What Does Alexa Mean?
| Gender | Female |
|---|---|
| Language(s) | Greek |
| Origin | |
| Meaning | defender of human |
| Other names | |
| See also | Alexander Alexandra Alexis Alexia Alex Alexa |
Alexa is a female form of Alex, It is variously a given name in its own right or a short form of Alexandra, both of which come from the Greek name Alexandros. It can be broken down into alexo meaning “to defend” and ander meaning “man”, making both Alexa and Alexandra mean “defender of man”. The similarly-spelled name Aleksa is a South Slavic masculine name.
Does Alexa stand for something?
Amazon chose the name Alexa because it ‘was inspired by the Library of Alexandria and is reflective of Alexa’s depth of knowledge,’ Lauren Raemhild, a public relations specialist for Amazon, said in a statement, referring to the late-pharaonic-era institution in Egypt.
Why do they call her Alexa?
History – Amazon Alexa devices on display in a retail store Alexa was developed out of a predecessor named Ivona which was invented in Poland, inspired by 2001: A Space Odyssey and bought by Amazon in 2013. In November 2014, Amazon announced Alexa alongside the Echo.
- Alexa was inspired by the computer voice and conversational system on board the Starship Enterprise in science fiction TV series and movies, beginning with Star Trek: The Original Series and Star Trek: The Next Generation,
- Amazon developers chose the name Alexa because it has a hard consonant with the X, which helps it be recognized with higher precision.
They have said the name is reminiscent of the Library of Alexandria, which is also used by Amazon Alexa Internet for the same reason. In June 2015, Amazon announced the Alexa Fund, a program that would invest in companies making voice control skills and technologies.
The US$ 200 million fund has invested in companies including Jargon, Ecobee, Orange Chef, Scout Alarm, Garageio, Toymail, MARA, and Mojio. In 2016, the Alexa Prize was announced to further advance the technology. In January 2017, the first Alexa Conference took place in Nashville, Tennessee, an independent gathering of the worldwide community of Alexa developers and enthusiasts.
Follow up conferences went under the name Project Voice and featured keynote speakers such as Amazon’s Head of Education for Alexa, Paul Cutsinger. At the Amazon Web Services Re: Invent conference in Las Vegas, Amazon announced Alexa for Business and the ability for app developers to have paid add-ons to their skills.
- In May 2018, Amazon announced it would include Alexa in 35,000 new homes built by Lennar,
- In November 2018, Amazon opened its first Alexa-themed pop-up shop inside of Toronto ‘s Eaton Centre, showcasing the use of home automation products with Amazon’s smart speakers.
- Amazon also sells Alexa devices at Amazon Books and Whole Foods Market locations, in addition to mall-based pop-ups throughout the United States.
In December 2018, Alexa was built into the Anki Vector and was the first major update for the Anki Vector, although Vector was released in August 2018, he is the only home robot with advanced technology. As of 2018, interaction and communication with Alexa were available only in English, German, French, Italian, Spanish, Portuguese, Japanese, and Hindi.
In Canada, Alexa is available in English and French (with the Quebec accent). In October 2019, Amazon announced the expansion of Alexa to Brazil, in Portuguese, together with Bose, Intelbras, and LG, Hoped-for revenue never materialized from people using voice ordering for Amazon products or services from partners like Domino’s Pizza and Uber,
Alexa does not play audio ads, and display ads were relatively unsuccessful. In 2019 an all-hands crisis meeting was called to address the issue, and a hiring freeze was instated. In 2022, with the division losing several billion dollars per quarter, the company started laying off Alexa employees en masse.
What does Alexa mean in Latin?
Since Alexa comes from the same origin, the meaning of Alexa is ‘ defender of man.’ Feminine forms of Alexander were not commonly used until the 20th century. English and Latin short form of Alexandra, meaning ‘defender of mankind’ Feminine form of Latin Alexius, meaning ‘defender’
Is Alexa a rare name?
Alexa Origin and Meaning – The name Alexa is girl’s name of Greek origin meaning “defending men”. Alexa was a steadily popular modern classic until Amazon’s virtual assistant Alexa was released in 2013. It remains relatively well used in the US despite this, though its standing keeps dropping for obvious reasons.
Is Alexa a good name?
Alexa Name Meaning – A shortened form of Alexandra, Alexa means “defender of man.” With her powerful meaning and electric sound, it’s no surprise that she’s a popular pick among today’s parents. Alexa is a name with modern appeal, filling the role as an updated form of a classic name.
Despite originating as a nickname, she stands tall on her own, holding her place as a name and avoiding the incomplete feel seen in other shortened forms of Alexandra like Lexi, Regal and mature, she avoids feeling too youthful. She’s adorable on a little one but does age gracefully along with her wearer.
If you’re a fan of nicknames, she does have a few including Ali and Lex, Alexa has some serious fashionista vibes, a name fit for a runway or photo studio. She has an energy that’s inescapable, carrying her down the streets of Manhattan and Paris alike.
Does Alexa have a secret?
