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Originally appeared in LinkedIn Future Singularity
The National Science Foundation (NSF) is actively Involved In promoting artificial intelligence (AI) research and development. A fact which I was admittedly unaware. Since the 1960s they have funded initiatives that aim to harness AI's capabilities to address societal challenges, enhance competitiveness, and promote responsible and ethical AI practices. The NSF seeks to provide researchers with the necessary resources, including computational infrastructure and access to pre-trained models, to advance AI innovation. Not that I'm surprised by the government involvement and interest, but I was taken aback by the basic timeline presented at the opening of this talk. Something that has been studies as far back as the ‘60s and has now exploded as nearly mainstream.
Expanding the Capabilities of Artificial Intelligence
During this roundtable discussion at SXSW 2024, the future of AI was explored by specialists from the NSF which included experts from academia, industry, and government to discuss the latest advancements in AI and its potential impact on various industries.
For those unaware, like myself, the NSF has played a significant role in fostering AI research for decades. By providing grants to universities and other institutions, NSF has laid the foundation for the current machine learning revolution. This has included providing financing for AI, machine learning, and reinforcement learning research. The NSF also has programs in place to encourage the ethical and responsible application of this rapidly developing technology because it recognizes these concerns in relation to AI.
However, what steps must we take to go beyond R&D and begin using AI to address societal issues? Healthcare, transportation, and energy efficiency are just a few of the areas where AI has the potential to be optimized, according to Division Director for Information and Intelligence Systems Michael Littman. He suggested that we can find fresh approaches to urgent problems by taking advantage of AI's analytical capabilities.
Ethical Considerations in AI Development
The NSF's program director, Jason Borenstein, emphasized the significance of ethical issues in AI research. His research focuses on resolving potential biases in things like general AI systems, self-driving cars, and even human-robot interaction. He's made it a goal to draw attention to issues with accountability, openness, and bias. To guarantee that AI systems are just, and used responsibly, ethical standards must be developed. This entails dealing with concerns like algorithmic fairness, data privacy, and the abuse of AI technologies with an end goal to do what we can to ensure that AI is used for the benefit of society while mitigating potential risks.
The U.S. government is actively supporting AI research and innovation. Tess DeBlanc-Knowles, from the NSF, discussed the National AI Research Resource (NAR), a pilot initiative aimed at providing researchers with access to computational resources and cutting-edge AI models. A formal agreement between the NSF and each private-sector partner will be created in collaboration with the federal technology team US Digital Service. This will guarantee that each partner "appropriately acknowledges its contributions to the NAR, its use of NAR resources, and any intellectual property that results from its participation." Notable collaborators offering computing power were NVIDIA and Microsoft. Through the provision of computing resources, datasets, and pre-trained models, this program seeks to close the knowledge gap between the academic community and the commercial sector. The NAR will increase national competitiveness in AI research and development, promote cooperation, and reduce entrance barriers. The NAR seeks to increase trust in AI systems by ensuring that their development is transparent, accountable, and ethical by lowering the barriers to entry for AI research.
Building Trust
For AI technologies to be widely adopted and used, trust is essential. In order to evaluate the reliability of AI systems, Dr. Michael Littman, Division Director for Information and Intelligence Systems at NSF, stressed the significance of comprehending user demands and giving them relevant information. He also emphasized Cynthia Rudin's work on interpretable AI, which uses machine learning to build models that are simple enough for people to understand. This method increases consumers' trust in these systems by enabling them to understand the reasoning behind AI decisions.
We can't overlook the critical role of human-computer interaction in building trust in AI systems. Users need to understand how AI models work and what factors influence their predictions. Not necessarily at the inner workings, but at a high-level of what goes into those systems. Providing clear explanations and enabling users to interact with models can enhance trust and promote informed decision-making.
The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography Director, Dr. Amy McGovern, emphasized the value of AI in enhancing weather prediction. Her group creates artificial intelligence techniques to improve weather forecasting, with an emphasis on extreme weather phenomena like hurricanes and tornadoes. Artificial intelligence can help communities prepare for and lessen the effects of catastrophic weather disasters by delivering more precise and timely predictions.
In Conclusion
The panel showcased the potential of AI to address societal challenges and the need for responsible development. AI has the enormous potential to solve difficult issues and enhance our lives. But the creation of AI must be approached with ethics, openness, and human-centered design at its core. We can use AI to power a more just, and sustainable future by encouraging cooperation between scientists, legislators, and business.
More on:
NSF: The U.S. National Science Foundation is an independent federal agency that supports science and engineering in all 50 states and U.S. territories.
The National Artificial Intelligence Research Resource Pilot (NAR/NAIRR) is a vision for a shared national research infrastructure for responsible discovery and innovation in AI.
