You are a professional terminologist. You are provided with the text below between ### ###. Your task is to EXTRACT THE MOST RELEVANT TERMS SPECIFIC TO THE LANGUAGE INDUSTRY AND ITS MOST RECENT DEVELOPMENTS.

Follow these instructions:

- Give me as output a list of terms in a column format.
- Order your output list alphabetically.

Now please extract the terminology. This is very important for my job. Thanks. ###TAUS Massively Multilingual AI Conference Rome
AI allows us to create virtual companions that can speak our language, any language, as well as humans. This is a technology breakthrough that shakes up the world. It can make businesses and governments a hundred times more efficient and it creates thousands of opportunities for innovation. If only we start thinking and operating truly and massively multilingual.


18 - 20 June 2024


Program

Day 1


Prompt Engineering for Localization Workshop


Quality Estimation Workshop

Welcome and opening remarks

How MTQE works and how it evolved
Evolution of the research, use cases and adoption of MTQE research, over time and an overview of the employed models and the scoring techniques


GEMBA: using Large Language Models to evaluate translation quality


Quality Estimation Use Cases

Integrating MTQE
Find out more about how you can implement a quality estimation step into your existing TMS or workflow management systems or directly into your MT workflow.


Getting Your Setup Right: Customization, Data, and Testing
Dive into the technical aspects of MTQE implementation, including customization, data requirements, language support, and strategies to evaluate.


Discussion and Q&A
Engage in open discussion, ask questions and exchange insights with presenters and fellow participants to further explore the nuances of MTQE.

Day 2
Theme of 2024 TAUS Massively Multilingual AI Conferences

No English Please - How to move towards truly multilingual AI

It started with a dream

Kalika Bali started her career with Lernout & Hauspie, the Belgian speech technology company that kicked off the first wave of consolidation in the localization industry in the 1990s. Who remembers these days? Lernout & Hauspie dazzled the world with magic speech devices. Too much too early perhaps, but Kalika carried the dream forward in her research work with Microsoft. In this opening address she will take us through the journey, her journey, from the discovery work in Flanders Language Valley to the launch of the multilingual speech technologies that the pioneers in Flanders Language Valley dreamed of.

Panel discussion

Execs from AI Labs discuss the challenges and opportunities of massively multilingual models and applications. English is the dominant language in the world of tech and AI research. Models are trained for more than 90% on English language data. Inevitably the LLMs look at the world through English filters. The focus of this session will be how to overcome this language bias and how to move towards a more democratic and inclusive world of AI. Models and algorithms alone can not solve this problem on their own. In other words: AI needs the language industry, and the language industry needs AI. How convergence of two industries can accelerate our path towards truly multilingual AI.

The Massively Multilingual Contest - Part I
A fast-paced innovators competition

Twelve innovators and disruptors have been selected to compete for the highest trust and scores from the audience presenting their AI solutions in six minutes.

Evolve

Evolving the translation process at scale beyond just translation quality assessment or automatic post-editing. How can organizations leverage a coherent combination of human specialists, process management and new generation AI with both excitement and realistic pragmatism baked in. At a lower cost than yesterday

Next-Generation MT

The unique capabilities of LLMs (large language models) create an unprecedented way to customize neural network behavior. LLMs are great for incorporating linguistic data and making machine translation customized with proper terms and phrases. memoQ AGT offers machine translation with instant domain adaptation accessible to anyone.

Ethics and Laws Surrounding GenAI

How to establish a human-centric policy towards AI

Will the EU AI Act serve as a model for the whole world, providing the necessary guardrails to the adoption and implementation of the AI systems? Or will it turn Europe into a digital ghetto, stifling innovation on our continent? How do we deal with the ethics around the usage of data and the privacy of users? And what about other laws that drive consumer activism and fuel mass claims, whilst on the other hand sharing of data becomes more important and will be propagated by law makers? Complex questions that affect everyone producing and consuming multilingual content.

In this session we will have experts from the legal and data sectors sharing their perspectives on a fair, competitive and compliant AI future for Europe.

The Massively Multilingual Enterprise

The corporate globalization perspective

AI is at the top of the corporate agenda with sizable investment budgets behind it. But are we ready to jump to touchless translation, to automatic multilingual content generation, polyglot chatbots and self-driving workflows? The stakes are high. Failure to deliver can set us back dramatically. In this conversation with globalization leaders and CTOs we hear exciting stories but we also get reality checks. The transition from just using Neural MT for translation automation to now introducing GenAI in every facet of enterprise content and customer contacts leads to unprecedented changes in processes, tooling, vendors and ethics. We have no choice, we must embrace AI. The organization needs to adapt.

PromptLoc: User-controlled precision AI-solutions for any language task

Current LLM-based products and services are limited to single tasks (e.g., custom translation) and offer users minimal control. This contrasts with the potential of LLMs to handle various language tasks and be user-friendly for non-experts. PromptLoc democratizes AI solutions by allowing users to customize and control operations through natural language prompts. These prompts can include dynamic placeholders for specific details in xliff files, making the tool flexible for any application. It's like a Swiss army knife for localization tasks, enabling creative solutions and expanding accessibility.



