Maximizing Value with AI: Transferring Insights from Predictive AI to Generative AI
Introduction:
In the ever-evolving landscape of AI, organizations are faced with new challenges and questions. From choosing the right projects to scaling and generating incremental value, the AI journey is complex. The rise of generative models, particularly ChatGPT, has further transformed the AI scene. This article explores the dos and don’ts of getting value with predictive AI, the technical challenges of generative AI, primary differentiators and challenges, and the changes data scientists must adapt to. To learn more, watch the webinar, “Value-Driven AI: Applying Lessons Learned from Predictive AI to Generative AI.” Written by Aslı Sabancı Demiröz, a Staff Machine Learning Engineer at DataRobot.
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The Evolving Landscape of AI: Lessons Learned from Predictive AI to Generative AI
If we look back five years, most enterprises were just getting started with machine learning and predictive AI, trying to figure out which projects they should choose. This is a question that is still incredibly important, but the AI landscape has now evolved dramatically, as have the questions enterprises are working to answer.
Back in the day, organizations found their first use cases harder than anticipated, and the questions just kept piling up. Should they go after big, bold projects or focus on steady streams of incremental value? How do they scale? What comes next?
Generative models, particularly ChatGPT, have completely changed the AI scene and forced organizations to ask entirely new questions. The big one is, which hard-earned lessons about getting value from predictive AI do we apply to generative AI?
Dos and Don’ts of Getting Value with Predictive AI
Companies that generate value from predictive AI tend to be aggressive about delivering those first use cases. They follow some key dos to ensure success. These include choosing the right projects and involving the right mix of stakeholders early. They also celebrate their successes to inspire others and create urgency.
On the flip side, there are some don’ts to avoid. Starting with the hardest and highest value problem introduces a lot of risk, so it’s best to avoid it. Additionally, deferring modeling until the data is perfect can perpetually defer value unnecessarily. Finally, focusing too much on perfecting organizational design and strategy can hinder the scaling of AI projects.
New Technical Challenges with Generative AI
As organizations venture into generative AI, they face new technical challenges. One of the primary challenges is increased computational requirements. Generative AI models require high-performance computation and hardware, either owned by the company or accessed through the cloud.
Another challenge lies in model evaluation. Predictive models have clear metrics like accuracy or AUC, whereas generative AI requires more subjective and complex evaluation metrics. Determining fair metrics to use on generative AI models is a harder task compared to evaluating predictive models.
Ethical considerations also come into play. Companies must ensure that generative AI outputs are mature, responsible, and not harmful to society or their organizations.
Differentiators and Challenges with Generative AI
Getting started with the right problems is crucial for success with generative AI. Organizations that target the wrong problems will struggle to achieve value quickly. Focusing on productivity instead of cost benefits yields better results. Moving too slowly is also a challenge that must be overcome.
The last mile of generative AI use cases differs from predictive AI. With generative AI, the outputs are in the form of human language, allowing for faster value propositions due to the interactivity of language.
The nature of data-related challenges changes with generative AI. These models excel at working with messy and multimodal data, reducing the time spent on data preparation and transformation.
Changes for Data Scientists with Generative AI
Generative AI brings about changes in the skillset of data scientists. They need to understand how generative AI models work, their shortcomings, and the strategies for generating meaningful outputs.
Increased computational requirements mean that data scientists may need to work with more complex hardware, requiring additional skills.
Model output evaluation becomes more important. Data scientists must experiment with different types of models and strategies, evaluating them efficiently and systematically to achieve the best results.
Monitoring also takes on a crucial role due to the ethical and legal concerns that generative AI models can raise. Closer monitoring and robust systems are necessary to ensure responsible use.
Finally, data scientists must consider new user experiences and incorporate them into the modeling workflow. Understanding the main personas involved in building generative AI solutions and how they contrast with predictive AI is essential.
What Lies Ahead for Generative AI?
As the field of generative AI continues to evolve, it is important for organizations to stay informed about the latest developments. Webinars, such as “Value-Driven AI: Applying Lessons Learned from Predictive AI to Generative AI,” provide insights into what to expect from generative AI, which use cases to start with, and other predictions.