It’s a legendary cheat code, which has been introduced to Alexa and Siri as an Easter egg. The ‘Super Mode’ doesn’t actually do anything, but instead is a whimsical tribute to the iconic video game cheat. The code to say is: ‘Alexa, Up, Up, Down, Down, Left, Right, Left, Right, B, A, Start’, as revealed on TikTok.
Can Alexa be a boy or girl?
How to enable a celebrity voice on your Echo(s): – Once you have purchased the celebrity voice, you can activate it on any additional Echo speakers you own.
Open the Alexa app.Tap on the Devices tab at the bottom.Tap on the Echo & Alexa button in the top left.Tap on the device you want to enable the celebrity voice on.Tap the Settings cog wheel button in the upper right.Scroll down to the option for Wake Word and tap on it.Choose from any celebrity voice you have enabled.
If you want a new wake word but don’t want to pay for a celebrity voice, Alexa has several options to choose from for free (they use the standard Alexa voices). See this guide for how to change your Alexa wake word. Update July 21st, 2022, 1:30PM ET: This article was originally published on July 22nd, 2021, and has been updated to account for changes in Alexa.
What’s Alexa’s gender?
Is Alexa female, male, or neutral? A cross-national and cross-gender comparison of perceptions of Alexa’s gender and status as a communicator , December 2022, 107426 Gender identity is not only a field with a rich flourishing of theories (from constructivism to essentialism) (Wood & Eagly, 2015) but it is also, for an increasing number of people, a neuralgic terrain involving social behaviors of rebellion, negotiation, or contestation of one’s own gender identity (Cerezo et al., 2020).
- Processes of stereotyping, and political ideology about what gender should be, intertwine to impose societal regulatory frameworks for gender identity that are more or less binding.
- According to Eagly’s (1987) Social Role Theory, commonly-held gender stereotypes first arise from the gendered division of labor in societies and contribute to shaping both how people perform gender and the expectations they form and hold for others (Eagly & Wood, 2012).
Against this backdrop, the gendering of machines and other artifacts or objects may further complicate the social framework in ways that reverberate back onto humans’ relationships and identities (Adams, 2020). Kirkham and Attfield (1996) forwarded a theory of gendered objects—as “two of the most fundamental components of the cultural framework which holds together our sense of social identity” (p.1)—which stresses the ease and normalcy with which ordinary objects are associated with genders and problematizes the effects that specific versions of masculinity and femininity exert from design to marketing to ownership, use practices, and cultural meanings.
- Within the big family of objects, new communication technologies like voice-based assistants (VBAs) carry direct and explicit gender cues through their embodied roles, voices, names, and personification in popular culture.
- Conceived in light of these two theoretical perspectives on the construction and meaning of gender, the aim of this paper is to explore users’ assignment of gender to Amazon’s Alexa.
We argue, on the one hand, that virtual agents, and in particular, those such as Alexa, to which a feminine name is assigned, contribute to reinforcing a retrieval ideology of the feminine as the place of social subordination and contempt and, on the other hand that theories of gender identity need to focus not only on humans but also on technologies such as virtual assistants and robots, since their gendering inevitably also implicates the gendering of humans.
- The literature on virtual agents has already pointed out how the assignment of machine gender is dramatically impacted by the cultural order in a given society (Bray, 2012; Søraa, 2017).
- In general, machine agents with more feminine cues are perceived as female, and agents with more masculine cues are perceived as male.
Alesich and Rigby (2017) raised the issue of the binary (male and female) vision that shapes many discussions in robotics, with, at maximum, the addition of genderless, which is the assumed category of machines. Previous research has shown that minimal visual gender cues on a robot suffice to assign gender (Jung et al., 2016).
Additionally, we refer to machines by names and pronouns, which arise from the robot’s perceived gender in many languages. As such, we need to consider the implications of the creation of gendered machines and how that might shape our collective imagination regarding gender (Alesich & Rigby, 2017). Additionally, a gendered machine voice and appearance can be matched with the stereotype of gender in an occupation role.
Tay et al. (2014) found that participants expressed more positive responses when gender-occupational role stereotypes and personality-occupational role stereotypes matched, showing that people react to robots according to social models. Bryant, Borenstein, and Howard (2020) documented that “perceived occupational competency is a better predictor for human trust than robot gender or participant gender” (p.13).
This “matching” between appearance and occupational role can influence people and their willingness to comply with machine actors’ requests (e.g., Carpenter et al., 2009; Nass & Moon, 2000). However, this willingness to comply can strengthen the gender and occupational stereotypes that already cause cultural issues.
Many scholars and designers have argued that we need to work to change these gendered stereotypes through gender-inclusive design (Wang & Young, 2014). Eyssel and Hegel (2012) maintained that we should “develop gender-neutral or counter-stereotypical machines to counteract the stability of personal and cultural stereotypes” (p.2224).
