General > The Big Picture
AGI
Cadavre Exquis:
Aunque llevaba un tiempo pensando en ello, aprovecho la publicación hace un par de días de un post sobre AGI (Artificial General Intelligence) por parte de Sam Altman (CEO de OpenAI) para crear un nuevo hilo al que ir trayendo noticias relacionadas con los avances en IA.
He estado dudando entre utilizar AI o AGI como título para el hilo. Al final, me he decantado por este último porque, aunque todavía no seamos totalmente conscientes de ello, la IA ya hace unos cuantos años que está presente en nuestras vidas de una forma más o menos evidente y creo que el debate debe centrarse no alrededor de la AI (también llamada Narrow AI) sino de la AGI por el impacto que tendra en la sociedad y, por tanto, en nuestras vidas.
Es muy probable que este hilo se solape con otros (e.g. STEM, El fin del trabajo, A brave new world: La sociedad por venir y, por supuesto, los de "PP-CC-Pisitófilos Creditofagos"), pero creo que la cantidad de noticias que van a producirse en un futuro cercano alrededor de la AGI precisan de un hilo específico para debatir sobre ello.
Saludos.
P.D.
Soy consciente de lo polémico del tema e incluso del problema que supone llegar a un acuerdo sobre lo que significa AGI; para intentar centrar el debate podemos partir de la definición que propone la propia OpenAI en su página "About" a día 26 de febrero de 2023:
--- Citar ---OpenAI’s mission is to ensure that artificial general intelligence
(AGI)—by which we mean highly autonomous systems that
outperform humans at most economically valuable work—benefits
all of humanity.
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https://openai.com/about/
Cadavre Exquis:
Post publicado el 24 de febrero de 2023 por Sam Altman, CEO de OpenAI, en el blog de la compañia:
--- Citar ---Planning for AGI and beyond
Sam Altman, February 24, 2023
Our mission is to ensure that artificial general intelligence—AI systems that are generally smarter than humans—benefits all of humanity.
If AGI is successfully created, this technology could help us elevate humanity by increasing abundance, turbocharging the global economy, and aiding in the discovery of new scientific knowledge that changes the limits of possibility.
AGI has the potential to give everyone incredible new capabilities; we can imagine a world where all of us have access to help with almost any cognitive task, providing a great force multiplier for human ingenuity and creativity.
On the other hand, AGI would also come with serious risk of misuse, drastic accidents, and societal disruption. Because the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right.[1]
Although we cannot predict exactly what will happen, and of course our current progress could hit a wall, we can articulate the principles we care about most:
* We want AGI to empower humanity to maximally flourish in the universe. We don’t expect the future to be an unqualified utopia, but we want to maximize the good and minimize the bad, and for AGI to be an amplifier of humanity.
* We want the benefits of, access to, and governance of AGI to be widely and fairly shared.
* We want to successfully navigate massive risks. In confronting these risks, we acknowledge that what seems right in theory often plays out more strangely than expected in practice. We believe we have to continuously learn and adapt by deploying less powerful versions of the technology in order to minimize “one shot to get it right” scenarios.
The short term
There are several things we think are important to do now to prepare for AGI.
First, as we create successively more powerful systems, we want to deploy them and gain experience with operating them in the real world. We believe this is the best way to carefully steward AGI into existence—a gradual transition to a world with AGI is better than a sudden one. We expect powerful AI to make the rate of progress in the world much faster, and we think it’s better to adjust to this incrementally.
A gradual transition gives people, policymakers, and institutions time to understand what’s happening, personally experience the benefits and downsides of these systems, adapt our economy, and to put regulation in place. It also allows for society and AI to co-evolve, and for people collectively to figure out what they want while the stakes are relatively low.
We currently believe the best way to successfully navigate AI deployment challenges is with a tight feedback loop of rapid learning and careful iteration. Society will face major questions about what AI systems are allowed to do, how to combat bias, how to deal with job displacement, and more. The optimal decisions will depend on the path the technology takes, and like any new field, most expert predictions have been wrong so far. This makes planning in a vacuum very difficult.[2]
Generally speaking, we think more usage of AI in the world will lead to good, and want to promote it (by putting models in our API, open-sourcing them, etc.). We believe that democratized access will also lead to more and better research, decentralized power, more benefits, and a broader set of people contributing new ideas.
As our systems get closer to AGI, we are becoming increasingly cautious with the creation and deployment of our models. Our decisions will require much more caution than society usually applies to new technologies, and more caution than many users would like. Some people in the AI field think the risks of AGI (and successor systems) are fictitious; we would be delighted if they turn out to be right, but we are going to operate as if these risks are existential.
