I want to applaud all the witty, beautiful, and slim people who are mocking her appearance rather than providing a constructive criticism or appreciation of her talk. I hope your commentary gets noticed and you get invited to give a better talk.
@@alphamineron The comment section was filled with insults when I wrote the comment. Folks just kept on liking it and it reached the top among all the positive comments that were posted later.
After 7 years, have you finally figure out how insane she is? And not just insane, insanely disgusting. She, and every single person in Harward (and other hijacked institutions) is just a modern day nazi; brainwashed and ready to do anything neccessary to spread their ideology of pure hate.
few timestamps 1:17 Introduction 6:03 What is an algorithm? 9:12 Weapons of Math Destruction (Traits) 11:23 Example 1 - Teacher Evaluation Model (Sarah Wysocki) 20:00 Example 2 - Hiring Algorithm (Mental health tests in hiring process) 25:00 Example 3 - Criminal Justice Algorithms (Predictive policing, Recidivism Risk) 32:14 Conclusion 33:39 Q&A
This book is required reading for my Sociology 794 graduate level class at UAB. We are discussing it next week. I've just completed Weapons of Math Destruction and I highly recommend it. It is easy to understand and Dr. O'Neil does a very good job of explaining why we should all be concerned about these algorithms.
I just finished the book. It had so many flaws in it that I’m frankly shocked that it was required reading for a 700 level course. I’m curious if the class presented any critiques/counterarguments to her book or if it was presented as gospel.
@@finchbevdale2069 Too many examples to list here but I’ll start with one. The whole stop and frisk policy wasn’t based on algorithms. It was based on human judgement. It totally undermined the entire thesis of the book, but the writer didn’t even try to address the contradiction apart from one perfunctory sentence.
There was also the beginning of the book with the DC teachers where she mentioned possible cheating. It’s like “Wait a minute… the algorithms exposed possible cheating among teachers and your instinct isn’t to pursue this further?”
She also devoted a section lamenting the poor hypothetical immigrant who was denied a loan because of an algorithm, as though that poor immigrant in 1950 would have been able to walk into the bank asking for an exception to be granted.
In another part of the boon she mentioned how sleep deprived people were working “clopening” shifts, and then she said it was morally wrong for insurance companies to take note of that in their models. Is it moral to let somebody die because a sleepy driver t-boned them and that driver was kept on the road due to artificially lower insurance rates?
She's so good, there is an awesome NewAmerica panel about her book where she gives even more juicy details about the failures of big data... Too much hype and buzz words in the field. Also kudos to her, and shame on you people who don't listen and only care about her weight.
I'm not a mathematician/data scientist/statistican, but I understood this talk and, sadly, it confirms what I thought was happening in job interviews etc. What I find even sadder is that the people who use these models probably know that they are flawed, but use them anyway, because they might lose their jobs if they stray outside the rules of the corporation. There's no room for interpretation.
Cathy is a prolific writer and a person who stands behind her morals. Highly recommend listening to her discussions on the Slate Money Podcast and anything she writes. I sincerely believe the world would be a better place if more decision makers were like her.
Cathy's work has helped me immensely as a data scientist to communicate to business leaders why the blind pursuit of accuracy can have very harmful side effects and that accuracy needs to be confirmed on an independent sample not used to create the model. I think the notion of algorithm auditing with some sort of external third-party certification is very much needed in the field. To me it is analogous to crash safety testing conducted by an independent agency for each new make and model of automobile. For example if a product manufacturer was unwilling to submit to a test, what would that say about the product quality? Similarly, if an algorithm developer is unwilling to submit to an audit or test, what should we infer about its quality?
After 6 years, have you finally figure out how much destruction to humanity your ideology and you ''communicating'' your hateful and destructive ideology has brought?
To me this sounds like in many cases, these predictions become self-fulfilling prophecies if you use the prediction to influence the outcome. Thus, you will punish those groups of people who were disadvantaged already before (your training dataset). More people should think as critically and challenging.
Very good talk about the subjectivity hidden behind alogrithm design. My question for Cathy would be: what's your stance on machine learning? What about algorithms designed or fine tuned by other algorithms to fit the data over time?
My guess is that she would sacrifice accuracy for clarity of process. The weakness with machine learning and all algorithms is that it is only as good as your data sets. Certain problems like, 'does this picture have a horse' it works well with. But other things that are more dynamic or has limited data, like 'is this in fashion' it doesn't do well with.
