Month: December 2023

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Generative AI for Public Sector: An API Opportunity

The disruptive power of AI extends to every industry, opening up unlimited possibilities for new business opportunities. It turns imagination into reality, insights into action, and possibility into discovery. Generative AI is a type of AI that produces content such as text, audio, code, videos, images, or any other content based on prompts input by the user. Generative AI models use complex computing processes like deep learning to analyze patterns from large sets of historical data to create new business opportunities.

Generative AI is a one of the most promising technologies that can help the public sector to improve productivity and service quality. However, it is important to ensure that the technology is used responsibly and ethically.

Generative AI can enable the public sector to improve productivity and service quality. Generative AI has a wide range of applications in the public sector. It can be used to extract information and automate paper-based processing. It can also be used to automate repetitive and mundane tasks, enabling staff to take on higher value work, optimize resource allocation, and enhance decision making. It also uses to summarize large amounts of information from different sources, such as public health data and economic indicators, to identify patterns, trends, and correlations for Government to take decision in favor public.

Here are a few examples of tasks that Generative AI can perform in the public sector:

  • Providing support to clients such as chatting, responding, and delegating task to correct department.
  • Writing and editing documents and emails
  • Coding tasks, such as debugging and generating templates and common solutions.
  • Summarizing information.
  • Research, translation, and learning

To ensure the responsible use of GenAI tools and maintain public trust , the public sector should align with the “FASTER” principles:

  • Fair: Content should comply with human rights, accessibility, procedural and unbiased obligations
  • Accountable: Content generated by these tools should make sure it is factual, legal, ethical, and compliant with the legal terms of use.
  • Secure: In pub-sec security is paramount goal. Content generated by Generative AI should appropriate for the security classification of the information and privacy & personal information are protected. Compliance with PII data.
  • Transparent: In Government sector, it is very important that your all procedural is transparent, and users know that they are interacting with an AI tool.
  • Educated: It is very important to document the strengths, limitations, and responsible use of the Generative AI tools. It should also highlight; how to create effective prompts and to identify potential weaknesses in the outputs.
  • Relevant: Generative AI tools should support user and organizational needs, contributes to improved outcomes and become relevant to society and business.

Since Generative AI has a wide range of benefits in the public sector, there are also some challenges associated with its use.

Here are Some of these challenges:

  1. Ethical dilemmas: Generative AI can be used to create deepfakes by manipulating videos and images. That can be used to spread misinformation and create confusion among public.
  2. Dependency on technology: Generative AI is dependent on the latest technology and underline system. It is based on how secure your data technology and how your data is communicating with AI models.
  3. Equity and accessibility issues: Generative automated certain task that led some job displacement. Which lead to accessibility and equity concern.
  4. Staff resistance to change: If Pub-Sec staff perceive Generative AI as a threat to their job then they may be resistance to change into Generative AI process.
  5. Project delays and failures: Generative AI projects are complex and time consuming. This may be delay or failure of project implementation.
  6. Regulatory issues: In Public Sector, data are fragmented which raises compliance and regulatory issue. This may be concerns about data privacy, security, and ownership.
  7. Cybersecurity risks: AI in the public sector raises cybersecurity risks. This may be concern about hacking, data breaches and other cyber threats.

API is helping GenAI to import the AI model and enable data for Generative AI. We can mitigate some of these risks by implementing API based approach for Generative AI in public sector.

Here are the few challenges in pub-sec Generative AI which is mitigated by API implementation.

