Artificial Intelligence Is The Last Human Invention

Advancements in the field of technology are inevitable for humans, and today, people’s dependence on their devices has altered their mindsets and views for the future. Our inherent desire to simplify our lives has motivated computer scientists to magnify Artificial Intelligence. It is an intricate system embodying the neural processes of the brain to replicate skills of a professional conveniently. Even though A.I’s (Artificial Intelligence) present-day limitations are preventing itself from automating the world, its capability to imitate any human abilities are frightening.

Google, Facebook, and Mount Sinai Hospital believe that Artificial intelligence is the last human invention as they incorporate A.I. into their business as a mainstream vision. The three key attributes making Artificial intelligence the last human invention are learning at an exponential rate, managing our advancing lifestyle, and providing insightful solutions. A developed set of techniques enable learning in artificial neural networks. These designing learning techniques are based on the chance, gradient, decision-based background-analysis, but also it generates new ideas based on its own merit. These techniques have to endow much deeper networks to be trained – now common networks with 5 to 10 hidden layers.

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And, it devotes out that these do far better on many problems than a simple neural network. The sake, of course, is the cleverness of a deep network to construct up a complicated hierarchy of concepts. It’s like the step-away conventional programming languages habit modular methods and ideas about abstraction to enable the appointment of complicate computer processes. Artificial Networks captivate a distinct form of language that change the views on computer science. The motive for interest in ANNs(Artificial Neural Networks) is that they can resolve problems. A common application of ANNs is Youtube translation suggestions for the upcoming videos based on the precedent genres and interests people have proposed. Scientists from Large Hadron Collider turned to ANNs to manage shipping companies to minimize delivery over a tough scattering of destinations.

Credit-card companies use them to ID fraudulent transactions, and they are even more accessible to smaller teams and individuals — Amazon, MetaMind, etc. tailor these artificial networks at a high fee that many can not afford. Google has been educative about their photo-analysis algorithms with the model of animals, and its services are stable at telling dogs from cats in regular formats. Translators have the ability to artfully merge expressions and tones in different languages with implemented slangs that are usable in the area. Virtual digital assistants: Siri, Alexa, and Cortana are the Big-Three major programs in our lives answering to simple tasks articulated to them. While voice systems are the most common vision in our households, some situations were screeching through your household is not the most viable option, particularly when your newborn is asleep next to you. Artificial intelligence in word-recognition, such as text from your mobile device, maybe the next step of AI that many may foresee.

These simulations have models that learn and grow using neural nets or a grapple-derived function. Emphasis is often, although not always, more on learning than on natural selection of methods. The world of artificial intelligence has become a possibility of the future with their analysis and proposed solutions, unlike any human. Those substitutes are the mass distribution and use of Internet-connected devices, which reproduce massive quantities of data, and cloud computing and software algorithms that can recognize patterns within data “I believe 2018 is the year that this will start to become mainstream, to begin to strike many aspects of our lives in a faithfully ubiquitous and meaningful passage,” says Ralph Haupter, the president of Microsoft Asia().

“The idea that computers have some amount of ‘intelligence’ is not new…So it has taken nearly 70 donkey’s for the right combination of factors to come together to move AI from concept to an increasingly ubiquitous reality,” By now you must be convinced of the fact that AI is impacting our lives on a daily base. Both Google and Apple along with other navigation services manner artificial intelligence to interpret hundreds of thousands of data point that they allow to give you real-time traffic data. When you are vocation an Uber, both the pricing and the car that agree your ride request is determined by AI. As you can see, AI plays a significant role in how we retch from point A to point B. As for the 2.7 million Americans who are engrossed as customer service representatives? Some may be redeployed to tasks that bots can’t do (copy bestowal with really irate customers). Companies relying on this technology say it can remedy eliminate human wandering, drastically increase speed in data retrieval, and remove obliquely from customer service interactions.

One of the biggest of these is – how do we keep the systems sure? Algorithms are based on data, so any change to that data will change the behavior and outcomes. “Almost anything wicked you can think of doing to a shape-learning model can be done right now,” before-mentioned one expert at a recent AI comparison in Spain. “And defending it is really, really hard. . 1. Healthcare One of the biggest benefits of AI is its ability to trawl through heavy amounts of data in record time. This assist researchers pinpoint areas of focus for their own research. For represent, a recent ground-breaking discovery on the disease Amyotrophic Lateral Sclerosis (ALS), was made through a partnership between Barrow Neurological Institute and the artificial intelligence company IBM Watson Health. IBM Watson, the affected intelligence electronic computer, reviewed thousands of pieces of research and was able to identify new genes associated with ALS. “Traditional researches tools are fast becoming inadequate to help data scientists and researchers keep pace with any global problems that AI could help us solve and find relevant insights among the now billions of documents which are spread all over the world,” pret. quoth the company in a oppress release.

