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Artificial intelligence technology is exploding and industries are rushing to adopt it as quickly as possible. Before your business plunges headlong into a confusing sea of opportunities, it’s important to explore how generative AI works, what red flags businesses should be aware of, and how to evolve toward an AI-ready business.
How Generative AI Really Works
One of the most common and powerful techniques for generative AI are large language models (LLMs), such as GPT-4 or Google’s BARD. These are neural networks that are trained on large amounts of text data from various sources such as books, websites, social media, and news articles. They learn the patterns and probabilities of language by guessing the next word in a sequence of words. For example, given the input “The sky is”, the model might predict “blue”, “clear”, “cloudy”, or “falling”.
By using different inputs and parameters, LLMs can generate different types of results, such as abstracts, headlines, stories, essays, reviews, subheadings, taglines, or codes. For example, given the input, “write a catchy slogan for a new brand of toothpaste,” the model might generate “smile with confidence,” “get rid of your worries,” “the toothpaste that cares,” or “shines like a star”. ”
Red flags companies should be aware of when using generative AI
While generative AI can offer many benefits and opportunities for businesses, it also has some drawbacks that need to be addressed. Here are some of the red flags that companies should be aware of before embracing generative AI.
Public vs. Private Information
As employees begin to experiment with generative AI, they will create prompts, generate text, and incorporate this new technology into their workflow. It is essential to have clear policies that delineate information that is released to the public versus private or proprietary information. Sending private information, even in an AI message, means that the information is no longer private. Start the conversation early to ensure teams can use generative AI without compromising proprietary information.
Generative AI models are not perfect and can sometimes produce inaccurate, irrelevant, or meaningless results. These outputs are often referred to as AI hallucinations or artifacts. They can be due to several factors, such as insufficient data quality or quantity, model bias or error, or malicious manipulation. For example, a generative AI model can generate fake news that spreads misinformation or propaganda. Therefore, companies need to be aware of the limitations and uncertainties of generative AI models and verify their results before using them for decision making or communication.
Using the wrong tool for the job
Generative AI models are not necessarily unique solutions that can solve any problem or task. While some models prioritize generalized responses and a chat-based interface, others are built for specific purposes. In other words, some models may be better at generating short texts than long texts; some may be better at generating factual texts than creative texts; some may be better for generating texts in one domain than in another domain.
Many generative AI platforms can be further trained for a specific niche like customer service, medical applications, marketing, or software development. It’s easy to just use the most popular product, even if it’s not the right tool for the job at hand. Businesses need to understand their goals and requirements and choose the right tool for the job.
Trash in; the trash out
Generative AI models are only as good as the data they are trained on. If the data is noisy, incomplete, inconsistent, or skewed, the model is likely to produce results that reflect these flaws. For example, a generative AI model trained on inappropriate or biased data can generate discriminatory copy and damage your brand’s reputation. Therefore, companies need to ensure they have high-quality data that is representative, diverse, and unbiased.
How to evolve towards an AI-ready company
Generative AI adoption is not a simple or straightforward process. It requires a strategic vision, a cultural change and a technical transformation. Here are some of the steps companies need to take to become an AI-ready business.
Find the right tools
As noted above, generative AI models are not interchangeable or universal. They have different capabilities and limitations depending on their architecture, training data, and parameters. Therefore, companies need to find the right tools that match their needs and goals. For example, an AI platform that creates images, such as DALL-E or Stable Diffusion, is probably not the best fit for a customer support team.
Platforms that specialize their interface for specific roles are emerging: writing platforms optimized for marketing results, chatbots optimized for general tasks and problem solving, developer-specific tools that connect to programming databases, medical diagnostic tools, and more. . Companies must evaluate the performance and quality of the generative AI models they use and compare them with alternative solutions or human experts.
Manage your brand
Every company must also think about control mechanisms. Where, say, a marketing team may have historically been the gatekeeper to brand messaging, it was also a bottleneck. With the ability for anyone in your organization to generate copy, it’s important to find tools that allow you to incorporate your brand cues, messaging, audiences, and voice. Having AI that incorporates brand standards is essential to debottlenecking brand copy without causing chaos.
Cultivate the right skills
AI generative models are not magic boxes that can generate perfect text without any human input or guidance. They require human skills and experience to use them effectively and responsibly. One of the most important skills for generative AI is rapid engineering: the art and science of designing inputs and parameters that get the desired results from models.
Rapid engineering involves understanding the logic and behavior of models, making clear and specific instructions, providing relevant examples and feedback, and testing and refining the results. Rapid engineering is a skill that anyone working with generative AI can learn and improve over time.
Establish new roles and workflows
Generative AI models are not standalone tools that can operate in isolation or replace human workers. They are collaboration tools that can increase and enhance human creativity and productivity. Therefore, companies need to establish new workflows that integrate generative AI models with human teams and processes.
Companies may need to create entirely new roles or functions, such as AI ombudsman or AI QA specialist, who can oversee and monitor the use and output of generative AI models and address issues when they arise. They may also need to implement new policies or protocols, such as ethical guidelines or quality standards, that can ensure the accountability and transparency of generative AI models.
Generative AI is no longer on the horizon; Has arrived
Generative AI is one of the most exciting and disruptive technologies of our time. It has the potential to transform the way we create and consume content across multiple domains and industries. However, the adoption of generative AI is not a trivial or risk-free endeavor. It requires careful planning, preparation and execution. Companies that embrace and master generative AI will gain a competitive advantage and create new opportunities for growth and innovation.
Yaniv Makover is the CEO and co-founder of Any word.
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