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AI STRUGGLES TO LEARN
Like this? Get AIDAILY, delivered to your inbox, every weekday. Subscribe to our newsletter at https://aidaily.us
AI can’t learn new things forever — an algorithm can fix that
AIs based on deep learning struggle with continuous learning, often failing to adapt to new tasks. Researchers have discovered that "waking up" dormant neurons in these systems can help them keep learning indefinitely. This algorithmic reset could make future AI systems more adaptable and flexible, addressing a significant limitation in current deep learning models.
Is AI Coming for Coders' Jobs?
AI is reshaping developer workflows but is far from replacing human coders, according to Jason Andersen's analysis. While generative AI can handle specific tasks, it lacks the creativity and adaptability required for complex coding. AI tools are evolving to enhance productivity by taking over routine tasks, but developers remain essential for innovation and problem-solving.
An ‘AI Scientist’ Is Inventing and Running Its Own Experiments
Researchers at the University of British Columbia have developed an "AI scientist" capable of designing, running, and refining its own experiments. While current results are modest, this open-ended learning approach could eventually lead to breakthroughs beyond human-guided AI. The challenge remains to ensure these AI systems operate safely as their capabilities grow.
Silicon Valley Is Coming Out in Force Against an AI-Safety Bill
California State Senator Scott Wiener's Senate Bill 1047, which mandates AI safety regulations for models costing over $100 million to train, faces intense opposition from major tech companies and politicians. Critics argue the bill could stifle innovation and hurt California's economy. Wiener defends the bill as necessary to prevent tangible risks, dismissing claims that it focuses on "science-fiction" threats.
Generative AI is Sliding into the 'Trough of Disillusionment'
Generative AI has moved past its peak of inflated expectations and is now in the "trough of disillusionment," according to Gartner's 2024 Hype Cycle. Companies are shifting focus from hype to concrete ROI, driving interest towards autonomous AI agents. While generative AI's long-term potential remains significant, the industry must overcome current challenges to achieve widespread productivity gains.
AI Companies Pitch Political Campaigns, But Few Are Interested
Despite a surge in AI tools offered by over 30 tech companies for the 2024 U.S. elections, political campaigns have been largely hesitant to adopt them. Some candidates who tried AI-backed services, like robocalls, found that voters were not receptive, leading campaigns to shy away from the technology.
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By Chris KalaboukisLike this? Get AIDAILY, delivered to your inbox, every weekday. Subscribe to our newsletter at https://aidaily.us
AI can’t learn new things forever — an algorithm can fix that
AIs based on deep learning struggle with continuous learning, often failing to adapt to new tasks. Researchers have discovered that "waking up" dormant neurons in these systems can help them keep learning indefinitely. This algorithmic reset could make future AI systems more adaptable and flexible, addressing a significant limitation in current deep learning models.
Is AI Coming for Coders' Jobs?
AI is reshaping developer workflows but is far from replacing human coders, according to Jason Andersen's analysis. While generative AI can handle specific tasks, it lacks the creativity and adaptability required for complex coding. AI tools are evolving to enhance productivity by taking over routine tasks, but developers remain essential for innovation and problem-solving.
An ‘AI Scientist’ Is Inventing and Running Its Own Experiments
Researchers at the University of British Columbia have developed an "AI scientist" capable of designing, running, and refining its own experiments. While current results are modest, this open-ended learning approach could eventually lead to breakthroughs beyond human-guided AI. The challenge remains to ensure these AI systems operate safely as their capabilities grow.
Silicon Valley Is Coming Out in Force Against an AI-Safety Bill
California State Senator Scott Wiener's Senate Bill 1047, which mandates AI safety regulations for models costing over $100 million to train, faces intense opposition from major tech companies and politicians. Critics argue the bill could stifle innovation and hurt California's economy. Wiener defends the bill as necessary to prevent tangible risks, dismissing claims that it focuses on "science-fiction" threats.
Generative AI is Sliding into the 'Trough of Disillusionment'
Generative AI has moved past its peak of inflated expectations and is now in the "trough of disillusionment," according to Gartner's 2024 Hype Cycle. Companies are shifting focus from hype to concrete ROI, driving interest towards autonomous AI agents. While generative AI's long-term potential remains significant, the industry must overcome current challenges to achieve widespread productivity gains.
AI Companies Pitch Political Campaigns, But Few Are Interested
Despite a surge in AI tools offered by over 30 tech companies for the 2024 U.S. elections, political campaigns have been largely hesitant to adopt them. Some candidates who tried AI-backed services, like robocalls, found that voters were not receptive, leading campaigns to shy away from the technology.