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man CEO Dario Amani Urgent payment In April, a need to understand how artificial intelligence models think.
This comes in a decisive time. As a person Battles In the global AI rankings, it is important to note what distinguishes it from the best artificial intelligence laboratories. Since its foundation in 2021, when seven Openai employees Cut off On concerns about the integrity of artificial intelligence, anthropology has built Amnesty International models that adhere to a set of human value principles, a system they call Amnesty International Constitutional Organization. These principles guarantee that the models are “Useful, honest and harmlessIn general, work in the interest of society. At the same time, the arm of the human being is Dive deeply To understand how you think its models in the world, and Why They produce useful answers (sometimes harmful).
The leading model of the anthropologist, Claude 3.7 Sonata, Take control Coding standards when launch in February, which proves that artificial intelligence models can outperform both performance and safety. The latest version of Claude 4.0 OPUS and Sonnet places Claude again The highest coding standards. However, in the rapid and increased artificial intelligence market, the Antarbur competitors such as Google Gemini 2.5 Pro and Open AI’s O3 have their impressive offers of coding, while they are Dominance Claude in mathematics, creative writing and comprehensive thinking through many languages.
If the ideas of Amodei are any indicator, then anthropologist plans for the future of artificial intelligence and its effects in critical areas such as medicine, psychology and law, where typical safety and human values are necessary. It appears: Anthropor is the pioneering AI laboratory, which strictly focuses on developing “interpretable” artificial intelligence, which allow us to understand, to some extent, what the model thinks and how it reaches a certain result.
Amazon and Google I have already invested billions of dollars in Anthropor even when their Amnesty International models are adopted, so the competitive advantage of the anthropologist is still emerging. Interpretated models, as Antarbur, can significantly reduce long -term operational costs associated with correction, scrutiny and risk relief in complex artificial intelligence spread.
Siash KapoorAmnesty International Aman notes that although the ability to explain is valuable, it is just one of the many tools to manage the risks of artificial intelligence. In his opinion, “the interpretation is neither necessary nor sufficient” to ensure the behavior of models safely-it matters more when it is associated with filters, verification and design centered around man. This most expansionist opinion sees that interpretation is part of a larger environmental system for control strategies, especially in the spread of artificial intelligence in the real world where models are components in broader decision -making systems.
The need for an interpretable AI
Until recently, many believed that Amnesty International was still years of progress like those that now help Claude, Gemini and Chatgpt boast Adoption of the exceptional market. While these models are already Pay the limits of human knowledgeThey are widely due to their quality in solving a wide range of practical problems that require solving creative problems or detailed analysis. Since the models are placed in the task on the growing problems increasingly, it is important to produce accurate answers.
Amodei fears that when AI responds to asking, “We have no idea … Why do you choose some words on others, or why he makes a mistake sometimes even though he is usually accurate.” Such errors – hallucinations of inaccurate information, or responses that are not in line with human values - will restore artificial intelligence models from reaching their full potential. In fact, we have seen many examples of Amnesty International continuing to struggle with it Hallucinogenic and Immoral behavior.
For Amodei, the best way to solve these problems is to understand how artificial intelligence thinks: “Our inability to understand the mechanisms of internal models means that we cannot predict a useful manner of these (harmful) behaviors, and therefore the struggle to exclude them … if it is possible to look at the models within the models, we may be able to prevent all the wings systematically, as well as the struggle with knowledge Dangerous.
Amodei also sees the current models as a barrier to spreading artificial intelligence models in “financial settings or high safety, because we cannot fully set the borders on their behavior, and a few errors may be very harmful.” In making decisions that directly affect humans, such as medical diagnosis or mortgage assessments, legal Systems Artificial intelligence is required to explain its decisions.
Imagine a financial institution using a large language model (LLM) to detect fraud – the interpretation can mean an explanation of the loan request for the customer as required by law. Or a manufacturing company that improves supply chains – understanding the reason for the artificial intelligence proposal that a specific resource can open efficiency and prevent unexpected bottlenecks.
