Targeting AI

The AI application startup, which was founded in 2016 and was valued at more than $2.1 billion in 2021, uses a reasoning engine to help employees search for information across the enterprise. Since its inception, a key ingredient in the company's success has been AI and generative AI technology. "We were the first company after Google to deploy BERT in production," said co-founder and president Varun Singh on the latest episode of Informa TechTarget's Targeting AI podcast. BERT was Google's first model with bidirectional encoding that enabled computers to understand large text spans. It was pretrained, so Moveworks did not have to train it from the ground up. It also did not require a lot of data. After using BERT to train its automation platform, Moveworks started using GPT-2  from OpenAI in 2020. This is two years before the mass popularization of the generative AI vendor's ChatGPT chatbot, mostly to generate synthetic data. Singh added that he and his team had failed to realize right away that the model could also be used for reasoning tasks. "It's not so much a mistake that was made or not, but it was just sort of as technology evolved, the moment a paradigm shift actually comes into full focus, you look back and you're like, 'We could have done that sooner because we had access to the models, but we didn't see how powerful they could be,'" he said. Since the shift, Moveworks has evolved from a platform with a reasoning engine to a platform for building AI agents. On Oct. 1, Moveworks launched Agentic Automation as part of its Creator Studio offering. The system enables developers to build AI agents. Throughout the evolution of its business, Moveworks has differentiated itself with its use of AI technology, Singh said. "Without AI, there's nothing Moveworks has to offer to the world," he said. "There's only value from Moveworks because of AI." 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.

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.

Open source AI models are closing the gap in the debate between open and closed models. Since the introduction of Meta Llama generative AI models in February 2023, more enterprises have started to run their AI applications on open source models. Cloud providers like Google have also noticed this shift and have accommodated enterprises by introducing models from open source vendors such as Mistral AI and Meta. At the same time, proprietary closed source generative AI models from OpenAI, Anthropic and others continue to attract widespread enterprise interest. But the growing popularity of open source and open models has also made way for AI vendors like Together AI that support enterprises using open source models. Together AI runs its own private cloud and provides model fine-tuning and deployment managed services. It also contributes to open source research models and databases. "We do believe that the future includes open source AI," said Jamie De Guerre, senior vice president of product at Together AI, on the latest episode of TechTarget's Targeting AI podcast. "We think that in the future there will be organizations that do that on top of a closed source model," De Guerre added. "However, there's also going to be a significant number of organizations in the future that deploy their applications on top of an open source model." Enterprises use and fine-tune open source models for concrete reasons, according to De Guerre. For one, open models offer more privacy controls in their infrastructure, he said. Enterprises also have more flexibility. When organizations customize open source models, the resulting model is something they own. "If you think of organizations making a significant investment in generative AI, we think that most of them will want to own their destiny," he said. "They'll want to own that future." Enterprises can also choose where to deploy their fine-tuned models. However, there are levels involved in what is fully open source and what is just an open model, De Guerre said. Open models refers to models from vendors that do not include the training data or the training code used to build the model, but only the weights used. "It still provides a lot of value because organizations can download it in their organization, deeply fine-tune it and own any resulting kind of fine-tuned version," De Guerre said. "But the models that go even further to release the training source code, as well as the training data used, really help the open community grow and help the open research around generative AI continue to innovate." 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.

Nearly two years after the mass consumerization of generative AI with the introduction of ChatGPT, the technology is now moving from experimentation to implementation. A recent survey by TechTarget's Enterprise Strategy Group found that generative AI adoption is growing. The analyst firm surveyed 832 professionals worldwide and found that adoption has increased in the last year. "We're in the acceleration phase," said Mark Beccue, an analyst at Enterprise Strategy Group and an author of the survey report, on the Targeting AI podcast. Organizations are using generative AI in areas such as software development, research, IT operations and customer service, according to the survey. However, there isn't a particular use case that is a top priority. Organizations are focusing on several applications of generative AI and still face some challenges when trying to adopt generative AI technology. One is a need for more infrastructure, Beccue said. "They feel that the changes are needed to support infrastructure before they can proceed with GenAI," he said. This might include adding platforms for enterprise generative AI projects or more development tools, he added. "It's really everything that gets you to being able to build an app," Beccue continued. Organizations also don't have consensus about what kind of AI model is best for their needs: open or closed source. "It's probably both," Beccue said. "People are thinking about how to use these things and they're understanding that not one model fits everything that they need. So, they're looking through to see what works for them in certain instances." The enterprises that have found quick success with generative AI are ones that invested in AI years before it was popularized by OpenAI's ChatGPT, Beccue said. He said these are companies like Adobe, ServiceNow -- which, for example, used machine learning, natural language understanding, process automation and AIOps since at least 2017 -- and Zoom. "They did it in a way where they said, 'We think there is potential here for this to help us do what we do better,'" he said. "That was their driver." This was what made them ready when generative AI hit the market. 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.

