Machine Translation: An Overview

Machine Translation relies on the use of algorithms and linguistic models to automatically translate text from one language to another. In recent years, machine translation technology has made significant progress thanks to the use of neural networks and deep learning, which have enabled translations that are more fluid and natural compared to previous methods based on rules and statistical translation. However, to ensure high-quality and relevant translation, MT needs to be trained and refined using specific data.

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Artificial Intelligence: A Broader Field

Artificial Intelligence, on the other hand, is a much broader field that includes the simulation of human intelligence through machine learning, deep learning, natural language processing (NLP), and more. AI can perform a wide range of tasks, from understanding natural language to generating text, from image recognition to aiding in medical diagnoses. While AI can be integrated with machine translation to create advanced translation systems, it is important to note that not all AI applications are specifically designed for translation.

The Benefits of a Trained Machine Translation System

When discussing machine translation tools and trained translation memories, the quality and consistency of the text can reach very high levels, often surpassing that of “generic” machine translation. Here are some key advantages of trained MT:

1. Personalization for Sector and Client: Translation memories specific to a sector and client allow for training the machine with a dataset that reflects the technical language and stylistic preferences of a given field. For instance, translating legal documents requires precise and formal language. A translation for the medical sector needs to be especially accurate and compliant with specialist terminology standards. A trained machine translation can ensure that these requirements are met, reducing the need for manual intervention and improving the final quality of the text.
2. Constant Updates: Specific translation memories can be regularly updated with new translations, feedback, and corrections, continuously improving the quality and adaptability of the system. This is a key advantage over generic translation models, which may not always be updated with relevant data for a particular client, sector, or geographic area. Ongoing updates ensure that translations keep pace with changes in terminology and style, maintaining a high level of consistency and precision.
3. Terminological Consistency: One of the main benefits of MT is its ability to maintain terminological consistency across large projects or repeated translations. Translation memories store previous translations and automatically reuse them when encountering similar terms or phrases, ensuring uniform terminology. This is particularly important for projects that require a high level of accuracy, such as technical manuals or patents.
4. Improved Quality: With training based on specific data and feedback, machines can learn from past errors and correct them in the future. This means that the quality of the translation not only remains high but also improves over time, adapting to the continuously evolving needs.

Conclusion

Machine translation trained with specific translation memories is one of the most advanced and high-performing options for achieving high-quality translations. Compared to artificial intelligence, machine translation offers a unique specialization and focus on linguistic translation. The ability to customize, update, and continuously improve translations with the use of specific memories makes machine translation the best choice for those seeking consistency, quality, and adaptability in content translation.

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L'articolo Machine Translation and Artificial Intelligence: A Comparison proviene da ASTW.

Luciano Floridi is one of the most authoritative voices in contemporary philosophy. Since 2013 he has been a full professor of Philosophy and Ethics of Information at the Oxford Internet Institute of Oxford University, where he has directed the Digital Ethics Lab since 2017. Currently, he is the director and founder of the Yale Digital Ethics Center. A hub dedicated to studying the impact that new technologies will have on humans in the years to come.

Called to share his vision of the interaction between artificial and natural intelligence, Floridi has taken up the theme of his latest publication: The Ethics of Artificial Intelligence. Principles, Challenges, and Opportunities.

The common thread can be aptly summed up with the metaphor used by the professor himself, that artificial intelligence will not replace human intelligence, just as frozen pizza has not taken the place of gourmet pizza.

Related:

• Artificial Intelligence, Podcasts and Translation
• Amazon and Generative AI for Clinical Documentation

A clear and concise comparison that, however, has been considered by many as overly simplistic.

Artificial and human intelligence

As Floridi himself clarified in a LinkedIn post: “[the metaphor] refers to an incorrect narrative whereby, in the history of technology, the new always and completely replaces the old (example: car – carriages) when in fact the new is often complementary and adds to the old (example: electric razor – hand razor, or motorcycle – bicycle). If we compare (and it is already an irreparable error, but we admit this hypothesis by logical exercise) artificial and natural intelligence (not even human, a dog is enough), at least we must grant that it is a question of complementarity and not of replacement. Ergo: two types of pizza”.

Now, leaving aside metaphors and comparisons, what clearly remains of the professor’s thinking is the primacy of natural over artificial intelligence.

The fact that AI has reached previously unthinkable peaks does not mean that it will be able to continue this climb in the future. And even if it were, the two intelligences are for Floridi incomparable and clearly distinguishable from each other.

In short, artificial intelligence will not kill human intelligence.

What do you think about? Tell us yours in the comments section of our social channels!

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L'articolo Artificial intelligence Will Not Kill Human Intelligence proviene da ASTW.

1. What is generative artificial intelligence?

Generative AI is a subcategory of AI that creates new data that looks like an input dataset. In the context of writing, this means creating texts that can follow predefined guidelines and parameters, such as those required in regulatory documentation.

2. Automation and efficiency

Automation is one of the main advantages of using generative AI in regulatory writing. The software can generate draft documents that comply with the guidelines, thus allowing the medical writer to focus on more complex and specific tasks.