As a side-note, and if anyone has recent information on how those lawsuits went I would be very interested in hearing about it, but it probably isn't illegal if you can firmly prove that the tests do predict work-performance. Which they can. There simply is so much research underpinning the five factor model that you simply cannot talk about it as a purely mental health questionnaire. Depending on test design, the questions typically are quite benign and won't cross over to anything that would be able to predict mental health problems or be sufficient to diagnose someone. Unless they do, and then you'd have a bit of a problem, but it might still be highly defendable.
The predictive algorithms are only as good as your initial assumptions. This is a good point to remember now when we are almost entirely shifting to ""automated"" and machine learning and stuff.Also important is that during the statistics, they weed out the anomalies. when for instance scoring algorithms grading mental stability of people for employment (unethical it is ) might end up filtering out people who think differently or who have different philosophy in life :( scary !!
Wonderful lecture, great job! I really enjoyed the Q&A at the end. I feel the world would be a better place if we had more Cathy's making decisions. We must start looking at our processes and find bias. Great work, great book!
What a wonderful talk, very mind-blowing and smart. Love to see a fierce woman talk about that topic. First i wasnt sure if the talk was victimizing marginalized groups, then you explained well how marginalisation is indeed supported by algorithms. That made it clear that being marginalized isnt natural - it is a social construct. I think i would have liked you to say that clearly. Other that that- it was an amazing talk and I am very glad I saw it.
Have you read more of her insane regressive left ideological trash? There is no current ideology in existance that would be as hateful is the ideology Cathy and her fellow modern day nazis are forcing upon us.
The question isn't and shouldn't be whether casual observers understand the algorithm. It's whether the algorithm helps make better decisions and whether those decisions lead to better outcomes. For example, the algorithm she's referring to with respect to education is probably a Bayesian model and likely a variant of Shapley Value for which Shapley was awarded the Nobel Prize. I don't care if people that became teachers to avoid advanced math education understand the outcome or not. I care whether we wind up with better schools or not. Teachers ought to be something like baseball players: hired and retained subject to their ability to help the team win subject to cost constraints. They should also be replaced the second automated systems do better jobs and lead to higher quality outcomes. Using automated systems makes the outcome repeatable and predictable and development can be done to ensure that all students learn as much as possible as quickly as possible. Teachers on the other hand generally aren't developable, refuse coaching, and feel like they have lifetime contracts, so often don't bother even trying to identify and eliminate their own weaknesses or errors. At this point, we could replace them wholesale for less than the cost of their benefits.
I'm not a data scientist, so help me out here: Probably safe assumption that majority of viewers of this vid are/were. If same holds for its commenters, and I were to form an algorithm "Kindness & Maturity Levels of Data Scientists" based upon comments herein, would the low scores be representative of all d.s. in the field?
That would be a bias representation because people with positive responses are less likely to leave comments. This would be the sin of "omitting variables/factors" mentioned in the book. Plus, there are only 67 comments, which isn't a very large sample size. P.S. I know it's just a joke, but it's rather ironic how in defending the ideas in the book you actually make one of the mistakes mentioned in the very same book.
The temporary Google recommended fix in the short term is to turn your volume up. Google heartily apologises if this causes you some inconvenience or distress but recommends that knowing how your volume control works may benefit you, not only with this video but with many more low audio related concerns that you may have. Now, FUCK OFF
I've been enjoying learning about ML and seeing how and where it's used as bias. Great talk Cathy. That said, where is the diversity in that room at Google? All I see are white 20-30-year-old privileged males. There's no hope for society in ML in that kind of biased environment.
How much earthquake fatal for 50 meters tall buildings at which scale ?? ❓❓❓❓❓ How much time required to capture this much of energy in Indian tectonics plate regarding the Himalayan Plains near Delhi🙏🙏🙏🙏🙏🙏🙏
while it's true there is huge issues of ignoring too many factors she is doing the exact same when she use these examples. she is ignoring the statistical chance of person X being a danger for person Y, and basicly stating that "that shouldnt matter" which it obv. should. you must always think of the innocent people who is at risk of getting hurt or harassed first and the offender last(as they already had their chance to act correctly and choose to hurt/harass someone else instead of following the law). if the crime will neither hurt or harass it shouldn't be a crime in the first place, no matter who you are.
