Targeting AI

When generative AI became the next big thing in tech, enterprise software giant Oracle bet heavily on a startup to provide it with foundation and large language models rather than scramble to develop its own. That then-fledgling company was Cohere. Founded in 2019, the generative AI vendor raised $270 million in a Series C round, and its investors included Oracle, Nvidia, Salesforce Ventures, and some private equity firms. In July, Cohere raised another $500 million and reached a market valuation of $5.5 billion. Cohere's open generative AI technology is now infused in many of Oracle's databases, a fixture among large enterprises. The tech giant has also tapped Cohere's powerful and scalable Control-R model for Oracle's popular vertical market applications, including those for finance, supply chain and human capital management. But while Oracle has put Cohere at the center of its generative AI and agentic AI strategy, the tech giant is also working closely with Meta. The social media colossus has gained a foothold in the enterprise AI market with its Llama family of open foundation models. Oracle is customizing Llama for its Oracle Cloud Infrastructure platform, along with Cohere's models. "We have made a decision to really partner deeply around the foundation models," said Greg Pavlik, executive vice president, AI and data management services at Oracle Cloud Infrastructure, on the Targeting AI podcast from TechTarget Editorial. "What we're looking for are companies that are experienced with creating high-quality generative AI models," he continued. "But more importantly … companies that are interested in enterprise and specifically business solutions." Pavlik said Oracle values the open architecture of the models from both Cohere and Meta, which makes it easier for Oracle to customize and fine-tune them for enterprise applications. "The advantage really of having a deep partnership is that we're able to sit down with the foundation model providers and look at the evolution of the models themselves, because they're not really static," he said. "A company will create a model and then they'll continually retrain it. "We see our role as to come in and proxy for the enterprise user, proxy for a number of verticals," Pavlik continued. "And then try to move the state of the art in the technology base closer and closer to the kinds of patterns and the kinds of scenarios that are important for enterprise users." Oracle also uses generative AI technology from other vendors and enables its customers to use other third-party models, he noted. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering AI software and systems. Together, they host the Targeting AI podcast.

At the beginning of the wave of generative AI hype, many feared that generative models would replace the jobs of creatives like artists and photographers. With generative AI models such as Dall-E and Midjourney seemingly creating unique works of art and images, some artists found themselves at a disadvantage. Some say the generative systems took their artwork, copied it and used it to produce their own images. In some cases, the generative systems allegedly outright stole the creative work. Two years later, artists have to some extent been reassured by the support of stock vendors like Getty Images. Instead of trailing behind generative AI tools such as Stable Diffusion, Getty created its own image-generating tool: Generative AI by Getty Images. Compared with other image generators, Getty has taken great lengths to restrict its model through the data set. The stock photography company maintains what it calls a clean data set. "A clean data set is really a training data set that a model is trained on that can lead to a commercially safe or responsible model," said Andrea Gagliano, senior director of AI and machine learning at Getty Images, on the latest episode of TechTarget Editorial's Targeting AI podcast. Getty's clean data set does not contain brands or intellectual property products, Gagliano said. The model's data set also does not include images of well-known people or likenesses of celebrities like Taylor Swift or presidential candidates. "We have taken the very cautious approach where our generator will not generate any known person or any celebrity," Gagliano said. "It will not generate Donald Trump," she said, referring to the President-elect. "And it will not generate Kamala Harris," referring to the vice president and former presidential candidate. "It has never seen a picture of Donald Trump," she continued. "The model has never seen a picture of Kamala Harris." Gagliano added that removing this possibility also guards against those who want to misuse the technology to create deepfakes. Therefore, any generated output is labeled synthetic or AI-generated. "We don't want any situation where we start to undermine the value of a real image," Gagliano said. Finally, the data set that Getty uses produces images with licenses on them, ensuring that creators get compensated. Thus, a portion of every dollar made by Generative AI by Getty Images is given to the creator who contributed to the data set. "The reason for that is the more unique imagery that we bring into the training data set, the more additive it is," Gagliano said. Getty updated its generative AI tools Tuesday. The new capabilities include Product Placement, which lets users upload their own product images and generate backgrounds, and Reference Image, which enables users to upload sample images to guide the color and composition of the AI-generated output. 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.  

