Following sound ethical principles for AI, such as these laid out by the European Union, can make these sorts of positive differences right from the start. From there, organizations should take the time to periodically reevaluate their AI tools to ensure they continue to align with their values and safeguard their users. Here, we’ll cover seven fundamental concepts that every company should consider before implementing AI solutions. 

1. Transparency in The Usage of AI Solutions 

Transparency is the foundation of ethical AI. When team members or clients interact with AI solutions, they should be able to easily follow how the tool reaches its conclusions or outputs. It is recommended that all processes involving the use of AI be identifiable and documented for future analyses and management. 

2. Support for Human Agency 

AI should empower, not replace, human decision-makers. Keeping human power at the fore ensures that these tools act as partners to human expertise, rather than substitutes. AI-driven systems can offer recommendations and insights, but as the European Union’s AI guidelines note, “The right of end users not to be subject to a decision based solely on automated processing should be enforced.” Following this approach ensures that technology enhances, rather than diminishes, the human touch on the job at hand. 

3. Reliability and Safety 

Reliability and safety are essential pillars of ethical AI. Before implementation, it is crucial to verify that AI works as intended and performs consistently over time. Reliable usage of AI technology is especially important in areas with high-stakes outcomes like healthcare or pharmaceuticals, in which even small errors can have significant consequences. 

4. Diversity, Non-discrimination & Fairness 

An ethical approach to AI usage respects diversity and prioritizes inclusion, which means it must be tested carefully for unintended biases and further evaluated to ensure it serves a wide range of users equitably. While AI systems aren’t inherently designed with inclusivity in mind, using them responsibly can support diverse populations and reinforce a company’s commitment to fairness. The goal should be to create or leverage tools that acknowledge and adapt to the unique backgrounds of all users.  

5. Privacy Protection and Data Security 

Data security is paramount in ethical AI use, as the technology relies on vast amounts of information. For clients, prioritizing their privacy means ensuring their sensitive or proprietary information is securely managed across platforms and departments. To protect client data, businesses should choose AI providers with robust security measures and transparent data practices that safeguard information at every stage. 

6. Societal & Environmental Wellbeing 

In an age of climate awareness, it is also essential to consider AI’s environmental footprint. AI can be energy-intensive, so opting for energy-efficient solutions internally or partnering with external providers with green practices is important. Environmentally responsible AI solutions are those that help solve business challenges in a sustainable way that respects our planet’s resources.  

7. Accountability 

Accountability ensures that companies remain responsible for their AI tools’ actions. Having clear processes to audit and evaluate AI decisions is essential. This way, businesses can address issues, learn from them, and continue to improve. Accountability in AI use demonstrates to users and stakeholders alike that you take responsibility for your actions. 

A Final Thought for Businesses 

Ethical AI implementation is not just about staying ahead in innovation; it’s about doing so in a way that upholds trust, transparency, and inclusivity. By following these principles, you will be well-positioned to implement AI responsibly, thus making a positive impact on both your clients and society as a whole. For industries like translation, healthcare or pharmaceuticals in which data security and cultural sensitivity are paramount, adhering to ethical AI principles helps deliver not only reliable results, but also peace of mind. 

The post AI Ethics Essentials: Key Principles for Responsible Implementation appeared first on Terra Translations.

What is Neural Machine Translation and How Does it Work? 

Neural Machine Translation (NMT) is an advanced AI technology designed to automatically translate text from one language to another. Unlike traditional translation methods that rely on predefined rules, NMT employs a neural network—a computer program that improves translation accuracy by considering the entire text’s context and learning from vast amounts of example data. 

NMT systems typically have two main components—one that reads and understands the original text and another that generates the translated text in the target language. This process mimics the human brain’s function, using interconnected nodes that enable the model to learn and enhance its capabilities over time. The ability of NMT systems to learn from context allows them to provide more fluent and coherent translations compared to older translation methods. 

What are Large Language Models and How Do They Work? 

Large Language Models (LLMs) are sophisticated AI systems designed to understand and generate human-like text. They are trained on vast datasets of text, which enables them to perform a wide range of language-related tasks beyond translation, such as text generation, summarization, and conversational AI. 