In conclusion, the AI landscape has changed significantly over the past five years. With generative AI, organizations face new challenges and opportunities. By learning from the lessons of predictive AI and adapting to the unique demands of generative AI, organizations can harness the full potential of AI technologies. It’s an exciting time to be at the forefront of AI innovation, and the possibilities are endless.
Sources:
– DataRobot: Value-Driven AI: Applying Lessons Learned from Predictive AI to Generative AI
Conclusion:
In conclusion, the AI landscape has evolved significantly over the past five years, leading organizations to ask new questions and face new challenges. To get value from predictive AI, companies should focus on choosing the right projects, involving the right stakeholders, and celebrating successes. However, with generative AI, organizations must consider increased computational requirements, model evaluation, and ethical concerns. Data scientists will need to adapt their skillset and understand new technologies. Despite these changes, the expertise of machine learning engineers, data engineers, domain experts, and AI ethics experts will still be crucial for success in generative AI. To learn more about generative AI and its use cases, watch the webinar “Value-Driven AI: Applying Lessons Learned from Predictive AI to Generative AI” by DataRobot.
Frequently Asked Questions:
1. What is value-driven AI?
Value-driven AI refers to the application of ethical principles and human values in the development and deployment of artificial intelligence systems. It ensures that AI technologies are designed and implemented in a way that aligns with societal goals, fairness, inclusivity, transparency, and accountability.
2. How does value-driven AI differ from predictive AI?
While predictive AI focuses on making accurate predictions based on data patterns, value-driven AI goes a step further. It aims to generate AI systems that not only predict outcomes but also consider the impact of those predictions on individuals, society, and ethical considerations.
3. Why is it essential to apply lessons learned from predictive AI to generative AI?
Generative AI involves the ability to create original content such as images, videos, or text. By applying lessons learned from predictive AI, we can ensure that generative AI systems are trained on unbiased and diverse datasets, avoid reinforcing harmful stereotypes, and consider the potential ethical implications of the generated content.
4. What are some challenges in implementing value-driven AI?
Implementing value-driven AI faces challenges such as defining the scope of ethical considerations, access to diverse and unbiased datasets, addressing algorithmic biases, and providing interpretability and transparency in AI systems. Overcoming these challenges requires interdisciplinary collaboration and ongoing research.
5. How can value-driven AI promote fairness and inclusivity?
Value-driven AI can promote fairness and inclusivity by ensuring that AI systems do not discriminate against individuals based on their race, gender, or other sensitive attributes. It involves identifying and mitigating biases in training data, algorithms, and decision-making processes to achieve equal opportunities and outcomes for all individuals.
6. What role does transparency play in value-driven AI?
Transparency is crucial in value-driven AI as it allows individuals to understand how AI systems make predictions. Transparent AI models provide insights into the factors considered, decision processes, and potential ethical implications, enabling stakeholders to hold the technology accountable and ensure it aligns with their values.
7. How can value-driven AI address privacy concerns?
Value-driven AI addresses privacy concerns by incorporating privacy principles and compliance measures in the development of AI systems. It involves adopting techniques such as differential privacy, data anonymization, and secure data handling to protect sensitive information and maintain individuals’ privacy rights.
8. What are some real-world applications of value-driven AI?
Value-driven AI has applications in various domains, including healthcare, finance, education, and social media. For example, in healthcare, AI systems can be designed to prioritize patient well-being, respect privacy, and ensure fairness in healthcare access and resource allocation.
9. How can businesses benefit from adopting value-driven AI?
Businesses adopting value-driven AI can gain a competitive advantage by building trust with their customers. By prioritizing ethical considerations, businesses can avoid potential reputational and legal risks associated with unfair or biased AI systems and establish themselves as responsible AI innovators.
10. What role does human involvement play in value-driven AI?
Human involvement is crucial in value-driven AI to ensure that the decisions made by AI systems align with human values. Human input is required in the development stage, including defining and refining ethical guidelines and continuously monitoring and auditing AI systems to ensure they behave in a manner that is consistent with societal values.
