Large-scale language models (LLMs) are the hottest emerging technology these days, but what exactly makes them different? There's no shortage of dystopian takes. For now, however, we need to focus on the social implications of widespread integration of LLMs. Today, LLMs already have the power and ability to transform and improve industries and positively contribute to society.
That said, you should use your LLM to address potential industry-wide issues. Although this technology is very promising, it is not without risks. First, let's discuss the long-term positive and negative effects that an LLM can have. Below, we explore the different ways in which an LLM can be used to transform unconventional industries and society as a whole.
Extend language accessibility with LLM
When it comes to LLMs, not all languages are created equal. The majority of the models are trained only on English and other widely spoken languages, with the exception of low-resource languages such as Swahili and Icelandic. This language gap creates new accessibility issues and poses a global barrier to true adoption of responsible and inclusive AI.
New initiatives such as AI Aya's Cohere are beginning to introduce more comprehensive models with fluency beyond the world's most common languages, but there is still a long way to go. There are over 7,000 signed and spoken languages in the world, but only seven are considered high-resource languages with large amounts of training data. A key element in realizing the potential of globally accessible LLMs is leveraging human expertise during the training and evaluation stages to ensure model accuracy and usefulness. Human insight helps ensure that the LLM uses local languages accurately and provides the model with important cultural context, which varies from country to country and is heavily influenced by language.
By enabling LLMs to deliver results in less common languages, we remove barriers to information, expand access to critical information and education, democratize the use of AI, and create more inclusive and equitable outcomes. Contribute to society.
How LLM connects people and nations
The second direction in which LLMs have a significant impact is on bureaucracy in the public sector. Recently, France announced ambitious plans to use LLM to simplify interactions between citizens and the state. The idea that the public sector could benefit from her LLM applies to all countries. Already in 2021, 99% of Singapore's government services will be end-to-end digital, and other countries are expected to follow suit. For a sector known for being slow to adopt new technology, an LLM offers an opportunity to progress quickly. It will only take a few years, or even a few quarters, before LLM-based solutions are making public services simpler, faster, and more intuitive around the world.
Does an LLM harm or help our mental health? It depends.
Finally, one of the favorite topics that dystopian fearmongers like to explore is the impact of AI on loneliness and psychological health. How many times have you heard something to the effect of, “This technology is driving us crazy?” You'll definitely remember Joaquin Phoenix and Scarlett Johansson's amazing performance in HER. You may even remember the eerie feeling you got after watching the movie Published in the Journal of Medical Internet Research shows how AI startup Replica helped people feel less lonely Have you heard of the paper that was published? Probably not, but the results are very promising.
With the advent of LLMs, a whole new industry is emerging. Companies like Woebot, Wysa, and Limbic use LLMs to provide therapy, resources, and information to better address mental health-related concerns. To further enable the adoption of these solutions in the medical field, high quality and highly specialized data are required. Behind every machine learning project is a general intuition that bigger and better data will lead to better results. Although LLM is in the early stages of this effort, we believe that engaging experts in the field to create high-quality, industry-specific datasets is essential to further improve LLM-based mental health solutions. it is clear.
Of course, every rose has its thorns, and there is no perfect solution. This same concept applies to his LLM. What LLM will look like is still up in the air and will depend largely on how we choose to implement it. Some things we know:
- More efficient and affordable models are needed to increase accessibility and adoption
- To ensure that your model is safe, accurate, and reliable, you must ensure that the datasets used to train your model come from expert, trusted, and responsible sources.
- Regulation and government oversight are needed to ensure the responsible use of LLMs
As the LLM continues to evolve, many challenges will arise. However, one thing is certain. The impact of LLM goes far beyond supporting text editing and code generation. This technology has the power to transform the way we learn, teach, organize our societies, and respond to problems. The ultimate challenge is our human condition and well-being. If we shift our focus from dystopian fears to how LLMs can improve different industries and societies, we can meet every challenge head-on and come out stronger than ever. Sho.
About the author
Dr. Ivan Yamshchikov leads the Data Advocates team at Toloka, a data solutions partner for AI development. He is also Professor of Semantic Data Processing and Cognitive Computing at the Center for AI and Robotics at the Würzburg-Schweinfurt University of Applied Sciences. His research interests include computational creativity, semantic data processing, and generative models.
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