3 very qualified ones would be enough.

Practically this would be like "calculate connector loads with 6 X bolts under Y kN of bolt pretension and the connector's drawing is in Z file."

This is an objective that requires creativity, knowledge and experience, but it is not dependent on emotions or emphaty, so I think it is possible.

But I have zero experience with machine learning or AI. So I have some questions:

1. Where to start with AI where you can automatize such objectives? What should I learn or how should I qualify myself?

2. I see a big challenge: Whereas you can find a lot of HTML code or poems on the internet, there is limited work on coding such specialized engineering software because most of such codes rot away in archives of geeks like me. The community is very small and very much disconnected, so such codes to sample from are not easy to acquire, I believe.

I heard that the bigger the data size is, the better. Is the "artificial intelligence" and the data size linearly proportionate? Or exponentially?

I hope I can convey where I am going with those thoughts. My concern is that it might not be easy to feed the code enough data.

I would appreciate any answer. :)
 
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sonny_1080

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Attorney General Anne Milgram uses "Moneyball"-esque techniques to reform the criminal justice system, with great success.

Anne Milgram: Why smart statistics are the key to fighting crime


When she became the attorney general of New Jersey in 2007, Anne Milgram quickly discovered a few startling facts: not only did her team not really know who they were putting in jail, but they had no way of understanding if their decisions were actually making the public safer. And so began her ongoing, inspirational quest to bring data analytics and statistical analysis to the US criminal justice system.
That's the most inspiring thing I've heard today.
 

ChrisV

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Great interactive Demo of how a Convolutional Neural Network works

 

ChrisV

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Great interactive Demo of how a Convolutional Neural Network works

Two more interactive Demos on Decision Trees:

 

Martin.G

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Nice resource you share @ChrisV.

I am reading Reinforcement learning: an introduction (Richard S. Sutton, Andrew G. Barto), a great book that cover all what you want to know about the subject, from the beginning of this discipline to today.
 

lowtek

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Nice resource you share @ChrisV.

I am reading Reinforcement learning: an introduction (Richard S. Sutton, Andrew G. Barto), a great book that cover all what you want to know about the subject, from the beginning of this discipline to today.
This is a fantastic text. I refer to it frequently.
 

ChrisV

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South Park creators Trey Parker and Matt Stone created a new AI-based television show "Sassy Justice," and it's great.


 

srodrigo

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I ended up doing it. I'm half way through. It's a good introduction, although it looks like I'd need a real project to work on later.
Just about to finish the Deep Learning certification on Coursera (managed to go back into it a year later and finish courses 4 and 5). I have mixed feelings about this:
  • The content is good, but the instructor sounds like my maths teacher (yeah, I get that this topic is about maths, but you know what I mean). It's difficult to keep focused without falling asleep.
  • Video edition is non existing. The guy speaks so low at the end of the sentences, that it's difficult to understand what he's saying. It doesn't help that he doesn't use a proper microphone either. Not to mention that mistakes and repetitions are not even removed. A joke.
  • I got sick of cats and cars images (one of the reasons why I got bored and did quit last year). There is a bit more variety towards the end though.
  • These courses are a good introduction, but I don't think you can jump into a job after them. I feel there's a lack of more challenging, real-world projects such as (from what I've heard) the ones on Udacity nanodegrees.

Happy to be nearly done and finish something, but I feel like I didn't learn much from it.
 

ChrisV

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Just about to finish the Deep Learning certification on Coursera (managed to go back into it a year later and finish courses 4 and 5). I have mixed feelings about this:
  • The content is good, but the instructor sounds like my maths teacher (yeah, I get that this topic is about maths, but you know what I mean). It's difficult to keep focused without falling asleep.
  • Video edition is non existing. The guy speaks so low at the end of the sentences, that it's difficult to understand what he's saying. It doesn't help that he doesn't use a proper microphone either. Not to mention that mistakes and repetitions are not even removed. A joke.
  • I got sick of cats and cars images (one of the reasons why I got bored and did quit last year). There is a bit more variety towards the end though.
  • These courses are a good introduction, but I don't think you can jump into a job after them. I feel there's a lack of more challenging, real-world projects such as (from what I've heard) the ones on Udacity nanodegrees.

Happy to be nearly done and finish something, but I feel like I didn't learn much from it.
Honestly I feel that way about most courses. The Coursera one and fast.ai were ZzZzZzzzz

The only courses that worked for me were Kirill Eremenko's. His courses are on Udemy snd SuperDataScience.com


For R the tidyverse / tidymodels websites are great:


In general it's better to read official documentation like that than reading dumb tutorials people put up.

