Targeting AI

It's looking like 2024 is the year of ROI for generative AI

Generative AI vendors and investors have turned their attention from last year's innovative frenzy to ROI, monetizing the language models that have revolutionized the tech world in a short time.
That's the outlook on 2024 from Kashyap Kompella, founder and analyst at RPA2AI Research, who was a guest on the Targeting AI podcast from TechTarget Editorial.
"If we think about it, 2023 really was the year of shock and awe for AI technology," Kompella said on the podcast. "But I think in 2024, there is going to be some amount of focus -- if not sole focus -- on return on investment."
At the same time, the tech landscape is seeing in 2024 an astonishing profusion of AI language models, from the ever-expanding power of large language models (LLMs) to the rise of small and open source models, and even models adapted for mobile devices, Kompella noted.
"The burst of technological innovation will continue," he said.
Investors looking at generative AI tech vehicles to pump venture funds into are hoping to hit "pay dirt" this year, as Kompella put it.
"But the businesses and the organizations that are looking to implement AI systems, they're going to be also focused on business value and return on investment," he said.
Meanwhile, 2024 is seeing a continuation and even ramping up of the litigation surrounding generative AI systems. There is also a growing emphasis on making generative AI systems safe by attempting to reduce or eliminate bias and inaccurate outputs.
Everyone from comedian and author Sarah Silverman and best-selling novelist John Grisham to The New York Times are suing generative AI vendors for misappropriating their work.
"Businesses are … becoming aware of some of the risks of using the AI systems." Kompella said. "So we'll see more indemnity clauses being offered by AI vendors."
Looking at the swelling generative AI market, Kompella also noted that venture capital activity in the arena is accelerating after a strong year in 2023.
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.

AI, hiring and the need for humans in the loop

The fear of AI technology eliminating thousands of jobs or affecting the hiring process continues to prevail in the age of generative AI.
While many believe that AI technology will augment workers, some are already seeing the effect of AI in the job market. Indeed, tech companies and other large enterprises have laid off thousands of workers in recent months, though staffing levels are mostly still higher than before the COVID-19 pandemic.
ResumeBuilder.com found in a November 2023 survey that of 750 business leaders, 44% reported AI technology would cause layoffs in 2024.
 
The presence of AI in the hiring process has also led to laws like New York's Local Law 144. It prevents employers from using an automated employment decision tool unless they prove they performed a bias audit beforehand.
This law and others are among the ways of proving accountability in the hiring process, said Cliff Jurkiewicz, vice president of global strategy at Phenom, an AI recruiting vendor.
"We must be accountable for the use of artificial intelligence, and the recommendations that it may be making in our decision-making," Jurkiewicz said on TechTarget Editorial's Targeting AI podcast.
While accountability is needed, removing all bias in hiring and recruiting is almost certainly unattainable, Jurkiewicz said.
"It is impossible to do that," he said. "It requires humans in the loop ... to be examining how these tools are functioning and being used in organizations."
Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. 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, unified communications software, analytics and data management technology. Together, they host the Targeting AI podcast.

B Corp Sama responds to AI data labeling criticism

Data labeling and annotation vendor Sama seeks to make an impact not only in the tech market but also in parts of the world where it's hard for people to partake in the digital economy.
As a women-led B Corporation chartered to do social and environmental good, Sama employs numerous people in countries such as Kenya and has created, said CEO Wendy Gonzalez on the latest episode of the Targeting AI podcast from TechTarget Editorial. She said the company has created more than 10,000 jobs in those regions.
Yet Sama has faced intense criticism for paying substandard wages to workers in Africa and also subjecting them to inhumane work environments by requiring them to view and then label offensive and violent images.
On the podcast, Gonzalez blamed some of the practices on its former client, generative AI giant OpenAI. She also argued that her company created decently paying jobs for people who otherwise would have trouble gaining employment.
"It went beyond the boundaries of work that we were comfortable doing," Gonzalez said. "It was only in existence for a handful of months."
Meanwhile, Sama's business mission is to help enterprises minimize the risk of AI model failure using its data annotating services.
New multi-cloud integration
Most recently, on Jan. 24, the vendor introduced a multi-cloud integration strategy in its platform to increase the speed of new project onboarding.
The integration allows enterprises to keep their data on one of the three top cloud providers – AWS, Microsoft and Google -- while still giving Sama access to the data.
It also enables faster onboarding to the Same platform and an integration suite compatible with Python SDKs and the Databricks platform.
The integration reduces the cost of data egress because it eliminates the need for organizations to move data around in a multi-cloud model deployment, said Gartner analyst Sid Nag.
"It speeds up application development via integration with other SDKs and programming language models while conforming to compliance and security models," Nag added.
However, it's unclear how the Sama product gets access to the data contained in an organization's primary cloud provider, Nag continued.
Ethics of data annotation and labeling
 
While Sama has found success in the data annotation niche, it has navigated a turbulent history in Africa.
Sama came under fire while performing contracted work for OpenAI in November 2021.
On behalf of OpenAI, Sama hired data labelers in Kenya for a take-home pay of about $2 per hour.
The labelers were charged with trying to remove toxic data from the training data sets of tools such as ChatGPT.
However, some of the workers accused Sama of making them read sexually disturbing texts while paying them unfairly low wages.
Although the work was beyond the norms of what Sama says it usually does in regions like Kenya, the incident still raised questions about the ethical implications of data labeling and what human workers are asked to do when removing toxic data from generative AI systems like ChatGPT.
For Gonzalez, it has to do with the types of jobs available for workers like those in Kenya and how those workers can be a part of the digital economy.
"If there were plentiful jobs, meaning you sort of take it or leave it, then that would be amazing," she said on the podcast "But that's not the situation. Being able to have people from around the world, globally in particular, the ones that have the greatest barriers to employment have access to the digital economy is important."
Complete and effective data is also important, she continued.
"You need a human in the loop to then validate that the AI or the model is interpreting that data as expected," Gonzalez said. "If it isn't, then you need to be able to flag that and then reflect and retrain that model."
Esther Ajao is a TechTarget Editorial news writer covering artificial intelligence software and systems. Shaun Sutner is a journalist with 34 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 artificial intelligence, customer experience and unified communications software, and analytics and data management technology. Together, they host the Targeting AI podcast.
 

Examining Microsoft venture fund M12’s AI investment approach

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.

Guiding generative AI toward responsible use

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.

Looking ahead: 2024 will see generative AI mature

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.
 

AI-assisted driving here long before autonomous vehicles

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.

Diving into Wayfair’s machine learning and AI odyssey

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.
 

Tech industry reaction to Biden’s AI executive order mixed

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.
"It's really looking at developing guidelines and best practices really across the whole field," said Katherine Hendrickson, a senior research lead at EpiSci, an AI military and aerospace software and hardware vendor, on the Targeting AI podcast from TechTarget News.
 
While the order holds much promise for AI system developers, Hendrickson said its main value is its focus on research and the government partnering with research centers, while also appearing to fund a number of AI sectors.
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. 

Venture capital firm helps launch early AI startups

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.