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But, being that it’s 2015, so much has changed since then. What makes a woman a wife isn’t about being docile, submissive, and letting the man run the show. Thanks to the Women’s Movement, there is an equality that makes both men want to find their partner and not their, well, maid.
We talked to 11 men about what makes a woman “wife material.” This is what they had to tell us.
1. You love in equal measure.
“It just boils down to love and attraction. I don’t mean any kind of romantic ideal, but like, proper love, where you love them so much that even when you’re mid-argument and super angry you still make sure they take an umbrella to work because it might rain. Any person who loves you that much in equal measure, and doesn’t mind risking a sex-related hip fracture when you’re both in your 70s is definitely marriage material.”
2. You have insatiable intellectual curiosity.
3. You accept without reservation.
“Total acceptance for who you are both the good and bad.”
4. You laugh.
5. You can cook.
“I know this is going to come off sexist, but it isn’t. I swear. That being said, wife material for me is a woman who can cook and cook well, like French pastry type well. Not because I want her in the kitchen but because I can’t cook to save my life but eating is a passion of mine. If her passion is to cook and mine is to eat then we can’t lose. I’d like to say once again that this isn’t me giving into gender stereotypes. Cooking and eating together is sexy. I guess I’m a regular old George Costanza.”
6. You challenge him.
“She has to challenge me to be my best self. That’s my main qualification. Also, I can’t marry someone who isn’t funny.”
7. You’re affectionate.
“Looking at my parents’ marriage there wasn’t as much emotional support as I think my mother needed from my dad. They fought too often and I never saw them, not once, show any sort of affection toward each other. Because of that, a woman who is affectionate and loving is someone with whom I want to grow old with. I don’t ever want my kids to wonder why mom and dad never hugged, kissed, or cuddled, like I always wondered. I want them to know we love each other and they were born from that love.”
8. You have your own life.
“In my mind, a woman with her own life is probably the coolest one to marry. And by ‘her own life’ I mean: her own career, her own set of friends, her own independent streak, her own dreams, and her own bunch of at least 15-20 vinyl records. This would certainly be a fair thing for a woman to want in a potential husband too, by the way.”
9. You don’t publicly embarrass.
“She doesn’t punk you in public in general but particularly in front of your friends. Bust chops? Okay. Disagree? Sure. Argue? Maybe. But if she’s wife material she’ll keep anything demeaning or embarrassing private.”
10. You love your past mistakes.
“I know this is cliché, but wife material for me is someone who loves the worst in me and is OK with all my screw-ups. I’ve made a lot of mistakes along the way and have my fair share of regrets, so if she can love that stuff and not hold it against me, then I’d call that wife material.”
11. You are considerate.
“‘Wife material’ indicators can range from doing the wallet dance (thanks, but we’ll pay) or something sweet like making our bed after a sleepover. It’s so simple, but speaks to a level of consideration you don’t always get in some people who might feel a little too entitled to your chivalry.”
This post originally appeared at YourTango.
Artificial Intelligence Explained. What Is Explainable Ai (Xai)?
Explainable AI (XAI) is a hot topic right now. We’ve recently seen a boom in AI, and that’s mainly because of the Deep Learning methods and the difference they’ve made. There are many more use cases of AI now compared to the times before Deep Learning was introduced.
The problem that we’re facing today is that many methods work, but we don’t elaborate on the details of whatever is done under the hood. But it’s very important to understand how the prediction is done, not just to understand the architecture of the method.
Explainable AI is useful for:
domain experts,
regulatory agencies,
managers and executive board members,
data scientists,
users that use machine learning, with or without the awareness.
The domain experts or the users of an AI or machine learning model (like doctors, for example) trust the model itself, gain scientific knowledge. The regulatory agencies certify the model’s compliance with the legislation in force. The managers assess the model’s regulatory compliance and need to understand the possible corporate applications of AI. The data scientists ensure and improve product efficiency or develop new functionalities.
Every other user affected by the model’s decision wants to understand the situation and verify if the decision is fair.
Goals for an explainable AI model to fulfill
There are many goals an XAI model should fulfill.
However, not every goal can be met with every method, and each goal has a different target audience.
Here are a few examples:
The domain experts and other users affected by the model should be able trust it.
We should be able to transfer the knowledge that we can gain from the model to other problems or challenges.
We should understand the models enough to ensure privacy of the data used for training the model and what’s done with the data during the prediction process. The European Union is already working on a directive on privacy and machine learning.
