In the age of generative AI, Microsoft has become one of the lead investors after its massive investment in ChatGPT creator OpenAI.

Since Microsoft's $13 billion investment in OpenAI, the AI market has seen changes including a tilt toward smaller and open source AI language models. Meanwhile, the tech giant's venture fund, M12, (which did not take part in the tech giant's deal with OpenAI) is still keeping its eye out for other AI startups that could be just as big as OpenAI.

M12 seeks technologies that are new and transformative in the market, said partner Michael Stewart.

"These are usually technologies where Microsoft does not have an existing large product," Stewart said on TechTarget Editorial's Targeting AI podcast. "[There's] less of a worry that Microsoft would be left behind in this unfolding story, as much as making sure they are aware of the most attractive, most competitive newest technologies that they could partner with."

In the hot AI market, there are more opportunities for AI startups to partner with big tech companies via investments than in the past, Stewart added.

"This is a very ripe environment for startups that have a partnership mindset to work with the majors," he said.

It's also critical that AI startups looking for investment understand where the generative AI technology is going, even if they are not all incorporating the technology.

Furthermore, startups must be willing to partner with investors and accept their input in the structure of their business model, Stewart said.

"It's very difficult for me to accept that investors who are buying a portion of the company have no say or even protection of their own investment as the company grows," he said. "We do look critically at structures that are really intended to foil the influence of boards."

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the Targeting AI podcast series.

When Juliette Powell and Art Kleiner started working on their book, The AI Dilemma: 7 Principles for Responsible Technology, generative AI had not yet exploded into the public consciousness.

But after OpenAI released its blockbuster AI chatbot, ChatGPT, in October 2022, the co-authors went back to revise their narrative to accommodate the sudden emergence of a transformative force in business and society, one that needs guidelines and regulations for responsible use perhaps more than any other new software technology.

"Now that we have generative AI in our hands … we also have to have the responsibility of how they will impact not just the people around us, but also the billions of people that are coming online every year who have no idea to what extent algorithms shape their lives," Powell said on the Targeting AI podcast from TechTarget Editorial. "So I feel like we have a larger responsibility."

Powell, like Kleiner, with whom she is a partner in a tech consultancy, is an adjunct professor at New York University's Interactive Telecommunications Program.

The authors' second principle, "Open the closed box," is about transparency and explainability -- the ability to look into AI systems and understand how they work and are trained, Kleiner said.

"That doesn't just mean the algorithm, it means also the company that created it and the people who engineered it and the whole system of sociotechnical activity, people and processes and code that fits together and creates it," he said.

Another of the principles at the core of the book is "people own their own data."

"One of the things that human beings do is hold biases and assumptions, especially about other people. And that when it's frozen into an AI system has dramatic effect, particularly on vulnerable populations," Kleiner said. "We are our own data."

The book is largely based on Powell's undergraduate thesis at Columbia University about the limits and possibilities in self-regulation of AI and drew on her consulting work at Intel.

As for regulation of AI technology, Powell and Kleiner are proponents to the extent that it fosters responsible use of AI.

"It's important that companies be held accountable," Powell said. "And I also think that it's incredibly important … for computer and systems engineers to actually be held accountable for their work, to actually be trained in responsible work ethics so that if people get harmed, there's actually some form of accountability."

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the "Targeting AI" podcast series.

With 2023 being the year for generative AI, 2024 will be the year the technology grows and develops.

Many industry experts think that instead of the hype slowing, it will blossom.

"In 2024, there will not be a trough of disillusionment with this tech, ever," said Mike Leone, an analyst at TechTarget's Enterprise Strategy Group, on the Targeting AI podcast from TechTarget Editorial. "We're jumping from hype to seeing productivity enhancements and improvements."

However, the year will likely bring about many more AI models with both mature and immature enterprise capabilities. Enterprises may also see cost and regulation policies that could affect enterprise adoption of generative AI, Leone added.

One development in the new year is a move away from large language models towards smaller models, said Usama Fayyad, executive director of The Institute for Experiential AI at Northeastern University.

"[There will be] a realization that bigger is not necessarily better all the time," Fayyad said. "Having more parameters makes a model less portable, less maintainable, often unstable, requires a lot more data and a lot more guidance."

Alternatively, smaller models are cheaper to train, maintain and revise, Fayyad added.

Regulation will also continue to develop in 2024, said Ricardo Baeza-Yates, director of research at The Institute for Experiential AI.

While the EU is already introducing AI policies, countries like China are expected to join in next year, Baeza-Yates said.

There will also be a push toward "grey models" instead of black box models, he added. Black box models are models that are unexplainable, while with grey models, there's a level of understanding of how the models work.

Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the Targeting AI podcast series.

Wide use of autonomous vehicles is far off in the hazy future.

But truck and "last-mile" delivery van fleets serving online shoppers are already using advanced AI technology to guide drivers to their destinations safely.

As Stefan Heck, CEO of Nauto, vendor of an AI-powered driver and fleet safety system, explained it on the Targeting AI podcast from TechTarget News, Nauto uses the same driving tools as autonomous vehicles, but leaves human drivers in charge.

