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Generative AI models are suddenly ubiquitous, but how should businesses approach and utilize this trending and fast-evolving tech? Ceiba looks at the origins and drawbacks of the generative AI boom and explains how companies can benefit.

Generative AI models and the secret to their success

  1. How did generative AI suddenly increase its influence?
  2. What are the drawbacks of generative AI?
  3. How can we overcome the limits of generative AI?
  4. Incorporating and optimizing generative AI in software development
  5. How will generative AI impact data processing?
  6. Generative AI and the fight for cyber security.

Generative artificial intelligence (AI) models have captured the world’s attention thanks to high-profile output in fields once deemed the sole preserve of human beings, like law and writing.

The best-known generative AI applications, such as text generator ChatGPT and art generators DALL-E, can produce human-standard emails, essays, poems, articles, computer code, jokes, and speeches.

Businesses are keen to take advantage of this ever-evolving tech and are applying it across multiple industries for increased productivity, error detection, and creative inspiration.

Here we consider how and why generative AI has captivated the global marketplace and discuss how businesses can incorporate its skillset.

Generative AI models CeibaBLOG

  1. How did generative AI suddenly increase its influence?

    Four key factors have determined the current global success of generative AI.

    Machines learn by analyzing data for patterns, sorting data according to similarities and differences and by receiving feedback on their actions.

    The models AI bases its intelligence on are expanding exponentially and there have been great advances in the quality of the factual data sources used to train it. 

    Computer power is greater than ever and foundation models – whereby machines undertake self-supervised learning on massive datasets – means AI can solve multiple problems using a single model.

    Developers have also witnessed a phenomenon known as ‘emergence’ whereby machines dramatically increase their capabilities as model sizes increase.

    You may also be interested in: How will rapid advances in generative AI change software development?

  2. What are the drawbacks of generative AI?

    Mass success and global exposure mean intense scrutiny, and generative AI has revealed its weaknesses as the result of the large-scale testing it endured in 2023.

    Users have highlighted false answers to prompts, the reproduction of data that is clearly out of date, and poor performances when it comes to straightforward tasks that involve simple math or reasoning.

    It is important to remember that generative AI relies on data and produces results similar to the knowledge it gains from that data. It cannot invent something completely new.

    The suggestion that generative AI will become a key player in the creative industries must therefore be tempered by the fact that generative AI cannot create anything from scratch.

    Machines’ lack of creativity and inventiveness at this stage differentiates them from their human counterparts and remains their fundamental weakness overall.

  3. How can we overcome the limits of artificial intelligence?

    Businesses can use various techniques to help the current wave of generative AI models perform to their very best and avoid unnecessary mistakes. 

    Adding certain words to the instructions given to machines, offering them access to the web or specific programming tools, and encouraging them to ask questions of themselves are all techniques that have met with marked success.

    Developers argue, however, that the best way to overcome the limits of today’s generative AI is to consider the way companies perceive and work alongside them. 

    Instead of viewing these models as sources of information or creativity, businesses should think of them as a conduit for ideas, links, and implementations, and use them to provoke and inspire human creativity and inventiveness.

  4. Incorporating and optimizing generative AI in software development

    Besides clear uses in the creative and customer service industries, generative AI offers opportunities across multiple industries, including software development.  

    The latest models have expanded the possibilities for internet search engines, for example, which can now focus on meaning rather than just a string of keywords, and automatic code generation, which is offered through software development collaboration tools.

    These AI-powered tools can quickly find and resolve the bugs in a piece of code and, given that debugging is a costly and time-consuming process, AI not only helps automate repetitive tasks but improves overall code implementation, allowing developers to focus on their myriad of other skills. 

    Code generation tools can also serve as smart assistants for developers, providing real-time prompts, suggestions, and insights during the decision making process.

  5. How can generative AI impact data processing?

    Data engineers are also reaping the rewards of generative AI models in data processing, particularly the fact these machines can help automate tasks related to data preprocessing and data transformation.

    AI-powered tools can also be used to analyze and optimize data pipelines, which maintains efficient data flow, reduces latency, and improves how data processing systems perform.

    Generative AI models’ ability to detect anomalies and inconsistencies in datasets is a huge advantage, helping to keep the quality of datasets up to standards and helping data engineers to identify potential problems.

  6. Generative AI and the fight for cyber security

    Generative AI has been a huge boon for the cyber security industry. The technology is already boosting the detection and response to cyber threats by quickly analyzing vast amounts of data and flagging the patterns that indicate an attack.

    AI-powered tools can also automate systems and software vulnerability assessments and penetration testing, ensuring these simulations are as accurate and effective as possible.

    Finally, in terms of fraud prevention, AI-driven fraud detection systems have been able to highlight fraud, such as phishing attacks and identity theft, as soon as it occurs, thus limiting the damage it inflicts and improving organizations’ security overall.

Generative AI is making waves for a reason. To find out how your business can incorporate and mitigate this transformative technology, contact Ceiba today.


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