Deploy Generative AI into your Enterprise
Introducing generative AI into your enterprise can be a game-changer for your business. This type of AI is capable of creating new content, such as images, video, and even text, that is indistinguishable from content created by humans. However, deploying generative AI can be challenging, especially when it comes to ensuring that the output is safe, fair, and unbiased.
Here are three ways you can start deploying this innovative technology into your enterprise systems.
1. Embedded Generative AI
SaaS platforms are now equipped with embedded Generative AI, which has been specifically designed to cater to various business cases. This powerful feature is seamlessly integrated into the platform, offering a myriad of benefits to users. The vendor manages model maintenance and training data, ensuring that the AI system remains up-to-date and efficient. Additionally, the vendor also takes care of potential risks and data privacy concerns, guaranteeing that the user's data is always protected. It is crucial to understand the measures taken by the vendor to safeguard user data and ensure its protection.
2. Public Models
This option involves API calls to a model that is provided as a service. Many companies like OpenAI, Amazon Web Services, and Microsoft Azure offer public APIs that can be connected to your software. There are several advantages to this approach, such as a low barrier to entry, higher sophistication, and speed.
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However, these public models are not always suitable for all enterprise applications as the data from the query may be retained and used for further development of the model, which may not align with some data residency and privacy obligations. Additionally, there is a potential for high costs as most public APIs have a fee structure that charges according to the number of queries and the length of processed text. It's important to get cost estimates and use smaller/cheaper models for narrower tasks. Finally, while rare, the provider of an API can choose to stop the service at any moment, which can be risky for businesses that depend on a pipeline whose flow they don't control.
3. Build-it-Yourself
When considering using a public model through an API, companies should be aware of its limitations. Creating and managing an open-source model themselves may be more advantageous as there are various open-source models available, each with unique strengths and weaknesses that may be more suitable for their specific needs. While a smaller model may have limited applications, it can still deliver satisfactory performance for a specific use case at a lower cost. Additionally, managing their open-source models allows companies to be independent of third-party API services.
However, creating and maintaining an LLM can be complex and requires a level of expertise in data science and engineering. Although you can start with an open source LLM model and fine tune it for enterprise specific use cases. Companies must assess whether they have the necessary skills and resources to build and maintain such a model in the long run. Moreover, community models available through open-source platforms are generally smaller and more targeted, while public APIs can cover a vast range of topics.
Therefore, companies must weigh the benefits of a tailored model versus a broader coverage of a public API and decide which approach best suits their needs and resources. Considering the tradeoffs of each approach, it's important to note that there is no clear winner. A one-size-fits-all approach simply won't work across all enterprises. Even within a single company, it's best to choose the model and architecture based on a use case by use case basis. To truly succeed with LLMs, companies must be agile and able to choose the right model for any given application. Innovation with LLMs is advancing rapidly, and only those who can remain flexible and adapt to these changes will reap the greatest benefits.
Irrespective of the deployment strategy, due diligence is necessary when implementing generative AI. Companies need to evaluate the risks, establish a responsible AI governance program, educate employees on the benefits and limitations of generative AI, and set up security policies. Dealing with bias is particularly challenging in large foundational models used for code, text, or image generation, where the training data set may be unknown, and the models' learning process is opaque. Therefore, companies must learn to leverage generative AI effectively while ensuring accuracy, control, and responsible use, or face the consequences of unethical and inaccurate results.
Intern at Scry AI
1yWell summarised. Ensuring fairness in AI models involves addressing bias, which is defined as unequal treatment based on protected attributes like gender or age. Fairness metrics, integrated into many AI systems or computed externally, include favorable percentages for each group, distribution of data for protected groups, and combinations of features related to one or more protected groups. To some extent, open-source libraries like Fairlearn and The AI Fairness 360 achieve fairness by computing metrics such as disparate impact ratio, statistical parity difference, equal opportunity, and equal odds to assess and enhance fairness. It is worth noting that fairness and bias differ because biases can be hidden, while fairness requires unbiased treatment concerning defined attributes. For example, training data may introduce biases of its own, which are often called Algorithmic Biases. Finally, after recognizing the dynamic nature of fairness, jurisdictions may alter the definition of fairness over time, making the task of updating AI models quite challenging. More about this topic: https://lnkd.in/gPjFMgy7