  • Security: According to recent finding Generative AI makes it easier for hacker to find and exploit vulnerabilities. If your Generative AI models are communicating with your organization data through API, it will mitigate vulnerabilities risk many folds. Government sector can implement strict control of their data in a number of ways like MFA or API access permission.
  • Data control: Through API implementation in Generative AI, pub-sec can eliminate any data leakage and data abuse. Through API governance they can monitor data usage by Generative AI models. Government sector can also implement API rate limiting or IP restriction for any API to get tighter control on their sensitive data.
  • Fairness and relevancy:  Accuracy of Generative AI model or LLM are based on independent and relevancy of data. Generative AI models in pub-sec only work when Generative AI model follows compliances and relevant to use-case. API implementation does make sure data is relevant and independent for LLM. API also restrict any unwanted data for AI models and reduce processing time to cleansing unwanted data.   
  • Data Separation: APIs keep data separated from Generative AI Models or LLM (Large Language Model) implementation. This enable LLM to work on different set of data at the same time and enable faster innovation within government sector.
  • Fast delivery: APIs enable faster delivery of generative AI models. During your development of LLM models you focus only on models not on data deliveries. This may enable two stream of development team. One team focus only on data delivery and second team can focus only on Large language models development. This may empower to team for faster project deliveries.

Public sector adoption on Generative AI is still in the early stages, but it needs to accelerate. This will enable faster public project deliveries and AI bot assistances.

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Generative AI: How API making powerful customer experiences

Generative AI is more like a child where you instruct child that don’t bounce basketball inside home, but child goes to bounce a soccer ball inside home. But this was not your expectation from child and then this action falls outside of your expectation. Now you add more parameters with your instruction then the child is more likely to get the response that you want.

Generative AI is the same, the more context and parameter we can give to generative AI the better our service replies, the better emails, the better product recommendations get from your Generative AI Models.

We’re all seeing some amazing demos of generative AI these days. Models trained on the whole internet are able to hold a conversation, explain their reasoning, and perform well at a broad variety of tasks.

You’ve probably started to play with Chat GPT, Google Bard, or Microsoft Bing. In your company folks are already experimenting with different ways of data to use it in their work.

These chat interfaces, as an initial proof of concept, are truly amazing. it’s already becoming clear, the ability to create significant business value and it will be dependent on your ability to INTEGRATE and MANAGE these systems and data.

But there are multiple barriers standing in the way of our ability to implement AI.

  • Fragmented data is hard to ingest into AI models.
  • Missing context leads to poor recommendations.
  • Lack of trust in how the LLMs will use your data.
  • Difficulty in acting on the recommendations because AI is completely detached from business processes.
  • And of course, overall security risks of accessing data across various systems.

Technology is moving fast, and the recent introduction of AI innovation is exciting, especially with the promise of increased productivity. If you look at a public source like Hugging Face, there are over 250k AI models compared to only 32 significant industry-produced machine learning models in 2022. If you pair these figures with the fact that the average enterprise has over 1000 applications, suddenly you have a lot of API integrations to account for.

Without addressing your system integration challenges, you risk deploying AI that results in generic data in, and generic insights out.

Generative AI and API ecosystem

Let’s find how API fits into this Large-language models (LLMs) or generative AI space.

You can start with an LLM of your choice, such as Salesforce CodeGen or OpenAI’s CoPilot.

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks.

As you know, big models incur big cost, and LLM’s are expensive.

So large language models are exposed as APIs to reduce cost. As we know, APIs are the easiest way to get data in and data out from these LLM. These LLM’s are open for anyone to use. These APIs are also pulling data from your existing system as well as legacy system. Now you are enabling APIs which is required for your business process and adding data context which is make sense to business use-case.

Next, you can establish control over the APIs for your LLM by applying governance and security policies using Universal API Management. In this way, you can assure that your organization is leveraging AI while remaining secure and conformant. Once your APIs are secured then you can add automation and integration flow with your APIs which communicate with your internal systems. Enabling AI data through API You can push and pull data from a variety of data sources, including 3rd party applications, to ensure that you are using the latest data with the latest technology and building a complete 360 view of your customer.

API Safely unlock generative AI capabilities through a layer of trust Use Universal API management (UPIM) to provide security and governance for AI driven systems. The integration and automation tools also ensure the customer 360 is all up to date with the latest data, making powerful customer experiences possible.