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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Also inside, Gartner predicts 80% of AI workforce will need upskilling by 2027, AI helps close ‘trust gap’ between companies, customers and more
Here are the most-read stories on AI Business this week.
The Nobel Prize for Physics has been awarded to John Hopfield and Geoffrey Hinton for their work developing methods that are the foundation of today’s machine learning.
The announcement was made Tuesday, highlighting that this year’s prize was about “machines that learn.”
This year’s winners embody that and were awarded the 2024 Nobel Prize for their foundational discoveries and inventions that enable machine learning with artificial neural networks.
“The laureates’ work has already been of the greatest benefit,” said Ellen Moons, chair of the Nobel Committee for Physics. “In physics, we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”
The age of artificial intelligence working independently is upon us as two tech behemoths – AI chip leader Nvidia and IT service giant Accenture – on Wednesday announced a partnership that promises to bring more agentic AI to businesses.
Agentic AI systems are a type of artificial intelligence that can achieve goals and make decisions without direct human supervision – planning actions, breaking down complex tasks, analyzing sensory input, making predictions and even interacting with other systems. The technology promises to boost productivity and control labor costs.
While GenAI launched an enterprise arms race to adopt AI, agentic AI could be the technology that really transforms the enterprise. According to a report from Emergen Research, the agentic market is valued at $30.89 billion in 2024 and has an expected compound annual growth rate (CAGR) of 31.68%. North America makes up about 20% of the total market.
Generative AI will spawn new roles in software engineering and operations through 2027 that will require 80% of the workforce to upskill, according to a new Gartner report.
Essential upskilling will include retrieval-augmented generation (RAG) skills, which is a technique for enhancing the accuracy and reliability of generative AI models. As well as developing a range of highly skilled AI engineers who can meet the rapidly increasing demand for AI-empowered software.
Contrary to “bold” claims and speculation that AI could reduce demand for human engineers, or even supplant them entirely, it will instead transform the future role of software engineers, said Philip Walsh, Sr Principal Analyst at the consultancy.
A new report by Vodafone Business has found that AI can help companies shrink the so-called Trust Gap with customers. The majority of those surveyed believe that AI-savvy businesses are more likely to make accurate predictions.
The Fit for the Future Report conducted in partnership with the London School of Economics surveyed 2,359 businesses and 5,289 individual customers across 10 markets and 11 key sectors of the economy about trust in business.
It revealed that businesses overestimated how much their customers trusted them, known as the Trust Gap, but that this gap can be reduced with AI.
In the survey, 62% of respondents said they trust organizations the same or even more when generative AI is used. In the U.S., 57% of respondents thought that AI-savvy businesses are more likely to make accurate predictions.
Radio astronomers at the Search for Extraterrestrial Intelligence (SETI) Institute are using AI to conduct the world’s first real-time search for fast radio bursts (FRBs), high-energy signals from space that may be a sign of life.
Nvidia announced at its AI Summit on Tuesday that SETI radio astronomers are using Nvidia Holoscan, a sensor-processing platform, and Nvidia IGX, an edge-computing solution, to better understand these rare astronomical phenomena.
SETI Institute operates the Allen Telescope Array in Northern California to search for evidence of extraterrestrial intelligence and to study transient astronomical events such as fast radio bursts.
Historically, analyzing radio signals from space has been a slow, offline process. Researchers would collect data and then process it later with custom-built programs.
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