Table of Contents
- Executive Summary
- Types of AI Models
- The AI Model Decision-Making Framework
- Factors to Consider
- Decision Triggers
This whitepaper offers a structured approach for business leaders and product managers to choose between generalized, specialized, and hybrid AI models. By evaluating various criteria such as business objectives, data sensitivity, and cost, you can make an informed decision tailored to your specific needs.
Choosing the right AI model can be a major challenge for startups new to the field. Large generalized models like GPT-3 can cost millions in compute costs as they scale, yet many founders underestimate these expenses. This whitepaper provides a straightforward framework to help startup founders and product leaders determine when to use generalized versus specialized AI models, weighing factors like cost, speed, and customization. With the right framework, startups can make informed AI decisions to meet business objectives without breaking the bank.
Types of AI Models
Before diving into the decision-making framework, let’s first understand the various types of AI models available. AI models fall into three broad categories:
- Generalized Models: The Swiss Army knives of AI, good for a broad range of tasks but not specialized.
- Specialized Models: Expert tools designed for specific tasks within particular industries.
- Hybrid Models: Combines features of both generalized and specialized models, offering a balanced approach.
Below we further break this down into a more nuanced and detailed list of available AI models.
1. Generalized Models
Generalized Intelligence Models: These models are akin to Swiss Army knives, capable of handling a multitude of tasks but not specialized in any. These models are useful for tasks that require natural language understanding, generation, and reasoning. They tend to focus on natural conversations and provide useful information to humans, without being limited to a narrow domain. However, they don’t have specialized capabilities tailored for specific industries or tasks. Some examples are:
- OpenAI’s GPT3 LLM which can be applied across a myriad of domains from language translation to chatbots, to content generation, text summarization and basic problem-solving across different industries.
- Claude by Anthropic for wide variety of NLP tasks like translation, QA, summarization and more. General capabilities to be applied across domains.
- DALL-E 2/3 by OpenAI for image generation based on text prompts. Enables creating original images for design, art and more.
Multi-modal Models: These models, capable of processing multiple types of inputs, also fall under the generalized category. For example:
- Language + Graphs and Data + Images: OpenAI GPT4 with advanced data analytics and its DallE3 mode can manage language and integrates other modalities like reading charts, creation of images, etc.
- Image captioning: OpenAI’s CLIP which can identify objects in images and automatically tag them for digital asset management. And it Enables users to search for products using images instead of text.
- Video summarization: This is the task of creating a short and informative summary of a longer video. For example, YouTube uses AI to generate video summaries for some of its content, such as news, sports, documentaries, etc.
- Speech recognition: This is the task of converting spoken words into text. For example, Siri is a virtual assistant that uses AI to recognize speech and perform various tasks for users.
2. Specialized Models
Expert Vertical AI: Specialized tools honed for specific tasks within particular industries. These models are useful for tasks that require domain-specific knowledge and expertise. For example:
- Protein folding prediction: DeepMind’s AlphaFold, which is tailored for predicting protein folding in the field of biotechnology. It assists in drug discovery and understanding diseases by predicting how proteins will fold
- Medical Diagnosis: IBM’s Watson is an expert vertical AI that uses natural language processing and machine learning to assist doctors in diagnosing and treating patients. Watson can analyze large amounts of medical data and provide evidence-based recommendations.
- Chess playing: DeepMind’s AlphaZero is an expert vertical AI that uses reinforcement learning to master chess and other board games. AlphaZero can learn from its own experience and surpass previous chess engines.
Ensembles of Models: Teams of specialized models each addressing a unique aspect of a problem also fall under this bucket. These models are useful for tasks that require combining different types of models or methods to achieve better results.Some examples are:
- Stock market prediction: This is the task of forecasting future stock prices or trends based on historical data and other factors. For example, an ensemble of models might use regression, classification, clustering, sentiment analysis, etc. to analyze data from various sources such as financial reports, news articles, social media, etc. and provide predictions and recommendations for investors.
