Implications of Advances in Machine Translation

Implications of Advances in Machine Translation
By Cathy Deng | April 2, 2021

On March 16, graduate student Han Gao wrote a two-star review of a new Chinese translation of the Uruguayan novel La tregua. Posted on the popular Chinese website Douban, her comments were brief, yet biting – she claimed that the translator, Ye Han, was unfit for the task, and that the final product showed “obvious signs of machine translation.” Eleven days later, Gao apologized and retracted her review. This development went viral because the apology had not exactly been voluntary – friends of the affronted translator had considered the review to be libel and reported it to Gao’s university, where officials counseled her into apologizing to avoid risking her own career prospects as a future translator.

Gao’s privacy was hotly discussed: netizens felt that though she’d posted under her real name, Gao should have been free to express her opinion without offended parties tracking down an organization with power over her offline identity. The translator and his friends had already voiced their disagreement and hurt; open discussion alone should have been sufficient, especially when no harm occurred beyond a level of emotional distress that is ostensibly par for the course for anyone who exposes their work to criticism by publishing it.

Another opinion, however, was that spreading misinformation should carry consequences because by the time the defamed party could respond, often the damage was already done. Hence, the next question was: was Gao’s post libelous? Quality may be a matter of opinion, but machine translation came down to integrity. To this end, another Douban user extracted snippets from the original novel and compared Han’s 2020 translation to a 1990 rendition by another translator, as well as to corresponding outputs from DeepL, a website providing free neural machine translation. This analysis was conducive to two main conclusions: that Han’s work was often similar in syntax and diction to the machine translation, more so than its predecessor; and that observers agreed that the machine translation was, in some cases, superior to its human competition. The former may seem incriminating, but the latter much less so: after all, if Han had seen the automated translation, wouldn’t she make it better, not worse? Perhaps similarities were caused merely by lack of training (Han was not formally educated in literary translation).

Researchers have developed methods to detect machine translations, such as assessing similarity between the text in question and its back-translation (e.g. translated from Chinese to Spanish, then back to Chinese). But is this a meaningful task for the field of literary translation? Machine learning has evolved such that models are capable of generating or translating text to be nearly indistinguishable from, or sometimes even more enjoyable than, the “real thing.” The argument that customers always “deserve” fully manual work is outdated. And relative to the detection of deep fakes, detecting machine translations is not as powerful in combating misinformation.

Yet I believe assessing similarity to machine translation remains a worthwhile pursuit. It may never be appropriate as a measure of professional integrity because the times of being able to ascertain whether the translator relied on automated methods are likely behind us. Similar to the way plagiarism detection tools are disproportionately harsh on international students, a machine detection tool for translation (currently only 75% accurate at best) may unfairly punish certain styles or decisions. Yet a low level of similarity may well be a fine indicator of quality if combined with other methods. If even professional literary translators might flock to a finite number of ever-advancing art machine translation platforms, it is the labor-intensive act of delivering something different that reveals the talent and hard work of the translator. Historically, some of the best translators worked in pairs, with one providing a more literal interpretation that the other then enriches with artistic flair; perhaps algorithms could now play the former role, but the ability to produce meaningful literature in the latter may be the mark of a translator who has earned their pay. After all, a machine can be optimized for accuracy or popularity or controversy, but only a person can rejigger its outputs to reach instead for truth and beauty – the aspects about which Gao expressed disappointment in her review.

A final note on quality: the average number of stars on Douban, like other review sites, were meant to indicate quality. Yet angry netizens have flooded the works of Han and her friends with one-star reviews, a popular tactic that all but eliminates any relationship between quality and average rating.

References

How private are Zoom meetings?

How private are Zoom meetings?
by Gerardo Mejia | April 2, 2021

This topic caught my attention, especially after the pandemic, because I see people using Zoom to replace human interaction more and more every day. Zoom is used throughout the day for multiple things including work, education, and personal meetings. At first, I thought that privacy issues were mostly limited to personal meetings, but I later learned that there are privacy concerns in both in education and in the workplace.

Personal Meetings

My initial interest in the topic was due to my observations of people using Zoom for things like birthday parties, bridal showers, baby showers, and other non-traditional uses of Zoom. I became interested on whether Zoom itself monitors or listens in on those calls. I was convinced that somewhere in their privacy policy it would state some type of loophole that would allow them to listen in on calls for the purposes of troubleshooting or ensuring the service was working. I was a bit disappointed, and relieved when I read that meetings themselves are considered “Customer Content” and that the company did not monitor, sell or use the customer content in any purpose other than to provide it to the customer.

