Sheamus McGovern’s Post

Key Insights on Generative AI from the World Economic Forum. #GenerativeAI #AI #ArtificialIntelligence #WorldEconomicForum #TechTrends

Great insights on Generative AI: from governing Generative AI to how it’s transforming areas such as education, health and finance. https://lnkd.in/gKzX6dus

How do we solve the growing gap between the exponential rise in data and the linear growth of data scientists? 📈 In conversation with Scott Hebner on AnalystANGLE, Darko Matovski, PhD, co-founder and CEO of causaLens, explains why AI data science agents are the key to unlocking value in the age of data overload: “We are generating more data than it can ever be analyzed … The trouble is that human data scientists have only been growing linearly … The only way to solve this is by AI, by creating AI data science agents that can kind of do the work of human data scientists,” Matovksi said. Discover how causaLens is addressing this challenge and empowering enterprises to bridge the talent gap in data science while driving innovation. 📺 Watch the full discussion: https://lnkd.in/dMkqjBT5 #CUBEConversations #theCUBE #AI #DataScience

Gina Rosenthal’s latest blog is a must-read for tech enthusiasts and industry leaders navigating the world of AI. She unpacks the buzz behind the term and explores why understanding its various meanings is more important than ever. From driving economic growth and reshaping global politics to transforming computing, Gina explores how AI is becoming a driving force behind economics, politics, and technology. She also clears up misconceptions by breaking down AI’s evolution — from perception and generative AI to agentic and physical AI. Don't miss Gina's post: https://hubs.ly/Q031Z_zK0 #DefineAI #AIinnovation #AI #Businesstech #Computing

The Artificial Intelligence Index Report 2024 is out by the Stanford Institute for Human-Centered Artificial Intelligence, an independent initiative of the #StanfordUniversity The top 10 takeways from this report are: 1. 1. AI beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning. 2. Industry continues to dominate frontier AI research. In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high. 3. Frontier models get way more expensive. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute. 4. The United States leads China, the EU, and the U.K. as the leading source of top AI models. In 2023, 61 notable AI models originated from U.S.based institutions, far outpacing the European Union’s 21 and China’s 15. 5. Robust and standardized evaluations for LLM responsibility are seriously lacking. There is a significant lack of standardization in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models. 6. Generative AI investment skyrockets. Despite a decline in overall AI private investment last year, funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. 7. The data is in: AI makes workers more productive and leads to higher quality work. In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. Still, other studies caution that using AI without proper oversight can lead to diminished performance. 8. Scientific progress accelerates even further, thanks to AI. In 2022, AI began to advance scientific discovery. 2023, however, saw the launch of even more significant science-related AI applications— from AlphaDev, which makes algorithmic sorting more efficient, to GNoME, which facilitates the process of materials discovery. 9. The number of AI regulations in the United States sharply increases. The number of AI- related regulations in the U.S. has risen significantly in the past year and over the last five years. 10. People across the globe are more cognizant of AI’s potential impact—and more nervous. For full report, visit https://lnkd.in/gWRGZGwC

Businesses and individuals alike are grappling with how to use generative AI to their benefit. To help guide them, researchers at the MIT Initiative on the Digital Economy are looking at how AI is being developed and exploring its potential and limitations. https://lnkd.in/d6qmbTRn

Conventional economic accounting proves to be inadequate for the dynamic field of AI research. The uncertainty surrounding what defines AI-related employment at this nascent stage of technological evolution challenges traditional economic metrics. https://lnkd.in/eugasVyr

I enjoyed this short viewpoint by Roger Highfield and Peter Coveney in JCIM Some thoughts that occurred during the read: 1. Being against hype is not the same as being against the thing being hyped. In fact, quite the opposite. If you read this and say, wow they are against AI being used in science, I think you miss the point. 2. I don’t know where I stand on the interpretability debate. Every reader of these words is using an incredible piece of technology lodged in their skull that they can’t interpret. This doesn’t seem to stop us from communicating testable hypotheses, meta-theories, axioms, and other implements of scientific reasoning. 3. I think the risk of scientific disciplines being hollowed out by AI hype is small but should be taken seriously. The AI distortion field will affect hiring, training, funding, and problem selection in every technical discipline. This can’t be avoided, but the response should be shaped to ensure that the core competencies of each discipline are preserved rather than discarded. 4. I don’t agree with all of arguments (one shouldn’t have to say this, but such is life), but I appreciate the blunt language. Don’t underestimate the value of having an argument plainly stated that you disagree with. This helps to refine your own position. https://t.co/KxG9b7zMDS

Very interesting view on the state of affairs in AI adoption by Stanford University's HAI. Take a look at private investment in AI by country and see how that ranking resembles the world defence budget ranking, with the US and China at the top. https://lnkd.in/d4tD3q7b

🔄Game-Changing Shift in AI Economics: DeepSeek AI Disrupts Financial Services Chinese firm DeepSeek just achieved what seemed impossible: developing a superior AI model at 1/100th the typical cost. Their US$6M LLM outperforms leading models and runs 30x cheaper. What does this mean for financial institutions? The AI landscape is shifting faster than expected. Banks need flexible, model-agnostic approaches to stay competitive and manage risk. Read Gilles Ubaghs' latest analysis on why financial institutions must rethink their AI strategy in this rapidly evolving space. 🔗https://lnkd.in/eVWhAuKA