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.

Democratic presidential candidate Kamala Harris is a product of two decades of California politics who has longstanding ties to the tech and AI communities in her home state. But in her role as President Joe Biden's vice president during the past four years, Harris was tasked with overseeing Biden's executive order on AI, with its emphasis on government regulation. And it was she who hosted leaders of tech giants at the White House last year and secured pledges from them to focus on AI safety. In sharp contrast is the GOP presidential nominee, Donald Trump. While Trump's running mate, Senator J.D. Vance (R-Ohio), has a background in tech venture capital, Trump himself has no tech experience but backs a largely hands-off approach to tech and AI companies. In simple terms, Trump is anti-regulation, while Harris favors a moderate regulatory stance on big tech and the suddenly emergent generative AI sector, a view that roughly parallels that of Biden. In this episode of the Targeting AI podcast from TechTarget Editorial, three commentators on the confluence of tech and AI and politics registered their analyses of the complex dynamics of the likely Harris-Trump faceoff. Makenzie Holland, big tech and federal regulation senior news writer at TechTarget, emphasized that "there is a huge focus from the Biden-Harris administration on AI safety and trustworthiness." Meanwhile, "we've obviously seen Trump attack the executive order," she noted. For R "Ray" Wang, founder and CEO of Constellation Research, the choice for the tech industry is fairly clear. "I stress the libertarian view because I think that's important to understand that tech doesn't necessarily want to be governed," Wang said. The other guest on the podcast, Darrell West, a senior fellow in the Governance Studies program at the Brookings Institute, has authored a book about policy making in the AI era. He also pointed out the marked divergence of Harris and Trump on tech and AI issues. "Even though she historically has been close to the tech sector, I actually think she will maintain Biden's tough line on a lot of issues because that's where the party is these days," West said. "And also that's where public opinion is on many tech issues." Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

For the past year, the Targeting AI podcast has explored a broad range of AI topics, none more than the fast-evolving and sometimes startling world of generative AI technology. From the first guest, Michael Bennett, AI policy adviser at Northeastern University, the podcast has focused intently on the popularization of generative AI, while also touching on traditional AI. While that first episode centered on the prospects of AI regulation, Bennett also spoke about some of the controversies then emerging in the nascent stages of generative AI. "Organizations who have licenses to use and to sell photographers' works are pushing back,” Bennett said during the inaugural episode of the Targeting AI podcast. While Bennett's point of view illuminated the regulatory and ethical dimensions of the explosively growing technology, Michael Stewart, a partner at Microsoft's venture firm M12, discussed the startup landscape. With the rise of foundation model providers such as Anthropic, Cohere and OpenAI, generative AI startups for the last 12 months chose to partner with and be subsidized by cloud giants -- namely Microsoft, Google and AWS –-- instead of seeking to be acquired. "This is a very ripe environment for startups that have a partnership mindset to work with the main tech companies,” Stewart said during the popular episode, which was downloaded more 1,000 times. The early stages of generative AI were marked by accusations of data misuse, particularly from artists, writers and authors. Our Targeting AI podcast hosts have also spoken to guests about data ownership and how large language models are affecting industries such as the music business. The podcast also explored new regulatory frameworks like President Joe Biden's executive order on AI. With some 27 guests from a diverse group of vendors and other organizations, the podcast took shape and laid the groundwork for a second year with plenty of new developments to explore. Coming up soon are episodes on Democratic presidential candidate Kamala Harris’ stances on AI and big tech antitrust actions, election deepfakes and tech giant Oracle's foray into generative AI. Listen to Targeting AI on Apple Podcasts, Spotify and all major podcast platforms, plus on TechTarget Editorial’s enterprise AI site. 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.