However, there was a small, although not too obvious loophole. Zoom considers this “Customer Content” to be under the user’s control, including its security, and thus it cannot guarantee that unauthorized parties will not access this content. I came to find out later that this is a major loophole that has been exploited in many instances. Although Zoom doesn’t take responsibility for this, there are many people that blame the company for not upgrading its security features. This all means that somebody would have to hack their way into my family’s private meeting in order to listen in. I believe that for most family gathering meetings the risk of this happening is not very high, so I would say it is safe to say that most family gathering zoom meetings are private as long as they are not the target of a hacker.

Education

I had initially thought that the education field was not heavily affected by zoom’s privacy or security issues. After all, most educators have trouble getting all their students to attend, and who is going to want to hack into a class? I was wrong about that too. The most notorious example occurred in China where Zoom assisted the Chinese government in censoring content that it did not agree with. It is also important to note that in addition to class content, schools also have other types of meetings that are more private in nature that put some sensitive information at risk like grades or school records. These could also become target of malicious hackers. In conclusion, while censorship may not be a large issue in the United States, there are some countries where this is a real issue.

Workplace

I remembered that Zoom is in my companies’ prohibited software list. I also learned that most tech companies have also banned their employees from using Zoom for work. I initially thought that this was due to Zoom’s privacy policy or terms of use policy allowing Zoom employees to listen in and thus making the meetings not secure enough as there could be a third-party listening in. It turns out that Zoom’s privacy policy states that they will not listen in or monitor in the meetings. However, like with personal meetings and education meetings, it is up to the company to secure its meetings and Zoom cannot guarantee that unauthorized users will access the content. This security issues make it so that Zoom cannot be held responsible if a company’s meeting is hacked and the meeting accessed by an unauthorized user. Companies are targeted by hackers all the time, so the risk, especially for high profile companies, of their zoom meetings being hacked is large.

zoom.us/privacy
www.techradar.com/news/more-top-companies-ban-zoom-following-security-fears
www.cnbc.com/2019/07/14/zoom-bug-tapped-into-consumers-deepest-fears.html
www.insidehighered.com/news/2020/06/12/scholars-raise-concerns-about-using-zoom-teach-about-china
nypost.com/2020/04/10/chinese-spies-are-trying-to-snoop-on-zoom-chats-experts-say/

Rise of Voice Assistants

Rise of Voice Assistants
by Lucas Lam | April 2, 2021

Many horror stories have surfaced as a result of the rise of Voice Assistants. From Alexa giving a couple some unwarranted advice to Alexa threatening someone with Chuck Norris, many creepy, perhaps crazy have surfaced. Without questioning the validity of these stories and getting deep into conspiracy theories, we recognize that the rise of voice assistants like the Echo from Amazon and Google Home from Google, has and will continue to give way to more privacy concerns. Yet, as it is getting harder and harder to get away from them going forward, we must understand what kind of data they are collecting, how we can take measures to protect our privacy, and how we can have peace of mind when using the product.

What are they collecting?
In the words of this “How-to Geek” article: “Alexa is always listening but not continually recording.” Voice Assistants are triggered by wake words. For Amazon’s device, the wake word is “Alexa”. When the blue ring light appears, the device captures audio input, sends it to the cloud to process the request, and a response gets sent back to the device. Anything said after a wake word is fair game for virtual assistants to record audio input. Every command that is given is stored, sent to the cloud for processing, and a response is sent back to the device to perform the task necessary. In Alexa’s Privacy Hub, it mentions that “all interactions with Alexa are encrypted in transit to Amazon’s cloud where they are securely stored,” explaining that the recording of audio input getting sent to the cloud and back is a secure process. Once a request is processed, the encounter is stored and collected, but users also have the ability to choose to delete the recordings once stored.

When users don’t actively delete their recordings, that’s information that these companies can harness to “understand” you better, give more targeted and customized responses, make more precise recommendations, etc. Though this can be considered creepy, the real threats don’t come when the virtual assistance understands your preferences better, it comes when that understanding gets into the hands of other people.