3. AI tools available for regulatory writing

There are various tools on the market, such as document management platforms and text generation software, which can be adapted for regulatory writing. Many of these tools offer machine learning capabilities that can be customized to a company’s specific regulatory needs.

4. Consistency and precision

AI can analyse huge amounts of data and guidelines to ensure that the documents generated are not only consistent but also accurate. This is critical in an industry where a small mistake can lead to legal and safety implications.

5. Adaptability to local regulations

Thanks to machine learning, AI systems can be trained to comply with the specific regulations of different markets, including Italy. This is particularly useful for companies operating internationally.

6. Ethical limitations and challenges

Despite the advantages, the use of AI in regulatory writing also raises ethical and practical issues. Responsibility for compliance remains with humans, and companies need to develop an ethical framework for the responsible use of AI.

Conclusion

Generative artificial intelligence has the potential to revolutionise regulatory writing in the medical and pharmaceutical sector, but it also poses technical, and, in some cases, ethical problems. A well-considered strategy for implementing AI can lead to greater efficiency, accuracy, and compliance.

Related:

• The Intersection Between Medical Devices and Drugs: A Guide to Regulatory Compliance

• The Importance of Regulatory Writing Training: Investing for the Future

L'articolo Generative Artificial Intelligence and Regulatory Writing proviene da ASTW.

Context: Podcasts are experiencing an extraordinary expansion. These episodes, often focused on specific topics, engaging stories, insights and worldwide news are gaining popularity at a surprising rate.

 Versatility is one of the main characteristics of this content. They can cover a wide range of topics, from travel to technology, from health to business, making them accessible to an ever-wider audience.

In addition, the accessibility and portability of podcast listening platforms, such as Spotify, Apple Podcasts and Google Podcasts (just to name a few), allow users to listen these episodes anywhere and anytime, according to their daily routines.

Those who follow our publications are well aware of the fundamental importance of linguistic accessibility in any field.

Breaking down language barriers is the primary goal of any language service providers around the world. In fact, we can easily affirm that one of the biggest challenges of our time is precisely to allow correct communication between people from different realities and languages-cultures.

And accessibility it’s exactly the founding element of this new project by Spotify.

Artificial intelligence meets podcasts

The latest news from the world of big tech came from Daniel Ek, CEO of Spotify, which presented in a Tweet the Voice Translation project:

“Podcasters – what if I told you could offer your pod to any listener around the world, in their own local language but still keep it in your own voice? That’s the pilot we’re launching! It’s called Voice Translation and using AI, translates podcasts episodes into alternate languages, all in the podcaster’s voice”.

Now, this feature is only available for three episodes of three different podcasts, but the responses on social media have already sparked a debate. Between those who see Voice Translation as a further step forward (or back) towards a dystopian and impersonal future, and those who instead loudly acclaim this latest innovation.

In the meantime, we can just remember the dangers of unsupervised machine translation and express the doubt, shared by many, about the quality of the translated content if the podcasters use a highly informal and familiar language, or in contrast, a highly specialised one.

As also reported by Slator:

“A jargon-filed, expert-targeted podcast like SlatorPod, for example, would be a challenge for any AI let alone an off-the-shelf, non-fine-tuned one”.

Maybe, the disclaimer inserted at the beginning of the test episodes, in which the use of artificial intelligence for translation and speech synthesis is made explicit, aims precisely to protect service providers from the possible negative implications of this new technology.

What are your thoughts about? Share with us in the comments on our social media channels!

For more information about our voice-over service, click here.

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L'articolo Artificial Intelligence, Podcasts and Translation proviene da ASTW.

Amazon Web Services (AWS) is the cloud system created by the Seattle giant in 2006. Since then, AWS has continuously experimented and included in its offer the most diverse services aimed at reducing the workload for professionals in various sectors.

From data processing to storage, from database management to augmented reality. As anticipated, the latest system developed by Amazon (or rather, AWS) is intended for medical staff with the aim of reducing the daily effort required for the compilation of patient medical records and the related data entry in electronic medical records.

Benefits: more time available to patients and less risk of burnout. Generative AI has found great application in various professional fields and promises great results in the health sector as well.

Related:

• Translation and Clinical Trials: How to Seize Opportunities

Amazon AWS, HealthScribe, and clinical documentation

Big promises and lots of expectations, but how does it work?

This new system combines speech recognition with generative artificial intelligence to automatically create preliminary clinical documentation from doctor-patient conversations.

Speech recognition allows the professional to free themselves from the burden of transcribing the conversations, automatically noting what has been said by both parties during medical examinations.

This technology is widely used by users around the world in various fields of application.

Over the years, numerous technology companies have developed systems of this type, designed to best meet the needs of users in different sectors of use. These include AWS, which in 2019 launched its own Transcribe Medical, able to report and track the information provided to the doctor by individual patients.

The next step offered by HealthScribe is enabled using a generative artificial intelligence system capable of processing transcripts and independently writing electronic medical records, extracting only the relevant information, and identifying the key terms for diagnosis.

