Recent advances in research, wherein scientists formulate hypotheses and meticulously derive conclusive results,
have elevated humanity above other species. The standard of living has exhibited a consistent upward trajectory
across successive centuries. The advent of the scientific and technological revolution has yielded therapeutic
interventions for once deemed incurable ailments. Additionally, domains encompassing habitation, transportation,
agricultural breeding, sustainable energy, telecommunications, electronics, among others, have experienced
substantial advancements in the 21st century.
In the preceding decade, the advent of Artificial Intelligence (AI) has wrought transformative effects on fields
including medicine, healthcare, agriculture, communication, marketing, speech recognition, and autonomous
vehicular systems, among numerous others. Notably, the introduction of DeepMind's AlphaFold has demonstrated
AI's prowess in addressing one of the most formidable challenges in human tasks: protein structure prediction.
Within the realm of AI, key branches include Machine Learning (ML) and Natural Language Processing (NLP).
Machine learning, in turn, is bifurcated into supervised learning (e.g., Random Forest, XGBoost, Support Vector
Machines) and unsupervised learning (e.g., clustering, principal component analysis) and Deep Learning (e.g.,
neural networks, convolutional neural networks, recurrent neural networks).
While strides in NLP have successfully extracted meaning from vast text datasets, the emergence of
Transformers
and Transfer Learning portends even greater achievements in the ensuing decade. Innovations such as OpenAI's
ChatGPT and Generative Adversarial Networks (GANs) hold significant promise across diverse sectors, including
the domains of medicine and healthcare.
To comprehend the intricacies of algorithms and elucidate the workings of 'Black Box' models, there is an
imperative for the development and adoption of Explainable and Interpretable AI/ML methodologies, exemplified
by
techniques like LIME, SHAP, Anchors, and other novel approaches. Given the present state of most ML models,
achieving a statistically sound interpretation of each feature's contribution to the model remains a
formidable
challenge, exacerbated by the inherent complexity of these algorithms, which presents a substantial barrier to
comprehending and interpreting such models.