Organizations classify documents so that their text data is easier to manage and utilize. Learn how 5 companies are using document classification in practice.
Discover the fundamental differences between AI development and traditional software engineering. This article explores six key areas where AI development diverges from conventional software development, providing valuable insights for effective planning, execution, and management of…
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With the growing number of ready-to-use or semi-ready-to-use AI tools, buy or build AI questions are top of mind for many leaders. There’s a big push to start using AI immediately, but many leaders also understand that not all AI tools will fit their organization’s needs.
For example, one of our customers invested heavily in developing machine learning models internally, from scratch. However, after deployment, they were inundated with customer complaints due to inaccuracies in model output and needed a way out of this problem.
Our evaluation pointed to several significant problems that would require the company to rebuild models entirely. To prevent further setbacks and to contain costs, instead of investing more in in-house development, we helped them transition to a third-party solution, which outperformed their existing one while keeping costs within expectations.
There can be many such twists and turns when it comes to implementing AI. You may have budget limitations, need more data, or your company’s IT infrastructure may also be too “simple” to support the use of AI.
You may also be faced with privacy concerns, cost complications, and other important organization-specific challenges which could push you into either building from scratch or buying an off-the-shelf solution.
Therefore, the best strategy for integrating AI is highly context-dependent.
In this article, we’ll look at the different ways to integrate AI and the pros and limitations or things to consider for each. This will help you assess what’s best for your organization.
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AI ethics is about releasing and implementing AI responsibly, paying attention to several considerations, from data etiquette to tool development risks, as discussed in a previous article. In this article, we’ll explore some of the ethical issues that arise with AI systems, particularly machine learning systems, when we overlook the ethical considerations of AI, often unintentionally.
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This is an abstractive summarization demo program. It was mainly used to summarize opinions, but since it does not rely on any domain information, it can be used to summarize any highly redundant text.
This paper presents a flexible framework for generating very short abstractive summaries. The key idea is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, we repeatedly find paths through this graph to produce concise summaries. We consider Opinosis a "shallow" abstractive summarizer as it uses the original text itself to generate summaries. This is unlike a true abstractive summarizer that would need a deeper level of natural language understanding.
While the evaluation is on an opinion dataset, the approach itself is general in that, it can be applied to any corpus containing high amounts of redundancies, for example, Twitter comments or user comments on blog/news articles. A very similar work to ours (published at the same time and at the same conference) is the following:
Multi-sentence compression: Finding shortest paths in word graphs
Proceedings of the 23rd International Conference on Computaional Linguistics (COLING 10). Beijing, China, August 23-27, 2010. Katja Filippova
Katja's work was evaluated on a news dataset (google news) for both English and Spanish while ours was evaluated on user reviews from various sources (English only). She studies the informativeness and grammaticality of sentences and in a similar way we evaluate these aspects by studying how close the Opinosis summaries are compared to the human composed summaries in terms of information overlap and readability (using a human assessor).
K. Ganesan, C. Zhai, and J. Han. Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), page 340--348. Beijing, China, Coling 2010 Organizing Committee, (August 2010)