Steven AI Talk

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Steven AI Talk(English)

  1. −3 H

    The 2026 AI Race: From Agentic Coding to Ad-Driven Models

    This transcript from IBM Technology’s "Mixture of Experts" explores the evolving landscape of enterprise artificial intelligence through 2026 and beyond. A central focus is OpenAI’s decision to integrate advertisements into ChatGPT, a move the panel views as an inevitable consequence of high inference costs. The experts also highlight the rise of "agentic" AI coding with Claude Code, noting a shift from simple assisted programming to autonomous agents that can handle complex tasks independently. Additionally, they analyze IBM’s Enterprise 2030 Report, which suggests that business leaders are transitioning from using AI for mere productivity to leveraging it for core product innovation. Finally, the discussion touches on Open Responses, a new effort by Hugging Face to standardize how AI agents interact with servers while navigating the protectionist trends of major tech labs.

    17 min

  2. −3 H

    The 2026 AI Race: From Agentic Coding to Ad-Driven Models

    This transcript from IBM Technology’s "Mixture of Experts" explores the evolving landscape of enterprise artificial intelligence through 2026 and beyond. A central focus is OpenAI’s decision to integrate advertisements into ChatGPT, a move the panel views as an inevitable consequence of high inference costs. The experts also highlight the rise of "agentic" AI coding with Claude Code, noting a shift from simple assisted programming to autonomous agents that can handle complex tasks independently. Additionally, they analyze IBM’s Enterprise 2030 Report, which suggests that business leaders are transitioning from using AI for mere productivity to leveraging it for core product innovation. Finally, the discussion touches on Open Responses, a new effort by Hugging Face to standardize how AI agents interact with servers while navigating the protectionist trends of major tech labs.

    7 min

  3. −1 D

    Privacy-Preserving AI: Federated Learning and Encrypted Agents

    The provided text explores the synergy between federated learning and encrypted AI agents to create a secure, decentralized framework for machine learning. Instead of gathering sensitive information into a central database, this approach allows individual devices or institutions to train models locally and share only the resulting mathematical updates. By utilizing advanced cryptographic techniques like homomorphic encryption, these systems can aggregate insights without ever exposing the underlying raw data to the central coordinator. This privacy-preserving architecture effectively solves the dilemma of training high-performing models while adhering to strict regulatory and ethical standards. Ultimately, these technologies ensure that data remains private and local while the collective intelligence of the global model continues to improve. This evolution represents a significant shift toward trustworthy AI, where collaboration no longer requires the compromise of information security.

    7 min

  4. −1 D

    Privacy-Preserving AI: Federated Learning and Encrypted Agents

    The provided text explores the synergy between federated learning and encrypted AI agents to create a secure, decentralized framework for machine learning. Instead of gathering sensitive information into a central database, this approach allows individual devices or institutions to train models locally and share only the resulting mathematical updates. By utilizing advanced cryptographic techniques like homomorphic encryption, these systems can aggregate insights without ever exposing the underlying raw data to the central coordinator. This privacy-preserving architecture effectively solves the dilemma of training high-performing models while adhering to strict regulatory and ethical standards. Ultimately, these technologies ensure that data remains private and local while the collective intelligence of the global model continues to improve. This evolution represents a significant shift toward trustworthy AI, where collaboration no longer requires the compromise of information security.

    15 min

  5. −2 D

    ADK: Building Autonomous AI Agents Beyond LLMs

    IBM's Agent Development Kit (ADK) enables autonomous AI agents to sense, reason, and act. Unlike passive LLMs, these tools use sensors and APIs to operate independently. Developers follow a six-step process, prioritizing safety, fairness, and trust in automation.

    15 min

  6. −2 D

    ADK: Building Autonomous AI Agents Beyond LLMs

    IBM's Agent Development Kit (ADK) enables autonomous AI agents to sense, reason, and act. Unlike passive LLMs, these tools use sensors and APIs to operate independently. Developers follow a six-step process, prioritizing safety, fairness, and trust in automation.

    7 min

  7. −3 D

    Securing and Governing Autonomous AI Agents

    Agentic AI offers autonomy but increases security risks like hijacking, prompt injection, and data poisoning. Effective governance requires human-in-the-loop oversight to prevent bias and ensure accountability through monitoring and AI firewalls.

    18 min

  8. −3 D

    Securing and Governing Autonomous AI Agents

    Agentic AI offers autonomy but increases security risks like hijacking, prompt injection, and data poisoning. Effective governance requires human-in-the-loop oversight to prevent bias and ensure accountability through monitoring and AI firewalls.

    8 min
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Steven AI Talk(English)

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  • Kanal
    LearnByDoing With Steven
  • Skapad av
    Steven
  • Aktiva år
    2025 - 2026
  • Avsnitt
    601
  • Åldersgräns
    Barnvänligt
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    © Steven
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    Steven AI Talk
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