Targeting AI

Hosts Shaun Sutner, TechTarget News senior news director, and AI news writer Esther Ajao interview AI experts from the tech vendor, analyst and consultant community, academia and the arts as well as AI technology users from enterprises and advocates for data privacy and responsible use of AI. Topics are related to news events in the AI world but the episodes are intended to have a longer, more ”evergreen” run and they are in-depth and somewhat long form, aiming for 45 minutes to an hour in duration. The podcast will occasionally host guests from inside TechTarget and its Enterprise Strategy Group and Xtelligent divisions as well and also include some news-oriented episodes featuring Sutner and Ajao reviewing the news.

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Episodes

Monday May 20, 2024

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. 

Monday May 06, 2024

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.
 

Monday Apr 22, 2024

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.

Monday Apr 08, 2024

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.
 

Monday Mar 25, 2024

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.

Monday Mar 11, 2024

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.
 
 

Monday Feb 26, 2024

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.

Monday Feb 12, 2024

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.

Monday Jan 29, 2024

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.
 

Tuesday Jan 16, 2024

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.

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