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Automated Research and Beyond: The Evolution of Artificial Intelligence

Welcome to STUDIO….a loading bar makes it way across the screen…the system scans your face…a voice with a slight east-german accent says: “Good morning, what would you like to do today?” Since 2025, similar solutions have taken the research world by a storm.

Before the emergence of automated research systems, researchers had given up attempts to perform comprehensive literature reviews on all but the most niche topics where the number of newly published articles still allowed it.

Some signals would not be meaningful for humans to interpret, they are latent propensities hidden deep in the way the content of the articles, as well as other aspects, form subtle connections with other articles.

Very roughly speaking, a machine intelligence based decision-making system involves collection and preparation of data, selecting, optimizing and testing of the model, and validating and governing the resulting solution.

All the individual activities that made up stages of using data to answer research questions, had been meticulously mapped out and parametrized to a degree that allowed the full automation of all of those processes.

Once all the involved processes had been clearly articulated in terms of the parameters they consist of — which was merely a question of time rather than feasibility as some had thought— it became a straightforward engineering problem to automate the related processes.

Once the workflows were automated, several AI research groups understood that to better capitalize on the promise of machine intelligence, it was critically important to assume a more balanced approach to system development.

Whereas capability development, such as new neural network architectures, had received the great majority of attention from researchers and developers for several decades, interaction and governance had largely been ignored.

The emergence of solution APIs and their convergence with voice technology grew the number of people with access to state-of-the-art AI from several million people in 2020 to several hundred million people in 2030.

Human researchers believe — with everything we know today — that it should not take more than five to ten years before the early 21st-century role of data scientists is made redundant by machine intelligence based systems.

The automated researcher is a precursor to pseudo-autonomous research systems that are not only able to test a hypothesis, but to take the results of the performed research, and formulate a new hypothesis based on the findings.

While the premise of an autonomous system is simple — the output of the system needs to be an acceptable input for the same system — much work is still required before autonomous research systems will play an important role in answering the most pressing questions of humanity.

Once processes related with hypothesis testing became mature, work on hypothesis formulation would no doubt accelerate exactly in the same way we had seen taking place with first Solution APIs followed by Problem APIs.

Once the machine would be able to formulate qualitative insights, expanding the capability to forming new questions based on available answers — including questions that had never been asked before — is going to be merely a matter of time.

How AI Will Sort through the Mess That Is Big Data

Big Data, the term isn’t mere a buzz-word and instead stands true to its name, referring to vast volumes of structured and unstructured data, that an organization comes across daily.

The exacerbating speed with which this vast variety of data is flowing in has led to huge loads of it, that is becoming catch-22 to manage now, especially the unstructured data received from numerous sources.

‘Big Data is too ‘Big’ to handle.’ IT professionals and computer scientists have already realized the need of Artificial Intelligence (AI), and therefore, data professionals mastered in business analytics will have quite a significant role to play in corporations that are all set to employ AI capabilities very soon, to pull out the valuable information from the ginormous mesh.

Artificial Intelligence will automate the complex analysis of the Big Data to fetch many meaningful and sensible data for accurate predictions and positive outcomes, that otherwise would have been extremely difficult to extract, by merely performing labor-intensive tasks by humans.

Big Data holds so much crucial in all aspects, as the AI reporter Nick Ismail says, in one of his articles, “There are vast amounts of enterprise data in various organizational silos as well as public domain data sources.”

study conducted to find out how Artificial Intelligence can help organizations come up with operational transformation, in conjunction with Big Data, revealed that Artificial intelligence could lead to unexpected business intelligence for organizations, so much that in some cases, it can even replace entire departments performing tasks that otherwise have been reserved strictly for humans.

It was mentioned in 2017 Market Guide for AIOps Platforms, that, “By 2022, 40% of all large enterprises will combine big data and machine learning functionality with supporting and partially replacing monitoring, service desk, and automation processes and tasks.”

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