Can you tell us a little bit about Exfluency and what the company does?
Exfluency is a technology company that provides hybrid intelligence solutions for multilingual communication. By leveraging AI and blockchain technology, we give tech-savvy businesses access to the latest language tools. Our goal is to make language assets as valuable as other corporate assets.
What technological trends have you noticed developing in the field of multilingual communication?
As in every other field, AI in general and ChatGPT in particular dominate the agenda. Companies operating in this language space are either panicking or scrambling to catch up. The main challenge is the size of the technology gap in this industry. Innovation, especially AI innovation, is not a plug-in.
What are the benefits of using an LLM?
Off-the-shelf LLMs (ChatGPT, Bard, etc.) have instant appeal. As if by magic, your well-crafted answers will appear on your screen. I can't help but be moved.
The real benefit of LLM comes from players that can provide immutable data to feed the model. They are what we feed them.
What does an LLM rely on when learning a language?
Overall, LLM learns languages by analyzing vast amounts of text data, understanding patterns and relationships, and using statistical methods to generate appropriate responses depending on the situation. The ability to generalize from data and generate consistent text makes it a versatile tool for a variety of language-related tasks.
Large-scale language models (LLMs) like GPT-4 rely on a combination of data, pattern recognition, and statistical relationships to learn languages. The main components they depend on are:
- data: LLM is trained on vast amounts of text data from the Internet. This data includes a wide range of sources such as books, articles, and websites. The diversity of data helps the model learn different language patterns, styles, and topics.
- patterns and relationships: LLM learns languages by identifying patterns and relationships in data. Analyze the co-occurrence of words, phrases, and sentences to understand how they fit together grammatically and semantically.
- statistical learning: LLM uses statistical methods to learn the probabilities of word sequences. Estimates the likelihood of a word occurring considering previous words in the sentence. This allows you to generate consistent, context-relevant text.
- Contextual information: LLM emphasizes understanding of context. They consider not only the preceding words, but also the entire context of a sentence or passage. This contextual information helps disambiguate words with multiple meanings and generate more accurate and contextually appropriate responses.
- attention mechanism: Many LLMs, including GPT-4, employ attention mechanisms. These mechanisms allow the model to weigh the importance of different words in a sentence based on context. This allows the model to focus on relevant information when generating a response.
- transfer learning: LLM uses a technique called transfer learning. These are pre-trained on large datasets and fine-tuned for specific tasks. This allows the model to adapt to perform specialized tasks such as translation, summarization, and conversation, while leveraging the extensive linguistic knowledge gained through pre-training.
- Encoder/decoder architecture: For certain tasks, such as translation and summarization, LLM uses an encoder/decoder architecture. Encoders process input text and transform it into a context-rich representation. The decoder uses it to produce output text in the desired language or format.
- feedback loop: LLM can learn from user interactions. As users provide corrections and feedback on the generated text, the model can adjust its responses and improve performance over time based on that feedback.
What are the challenges of using the LLM?
The fundamental problem that has existed since we started providing data to the likes of Google and Facebook is that “we'' are the product. Major companies are making untold billions of dollars thanks to our rush to feed their apps with our data. For example, ChatGPT is experiencing the fastest onboarding growth in history. Consider how Microsoft has profited from the millions of prompts people have already thrown at it.
Open LLMs are hallucinatory and the answers to their prompts are so well thought out that you can easily be fooled into believing what they say.
Even worse, there are no references/links indicating where the answer came from.
How can these challenges be overcome?
LLM is what we give them. Blockchain technology allows you to create an immutable audit trail and use it to create immutable, clean data. No need to search the internet. This way, you can have complete control over what data is entered, maintain its confidentiality, and support it with a wealth of useful metadata. Multilingual support is also possible!
This data is then stored in a database so we can also provide the necessary source links. If you don't completely believe the answer to a prompt, open the source data directly to see who wrote it, when it was written, in what language, and in what context.
What advice would you give to companies looking to use anonymized private LLM for multilingual communications?
Make sure your data is immutable, multilingual, and of high quality, and keep it for your own eyes only. An LLM can be a real game changer.
What do you think the future holds for multilingual communication?
Language, like many other fields, will embrace forms of hybrid intelligence. For example, in the Exfluency ecosystem, AI-driven workflows handle 90% of translations. Then our highly qualified bilingual subject matter experts only need to focus on his last 10%. This balance will change over time, with AI becoming an increasingly larger part of the workload. However, human input remains important. This concept is encapsulated in our strap line. Powered by technology and perfected by people.
What plans does Exfluency have for the next year?
a lot! We aim to deploy this technology to new verticals and build a small business community to serve small businesses. There is also significant interest in knowledge mining apps designed to exploit the information hidden in millions of linguistic assets. 2024 is going to be an exciting year!
- Jaromir Dzialo is the co-founder and CTO of Exfluency. Exfluency provides affordable, AI-powered language and security solutions with a global talent network for organizations of all sizes.
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