AI has finally entered the mainstream in recent years. But in the research ecosystem, what do we mean with AI and how is it already being used? We did what everyone has been doing and asked the AI for some answers.
Anyone would be forgiven for thinking that artificial intelligence (AI) has just been invented, given the great interest that has suddenly generated in it, from 2023. However, AI actually has a long history, being conceived for the first time in the 1950s and has gradually become a reality in later decades. However, there has been a real step forward with a generative AI, which can create apparently impeccable text or images in just seconds. But is it really so new?
For those who write blog posts or even an SMS message, they could be accustomed to accepting one or two suggestions of the predictive text function, in itself a form of AI. When you realize that the words you have chosen have not been yours, you can see how the lines have begun to blur with the generative capabilities of the most advanced. However, caution should be used since AI is more than capable of making mistakes: only read the CEO of Digital Sciences Daniel Hook in ‘The Lone Banana Problem’. In addition, the lack of context or key detail can lead to misrepresentation or redundancy in the content that is being created.
Digital Science and Ia
Digital science has been using IA and related technologies such as automatic learning (ML) and natural language processing (NLP) for several years; In fact, the dimensions have had https://www.dimensions.ai As URL since its launch in 2018, and has become a trusted partner for hundreds of organizations that depend on their experience in AI. These are the ways in which digital science and their portfolio companies currently use AI -related technologies:
Dimensions
The largest connected research database, which covers publications, conferences summaries, subsidies, patents, clinical trials and more
In Lachem
Data intelligence for life sciences and chemical industries, with one of the world’s largest chemical databases
Readcube
The literature and literature discovery management solution with AI that transforms how your team works with academic literature
Ask Chatgpt
So what happens if we ask a generative model how digital science uses AI? Well, here is some text that we received courtesy of the OpenAI chatgpt:
“Digital science, as a company that provides scientific software and research solutions, uses the in several ways. Here are some examples:
- Text and data mining: AI algorithms are used to analyze large volumes of scientific literature, extract valuable information, identify trends and accelerate the discovery process.
- Natural language processing (NLP): Digital Science uses PNL techniques to improve the understanding and processing of scientific texts, allowing advanced search capabilities, automatic categorization and semantic analysis.
- Predictive analysis: AI models are trained in scientific data to predict the results, such as the success or impact of research projects, financing opportunities or possible collaborations, helping researchers make informed decisions.
- Recommendation systems: By using automatic learning algorithms, digital science can provide personalized recommendations to researchers, suggesting relevant articles, magazines or research resources based on their previous interests and activities.
- Data visualization and exploration: The tools with AI allow the creation of interactive views and data exploration platforms, which allows scientists to obtain deeper ideas of complex data sets and facilitate data -based decision making.
These are just some examples of how Digital Science incorporates AI in their offers. The AI field is quickly evolving, and the new applications are emerging continuously to support scientific research and discovery. “
Source: Chatgpt
While all the information presented by AI is correct, it is broken down by technical functionality, it only presents ‘examples’ of how technology is used and not in which it is used. As such, it is technically very successful, but of little interest for most of the audiences that would lose the context of how technology is really applied.
Ai vs human
In 2023, while all the generative excitation AI was at its maximum point, Digital Science launched a campaign called ‘Mind the Trust Gap’ that sought to underline the importance of confidence in research among those who carry out research itself and the rest of us who depend on its integrity and quality. The developments in AI technology are often compared to humans as if it were some kind of competition or even battle, but perhaps it is more sensible to see it as a relationship that enters a new phase, and trust is key to that relationship. While we can use with transparency and openness as the previous examples, we can build a better understanding of the world around us.
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