“The discovery gives ALS researchers unworn insights that will pave the highway for the disclosure of new drug targets and therapies to combat one of the world’s most devastating and deadly diseases.” Another promising use for AI within healthcare is its ability to soothsay the outcome of drug treatments. For instance, cancer patients have often disposed of the same illegal drug, and then supervise to see the effectiveness of that drug. AI could use data to predict which patients could benefit from using a particular drug, providing a highly personalized advance, and saving valuable time and money.

3. Transforming how we teach Earlier this year, students at Georgia Tech university in the US were startled to discover that their helpful education accessory had in performance been a robot all along. After commencing teething problems, the robot started answering the students’ questions with 97% certainty. The college designed the robot after their research showed that one of the main factors behind students dropping out is a want of support. People learn differently, at different speeds and with separate starting moment. Artificial intelligence could usher in a futurition where we all learn in a much more personalized distance. But no culture system in the world can afford a tutor for every child, so this is where AI might be able to step in.

Artificial tutors, made to look and sound as much like humans as possible, could take the lead in delivering personalized education. Technology inclines is at breakneck haste creating opportunities for increasing our influences on the worldwide networks, and our mobile devices are superseding the gadgets in our houses. Artificial intelligence is the holy grail to all human inventions with its learning curve, human control, and simulating ideas ending worldly issues.

Once humans can develop source code for an AI program, the possibility for inventions and solutions is endless, and people would not need to invest in answering unhuman problems. The idea of Artificial Intelligence seemed like a distant dream without an end a few years ago. While an abstract concept of interpreting any sourced information through numbers may be overwhelming, humans need to prepare a dramatic change in everyday practices of AI.

The Gender Pay Gap Situation

Despite numerous feminist movements and policies put in place to promote gender inequality, women still do not get paid as much as men. The gender pay gap is the difference between what the average man and woman makes. Wade (2018) found that full time working women make $0.82 for every dollar that a full time working man makes. The gap has slowly gotten smaller since women were first allowed to work, however, it still persists. According to the Institute for Women’s Policy Research (2018), “if change continues at the same slow pace as it has done for the past fifty years, it will take 40 years—or until 2059—for women to finally reach pay parity.” If unequal pay persists for another 40 years, it may cause significant problems for our economy. The gender pay gap is caused by gender job segregation, discrimination, and the ideologies about motherhood and work.

“Gender job segregation is the practice of filling occupations with mostly male or mostly female workers,” (Wade, 2018, p. 326). People often associate different jobs with different genders. For example, construction work is seen as masculine, whereas, childcare work is seen as more feminine. People associate gender with these jobs based off of the stereotypical traits that each gender has. For example, men are seen as strong and tough which would make them good construction workers, and women are seen as compassionate and nurturing which would make them good caregivers. According to Cech (2013), gender job segregation is one of the largest factors contributing to the gender pay gap.

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Wade (2018) found that there are three main hypotheses that explain the cause of gender job segregation: socialization hypothesis, employer selection hypothesis, and the selective exit hypothesis. As discussed earlier, people associate gender with different jobs, and they also associate stereotypical traits with each gender. “The socialization hypothesis suggests that men and women respond to gender stereotypes when planning, training, and applying for jobs” (Wade, 2018, p. 329). The employer selection hypothesis is similar to the socialization hypothesis except it refers to the employer. Instead of people deciding their career based off of gender stereotypes, the employer chooses workers based on gender stereotypes. The hypothesis suggests that employers will choose men for stereotypical masculine jobs, and women for stereotypical feminine jobs (Wade, 2018, p. 330). Both of these hypotheses suggest that people strongly associate different jobs and traits with different genders, and that people base their decisions off of them. The last hypothesis is the selective exit hypothesis. According to Wade (2018), the selective exit hypothesis suggests that when women are hired into male dominated occupations, they often quit and join female dominated occupations. This often happens because they experience a negative work environment.