Day 3
Opening Day 2 of the M2 AI Conference

Farewell MT - Welcome AI

The immersion of MT into AI.

Leaders from the world of MT are the patriarchs of AI. In this session they discuss the transformation in their programs as a result of the remarkable success of Large Language Models. The question of human parity in MT, causing heated debates some six years ago, is back on the agenda. What keeps the heads of MT programs busy these days and what should they worry about in the next year or two? If MT is getting better so quickly now it becomes harder and harder to find the errors. What does that mean for the modern translation practice? Is there still a place for TMS systems and what about specialization of MT for domains and customers?

Showcasing a Revolution in Multilingual Content

The (language) industry embraces GenAI and rolls out new (revolutionary) strategies, programs and products

Convergence of AI and language technology leads to unprecedented innovation. In this session we will have language service and technology providers discuss the challenges and opportunities of the shifts to machine intelligence. The challenges include the uncertainties, the risks of failures and the inevitable tactics of trial and error. The opportunities on the other hand are the opening of new (language) markets, the launch of new services and the complete overhaul of traditional localization processes and workflows. They will share what they venture, what went wrong, what went well and how they see the landscape changing.

Questioning Quality and Evaluating LLMs

How do we trust and manage the quality of content in a hyper-automated world?

Who is out there to help manage risk and put in the checks and controls necessary for accuracy and reliability of massive multilingual content. A session dedicated to quality. 

A panel consisting of experienced users (of Quality Estimation) and AI developers discuss the advancements in automatic review of content. We cover the whole palette of inspection and analysis, from automatically detecting the use of LLMs to sentiment analysis and automatic generation of MQM error types. Technically (and theoretically) there seems to be no limit to what AI can do to automate the quality function. But the daily practice in a global enterprise and in a translation company is still different. Regulations, policies and analyses require that we tread carefully.

Updates on the Meetween Project

Any Questions

Any Questions? (based on the BBC current affairs program) is an opportunity to explore a range of topics within the conference theme, in an informal and personalized format which allows any attendee to ask questions, and the panel of experts to respond with their own take on the subject.  So whether it’s around AI, or NMT, or process and workflow management, or resourcing and global supply chains, this session explores the matter with unscripted, personal responses.

The Massively Multilingual Contest - Award Ceremony

After the presentations from each contestant, the audience receives a link to vote for their favorite. At the end of the two days, the votes will be counted and the winner will be announced. The prize is a coveted TAUS Massively Multilingual Award.

A Deep Dive Into the State-of-the-Art AI Solution for Translation

While many companies are looking to large language models (LLMs) for their translation and localization needs, LLMs are not the state-of-the-art solution. Neither is generic neural machine translation (MT). The modern enterprise requires a dynamic, customizable solution that can quickly adapt to its ever-changing needs. The answer is adaptive neural MT, which, according to a recent comparative MT evaluation by Achim Ruopp, founder of Polyglot Technology, outperforms leading public MT systems and the best LLM for translation, GPT-4. Let’s explore why adaptive neural MT is the cutting-edge solution for businesses.

To improve any artificial intelligence (AI), you must find errors, fix them, and retrain the system. The strength of adaptive neural MT is its ability to quickly and continuously learn from customers’ data, ongoing corrections, and document context, helping it improve as MT usage expands within an enterprise. This constant corrective feedback ensures that translations match the intended tone and terminology for the target audience and increases overall productivity.

While LLMs can sometimes provide high-quality output with much coaxing and effort, they can also be unreliable and inconsistent, much more prone to hallucinations, slow, and difficult to embed in highly automated translation workflows. LLMs are also currently limited in their language coverage, and these limitations will take time to resolve. 

In addition, the most successful AI deployments need continuous learning and improvement capabilities to meet evolving business and use case requirements. This activity demands rapid error detection capabilities, the ability to incorporate closely integrated corrective feedback, and automated and dynamic updates to the translation model to keep it in step with the latest requirements.

Translated’s ModernMT offers all of these crucial benefits and more. Now enhanced with Human-in-the-Loop, ModernMT is the first dynamic, adaptive MT solution that combines advances in AI with human reviews in the background to deliver continuous, real-time quality improvements. Every day, based on user requirements across languages and projects, a percentage of the delivered machine-translated content is automatically sent to professional translators for review and revision, providing feedback for immediate improvement of the MT system. Everything happens in the background, without any need for intervention by the user after they have configured their approach in the dashboard. You can learn more from Translated’s VP of AI Solutions, John Tinsley, in the video above.