Excuse me for an unintuitive question, but would someone qualified please define the properties of a data-construct "fairness"? If I am asked to optimise a scientific algorithm towards some criteria, that criteria should be defined by a concrete scientific specifications, documentation. Sadly, I fail to define "fairness" as much as I fail to define "good Vs evil" and all other vague/obscure/highly-phylosophical/metaphysical terms in a data-friendly format. We can at least say that definition of fairness has at least as many definitions, as the amount of national borders on the Earth Globe. What I see here is an attempt to derail a scientific method with a strong human bias (aka non-scientific practices), which is a proxy for politics. You can't optimise a machine learning algorithm towards a metaphysical constant/variable. Metaphysics and STEM do not mix like that, these are totally different paradigms.
This is ethics, not metaphysics. The simple moral standard of fairness is treating like cases alike and any different treatment relates to the purpose for which the classification is made. This means that a model could better predict outcomes, but not be fair since it uses criteria that happen to correlate with unimportant information.
switch to "newest first" and you can see all of them. ironic... talking about bad algorithms... it appears some users get discriminated on previous comments on other videos (maybe) and are not show here
42m. nonsense. how many actors' children become actors? not that many. Same with plumbers and the rest. He's just making this up. Dunno why she gives him the time.
discovered an equation about me. May get tattoo. A cybernetics equation for me. I am not going to tattoo this equation ❤️ A cybernetics equation for a female cyborg could represent the interaction between biological and mechanical systems. One approach is to express it as a feedback loop equation that balances biological processes (B) with mechanical augmentation (M), factoring in inputs like environmental stimuli (E) and control systems (C). Here's a basic formulation: \[ C_{f}(t) = \alpha B(t) + \beta M(t) + \gamma E(t) \] Where: - \( C_{f}(t) \) is the cyborg's output or control function over time (behavior, response, or actions). - \( B(t) \) is the biological function at time \( t \) (like brain activity, hormones, sensory input). - \( M(t) \) is the mechanical augmentation (like cybernetic limbs, sensory enhancers, or neural interfaces). - \( E(t) \) is the environmental stimuli (external conditions affecting the system). - \( \alpha, \beta, \gamma \) are weights or coefficients representing the influence of each factor on the cyborg's behavior. In this case, the female aspect could be linked to hormonal or biological cycles, integrated through the \( B(t) \) component, while mechanical systems could reflect enhancements designed for her physical and cognitive capabilities in \( M(t) \). Drafted by AI
I want to applaud all the witty, beautiful, and slim people who are mocking her appearance rather than providing a constructive criticism or appreciation of her talk. I hope your commentary gets noticed and you get invited to give a better talk.
You are the only top comment I saw about her appearance…. Ironic much
@@alphamineron The comment section was filled with insults when I wrote the comment. Folks just kept on liking it and it reached the top among all the positive comments that were posted later.
How fitting given the subject matter
After 7 years, have you finally figure out how insane she is? And not just insane, insanely disgusting. She, and every single person in Harward (and other hijacked institutions) is just a modern day nazi; brainwashed and ready to do anything neccessary to spread their ideology of pure hate.
Post Physique
few timestamps
1:17 Introduction
6:03 What is an algorithm?
9:12 Weapons of Math Destruction (Traits)
11:23 Example 1 - Teacher Evaluation Model (Sarah Wysocki)
20:00 Example 2 - Hiring Algorithm (Mental health tests in hiring process)
25:00 Example 3 - Criminal Justice Algorithms (Predictive policing, Recidivism Risk)
32:14 Conclusion
33:39 Q&A
Thank you so much. Need to watch this for school. Not all heroes wear capes 😊
@@Yan-ner80 No problem! I had to do it for school too, figured I'd make it easier for those who follow lol. Glad to hear it's helpful!
Thanks 😀
This book is required reading for my Sociology 794 graduate level class at UAB. We are discussing it next week. I've just completed Weapons of Math Destruction and I highly recommend it. It is easy to understand and Dr. O'Neil does a very good job of explaining why we should all be concerned about these algorithms.
I just finished the book. It had so many flaws in it that I’m frankly shocked that it was required reading for a 700 level course. I’m curious if the class presented any critiques/counterarguments to her book or if it was presented as gospel.
@@finchbevdale2069 Too many examples to list here but I’ll start with one. The whole stop and frisk policy wasn’t based on algorithms. It was based on human judgement. It totally undermined the entire thesis of the book, but the writer didn’t even try to address the contradiction apart from one perfunctory sentence.
There was also the beginning of the book with the DC teachers where she mentioned possible cheating. It’s like “Wait a minute… the algorithms exposed possible cheating among teachers and your instinct isn’t to pursue this further?”