AWS is quietly building a generative AI ecosystem in which its customers can use many large language models from different vendors, or choose to employ the tech giant's own models, Q personal assistants, GenAI platforms and Trainium and Inferentia AI chips. AWS says it has more than130,000 partners, and hundreds of thousands of AWS customers use AWS AI and machine learning services. The tech giant provides not only the GenAI tools, but also the cloud infrastructure that undergirds GenAI deployment in enterprises. "We believe that there's no one model that's going to meet all the customer use cases," said Rohan Karmarkar, managing director of partner solutions architecture at AWS, on the Targeting AI podcast from TechTarget Editorial. "And if the customers want to really unlock the value, they might use different models or a combination of different models for the same use case." Customers find and deploy the LLMs on Amazon Bedrock, the tech giant's GenAI platform. The models are from leading GenAI vendors such as Anthropic, AI21 Labs, Cohere, Meta, Mistral and Stability AI, and also include models from AWS' Titan line. Karmarkar said AWS differentiates itself from its hyperscaler competitors, which all have their own GenAI systems, with an array of tooling needed to implement GenAI applications as well as AI GPUs from AI hardware giant Nvidia and AWS' own custom silicon infrastructure. AWS also prides itself on its security technology and GenAI competency system that pre-vets and validates partners' competencies in putting GenAI to work for enterprise applications. The tech giant is also agnostic on the question of proprietary versus open source and open models, a big debate in the GenAI world at the moment. "There's no one decision criteria. I don't think we are pushing one [model] over another," Karmarkar said. "We're seeing a lot of customers using Anthropic, the Claude 3 model, which has got some of the best performance out there in the industry." "It's not an open source model, but we've also seen customers use Mistral and [Meta] Llama, which have much more openness," he added. 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. He is a veteran journalist with more than 35 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. They co-host the Targeting AI podcast.

The biggest global retailer sees itself as a tech giant. And with 25,000 engineers and its own software ecosystem, Walmart isn't waiting to see how GenAI technology will play out. The company is already providing its employees -- referred to by the retailer as associates -- with in-house GenAI tools such as the My Assistant conversational chatbot. Associates can use the consumer-grade ChatGPT-like tool to frame a press release, write out guiding principles for a project, or for whatever they want to accomplish. "What we're finding is as we teach our business partners what is possible, they come up with an endless set of use cases," said David Glick, senior vice president of enterprise business services at Walmart, on the Targeting AI podcast from TechTarget Editorial. Another point of emphasis for Walmart and GenAI is associate healthcare insurance claims. Walmart built a summarization agent that has reduced the time it takes to process complicated claims from a day or two to an hour or two, Glick said. An important area in which Glick is implementing GenAI technology is in payroll. "What I consider our most sacrosanct duty is to pay our associates accurately and timely," he said. Over the years, humans have monitored payroll. Now GenAI is helping them. "We want to scale up AI for anomaly detection so that we're looking at where we see things that might be wrong," Glick said. "And how do we have someone investigate and follow up on that." Meanwhile, as for the "build or buy" dilemma, Walmart tends to come down on the build side. The company uses a variety of large language models and has built its own machine learning platform, Element, for them to sit atop. "The nice thing about that is that we can have a team that's completely focused on what is the best set of LLMs to use," Glick said. "We're looking at every piece of the organization and figuring out how can we support it with generative AI." 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. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. They co-host the Targeting AI podcast.

