Gov. Gavin Newsom’s recent veto of California’s Senate Bill (SB) 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, reignited the debate about how best to regulate the rapidly evolving field of artificial intelligence. Newsom’s veto illustrates a cautious approach to regulating a new technology and could pave the way for more pragmatic AI safety policies.
An emerging body of research suggests that large language models (LLMs) can “deceive” humans by offering fabricated explanations for their behavior or concealing the truth of their actions from human users. The implications are worrisome, particularly because researchers do not fully understand why or when LLMs engage in this behavior.
I was excited to contribute to a group of over 20 prominent AI researchers, legal experts, and tech industry leaders from institutions including OpenAI, the Partnership on AI, Microsoft, the University of Oxford, a16z crypto, and MIT on a paper proposing "personhood credentials" (PHCs) as a potential solution to the growing challenge of AI-powered online deception. PHCs would provide a privacy-preserving way for individuals to verify their humanity online without disclosing personal information. While implementation details remain to be determined, the core concept warrants serious consideration from policymakers and tech leaders as AI capabilities rapidly advance, threatening to erode the trust and accountability essential for societies to function.
The increasing capabilities of AI have sparked an important debate: Should the "weights" that define a model's intelligence be openly accessible or tightly controlled? This article dives deep into the arguments on both sides. Proponents say open-weight models accelerate innovation, enable greater scrutiny for safety, and democratize AI capabilities. Critics warn of risks like misuse for disinformation or military purposes by adversaries. The piece examines recent developments like frontier open-weight models from Meta and Mistral, Mark Zuckerberg's case for openness, concerns about China's AI ambitions, and the US government's cautious approach.
The recent AI policy roadmap from the bipartisan AI working group in Congress strikes a thoughtful balance between promoting AI innovation and addressing potential risks. It lays out a nuanced approach including increased AI safety efforts, crucial investments in domestic STEM talent, protections for children in the age of AI, and "grand challenge" programs to spur breakthroughs - all while avoiding hasty overregulation that could stifle progress
Gov. Gavin Newsom’s recent veto of California’s Senate Bill (SB) 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, reignited the debate about how best to regulate the rapidly evolving field of artificial intelligence. Newsom’s veto illustrates a cautious approach to regulating a new technology and could pave the way for more pragmatic AI safety policies.
New research reveals the transformative potential and complex realities of AI in education. From AI-powered grading systems that match human accuracy while saving countless hours, to AI tutors that both enhance and hinder learning, to AI's ability to reason like humans and predict social science experiments, these studies paint a nuanced picture of AI's role in the future of education. As the field rapidly evolves, enthusiasts and skeptics alike must grapple with the profound implications of AI for teaching and learning and ensure their views are shaped by research.
I was excited to contribute to a group of over 20 prominent AI researchers, legal experts, and tech industry leaders from institutions including OpenAI, the Partnership on AI, Microsoft, the University of Oxford, a16z crypto, and MIT on a paper proposing "personhood credentials" (PHCs) as a potential solution to the growing challenge of AI-powered online deception. PHCs would provide a privacy-preserving way for individuals to verify their humanity online without disclosing personal information. While implementation details remain to be determined, the core concept warrants serious consideration from policymakers and tech leaders as AI capabilities rapidly advance, threatening to erode the trust and accountability essential for societies to function.
The increasing capabilities of AI have sparked an important debate: Should the "weights" that define a model's intelligence be openly accessible or tightly controlled? This article dives deep into the arguments on both sides. Proponents say open-weight models accelerate innovation, enable greater scrutiny for safety, and democratize AI capabilities. Critics warn of risks like misuse for disinformation or military purposes by adversaries. The piece examines recent developments like frontier open-weight models from Meta and Mistral, Mark Zuckerberg's case for openness, concerns about China's AI ambitions, and the US government's cautious approach.
The recent AI policy roadmap from the bipartisan AI working group in Congress strikes a thoughtful balance between promoting AI innovation and addressing potential risks. It lays out a nuanced approach including increased AI safety efforts, crucial investments in domestic STEM talent, protections for children in the age of AI, and "grand challenge" programs to spur breakthroughs - all while avoiding hasty overregulation that could stifle progress
In the rapidly evolving landscape of artificial intelligence (AI), the dialogue often veers between the extremes of stringent regulation, like the European Union’s AI Act, and laissez-faire approaches that risk unbridled technological advances without sufficient safeguards. Amidst this polarized debate, the Coalition for Health AI (CHAI) has emerged as a promising alternative approach that addresses the ethical, social, and economic complexities introduced by AI, while also supporting continued innovation.