Potential Threats
What are some of the real threats when it comes to virtual assistants?

Any mishaps in triggering the wake word will lead to unwelcomed eavesdropping. Again, these voice assistants are always listening for their wake words, so a word that is mistaken for “Alexa” will inadvertently record audio input and return a response. That is why it is of upmost importance that companies optimize their algorithms so that they mitigate the false positives and increases the precision of detecting wake words. One major threat is that these recordings can land on the hands of people working on the product, from the machine learning engineers to the transcribers who work with this kind of data to improve the services of the device. Though personally identifiable information should be encrypted, an article in Bloomberg revealed that transcribers potentially have access to first names, device serial numbers, and account numbers.

Hacking is another possible threat. According to an article from Popular Mechanics, a German Sercuity consulting firm found that voice data can be hacked into through third-party apps. Hackers can attempt phishing by getting these virtual assistance to ask for a password or sensitive information in order for a request to be processed. Active security measures must be implemented in place to prevent such activity.

**What to do?**
There are some possible threats, and it’s consequences can escalate. Odds of something like this happening to an average joe is rare, but even if one is fearful of the consequences, many things can be done to protect one’s data privacy, from setting up automatic voice deletions to going file by file to delete the recordings. Careful use and careful investigation on your ability to protect your own privacy can give you a greater peace of mind every time you go home and talk to Alexa.

References:

www.bloomberg.com/news/articles/2019-04-10/is-anyone-listening-to-you-on-alexa-a-global-team-reviews-audio
tiphero.com/creepiest-things-alexa-has-done
www.amazon.com/Alexa-Privacy-Hub/b?ie=UTF8&node=19149155011
www.washingtonpost.com/technology/2019/05/06/alexa-has-been-eavesdropping-you-this-whole-time
www.howtogeek.com/427686/how-alexa-listens-for-wake-words/

Invisible Data: Its Impact on Ethics, Privacy and Policy

Invisible Data: Its Impact on Ethics, Privacy and Policy
By Anil Imander | April 5, 2021

A Tale of Two Data Domains

In the year 1600, Giordano Bruno, an Italian philosopher and mystic, was charged with heresy. He was paraded through the streets of Rome, tied to a stake, and then set afire. To ensure his silence in his last minutes, a metal spike was driven through his tongue. His crime – believing that earth is another planet revolving around the sun!

Almost exactly a century later, in 1705, the queen of England knighted Isaac Newton. One of the achievements of Newton was the same one for which Giordano Bruno was burnt alive – proving that earth is another planet revolving around the sun!

Isn’t this strange! Same set of data and interpretations but completely different treatment of the subjects.

What happened?

Several things changed during the 100 years between Bruno and Newton. The predictions of Copernicus, data collection of Tycho Brahe and Kepler’s laws remained the same. Newton did come up with a better explanation of observed phenomenon using Calculus but the most important change was not in data or its interpretations. The real change was invisible –  most importantly Newton had political support from royalty, the protestent sect of Christianity was more receptive to ideas challenging the church and the Bible. Many noted scientists had used Newton’s laws to understand and explain the observed world and many in the business world had found practical applications to Newton’s laws. Newton had suddenly become a rockstar in the eyes of the world.  

This historical incident and thousands of such incidences highlight the fact that data has two distinct domains – Visible and Invisible.

The visible domain deals with the actual data collection, algorithm, model building and analysis. This is the focus of today’s data craze. The visible domain is the field of Big Data, Statistics, Advance Analytics, Data Science, Data Visualization and Machine Learning

The invisible domain is the human side of data. It is difficult to comprehend, not easily understood, not well defined, and is subjective. We tend to believe that data has no emotions, belief systems, culture, biases or prejudices. But data in itself is completely useless unless we, human beings, can interpret and analyze it to make decisions. But unlike data, human beings are full of emotions, cultural limitations, biases and prejudices. This human side is a critical component of the invisible data. This may come as a surprise to many readers but the invisible side of data is sometimes more critical than visible facts when it comes to making impactful decisions and policies.

The visible facts of data is a necessary condition for making effective decisions and policies but it is not sufficient unless we consider the invisible side of data.

So going back to Bruno and Newton’s example – in a way the visible data had remained the same but the invisible data was changed within the 100 years between Bruno and Newton.