Every model should be done in a way that doesn’t affect any minorities. In other words, every model should be fair and ethical.
Every model should be robust and informative. We should be confident that the prediction is valuable and related to the user’s decision.
And finally, every model should be accessible to non-technical people. They should understand how it works and, in some cases, be able to interact with it.
Not every XAI model needs to fulfill all of the goals, and not every model that meets one of the goals above is an XAI model.
Levels of transparency of an XAI model
The transparency of a model can be divided into three levels, depending on how transparent the model is.
→ Transparency level 1:
We should achieve a model that is fully simulatable, which means it can be fully simulated by a human. Simulatable models are the most demanded type. Most machine learning projects use shallow and scikit-learn methods to increase the chances that their model will be simulatable.
→ Transparency level 2:
The second level of transparency is reached when a model can be decomposed. This means that we are able to divide the model into parts and explain how each part works and how it processes the data. In many models, especially in models based on neural networks, we can only explain just one part of the whole model in detail.
→ Transparency level 3:
The last level is algorithmic transparency, which means that we understand how the model produces the output. In most cases, it can be achieved with simple methods easily understood by the user.
How to explain a model?
There are many ways to explain how a model works (it’s also called post-hoc explainability).
Typically, we use text to explain it, but we can also use symbols and chúng tôi most popular method for explanation is based on charts. It’s an easily interpretable way for humans to understand how a model works. We simply take a subspace of the model and explain it in a couple of different ways.
Another easy to understand method for explanation is doing it through an example. We take some input data and explain what happens with it during the process, step by step.
If the model is too complex, we may simplify it and explain the way it works on a simplified model.
Black-box method example
For the black-box method example, we’ll use a three layer network. Each layer is a dense. It’s simple, but still complex enough to be considered a black-box method, even if we achieve almost 99% of accuracy.
We can draw the weights of the layers. It’s very hard or even impossible to interpret them.
The code above will produce about tens of lines like the one below:
[array([[ 0.00166182, 0.04952418, 0.08845846, …, 0.00472951,
-0.04272532, 0.04789469],
[-0.0524085 , -0.03233211, -0.0232333 , …, -0.0056492 ,
0.04325055, -0.06916995],
[-0.01691317, 0.02450813, 0.06632359, …, -0.06094756,
-0.14761966, -0.02945693],
…,
These are the values of the weights of just one layer. In many neural networks, the number of weights is counted in millions. It’s hard to explain each weight, in many cases even impossible. That’s why we call such methods black-box methods.
White-box method example
There are plenty of white-box methods. One method that is well-known and easy to interpret is the decision tree. Modified versions are used in Kaggle competitions with success.
it’s simple,
it’s interpretable,
it’s easy to convert to a set of rules,
it’s a feature importance tool.
We can easily draw the tree and see all the decisions that are made on each node.
It can be effortlessly replaced with a set or rules ( if statements).
Python tools for explainable AI
In Python, we have a number of tools to understand how the model works.
What Does It Mean To Be Vested In My 401(K)?
You know you should contribute to your 401(k) on a regular basis, that you should match your employer’s contribution, maybe even that you should invest more aggressively when you’re young, then adjust to a more conservative approach as you near retirement age.
But do you know what it means to be vested in your 401(k)? We explain what this means and why it’s important.
Vested Definition
Putting it simply, vested is a term used to determine how much of your 401(k) funds you can take with you when you leave your company. Vesting refers to the ownership of your 401(k).
While all the money that you personally have contributed to your 401(k) is yours and will go with you if you choose to leave your position, the terms may be a bit different when it comes to your employer’s match of that money. Many employers set up vesting guidelines regarding what they contribute to their employee’s 401(k)s.
Many companies’ policies range from three to seven years in order for you to be fully vested in your 401(k). Some may allow you to be vested for a percentage of that amount, which increases each year until you reach the maximum amount.
What Happens If I Leave Before I Am Fully Vested in My 401(k)?
Let’s say you have a plan that increases the amount you are vested in your plan each year by 20%. This means that you will be fully vested (i.e. the employer-matching funds will belong to you) after five years at your job. But if you leave your job after three years, you will be 60% vested, meaning that you will be entitled to 60% of the amount of money that your employer contributed to your 401(k).
If your employer does not have a plan that increases your vested amount each year but instead becomes fully-vested when you’re at the company for a certain period of time, you will lose all the money your employer has contributed to your 401(k) plan if you leave before that period is up.
