HPE GreenLake’s Extensive Language Models: Promoting Ethical AI Usage, Supported by IEEE

**AI Driving Demand for Storage and Memory**

AI technologies rely on large amounts of data for training, which translates to increased storage and memory requirements. This article discusses how AI is expected to drive the demand for storage and memory in the future.

**HPE’s GreenLake AI Cloud for Large Language Models**

During its HPE Discover event, Hewlett Packard Enterprise (HPE) unveiled its GreenLake AI cloud designed for large language models (LLMs). GreenLake offers a cloud service that enables enterprises to privately train, tune, and deploy AI at scale. HPE emphasizes that these services will be powered by world-leading supercomputers and AI software running on renewable energy sources.

**Partnership with Aleph Alpha for LLM Usage**

HPE has partnered with Aleph Alpha, a German AI startup, to provide a readily available LLM for use in text and image processing and analysis. In addition to this, HPE plans to offer more AI applications in the future through its GreenLake platform. These applications will cater to various industries such as climate modeling, healthcare, life sciences, financial services, manufacturing, and transportation.

**Increasing Adoption of AI-Based Services**

HPE’s venture into AI-based services is part of a trend among large enterprise and cloud providers. The use of AI has become a prominent feature in digital transformation efforts, evident through the integration of advanced AI capabilities in search engines and content creation platforms. However, the widespread adoption of AI also raises concerns about its potential misuse and the associated risks.

**IEEE’s Involvement in Addressing AI Ethics**

In a recent statement released by IEEE’s Standards Association, the organization expressed its position on the benefits, potential harms, and need for appropriate standards in the field of AI. IEEE is actively developing a range of standards and methodologies to tackle issues related to safety, biases, transparency, privacy, and corporate governance in AI applications.

**Ethical Concerns and Limitations of Generative AI Models**

Generative AI models, including large language models, offer immense potential for various sectors. However, they also raise ethical concerns due to the integration of data, algorithms, sensors, and actuators with inherent values, biases, and unforeseen impacts in constantly evolving socio-technical environments. IEEE emphasizes the importance of technological guardrails and societal safeguards before deploying flawed AI systems into everyday life.

**IEEE’s Contribution to Quality Assurance of Large Language Models**

IEEE’s initiatives, such as the development of the IEEE P7009™ standards project, aim to provide fail-safe design methodologies and tools for autonomous and semi-autonomous systems. These efforts can be instrumental in addressing the key issues of quality assurance associated with large language models.

**Addressing Ethical and Societal Concerns**

To prevent harmful outcomes, developers, users, and regulators must address ethical and societal concerns associated with AI. Transparency plays a crucial role in providing detailed information about the models’ corpus, architecture, guardrails, and data handling policies. Additionally, measures should be put in place to counter the development and dissemination of AI-driven misinformation.

**Shared Responsibility in AI Development and Use**

Given the impact of AI applications on democratic institutions, societal cohesion, and mental health, governments, industries, scientists, and engineers must recognize the significance of openness, international collaboration, and critical discourse. This collective responsibility is vital to ensure the responsible development and use of AI technologies.


HPE’s GreenLake AI cloud, along with partnerships such as the one with Aleph Alpha, highlights the growing demand for AI-based services and the need for increased storage and memory capacity. However, as AI continues to advance, organizations like IEEE are actively addressing ethical concerns and promoting transparency and responsible development practices. With careful attention to these issues, AI can fulfill its transformative potential while minimizing the risks it poses.

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