She also devoted a section lamenting the poor hypothetical immigrant who was denied a loan because of an algorithm, as though that poor immigrant in 1950 would have been able to walk into the bank asking for an exception to be granted.
In another part of the boon she mentioned how sleep deprived people were working “clopening” shifts, and then she said it was morally wrong for insurance companies to take note of that in their models. Is it moral to let somebody die because a sleepy driver t-boned them and that driver was kept on the road due to artificially lower insurance rates?
She's so good, there is an awesome NewAmerica panel about her book where she gives even more juicy details about the failures of big data... Too much hype and buzz words in the field. Also kudos to her, and shame on you people who don't listen and only care about her weight.
I'm not a mathematician/data scientist/statistican, but I understood this talk and, sadly, it confirms what I thought was happening in job interviews etc. What I find even sadder is that the people who use these models probably know that they are flawed, but use them anyway, because they might lose their jobs if they stray outside the rules of the corporation. There's no room for interpretation.
Cathy is a prolific writer and a person who stands behind her morals. Highly recommend listening to her discussions on the Slate Money Podcast and anything she writes. I sincerely believe the world would be a better place if more decision makers were like her.
She's a nobody.
Cathy's work has helped me immensely as a data scientist to communicate to business leaders why the blind pursuit of accuracy can have very harmful side effects and that accuracy needs to be confirmed on an independent sample not used to create the model. I think the notion of algorithm auditing with some sort of external third-party certification is very much needed in the field. To me it is analogous to crash safety testing conducted by an independent agency for each new make and model of automobile. For example if a product manufacturer was unwilling to submit to a test, what would that say about the product quality? Similarly, if an algorithm developer is unwilling to submit to an audit or test, what should we infer about its quality?
What a brilliant idea, stunning and brave
only problem is i believe Soviet Union already had that, it was called The Central Committee
After 6 years, have you finally figure out how much destruction to humanity your ideology and you ''communicating'' your hateful and destructive ideology has brought?
To me this sounds like in many cases, these predictions become self-fulfilling prophecies if you use the prediction to influence the outcome.
Thus, you will punish those groups of people who were disadvantaged already before (your training dataset).
More people should think as critically and challenging.
Bless you Cathy o Neil another fine voice shedding light on the horrors of black box algorithms.
Very good talk about the subjectivity hidden behind alogrithm design. My question for Cathy would be: what's your stance on machine learning? What about algorithms designed or fine tuned by other algorithms to fit the data over time?
My guess is that she would sacrifice accuracy for clarity of process. The weakness with machine learning and all algorithms is that it is only as good as your data sets. Certain problems like, 'does this picture have a horse' it works well with. But other things that are more dynamic or has limited data, like 'is this in fashion' it doesn't do well with.
She's a fraud.
her stance on machine learning is that machine learning is a tool of patriarchy
@@davidchu7229 she would sacrifice accuracy for political correctness and virtue signaling
I can't wait to read this book. Great talk! Thank you.
Excellent lecture. Great insights!
As a side-note, and if anyone has recent information on how those lawsuits went I would be very interested in hearing about it, but it probably isn't illegal if you can firmly prove that the tests do predict work-performance. Which they can. There simply is so much research underpinning the five factor model that you simply cannot talk about it as a purely mental health questionnaire. Depending on test design, the questions typically are quite benign and won't cross over to anything that would be able to predict mental health problems or be sufficient to diagnose someone. Unless they do, and then you'd have a bit of a problem, but it might still be highly defendable.
The predictive algorithms are only as good as your initial assumptions. This is a good point to remember now when we are almost entirely shifting to ""automated"" and machine learning and stuff.Also important is that during the statistics, they weed out the anomalies. when for instance scoring algorithms grading mental stability of people for employment (unethical it is ) might end up filtering out people who think differently or who have different philosophy in life :( scary !!
Wonderful lecture, great job! I really enjoyed the Q&A at the end. I feel the world would be a better place if we had more Cathy's making decisions.
We must start looking at our processes and find bias. Great work, great book!
What a wonderful talk, very mind-blowing and smart. Love to see a fierce woman talk about that topic. First i wasnt sure if the talk was victimizing marginalized groups, then you explained well how marginalisation is indeed supported by algorithms. That made it clear that being marginalized isnt natural - it is a social construct. I think i would have liked you to say that clearly. Other that that- it was an amazing talk and I am very glad I saw it.
