Janna Lipenkova
Janna Lipenkova
Contributor, The Yuan

Janna Lipenkova is a computational linguist who speaks seven languages. She heads two companies, Anacode and Equintel, that use AI to deliver cutting-edge business intelligence and guide clients towards smarter decisions, strategy, and execution.


Shaping the human-AI partnership requires trust and active user cooperation
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The best way to shape the relationship between humans and AI is to help AI users become more AI literate, interact with AI systems more effectively, and give them a greater say in the design and implementation of these systems, argues computational linguist Janna Lipenkova.
Janna Lipenkova  |  Aug 09, 2024
Implementing Text2SQL is crucial for powering data-driven organizations
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As data-driven organizations look for the best ways to power their businesses and remain competitive in an increasingly digital world, this article seeks to help them do so by providing a guide to implementing conversational access to enterprise data using Text2SQL.
Janna Lipenkova  |  Sep 11, 2023
Four LLM trends since ChatGPT and their implications for AI builders
Domain knowledge
ChatGPT and other autoregressive GAIs have elicited kudos as the last word in AI, but computational linguist Janna Lipenkova begs to differ. Autoencoding analytical AI blazes the future of LLMs, she says, and shows the workings of and demonstrates how to fine-tune all LLM models.
Janna Lipenkova  |  Jul 20, 2023
Overcoming the limitations of large language models
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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.
Janna Lipenkova  |  Feb 24, 2023
Choosing the right language model for your NLP use case
New era
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.
Janna Lipenkova  |  Oct 21, 2022
COVID-19 and AI - a Retrospective on Major Trends
Post-pandemic
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.
Janna Lipenkova  |  May 27, 2022
NLP Ferrets Out Propaganda
New era
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.
Janna Lipenkova  |  May 12, 2022
From Cancel Culture to the Metaverse: Detecting Trends in the Media Spotlight
Metaverse
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.
Janna Lipenkova  |  Mar 23, 2022
AI Takes Shortcuts in Drug Discovery
Domain knowledge
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.
Janna Lipenkova  |  Jun 30, 2021