You may think that we might have grown since the time of Newton – we have more data, more tools, more algorithms, advanced technologies and thousands of skilled resources. But we are still not far off from where we were – in fact the situation is even more complicated than before.

There is preponderance of data today that supports the theory that humans are responsible for climate change but almost 50 % of the people in the US do not believe that. The per capita expenditure in health care in the US is twice the amount of any developed nations in spite of a significant percentage of the people being not insured or underinsured. Yet many politicians ignore the facts on the table and are totally against incorporating any of the ideas from other developed nations into their plan whether becoming part of the “paris accord” or adopting a regulated health care system.

Why is the data itself not sufficient? There are many such examples in both business and social settings that clearly point out that along with visible facts, the invisible side of data is equally or in many cases more important than the hard facts.

Data Scientists, Data Engineers and Statisticians are well versed with visible data – raw & derived data, structures, algorithms, statistics, tools and visualization. But unless they are also well versed with the invisible side of data – they are ineffective.

The invisible side of data is the field of behavioral scientists, social scientists, philosophers, politicians, and policy makers. Unless we bring them along with the ride, just the datasets will not be sufficient.  

Four challenges of Invisible Data:

I believe that the invisible data domain has critical components that all data scientists and policymakers should be aware of. Typically, the invisible data domain is either ignored, marginalized or misunderstood. I have identified four focus areas of the invisible data domain. They are as follows.

  1. Human Evolutionary Limitations: Our biases, fallacies, illusions, beliefs etc.
  2. Brute Data Environments: Complex issues, cancer research, climate change
  3. Data Mirages: Black swans, statistical anomalies, data tricks etc.
  4. Technology Advancements: Free will, consciousness, data ownership

Human Evolutionary Limitations

Through the process of evolution we have learnt to avoid more of Type I errors (false positives) than Type II errors (false negatives). Type I errors are costlier than Type II errors – it is better to not pick up the rope thinking that its a snake than to pick up a snake thinking that its a rope. This is just one simple example of how the brain works and creates cognitive challenges. Our thinking is riddled with behavioral fallacies. I am going to use some of the work done by Nobel Laureate, Daniel Kahneman, to discuss this topic. Kahneman shows that our brains are highly evolved to perform many tasks with great efficiency, but they are often ill-suited to accurately carry out tasks that require complex mental processing.

By exploiting these weaknesses in the way our brains process information, social media platforms, governments, media, and populist leaders, are able exercise a form of collective mind control over masses.

Two Systems

Kahneman introduces two characters of our mind:

  • System 1: This operates automatically and immediately, with little or no effort and no sense of voluntary control.
  • System 2:  This allocates attention to mental activities that demand dedicated attention like performing complex computations.

These two systems co-exist in the human brain and together help us navigate life; they aren’t literal or physical, but conceptual. System 1 is an intuitive system that cannot be turned off; it helps us perform most of the cognitive tasks that everyday life requires, such as identify threats, navigate our way home on familiar roads, recognize friends, and so on. System 2 can help us analyze complex problems like proving a theorem or doing crossword puzzles. System 2 takes effort and energy to engage it. System 2 is also lazy and tends to take shortcuts at the behest of System 1.

This gives rise to many cognitive challenges and fallacies. Kahneman has identified several fallacies that impact our critical thinking and make data interpretation challenging. A subset are as follows – I will be including more as part of my final project.

Cognitive Ease

Whatever is easier for System 2 is more likely to be believed. Ease arises from idea repetition, clear display, a primed idea, and even one’s own good mood. It turns out that even the repetition of a falsehood can lead people to accept it, despite knowing it’s untrue, since the concept becomes familiar and is cognitively easy to process.

Answering an Easier Question

Often when dealing with a complex or difficult issue, we transform the question into an easier one that we can answer. In other words, we use a heuristic; for example, when asked “How happy are you with life”, we answer the question, “How’s my married life or How is my job”. While these heuristics can be useful, they often lead to incorrect conclusions.

Anchors

Anchoring is a form of priming the mind with an expectation. An example are the questions: “Is the height of the tallest redwood more or less than x feet? What is your best guess about the height of the tallest redwood?” When x was 1200, answers to the second question was 844; when x was 180, the answer was 282.

