Targeting AI

Examining developers' perception of AI tools at GitHub

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.

Musicians and the fight for fairness in the age of GenAI

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.
 

Security, bias risks are inherent in GenAI black box models

From bias to hallucinations, it is apparent that generative AI models are far from perfect and present risks.
Most recently, tech giants -- notably Google -- have run into trouble after their models made egregious mistakes that reflect the inherent problem with the data sets upon which large language models (LLMs) are based.
Microsoft faced criticism when its models from partner OpenAI generated disturbing images of monsters and women.
The problem is due to the architecture of the LLMs, according to Gary McGraw, co-founder of the Berryville Institute of Machine Learning.
Because most foundation models are a black box that contain security flaws within their architecture, users have little ability to manage the risks, McGraw said on the Targeting AI podcast from TechTarget Editorial.
In January, the Berryville Institute published a report highlighting some risks associated with LLMs, including data debt, prompt manipulation and recursive pollution.
"These are some risks that need to be thought about while you're building your LLM application so that you don't put your business, your enterprise, your business, at more risk than you want to take on when you adopt this technology," McGraw said.
The risks are embedded in both closed and open source models and small and large language models, he added.
"When people build their own language model, what they're often doing ... is taking a foundation model that's already developed and they're training it a little bit further with their own proprietary prompting," he continued. "These steps do not eradicate the risks that are built into the black box. In fact, all they do is hide them even further."
These risks can be dangerous for real-world situations such as the 2024 election, McGraw said. Since the language models are built from data from all over the web -- both good and unreliable -- LLMs trained on that data can be used to produce false and malicious information about the election.
"Using this technology, we need some way of controlling the output so that it doesn't get back out there into the world and just cause more confusion among people who don't know which way is up," he 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.

A look at independent AI hardware and software vendor SambaNova's open source strategy

AI hardware and software provider SambaNova Systems seeks to put enterprise customers in charge of their data while using open source models.
A smaller competitor of AI hardware vendor Nvidia, the AI vendor is trying to distinguish itself by helping enterprises train and deploy large models that they can't train on Nvidia's systems.
"What we try to focus on is how do we actually create a hardware platform that allows these companies to take these hard problems where the models are really big and deploy them in a reasonable way," co-founder and CEO Rodrigo Liang said on the Targeting AI podcast from TechTarget Editorial.
One way the vendor does this is by focusing on open source models.
"What we decided to do some years ago was [go] fully into open source," Liang said. "We want to open the model so that everybody at any given point in time can look at the entire model and how it was trained."
SambaNova introduced Sambaverse on March 6.
In SambaNova's terms, Sambaverse is a playground and API where developers can test available open source large language models from a single endpoint and compare their responses for any given application.
The new playground comes one week after the vendor unveiled Samba-1, a trillion-parameter generative AI model for the enterprise. The model comprises more than 50 open source generative AI models.
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.
 
 

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.