We have already reached a point where AI can considerably boost our intelligence and help us produce better results more quickly, even though Artificial General Intelligence, or the “singularity,” as some call it, maybe decades away.
There is currently no field in which AI has not shown itself to be beneficial. AI has established itself in every field, from flying to flying games to diagnosing disease to automatically cleaning up selfies. Over 8 million active researchers spend more than $1.5 trillion on academic research annually to enhance human knowledge and intelligence.
The average population may not have realized or even given much thought to the requirement for speed and accuracy when it comes to research until a year ago. However, many are becoming aware of and feeling the agony of the pace of research in the race to develop an antiviral medicine or a vaccine due to the recent COVID-19 scenario.
While some of the outcomes of academic research are acclaimed, it is simple to overlook the innumerable steps and procedures that must be completed before any findings can be obtained. Moreover, the study’s outcomes are frequently neither revolutionary nor immediately practical.
The discovery phase of the research lifecycle is one of the initial stages. Only 50% of the papers that researchers read are helpful; on average, they spend 4 hours per week browsing through research and 5 hours reading articles. AI can help scholars find the appropriate publications to read in this situation.
Numerous tools enable natural language processing and search using machine-learned ideas, helping researchers focus their reading and find the pertinent study much more quickly. The real research phase follows, which entails data collection, experiments based on multiple hypotheses, data collection, analysis, representation, and conclusion-making.
Many AI open-source tools, including Python, R, Pandas, Scikit, and Spark, and paid AI tools, like Mathematica, Matlab, and SAS can be extremely helpful for the aforementioned tasks, especially when they are geared toward statistical machine learning. To help them with their study, several research laboratories are using cutting-edge AI streams, including computer vision, robotic arms, IOT, and speech and audio.
The publication and distribution of research—the laborious, time-consuming, yet crucial last step of the process—is the last and most crucial phase for researchers.
In addition to automated solutions for styling figures, tables, captions, and citations, there are many AI tools available that researchers can use to assist with writing manuscripts, correcting grammar and language, and formatting them in accordance with target journal standards. Editing services are available to assist with manuscript preparation, formatting, and language correction.
The need of the hour is to create cutting-edge goods for publishers and commercial and technological solutions for stakeholders in the research environment; thus, we are thrilled to have joined the AI and deep-learning field.
Our goal is to put the researcher at the heart of research through effective initiatives like researcher life. We have already created a number of AI-powered tools that support researchers in concentrating on their primary task—research. Though, we still have a long way to go as a community before AI is completely integrated into the research ecosystem.