Tam and Khosla (2016) argue that we must deconstruct gender stereotypes in robot appearance. Carpenter et al. (2009) contended that gendered robots will be more effective in some circumstances, and genderless robots may work better in other cases. In the current study, we examine VBAs, specifically Amazon’s Alexa, in relation to gender perceptions.
While much has been discussed about the problematic gender dynamics of Alexa, we will seek to provide a systematic investigation of how users perceive and understand Alexa in terms of gender. There are some indications of gender differences in perceptions of social robots.
- For example, Nomura (2017) demonstrated that women are less likely to have a favorable view of robots.
- Showkat and Grimm (2018) documented that the sex differences previously identified in information processing styles, such as tinkering (men are more likely to tinker than women), have also emerged in human-robot interaction.
Schermerhorn et al. (2008) found that there are evident differences in how men and women conceptualize, react to, and coexist with robots: men tend to think of the robot as more humanlike, show some “social facilitation” for an arithmetic task and express more socially responding on a survey administered by a robot.
Women, on the contrary, perceived the robot as more machine-like and did not feel facilitated by the robot in the arithmetic task. In a large European study ( N = 26,751), Taipale et al. (2015) found that men had slightly more positive views of robots than women when controlling for socio-demographic variables.
However, other studies have not found significant sex differences (e.g., Rea, Wang & Young, 2015; Obaid et al., 2016; Reich-Stiebert & Eyssel, 2017). The studies crossing a human’s gender and a robot’s gender are few and inconsistent. Otterbacher and Talias (2017) demonstrated that the cross-gender effect is more salient for men participants than for women.
This result supports Schermerhorn et al.’s (2008) study which showed that men are more likely than women to treat a robot as a social agent rather than simply a machine. Eyssel, De Ruiter, Kuchenbrandt, Bobinger, and Hegel (2012), on the contrary, found a preference for same-gendered robots based on voice-gender cues.
Siegel, Breazeal, and Norton (2009), investigating robots’ persuasiveness based on the dimensions of trust, credibility, and engagement, found that participants rated the robot of another sex as more credible, trustworthy, and engaging effects were much more substantial between male participants and the female robot.
Kuchenbrandt et al. (2014) documented that participants who worked with a robot in a stereotypical female work arena made significantly more errors than when performing typically male tasks and were less prone to accept help from the robot than participants interacting with the robot involved in a typically male task.
However, male and female robots were perceived as equally competent within a stereotypical female task but the female robot was perceived as less competent than the male robot. These results partly contradict previous findings that people prefer a match of gender robots and task features.
For the current study, we focus on VBAs and their relation to gender perceptions. Feminist scholarship has expressed strong concerns regarding how AI systems have gender bias embedded in the frameworks (e.g., Adam, 1998; Broussard, 2018; Noble, 2018). Like many science fiction products, Cortana, SIRI, and Alexa have built-in assumptions about gender by proposing futuristic innovations and outdated gender roles (Pluretti et al., 2016).
These devices are designed to have credible conversations, perform routine tasks, and entertain users. Additionally, these products are used by millions of people worldwide (Leskin, 2018; Strengers & Kennedy, 2020). Specifically, this study focuses on Amazon’s Alexa.
Alexa arrived in households in 2015, bound to a physical device but without an anthropomorphized body. Alexa epitomizes “the rhetorical phenomenon of persona as a way of analyzing the presentation of a gendered self, characterized by narrative constitution of a normatively feminine identity” (Woods, 2018, p.335).
The gendered narration of Alexa, based on the simulation of gendered occupational roles, is a communicative process that requires, like any other, cooperation by the interlocutors inside the process; otherwise, it does not work. VBAs like Alexa are linked to the idea of a female personal secretary or administrative assistant (Lingel & Crawford, 2020; Schiller & McMahon, 2019).
Others have argued that Alexa performs the work of “female domesticity” (Walker, 2020, p.13). We will first examine the more professional role occupied by Alexa. What are the cultural motivations for these choices? With the development of computerization in the workplace, administrative assistants have primarily been given only to high-level managers.
This burden of this administrative work rests heavily on women, with men historically serving as the managers. Mid-level workers often have this work placed on themselves and rely on computers for partial automation. Much of this patterned information labor has been outsourced to call and service centers (Lingel & Crawford, 2020).
Having computers take on some of the tasks that women traditionally performed has created an environment in which a genderized VBA (such as Alexa) can operate. Alexa allowed ordinary people to utilize some of these technologies for the automation of routine tasks. Alexa, whose name is generally associated with women (derived from the Greek alex + anders = “defender of man”), is feminine presenting (Loideain & Adams, 2020).
Further, Amazon has personified Alexa as a bodily woman in media ads ( Alexa Loses Her Voice, Superbowl LII) and in ways that reflect intersectional gender and racial stereotypes of women (i.e., over-sexualization, false beauty standards) (Schlamb, 2018).
Although users can change Alexa’s voice, the default voice is feminine. Alexa represents an administrative assistant for everyday users. Users can ask Alexa to perform tasks, order to-go food, entertain them with music or jokes, keep up with grocery lists for purchases, or give mundane information like the time and temperature.
