John Hopfield, and Geoff Hinton.
You've heard me mention both their names.
Hopfield began life as a molecular biologist. Now he's considered the father of machine learning.
Every AI in existence today, uses Hopfield's methods or some variation thereof. ChatGPT wouldn't exist at all without them.
Hinton (and his partner Sejnowski who's now at the Salk Institute) linked Hopfield's methods with thermodynamics, creating the first artificial neural network that could learn to read and write.
I would consider both men physicists. Hopfield was a student of John Wheeler's. Hinton worked at Google for a while on the quantum computing stuff, but he quit because he didn't like what he saw, and now he's warning the world about abuses of AI.
John Hopfield:
View attachment 1024413
en.wikipedia.org
The paper that started it all:
Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of ...
www.ncbi.nlm.nih.gov
Geoffrey Hinton:
View attachment 1024414
en.wikipedia.org
The first artificial neural network that could learn to read and talk;
John Hopfield's Concerns About AI
John Hopfield has expressed significant worries regarding the implications of artificial intelligence, particularly in light of his contributions to the field. His concerns can be summarized as follows:
1.
Potential for Loss of Control: Hopfield has highlighted the
"threat of these things getting out of control," indicating his apprehension about the unpredictable nature of advanced AI systems. He fears that as AI technologies evolve, they may operate beyond human oversight, leading to unintended consequences.
2.
Ethical Implications: He draws parallels between the current state of AI and the early days of gene editing, suggesting that just as scientists worked to establish ethical guidelines for gene editing, similar measures are necessary for AI. He believes that without proper ethical frameworks, the rapid advancement of AI could lead to harmful outcomes.
3.
Existential Risks: Hopfield has articulated a broader concern about the capabilities of AI, stating,
"I worry about anything that says I'm big, I'm fast, I'm bigger than you are, I'm faster than you, and I can also outrun you." This metaphor reflects his anxiety about the potential dominance of AI systems over human capabilities and the implications of coexisting with such powerful technologies.
In summary, John Hopfield's worries about AI center around the potential for loss of control, the need for ethical guidelines, and the existential risks posed by increasingly capable AI systems. His insights underscore the importance of addressing these challenges as AI continues to develop.
Here’s a list of strategies to address potential problems with AI:
1.
Establish Ethical Guidelines: Develop comprehensive ethical standards that govern AI development and deployment, focusing on transparency, accountability, and fairness.
2.
Regulate AI Development: Create regulatory frameworks to ensure that AI systems adhere to safety and ethical norms. This includes risk assessments and compliance checks.
3.
Promote Explainability: Encourage the design of AI systems that provide clear insights into their decision-making processes, making it easier for users to understand and trust their outputs.
4.
Engage Stakeholders: Involve diverse stakeholders, including ethicists, technologists, and community representatives, in the AI development process to identify potential risks and societal impacts.
5.
Invest in Safety Research: Fund research aimed at developing robust AI safety measures that prevent harmful outcomes, including alignment with human values.
6.
Implement Robust Testing: Prioritize extensive testing of AI systems in controlled environments to identify and mitigate potential failures before deployment.
7.
Monitor and Audit: Establish ongoing monitoring and auditing mechanisms for deployed AI systems to ensure compliance with ethical guidelines and regulatory standards.
8.
Foster Public Awareness: Educate the public about AI technologies, their benefits, and risks, empowering society to engage in informed discussions about their use.
9.
Create AI Governance Bodies: Form independent organizations tasked with overseeing AI developments and addressing emerging challenges in real-time.
10.
Encourage Collaborative Efforts: Foster international collaboration among governments, institutions, and industry players to share best practices and address global AI challenges collectively.
These strategies can help mitigate the risks associated with AI while maximizing its benefits.
==>You know, AI isn’t really the big issue here. The real problem is those classic deadly sins. They mess with everyone’s heads! lol.
Sources :
Pair awarded ‘for foundational discoveries and inventions that enable machine learning with artificial neural networks’.
www.aljazeera.com
Hopfield, the Howard A. Prior Professor in the Life Sciences, Emeritus, and professor of molecular biology, emeritus, shares the 2024 Nobel Prize with Toronto's Geoffrey E. Hinton.
www.princeton.edu
Hopfield co-founded the Department of Computation and Neural Systems at Caltech in 1986
www.caltech.edu