"We're not trying to replace the driver at all. We're a co-pilot or a guide, an advisor or safety warning system for the driver," Heck said on the podcast. "We use similar AI to what an autonomous vehicle does in terms of understanding what's happening."

Nauto's predictive AI package uses sensors, a dual-facing camera, computer vision and neural network technology to see, understand and anticipate driving conditions in real time and issue verbal assist alerts to drivers if they take their eyes off the road or hands off the steering wheel or act sleepy.

But unlike the tech in expensive autonomous vehicles, which are still largely in the testing phase and have run into serious safety and other operating problems in San Francisco and elsewhere, Nauto's system is more approachable at a cost of about $500 per vehicle.

As for privacy considerations, while drivers are fully aware the AI system is there and can't turn it off while they're driving, Heck said the vendor tries to make it as non-intrusive as possible so drivers don't get annoyed.

And the Nauto onboard box, mounted on the windshield, is polite, Heck argued.

"It is an algorithm looking in real time for certain risks and behaviors only," he said. "We don't have an algorithm that says … 'Stefan's picking his nose today.' But we do look for, did you fall asleep? Did you not see the stop sign where you're not paying attention?

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the "Targeting AI" podcast series.

Wayfair's machine learning strategy has been critical to its growth.

The online furniture retailer's machine learning and AI journey started in 2013.

"It was about 'We think we can do better business, make our dollars go longer if we actually optimize this toolkit,'" said Tulia Plumettaz, Wayfair's director of machine learning, during the Targeting AI podcast from TechTarget News.

Wayfair started with putting machine learning technology to work to enhance its marketing. This meant using machine learning and AI technology to find the best medium to place its ads.

Soon, the online retail giant was expanding its use of the technology to price algorithmically and understand how price changes will change demand.

When Wayfair first engaged with AI, the company was mostly a "build shop," meaning it developed its AI and machine learning systems in-house, Plumettaz said.

However, the company has since pivoted to a hybrid approach and started partnering with third-party vendors, notably Google Cloud. Wayfair has also tested generative AI technology from OpenAI, even though the company has historically been a Google shop, Plumettaz said.

"We see the longevity of these partnerships as a mechanism of saying, 'Hey, we can use that to inform product,'" she said. "We see ourselves pretty much with a lot of vendors, as we want to be a partner as you're building your product rather than a transactional relation of, 'I buy a service from you.'"

Regarding generative AI, the retailer has integrated the technology into products such as Decorify, a generative AI design tool. It is also incorporating the technology internally and in some sales operations.

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the "Targeting AI" podcast series.

The tech industry is dealing with the implications of an executive order on AI signed by President Joe Biden Oct. 30.

The order aims to establish new standards for AI safety and security, while protecting the privacy of American citizens, promoting innovation and spurring development of responsible AI.

The order also shows how the federal government is promoting AI technology internally, said Forrester analyst Alla Valente.

"From the language of this EO, what's clear is that the federal government is now being mandated to leverage AI, and then use that AI to improve how it does everything it does," she said.

However, AI vendors in both the private and federal sectors should pay attention to the order, especially in the areas in which there is a call for standards in AI safety and security, Valente added.

The executive order discusses the need for new standards to test AI, built on the National Institute of Standards and Technology's framework.

"What the executive order is hoping to do is identify some of the risks as early as possible," Valente said. If that's accomplished, risk and security management practices can be embedded earlier in the development cycle of the AI lifecycle, she added.

While the intent of the executive order is to create standards and safety guardrails around AI systems, the lack of actionable steps stood out to Gopi Polavarapu, chief solutions officer at Kore.ai.

"From a vendor perspective, it's a welcome governance that's coming from the government, but at the end of the day, we need to know what those standards are, how that's going to be enforced," Polavarapu said. Kore.ai is a startup vendor of conversational AI tools for enterprises.

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. 

The success of an AI startup depends on not only the technology and the problem the startup seeks to solve within the market, but also the support it has from investors and venture capital firms.

One venture capital (VC) firm that prides itself on working closely with the founders of startups is Glasswing Ventures.

As an early VC firm, Glasswing is focused on investing in AI-enabled companies and what it calls "frontier tech" B2B companies, according to Kleida Martiro, a partner at the company.

"We have built strong convictions around certain areas where AI could really revolutionize certain industries," Martiro said during a Targeting AI podcast discussion. Those convictions have led Glasswing to create a mission oriented toward "connecting and protecting" building AI startups.

When in the connect part of the mission, the VC firm looks for startups developing smart data infrastructure and automation, and vertical applications. The protect part is centered around security, which includes data governance and cybersecurity.

Glasswing focuses on seed and pre-seed financing of startups in earliest stages. It guides startups by connecting them to customers, talent and more fundraising.

"We serve as a true partner, we get involved as much as the startup wants us to get involved and we step aside when they don't need our help," Martiro said. "We're very much founder-first. They're part of our extended family."

Startups working with Glasswing need to demonstrate that their technology addresses critical need in the market. The startups also need to start with real talent.

