As one of the top cloud providers, Google Cloud also stands at the forefront of the generative AI market.

Over the past two years, Google has been enmeshed in a push and pull with its chief competitors -- AWS, Microsoft and OpenAI -- in the race to dominate generative AI.

Google has introduced a slate of new generative AI products in the past year, including its main proprietary large language model (LLM), Gemini and the Vertex AI Model Garden. Last week, it also debuted Audio Overview, which turns documents into audio discussions.

The tech giant has also faced criticism that it might be falling behind on generative AI challenges such as the malfunctioning of its initial image generator.

Part of Google's strategy with generative AI is not only providing the technology through its own LLMs and those of many other vendors in the Model Garden, but also constantly advancing generative AI, said Warren Barkley, head of product at Google for Vertex AI, GenAI and machine learning, on the Targeting AI podcast from TechTarget Editorial.

"A lot of what we did in the early days, and we continue to do now is … make it easy for people to go to the next generation and continue to move forward," Barkley said. "The models that we built 18 months ago are a shadow of the things that we have today. And so, making sure that you have ways for people to upgrade and continue to get that innovation is a big part of some of the things that we had to change."

Google is also focused on helping customers choose the right models for their particular applications.

The Model Garden offers more than 100 closed and open models.

"One thing that our most sophisticated customers are struggling with is how to evaluate models," Barkley said.

To help customers choose, Google recently introduced some evaluation tools that allow users to put in a prompt and compare the way models respond.

The vendor is also working on AI reasoning techniques and sees that as moving the generative AI market forward.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

The growth of generative AI has put diversity front and center.

In the last year, there have been concerns that GenAI systems such as ChatGPT and Google Gemini are not trained with enough diverse data sets.

For instance, the introduction of the Lensa app two years ago allowed people of color to generate avatars of themselves. Concerns were raised, however, after some users said Lensa's generated images changed their skin color.

Incidents with AI tools like Lensa show that AI creators might not have enough diversity in their data set.

Alternatively, there have also been incidents where it's clear that AI systems misrepresented diversity. For example, Google shut down Gemini's image generator earlier this year after users started generating inaccurate depictions of historical figures. For example, it generated images of well-known white people, such as the Pope, as Black people.

Google has since opened the model back up. Last week, the cloud provider revealed that its new AI model, Imagen 3, will be rolled out to its Gemini AI model. The model will produce images of people again but won't support generation of photorealistic identifiable individuals.

Despite the hiccup in the beginning stages of the technology, hope exists, said David C. Williams, assistant vice president of automation at AT&T.

While Williams leads a team that previously used RPA, or robotics process automation, to drive business needs at AT&T, the team is now pivoting to generative AI. The shift has given Williams a view of how GenAI could affect diversity.

"Generative AI is going to force diversity," Williams said on the latest Targeting AI episode.

Cloud providers such as Google must include diversity in their data sets because not having it could lead to alienation from people of color, he continued. If creators of these systems fail to have diverse systems that show representation, that could lead many people of color to simply stop using the systems, which won't help their business.

On the other hand, people of color and women will gain new opportunities because of generative AI.

"Those that embrace generative AI and figure out how to use it in the workplace will have an incredibly different value proposition than the rest," Williams said.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

The growth of deepfakes in the past few years is a threat to not only organizations but also the U.S. general election in November.

Information security vendor Pindrop saw a sharp rise in deepfakes in the first few months of the year compared to the previous year.

Deepfakes of Vice President Kamala Harris, former President Donald Trump, President Joe Biden and state-level candidates have circulated in the runup to the November U.S. general election.

"Last year, we were seeing about one deepfake every single month," Vijay Balasubramaniyan, co-founder and CEO at Pindrop, said on the Targeting AI podcast. "Starting this year ... we started seeing a deepfake every single day across every single customer."

A big reason for the stark increase is the growth of generative AI systems and voice cloning apps. Meanwhile, many people can't distinguish between a deepfake voice and an authentic one.

While about 120 voice cloning apps were on the market last year, this year users (both legitimate and illegitimate) can choose among more than 350 voice cloning apps.

Moreover, Balasubramaniyan said, fraudsters are using generative AI technology to scale their attacks.

For example, generative AI systems can create deepfakes in many different languages -- a series of large language models from Meta can translate some 4,000 languages. Fraudsters can use these systems to create deepfakes that can respond to questions depending on which words are spoken.

"They have managed to scale their attacks in massive ways, and in ways that we have not seen before generative AI. We're seeing that now," Balasubramaniyan said.

The massive progression of deepfake technology means organizations must remain aware and vigilant, said Harman Kaur, vice president of AI at Tanium, on the podcast. Tanium is a cybersecurity and management vendor based in Kirkland, Wash.

"You have to have a plan to respond," Kaur said. "Do you have the tools to understand what type of threat has been invited into your network, and do you have the tools to fix it?"

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Together, they host the Targeting AI podcast series.