One of the reasons why gender job segregation is one of the largest factors contributing to the gender pay gap is because female-dominated occupations are valued less than male-dominated occupations, therefore, female-dominated occupations have significantly lower wages than male-dominated occupations. The Bureau of Labor Statistics found that the top 5 highest paying jobs are more than 60% male, and 72% of the lowest paying jobs are majority female (as cited in Wade, 2018, p. 334). “A common explanation for the association is found in devaluation theory, according to which women’s work suffers from discrimination in pay because the traits and skills identifies with femininity are valued less than are masculine traits” (Mandel, 2013, p. 1183). The devaluation theory basically suggests that the reason why female-dominated occupations have lower wages is because the stereotypical traits associated with being a female are undervalued compared to those of a man. Wade (2018) states that a lot of people view work done by women as a natural part of being a woman, rather than a skill and should, therefore, be compensated less. Women’s work and skills are so undervalued that when they do move into male-occupations, those occupations start to be paid les as well. Mandel (2013) found that when more women moved into highly paid occupations, the wages decreased. When occupations start to include more women, their wages will decrease. Women will experience lower wages no matter the job, occupation, or field they go into because their traits and skills are very undervalued, and people believe they shouldn’t be paid as much because of it.

Another main contributor to the gender pay gap is gender discrimination in the workplace. Gender ideologies and stereotypes are the main cause of gender discrimination. According to Bobbitt-Zeher (2011, p. 766), “Consciously or not, individuals translate ideas about gender into discriminatory behaviors through sex categorization and gender stereotyping.” Bobbitt-Zeher (2011) also states that there are two different kind of gender stereotypes, descriptive and prescriptive gender stereotypes. “Descriptive stereotypes concern beliefs about traits that one gender has; prescriptive stereotypes involve beliefs about traits one gender should have” (Bobbitt-Zeher, 2011, p. 766). These stereotypes can lead to discrimination when the traits associated with the gender don’t match with the traits associated with the occupation (Bobbitt-Zeher, 2011). Gender stereotypes influence the way we view others, as well as how we act and behave around or towards them, therefore, they can easily lead to discrimination. There are many types of gender discrimination in the workplace. Wade (2018, p. 341) found that “Scholars have identified three forms it takes: men’s hostile and benevolent sexism, women’s double binds, and employer’s preference for men.”

Sexism in the workplace creates a hostile environment for all employees. Hostile sexism is the most obvious type of sexism in the workplace. Yi (n.d.) describes hostile sexism as “any antagonism toward women who challenge male power.” According to Wade (2018), men who hold hostile sexist beliefs think that women should be at home with their children, and these men might also see women as a threat to their identity and authority. Hostile sexism can often lead to sexual harassment and assault. Women working in male-dominated occupations have an increased risk of experiencing sexual harassment and assault. One study found that 60% of female construction workers experienced sexual harassment at least once, and 30% of these workers experienced sexual harassment on a daily basis (Shaw, Hegewisch, & Hess, 2018). Benevolent sexism is more subtle than hostile sexism. Benevolent sexism is when men try to shield and protect women from having to do masculine tasks, which can prevent women from being able to do their jobs (Wade, 2018). Yi (n.d) found that men who perpetrate benevolent sexism view women as delicate and fragile and believe that they need to be protected. Benevolent sexist beliefs stem from stereotypes about women, and ultimately hurt their chances of being able to advance their careers.

Due to the sexism and discriminate women experience at work, especially those in male-dominated occupations, women face a double bind. The Cambridge Dictionary defines double bind as “a difficult situation in which, whatever action you decide to take, you cannot escape unpleasant results” (Double bind, n.d.). Since women are stereotyped as being fragile, nurturing, emotional, and delicate, they are often seen as unsuitable for masculine occupations. Women in male-dominated occupations need to be more masculine so they are seen as more competent and suitable for their jobs. “To be successful at her job, a woman needs to do masculinity, but to be accepted by her boss, colleagues, and clients, she needs to do femininity” (Wade, 2018, p. 345). This is a lose-lose situation for women. If they are more masculine, they will succeed at their jobs, however, this makes them vulnerable for discrimination from their boss and coworkers. Wade (2018, p. 345) found that “feminine women are seen as likeable but incompetent, while women who do masculinity are seen as competent but not likeable.” This makes it impossible for women to be good at their jobs and like by their boss and coworkers. No matter what they do, they are discriminated against.

