The world, specifically those involved in tech space is going gaga over the possible role artificial intelligence (AI) technology could play in solving current and future challenges. Well, whether it could do so or not is a matter of debate, but some common concerns raised by tech experts are the efficacy of AI-generated insight as the accuracy depends on the quality and quantity of available static and dynamic data. In the current scenario finding, gathering, and refining data and making them valuable for any AI model is a gigantic task, especially for those planning to leverage the power of AI as high quality data is highly scarce.
Although the pace at which the tech-landscape is evolving is highly encouraging, it is still a far cry for small and medium enterprises to effectively implement AI technologies for their unique requirements. Thanks to a remarkable jump in interest in AI technologies, it is attracting huge investments, paving the way for further innovations. Tech majors are already releasing one model after another, and most are focused on improving operational efficiency with better accuracy and at remarkable speed. It is well-accepted across academic and entrepreneurial circles that AI has the potential to transform the way the world works, but it all depends on the quality of the data the AI model is using to generate the required output.
Following the success of OpenAI, the investor fraternity looks very positive and makes funds available to those willing to add more value to AI-based technologies. The latest in the league is Mistral AI, a French entrant aiming to compete with players like OpenAI, Bard, etc. in large language models (LLMs) and generative AI. The company is addressing the core issue of ‘quality data’ for making any AI system highly efficient so that it could not only generate quality insight at speed but help the system learn smartly and make it more robust at an unimaginable scale. A $113 million seed round in just four weeks since inception, says everything about the value of ‘quality data’ for the future of AI.
Ask AI professionals and they will unequivocally underline the dependency of any AI model efficacy on the quality of the input data. Mistral AI is offering the creation of robust models for recognizing patterns accurately, predicting outcomes at speed, and generating valuable insights. No data is considered bad, but it will fall in the ‘high-quality’ category only when it is not just accurate, complete, and properly labeled, but contextually relevant for unique requirements. Just like any production line mantra “garbage in, garbage out” the AI system is also as good as the quality of input data, as the system trains itself to handle big data based on those training data. Less than 100% accurate data leads to the production of inaccurate output, thus making the system useless.
The importance of quality data is well acknowledged, especially in advanced AI applications like medical, defense, or locomotion. The factors like time, energy, volume, and human error make data labeling a challenging task. However, this opens a space for innovation where data could be labeled using machine learning (ML) models. Visualizing the need and challenge of handling big data, a French startup with a presence in New York, Kili Technology, developed a data labeling tool to automate labeling using advanced ML models so that the process could be made more accurate, efficient, and future-ready to handle data at scale. Active learning and improvisation help is making the system more and more accurate over a period of time, thus making the output more reliable.
No matter how advanced and promising the core AI technology is, it will be as good as the input data. With the exponential jump in interest in AI models for specific requirements, whether system automation, operational efficiency gain, or reducing manual involvement in any process, the need for high-quality data and data labeling tools is bound to surge in the coming days.
Who will be able to participate and compete in the AI race? The answer is pretty simple: only those who could capture, label, and leverage data effectively. Of course, the prowess to code super algorithms is an obvious prerequisite, which is likely to be available in abundance, thanks to the talents groomed by sophisticated AI tools.
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Original Source of the original story >> The Role of High-Quality Data to Thrive in AI Era