Oh and another essential tip: find a community where you can ask questions. Discord is probably the best, but there are also AI Facebook groups, AI/ML subreddits... just somewhere you can ask questions if you get stuck.
 
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Martin.G

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Honestly I feel that way about most courses. The Coursera one and fast.ai were ZzZzZzzzz

The only courses that worked for me were Kirill Eremenko's. His courses are on Udemy snd SuperDataScience.com


For R the tidyverse / tidymodels websites are great:


There are very good courses here. This is gold.
 

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ChrisV

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I just remembered I wrote this article a while ago:

Data Science and Artificial Intelligence are distinct but similar fields, so the information in that article can also help you with AI / Machine Learning.

1604768170697.png



It's based on the R programming language, but many of the suggestions can be applied to python as well.

The best way to learn something is to learn the top ~20% of most important things, which will generally get you 80% of the results. In the article I suggest you just do it. Don't waste time learning a shitload of theory at first. You can learn that later. It's best to focus on the actual things that professionals use.

1604768299645.png

Jeremy Howard likens learning AI theory to learning baseball. Imagine if you wanted to learn to play baseball. But instead of just going out and playing baseball they sat you in a classroom and taught you the physics of the game. They taught you equations and theory and made you memorize it. This is obviously a really silly way to learn and by the time you got to even play a game you would have probably lost interest. He talks about it here.

How this relates to my article is: the best way to learn Data Science or Machine Learning is to do Data Science or Machine Learning. Kaggle.com has all sorts of datasets you can download and experiment on. That way by actually doing the work you learn the most important things used in production (the 20%.)

Some notable tips from my article:

Write the code. Don't copy and paste code you find in tutorials or StackOverflow. Make sure you actually physically type the code. You need the muscle memory. and you need feedback on what you're doing right or wrong. When programming it's so easy to put a period or closing bracket in the wrong place and unless you're actually typing the code you'll never know what you're doing wrong. Act like Copy and Pasting is a felony.

When you learn something: put it into practice immediately. Our cognitive memory is terrible, but out experiential memory is great.

Things need to be ingrained in your mind. You create different neural pathways when you do something. You don’t even necessarily have to intellectually know something to have the neural pathways. Have you ever needed to give someone a phone number, but without the number pad in front of you, you couldn’t remember it? When there’s a phone in front of you. you can dial the phone number just fine, but when someone asks you you’re like ‘uhhhh, let me go look at a number pad.’ Or the same with directions sometimes. You cant give someone directions but you ‘know the way if I just drove there.’ This is why doing is so important.

You have a muscle memory where you know exactly what you’re doing…. but you can’t intellectualize it. And the opposite can happen; you can intellectualize something, but have no idea how to actually do it. Don’t make that mistake!

You need to run the command immediately after entering to see if there are any errors. You absolutely need feedback. If you type it into the script, and don’t enter the command, you can easily engrain an incorrect syntax into your brain over and over.

Here are some evidence-based strategies for the best ways to learn things:

1604769472422.png

Btw, I have a bunch of life-improvement related articles on Medium as well.

 

WhoDatBoy

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Hey guys, very cool to see that there's quite some people on the forum with experience in the fields of Data Science and AI.

Let me quickly introduce myself, I'm a 21 y/o Dutch dude currently in the last year of my Mechanical Engineering Bachelor at a technical university. I don't want to continue in the field of Mechanical Engineering, although I definitely learnt a lot of valuable skills that I might be able to use in future business ventures (like engineering design).

I'm very interested in Data Science and AI and the many applications, and am currently thinking about maybe doing getting a Master's degree in that direction. My university has introduced a new master, focused specifically on Data Science and AI. The other option is a Master called "Data Science and Entrepreneurship", which focuses on how to add value in business with Data Science. The latter is actually taught at an institute that focuses entirely on Data Science, which has its own benefits (entrepreneurial peers, industry connections, startup incubator and support even after finishing the Master). I'm not yet sure which one to pick, and not even sure if Data Science is really for me.

I was wondering if anyone could offer any advice on the best (and quickest way, seeing as I have to decide within half a year) to find out if a career in Data Science (especially AI) is something for me. Also, opinions on which master you would choose if you were in my shoes (I can send links of the official websites via PM) are very welcome. Perhaps some of you would even recommend to skip uni and just do courses and projects myself; it would be awesome if you could share your thoughts!