LLMs use deep learning techniques with multiple layers of neural networks. Each layer refines the model’s understanding of the data, employing an attention mechanism that focuses on specific parts of the input data. This process allows LLMs to generate text by predicting the next word in a sequence based on the input they receive, making them versatile in generating coherent and contextually relevant text. 

Pros and Cons of NMT and LLMs 

No technology is perfect, so let’s take a look at both the advantages and disadvantages of NMT and LLMs.  

The Takeaway 

NMT and LLMs represent significant advancements in artificial intelligence, revolutionizing how we approach translation and other language-related tasks. While these tools offer substantial benefits, it’s crucial to view them as complementary to human expertise, particularly in ensuring accuracy, cultural sensitivity, and nuanced understanding. Professionals should leverage these technologies to enhance their workflows while remaining vigilant to their limitations and the importance of human oversight.

The post Neural Machine Translation vs Large Language Models appeared first on Terra Translations.

What is ChatGPT?

ChatGPT is a highly advanced chatbot that utilizes cutting-edge technology to engage in natural and varied conversations with users. It is designed to be adaptive and responsive to the different needs and contexts of its users, allowing it to be applied effectively in multiple industries, including retail, education, and marketing.

With its exceptional ability to interact with humans, ChatGPT can perform a wide range of tasks that can save time and improve efficiency. It can help businesses generate marketing copy, write lyrics, navigate complex coding issues, provide language translation services, summarize information, and even support students with their homework.

ChatGPT has quickly become a groundbreaking innovation in the technology industry, and its rapid progress is evident with the release of a newer version, GPT-4. This latest update offers even more functions and enhanced capabilities that will undoubtedly take ChatGPT to the next level.

How does ChatGPT work?

ChatGPT is built on the foundation of the GPT (Generative Pre-trained Transformer) model, which is a type of artificial neural network widely used in natural language processing (NLP).

This chatbot works by analyzing a vast corpus of text data to understand the nuances of language and respond to users’ requests accurately. It can understand and interpret the meaning of a user’s input through various techniques, such as sentiment analysis, entity recognition, and machine translation, to provide appropriate responses.

One of the significant advantages of ChatGPT is its ability to adapt to different languages. Initially developed and trained in English, it now supports around 95 languages worldwide. This makes the chatbot more accessible to users from different regions, with different language preferences.

Furthermore, ChatGPT leverages the power of machine learning artificial intelligence (AI) to deliver seamless conversational experiences. It uses a range of methods, such as context-based response generation, personalized recommendations, and empathetic interactions, to enhance the chat experience and build a deeper connection with users.

ChatGPT challenges and limitations

ChatGPT is one of the newest advancements in artificial intelligence that allows users to generate human-like responses to certain queries. However, ChatGPT is not without its challenges and limitations. The main challenge with ChatGPT is the risk of data bias and the spread of misinformation. This is because AI models like ChatGPT can be influenced by the data sets they are trained on, which can lead to biased outputs.

Another limitation of ChatGPT is that it still may not be able to fully understand the context and subtlety of human communication. For instance, ChatGPT may not be able to comprehend sarcasm or irony, leading to inappropriate responses. These limitations could be harmful if ChatGPT is used in sensitive settings such as mental health support or legal advice.

It is also important to consider the ethical implications of using ChatGPT. As AI algorithms are not able to comprehend moral principles, they may produce outputs that do not align with human values. Additionally, the use of ChatGPT raises concerns about data privacy and the protection of personal information.

Lastly, ChatGPT requires continuous improvement in its development, which means that it may need to be regularly monitored and updated to ensure its accuracy and avoid the spread of misinformation. Another limitation ChatGPT warns its users about is that it has limited knowledge of the world and events after 2021. Additionally, developers of ChatGPT should consider ways to improve the model’s ability to comprehend nuance and context in human communication.

The Takeaway

In conclusion, while ChatGPT offers a groundbreaking development in AI, its challenges and limitations cannot be ignored. As such, it’s crucial that users stop for a moment and analyze the use they are about to make of it. This can be a great tool when it comes to finding inspiration and generating ideas, but it should not be relied on completely.