For this reason, Amodei explains that “Anthropor doubles the interpretation, and we have a goal to reach” the interpretation can reliably discover most of the form of the model “by 2027.”
To this end, Antarbur recently participated in $ 50 million investment in GoodfireAmnesty International’s research laboratory makes great progress on “brain scanning operations”. Their models inspection platform, EMber, is a tool that does not know that the concepts learned within the models and allow users to deal with them. In a conversation Expatible offerThe company explained how the Eber can get to know paint These concepts are on fabric to create new images that follow the user design.
Anthropor’s investment in Emberv alludes to the fact that the development of interpretable models is difficult enough so that the Antarbur does not have the workforce to achieve the interpretation on its own. Interpretable creative models require new tools and skilled developers to build
A wider context: the perspective of the artificial intelligence researcher
To destroy a Amodei perspective and add a context that affects the need, I met Venturebeat Kapoor, with Amnesty International Safety Researcher in Princeton. Kapoor participated in writing the book Amnesty International Snake OilA critical study of exaggerated demands surrounding the capabilities of leading artificial intelligence models. He is also a co -author of “Amnesty International as ordinary technology“It calls for dealing with artificial intelligence as a standard transformational tool such as the Internet or electricity, and enhances a realistic perspective about its integration in daily systems.
Kapoor does not oppose that the ability to interpret is valuable. However, he is skeptical of his treatment as the central column to align artificial intelligence. “It is not a silver bullet,” Kapoor told Venturebeat. He said that many of the most effective safety technologies, such as post -response filtering, do not require opening the model at all.
It also warns of what researchers call “the fallacy of mystery” – the idea that if we do not fully understand the system inside the system, we cannot use it or organize it responsibly. In practice, complete transparency is not how to evaluate most techniques. What matters is whether the system leads reliably under real circumstances.
This is not the first time that Amodei has warned of the risk of AI outperformed our understanding. In October 2024 mail“Love Grace machines”, drawing a vision of increasing models models that can take real meaningful measures (and perhaps double our life).
According to Kapoor, there is an important discrimination that must be done here between the model ability and power. There is no doubt that the typical capabilities are increasing quickly, and may soon develop enough intelligence to find solutions to many complex problems that challenge humanity today. But the model is only strong like the facades that we offer to interact with the real world, including location and how to spread models.
Amodei separately argued that the United States should maintain progress in developing artificial intelligence, partly through Export controls That limits access to strong models. The idea is that authoritarian governments may irregularly use Frontier AI – or seize the geopolitical and economic edge that comes with their publication first.
For Kapoor, “Even the biggest supporter of export control elements agree that he will give us at least one or two years.” He believes that we should deal with artificial intelligence as “Regular technology“Like electricity or the Internet. Although the revolutionary, it took decades until both technologies are fully fulfilled throughout society. Kapoor believes that it is the same for artificial intelligence: the best way to maintain the geopolitical edge is to focus on the“ long game ”to transform industries to use artificial intelligence.
Others criticize Amodei
Kapoor is not the only one who criticizes the position of Amani. Last week at Viveatech in Paris, Yansen Huang, CEO of NVIDIA, Declare With Amodei views. Huang wondered whether the authority to develop artificial intelligence should be limited to a few strong entities such as anthropor. He said: “If you want things to take place safely and responsibly, then you do it in the open … Do not do it in a dark room and tell me that it is safe.”
In response, man I mentioned“Dario never claimed that“ Antarbur ”can only build artificial intelligence safely and strongly. As the general record will appear, Dario called for the national transparency standard for artificial intelligence developers (including Antarbur) until the public and policy makers realize“ models and risks ”, and they can be prepared accordingly.
It should also be noted that the Antarbur is not alone in its endeavor to explain: I made the deep interpretation team from Google, led by Neil Nanda, too. Serious contributions To interpret research.
Ultimately, the best AI laboratories and researchers provide strong evidence that the ability to explain can be a major discrimination in the competitive artificial intelligence market. Institutions that give priority to explaining the ability early may gain a great competitive advantage by building more confident, compatible, and adaptable intelligence systems.
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