The rapid advancement of generative AI is generating excitement across various industries. Gen AI has the potential to revolutionize the way we work and think in healthcare, finance, transportation, manufacturing, media, retail, and energy. However, we are currently in what can be considered “Act 1” of the gen AI story. While early experiments have showcased the possibilities, they have also revealed challenges and limitations. It is crucial to progress to “Act 2” by addressing these challenges and operationalizing gen AI.
One key aspect that needs improvement is accuracy. While gen AI has demonstrated its power, it has also shown inaccuracies and “hallucinations” that disqualify it for broader usage. Ensuring the quality of gen AI and resolving these accuracy issues is essential. Another challenge is bias. Early pilot programs have highlighted that gen AI is still influenced by biased training data, leading to biased results. To earn the trust of users, this flaw must be addressed.
Ethics is another critical consideration. Regulators, thought leaders, and ethicists have emphasized the need for guardrails and safeguards to prevent misuse, disinformation, fraud, and misrepresentations. Responsible AI must be a primary concern.
Scalability is a significant challenge in gen AI. The computing resources required to build and operate gen AI applications at scale are unprecedented. The growth in training compute for machine learning models, the amount of data used, and the size of models have all increased exponentially. This trajectory is expected to continue, necessitating a focus on scalability.
Cost is also a factor to consider. The compute-intensive nature of gen AI comes at a high price. While preliminary proof-of-concept demonstrations may overlook economic feasibility, mass-market gen AI applications must provide benefits at an acceptable cost to enable broad usage.
To progress to Act 2, companies must differentiate with data. The quality of gen AI is heavily reliant on the quality of training data. Proper resources should be devoted to data-cleansing routines and a thoughtful data strategy.
Choosing the right hybrid mixture of models is also crucial. While it may be tempting to rely on one large model, a heterogeneous architecture that integrates multiple models is more effective. Each model has its strengths and weaknesses, and a blended portfolio of models can support various AI initiatives.
Integrating AI responsibly is essential. Standards and practices emphasizing ethics and responsible use must be in place. AI should prioritize education and integrity, detecting and preventing harmful or inappropriate content.
Focus should also be on cost, performance, and scale. Gen AI success relies on a low-cost, highly performant ML infrastructure that enables rapid training. Scaling an application exposes unexpected scenarios, so plans and infrastructure must accommodate these deployments.
Lastly, gen AI must be usable and accessible. Efforts should be directed towards non-experts and non-coders, allowing them to tap into AI’s power. Making gen AI broadly accessible within security parameters and integrating it into existing workflows is crucial.
The journey from Act 1 to Act 2 will not be straightforward, but it is necessary to catch up to the hype surrounding gen AI. Progressing from exciting technology demonstrations to mature, reliable, and cost-effective solutions requires effort and a focus on addressing challenges. By rolling up our sleeves and committing to the development of gen AI, we can unlock its full potential.