In 2017, over a million users asked Alexa to marry her, clearly provoking Alexa’s language processing system, which answers with a catchy quip about being in the cloud while the user is down on Earth (Leskin, 2018). Because users can tell Alexa to manage domestic labor tasks, a built-in master-servant relationship encoded in Alexa is also “racially coded given the historical racialization of domestic labor itself” (Schiller & McMahon, 2019, p.10).
- This master-servant relationship represents the inoculation of regressive behaviors (starting from communication) within the household, both towards women (the first to be attacked) and children.
- Although a good deal has been written about the speculative gender dynamics and problematics of VBAs like Alexa, there is a need for systematic investigations of how users themselves perceive and understand Alexa in terms of gender.
There is a pervasive assumption in the scholarly and public commentary that Alexa is female, but we lack empirical evidence about how lay people understand or assign gender to the persona. Therefore, our first research question is: We explored this issue by asking respondents to choose what gender they perceive Alexa to be from a list of options that included female, male, neutral, or in a different way.
However, the explicit gender label people affix to Alexa (as female, male, neutral, or otherwise) tells only part of the story of their broader gendering practices toward this VBA. We know in fact that names as well as pronouns and adjectives are “doing” words, important in the social construction of gender (Pilcher, 2017).
In our case, the general consistency between ascriptions of human personhood and subsequent gender pronoun use may be complicated, because virtual agents blur the lines between person and thing, subject and object (Etzrodt & Engesser, 2021). For instance, Purington et al.
- 2017, May) found that more than half of the Amazon user reviews of Echo/Alexa they analyzed included the personified name “Alexa” but that most reviewers employed object pronouns instead of fully personifying Alexa as a subject.
- Therefore, our second research question was: To investigate how conceptualizations of Alexa’s gender manifest within spontaneous language, we performed content and thematic analyses of respondents’ language choices (i.e., pronoun usage, noun forms) in their writing about Alexa.
This enabled us to see whether and to what degree respondents’ assignment of explicit gender to Alexa aligns with their gendering linguistic practices. This is important given that, presumably, when speaking or writing about another person, the choice of pronouns (and noun forms, in Italian) should be governed by the perceived gender of that person.
- Thus, we anticipated that respondents would generally perceive Alexa as female, but a large margin of incertitude regarding gender would be evidenced in their ways of writing about the VBA.
- Furthermore, we explored conceptualizations of Alexa’s gender indirectly by investigating the social status of this VBA.
Because gender is linked to social status (Rosette et al., 2018), respondents’ vision of Alexa’s standing as a communicator could shed additional light on Alexa’s gender assignment holistically. Therefore, we posed a third research question: Next, we inquired as to whether people perceive gender differences in interactions with Alexa.
- As we have reported in the preceding sections, previous research demonstrates some differences in women’s and men’s attitudes, preferences, and use practices involving robotic technologies, as well as effects linked to a match or mismatch of human and machine gender.
- Often, people tell the truth by attributing to others what they do not want to reveal about themselves or what they cannot see as clearly in themselves.
Therefore, we focused on respondents’ perceptions of whether (and, if so, how) other users treat Alexa differently on the basis of their gender. By relying on observations of other people’s interactions with Alexa, we were also able to consider the perceptions of users and non-users alike.
Specifically, we asked: Finally, it is quite likely that there are cultural and gender differences among the respondents of this study. Users of Alexa are not a homogenous group and culture is one aspect of their differences that may play a role in perceptions of gender. Not only do certain cultures differ in the degree to which Alexa is diffused and adopted, but also in their general attitudes about gender roles (e.g., Block, 1973) and the ways in which gender is infused and constructed in their languages (i.e., the shaping influence of grammatical gender; Thiery, 2016).
The US and Italy are nations that differ in both the exposure and usage rates of Alexa as well as the structure of their dominant languages: In Italian, as opposed to English, not only pronouns but also nouns and their adjective forms are grammatically gendered.
A person’s gender, as well as their country, may influence how they understand and treat Alexa in terms of gender. Thus, our last research question examines the role of respondents’ country, as a proxy for culture, and their gender in forming perceptions of Alexa’s gender. Precisely, we pose the question: In the following sections, we describe the methods, present the main findings of our research, and discuss the results and their implications for understanding the relationship between gender and VBAs like Alexa.
In 2021, following Institutional Review Board approval, we administered a survey at a large US midwestern research university and at a public northeastern Italian university (total n = 655). The US sample ( n = 322) was comprised of student participants recruited from a research pool for communication students.
The majority of US respondents were women (62.8%), followed by men (32.1%), nonbinary (7%), and those who preferred to self-describe differently or to not answer (3%). In Italy, the In the sections that follow, we report the results pertaining to each of the five research questions. We address the RQs in the order in which they are listed above, with one exception: For RQ5 (respondent culture and gender comparisons), we present the results alongside the combined-sample analyses employed to answer RQs1-4.