However, there is no shortage of critical issues highlighted by various stakeholders, starting with the lack of data relating to the accuracy of the service. The concerns also include the possible biases involved in this type of system and the dangers resulting from a lack of supervision of what has been transcribed and processed.

For now, we can only await further details.

Useful links:

• ASTW Medical Translation
• Amazon Press Center

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L'articolo Amazon and Generative AI for Clinical Documentation proviene da ASTW.

Pareschi is one of the Italian literary translators most appreciated by the public and publishing houses. She has translated works such as Jonathan Franzen’s Le correzioni, Shirley Jackson’s Paranoia, and Ernest Hemingway’s Il vecchio e il mare – New Edition. She is also the writer of I jeans di Bruce Springsteen e altri sogni americani.

As reported by several news agencies and by Pareschi herself through her Twitter profile, the input came during a conference held in the Italian Senate entitled “Artificial intelligence and the future of cultural diversity”.

“I have five years left, I’m told. Five years to continue doing my job, literary translation, before being replaced by a machine that will be able to translate exactly like me”.

These are the words reported by various information channels, capable of stimulating the fears of translators who have seen the concept of substitution as the greatest danger.

In general, when we talk about machine translation, we refer to the fields of translation where human creativity plays a marginal role. We are talking about technical and sectoral translations, where the focus lies on the precision of the text, in compliance with industry requirements, terminological accuracy and, of itself, semantic accuracy.

But what are the dangers in literary translation?

Will good enough quality be the future?

Again, reporting what Pareschi said: “The most immediate danger, however, is that the principle of good enough quality passes. Translating a book with AI will be faster and cost less, and if the quality suffers, it doesn’t matter; the frog quickly gets used to boiling water”.

Technological advances, even in the linguistic field, are often aimed at savings.

The implementation of machine translation guarantees a reduction in costs and time related to the translation process. A reduction that, in the aforementioned areas and with the presence of a human linguist, does not negatively affect the quality of the text.

However, the same cannot be said for literary translation.

When translating works of this type, it is not only the textual aspect that captures the concentration of the linguist. It is necessary to translate the emotions, the rhythm, the pauses, the stylistic choices of the author, and any other nuance.

A task that requires completely human cognitive effort and sensitivity, which makes the presence of the translator an essential condition. It is therefore surprising that an artificial intelligence can replace its talents. Although, considering the sudden developments we have witnessed in recent times, a time span of 5 years could lead AI to achieve goals that are unthinkable today.

In any case, according to Pareschi, quality will be affected, assuming a future scenario in which the market will be divided into two bands: “a mass one in which translations will be generated by machines [with a quality considered acceptable] and an elite one for those who want translations made by humans”.

What are your thoughts? Share your opinion in the comments section of our social channels!

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L'articolo The danger of good enough quality proviene da ASTW.

How many of us have never used, if only out of curiosity, ChatGPT? The success of the last few months suggests a clear answer. And while it is certainly true that the quality of information provided by the tool, as well as its ability to intuit user requests, are nothing short of amazing, it is equally true that these systems are not perfect at all.

It is enough to query the OpenAI chatbot on a topic for which we have deep knowledge to realize the possible inaccuracies and misleading information provided by this tool.

But the risks for the user do not end there, as correctly pointed out by Gen Quinn (a patent attorney) in his contribution to IPWatchdog.

The issue concerns the possible lack of privacy of the data entered by users into the AI system. Will my information be used to train the linguistic model? Or shared with other users if their prompt is related to the topic I am dealing with?

Most likely, yes. Or rather, probably yes if the system used is ChatGPT itself.

This is because, as expressed by OpenAI, the data could be used to improve chatbot responses and increase the quality of the model (*). The use of these artificial intelligence tools in the legal field may therefore lead to possible violations of professional secrecy.

Artificial intelligence and the legal field

Given these premises, it is hardly astonishing that in recent weeks two technological giants, such as Samsung and Apple, have limited the use of these AI systems by their employees. Worried about the possible loss of relevant data and the plausible advantages unintentionally provided to competitors.

The secrecy of confidential information is certainly a fundamental issue for technology companies, but not only for them. Associated firms and legal representatives are, in fact, obliged to guarantee the confidentiality of the information provided by clients in the context of their work.

The use of generative artificial intelligence tools could easily speed up the processes of drafting and preparing the necessary documentation, especially if we consider the quality achieved by these systems once directed by the user towards a specific field of action – limited to the production of text based on the prompts provided.

As anticipated, however, this information could be usable by other users for the most disparate purposes, leading the represented party to even serious consequences.

Risks that competent law firms and lawyers will certainly be able to mitigate, basing their work on safe and guaranteed tools.

In conclusion, relying on experienced professionals capable of carrying out their work with full awareness, including careful use of any artificial intelligence systems, is the wisest and safest choice to protect yourself.

* It is possible to ask OpenAI not to process your data. However, the company reserves the right not to accept the request if it is not considered valid.

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L'articolo The risks of Artificial Intelligence in the legal field proviene da ASTW.