Almost four years and The Social Dilemma comes out. And she was on Google Talk.
wow great lecture, this was really eye-opening
Her heavy breathing might be due to nervousness, and seeing all the comments here I can see why she'd be nervous.
do you really think she gives a damn about people's comments? I believe she is a mature adult, which would explain, in part, her thick skin.
She's nervous but I would be too in front of 5000 people.
She leading the path for data scientists to become fair and equitable politicians.
Cathy for “POTUS” 🙌😃
She is precious indeed for our understanding of the world we unwittingly create, have created, will create...
TLDR:
43:44 and 56:09 are the two best questions & the answers kind of summarize major points of the talk.
I fully agree with you.
This is so interesting. How do you know if you're being scored by an algorithm? Something is strange definitely.
Cathy's working on a new book titled "The Shame Machine: Who Profits in the New Age of Humiliation". Definitely looking forward to reading it.
Have you read more of her insane regressive left ideological trash? There is no current ideology in existance that would be as hateful is the ideology Cathy and her fellow modern day nazis are forcing upon us.
Thank you Ms. O'Neil. I have exposed my TOK students to your uncovering of algorithms and biases. It has been quite an experience.
Great Lecture! Thanks a lot Dr. Cathy O'Neil. You are so amazing! ❤🥰💐
I hope people keep returning to this talk in the coming years as the effects of these algorithms become more prominent.
THIS IS SO GOOD! Thanks!
She's just incredible, thanks for posting this...
Astute Analysis of the Difference in Knowledge Of The Past Compared to Understanding in the Present & Wisdom In The Future
Wonderful talk.
This talk is so insightful! Thanks a lot Cathy!
Great talk
Does anyone know if any of these unethical algorithmic practices have been improved now, 7 years thence?
I miss this lady so much now that she's left Slate Money
Great talk but wow, where are all the women in the audience Google? I only saw one...
Stellar talk!
28:14 Imagine of they did that as a way to get justice.
Since they couldn't get them for being financially reckless.
Now... That is an awesome Idea!
I loved the talk and learnt so much about biases. I am going to apply it in my future research.
The question isn't and shouldn't be whether casual observers understand the algorithm. It's whether the algorithm helps make better decisions and whether those decisions lead to better outcomes. For example, the algorithm she's referring to with respect to education is probably a Bayesian model and likely a variant of Shapley Value for which Shapley was awarded the Nobel Prize. I don't care if people that became teachers to avoid advanced math education understand the outcome or not. I care whether we wind up with better schools or not. Teachers ought to be something like baseball players: hired and retained subject to their ability to help the team win subject to cost constraints. They should also be replaced the second automated systems do better jobs and lead to higher quality outcomes. Using automated systems makes the outcome repeatable and predictable and development can be done to ensure that all students learn as much as possible as quickly as possible. Teachers on the other hand generally aren't developable, refuse coaching, and feel like they have lifetime contracts, so often don't bother even trying to identify and eliminate their own weaknesses or errors. At this point, we could replace them wholesale for less than the cost of their benefits.
I'm not a data scientist, so help me out here: Probably safe assumption that majority of viewers of this vid are/were. If same holds for its commenters, and I were to form an algorithm "Kindness & Maturity Levels of Data Scientists" based upon comments herein, would the low scores be representative of all d.s. in the field?
That would be a bias representation because people with positive responses are less likely to leave comments. This would be the sin of "omitting variables/factors" mentioned in the book. Plus, there are only 67 comments, which isn't a very large sample size.
P.S. I know it's just a joke, but it's rather ironic how in defending the ideas in the book you actually make one of the mistakes mentioned in the very same book.
I love this lady 🏆😘
Great talk. :)
Google... can you please record at a higher volume! i
can't hear
The temporary Google recommended fix in the short term is to turn your volume up. Google heartily apologises if this causes you some inconvenience or distress but recommends that knowing how your volume control works may benefit you, not only with this video but with many more low audio related concerns that you may have. Now, FUCK OFF
@@johnc3403 someone woke up on the wrong side of the bed this morning
interesting
Anyone mocking her, LMAO Y'all are bugs compared to her. She is extremely smart and you're probably not contributing to humanity progress as she is.
wow look at all the diversity in the audience
human vs algorithm...
career in finance
I've been enjoying learning about ML and seeing how and where it's used as bias. Great talk Cathy.