- Face recognition: This is the task of identifying or verifying a person’s identity based on their face. For example, an ensemble of models might use convolutional neural networks, support vector machines, principal component analysis, etc. to extract features from face images and compare them with a database of known faces.
- Machine translation: This is the task of translating text or speech from one language to another. For example, an ensemble of models might use recurrent neural networks, attention mechanisms, transformers, etc. to encode the source language and decode the target language.
3. Hybrid Models
Expert Company AI: Tailored instruments for specific business needs, combining features of both generalized and specialized models. These models are useful for tasks that require customizing or adapting existing models to specific business needs or scenarios For example:
- Inventory management: This is the task of optimizing the supply and demand of goods or services. For example, a retail company might use a hybrid model that combines a generalized intelligence model for demand forecasting and a specialized model for inventory optimization. The hybrid model can help the company reduce costs, increase sales, and improve customer satisfaction.
- Customer service: This is the task of providing assistance or support to customers or clients. For example, a banking company might use a hybrid model that combines a chatbot for answering common questions and a human agent for handling complex issues. The hybrid model can help the company improve customer experience, loyalty, and retention.
- Fraud detection: This is the task of identifying or preventing fraudulent or illegal activities. For example, a credit card company might use a hybrid model that combines a generalized intelligence model for anomaly detection and a specialized model for risk assessment. The hybrid model can help the company protect its customers and assets from fraudsters.
Layered, Modular Architectures: Like LEGO blocks, these models can be tailored by interchanging parts to suit both generalized and specialized tasks. These models are useful for tasks that require flexibility and scalability. Some examples are:
- Natural language generation: This is the task of producing natural language text from non-linguistic data or inputs. For example, a layered, modular architecture might consist of modules for content selection, text planning, sentence generation, and text realization. The modules can be interchanged or modified depending on the input data and the output format.
- Image generation: This is the task of producing realistic images from sketches, texts, noises, etc. For example, a layered, modular architecture might consist of modules for image synthesis, style transfer, colorization, and enhancement. The modules can be interchanged or modified depending on the input data and the output quality.
- Music generation: This is the task of producing musical compositions from genres, moods, lyrics, etc. For example, a layered, modular architecture might consist of modules for melody generation, harmony generation, rhythm generation, and instrument selection. The modules can be interchanged or modified depending on the input data and the output style.
AutoML and Auto-tuned Models: These self-optimizing models can be tailored to specific tasks while retaining a level of general applicability. AutoML and Auto-tuned Models: These models are useful for tasks that require automation and optimization. Some examples are:
- Model selection: This is the task of choosing the best model or algorithm for a given problem or dataset. For example, AutoML is a technique that uses AI to automatically search for and evaluate different models based on predefined criteria such as accuracy, speed, complexity, etc.
- Hyperparameter tuning: This is the task of finding the optimal values for the parameters that control the behavior or performance of a model or algorithm. For example, Auto-tuned Models are models that use AI to automatically adjust their hyperparameters based on feedback from validation data or real-world scenarios.
- Model deployment: This is the task of deploying a model or algorithm to production or operation. For example, AutoML and Auto-tuned Models can also help with model deployment by automatically selecting the best platform, environment, configuration, etc. for running the model efficiently and effectively.
Bottom Line – First Identify Which Category Is the Best Fit
- Generalized Models are like Swiss Army knives, capable of handling a multitude of tasks but not specialized in any. They can process and integrate multiple types of inputs, such as texts, images, sounds, etc. They can also perform natural language understanding, generation, and reasoning across various domains and industries.
- Specialized Models are narrow models tailored for specific tasks or industries. They are like expert tools, honed for specific tasks within particular domains or industries. They require domain-specific knowledge and expertise to perform well. They can also combine different types of models or methods to achieve better results.
- Hybrid Models combine generalized and specialized models to achieve the best of both. They are like tailored instruments, customized or adapted to specific business needs or scenarios. They can also self-optimize or self-adjust based on feedback or new data.
