19 : 08 : 00 : 47

19 : 08 : 00 : 47

19 : 08 : 00 : 47

19 : 08 : 00 : 47

Keep Apart Research Going: Donate Today

Oct 27, 2024

Policy Analysis: AI and Sustainability: Climate Impact Monitoring

Parikirt Oggu & Shawn Reginauld

Details

Details

Arrow
Arrow
Arrow
Arrow
Arrow
Arrow

Organizations are responsible for reporting two emission metrics: direct and indirect

emissions. Reporting direct emissions is fairly standard given activity related to the

generation of such emissions typically being performed within a controlled environment

and on-site, thus making it easier to account for all of the activities that contribute to

such emissions. However, indirect emissions stem from activities such as energy usage

(relying on national grid estimates) and operations within a value chain that make

quantifying such values difficult. Thus, the subjectivity involved with reporting indirect

emissions and often relying on industry estimates to report such values, can

unintentionally report erroneous estimates that misguide our perception and subsequent

action in combating climate change. Leveraging an artificial intelligence (AI) platform

within climate monitoring is critical towards evaluating the specific contributions of

operations within enterprise resource planning (ERP) and supply chain operations,

which can provide an accurate pulse on the total emissions while increasing

transparency amongst all organizations with regards to reporting behavior, to help

shape sustainable practices to combat climate change.

Cite this work:

@misc {

title={

Policy Analysis: AI and Sustainability: Climate Impact Monitoring

},

author={

Parikirt Oggu & Shawn Reginauld

},

date={

10/27/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Apr 14, 2025

Read More

Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Read More

Jan 24, 2025

CoTEP: A Multi-Modal Chain of Thought Evaluation Platform for the Next Generation of SOTA AI Models

As advanced state-of-the-art models like OpenAI's o-1 series, the upcoming o-3 family, Gemini 2.0 Flash Thinking and DeepSeek display increasingly sophisticated chain-of-thought (CoT) capabilities, our safety evaluations have not yet caught up. We propose building a platform that allows us to gather systematic evaluations of AI reasoning processes to create comprehensive safety benchmarks. Our Chain of Thought Evaluation Platform (CoTEP) will help establish standards for assessing AI reasoning and ensure development of more robust, trustworthy AI systems through industry and government collaboration.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.