Transformers

Transformers in generative artificial intelligence (AI) are neural networks developed to enhance the ability of AI to more accurately sequence an input and to an output. This ability for generative AI to gain some level of context around an input solved a massive problem with sequence transduction. Sequence transduction is any instance where an input sequence is transformed into an output sequence, and happens often in text-to-speech, translations, speech recognition, and more.

Before transformers, AI was limited in its ability to operate more like a human. By applying transformers, AI has dramatically improved its ability to process sequences more accurately and efficiently.

Transformers are Garnering a Lot of Attention — and for Good Reason

Prior to transformers, AI relied on recurrent neural networks (RNNs). These networks processed data sequentially, one word at a time in the input, without understanding the relationship between the words when forming the output. Developers have long wanted to understand how to create a pathway that operates similar to the human brain’s natural language processing abilities by understanding how the brain applies context to statements, phrases or sentences.

Recently, researchers discovered that the limbic system, or the side of the brain closest tied to memories, takes spatial information and tracks it alongside the memories of the brain. Transformers in AI were developed to work in a similar capacity, aligning logical inputs with context to create a more human-like output. By recreating the human brain’s ability to understand the relationships between words, AI now knows which words are most important, which words hold various meanings, and thus can become more conversational in nature with more accuracy in tone.

Transformers Can Enhance Enterprise Operations

There are impressive advantages to how transformers are used at the enterprise level. Prior to them coming on the scene in 2017, AI couldn’t be relied upon for tasks such as customer service chatbots. Today, transformers in natural language processing allow information to be processed in multiple parts of a sequence simultaneously, allowing AI models to be trained faster and operate with more accuracy.

As the neural networks become more advanced, transformers will have more long-term dependencies in the text and images for analysis, which will dramatically improve accuracy in both understanding inputs and generating coherent outputs. This adaptability opens them up for more use cases and domain-specific tasks.

Challenges of Transformers With AI

While the promise of transformers is clear, there are still many hurdles that must be taken into consideration when adopting AI into your organization.

Perhaps the biggest challenge with transformers is the amount of computational costs it requires. AI models need thousands of GPUs around the clock to be trained and to continue to run efficiently. Having this technological capability in house is expensive, so organizations that don’t operate a private AI network run the risk of relying on external databases, which can tarnish the outputs and put the brand at risk of misunderstandings, AI hallucinations, and biases.

Analyzing where and how to adopt AI into an organization requires looking at how the challenges of developments, such as transformers, can impact the overall brand and customer experience. We review this through the lens of the StoryVesting framework where we can better understand the human impact of new systems like AI. Without having a framework as a guardrail for making these critical decisions about where and how to bring AI into the operational fold, organizations run the risk of being adversely affected.

Use Cases for Transformers in Enterprise

Transformers have taken the development world by storm. While there are advantages and challenges to leveraging this technology, proper deployment can yield outstanding business outcomes.

Bolster the Customer Experience

Transformers are paramount to customer experience where chatbots are used. Rather than relying on natural language programming (NLP) to parse text together, transformers allow AI to speak more conversationally, improving the customer experience where chatbots are used.

Improve the Hiring Process

Because transformers are able to analyze text and images at a deeper level, this type of AI can help human resources departments find top talent and retain top talent with more ease. By going beyond basic keyword searches in resumes and feedback forms, HR can extract better insights to build the workforce.

Analyze Multiple Data Loops

Enterprises have a multitude of data points and data layers. On the surface, these data points can feel overwhelming and impossible to analyze or interpret. With transformers, teams can leverage AI to get faster insights and more accurate assumptions by pinpointing patterns with more ease.

Lower Silo Walls

When teams have access to the same insights pulled together with transformers, they’re able to work off the same data sets and lower the departmental walls. This improves the employee experience and boosts productivity, allowing teams to work in sync rather than in silos.

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Transformers

Transformers are Garnering a Lot of Attention — and for Good Reason

Transformers Can Enhance Enterprise Operations

Challenges of Transformers With AI