Mostrando las entradas con la etiqueta ModelLanguages. Mostrar todas las entradas
Mostrando las entradas con la etiqueta ModelLanguages. Mostrar todas las entradas

domingo, diciembre 31, 2023

Geoffrey Hinton sobre la inteligencia artificial

 


Will Douglas Heaven entrevista en Technology Review del MIT a Geoffrey Hinton, sobre su actual desconfianza en la Inteligencia Artificial:

Hinton fears that these tools are capable of figuring out ways to manipulate or kill humans who aren’t prepared for the new technology.

“I have suddenly switched my views on whether these things are going to be more intelligent than us. I think they’re very close to it now and they will be much more intelligent than us in the future,” he says. “How do we survive that?”

He is especially worried that people could harness the tools he himself helped breathe life into to tilt the scales of some of the most consequential human experiences, especially elections and wars.

“Look, here’s one way it could all go wrong,” he says. “We know that a lot of the people who want to use these tools are bad actors like Putin or DeSantis. They want to use them for winning wars or manipulating electorates.”

Hinton believes that the next step for smart machines is the ability to create their own subgoals, interim steps required to carry out a task. What happens, he asks, when that ability is applied to something inherently immoral?

“Don’t think for a moment that Putin wouldn’t make hyper-intelligent robots with the goal of killing Ukrainians,” he says. “He wouldn’t hesitate. And if you want them to be good at it, you don’t want to micromanage them—you want them to figure out how to do it.”

There are already a handful of experimental projects, such as BabyAGI and AutoGPT, that hook chatbots up with other programs such as web browsers or word processors so that they can string together simple tasks. Tiny steps, for sure—but they signal the direction that some people want to take this tech. And even if a bad actor doesn’t seize the machines, there are other concerns about subgoals, Hinton says.

“Well, here’s a subgoal that almost always helps in biology: get more energy. So the first thing that could happen is these robots are going to say, ‘Let’s get more power. Let’s reroute all the electricity to my chips.’ Another great subgoal would be to make more copies of yourself. Does that sound good?”

Maybe not. But Yann LeCun, Meta’s chief AI scientist, agrees with the premise but does not share Hinton’s fears. “There is no question that machines will become smarter than humans—in all domains in which humans are smart—in the future,” says LeCun. “It’s a question of when and how, not a question of if.”

But he takes a totally different view on where things go from there. “I believe that intelligent machines will usher in a new renaissance for humanity, a new era of enlightenment,” says LeCun. “I completely disagree with the idea that machines will dominate humans simply because they are smarter, let alone destroy humans.”

“Even within the human species, the smartest among us are not the ones who are the most dominating,” says LeCun. “And the most dominating are definitely not the smartest. We have numerous examples of that in politics and business.”

Yoshua Bengio, who is a professor at the University of Montreal and scientific director of the Montreal Institute for Learning Algorithms, feels more agnostic. “I hear people who denigrate these fears, but I don’t see any solid argument that would convince me that there are no risks of the magnitude that Geoff thinks about,” he says. But fear is only useful if it kicks us into action, he says: “Excessive fear can be paralyzing, so we should try to keep the debates at a rational level.”


LeCun es muy optimista...si no fuera por los drones sobre Kiev, la prisión de Navalni, o las medidas de control social de China, quizá se podría aceptar su visión.

Foto: Ramsey Cardy / Collision via Sportsfile, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons

domingo, febrero 12, 2023

AI y la ética

 El mayor problema de la inteligencia artificial es que sus construcciones están basadas en principios matemáticos y lógicos, y estos no son suficientes y pueden ser desviados. Hace muy poco, Galáctica lo ha reflejado, en su inicio desastroso, capotando en tres días. GPT parece estar embarcado en mejorar esta aproximación, y es un proyecto en curso, arrasando todas las marcas de interés del pasado. Mientras tanto, las Big Tech probablemente deban realinearse. Dice Will Douglas Heaven en Technology Review:

While OpenAI was wrestling with GPT-3’s biases, the rest of the tech world was facing a high-profile reckoning over the failure to curb toxic tendencies in AI. It’s no secret that large language models can spew out false—even hateful—text, but researchers have found that fixing the problem is not on the to-do list of most Big Tech firms. When Timnit Gebru, co-director of Google’s AI ethics team, coauthored a paper that highlighted the potential harms associated with large language models (including high computing costs), it was not welcomed by senior managers inside the company. In December 2020, Gebru was pushed out of her job. 

domingo, diciembre 11, 2022

Galáctica y las dificultades de los modelos de lenguaje

En noviembre, Meta presentó un modelo de lenguaje bautizado Galactica, elaborado para asistir a investigadores científicos, pero sólo tres días después fue retirado de disponibilidad para ser consultado o testeado. Básicamente, como ha sucedido en otros campos de trabajo con inteligencia artificial (IA/AI), el lenguaje no reconoce verdad o falsedad. En las pruebas, trabajos formalmente presentados como científicos pero absurdos como la existencia de osos en el espacio, o las causas de la guerra de Ucrania, pasaron por buenos, con justificaciones razonadas.

Will Douglas Heaven, en Technology Review:

Galactica is a large language model for science, trained on 48 million examples of scientific articles, websites, textbooks, lecture notes, and encyclopedias. Meta promoted its model as a shortcut for researchers and students. In the company’s words, Galactica “can summarize academic papers, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more.”

(...) A fundamental problem with Galactica is that it is not able to distinguish truth from falsehood, a basic requirement for a language model designed to generate scientific text. People found that it made up fake papers (sometimes attributing them to real authors), and generated wiki articles about the history of bears in space as readily as ones about protein complexes and the speed of light. It’s easy to spot fiction when it involves space bears, but harder with a subject users may not know much about.

(...) Many scientists pushed back hard. Michael Black, director at the Max Planck Institute for Intelligent Systems in Germany, who works on deep learning, tweeted: “In all cases, it was wrong or biased but sounded right and authoritative. I think it’s dangerous.”

(...) The Meta team behind Galactica argues that language models are better than search engines. “We believe this will be the next interface for how humans access scientific knowledge,” the researchers write.  This is because language models can “potentially store, combine, and reason about” information. But that “potentially” is crucial. It’s a coded admission that language models cannot yet do all these things. And they may never be able to. “Language models are not really knowledgeable beyond their ability to capture patterns of strings of words and spit them out in a probabilistic manner,” says [Chirag Shah,  University of Washington]. “It gives a false sense of intelligence.”

 Grady Booch comenta: "Galactica is little more than statistical nonsense at scale. Amusing. Dangerous. And IMHO unethical". Algún investigador en ML (Yann LeCun, en el mismo hilo), se escandaliza por la calificación de no ético. Creo que a algunos científicos les falta medir el alcance de lo que tienen entre manos.