What’s the biggest bottleneck in machine learning today?

One of the biggest machine learning bottlenecks of the modern era has been found in data quality and availability. Despite the presence of large amounts of data, they are mostly noisy, unstructured, biased or incomplete. ml models require clean well labeled data to perform well, however, data annotation and curation on a large scale is expensive and time-consuming. On top of this, in most of the areas of interest that are sensitive such as healthcare, finance or scientific research, data is either scarce due to privacy or veiled by organizational silos that these researchers and engineers can barely access. Even the most advanced algorithms would not be operative without trusted information.

The other bottleneck of major significance is the computational resources and scalability. The price of running large scale models such as GPT or diffusion models is already a massive amount of hardware in the form of a giant array of GPUs, giant memory or enormous amounts of power. This puts the present state-of-the-art AI development within the grasp of only a few tech giants with big pockets, which leads to an unequal distribution of innovation. Even smaller organizations and research laboratories can impede the urge to democratize the creation of machine learning due to the hardware necessary to train or optimize more complex models.

A third difficulty is the readability and reliability of ML systems. Models can predict very accurately but with a deep neural net it becomes very difficult to understand why they have come up with a given decision. Such a black-box character is an obstacle to application in more critical areas like medicine, law and autonomous driving, where accountability and exposition are required. The problem of bias and fairness is also aggravated by the inability to decode models because latent biases in training data may cause discriminatory scores without a clear view of the approach that was implemented to derive the score.

Finally is the integration and real world deployment bottleneck. Most machine learning models perform well in a research but not in the field of deployment. Models may worsen over time due to such issues like data drift, issues related to environment variability and scalability. In addition, to reduce the gap between machine learning research and the actual business or societal change, effective collaboration among engineers, experts in the domain, and policymakers is necessary, which is overlooked. It is one of the main points that should be resolved to ensure that AI is not going to remain a laboratory success only but that it can be adapted into a real-life value that will become sustainable