The post ChatGPT: An Introduction to the Revolutionary Language Model appeared first on Terra Translations.

How Errors Can Occur

Wikipedia serves as a good example of how machine translation errors can occur and reinforce gender bias. Wikipedia’s entries tend to be geographically diverse, lengthy, and refer to subjects in the third person, which leads to the use of a lot of pronouns. Because of this, Wikipedia entries (particularly biographies) often have potential to cause machine translation errors related to gender, especially if an article refers to a person explicitly early in a sentence, but not later on. 

How Errors Can Be Resolved

Let’s look at Google as an example of a company aiming to resolve machine translation mistakes regarding gender. Google acknowledges that its translation tools struggle with errors that lead to reinforcing gender bias. They believe that they need to advance translation techniques to surpass single sentences. Doing this requires setting new metrics for measuring their progress and creating datasets with the most commonly encountered context-related errors. They’re facing a significant challenge. Translation errors related to gender are susceptible, as they can incorrectly refer to someone and how they self-identify. 

Google is working towards long-term improvements on their machine learning systems so they can continuously improve how they translate pronouns and gender.

The Takeaway

In recent years there’s been more awareness that these biases exist and machine translation engineers are trying their best to resolve this issue quickly, but it’s no easy endeavor since gender works so differently in all languages. Even though many advancements have been made in the machine translation industry, work still needs to be done. In all reality, a human translator is much better equipped to handle such sensitive issues like gender. 

It has taken many years to improve machine translation quality, and additional improvements will take more time to make. However, this issue can’t wait that long to be addressed. Errors that reinforce gender bias are critical to work on now, considering the recent relevance gender inclusivity has taken recently. If a company wants to prioritize inclusive language, it’s not safe to use an automated solution. Gender is a sensitive topic; with a translation, you want to ensure your message is conveyed discreetly. Right now, human translators are researching and staying up to date with the latest trends in the languages they work with. This is necessary, as everything is changing so fast. Companies should turn to these professionals to ensure their brand is not hurt by a careless machine translation mistake.

The post Does machine translation reinforce gender bias? appeared first on Terra Translations.

Machine Translation

Machine translation technology has made massive strides in recent years. Long gone are the days of rule based machine translation. Thanks to the introduction of AI and machine learning, machine translation output quality has been improving at a faster rate. It’s worth noting that there are still limitations to this technology — limitations that require human intervention to overcome. In order for machine translation engines to learn, someone has to teach them. As a result, linguists must be involved both before and after a translation takes place. Linguists must “train” the engines to predict how a translator would proceed through post-edition work. 

Post-edition occurs when a human translator corrects the machine-generated text and provides explanations for each correction. They then explain why they decided to do what they did, which helps improve the machine translation’s capabilities. Next, engineers process this information and feed the engine with more data. The goal is that as time passes, less human interference is required to produce human-quality translations. To achieve this goal, more and more industry-specific machine translation engines are being created and companies now have the possibility of training machine translation engines for their use.

Transcription Services

In the case of transcription services, we’re seeing an increase in the use of speech recognition software. It’s important to pause here and draw a line between speech recognition and dictation. Dictation occurs when the speaker purposefully modulates and uses commands to be understood, which can be easier to decode because the speaker is usually intentionally clear. The difficult task is to decode speech when it’s not dictated, such as during a lecture or an interview.

Similar to machine translation, speech recognition software requires training. The developers of speech recognition software apps are collecting massive amounts of data from the users’ recorded sentences and correcting the transcribed text to train the software and make it more accurate when it comes to elements like accents, jargon, and speed. This is no easy task as no one human speaks precisely the same. Similar to how no two people are alike, deviations in speech patterns and accents must be taken into account. Any type of anomaly, like an accent, can cause speech recognition software to misinterpret certain aspects of a conversation. This is why having a human review of the output — no matter how powerful the technology is — is imperative. If you’re struggling to visualize this technology, pick up your smartphone. Often, when you get a new smartphone, you have to train your phone’s digital assistant (like Apple’s Siri) to recognize your specific voice. In many cases, the phone will only respond to your voice and won’t work for someone else.

