Lifeboat Foundation

In November 2022, OpenAI launched ChatGPT.

From President Biden’s executive order on AI to the E.U. AI Act, governments scrambled to regulate AI this year.

Elucidating human contact networks could help predict and prevent the transmission of SARS-CoV-2 and future pandemic threats. A new study from Scripps Research scientists and collaborators points to which public health protocols worked to mitigate the spread of COVID-19—and which ones didn’t.

In the study, published online in Cell on December 14, 2023, the Scripps Research-led team of scientists investigated the efficacy of different mandates—including stay-at-home measures, social distancing and travel restrictions —at preventing local and regional transmission during different phases of the COVID-19 pandemic.

They found that local transmission was driven by the amount of travel between locations, not by how geographically nearby they were. The study also revealed that the partial closure of the U.S.-Mexico border was ineffective at preventing cross-border transmission of the virus. These findings, in combination with ongoing genomic surveillance, could help guide public health policy to prevent future pandemics and mitigate the new “endemic” phase of COVID-19.

Twenty-four years ago, Ray Kurzweil predicted computers would reach human-level intelligence by 2029. This was met with great concern and criticism. In the past six months technology experts have come around to agree with him. According to Kurzweil, over the next two decades, AI is going to change what it means to be human. We are going to invent new means of expression that will soar past human language, art, and science of today. All of the concepts that we rely on to give meaning to our lives, including death itself, will be transformed.\
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Speakers:\
Ray Kurzweil\
Inventor, Futurist \& Best-selling author of ‘The Singularity is Near’\
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Reinhard Scholl\
Deputy Director, Telecommunication Standardization Bureau\
International Telecommunication Union (ITU)\
Co-founder and Managing Director, AI for Good\
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The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.\
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Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\
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🔴 Watch the latest #AIforGood videos!\
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What is AI for Good?\
We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\
More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\
AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\
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Disclaimer:\
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.

If we’re not careful, Microsoft, Amazon, and other large companies will leverage their position to set the policy agenda for AI, as they have in many other sectors.

A recent study published in IEEE Transactions on Control of Network Systems discusses how artificial intelligence (AI) can be used to control microgrids in the event of a long-term power outage caused by natural disasters or human error. This study was conducted by a team of researchers at UC Santa Cruz and holds the potential to improve power restoration techniques, which are traditionally controlled by local utility companies. One benefit of microgrids is they can function to power a small area, such as a town, until the primary utility source comes back online.

“Nowadays, microgrids are really the thing that both people in industry and in academia are focusing on for the future power distribution systems,” said Dr. Yu Zhang, who is an assistant professor of electrical and computer engineering at UC Santa Cruz and co-author on the study.

For the study, the researchers used an AI-based approach to develop a novel method where microgrids could draw power from renewable energy sources while being disconnected from the primary utility source, known as “islanding mode”, but can also function while being connected to the source, as well. This new method, which they refer to as constrained policy optimization (CPO), uses a machine learning algorithm that learns from outside input, such as real-time changes in environmental or power conditions, and makes the best-informed decisions on what to do next.

RIO DE JANEIRO (AP) — City lawmakers in Brazil have enacted what appears to be the nation’s first legislation written entirely by artificial intelligence — even if they didn’t know it at the time.

The experimental ordinance was passed in October in the southern city of Porto Alegre and city councilman Ramiro Rosário revealed this week that it was written by a chatbot, sparking objections and raising questions about the role of artificial intelligence in public policy.

Rosário told The Associated Press that he asked OpenAI’s chatbot ChatGPT to craft a proposal to prevent the city from charging taxpayers to replace water consumption meters if they are stolen. He then presented it to his 35 peers on the council without making a single change or even letting them know about its unprecedented origin.

Just weeks before the management shakeup at OpenAI rocked Silicon Valley and made international news, the company’s cofounder and chief scientist Ilya Sutskever explored the transformative potential of artificial general intelligence (AGI), highlighting how it could surpass human intelligence and profoundly transform every aspect of life. Hear his take on the promises and perils of AGI — and his optimistic case for how unprecedented collaboration will ensure its safe and beneficial development. (Recorded October 17, 2023)

If you love watching TED Talks like this one, become a TED Member to support our mission of spreading ideas: https://ted.com/membership.

Continue reading “The Exciting, Perilous Journey Toward AGI | Ilya Sutskever | TED” »

I suggest two responses to this difficult challenge for the United States and its allies: At the time of attack, the allies should respond with nonnuclear retaliation as long as politically feasible, in order to prevent further nuclear escalation. However, this will be difficult given the likely post-strike panic and hysteria. So, in preparation, the US should deconcentrate its northeast Asian conventional footprint, to reduce North Korean opportunities to engage in nuclear blackmail regarding regional American clusters of military equipment and personnel, and to reduce potential US casualties and consequent massive retaliation pressures if North Korea does launch a nuclear attack.

North Korean first-use incentives. The incentives for North Korea to use nuclear weapons first in a major conflict are powerful:

Operationally, North Korea will likely have only a very short time window to use its weapons of mass destruction. The Americans will almost certainly try to immediately suppress Northern missiles. An imminent, massive US-South Korea disarming strike creates an extreme use-it-or-lose-it dilemma for Pyongyang. If Kim Jong-Un does not use his nuclear weapons at the start of hostilities, most will be destroyed a short time later by allied airpower, turning an inter-Korean conflict into a conventional war that the North will probably lose. Frighteningly, this may encourage Kim to also release his strategic nuclear weapons almost immediately after fighting begins.

Large Language Models (LLMs) have shown great capabilities in various natural language tasks such as text summarization, question answering, generating code, etc., emerging as a powerful solution to many real-world problems. One area where these models struggle, though, is goal-directed conversations where they have to accomplish a goal through conversing, for example, acting as an effective travel agent to provide tailored travel plans. In practice, they generally provide verbose and non-personalized responses.

Models trained with supervised fine-tuning or single-step reinforcement learning (RL) commonly struggle with such tasks as they are not optimized for overall conversational outcomes after multiple interactions. Moreover, another area where they lack is dealing with uncertainty in such conversations. In this paper, the researchers from UC Berkeley have explored a new method to adapt LLMs with RL for goal-directed dialogues. Their contributions include an optimized zero-shot algorithm and a novel system called imagination engine (IE) that generates task-relevant and diverse questions to train downstream agents.

Since the IE cannot produce effective agents by itself, the researchers utilize an LLM to generate possible scenarios. To enhance the effectiveness of an agent in achieving desired outcomes, multi-step reinforcement learning is necessary to determine the optimal strategy. The researchers have made one modification to this approach. Instead of using any on-policy samples, they used offline value-based RL to learn a policy from the synthetic data itself.