In the following sections, we will discuss the results for each research question sequentially and relate them to the broader debate regarding Alexa and gender. Our findings support the concerns often raised in the current debate on Alexa: Alexa is widely considered female in at least two countries differing in usage/adoption rates and linguistic approaches to gender.
B. Tay et al. A.S. Rosette et al. Y. Mou et al. N.N. Loideain et al. A. Edwards et al. A. Adam R. Adams S. Alesich et al. J.H. Block F. Bray
M. Broussard D.A. Bryant et al. J. Carpenter et al. A. Cerezo et al. A.H. Eagly A.H. Eagly et al. A. Edwards A. Edwards et al. K. Etzrodt et al. F. Eyssel et al. F. Eyssel et al. L. Fortunati et al. L.M. Fritz A.L. Guzman A.L. Guzman E.H. Jung et al. T. Kanda et al. P. Kirkham et al.
One communication pattern afforded by smartphones is sexting. When considering sexting as an intimate form of sexual-self disclosure, concepts like perceived appropriateness must be considered. The current project assessed how the frequency and perceived appropriateness of sexting varied across relationship stages and across different attachment tendencies among emerging adults. Results from a cross-sectional survey ( N = 133) indicate linear relationships, such that sexting tended to increase in frequency and perceived appropriateness alongside the stages of relationship development. Further, relationship stage did not interact with attachment tendencies, but attachment avoidance demonstrated a negative relationship with sexting frequency. Theoretical and practical implications are discussed further. Visual search is facilitated when a target item is positioned within an invariant arrangement of task-irrelevant distractor elements (relative to non-repeated arrangements), because learnt target-distractor spatial associations guide visual search. While such configural search templates stored in long-term memory (LTM) cue focal attention towards the search-for target after only a few display repetitions, adaptation of existing configural LTM requires extensive training. The current work examined the important question whether individuals claimed to have better attention performance (i.e., action video game players; AVGP) show improved acquisition vs. adaptation of configural LTM (relative to no-gamers; NAVGP) in a visual-search task with repeated and non-repeated search configurations and consisting of an initial learning phase and, following target relocation, a subsequent adaptation phase. We found that contextual facilitation of search reaction times was more pronounced for AVGP relative to NAVGP in initial learning, probably reflecting enhanced learn-to-learn capabilities in the former individuals. However, this advantage did not carry over to the adaptation phase, in which gamers and non-gamers exhibited similar performance and suggesting that attention control required for overcoming visual distraction from previously learned (but no more relevant) target positions is relatively uninfluenced by action-game experience. Older adults tend to under-utilize digital technology and online services that can yield substantial benefits to their health and wellbeing. Addressing this problem requires determining robust and consistent predictors of older adults’ Internet use over time. Informed by current models of technology use in aging, the present study took a data-driven approach to determine the predictors of Internet use among older adults. Machine learning was applied to a large, nationally representative sample of older Americans with data for hundreds of variables – both before and after the advent of smartphones and tablets. Machine learning models achieved classification accuracy slightly higher than a theory-driven benchmark model, with results largely supporting current models of aging and technology use. Specifically, data from 2002 and 2016 indicated that age, socioeconomics, and cognitive functions that decline with age (immediate memory, delayed memory, and visuospatial skills) were the most robust and consistent predictors of Internet use among older adults. Machine learning also discovered additional factors that should be considered in models of technology use in aging, such as alcohol use. Taken with prior literature, these results suggest that automaticity should be a high design priority to reduce the age-related cognitive challenges that impact technology use. This study followed a longitudinal design to objectively monitor practice behaviors of professional and semi-professional esports players over a year. Publicly available data were collected from 30 male Counter-Strike: Global Offensive players (age: 23.76 ± 2.88y). Players were classified into two groups: professional (n = 18) or semi-professional (n = 12). The total hours of practice (all game-specific practice) and the competitive hours of practice (time spent in competitive modes only) were collected weekly. Generalised Estimating Equations were used to compare the practice behaviors of the two groups. Professional and semi-professional esports players completed an average of 30.9 ± 8.2 h and 24.7 ± 3.6 h per week of total game-specific practice, respectively, and 19.6 ± 6.9 and 15.0 ± 2.7 h of competitive practice, respectively. A significant week ∗ group interaction was observed for total practice time (Wald χ2 = 9.48, p = 0.002) and total competition practice time (Wald χ2 = 7.54, p = 0.006). Specifically, professional esports players completed 6.6 (SE = 2.2) hr per week more of total practice hours than semi-professional players, of which 4.8 (SE = 1.8) hr were competitive practice. This sample of expert esports performers complete high volumes of practice which can be monitored via publicly accessible repositories. Online data collection services are increasingly common for testing mass communication theory. However, how consistent are the theoretical tenets of theory when tested across different online data services? A pre-registered online survey ( N = 1546) examined the influence of the presumed influence model across subjects simultaneously recruited from Amazon Mechanical Tur k and Qualtrics Panels, Results revealed that model parameters were mostly consistent with the IPI theory regardless of data source. Methodological implications are discussed. This study explores the antecedents and consequences of unfriending in social media settings. Employing an online panel survey ( N = 990), this study investigates how exposure to hate speech is associated with political talk through social media unfriending. Findings suggest that social media users who are often exposed to hate speech towards specific groups and relevant issues are more likely to unfriend others (i.e., blocking and unfollowing) in social media. Those who unfriend others are less likely to talk about public and political agendas with those with cross-cutting views but tend to often engage in like-minded political talk. In addition, this study found indirect-effects associations, indicating that social media users who are exposed to hate speech are less likely to engage in cross-cutting talk but more likely to participate in like-minded talk via unfriending other users in social media.