The AI Model Decision-Making Framework
Creating a decision-making framework to determine which AI model to use requires a well-structured approach that takes into consideration the specific needs, constraints, and objectives of a given project or organization.
You can ask yourself some questions to guide your decision-making process, such as:
- What problem(s) are you trying to solve with AI?
- What data type, volume, and quality do you have?
- Which performance metrics are key: accuracy, speed, scalability, or explainability?
- How frequently will the model be updated or retrained?
- Is the model easy to integrate with your existing system?
- What level of customization and control is needed for the model?
- How do you ensure the model’s ethical use and transparency?
- What’s your budget?
Factors to Consider
In this section, we will compare the three types of AI models based on different factors that may affect your decision. These factors include:
- Business Objectives: This factor reflects the purpose and scope of your AI project. Depending on your objectives, you may need an AI model that can handle a variety of tasks or a specific task.
- Data Sensitivity: This factor reflects how secure or confidential your data is. Depending on your data sensitivity, you may need an AI model that can process your data in-house or on the cloud.
- Cost Implication: This factor reflects how much money you need to spend or save for your AI project. Depending on your cost structure, you may need an AI model that is cheap or expensive to start or maintain.
- Time-to-Market: This factor reflects how fast or slow you need to launch or deploy your AI project. Depending on your time-to-market, you may need an AI model that is quick or slow to implement or update.
- Expertise Required: This factor reflects how much skill or knowledge you need to create or use your AI model. Depending on your expertise level, you may need an AI model that is easy or difficult to understand or control.
- Scalability: This factor reflects how well your AI model can handle increasing or decreasing demand or complexity. Depending on your scalability needs, you may need an AI model that is easy or difficult to scale up or down.
- Regulatory Compliance: This factor reflects how well your AI model meets the legal or ethical standards of your industry or domain. Depending on your regulatory needs, you may need an AI model that is compliant or non-compliant with specific criteria.
- Customization: This factor reflects how much you can modify or personalize your AI model for your problem or task. Depending on your customization needs, you may need an AI model that is limited or flexible in its parameters, features, and outputs.
- Future-proofing: This factor reflects how well your AI model can adapt to future needs or trends in your industry or domain. Depending on your future-proofing needs, you may need an AI model that is adaptable or stable in its performance and functionality.
|Factor||Generalized Models||Specialized Models||Hybrid Models|
|Business Objectives||Best suited for horizontal objectives like conversational AI, content generation, open-domain question answering. May struggle with industry or task-specific objectives without customization.||Ideal for niche vertical objectives like predictive maintenance, fraud detection, medical diagnosis. Require expertise to tailor precisely to domain.||Well-suited for objectives that require both horizontal and vertical capabilities. Customizable to balance general intelligence with specialized tuning.|
|Data Sensitivity||Low inherent security due to reliance on vast data. Encryption and access controls can help, but risks remain.||High security as models trained in-house on proprietary data. Strict controls on access and dissemination.||Moderate security with balanced approach. Sensitive data kept in-house while leveraging public data. Multiple security layers.|
|Cost Implications||Low startup costs but can become exponentially expensive at scale if reliant on 3rd party API. Caching and rate limits can help manage.||High upfront investment for in-house infrastructure and experts. But avoiding ongoing API costs can improve long-term value.||Balance of upfront and ongoing costs. Infrastructure and experts needed but can leverage external APIs judiciously.|
|Time to Market||Quick to implement with minimal customization needed. Enables faster experimentation and iteration.||Slower deployment due to upfront development and training requirements. But highly tailored to use case.||Faster than specialized models with some customization. But slower than generalized models.|
|Expertise Required||IT skills often sufficient to leverage pretrained models. AI/ML experts helpful for customization.||Deep AI/ML expertise critical. Also requires domain knowledge to tailor precisely.||AI/ML and some domain expertise required to balance customization and leverage of pretrained models.|
|Scalability||Can scale to high workloads easily with built-in support from large providers.||Scaling requires additional in-house resources and infrastructure. Limits on scalability.||Mix of internal and external resources allows for solid scalability. But limited compared to generalized models.|
|Regulatory Compliance||Standardized models may lack transparency. 3rd-party reliance introduces compliance risks.||Highly customizable to meet regulatory needs. But still issues around bias and explainability.||Customizable to requirements with more control than generalized. Provides audit trails.|
|Customization||Limited ability to customize architecture or parameters of pretrained models.||Highly customizable and tailored since built in-house. But expensive to change significantly.||Flexible customization options. Can tune pretrained models and swap custom modules.|
|Future Proofing||Slower to adapt models to new data or use cases. Retraining costs are high.||Easier to update with new data. But new uses cases require rebuilding models.||Ability to selectively retrain parts of model. But still significant work to adapt.|
As you can see, there is no one-size-fits-all solution for choosing an AI model. You need to consider your goals, data, and constraints and weigh the pros and cons of each type of AI model.
