Self-supervised learning (SSL) has expanded the reach of speech technologies to many languages by minimizing the need for labeled data. However, current models only support 100-150 of the world’s 7,000+ languages. This limitation is largely due to the scarcity of transcribed speech, as only about half of these languages have formal writing systems, and even… →

Generative AI jailbreaking involves crafting prompts that trick the AI into ignoring its safety guidelines, allowing the user to potentially generate harmful or unsafe content the model was designed to avoid. Jailbreaking could enable users to access instructions for illegal activities, like creating weapons or hacking systems, or provide access to sensitive data that the… →

The design and deployment of efficient AI agents have become a critical focus in the LLM world. Recently, Anthropic has highlighted several highly effective design patterns that are being utilized successfully in real-world applications. While discussed in the context of Claude’s models, these patterns offer valuable insights that can be generalized to other LLMs. The… →

Large Language Models (LLMs) are rapidly developing with advances in both the models’ capabilities and applications across multiple disciplines. In a recent LinkedIn post, a user discussed recent trends in LLM research, including various types of LLMs and their examples.  Multi-Modal LLMs  With the ability to integrate several types of input, including text, photos, and… →

Many challenges are faced while challenges fine-tuning and refining language model systems. Engineers at Google and Meta spend twelve to eighteen months transitioning a model from the research phase to the production phase. And that’s not just because they execute a single tuning task and then move on. They refine it iteratively, starting with supervised… →

The field of deep reinforcement learning (DRL) is expanding the capabilities of robotic control. However, there has been a growing trend of increasing algorithm complexity. As a result, the latest algorithms need many implementation details to perform well on different levels, causing issues with reproducibility. Moreover, even state-of-the-art DRL models have simple problems, like the… →

Large Language Models (LLMs), trained on vast amounts of data, have shown remarkable abilities in natural language generation and understanding. General-purpose corpora, comprising a diverse range of online text, are utilized for their training, examples of which are Wikipedia and CommonCrawl. Although these universal models work well on a wide range of tasks, a distributional… →

While large language models (LLMs) have been proven to be pivotal in natural language processing (NLP), these models require immense computational resources and time for training, posing a significant and one of the most crucial challenges for researchers and developers. This enormous computational cost and memory requirement can be a barrier to both research and… →