At some point, the balance between the upsides and downsides of deployments (such as empowering malicious actors, creating social and economic disruptions, and accelerating an unsafe race) could shift, in which case we would significantly change our plans around continuous deployment.
Second, we are working towards creating increasingly aligned and steerable models. Our shift from models like the first version of GPT-3 to InstructGPT and ChatGPT is an early example of this.
In particular, we think it’s important that society agree on extremely wide bounds of how AI can be used, but that within those bounds, individual users have a lot of discretion. Our eventual hope is that the institutions of the world agree on what these wide bounds should be; in the shorter term we plan to run experiments for external input. The institutions of the world will need to be strengthened with additional capabilities and experience to be prepared for complex decisions about AGI.
The “default setting” of our products will likely be quite constrained, but we plan to make it easy for users to change the behavior of the AI they’re using. We believe in empowering individuals to make their own decisions and the inherent power of diversity of ideas.
We will need to develop new alignment techniques as our models become more powerful (and tests to understand when our current techniques are failing). Our plan in the shorter term is to use AI to help humans evaluate the outputs of more complex models and monitor complex systems, and in the longer term to use AI to help us come up with new ideas for better alignment techniques.
Importantly, we think we often have to make progress on AI safety and capabilities together. It’s a false dichotomy to talk about them separately; they are correlated in many ways. Our best safety work has come from working with our most capable models. That said, it’s important that the ratio of safety progress to capability progress increases.
Third, we hope for a global conversation about three key questions: how to govern these systems, how to fairly distribute the benefits they generate, and how to fairly share access.
In addition to these three areas, we have attempted to set up our structure in a way that aligns our incentives with a good outcome. We have a clause in our Charter about assisting other organizations to advance safety instead of racing with them in late-stage AGI development. We have a cap on the returns our shareholders can earn so that we aren’t incentivized to attempt to capture value without bound and risk deploying something potentially catastrophically dangerous (and of course as a way to share the benefits with society). We have a nonprofit that governs us and lets us operate for the good of humanity (and can override any for-profit interests), including letting us do things like cancel our equity obligations to shareholders if needed for safety and sponsor the world’s most comprehensive UBI experiment.
We think it’s important that efforts like ours submit to independent audits before releasing new systems; we will talk about this in more detail later this year. At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models. We think public standards about when an AGI effort should stop a training run, decide a model is safe to release, or pull a model from production use are important. Finally, we think it’s important that major world governments have insight about training runs above a certain scale.
The long term
We believe that future of humanity should be determined by humanity, and that it’s important to share information about progress with the public. There should be great scrutiny of all efforts attempting to build AGI and public consultation for major decisions.
The first AGI will be just a point along the continuum of intelligence. We think it’s likely that progress will continue from there, possibly sustaining the rate of progress we’ve seen over the past decade for a long period of time. If this is true, the world could become extremely different from how it is today, and the risks could be extraordinary. A misaligned superintelligent AGI could cause grievous harm to the world; an autocratic regime with a decisive superintelligence lead could do that too.
AI that can accelerate science is a special case worth thinking about, and perhaps more impactful than everything else. It’s possible that AGI capable enough to accelerate its own progress could cause major changes to happen surprisingly quickly (and even if the transition starts slowly, we expect it to happen pretty quickly in the final stages). We think a slower takeoff is easier to make safe, and coordination among AGI efforts to slow down at critical junctures will likely be important (even in a world where we don’t need to do this to solve technical alignment problems, slowing down may be important to give society enough time to adapt).
Successfully transitioning to a world with superintelligence is perhaps the most important—and hopeful, and scary—project in human history. Success is far from guaranteed, and the stakes (boundless downside and boundless upside) will hopefully unite all of us.
We can imagine a world in which humanity flourishes to a degree that is probably impossible for any of us to fully visualize yet. We hope to contribute to the world an AGI aligned with such flourishing.
Footnotes1.We seem to have been given lots of gifts relative to what we expected earlier: for example, it seems like creating AGI will require huge amounts of compute and thus the world will know who is working on it, it seems like the original conception of hyper-evolved RL agents competing with each other and evolving intelligence in a way we can’t really observe is less likely than it originally seemed, almost no one predicted we’d make this much progress on pre-trained language models that can learn from the collective preferences and output of humanity, etc.
