In 2023, advancements in generative AI include improved complexity and understanding in language models like GPT-4, enabling more comprehensive applications. Visual generation models are now more precise, delivering photorealistic outputs. Progress in cross-modality AI has led to complex multi-modal outputs from mixed data types. Generative models in reinforcement learning have become more sophisticated, while tools to control and understand such models have developed. These models have also become more efficient, enabling use on lower-power devices for privacy-centric applications. Preliminary steps have also been taken in implementing generative models on quantum computers.

The fast-paced advancements and significant breakthroughs in these tools sparked a growing interest among both professionals and ordinary users. We asked Andrii Dankevych, Senior Director, Operational Excellence, to share the vision regarding generative AI tool usage and discuss its risks and benefits for professional and personal purposes. In the following article, Andrii helps understand generative AI and identifies the scope and potential risks associated with using these tools.

Understanding generative AI

Two popular AI tools are ChatGPT, designed for general use, and GitHub Copilot, crafted specifically for developers. Additionally, more niche AI tools can generate images, designs, BA diagrams, and business processes. While they are not yet well-known, they are in the experimentation phase.

The benefits of AI have become apparent to all. The AI revolution has decreased the cost of cognition, allowing us to process and generate ideas using artificial intelligence. Similarly, the advent of the Internet once reduced the cost of acquiring information. Today, the challenge lies in processing this vast amount of information, and that’s where AI comes into play. Individuals who work with generative AI tools gain an additional skill set akin to a superpower. While AI may not replace humans, people who leverage AI will certainly outpace those who don’t. AI tools such as virtual assistants and copilots will likely become widespread in various industries, enabling faster and superior problem-solving, though they will still require human supervision.

It’s essential to learn how to use AI in any field, even as a personal assistant for generating and discussing ideas. These AI applications are less than a year old but have garnered immense popularity and a significant user base. However, standard rules for incorporating AI into work processes have yet to be established.

Understanding risks of AI use: Data leaks, bias, “hallucinations,” and intellectual property concerns

Like any third-party service, AI presents risks, such as data leaks. Once we transfer data, we lose control over it. Other risks include biased AI responses and “hallucinations,” where AI synthesizes information in a way that resembles reality, but the output may not be 100% correct. Also, loss of control over data and updates presents a security risk. We must obtain the client’s consent before transferring code, ideas, thoughts, or implementation options for analysis. Furthermore, we risk infringing on intellectual property rights when using AI-generated information since we can’t verify whether third-party intellectual property was used in generating the response. Here is the list of the main risks of generative AI usage:

• Risk of data leaks when using third-party services like AI.
• Loss of control over data once it is transferred.
• Potential for biased AI responses.
• Risk of AI “hallucinations,” where AI synthesizes information that resembles reality, but the output may not be entirely accurate.
• Loss of control over data updates, presenting a security risk.
• Client consent is required before transferring any intellectual content to avoid legal and ethical issues.
• Using AI-generated information may cause intellectual property rights infringement due to verification difficulties.

Understanding the scope of AI: Navigating challenges and maximizing efficiency when generating text, brainstorming, and coding

There are different areas of AI usage, and each requires a correct attitude and understanding of the potential pitfalls and shortcomings of the current version of the specific AI tool. Let’s examine some of them.

Basic text generation: We supply AI with specific data points in this scenario. The core of the text is based on this data, while the AI shapes the text according to the specific request.

Brainstorming: The results can be unpredictable when we ask AI to generate ideas or solutions to problems. AI might suggest options that are not suitable for your particular case. If you interact with AI via a software interface, a “temperature” setting ranges from 0 to 1. With that setting at 0, AI will provide straight, concise answers. As you increase the temperature, the responses become more creative. While generating text or brainstorming, setting the temperature around 0.7 often yields more inventive answers.

Coding: Extra caution is necessary here. While AI can generate simple functions, it often needs help with more complex ones. Even if it appears statistically accurate, there may be no logical coherence. Thus, it is essential to verify that the generated code works consistently.

Don’t assume that AI can resolve everything. The key is to break down larger problems into smaller, manageable tasks. Effectively framing a task for AI is already a significant step toward success. When delegating it to AI, consider the task’s form, segments, and complexity. Here are some tips for using generative AI effectively:

• Problem decomposition: Large and complex problems can overwhelm even the most sophisticated AI systems. Therefore, it’s essential to break down such problems into smaller, more manageable tasks. This process, known as problem decomposition, helps simplify the problem domain and allows AI to focus on one task at a time.
• Task framing: The way a task is framed can significantly affect AI performance. Ideally, tasks should be defined clearly and unambiguously. The tasks should contain enough context for AI to understand what’s expected without overwhelming it with too much information.
• Understand the task form, segments, and complexity: Before delegating a task to AI, it’s important to fully understand the task form, segments, and complexity. Some tasks may appear simple but have complex subtasks that are challenging for AI. On the contrary, other tasks may be complex but can be easily broken down into more straightforward ones that AI can handle. Knowing the task inside and out will help you make a more informed decision about whether and how to delegate it to AI.
• Continuous learning and adjustment: AI systems are not static, they learn and adapt over time. Therefore, it’s crucial to continuously monitor their performance, provide feedback, and adjust their training as needed. This active engagement can significantly improve the efficiency and effectiveness of the AI system.
• Ethical considerations: Remember that using AI also comes with ethical responsibilities, such as ensuring that data privacy is respected and that any AI-generated information doesn’t infringe on intellectual property rights. Consider these aspects when deciding how to use AI.

By following these strategies, you can leverage the power of generative AI in an efficient, effective, and ethical way.

Responsible AI use: Guidance for coding, self-education, and intellectual property protection

Using AI technology should not be prohibited. Indeed, it should be encouraged, but with an emphasis on careful and considerate implementation. Tasks such as coding or text generation are subject to higher-level approval when a particular AI-driven tool is utilized. The technology should only be used for task-related objectives via official accounts. Personal use on these accounts is strictly forbidden.

Incorporating AI tools for self-learning is highly advisable, but only personal devices and accounts can be used for this purpose. The deployment of various interactive platforms and the creation of custom aids through programming interfaces are recommended.

It is important to remember that all work completed within a project framework, including the code and all project-related information, is considered client property. The technology poses a risk of transferring this information to external services, particularly in the absence of client consent. Therefore, adherence to specified guidelines is necessary to avoid any infringement of intellectual property rights when AI-generated information is used.

Generative AI leap in 2023: Balancing potential, responsibility, and ethical use for a brighter technological era

In conclusion, the year 2023 marks a significant leap in the development and application of generative AI. The groundbreaking capabilities of AI-based tools, such as GPT-4, revolutionize various tasks, ranging from coding to text generation. However, it’s important to understand their advantages, potential pitfalls, and ethical implications.

Embracing these advanced technologies requires a balanced approach of curiosity, openness, caution, and respect for intellectual property. On the way to integrating AI into our daily professional and personal activities, we should be responsible and understand both its power and limitations. Particularly, we should follow the established guidelines to ensure optimal efficiency and ethical conduct.

Furthermore, it’s crucial to remember that despite AI’s immense potential, human supervision and judgment remain integral. While AI tools can significantly enhance productivity and creativity, they are not standalone solutions but supportive mechanisms that require human interaction, feedback, and continuous learning.

Lastly, these days AI intensely mixes with quantum computing, providing both unprecedented opportunities and challenges. Therefore, staying informed, adaptable, and responsible when using AI will help us harness its potential and avoid possible risks. It’s all about adopting a smart, efficient, and ethical approach to technology.

It is safe to predict that the use of AI could soon become mandatory.