"When investing at such an early stage, it really comes down to the team," Martiro said. "Backing a team that can execute, has the vision, has the technical chops and the technical skills very much married with the business ... and backing good people who are hustlers is truly what makes it at this stage."

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Together, they host the Targeting AI podcast series.

Customer experience chatbots that not only fail to deliver but also fall short of their human counterparts are the bane of CX designers' vision of an automated future.

Now, the arrival of generative AI technology is promising to correct dysfunctional chatbots' missteps, ease the burden on overworked and underappreciated human customer service agents and satisfy frustrated consumers.

But CX expert Don Fluckinger, a veteran tech journalist who has also worked as a CX industry analyst, casts a skeptical eye on claims made on behalf of generative AI and takes a cautionary view of automation and chatbots themselves.

"Losing jobs is never all right," Fluckinger said on TechTarget News' Targeting AI podcast. "But would it be OK for generative AI to more effectively answer customer questions so that humans could monitor what it's doing and not spewing out deceptive or wrong information? That would be good."

Many call centers already have AI-powered interactive voice response (IVR) systems, Fluckinger noted.

And yet, these don't work all that well.

"I've seen demos of these at conferences, on exhibition floors. I've read about them, but I have never run into it in real life yet," Fluckinger said. "The IVRs I hit are always pretty dumb."

Meanwhile, better IVR systems could be on the horizon, and generative AI could help.

Fluckinger noted, though, that while better call center and other CX platforms infused with generative AI technology are coming, they have to be tested and integrated with current systems.

And, finally, companies have to buy the new technology. But the industry isn't there yet.

Note: At the time this podcast was recorded, Fluckinger was a CX analyst for TechTarget's Enterprise Strategy Group. He now covers digital experience systems, end-user computing and the CPU/GPU market for TechTarget Editorial's news unit.

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the "Targeting AI" podcast series.

Oyster is keeping its distance from the generative AI craze, at least for now.

When the vendor, whose platform helps companies with hiring, paying and managing employees in 180 countries around the world, recently came out with a new chatbot, Pearl, it fueled it with basic conversational AI, not the generative variety.

That's largely because Oyster wanted to skirt generative AI's by now well-known risks of outputting inaccurate and biased information, said Michael McCormick, senior vice president of product and engineering at Oyster, on this week's episode of TechTarget Editorial's Targeting AI podcast.

The vendor is a certified B Corporation with a mandate to focus on social and environmental performance.

"One of the big problems with generative AI that everyone knows about is the tendency it can hallucinate," McCormick said. "We've seen examples of people resting control away from the intent of the generative AI programmers, and convincing the generative AI to do and say all sorts of awful things.

"And there is not enough data capturing the experience of underserved and underrepresented groups," he added. "And so there's a huge amount of risk if you try to have guidance from systems like that in the HR space."

Pearl is Oyster's first public foray into using AI to interact with users of its platform. Essentially, the chatbot answers, in conversational format, questions about hiring and remote employment regulations in a world of distributed work in dozens of far-flung countries.

The chatbot is trained on Oyster's wealth of static information about global HR policies, taxes and benefits. So essentially it functions as a private large language model, with Oyster employees serving as "humans in the loop" to ensure that Pearl gives simple, consistent and accurate advice, thus further minimizing generative AI risk.

"If you give an individual the ability to have a direct conversation with a generative AI, you give up control of what might happen," McCormick said. "And you're at the mercy of OpenAI or Bard or whomever in terms of how they try to control that."

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas. Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the "Targeting AI" podcast series.

Much of the world became aware of generative AI and large language models with the release of Dall-E and ChatGPT last year, but Conversica CEO Jim Kaskade has known about the technology since 2019.

During a walk with a top AI executive at Google, Kaskade said he learned about a lot about where the tech giant was heading with generative AI technology.

Once he became CEO of the AI vendor specializing in digital assistants, he looked for ways to apply the technology in a way that was disruptive on the scale of earlier world-changing technologies.

Kaskade's company's brand of disruption is conversational AI and the generative AI-powered digital assistants that he sees as an automated workforce that will eventually ease the burden of much menial work now done by humans.

The application of LLMs in the form of OpenAI's ChatGPT and other similar systems has seen quick adoption worldwide compared to similarly disruptive technologies such as electricity, telephone communications and television, but not all organizations are comfortable with the technology.

That uneasiness is analogous with the discussion in recent years about public cloud versus private and hybrid cloud, Kaskade said.

"It's just a sequence of been there, done that," he said on Tech Target Editorial's Targeting AI podcast. "Once people get really comfortable with the amount of governance that's put around the public application [product], the public cloud solutions, then the big enterprises will start to move from private LLM to public LLM. It'll take the same period of time as it did with cloud."

The more comfortable companies and people are with AI technology, the more benefits they can gain from it.

"Look at what happened with the computer, the PC, look what happened with the phone, look what happened with the world wide web," Kaskade said. "AI is going to be more disruptive than any of those or all of them added together."

Esther Ajao is a TechTarget news writer covering artificial intelligence software and systems. Together, they host the Targeting AI podcast series.

Shaun Sutner is senior news director for TechTarget Editorial's enterprise AI, business analytics, data management, customer experience and unified communications coverage areas.