AGI could happen soon or far in the future; the takeoff speed from the initial AGI to more powerful successor systems could be slow or fast. Many of us think the safest quadrant in this two-by-two matrix is short timelines and slow takeoff speeds; shorter timelines seem more amenable to coordination and more likely to lead to a slower takeoff due to less of a compute overhang, and a slower takeoff gives us more time to figure out empirically how to solve the safety problem and how to adapt.2.For example, when we first started OpenAI, we didn’t expect scaling to be as important as it has turned out to be. When we realized it was going to be critical, we also realized our original structure wasn’t going to work—we simply wouldn’t be able to raise enough money to accomplish our mission as a nonprofit—and so we came up with a new structure.
As another example, we now believe we were wrong in our original thinking about openness, and have pivoted from thinking we should release everything (though we open source some things, and expect to open source more exciting things in the future!) to thinking that we should figure out how to safely share access to and benefits of the systems. We still believe the benefits of society understanding what is happening are huge and that enabling such understanding is the best way to make sure that what gets built is what society collectively wants (obviously there’s a lot of nuance and conflict here).AuthorsSam AltmanAcknowledgmentsThanks to Brian Chesky, Paul Christiano, Jack Clark, Holden Karnofsky, Tasha McCauley, Nate Soares, Kevin Scott, Brad Smith, Helen Toner, Allan Dafoe, and the OpenAI team for reviewing drafts of this.Filed UnderSafety & Alignment
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Saludos.
Cadavre Exquis:
Sin duda, la parte más compleja de la AGI es el tema del alineamiento o alineación de la Inteligencia Artificial.
Nick Bostrom escribió en 2014 un libro dedicado precisamente a este problema. Está publicado tanto en inglés como en castellano.
Saludos.
Cadavre Exquis:
Curiosamente, Pablo Gil dedicó su vídeo de este viernes a la IA y, aunque todavía se le ve un poco despistado al respecto, sí que intuye que va a suponer una revolución tanto o más grande que lo que fue la Revolución Industrial o la el acceso generalizado a Internet.
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Saludos.
Cadavre Exquis:
--- Citar ---Microsoft Tests ChatGPT's Ability to Control Robots
Posted by EditorDavid on Sunday February 26, 2023 @07:34AM from the AI-robot dept.
"We extended the capabilities of ChatGPT to robotics," brags a blog post from Microsoft's Autonomous Systems and Robotics research group, "and controlled multiple platforms such as robot arms, drones, and home assistant robots intuitively with language."
They're exploring how to use ChatGPT to "make natural human-robot interactions possible... to see if ChatGPT can think beyond text, and reason about the physical world to help with robotics tasks."
--- Citar ---We want to help people interact with robots more easily, without needing to learn complex programming languages or details about robotic systems. The key challenge here is teaching ChatGPT how to solve problems considering the laws of physics, the context of the operating environment, and how the robot's physical actions can change the state of the world.
It turns out that ChatGPT can do a lot by itself, but it still needs some help. Our technical paper describes a series of design principles that can be used to guide language models towards solving robotics tasks. These include, and are not limited to, special prompting structures, high-level APIs, and human feedback via text.... In our work we show multiple examples of ChatGPT solving robotics puzzles, along with complex robot deployments in the manipulation, aerial, and navigation domains....
We gave ChatGPT access to functions that control a real drone, and it proved to be an extremely intuitive language-based interface between the non-technical user and the robot. ChatGPT asked clarification questions when the user's instructions were ambiguous, and wrote complex code structures for the drone such as a zig-zag pattern to visually inspect shelves. It even figured out how to take a selfie! We also used ChatGPT in a simulated industrial inspection scenario with the Microsoft AirSim simulator. The model was able to effectively parse the user's high-level intent and geometrical cues to control the drone accurately....
We are excited to release these technologies with the aim of bringing robotics to the reach of a wider audience. We believe that language-based robotics control will be fundamental to bring robotics out of science labs, and into the hands of everyday users.
That said, we do emphasize that the outputs from ChatGPT are not meant to be deployed directly on robots without careful analysis. We encourage users to harness the power of simulations in order to evaluate these algorithms before potential real life deployments, and to always take the necessary safety precautions. Our work represents only a small fraction of what is possible within the intersection of large language models operating in the robotics space, and we hope to inspire much of the work to come.tics to the reach of a wider audience. We believe that language-based robotics control will be fundamental to bring robotics out of science labs, and into the hands of everyday users.
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ZDNet points out that Google Research and Alphabet's Everyday Robots "have also worked on similar robotics challenges using a large language models called PaLM, or Pathways Language Model, which helped a robot to process open-ended prompts and respond in reasonable ways."
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Saludos.
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