President-elect Donald Trump during his election campaign offered clues about how his administration would handle the fast-growing AI sector. One thing is clear: AI, to the extent that it is regulated, is headed for deregulation. "It's likely going to mean less regulation for the AI industry," said Makenzie Holland, senior news writer at TechTarget Editorial covering tech regulation and compliance, on the Targeting AI podcast. "Being against regulation and [for] deregulation is a huge theme across his platform." Trump views rules and regulations on business as costly and burdensome, Holland noted. The former president and longtime businessman's outlook presumably includes independent AI vendors and the tech giants that also develop and sell the powerful generative AI models that have swept the tech world. President Joe Biden's wide-ranging executive order on AI has been the strongest articulation of how the federal government views AI policy. However, it's unclear which elements of the Democratic president's plan Trump will scrap and which he'll keep. Trump established the National Artificial Intelligence Initiative Office at the end of his first term as president in 2021. David Nicholson, chief technology advisor at Futurum Group, said on the podcast that Trump will likely retain some aspects of the executive order with bipartisan support. Among these is the federal government's recognition that it should guide and promote AI technology. "[Trump will] definitely not scrap it wholesale," Nicholson said. "There's something behind a lot of those concerns ... and pretty bipartisan concern that AI is a genie that we only want to let out of the bottle, if possible, very carefully." Holland, however, doesn't expect many regulatory proposals in Biden's executive order to survive the next Trump presidency. Trump is also likely to dramatically de-emphasize the AI safety concerns and regulatory proposals that feature prominently in Biden's executive order, she said. Meanwhile, concerning Elon Musk -- a major Trump backer and owner of the social media platform X, formerly Twitter, and generative AI vendor xAI -- the issue is complicated, Nicholson said. Musk has been a trenchant critic of xAI competitor OpenAI, alleging in a lawsuit that the rival vendor abandoned its commitment to openness in AI technology. However, Nicholson noted that Musk's definition of transparency in training large language models is unorthodox, insisting that models be "honest" and not contain political bias. "Having the ear of the president and the administration, I think he could be meaningful in that regard," Nicholson said. "[Musk] is going to be the loudest voice in the room when it comes to a lot of this stuff." While Trump is expected to try to reverse or ignore much of Biden's agenda, one major piece of bipartisan legislation passed during Biden's tenure, the CHIPS and Science Act of 2022, is likely to survive because it emphasizes reviving manufacturing and technology development in the U.S., Nicholson said. But the Federal Trade Commission's and Department of Justice's active stances on AI rulemaking and big tech regulation -- the DOJ successfully sued Google for monopolizing the search engine business -- are ripe for a Trump rollback. "The FTC is likely to face a shake-up, as far as Lina Khan's job probably is on the line," Holland said, referring to the activist FTC chair, who has vigorously pursued a number of big tech vendors. "Trump's entire platform is about deregulation and being against regulation. That's automatically going to impact these enforcement agencies, which, in some capacity, can make their own rules," Holland said. In the absence of meaningful federal regulation of AI, the U.S. is moving toward a state-by-state regulatory patchwork. 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. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.  

When Candace Mitchell was young, she discovered a love for computers and haircare. Her interest in technology led her to study coding in high school, leading her to build websites. Meanwhile, she also considered going to cosmetology school. She found a middle ground in beauty technology, later becoming co-founder and CEO of Myavana, a Black-owned beauty technology vendor. Myavana uses AI technology to analyze hair strands and make haircare recommendations. Myavana started with a hair analysis kit; the startup's technology uses machine learning to identify and analyze the different unique combinations in people's hair. "Our research shows us that there are actually 972 unique combinations of hair profiles," Mitchell said on the latest episode of the Targeting AI podcast. "Using machine learning is how we can automate the process of the analysis and generate those product recommendations." While Myavana works with consumers, it found that its data on hair is also valuable to enterprises interested in the haircare business. "When you come to Myavana, you can target consumers based on their hair goals and hair challenges," Mitchell said. "That's the cool thing with AI -- it has uncovered new data that is helpful for businesses and how to target consumers. And again, just making it personalized." Myavana recently raised $5.9 million in seed round funding. While the vendor developed proprietary technology, it runs its model on AWS. It also built a conversational AI chatbot with Google. 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.