This cutting-edge solution is powered by an adaptive Machine Translation Quality Estimator (MTQE), which rapidly analyzes all translated content and ranks segments by the likelihood of errors. This way, human reviews are directed where they’re needed most. By addressing challenging errors and retraining the MT system in real time, we can more efficiently address problematic segments in subsequent translations, leading to continuous improvement of the MT system. Translated’s adaptive MTQE has another distinct advantage over generic MTQE models. Similar to adaptive MT, it learns from customer data to make better decisions about quality. This approach allows users to solve a large proportion of significant problems in a corpus by focusing revisions on a small percentage of the worst-scoring translations. 

This crucial difference is critical for companies localizing large volumes of data, as human oversight of these volumes is near-impossible. It’s a prudent approach to managing risk when companies want to go multilingual at scale, enabling faster turnaround times and increased efficiency.

While adaptive neural MT is the state-of-the-art solution for enterprises, that may change in the coming months or years. LLMs hold great promise for the future of automated translation, but right now, they’re not ready to replace MT for enterprises. However, according to recent experiments conducted by Translated, they’re likely to reach this stage very soon. As with any AI, implementing LLMs for enterprise translation will require real-time adaptivity to meet business needs and provide continuous, automated quality improvements.


2023 was the first full year of GenAI, kicked off to the
public with ChatGPT’s release in November 2022.
Since then, a cavalcade of alternative LLM platforms
– both proprietary ones from big tech companies
and open-source models – have been announced,
and there is no slowing down. The incredibly rapid
development in and the hype around LLMs implies
that all previous technology trends and predictions
are annulled.
Last year was all about testing to evaluate LLMs'
utility, quality, and implementation strategies. The
experiments can be categorized as a) augmenting,
b) upgrading, or c) expanding value. Augmentation
involves integrating the new AI models to enhance
existing technologies and workflows (such as
machine translation quality estimation or automated
post-editing), upgrades entail replacing prevailing
technological frameworks with LLMs (such as
utilizing them for machine translation or optical
character recognition), and expansion of value
creation emerges when LLMs are employed to
generate innovative benefits that were previously
unattainable or impractical with technology (such as
source optimization, large-scale translation memory
cleanups, multilingual content generation, or linguistic
bug detection), extending the range of services LSPs
can offer, their reach to new potential buyers, and
the competitive landscape they need to cope with. In
addition, AI may prove to be more useful as a platform
for the orchestration of traditional tools in the existing
and evolving language technology ecosystem.
The prevailing sentiment from almost all stakeholders
is that piloting LLMs is relatively easy and has
considerable potential. However, harvesting widescale benefits is more challenging than initially
anticipated. The term “wide-scale” is key here:
driven by demand from senior management levels,
enterprises try to leverage GenAI across
their departments, and content and language
services is just one of those many functions.
Experiments show that company-wide AI
initiatives are no less – if not more – complex
than digital transformations, which itself is not
a completed journey for many organizations.
Nevertheless, the great promise of LLMs is
their versatility in various natural language
tasks, even if their performance may – yet
– lag behind purpose-built tools or human
expertise. Text summarization, rewriting, tone
corrections, question answering, retrieval
augmented generation (RAG), poem writing,
and many more functions are suddenly made
available in a single tool, even in multiple
languages. The latest versions of platforms,
such as OpenAI’s GPT-4 or Google’s Gemini,
even offer multimodal operations, voice and
image inputs, and the race is still hot between
big tech and AI labs for the next big thing,
video generation included.
All that said, we haven’t seen any actual mass
application of LLMs within the industry.
Some of the key factors contributing to this
are the rapid pace of development and the
proliferation of platforms. Similarly as with
machine translation, LLMs do not represent
a singular, unified technology, and there
is no single clear-cut platform that would
comprehensively serve all the use cases –
and languages – in the most efficient way.

Technology Trends And Predictions | NIMDZI 100
Also, the largest and most capable generalpurpose LLMs have very high computational
requirements, high latency, hallucination issues,
buggy prompt engineering, and often unverified
quality outcomes. As enterprises and LSPs have
over time made significant investments into
their language technology stacks, the current
challenges with LLMs – although temptingly
capable looking – make them a dubious option
to solve old problems in a new way.
In addition, the speed of advancement in the
foundational models of GenAI – especially
spurred by big tech – warrants some caution
when making strategic, long-term decisions for
any of the current LLM solutions. The prospect
of domain- and task-specific, fine-tuned small
language models is also on the horizon. The
threat (and opportunity) of a new disrupting
architecture, modality, or provider emerging
in a short timeframe would render carefully
considered ROI projections obsolete.
We foresee that 2024
will be about solving
the complexities of
operationalizing GenAI
in the industry.
This entails overcoming challenges related
to data governance, repeatability, scalability,
integration with legacy systems, and talent
upskilling related to AI deployment. LSPs and
buyer-side localization programs sit on a large
pool of language and content data that will
prove invaluable in the deeper adoption of
LLMs. Consequently, 2024 is going to witness
concerted efforts both on the buyer and provider
side towards streamlining AI implementation
and creating tangible benefits amidst these
complexities.###