Brute Data Environments

During the last solar eclipse, people travelled 100s of miles in the USA to witness the phenomenon. Thanks to the predictions of scientists, we knew exactly what time and day to expect the eclipse. Even though we have no independent capacity to verify the calculations. We tend to trust scientists.

On the other hand, the global warming scientists have been predicting the likely consequences of our emissions of industrial gases. These forecasts are critically important, because the experts see grave risks to our civilization. And yet, half the population of the USA ignores or distrusts the scientists.

Why this dichotomy?

The reason is, unlike the prediction of eclipse the climate dystopia is not immediate, it cannot predict the future as precisely as eclipse, it requires collective action at a global scale and there is no financial motivation.

If the environmentalists had predicted the Texas snowstorm of last month accurately and ahead of time to avoid its adverse impact, probably the majority of the people in the world would have started believing in global warming. But the issue of global warming is not deterministic like predicting an eclipse.

I call issues like “global warming” as issues of a brute data environment. The problem is not deterministic like predicting eclipse, it is more of a probabilistic and therefore open to interpretation. Many problems fall into this category – world hunger, cancer research, income inequality and many more.  

Data Mirages

Even though we have abundance of data today, there are some inherent data problems that must not be ignored. I call them data mirages. These are statistical fallacies that can play tricks on our minds. 

Survivorship Bias

Drawing conclusions from an incomplete set of data, because that data has ‘survived’ some selection criteria. When analyzing data, it’s important to ask yourself what data you don’t have. Sometimes, the full picture is obscured because the data you’ve got has survived a selection of some sort. For example, in WWII, a team was asked where the best place was to fit armour to a plane. The planes that came back from battle had bullet holes everywhere except the engine and cockpit. The team decided it was best to fit armour where there were no bullet holes, because planes shot in those places had not returned.

Cobra Effect

When an incentive produces the opposite result intended. Also known as a Perverse Incentive. Named from a historic legend, the Cobra Effect occurs when an incentive for solving a problem creates unintended negative consequences. It’s said that in the 1800s, the British Empire wanted to reduce cobra bite deaths in India. They offered a financial incentive for every cobra skin brought to them to motivate cobra hunting. But instead, people began farming them. When the government realized the incentive wasn’t working, they removed it so cobra farmers released their snakes, increasing the population. When setting incentives or goals, make sure you’re not accidentally encouraging the wrong behaviour.

Sampling Bias

Drawing conclusions from a set of data that isn’t representative of the population you’re trying to understand. A classic problem in election polling where people taking part in a poll aren’t representative of the total population, either due to self-selection or bias from the analysts. One famous example occurred in 1948 when The Chicago Tribune mistakenly predicted, based on a phone survey, that Thomas E. Dewey would become the next US president. They hadn’t considered that only a certain demographic could afford telephones, excluding entire segments of the population from their survey. Make sure to consider whether your research participants are truly representative and not subject to some sampling bias.

Technology Advancements

As per Yuval Noah Harari, one of the preeminent philosophers in Artificial Intelligence, there is a new equation that has thrown a monkey wrench into our belief system.

B * C * D = AHH

What he means is that the advancements in BioTech (B) combined with advancement in computer technology (‘C’) combined with Data (D) will provide the ability to hack human beings (AHH). Artificial intelligence is creating a new world for us where the traditional human values or human traits are becoming obsolete.

Technologies like CRISPR have already created a moral and ethical issue by providing the ability to create designer babies while technologies like Machine Learning have reignited the issue of bias by using “racist” data for training. The field of Artificial Intelligence is going to combine the two distinct domains of biology and computer technology into one.

This is going to create new challenges to the field of privacy, bias, joblessness, ethics and diversity while introducing unique issues like free will, consciousness, and the rise of machines. Some of the issues that we must consider and pay close attention to are as follows:

Transfer of authority to machines:

A couple of days ago I was sending an email using my gmail account. As soon as I hit the send button, a message popped up “did you forget the attachment?” Indeed I had forgotten to include the attachment and Google had figured that out interpreting my email text. It was scary but I was also thankful to Google! Within the last decade or more, we have come to entrust eHarmony for choosing a partner or Google to conduct search or Netflix to decide a movie for us or Amazon to recommend a book. Self-driving cars are taking over our driving needs and AI physicians are taking over the need for real doctors. We love to transfer authority and responsibility to machines. We trust the algorithms more than our own ability to make decisions for us.

