The American Translators Association (ATA) held a virtual conference on May 20, 2023, focusing on machine translation (MT) and artificial intelligence (AI), and the implications for the way translators and interpreters work. The conference was moderated by Nora Díaz, Robert Sette, and Andy Benzo. The keynote speech was delivered by Jay Marciano, current director of MT outreach and strategy at Lengoo and president of the Association for Machine Translation in the Americas (AMTA). Marciano introduced his speech with the idea of thinking about language in a mathematical way, “working with language algorithmically,” and walked attendees through a few examples of how this applies (e.g., the many pages of stats produced from a single Wordle game and the billions of ways a deck of cards can be shuffled). The point of the examples was to show that language is far more complex than the calculations used for the examples, and yet humans can understand the calculations. Natural language processing (NLP) scientists had to work with this complexity to get to today’s technology, including MT and large language models (LLMs). Marciano explained the application of generative AI to translation as a way to have an LLM conduct an analysis of existing translations that results in high quality output. He then mentioned the emergence of AI-generated multilingual content, with which he welcomed attendees to the “post-post editing world.” Finally, Marciano provided a list of AI-related jobs for which linguists already have many applicable skills (e.g., data curator, terminologist, language process analyst, etc.), even in the midst of great uncertainty. He also encouraged people to connect with others and understand that their competition is not AI, but those individuals who are better at using AI. Jonathan Downie, an interpreting consultant, researcher, conference interpreter, and author, presented a session titled “Finding the Value in Human Translation and Interpreting When Machines Are So Good.” Downie began by giving attendees what he called a dose of reality, exemplified by technologies that can translate and interpret well. He also spoke about the parallel situation of language experts being underpaid and struggling for some time now, as is the case with professionals in the audiovisual translation and interpreting sectors (supported by AVTE and ITI survey figures). Many linguists are diversifying inside and outside of their professions or wondering if it is time to do that or leave their professions entirely, added Downie. According to the speaker, technology vendors are portraying language differences as problems that need solving through “smart engineering,” whereas humans market their services as qualified, accurate, but also invisible. He added that being invisible does not help the linguists’ case against machines. Downie recommended using marketing messaging to convey how translators and interpreters make a difference, and reminded attendees that some work is definitely going to the machines and that linguists will need to systematize, specialize or diversify. Matthew Schlecht, a chemist and a scientific/medical polyglot translator, editor, and writer, showed attendees the perspective of a linguist in today’s MT post-editing (MTPE). Schlecht explained the differences between light and full editing, and proceeded to explain his workflow on patent MTPE. After showing a few examples of the kinds of issues found in MT output in several language combinations, including different quality levels from MT engines like Google Translate and DeepL, Schlecht also mentioned how segmentation in certain language pairs can pose a problem, such as between English and Japanese. To the question of whether a linguist can make a living doing MTPE, Schlecht replied yes, and he has been doing this work for over six years. He also echoed the message from the two previous speakers in that the survivors of a technological tidal wave will be those who adapt to changes. Yuri Balashov, an ATA-certified translator, professor of philosophy and faculty fellow in the Institute for Artificial Intelligence at the University of Georgia, offered a presentation centered around topics like domain adaptation and emerging trends in LLMs. The presentation started with a summary of the history of TM and MT, to today’s neural MT and transformers, and moved on to how humans work with these technologies. On the subject of domain adaptation, Balashov stressed the value of translator specialization to make contextual decisions in machine-translated texts. He proposes that domain adaptation is easy for humans as they are able to discern multiple combined/connected meanings (drawing a comparison with neural networks). At present, added Balashov, MT has trouble with domain adaptation, even as numerous groups of researchers work on fine-tuning engines using various methods. Some of the largest MT engines, like Google AutoML Translation, however, show promising domain-specific results. To illustrate his own experience with MT-TMS integrations, Balashov described how he worked with DeepL and ModernMT, which he considered easy to integrate. He mentioned how DeepL is superior to a lot of other MT engines, and attributes the MT performance to better data, not better algorithms. ModernMT was superior in autocorrecting MT output in his experience. Balashov also gave examples of tone, terminology/glossary adaptation, and fuzzy match “repair” functionalities, as well as ways to easily incorporate certain MT engines in a few TMSs, like memoQ. The presentation also included an overview of Large Language Models. Balashov finished with a few closing quotes from industry experts and firms, including Slator’s Florian Faes. The conference ended with a virtual town hall meeting that included Matthew Schlecht, Jost Zetzsche, Carola F. Berger, Daniel Sebesta, and Johanna Klemm. The discussion began with Jost’s explanation of the difference between different types of artificial intelligence, reminding attendees that the tools discussed all “fall into the category of narrow artificial intelligence,” defined as “the ability of a machine to process large amounts of data and make predictions exclusively on the basis of that data.” The panelists also discussed how the expertise of translators is still valid today, what’s next now that all data has already been used to train GPT-4, and the need to be knowledgeable about MT and not be afraid of changes, among other topics.