The Takeaway

As impressive as machine translation and tools like speech recognition software are, they still require human supervision in order to achieve the best results. There are very necessary human touches that can take the content created by AI and make it more accurate and effective. The post-editing of machine translated texts and transcription QA checks are a necessary step a human linguist must take, as there are times when the sensitivity of the materials require that no risks are taken (such as with medical translation) and that humans are the ones making the important decisions.

The post How Humans Shape AI and Fuel Translation Technologies appeared first on Terra Translations.

What are the Benefits of SEO?

There are many benefits of using SEO techniques, primarily working towards helping a website get as many eyes on it as possible. Some of these benefits include using SEO to:

• Help internet users find the answers and solutions they are looking for
• Increase website visibility and traffic, as well as brand discovery
• Provide growth opportunities for businesses
• Grow traffic and sales through targeted searches

Why Machine Translation Can’t Do the Job

Translating a website into a new language can give businesses the opportunity to vastly grow their audiences and brand reach. However, their efforts have to go past a straightforward translation and must take the proper keywords for each specific market into account. Even if the internet users are searching for the same thing, they may not search for it in the same way. 

While it may be tempting to use machine translation because it is time and cost-efficient, the truth is, it can not assist with international SEO needs in the same way a human translator can. Having a list of translated keywords is not always enough to gain the same SEO traction. To properly take advantage of international SEO, there has to be researched into the target market and a high level of cultural insight in order to be truly effective. The ideal keywords can change greatly when working with a new language and location. In effect, translating SEO keywords is more similar in nature to transcreation, as you can’t directly translate keywords and instead need to come up with new keywords that suit the needs and habits of the target audience. 

On top of the nuanced SEO requirements that Machine Translation can’t handle, Google recently updated their Quality Guidelines to state that using automatically generated text that is translated by an automatic tool is no longer allowed unless it undergoes a human review before publication — a process known as post editing.

How a Linguist Can Help

A linguist can step in where Machine Translation cannot thanks to having a diverse skill set and a deeper understanding of cultural context and the target audience, as well as SEO and marketing best practices. A linguist with a strong grasp on all of these elements can research which search engines are most popular in a target market, they can complete a detailed keyword analysis, and they can leverage SERP analysis and the ranking factors search engines utilize. 

Alongside understanding the needs of international SEO, linguists specialized in this field also have web writing skills and can create content that is engaging for the audience and utilizes SEO keywords to their advantage.

The post What is International SEO and Why is it Important? appeared first on Terra Translations.

Post-editing implies reviewing a MT output in order to improve it and to obtain a semantically and syntactically accurate target text. This service is a specialized task that requires a specific set of skills, expertise and competencies.

Trained post-editors are aware of the most common mistakes MT makes and quickly implement the changes needed. Let’s analyze some of the most common errors addressed in the post-editing stage.

Mistranslations and omissions

Whether a document or project need deep or light post-editing, there are mistakes that post-editors always correct in the post-editing stage. They scan the output text for omitted or added words, phrases or segments. Additionally, they will correct mistranslations, semantic and syntactic errors by applying quick and short changes. Correcting numerical and tag mismatches between source and target text is also a must during post-editing.

Furthermore, if specified for a project, reviewers evaluate if the output complies with stylistic guidelines and correct it accordingly.

With all these basic improvements, post-editing ensures that the target text is accurately translated and properly formatted.

Limits of AI

Mistranslations or omissions are common errors that can be found even in human translation. But other mistakes are related to the capabilities of the artificial intelligence engine. Some of them are the following:

• Post-editors spot errors in the output that can be due to a spelling error in the source text. When the misspelled word or cipher exists, the engine translates it, but the target text will convey the wrong meaning. Because vendors master specific domains, they are able to spot those errors.

• If there are acronym preferences specified, post-editors will ensure they are properly translated into the target text. This is because the MT engine might accurately translate well-known acronyms (e.g., WHO>OMS), but non-familiar ones can be left untranslated. Also, there might be inconsistencies in how they are translated or explained in the target text.
• Depending on the engine (if it’s, for instance, example based, ruled based or neural), some types tend to mirror the letter case of words. Post-editors correct any capitalization mistake generated by differences in the capitalization rules between target and source text. 