P.S.
One of the most important reasons I want to learn Data Science is that I feel like it gives me skills that allow me to add value in many different fields. I'll probably work for a company after I graduate (perhaps in finance) to earn money for business ventures. My dream is to one day be able to spend my time inventing and innovating, without having to worry about my personal finances.

Thanks a lot in advance guys. I hope I can eventually offer something in return for your efforts.
 

WillHurtDontCare

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The other option is a Master called "Data Science and Entrepreneurship",

You know what would be better than doing this?

Reading Cas$hvertising, getting clients, then learning how to use data science tools to solve specific problems that make you money.

You don't need a degree to use data science tools. Take OCR (optical character recognition) for example. You could spend time learning all of the ideas that went into building it, OR, you could learn how to use the Tesseract API with Python to data mine some text out of image documents and check if you got the results that you wanted. People will pay you to do this.

Graduate school is the new seminary - you don't go there to make serious money; you go there to be indoctrinated into the priestly caste and so that people acknowledge you for being wise.

You know what is way better than spending money to learn? Making money to learn! And you will learn data science 100x more effectively if you're solving REAL business problems.

Join some professional networking group like BNI and apply as an analyst of sorts. If you have an undergraduate in mechanical engineer, people already assume that you're smart. From there you can start researching the industries and professions of the people in your group, then once you understand their problems, you can start to offer potential solutions.

And just to summarize this post - you know that you can start working on a problem before you have the full solution, right/
 

Kid

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You know what would be better than doing this?

Reading Cas$hvertising, getting clients, then learning how to use data science tools to solve specific problems that make you money.

You don't need a degree to use data science tools. Take OCR (optical character recognition) for example. You could spend time learning all of the ideas that went into building it, OR, you could learn how to use the Tesseract API with Python to data mine some text out of image documents and check if you got the results that you wanted. People will pay you to do this.

Graduate school is the new seminary - you don't go there to make serious money; you go there to be indoctrinated into the priestly caste and so that people acknowledge you for being wise.

You know what is way better than spending money to learn? Making money to learn! And you will learn data science 100x more effectively if you're solving REAL business problems.

Join some professional networking group like BNI and apply as an analyst of sorts. If you have an undergraduate in mechanical engineer, people already assume that you're smart. From there you can start researching the industries and professions of the people in your group, then once you understand their problems, you can start to offer potential solutions.

And just to summarize this post - you know that you can start working on a problem before you have the full solution, right/
Same opinion here.

Data science is something you can learn on your own.
Its not like University has some knowledge that you can't get.
Probably the opposite is true- you would learn more
on your own than following some curriculum.
 

WhoDatBoy

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Thanks for your replies, really appreciate it.

Graduate school is the new seminary - you don't go there to make serious money; you go there to be indoctrinated into the priestly caste and so that people acknowledge you for being wise.
I agree with this for a great part. Doing the master's would take another 3 years of my life (including the extra courses I would have to do before even being admitted). My dad, being a slowlaner, will finanically support me until I'm 23, as long as I study and get decent results. I think that this fact and my fears in general still push me towards the choice of doing a master.

I'll have to think better about what it is exactly that I want to accomplish. It might actually be a better to just learn developing and use AI and Data Science tools at a later stage, so that I can create a full product, like a SaaS. I feel like it is more challenging to create a product for a particular market if I just learn AI and Data Science, and it would quickly lean towards freelancing jobs, which is not exactly my goal in the long run.

Thanks for the kick in the butt. I don't have many people around me irl with this perspective.

Join some professional networking group like BNI and apply as an analyst of sorts. If you have an undergraduate in mechanical engineer, people already assume that you're smart. From there you can start researching the industries and professions of the people in your group, then once you understand their problems, you can start to offer potential solutions.
Would you say that only learning about Data Science and AI would be sufficient to eventually create a full product that I can sell to multiple businesses/persons (instead of doing freelance work)? Or would you recommend focusing on learning something like software engineering first, so that I would be more capable to build a full product?

And just to summarize this post - you know that you can start working on a problem before you have the full solution, right/
I certainly know that is true, although I do think one should at least have something like a portfolio as a proof of their capabilities, right?

I'll take some time to think about what I really want to do and get to trying it out, as soon as I have some free time again (currently in exam period and doing my final thesis).

Thanks again.
 

WhoDatBoy

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Oh and I immediately got Ca$hvertising on my kindle, so as soon as I finish my current book I'll get to it. Very curious about it and hope I can immediately apply the knowledge in the new eCommerce store that I'll start after my exams.