So be sure to familiarize yourself with your employer’s vesting policy, or it could cost you big. You may even consider staying at your job longer than you originally planned in order for your 401(k) to fully vest.
Why Do Employers Have Vesting Policies?
One reason employers have vesting policies is to encourage the longevity of their employees. Many employees will stay in their jobs until they are fully vested in their 401(k)s in order to gain the most financial benefit. For employees, this may be a consideration when it comes time to look for a new job.
On that note, it is always important to consider the financial impact of a new job. If your salary is going to increase significantly, you may be willing to take the hit on your 401(k) balance, especially if you have only been with the company for a year or two. However, if you are close to the point of being fully vested in your 401(k), it may be more beneficial to wait a few months or even a year to allow your 401(k) to become fully vested before switching jobs.
How Can I Determine What Guidelines Affect Me?
To fully understand the vesting policies of your company, speak with the human resources department. They should be able to explain your company’s vesting policy and schedule. Being aware of this policy can help you to make the most of your retirement contributions and accounts.
It can also help you determine the right time to begin looking for a new job. For example, if you are only six months away from becoming fully vested in your retirement account, it may be worth waiting to switch jobs.
How Does Vesting Affect How Much I Should Contribute to Retirement?
You should aim to contribute 10 to 15 percent of your income to retirement. This total can include your employer match. If this amount is a bit out of your reach, then you should aim to contribute at least the same amount your employer matches. After all, it’s basically free money.
And if you know you are going to leave a particular job before your 401(k) is fully vested, you may want to increase your contributions to cover the loss if you change jobs.
And remember: When it comes to retirement, it’s always better to save more, rather than less. Your future self will thank you.
Updated by Rachel Morgan Cautero
What Does .Com Mean? Get The History Behind .Com
.com was introduced as one of the first top-level domains (TLDs) when the Domain Name System (DNS) was first implemented for use on the internet in January 1985. Originally created to represent the “commercial” intent of a website, .com has since been at the epicenter of the digital revolution that has reshaped the way people work, live, play and connect with family and friends.
Detailed History of .com
Jon Postel in 1994, with hand-drawn map of internet top-level domains. Photo by Irene Fertik, USC News Service. ©1994, USC.
The need for some sort of organizing principles became more and more apparent as more entities connected into the fledgling internet. Bringing order to the increasingly chaotic universe fell to the legendary Jon Postel and his colleagues at the University of Southern California’s Information Sciences Institute.
While we know that the first .com was assigned to chúng tôi on March 15, 1985, the genesis of .com is less clear. According to Craig Partridge, Professor & Department Chair of Computer Science at Colorado State University, the name for domains evolved as the system was created. At first, .cor was proposed as the domain for corporations, but when the final version came out it was switched to .com.
Jack Haverty, an internet pioneer at MIT, said they weren’t really thinking about business when they were developing the top-level domains. “I think .com originally was derived from “company” rather than “commercial.” The .com’s weren’t thought of as “businesses” in the sense of places that consumers go to buy things,” he wrote in an email. “They were companies doing government contract work. The internet was not chartered to interconnect businesses—it was a military command-and-control prototype network, being built by educational and governmental entities, and contractors.” Still, they seemed to understand that some kind of commerce was coming.
“I don’t recall anybody ever thinking we were creating an organizational structure to encompass hundreds of millions of entities covering the entire planet in support of all human activities. And it certainly wasn’t supposed to last for 30+ years, even as an experiment. It just happened to turn out that way.” - Jack Haverty, Internet Pioneer
Verisign’s Role in .com
Every domain name is powered by a registry operator. As the registry operator for .com, Verisign enables the world to connect online with reliability and confidence, anytime, anywhere.
With a current average of approximately 235 billion DNS lookups performed daily—and peaks far in excess of this—it is vital that Verisign’s internet services be operational around the clock. To make this possible, we have designed a sophisticated service from the ground up to address multiple complex, high-volume, real-time demands. This includes diverse hardware, operating systems, middleware and custom applications, power provider and network provider diversity and a number of other protections.
Verisign ensures the security, stability and resiliency of key internet infrastructure and services, including the .com and .net domains and two of the internet’s root servers, as well as performs the root-zone maintainer functions for the core of the internet’s DNS. Our commitment is ensuring that an infrastructure powered by Verisign is always operating at the highest level to enable the innovation required to address the needs of the future, while also addressing the needs of today.
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