Large language models, or LLMs, have garnered a lot of attention lately, but many exciting developments and applications are yet to come. In this article, Janna Lipenkova contrasts the learning process of LLMs with human learning, discusses the gaps and presents a range of methods to augment and enhance LLMs with human-like cognitive capabilities.

Computational linguist and AI leader Janna Lipenkova takes a closer look at what goes into natural language processing and which models are best applied in which scenarios. This requires reviewing in detail how language models are built and trained, and how they can be fine-tuned for optimal results.

The decade leading up the outbreak of the COVID-19 pandemic saw major growth in many areas of AI, including its many non-medical applications, and although the pandemic has altered the trajectory of some of these, it has also turbocharged existing trends and made the world even more digitalized than could have been imagined even just a few years ago.

As the world is caught up in the throes of major crises, propaganda is heavily used as a way of controlling public opinion and neutralizing dissent. The propagandistic toolbox is also getting more and more sophisticated - which thus makes it harder to identify propaganda in public and social media. This article reviews the major linguistic expressions of propaganda and discusses how Natural Language Processing can be used to automatically spot it, while also noting the complexity and limitations of this task.

Computational linguist Janna Lipenkova discusses how trending topics in the media can be extracted using natural language processing by looking at prominent time series patterns, analyzing the meaning of a topic by constructing and inspecting its semantic context, and witnessing changes over time in the semantic context of a topic.

The pharma industry faces extreme pressure when it comes to drug discovery and development for new or rare diseases. However, new computational techniques can help the industry to step up to the challenge. AI and machine learning can be applied in this process for a more efficient, flexible drug discovery process.