: Is Alexa female, male, or neutral? A cross-national and cross-gender comparison of perceptions of Alexa’s gender and status as a communicator
What are the three names of Alexa?
Step 7: Choose Alexa’s new name! – Finally, here’s how to change Alexa’s name: Your screen will say “Wake Word,” and offer four different options underneath it. The other options for Alexa’s name are “Amazon,” “Echo,” and “Computer.” In a recent addition to the classic options, one can now select “Ziggy” as a new name as well. Once you’ve made your selection, don’t forget to hit “OK!” 9 / 10 seewhatmitchsee/Getty Images
Is Alexa a Italian name?
The name Alexa is of Greek origin and means ‘defender of mankind’. It is the feminine form of Alex/Alexander. It also the short form of Alexandra.
What religion is the name Alexa?
What is the Religion of the Name Alexa? The religion of the name Alexa is Christianity.
What is Alexa’s birthday?
Alexa has been around since 2014. Amazon introduced Alexa on Nov.6, 2014 (hence her birthday).
What is a friendly name Alexa?
Global Alexa catalog – You can use the global Alexa catalog of pre-defined friendly names in your skills. Each item in the catalog is an asset identifier that supports one or more friendly names. For each asset identifier that you specify in your skill, your users get access to multiple friendly names.
| Asset identifier | Supported friendly names |
|---|---|
| Alexa.Button.OffButton | Off button |
| Alexa.Button.OnButton | On button |
| Alexa.Button.BrightenButton | Brighten button |
| Alexa.Button.DimButton | Dim button |
| Alexa.Button.MainButton | Main button |
| Alexa.Button.TopButton | Top button |
| Alexa.Button.BottomButton | Bottom button |
| Alexa.Button.CenterButton | Center button |
| Alexa.Button.MiddleButton | Middle button |
| Alexa.Button.UpButton | Up button |
| Alexa.Button.DownButton | Down button |
| Alexa.Button.LeftButton | Left button |
| Alexa.Button.RightButton | Right button |
| Alexa.Button.FirstButton | First button |
| Alexa.Button.SecondButton | Second button |
| Alexa.Button.ThirdButton | Third button |
| Alexa.Button.FourthButton | Fourth button |
| Alexa.Button.FifthButton | Fifth button |
| Alexa.Button.SixthButton | Sixth button |
| Alexa.Button.SeventhButton | Seventh button |
| Alexa.Button.EighthButton | Eighth button |
| Alexa.Button.DoublePress | Double press |
| Alexa.Button.DoublePush | Double push |
| Alexa.Button.LongPress | Long press |
| Alexa.Button.LongPush | Long push |
| Alexa.Button.SinglePress | Single press |
| Alexa.Button.SinglePush | Single push |
| Alexa.DeviceName.AirPurifier | Air Purifier, Air Cleaner, Clean Air Machine |
| Alexa.DeviceName.Camera | Camera |
| Alexa.DeviceName.Fan | Fan, Blower |
| Alexa.DeviceName.Router | Router, Internet Router, Network Router, Wi-Fi Router, Net Router |
| Alexa.DeviceName.Shade | Shade, Blind, Curtain, Roller, Shutter, Drape, Awning, Window shade, Interior blind |
| Alexa.DeviceName.Shower | Shower |
| Alexa.DeviceName.SpaceHeater | Space Heater, Portable Heater |
| Alexa.DeviceName.Washer | Washer, Washing Machine |
| Alexa.Gesture.DoubleClick | Double click |
| Alexa.Gestures.DoubleTap | Double tap |
| Alexa.Gesture.SingleClick | Single click |
| Alexa.Gesture.SwipeDown | Swipe down |
| Alexa.Gesture.SwipeLeft | Swipe left |
| Alexa.Gesture.SwipeRight | Swipe right |
| Alexa.Gesture.SwipeUp | Swipe up |
| Alexa.Gesture.Tap | Tap |
| Alexa.Setting.2GGuestWiFi | 2.4G Guest Wi-Fi, 2.4G Guest Network, Guest Network 2.4G, 2G Guest Wi-Fi |
| Alexa.Setting.5GGuestWiFi | 5G Guest Wi-Fi, 5G Guest Network, Guest Network 5G, 5G Guest Wi-Fi |
| Alexa.Setting.Auto | Auto, Automatic, Automatic Mode, Auto Mode |
| Alexa.Setting.Direction | Direction |
| Alexa.Setting.DryCycle | Dry Cycle, Dry Preset, Dry Setting, Dryer Cycle, Dryer Preset, Dryer Setting |