While Apple garnered wide attention for its recent embrace of generative AI for iPhones and Macs, rival end point device maker Lenovo already had a similar strategy in place. The multinational consumer products vendor, based in China, is known for its ThinkPad line of laptops and for mobile phones made by its Motorola subsidiary. But Lenovo also has for a few years been advancing a “pocket to cloud” approach to computing. That strategy now includes GenAI capabilities residing on smartphones, AI PCs and laptops and more powerful cloud processing power in Lenovo data centers and customers’ private clouds. Since OpenAI’s ChatGPT large language model (LLM) disrupted the tech world in November 2022, GenAI systems have largely been cloud-based. Queries from edge devices run a GenAI prompt in the cloud, which returns the output to the user’s device. Lenovo’s strategy -- somewhat like Apple’s new one -- is to flip that paradigm and locate GenAI processing at the edge, routing outbound prompts to the data center or private cloud when necessary. The benefits include security, privacy, personalization and lower latency -- resulting in faster LLM responses and reducing the need for expensive compute, according to Lenovo. “Running these workloads at edge, on device, I'm not taking potentially proprietary IP and pushing that up into the cloud and certainly not the public cloud,” said Tom Butler, executive director, worldwide communication commercial portfolio at Lenovo, on the Targeting AI podcast from TechTarget Editorial. The edge devices that Lenovo talks about aren’t limited to the ones in your pocket and on your desk. They also include remote cameras and sensors in IoT AI applications such as monitoring manufacturing processes and facility security. “You have to process this data where it's created,” said Charles Ferland, vice president, general manager of edge computing at Lenovo, on the podcast. “And that is running on edge devices that are deployed in a gas station, convenience store, hospital, clinics -- wherever you want.” Meanwhile, Lenovo in recent months rolled out partnerships with some big players in GenAI including Nvidia and Qualcomm. The vendor is also heavily invested in working with neural processing units, or NPUs, in edge devices and innovative cooling systems for AI servers in its data centers. Shaun Sutner is a journalist with 35 years of experience, including 25 years as a reporter for daily newspapers. He is a senior news director for TechTarget Editorial's information management team, covering AI, analytics and data management technology. Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.

The rise of generative AI has also brought renewed interest and growth in open source technology. But the question of open source is still "open" in generative AI. Sometimes, the code is open -- other times, the training data and weights are open. A leader in the open source large language model arena is Meta. However, despite the popularity of the social media's giant's Llama family of large language models (LLMs), some say Meta's LLMs are not fully open source. One vendor that built on top of Llama is Lightning AI. LightningAI is known for PyTorch Lightning, an open source Python library that provides a high level of support for PyTorch, a deep learning framework. Lightning in March rolled out Thunder, a source-to-source compiler for PyTorch. Thunder speeds up training and serves generative AI (GenAI) models across multiple GPUs. In April 2023, Lightning introduced Lit-Llama.  The vendor created the Lit-Llama model starting with code from NanoGPT (a small-scale GPT for text generation created by Andrej Karpathy, a co-founder of OpenAI and former director of AI at Tesla). Lit-Llama is a fully open implementation of Llama source code, according to Lightning. Being able to create on top of Llama highlights the importance of "hackable" technology, Lightning AI CTO Luca Antiga said on the Targeting AI podcast from TechTarget Editorial. "The moment it's hackable is the moment people can build on top of it," Antiga said. However, mechanisms of open source are yet to be fully developed in GenAI technology, Antiga continued. It's also unlikely that open source models will outperform proprietary models. "Open source will tend to keep model size low and more and more capable, which is really enabling and really groundbreaking, and closed source will try to win out by scaling out, probably," Antiga said. "It's a very nice race." 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.

In intellectual tech circles, a debate over artificial general intelligence and the AI future is raging.  Dan Faggella is in the middle of this highly charged discussion, arguing on various platforms that artificial general intelligence (AGI) will be here sooner than many people think, and it will likely take the place of human civilization.  "It is most likely, in my opinion, that should we have AGI, it won't follow too long from there that humanity would be attenuated. So, we would fade out," Faggella said on the Targeting AI podcast from TechTarget Editorial.  "The bigger question is how do we fade out? Is it friendly? Is it bad?" he said. "I don't think we'll have much control, by the way, but I think maybe we could try to make sure that we've got a nice way of bowing out."  In addition to his role as an AI thinker, Faggella is a podcaster and founder and CEO of AI research and publishing firm Emerj Artificial Intelligence Research.  In the podcast episode, Faggella touches on a wide range of subjects beyond the long-term AI future. He takes on election deepfakes (probably not as dangerous as feared, and the tech could also be used for good) and AI regulation (there should be the right amount of it), as well as robots and how generative AI models will soon become an integral part of daily life.  "The constant interactions with these machines will be a wildly divergent change in the human experience," Faggella said. "I do suspect absolutely, fully and completely that most of us will have some kind of agent that we're able to interact with all the time.  Meanwhile, Faggella has put forth a vision of what an AGI-spawned "worthy successor" to humans could look like in the AI future. He has written about the worthy successor as "an entity with more capability, intelligence, ability to survive and (subsequently) moral value than all of humanity."  On the podcast, he talked about a future inhabited by a post-human incarnation of AI.  "Keeping the torch of life alive would mean a post-human intelligence that could go populate galaxies, that could maybe escape into other dimensions, that could visit vastly different portions of space that we don't currently understand," he said.  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. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Together, they host the Targeting AI podcast.