Joblessness and emergence of the “useless class”:

Ever since the Industrial Revolution of the 1840s we have dealt with the idea of machines pushing people out of the job market. In the Industrial Revolution and to some extent in the Computer Revolution of 1980’s and 1990’s, the machines competed for manual skills or clerical skills. But with Artificial Intelligence, machines are also competing in cognitive and decision making skills of human beings.

Per Yuval Noah Harari – the Industrial Revolution created the proletariat class but the AI Revolution will create a “useless class.” Those who lost jobs in agriculture or handicraft during the Industrial Revolution could train themselves for Industrial jobs but the new AI Revolution is creating a class of people who will not only be unemployed but also unemployable!

Invisible Data: Impact on Ethics, Privacy and Policy

The abundance of data has created several challenges in terms of privacy, security, ethics, morals and establishing policies. Mere collection of data makes it vulnerable for hacking, aggregating and de-anonymizing. These are clear problems in the domain of visible data but these become even more complicated when we bring in invisible data in the mix. Following are few suggestions that we must explore:

Data Ownership and Usage

After the agricultural revolution, land was a key asset and decisions about its ownership were critical in managing society. After the Industrial Revolution, the focus shifted from land to factories and machines. The entire twentieth century was riddled with the ownership issue of land, factories and machines. This gave rise to two sets of political systems – liberal democracy and capitalism on one side and communism and central ownership on the other side. Today the key asset is data and decisions about its ownership and use will enable us to set the right policies. We may experience the same turmoil we went through while dealing with the issue of democracy vs communism.

The individual or the community

On most moral issues, there are two competing perspectives. One emphasizes individual rights, personal liberty, and a deference to personal choice. Stemming from John Locke and other Enlightenment thinkers of the seventeenth century, this tradition recognizes that people will have different beliefs about what is good for their lives, and it argues that the state should give them a lot of liberty to make their own choices, as long as they do not harm others.

The contrasting perspectives are those that view justice and morality through the lens of what is best for the society and perhaps even the species. Examples include vaccinations and wearing masks during a pandemic. The emphasis on seeking the greatest amount of happiness in a society even if that means trampling on the liberty of some individuals.

AI Benevolence

Today when we talk about AI, we are overwhelmed by two types of feelings. One is of awe, surprise, fascination and admiration and the other is of fear, dystopia and confusion. We tend to consider AI as both omnipotent and omniscient. There are the same adjectives we use for “God”. The AI concerns have some legitimate basis but like “God” we should also look to AI for benevolence. Long term strategies must include intense focus on using AI technology to enhance human welfare. Once we switch our focus from AI being a “big brother” to AI being a “friend” our policies, education and advancement will take a different turn.

Cross Pollination of Disciplines

As we saw already that the invisible data spans many disciplines from history to philosophy, to society to politics to behavioral science to justice and more. The new advancements in AI must include cross-pollination between humanists, social scientists, civil society, government and philosophers. Even our educational system must embrace cross pollination of disciplines, ideas and domains.

Somatic vs Germline Editing

Who decides what is right – somatic vs germline editing to cure diseases?

Somatic gene therapies involve modifying a patient’s DNA to treat or cure a disease caused by a genetic mutation. In one clinical trial, for example, scientists take blood stem cells from a patient, use CRISPR techniques to correct the genetic mutation causing them to produce defective blood cells, then infuse the “corrected” cells back into the patient, where they produce healthy hemoglobin. The treatment changes the patient’s blood cells, but not his or her sperm or eggs.

Germline human genome editing, on the other hand, alters the genome of a human embryo at its earliest stages. This may affect every cell, which means it has an impact not only on the person who may result, but possibly on his or her descendants. There are, therefore, substantial restrictions on its use.

Treatment: What is normal?

BioTech advancements like CRISPR can treat several disabilities. But many of these so-called disabilities often build character, teach acceptance, and instill resilience. They may even be correlated to creativity.

In the case of Miles Davis, the pain of sickle cell drove him to drugs and drink. It may have even driven him to his death. It also, however, may have driven him to be the creative artist who could produce his signature blue compositions.

Vincent van Gogh had either schizophrenia or bipolar disorder. So did the mathematician John Nash. People with bipolar disorder include Ernest Hemingway, Mariah Carey, Francis Ford Coppola, and hundreds of other artists and creators.