| Alexa.Setting.FanSpeed | Fan Speed, Airflow speed, Wind Speed, Air speed, Air velocity |
| Alexa.Setting.GuestWiFi | Guest Wi-Fi, Guest Network, Guest Net |
| Alexa.Setting.Heat | Heat |
| Alexa.Setting.Mode | Mode |
| Alexa.Setting.Night | Night, Night Mode |
| Alexa.Setting.Opening | Opening, Height, Lift, Width |
| Alexa.Setting.Oscillate | Oscillate, Swivel, Oscillation, Spin, Back and forth |
| Alexa.Setting.Preset | Preset, Setting |
| Alexa.Setting.Quiet | Quiet, Quiet Mode, Noiseless, Silent |
| Alexa.Setting.Temperature | Temperature, Temp |
| Alexa.Setting.WashCycle | Wash Cycle, Wash Preset, Wash setting |
| Alexa.Setting.WaterTemperature | Water Temperature, Water Temp, Water Heat |
| Alexa.Shower.HandHeld | Handheld, Handheld Shower, Shower Wand, Hand Shower |
| Alexa.Shower.RainHead | Rain Head, Overhead shower, Rain Shower, Rain Spout, Rain Faucet |
| Alexa.Unit.Angle.Degrees | Degrees, Degree |
| Alexa.Unit.Angle.Radians | Radians, Radian |
| Alexa.Unit.Distance.Feet | Feet, Foot |
| Alexa.Unit.Distance.Inches | Inches, Inch |
| Alexa.Unit.Distance.Kilometers | Kilometers |
| Alexa.Unit.Distance.Meters | Meters, Meter, m |
| Alexa.Unit.Distance.Miles | Miles, Mile |
| Alexa.Unit.Distance.Yards | Yards, Yard |
| Alexa.Unit.Mass.Grams | Grams, Gram, g |
| Alexa.Unit.Mass.Kilograms | Kilograms, Kilogram, kg |
| Alexa.Unit.Percent | Percent |
| Alexa.Unit.Temperature.Celsius | Celsius, Degrees Celsius, Degrees, C, Centigrade, Degrees Centigrade |
| Alexa.Unit.Temperature.Degrees | Degrees, Degree |
| Alexa.Unit.Temperature.Fahrenheit | Fahrenheit, Degrees Fahrenheit, Degrees F, Degrees, F |
| Alexa.Unit.Temperature.Kelvin | Kelvin, Degrees Kelvin, Degrees K, Degrees, K |
| Alexa.Unit.Volume.CubicFeet | Cubic Feet, Cubic Foot |
| Alexa.Unit.Volume.CubicMeters | Cubic Meters, Cubic Meter, Meters Cubed |
| Alexa.Unit.Volume.Gallons | Gallons, Gallon |
| Alexa.Unit.Volume.Liters | Liters, Liter, L |
| Alexa.Unit.Volume.Pints | Pints, Pint |
| Alexa.Unit.Volume.Quarts | Quarts, Quart |
| Alexa.Unit.Weight.Ounces | Ounces, Ounce, oz |
| Alexa.Unit.Weight.Pounds | Pounds, Pound, lbs |
| Alexa.Value.Close | Close |
| Alexa.Value.Delicate | Delicates, Delicate |
| Alexa.Value.High | High |
| Alexa.Value.Low | Low |
| Alexa.Value.Maximum | Maximum, Max |
| Alexa.Value.Medium | Medium, Mid |
| Alexa.Value.Minimum | Minimum, Min |
| Alexa.Value.Open | Open |
| Alexa.Value.QuickWash | Quick Wash, Fast Wash, Wash Quickly, Speed Wash |
What is the cute nickname?
Looking for cute nicknames for your girlfriend, boyfriend, or kids? Here’s your master list of cute nicknames from around the world. Pumpkin, peanut, bubby, baby, babe, bae, honey, darling, sugar, sweetie, honeybunch English is packed full of fun, creative, and cute nicknames to call your loved ones.
From food to animals, to just plain gibberish words – lots of us love giving a cute nickname to our significant other, family, friends, and children. And it’s not just English. In every language, people have terms of endearment to show their love and affection. Learning these can add new depth to your language learning – especially if you’re learning a language to speak with loved ones.
Learning pet names is also a fun way to expand your vocabulary, and it develops your cultural understanding. Many of the words used as nicknames reflect social relationships and are intertwined with the values of a particular culture, Let’s get into it! Here are some cute nicknames from around the world. 
What are the four names for Alexa?
The other options for Alexa’s name are, “Amazon,” “Echo,” and “Computer.” In a recent addition to the classic options, one can now select “Ziggy” as a new name as well.
Can Alexa record you without you knowing?
