IBM Demystifies AI Governance

PLUS: Google DeepMind's GraphCast AI beats standard model ECMWF in accuracy.

Good morning! Today is Wednesday, November 15th and in today's news IBM is releasing an AI governance tool and Google Deepmind’s new weather algorithm outperforms supercomputers using only a laptop. New to The Intelligence Age? Sign up here.
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News & Insights

IBM WatsonX Tool For AI Governance Will Soon Be Available

IBM's WatsonX Governance tool is poised to demystify AI deployment. Set for release early next month, this platform serves as a pivotal ally for businesses grappling with the opaque nature of AI operations. Kareem Yusuf, IBM's senior vice-president, notes the tool's crucial role in automating governance and ensuring transparent model management – a beacon of clarity for CEOs eager to leverage AI's potential yet wary of inherent risks.

The introduction of WatsonX Governance signals IBM's commitment to facilitating safer, more regulated AI usage. It translates convoluted regulations into actionable policies, a feature growing in significance as global AI legislation evolves. This isn't mere compliance; it's a strategic move to future-proof enterprises against the unpredictable tide of AI innovation. Could WatsonX become the gold standard for AI governance? siliconrepublic

AI Is At An Inflection Point, Fei-Fei Li Says

Generative AI has matured, evolving from a novel concept to a robust suite of tools driving real-world applications. Fei-Fei Li, esteemed co-director of Stanford's Human-Centered AI Institute, acknowledges this shift. "It's behind concrete tools that people can try out for themselves," she notes, underscoring the technology's palpable impact. Businesses have caught on, integrating AI to enhance profitability. This is important: AI's influence is no longer hypothetical—it's operational and pervasive.

Li, while optimistic, spotlights pressing challenges—misinformation, workforce disruption, bias, and privacy. "I absolutely respect that... but there are other risks... more pressing and urgent," she contends. The debate over AI's catastrophic risks is less immediate than the tangible issues we confront daily. The concentration of AI power poses another risk, potentially sidelining public-sector research and diverse perspectives. A national investment in AI research is imperative for maintaining a balanced technological ecosystem. technologyreview

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Practical AI

A Balancing Act: Using AI For Internal Workplace Communications

In the matrix of modern workplaces, AI has carved a niche in streamlining internal communications. Yet, the human element cannot be understated. Ciara Flaherty of Springboard Communications insists, "Human oversight is essential to get the benefit of AI in workplace communication." AI tools adeptly tailor messages, but they lack the empathy and depth of human interaction. Balance is key.

Efficiency can't overshadow the need for a personal touch. While AI, including ChatGPT, Jasper, or Anyword, boosts content creation speed, it may strip communications of authenticity. "Also by delivering real-time updates and alerts to employees, we find that they can be well-informed about events or changes in the moment," Flaherty notes. But without human oversight, messages risk being disregarded as insincere. AI is a tool, not a panacea. siliconrepublic

Around The World

More News

  • YouTube updates its policy requiring creators to disclose AI-generated content in their videos, emphasizing transparency, and introduces guidelines for labeling AI material, particularly for sensitive topics, while allowing affected individuals to request the removal of unauthorized voice imitations unless deemed parody or satire.

  • Google Study Reveals AI Surpasses Traditional Methods in Weather Forecasting Accuracy for the First Time, Marking a Significant Milestone in Meteorological Predictive Science.

Trending Today

Research Review

Google DeepMind’s AI Weather Forecaster Handily Beats A Global Standard

Google DeepMind has raised the bar. Its AI, GraphCast, has outperformed the ECMWF, a titan in atmospheric prediction, in forecasting accuracy for over 1,300 variables. GraphCast's ability to deliver rapid predictions on a laptop contrasts with the ECMWF's supercomputer dependency. This efficiency redefines expectations for meteorological AI, providing a glimpse into a future where advanced forecasts are both swift and accessible.

The implications are profound. Graph neural networks (GNNs) have enabled a leap in predictive prowess, particularly for weather phenomena. By modeling the atmosphere's complex interactions, GNNs like GraphCast offer detailed insights with minimal computational power. But here's the catch: AI models have shown a tendency to underpredict extreme events. And with climate change altering weather patterns, can we rely solely on historical data to train these systems? arstechnica

Written by Isaac R. Ward, Casey Clifton, and Alex Brogan.

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