• Some projects may have the specification of leaving untranslated certain terms or phrases, for example, codes of web pages, proper names or institution names. While reviewing the output, the post-editor ensures the target text complies with that requirement.
• Sometimes, the MT engine misreads punctuation by interpreting it wrongly or mirroring the source text’s punctuation. Post-editors must be aware of the most common punctuation mistakes (for instance, mistranslation of the long dash and colon in English into Spanish text pairs) and correct them accordingly.

• The MT output can be grammatically and syntactically correct, but still don’t comply with, for example, the character limit specified for a project. Post-editors will bear in mind the specific requirements and apply the appropriate changes.

Leave it to the experts

Relying on expert post-editors ensures that providers with a specific background and know-how handle the MT workflows. Experience and expertise allow vendors to implement the required improvements in MT outputs without sacrificing time nor productivity.

The post Post-editing Highlights: What to Correct appeared first on Terra Translations.

One of the most requested tasks in the MT workflow is post-edition, the process of improving a machine translation output. Only certain professionals stand out in this task. They are a specific type of editors that have the required technical, psychological, and linguistic skills. Let’s find out what you need to be an ideal post-editor. 

Papers please

First and foremost, a certification in translation, language studies, or linguistics is a must in the profile of a post-editor. If not, as per ISO 18587:2017, the post-editor must have at least five years’ experience in translating or post-editing. These are requirements that intend to guarantee translation service providers work with top-quality professionals.

A whole lot of competences

A quality-oriented translation workflow is rooted in the proper selection of the professionals involved in a project. This is also the case for MT workflows. The following list summarizes the competencies that are part of the ideal post-editor profile. 

Post-editors are, like any other translation professional, proficient in both source and target language and culture. They know how to conduct efficient research of terminology and manage the information. Also, they master the specific domains, since this implies an expert understanding of the source text. 

Lastly, post-editors must be skilled in IT resources, like CAT tools, but also be acquainted with MT systems. The post-editors that fulfill the required profile know MT models (neural, statistical, example-based, rule-based) and their differences. Furthermore, they are aware of the most common errors in each system. Thus, they can manage more efficiently their attention and spot mistakes quickly.

The two A’s: Aptitude and Attitude

There are differences between the profiles of MT post-editors and TEP editors. Both are detail-oriented linguists, but in addition, post-editors must be fast and efficient, implementing minor and quick changes in the short time provided for the edition. 

Moreover, a salient feature of post-editors is their predisposition or flexible attitude. Sometimes language professionals are reluctant to the implementation of MT. But MT is just one solution in the fast-growing translation industry, whose core business remains the same, regardless of its growth or of the MT implementation. Successful post-editors are confident and creative, and they adapt willingly to the new roles the industry has to offer.

The post Linguist Profiling: What Makes an Ideal Candidate for Post-editor appeared first on Terra Translations.

Machine Translation is Here to Stay

Bibiana is aware that we live in a globalized and constantly evolving world. The adoption of machine translation is one change that she believes translators and linguists need to accept and not feel threatened by, “The truth is that a machine will never be able to completely replace human labor in the translation process, at least for now, and it will always take the touch of a translator or editor to deliver a verbose, meaningful, and error-free deliverable to the client,” Bibiana said.

Bibiana has found that machine translation is extremely effective in handling certain subject matters, such as those relating to medical, technology, and engineering industries. For subject matter that requires more creativity, such as marketing and advertising, she doesn’t feel machine translation can hit the mark. 

At the end of the day, one of the benefits of machine translation in Bibiana’s opinion is client satisfaction, at least in regard to saving time. Some clients require a fast turnaround, especially if they handle large volumes of text, and they may not have time to wait for a human translation. By using machine translation, and then utilizing human labor for the post-edition process, the client can have a deliverable of acceptable quality in a quicker time frame. The decision to use machine translation during the process depends on the quality expectations of the client and what their priorities are. “We have clients who have found these machine translation tools to be really high quality in the cases of highly technical translation projects. In many instances, we find practically no differences between what a person and machine translation can translate when there is little room for creativity during the process. This tool even recognizes the client’s translation memory and glossary, which guarantees the correct application of both,” Bibiana said. 