Can Alexa Record Conversations Without Your Knowing? – While Alexa offers assistance in so many areas of life, it’s natural to wonder: Can Alexa record conversations? Does Alexa listen to everything you say? Does Alexa spy on you? You need to know that Alexa is technically always listening, even without explicitly triggering an Alexa device.
- Alexa does not actively record and store all your conversations, but it’s always listening for “Alexa,” the wake word.
- Once you say it, anything you say that follows is recorded and stored in the cloud.
- On occasion, Alexa may think you’ve said its name when you haven’t.
- There are reported instances of Alexa sending conversations to people’s co-workers or even strangers.
These incidents shed some light on the imperfect nature of voice assistant technology. But unfortunately, this is where the problem lies—there are times where Alexa will record conversations without you knowing. One of the reasons Alexa will record conversations is to learn more about you, the user.
What was Alexa originally called?
History – The first-generation Amazon Echo Work on the Amazon Echo began in 2011, known as “Project D”. It was named this because the Kindle was Project A and the Fire Phone was Project B. The Amazon Echo was an offshoot of Project C. Project C is unknown, even though the work on it has stopped.
The Amazon Echo was originally supposed to be called the Amazon Flash. The wake word, the word that makes the device responsive, for the Echo used to be “Amazon”. Both of these attributes were disliked by Lab126, the division of Amazon that conducts research and development and creates computer hardware.
Lab126 believed that “Amazon” is too much of a commonly used word, and the device would react when it was not intended to. Jeff Bezos, the CEO of Amazon, ended up being influenced by Lab126 to change the name of the device to the Amazon Echo and the wake word to “Alexa”.
- The Amazon Echo was originally pitched as only a smart speaker, it was not originally intended to be a smart home hub, as it is now, until after it was launched.
- As Alexa, the artificial intelligence (A.I.) that powers the Amazon Echo, improved, the device became more of a controlling center for smart home appliances.
Dave Isbitski, the chief developer evangelist for the Echo and Alexa, received calls from smart home manufacturers to discuss connecting their devices, after the release of the Amazon Echo. But smart home devices had a problem: people were not buying smart home devices because they often required an extra app in order to be used, which was not much better than just using the device manually.
The Amazon Echo (1st Generation) was initially released in March 2014 for Amazon Prime and invited members, and was marketed alongside the voice of the product, Alexa. Alexa is a voice associated with the Amazon Echo that will respond to questions and requests through artificial intelligence. Amazon has claimed that the voice of Alexa was inspired by electronic communications systems featured in the television series Star Trek: The Original Series and Star Trek: The Next Generation,
The Echo featured prominently in Amazon’s first Super Bowl broadcast television advertisement in 2016. In March 2016 Amazon released a byproduct of the Amazon Echo, called the Amazon Echo Dot, This device is an ice hockey puck sized version of the original Amazon Echo released in 2014, and it has the same capabilities.
This product was designed to be used in smaller rooms such as bedrooms due to its limited speaker capabilities (size) or to be paired with an external speaker. In November 2016 the second generation of the Echo Dot was released for a lower price with improved voice recognition and new colors. The second generation of the Amazon Echo was released in October 2017.
This update offered better voice recognition and a fabric covering exterior. Subsequently, other variants of the Amazon Echo have been released. In May 2017 Amazon released the now-discontinued Amazon Tap, a portable, slightly smaller version of the Amazon Echo.
Although the two products are similar the Tap is battery powered, portable, and requires the touch of a button in order to enable voice commands. In April 2017 the Amazon Echo Look was released to invitees only, as an Amazon Echo with a built in camera. It was designed as a speaker, that is also handy with artificial intelligence that has smart algorithms to help users pick out outfits.
It was released to the general public in August 2018. In June 2018 the Amazon Echo Show was released to the public as a device with a 7-inch screen used for streaming media, making video calls and the use of Alexa. The second generation of the device was made available in November 2018 and features a 10-inch screen with improved speakers.
What is another word for Alexa?
How to change Alexa’s name – 
Changing Alexa’s name is simple, but at the moment, you only have a few Amazon-sanctioned alternatives: Computer, Echo, Amazon, or sometimes Ziggy. Ziggy is probably the least likely to cause accidental triggers, although Computer is no doubt tempting for Star Trek fans. Here are the steps to switch names:
Open the Alexa app on your Android or iOS device. Select the Devices tab. Go to Echo & Alexa, Choose the device you want to change the name of. Tap on the Settings gear icon. Tap on Wake Word, Select your new name.
Remember that you can only change names on a speaker-by-speaker basis. This can be time-consuming if you have several devices and want to make changes across the board, but you can also exploit the arrangement to trigger specific speakers, or let family members use names they prefer. More:
What are the four names for Alexa?
The other options for Alexa’s name are, “Amazon,” “Echo,” and “Computer.” In a recent addition to the classic options, one can now select “Ziggy” as a new name as well.
Why did Jeff Bezos choose Alexa?
Ultimately, Amazon decided on Alexa, which was also Bezos’ idea: it’s an homage to the library of Alexandria in Egypt, which was one of the largest centers for knowledge and learning in ancient times.