Decision Triggers: Navigating Crucial Considerations
As you consider adopting an AI model, there are pivotal factors—referred to as ‘Decision Triggers’—that can significantly influence the success and efficiency of your AI initiative. These triggers serve as red flags or signposts to guide you in your selection process. Let’s delve into each:
If your business operations involve proprietary algorithms or confidential data, specialized models are often the most suitable choice. Their design can be adapted to respect the unique constraints of your business while ensuring data security.
Speed to Market (Fast GTM)
When speed to market is a high priority, generalized models often come to the rescue. They are pre-trained and quicker to deploy, allowing you to implement your AI strategy in a relatively shorter span.
Understanding the Total Cost of Ownership (TCO) is critical. While generalized models may appear cost-effective initially, scaling or customizing them could increase expenses. Specialized models, on the other hand, often have higher upfront costs but may prove more economical in the long run.
In industries where compliance with strict regulations is mandatory—such as healthcare or finance—specialized models are almost invariably the go-to choice. These models can be designed to adhere to industry-specific guidelines, offering an extra layer of assurance.
If your focus is on scaling your operations quickly and broadly, generalized models typically offer the agility and versatility you need. They can handle a large influx of data and diverse tasks without requiring significant modifications.
When your project demands a blend of flexibility and standardization, hybrid models offer a golden middle path. These models enable you to tailor the AI functionalities to your specific needs while maintaining a level of general applicability.
If your operation has a feedback mechanism or a steady influx of new data, hybrid models are particularly advantageous. They can adapt and improve over time, optimizing performance and output quality.
Finally, if you find yourself in a situation where trade-offs between accuracy, speed, scalability, and explainability are inevitable, hybrid models offer the most balanced approach. They allow for a nuanced interplay between these competing factors, enabling you to align the model more closely with your specific objectives.
By carefully assessing each of these decision triggers in the context of your project’s unique needs and objectives, you can make a more informed and strategic choice in selecting the most appropriate AI model.
Each of these decision triggers highlights a critical consideration that can significantly impact the success and effectiveness of your AI initiative. By carefully assessing each trigger in relation to your project’s specific needs and objectives, you can make a more informed decision on the most suitable AI model to adopt.
Choosing the right AI model for your organization is a complex yet critical decision. This whitepaper provides a comprehensive yet practical framework to guide you through this selection process, considering factors like business objectives, data sensitivity, and cost. Our decision-making framework, complete with a dynamic scoring algorithm, offers a tailored approach to help you identify the most suitable AI model—be it generalized, specialized, or hybrid.
The included decision triggers act as crucial signposts, highlighting pivotal factors that could significantly impact your AI project’s success. As the AI landscape continues to evolve, this framework is designed to be a living tool, adaptable to new developments and your changing needs. It aims to simplify the complexities of AI model selection, helping you make informed, strategic choices for both immediate and long-term success.