Tech executives looking to get the most value for their organizations from generative AI need to understand the basics of the technology, according to a report released Tuesday by Forrester Research.

“The tech world has grown frustrated with several recent bubbles delivering no real value, but generative AI is already improving content creation, software development and knowledge management across enterprises,” the report said.

“However, publicity breeds bad information and misconceptions,” it continued. “Tech executives need to know some basics such as what generative AI is, how it can be used, what the future holds for generative AI, and what to do with it in the short term.”

To get a handle on what generative AI is, tech executives need to clear up some misconceptions about the technology.

“It sounds simple, but the biggest misconception I encounter over and over again is that generative AI and ChatGPT are not the same thing,” said Rowan Curran, Forrester analyst and one of the report’s authors.

“When executives look at these things, it’s important to look at them as a broader technology that happened to capture our imagination through the chatbot interface,” he told TechNewsWorld.

“ChatGPT is an application wrapped around the GPT-4 or GPT 3.5 Turbo model,” he said. “Tech executives need to look at the model in addition to the application.”

Smart ain’t as smart as it sounds

Generative AI is a big language model, which means it’s capable of pretty much anything related to language, explained Sagi Eliyahu, co-founder and CEO of Tonkian, a Palo Alto, California-based maker. Creator of a process experience platform that includes AI-enabled features.

“Since we as humans communicate and even think in words, LLMs now look like they are capable of anything,” he told TechNewsWorld.

“But even though they appear capable of ‘thinking’, language models are ultimately constrained by the data they have been trained on,” he said. “Like any technology, it’s only as useful as how you leverage it in the existing culture.”

“People think because it sounds smart, it is smart,” said Daniel Castro, director of the Center for Data Innovation, an international think tank studying the intersection of data, technology and public policy.

“People shouldn’t rely on it for facts or as a substitute for human expertise,” he told TechNewsWorld. “Instead, they should use it as a tool to generate ideas and enhance human skills. Generative AI has many important use cases, but it is still a long way from artificial general intelligence.”

Mistaking generative AI for artificial general intelligence — a type of AI that can perform any intellectual task that a human can do — is another misconception, said Anderl Group, president and CEO of an advisory services firm in Bend, Ore. said principal analyst Rob Enderle.

“AGI is still years away in our future,” he told TechNewsWorld.

“What generative AI is, is a big language model that can interact with you,” he said. “It is the beginning of a new user interface based on voice and presence that is by design more human-like in use.”

variety of use cases

The use of “chat” in generative AI like ChatGPT can confuse even executives who are the nimrods of AI. Mark N., president and principal analyst at SmartTech Research in San Jose, Calif. “They confuse generative AI with the simple chatbots commonly used for customer service on websites,” Vena said.

“Those chatbots are not general AI-based, because they get their responses from a limited universe of general questions, which are typically specific to a single topic,” he told TechNewsWorld. “Zen AI curates its own content, in principle, for all content on the internet, so it is much more real-time from a contextual content perspective and can answer a vast array of questions.”

Acknowledging that generative AI is still relatively immature, Forrester said technical executions could capitalize on a variety of use cases, including:

  • increasing developer productivity through text-to-code generation tools;
  • enabling visual designers to iterate and quickly ideate with a text-to-image generator;
  • empowering marketers to create product descriptions that match their preferred brand language and tone; And
  • Enhancing the appearance of officers by allowing synthetic avatars of themselves to appear in video without being recorded themselves.

“One of the most under-appreciated aspects of generative AI is its potential to enable more people to create software than ever before,” said Bob O’Donnell, founder and principal analyst at technology market research and consulting firm Technalysis Research in Foster City. Is.” , California.

“No-code, low-code development tools have been available for years, but you still need to be very technical to get them to work,” he told TechNewsWorld.

“One of the more interesting applications of generative AI is the ability to build code from details,” he continued. “It means that someone with an idea without programming expertise can do a lot of interesting things. It’s going to be incredibly impactful for businesses.

from excitement to magic

Forrester said that while generative AI is exciting today, the applications of tomorrow will seem like magic.

For example, a future analytics platform with embedded generative AI capabilities may allow a user to submit a query such as: “Create an infographic of our last year’s sales revenue, operating expenses, and customer satisfaction and summarize our last three Include an explanation for the trends that do.” Quarterly Report.

“AI now allows end users to make the leap from research to something much more useful — resolution,” Eliyahu said.

“And not just any kind of resolution, but resolution that is differentiated, rapid, personal and context-aware,” he continued. “At the end of the day, this is what people really want and need out of technology – tools for their requests, questions and problems to be understood and resolved immediately.”

Forrester admits that problems have plagued generative AI. Text generators can produce coherent nonsense, as well as have harmful biases baked into their data, it noted. Questions about copyright and intellectual property also remain unanswered.

“Beyond the potential for hallucinations, which we have seen in a lot of these models, AI is not going to be the solution to everything,” said Will Duffield, a policy analyst at the Cato Institute, a Washington, DC think tank. ,

“There’s always a risk of trying to overfit a new technology to solve problems it’s not yet designed to solve,” he told TechNewsWorld.

Seek General AI vendor input

Still, Forrester encourages tech executives to start experimenting with generative AI over the next six to nine months.

“It’s really important for organizations to start experimenting in this space and start engaging with their vendor partners to understand what they’re doing,” Curran advised. “Most vendors have something on their road map for how they are going to provide generative AI capability.”

He also recommended taking a broader look at the tech execution vendor landscape. “It’s a lot bigger than some of the players who have gotten all the attention over the last several months,” he said.

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