Global Artificial Intelligence Portal

Using AI Chatbots for Data Analysis: A Step-by-Step Guide for Non-Programmers.   Introduction. Imagine a university researcher receiving a spreadsheet containing 5,000 survey responses. The researcher wants to know which factors are associated with student satisfaction, whether responses differ between groups, whether unusual observations exist, and which variables deserve further investigation. Traditionally, answering these questions might require spreadsheet formulas, statistical software, programming, visualization tools, and substantial technical knowledge. Conversational data-analysis systems are changing this workflow. A non-programmer can describe an analytical task using ordinary language: “Calculate the average satisfaction score for each study level, identify unusual values, and create a chart comparing the groups.” A capable conversational system may translate that request into executable analysis code, perform calculations, create visualizations, and explain the result...

  AI in Biology, Chemistry, Physics, and Astronomy: The Rise of AI for Scientific Discovery. Introduction. Modern science faces a problem that previous generations of researchers rarely encountered at the same scale: there is simply too much information. Genomic databases contain enormous numbers of DNA sequences. Chemistry produces vast libraries of molecules and reactions. Physics experiments generate complex datasets from particle detectors, simulations, and instruments. Astronomy continuously produces observations containing billions of stars, galaxies, and other celestial objects. Human researchers cannot manually examine all of these possibilities. This is where artificial intelligence is becoming increasingly important. AI can process enormous datasets, identify patterns, estimate relationships, generate hypotheses, predict molecular or physical properties, and help researchers decide which experiments deserve further investigation. The transformation is already measurable. ...