Navigating the AI Boom: Key Trends Revolutionizing the Future
Artificial Intelligence, a word that’s been plastered everywhere from GreenTech to Telemedicine, has been disrupting the market more than ever. Having fully propelled into the technology generation of the 21st century, Artificial Intelligence is shifting the way we are approaching tasks that have been done by human capital for decades, including in the private markets.
AI can help throughout investment analysis. Its ability to analyze trends in vast amounts of data can help provide clear market conditions for investors. The due diligence that AI provides can help foresee risks or market turmoil that is headed our way that humans themselves can’t identify.
Machine Learning’s reliance on humans’ ability to code makes it harder for people without a programming background to utilize its benefits. However, AutoML makes it easier for users with minimal to no experience in data engineering to train models. AutoML reduces the manual aspects of the model creation process to streamline the ML pipeline.
MLOps is a function that can actively enhance the efficiency of the model creation process. Around a decade ago, the development side of a platform, the software developers, would develop the code and necessary features for a specific task whereas the operations team would focus on the deployment and maintenance of the platform. However, without the integration of the two sides, creating programs for specific tasks became harder as the software development team wouldn’t know which tasks they’d be integrating the software for. Thus, DevOps (Development Operations) was born. In recent years, it came to the forefront of engineers that this system could be used for the Machine Learning pipeline, hence the name MLOps. MLOps uses continuous integration and continuous delivery to test new features and make sure new blocks of codes don’t break existing systems. MLOps can be used for forecasting future demand of investors or sales predictions.
Multimodal machine learning’s ability to utilize data on various verticals through audio, images, and texts allows it to offer more accurate predictions. Multimodal ML can be used in call centers to offer the best solutions for clients regardless of if the customer service is being provided through a chat bot, a hotline number, etc,.
The biggest trend we can foreshadow for the future would be Generative AI, also known as GenAI. GenAI’s ability to create new content can create new image processing, models, and algorithms which can help in model optimization. Traditional AI on the other hand focuses on detecting patterns and the classification of data. Neural Architecture Search (NAS), a traditionally tedious process done by hand, can be automated through GenAI. NAS is the process of finding the best model to most efficiently complete a specific task, which Generative AI can complete itself sufficiently with minimal human intervention.
AI has also posed a variety of challenges and concerns for users as well. AI relies on the training data to make inferences, however if this data isn’t consistently updated, the results may be inaccurate. Trading is heavily reliant on timeliness and precision however one small mishap could cost nearly millions of dollars, so keeping up with market trends will have to be prioritized if we are looking to see AI more integrated in higher frequency markets.
Artificial intelligence, while it poses its own concerns, can be used to automate numerous day to day tasks to streamline workflow. The more training machine learning models receive, the more accurate results will be yielded. This trial and error period of AI/ML is crucial for the future of technology’s integration with society which can turn ten fold for society as a whole.