It's difficult to understand some technologies because they're better experienced than described. I've found GenAI to be one example where it's difficult to grasp the full range of capabilities unless you see some of it in action. Over the last year, I've given a number of presentations that tried to contextualize GenAI for the audience by demonstrating relevant use cases. I compiled them in this long master deck, which I periodically update and am sharing in the hope that it may spark some ideas for you.
I had a great time joining James Pethokoukis on his podcast, Faster, Please! We touched on a number of areas including how AI can help improve teaching and learning.
I am thrilled to share that I have been appointed to the board of the Federation of American Scientists (FAS). It is an incredible honor to have the opportunity to contribute to the vital work of this important organization, particularly during a time when science and technology are progressing at an unprecedented pace.
Recent studies reveal autonomous vehicles (AVs) boast impressive safety records, with far fewer and less severe crashes compared to conventional cars. Advanced AV systems that constantly monitor surroundings, precisely follow traffic laws, and avoid dangerous situations contribute to their superior performance. With rapid technological improvements enabling AVs to learn from analyzing massive amounts of human driving data, they are poised to drastically reduce traffic accidents and fatalities. Further real-world experience for AVs promises to refine their systems and enhance safety even more. Regulators should enable this progress while enacting reasonable safeguards.
The capabilities of AI promise not only efficiency but potentially a more accessible interface for citizens. As governments begin to integrate these technologies into their service-delivery mechanisms, it is imperative to approach the adoption with due diligence, guided by robust frameworks like NIST’s AI RMF. With a combination of strategic foresight, stakeholder engagement, and capacity building, governments can harness the power of AI to truly transform public service, making it more responsive and citizen-centric than ever before.
There is an incredible amount of excitement and confusion around what this wave of generative AI means for education. These technologies are rapidly improving, and developers are introducing capabilities that would have been considered science fiction just a few years ago. In my latest Education Next piece, I provide an overview of generative AI and explore how this technology will influence how students learn, how teachers work, and ultimately how we structure our education system.
The Biden administration recently secured voluntary commitments from major AI companies to manage risks associated with AI, including ensuring products are safe and transparent about capabilities and limitations. While a step in the right direction, the vague pledges largely reaffirm existing activities and oversight mechanisms are unclear. This embodies the precautionary principle which can hamper innovation if taken too far through accumulating requirements. There should be a balance between risk mitigation and advancing AI to realize its benefits. The commitments are also oriented toward avoiding harms rather than proactively leveraging AI's potential to address pressing societal challenges like climate change and learning loss. A more ambitious, benefit-focused approach is needed alongside reasonable precautions.
The rise of artificial intelligence (AI) coincides with a concerning public health crisis of loneliness and isolation, particularly among young people. According to a CDC survey, a shocking 44% of adolescents are dealing with constant feelings of sadness and hopelessness. As AI technology becomes more prevalent, concerns are growing about its potential to worsen these emotional struggles. This new technological context necessitates a deeper investigation into the role AI might play in intensifying these existing societal issues.
Over the past decade, many have been disappointed by the unfulfilled promises of technology transforming education. However, recent advancements in AI, such as OpenAI's GPT-4, may signal a genuine breakthrough. These large-language models have smarter capabilities, function as reasoning engines, use language as an interface, and are scaling rapidly thanks to tech giants. As a result, AI-powered tutoring and teaching assistants are emerging to provide individualized learning, automate administrative tasks, and offer constructive critiques on student writing. While there are limitations, it is expected that future iterations will address these issues. Harnessing AI's potential could lead to a future where education is more effective, equitable, and personalized, with teachers focusing on fostering meaningful connections with their students.
The rise of AI technologies like ChatGPT promises to boost global GDP by 7% in a decade, according to Goldman Sachs. However, this may disrupt 63% of US jobs, including higher-skilled professions like auditors and interpreters. These AI tools will change the nature of work, shifting focus from mundane tasks to more advanced, human-centric activities. Surprisingly, AI could benefit the least skilled workers, narrowing the performance gap among employees. While job displacement is inevitable, the larger disruption may be the creation of new, hybrid jobs combining domain expertise with AI skills. Policymakers must proactively invest in education and workforce training to ease these transitions and capitalize on AI's productivity potential.
Policymakers have yet to fully understand the profound effect the AI revolution will have on our economy, national security, and social well-being. AI, for all its benefits, will also present new risks, challenges, and tensions with our values. To prevent the worst consequences of AI, clear regulations are needed to harness its benefits while managing its risks.
OpenAI. Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory wrote a provocative report surfacing misinformation risks posed by AI. The report provides a thoughtful framework through which to think about the threat of AI-enabled influence operations and some of the steps that can minimize those risks along with their associated trade-offs.