Machine Translation Has Its Faults

Bibiana acknowledges that machine translation has its difficulties, which is why pairing it with a human translator, linguist, or editor can make all the difference. Four shortcomings that Bibiana is wary of include:

• Complex formats. Most machine translation engines do not recognize formats such as bold, italics, underlined text, subscript and superscript, colors, and the tags that are generated in a conversion.

• Table headings. Machine translation tools often break the words from the heading. When translating, sometimes the order of the words must be reversed. This process cannot be recognized by an automatic translation tool and it translates the words literally.

• Segmentation. When a program takes a source file, it may cut sentences, therefore in this case, the machine translation engine doesn’t recognize the cut sentence and translates it as two separate and meaningless sentences.

• Inconsistencies. Machine translation engines are usually inconsistent with the translation of the same term and often confuse the indistinct use of informal or formal tone or verb tenses.

Bibiana urges against solely utilizing machine translation tools for the sake of saving money, as some tools may leave much to be desired without human intervention and the work may end up needing a complete retranslation.

Working Together is Key

Machine translation can not stand on its own and that should provide some comfort to linguists and translators that feel their territory is being infringed upon. “As we see it, in one case or the other, human work is essential,” Bibiana said. She believes that we can no longer continue to ignore and oppose the implementation of machine translation. She urges that we must make sense of it and acknowledge the many benefits associated with pairing machine translation with a skilled human touch.

The post Lead Linguist Bibiana Cirera’s View on Machine Translation appeared first on Terra Translations.

1. Pre-editing

In order to obtain a better output after implementing the MT engine, post-editors will prepare the source text. This is because there are texts that are more suitable for MT than others. Pre-editing is the process of preparing the source text before MT to obtain a better MT raw output. The most common actions required in this step are the following: 

• Manage terminology
• Apply style guides
• Shorten sentence length
• Reduce long noun phrases

2. Machine Translation

At this stage, the MT engine translates the source text. The device can be integrated in a CAT tool, it can be a client’s engine or Google Translate, among other options. Depending on the project’s scope or requirements, a sample may be machine-translated to check the output. According to the results — and if needed — the project’s team makes adjustments in the source text or the engine.

3. Post-editing

Depending on the client’s requests and needs, the translated output can be delivered without post-editing at all (raw output), or with light post-editing or deep post-editing. Regardless of which process is applied, there are certain rules that determine the post-editing process. According to the Translation Automation User Society (TAUS), during the post-editing task, the post-editor should bear in mind these rules:

1. Do not retranslate the text
2. Decide changes quickly (“2-second rule”)
3. Translate the whole text, unless some phrases are classified as untranslatable
4. Correct incomprehensible sentences
5. Delete inaccurate sentences if they are irrelevant and difficult to correct
6. Focus on semantic and syntactic mistakes
7. Don’t correct stylistic errors (their correction is subject to prior agreement)
8. Don’t replace recurring terms with synonyms

4. Feedback and Evaluation

When developing an MT engine, the post-editor not only corrects the text, but also provides feedback to the engineers. Usually, the evaluation is made using standardized forms. This is a very important step that helps improve the MT device. The MT team retrains the engine based on the feedback provided (changing configurations, uploading new bilingual samples, for instance). With this step, the engine is “trained” so the quality of the MT output improves gradually. 

The Zero Step

Like in any other localization project, there is a step that cannot be skipped. For a successful delivery, it’s essential to have a prior agreement with clients about what they expect of the MT workflow. Specifically, what kind of post-editing process will be applied (none, light, or deep), style preferences, proper nouns treatment, date format and untranslatable phrases, among others, are details that need to be specified before the project starts. This kind of agreement is the foundation of any localization task.

¹As we can see in the chart, the skills, and expertise of linguists play a key part in the MT’s workflow.

The post 4 Stages and 8 Rules for Successful Post-editing appeared first on Terra Translations.