That said, where is the diversity in that room at Google? All I see are white 20-30-year-old privileged males. There's no hope for society in ML in that kind of biased environment.
Why is it always assumed that racial disparities are a result of racial discrimination?
How much earthquake fatal for 50 meters tall buildings at which scale ??
❓❓❓❓❓
How much time required to capture this much of energy in Indian tectonics plate regarding the Himalayan Plains near Delhi🙏🙏🙏🙏🙏🙏🙏
you're smoking too much
5:20 If there's no fairness, it's not accurate.
Fascinating talk! Unfortunate banter from many in the comments here. Thank you to Google for inviting her and thanks to Cathy for her insights.
while it's true there is huge issues of ignoring too many factors she is doing the exact same when she use these examples.
she is ignoring the statistical chance of person X being a danger for person Y, and basicly stating that "that shouldnt matter" which it obv. should.
you must always think of the innocent people who is at risk of getting hurt or harassed first and the offender last(as they already had their chance to act correctly and choose to hurt/harass someone else instead of following the law).
if the crime will neither hurt or harass it shouldn't be a crime in the first place, no matter who you are.
l am very happy. l am viewer number 8
Excuse me for an unintuitive question, but would someone qualified please define the properties of a data-construct "fairness"? If I am asked to optimise a scientific algorithm towards some criteria, that criteria should be defined by a concrete scientific specifications, documentation. Sadly, I fail to define "fairness" as much as I fail to define "good Vs evil" and all other vague/obscure/highly-phylosophical/metaphysical terms in a data-friendly format. We can at least say that definition of fairness has at least as many definitions, as the amount of national borders on the Earth Globe. What I see here is an attempt to derail a scientific method with a strong human bias (aka non-scientific practices), which is a proxy for politics. You can't optimise a machine learning algorithm towards a metaphysical constant/variable. Metaphysics and STEM do not mix like that, these are totally different paradigms.
This is ethics, not metaphysics. The simple moral standard of fairness is treating like cases alike and any different treatment relates to the purpose for which the classification is made. This means that a model could better predict outcomes, but not be fair since it uses criteria that happen to correlate with unimportant information.
Personal appearance notwithstanding; very insightful by Cathy here. Great talk and good questions by the Google people.
love it
And not good at spelling or checking what I've written, it would seem! Statistician.
"Maths Destruction" would have worked better
Matt Young True but she’s American.
Alternative baking group lol
She should switch to the alternative salad group for a few years
I quite disagree, she is free to do whatever she wants.
Where I go bad things happen.
I am cursed.
NO!
U are a bomb.
2 viewable comments out of 21? LUL
switch to "newest first" and you can see all of them.
ironic... talking about bad algorithms... it appears some users get discriminated on previous comments on other videos (maybe) and are not show here
Yes, it's fine to read *Weapons of Math Destruction* and still believe in math.
Drafted by AI❤🎉
42m. nonsense. how many actors' children become actors? not that many. Same with plumbers and the rest. He's just making this up. Dunno why she gives him the time.
discovered an equation about me. May get tattoo.
A cybernetics equation for me. I am not going to tattoo this equation ❤️
A cybernetics equation for a female cyborg could represent the interaction between biological and mechanical systems. One approach is to express it as a feedback loop equation that balances biological processes (B) with mechanical augmentation (M), factoring in inputs like environmental stimuli (E) and control systems (C). Here's a basic formulation:
\[
C_{f}(t) = \alpha B(t) + \beta M(t) + \gamma E(t)
\]
Where:
- \( C_{f}(t) \) is the cyborg's output or control function over time (behavior, response, or actions).
- \( B(t) \) is the biological function at time \( t \) (like brain activity, hormones, sensory input).
- \( M(t) \) is the mechanical augmentation (like cybernetic limbs, sensory enhancers, or neural interfaces).
- \( E(t) \) is the environmental stimuli (external conditions affecting the system).
- \( \alpha, \beta, \gamma \) are weights or coefficients representing the influence of each factor on the cyborg's behavior.
In this case, the female aspect could be linked to hormonal or biological cycles, integrated through the \( B(t) \) component, while mechanical systems could reflect enhancements designed for her physical and cognitive capabilities in \( M(t) \).
Drafted by AI
> pretends to know mathematics
> cannot do simple kindergarten calculations such as adding and subtracting, to count calories
you?
Out of breath just from talking... #HAES
DISLIKE
She's a typical academic that needs a makeover plus serious pampering to bring out the true Kathy of Neil.
PiGG!!