Working women also have to deal with employer’s preference for men. This is the case in both male and female-dominated occupations. The effects of sexism and double binds make it hard for women to do their jobs effectively, as well as receive proper training and feedback from their employers. This makes it harder for women to advance their careers. Women in male-dominated occupations don’t receive the same opportunities as men do. This includes promotions, raises, and training. Wade (2018, p. 346) describes the glass ceiling as “an invisible barrier between women and top positions in masculine occupations.” Even when women have the same amount, or even more, of education, training, and experience as men, they are still passed over for promotions, raises, and other opportunities. When women do get passed the glass ceiling and are hired into a top position, they have a much higher risk of failing compared to men, this is known as the glass cliff (Wade, 2018). Women face many obstacles in male-dominated occupations that are impossible to get around, but they also face many obstacles in female-dominated occupations as well. Men in feminine occupations experience the glass escalator which Wade (2018, p.347) describes as “an invisible ride to the top offered to men in female-dominated occupations.” In every occupation and field, men are given more opportunities than women despite their level of education, experience, or training. Women face impossible obstacles and are discriminated against in all occupations, which leads to them having lower wages and benefits, and eventually can lead to them having lower ambition and aspirations.

The final contributing factor to the gender pay gap is the ideologies about motherhood and work. Mothers experience an even greater amount of discrimination at work than women who don’t have any children, because of how people view ideal workers and ideal mothers. Wade (2018, p. 348) describes the ideal worker norm as “the idea that an employee should commit their energies to their job without the distraction of family responsibilities.” This concept portrays mothers as non-ideal workers because they have children. Bernard and Correll (2010,) found that women with children are seen as less qualified and committed by their employers than those without children. Since their employers view them as a less ideal worker, their pay is significantly decreased; this is referred to as the motherhood penalty (Wade, 2018). The same concept does not apply to fathers. Men with children receive an increase in pay which is known as the fatherhood premium (Wade, 2018). According to Bernard and Correl (2010), when mothers do prove themselves to be qualified and competent employees, they are still discriminated against due to prescriptive stereotypes of how mothers should act and behave. When mothers do succeed at work, then they are spending less time and effort at home with their children, which makes their boss and coworkers view them as a bad mother. Working moms experience their own double bind. If they are more focused and dedicated to caring for their children, then they are seen as bad workers, if they are focused and dedicated at work, then they are seen as bad mothers. Working mothers may also be put on what is known as the mommy track. Wade (2018, p. 349) describes the mommy track as “a workplace euphemism that refers to expecting less intense commitment from mothers, with the understanding that they’re sacrificing the right to expect equal pay, regular raises, or promotions.” This often happens after women take time off to care for their young children, which sets them back in their careers and makes it harder for them to reenter the workforce.

The gender pay gap has been negatively effecting women since they were first allowed the right to work, and despite many policies put in place to protect women’s rights and equality, the gender pay gap still persists. There are several causes that contribute to the gender pay gap including gender job segregation, discrimination, and ideologies about motherhood and work. The only way to truly diminish the gender pay gap is to fix these main causes, however, in order to do that, people will have to change all of their beliefs and ideas about women. This may seem impossible but there are smaller steps to take such as spreading awareness and educating people about the gender pay gap. Achieving equal pay would greatly help our economy and those living in poverty.


Benard, S., & Correll, S. (2010). Normative discrimination and the motherhood penalty. Gender and Society, 24(5), 616-646. Retrieved from

Bobbitt-Zeher, D. (2011). Gender discrimination at work: Connecting gender stereotypes, institutional policies, and gender composition of workplace. Gender and Society, 25(6), 764-786. Retrieved from

Cech, E. (2013). The self-expressive edge of occupational sex segregation. American Journal of Sociology, 119(3), 747-789. doi:10.1086/673969

Double bind. (n.d.) In Cambridge English Dictionary. Retrieved from

Institute for Women’s Policy Research. (2018, September 12). Pay equity & discrimination. Retrieved from

Mandel, H. (2013). Up the down staircase: Women’s upward mobility and the wage penalty for occupational feminization, 1970-2007. Social Forces, 91(4), 1183-1207. Retrieved from

Shaw, E., Hegewisch, A., & Hess, C. (2018, October 15). Sexual harassment and assault at work: Understanding the costs. Retrieved from

Wade, L., & Ferree, M. M. (2018). Work. Gender: Ideas, interactions, and institutions (2nd ed., pp. 320-355). New York, NY: W. W. Norton & Company.

Yi, J. (n.d.). The role of benevolent sexism in gender inequality. Retrieved from

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