Vice President of AI & Quantum Computing, Paul Smith-Goodson, dives in as he sheds light on the life-saving potential of AI by examining its practical applications in the creation of new antibiotics and other scientific AI tools.
Today, AI researchers continue to push the boundaries by developing new algorithms and models that can tackle increasingly complex tasks. AI and the size of models continues to evolve at an unprecedented pace, producing responses that are more human-like and expanding the range of tasks it can perform. Breakthroughs and applications are still being made in areas such as natural language processing , computer vision, and robotics.
Synthetic data is critical for business applications as it offers solutions when real data is scarce or inadequate. One of the key advantages of synthetic data is its ability to be generated in vast quantities, making it ideal for training AI models. Furthermore, synthetic data can be designed to encompass a diverse range of variations and examples, leading to better generalization and usability of the model.
The self-attention mechanism is a defining feature that sets transformers apart from other encoder-decoder architectures. This mechanism proves especially beneficial in natural language processing as it enables the model to grasp the relationships between words in a sentence and recognize long-term dependencies. The transformer accomplishes this by assigning weights to each element in the sequence, based on its relevance to the task.
The illustration on the right side of the graphic depicts a chemistry transformer model, which connects atoms to molecules and to chemistry. The same concept could be applied to build foundation models for biology or other related fields by representing biological or chemical molecules as text. Despite the challenges posed by small data sets for generative AI models, for each of the domains in the above graphic, a vast amount of unlabeled data exists in businesses. This data provides an opportunity to train custom foundational models, enabling the solution of previously thought unsolvable problems. This aligns with IBM Research's focus on exploring new AI capabilities through generative AI and pushing the boundaries of AI science.
antimicrobial design Source: IBM Research, Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics. Nature Biomed. Eng., March 2021In a collaborative effort between Dr. Das and her team at IBM, as well as other organizations, they conducted a study to find innovative solutions to the problem of antimicrobial resistance. The study utilized AI to synthesize and evaluate 20 unique antimicrobial peptide designs, chosen from a pool of 90,000 sequences.
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