Salesforce was an early adopter of generative AI, seizing on large language model technology from OpenAI to integrate into its own applications. But the CRM and CX giant quickly evolved an open model strategy. It now gives customers access to multiple third-party LLMs while providing its own AI trust layer to try to ensure that Salesforce users can safely rely on AI-generated outputs. Jayesh Govindarajan, senior vice president at Salesforce AI, calls this approach "BYOLLLM," or bring your own LLLM. "The Salesforce LLM strategy is to provide an open-model ecosystem for our customers," Govindarajan said on the Targeting AI podcast from TechTarget Editorial. "Salesforce-developed models are, of course, available out of the box on the AI stack, but customers can also bring their own LLMs. And to support this level of choice and diversity, the trust layer is model-agnostic," he continued. As befits its core customer base, Salesforce sees sales, marketing and customer service applications as most ripe for generative AI, and that is where the vendor is focusing on the technology as a productivity engine, Govindarajan said. Similar conversations, whether taking place in email or other messaging formats, can be automated with generative AI so the technology is embedded in daily workflows. An example Govindarajan cited is using generative AI to let a marketing person easily make a marketing campaign multilingual. "How do we make a customer service person more efficient? How do we make a rock star salesperson 10 times more successful? How do we make a marketing manager create campaigns that convert really well?" Govindarajan said. "It's not easy to do that. You want to do it with safety, security, and trust," he said. "As you know, the systems can go off. So, you want to have the right guardrails in place to be able to shape it into the right form." 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. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

The explosive popularity of generative AI has been accompanied by the question of whether developers are finding great uses for the new technology. While the hype around GenAI has grown, the perception of its usefulness for developers has changed. "Developers are eager to kind of embrace AI more into their complex tasks, but not for every part, and they're not open to the same degree," GitHub researcher Eirini Kalliamvakou said on the Targeting AI podcast from TechTarget Editorial. On Jan. 17, Kalliamvakou released new findings that showed the evolution of developers' expectations of and perspectives on AI tools. For many developers, GenAI tools are like a second brain and serve mainly to reduce some of the cognitive burden they feel performing certain tasks. Cognitive burden in coding is produced by tasks that require more energy than developers would like to invest. "They feel that it is not worth their time," Kalliamvakou said. "This is a sort of task that is ripe for automation." Many developers are also using AI tools to quickly make sense of a lot of information and understand the context of what they need to do. While many developers find AI tools helpful, others experience AI skepticism, she added. Developers who are skeptical about AI had tried AI tools and were not satisfied. "They felt the tools are not good enough," Kalliamvakou continued. This is because the tools sometimes gave inaccurate responses and were not helpful. "What they were saying was AI [tools] at the moment, they cannot be trusted, they cannot give ground truths,"  she said. The two groups of developers are important to keep in mind for GitHub and other AI vendors creating tools that developers will use. 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 technology has led to concerns about the data AI technology companies use to train their systems. Authors, journalists and now musicians have accused generative AI vendors of using copyrighted material to train large language models. More than 200 musicians signed an open letter released Tuesday by the Artists Rights Alliance calling on AI developers to stop their "assault on human creativity." While the artists argue that responsible use of generative AI technology could help the music industry, they also maintain that irresponsible use could threaten the livelihoods of many. The problem is permissions, said Jenn Anderson-Miller, co-founder and CEO of music licensing firm Audiosocket, on the Targeting AI podcast from TechTarget Editorial. "It's widely understood that a lot of these training models have trained on copyrighted material without the permission of the rights holders," Anderson-Miller said. While it's true that the musicians did not produce evidence of how their works have been infringed on, generative AI vendors such as OpenAI have failed to prove that they didn't infringe on copyrighted works, she said. For Anderson-Miller, one solution to the problem is creating a collaborative effort with musicians that would include licensing. As a company that represents more than 3,000 artists, Audiosocket recently inserted an AI clause in its artist agreement. In the clause, Audiosocket defined traditional and generative AI and said it plans to support the ecosystem of traditional AI. "We don't see this as directly threatening our artists," Anderson-Miller said. "We see this as, if anything, it's helping our artists." 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.