Cheers
 

WillHurtDontCare

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My dad, being a slowlaner, will finanically support me until I'm 23, as long as I study and get decent results.

Find a problem worth solving, reach out to people who have it, then get 2-3 clients asking to solve it. You might not even need financial support from your dad.
 

ragnarcallan

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Hey guys I have an idea if anyone would be interested.

Would any of you guys like to take part in the Fastlane Artifical Intelligence & Machine Learning Fantasy Football Competition.

Here's how it works...we basically use AI / ML Algorithms to create a Fantasy Football or Fantasy Baseball team, Moneyball style. I think since Baseball season is almost over, our best bet is Fantasy Football.

Even if you're not experienced, I think this would be an awesome project to apply analytics to a real life project. Even if you're not good at it yet... f--- it... no better way to learn than by doing.

Would anyone be down?

@PedroG, @ZF Lee, @Empires, @S.Y., @Strategery, @srodrigo, @spreng, @Fastlane Liam , and anyone else
Would definitely be interested in this
 

ChrisV

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So this is by far the easiest way I've seen to get started with Machine Learning.

Apple has a simple-to-understand point-and-click Machine Learning app called Create ML, which requires no coding experience. (You'll eventually want to learn coding but if you don't know how to code you can get started right away.)

Here's how it works. For simplicity's sake, let's make a quick Image Classifier. Oh my. It's a model that will tell you if an image contains a Lion , a Tiger or a Bear.

You simply put all your Lion pictures in one folder, all your Tiger pictures in another, and all year Bear pictures in another, and tell Create ML which folder is which

1612216038209.png
1612216082830.png

1612215837363.png

1612216260061.png

1612216320240.png

Pretty cool. Apple is known for making powerful tools accessible and easy to use. This is normally how you would program a Machine Learning model:

1612216410685.png

If you don't have experience programming, or aren't sure if you want to pursue it this is a great place to start. Or if you plan on doing iOS / macOS development.

More info on Create ML:

 

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ChrisV

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Oh, Create ML is only available on macOS. If you don't have a mac you can rent one in the cloud here, here or here for ~$20/mo.

Also, there are a bunch of other types of models you can train in Create ML:

Screen Shot 2021-02-01 at 7.07.37 PM.png

Honestly, I recommend trying Create ML even if you do have coding experience as it will give you a mental framework as to what is going on when you open a more complicated program like TensorFlow or PyTorch.

Create ML Tutorial
 
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neofox

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While I'm evaluating business ideas, I came across GPT-3 which I now have access to. I have some ideas how to use it but I actually don't know how to code!

I did see yesterday that there is GPT Neo which is an opensource version of GPT-3. Does anyone know anything about it? Is it as good as GPT-3?
 

Kal-El1998

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Does AI present great benefits? Yes 100%. But we have to draw a line somewhere to save the labor market. Like it or not, work is the only thing people look forward to / have a purpose to keep living for. Sad but true. If we outsource literally everything to computers...how could the masses possibly earn any kind of living?
 

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[...] If we outsource literally everything to computers...how could the masses possibly earn any kind of living?

Instead of viewing automation as a path to job reduction, learn to embrace it as liberation from repetitive tasks.

Implemented effectively and sensitively, it could help us become more productive while giving people time to focus on the more strategic or creative tasks that are more closely aligned with an organization's purpose.

Instead of the more simple and routine tasks that are being automated with AI, a theme to consider is the human-robot collaboration. This collaboration will increasingly deliver tasks that require high cognitive as well as emotional skills.

AI doesn't come at the cost to humans, instead it adds layers of efficiencies that allow for increased standards of living. Consider the industrial revolution and how it changed our standard of living - and think about the number of sectors that are now being industrialized via AI.
 

Kal-El1998

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Instead of viewing automation as a path to job reduction, learn to embrace it as liberation from repetitive tasks.

Implemented effectively and sensitively, it could help us become more productive while giving people time to focus on the more strategic or creative tasks that are more closely aligned with an organization's purpose.

Instead of the more simple and routine tasks that are being automated with AI, a theme to consider is the human-robot collaboration. This collaboration will increasingly deliver tasks that require high cognitive as well as emotional skills.

AI doesn't come at the cost to humans, instead it adds layers of efficiencies that allow for increased standards of living. Consider the industrial revolution and how it changed our standard of living - and think about the number of sectors that are now being industrialized via AI.
As much as I want to agree with that theory. I have to disagree. Those at the top are just going to see it as an easy way to cut people and increase their profit. What you're saying would work if everyone was pure of heart, but that's not the reality we live in.
 

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