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El progreso reciente en IA ha sido sorprendente. Apenas ha pasado una semana sin que un nuevo algoritmo, aplicación o implicación aparezca en los titulares. Pero OpenAI, la fuente de gran parte del revuelo, completó recientemente su algoritmo insignia, GPT-4 , y según el CEO de OpenAI, Sam Altman, su sucesor, GPT-5, aún no ha comenzado a entrenarse . (more…)

En el marco del Google Marketing Live 2023, el evento anual que reúne a figuras de prestigio del ámbito de la publicidad y el marketing, el gigante tecnológico ha anunciado novedades impulsadas por IA relativas a su servicio Google Ads. (more…)

Editor’s Note: The following is a brief letter from Ray Kurzweil, a director of engineering at Google and cofounder and member of the board at Singularity Group, Singularity Hub’s parent company, in response to the Future of Life Institute’s recent letter, “Pause Giant AI Experiments: An Open Letter.”

The FLI letter addresses the risks of accelerating progress in AI and the ensuing race to commercialize the technology and calls for a pause in the development of algorithms more powerful than OpenAI’s GPT-4, the large language model behind the company’s ChatGPT Plus and Microsoft’s Bing chatbot. The FLI letter has thousands of signatories—including deep learning pioneer, Yoshua Bengio, University of California Berkeley professor of computer science, Stuart Russell, Stability AI CEO, Emad Mostaque, Elon Musk, and many others—and has stirred vigorous debate in the AI community.

…

Regarding the open letter to “pause” research on AI “more powerful than GPT-4,” this criterion is too vague to be practical. And the proposal faces a serious coordination problem: those that agree to a pause may fall far behind corporations or nations that disagree. There are tremendous benefits to advancing AI in critical fields such as medicine and health, education, pursuit of renewable energy sources to replace fossil fuels, and scores of other fields. I didn’t sign, because I believe we can address the signers’ safety concerns in a more tailored way that doesn’t compromise these vital lines of research.

I participated in the Asilomar AI Principles Conference in 2017 and was actively involved in the creation of guidelines to create artificial intelligence in an ethical manner. So I know that safety is a critical issue. But more nuance is needed if we wish to unlock AI’s profound advantages to health and productivity while avoiding the real perils.

— Ray Kurzweil
Inventor, best-selling author, and futurist

Image Credit: DeepMind / Unsplash

 

Fuente:

Kurzweil, R. (2023, 5 mayo). A Note From Ray Kurzweil on the Recent Call to Pause Work on AI More Powerful Than GPT-4. Singularity Hub. https://singularityhub.com/2023/05/05/a-note-from-ray-kurzweil-on-the-recent-call-to-pause-work-on-ai-more-powerful-than-gpt-4/

To build a machine, one must know what its parts are and how they fit together. To understand the machine, one needs to know what each part does and how it contributes to its function. In other words, one should be able to explain the “mechanics” of how it works. (more…)

Seleccionar objetos separados en una escena visual nos parece intuitivo, pero las máquinas luchan con esta tarea. Ahora, un nuevo modelo de IA de Meta ha desarrollado una idea amplia de lo que es un objeto, lo que le permite separar objetos incluso si nunca antes los había visto. (more…)

Google ha comenzado a poner a disposición del público su chatbot de inteligencia artificial (IA) que ha bautizado con el nombre de Bard.

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Estas dos grandes compañías tecnológicas rivalizarán ahora por acaparar el mercado. (more…)

There’s a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some jobs altogether.

But in a study of science laboratories I carried out with three colleagues at the University of Manchester, the introduction of automated processes that aim to simplify work—and free people’s time—can also make that work more complex, generating new tasks that many workers might perceive as mundane.

In the study, published in Research Policy, we looked at the work of scientists in a field called synthetic biology, or synbio for short. Synbio is concerned with redesigning organisms to have new abilities. It is involved in growing meat in the lab, in new ways of producing fertilizers, and in the discovery of new drugs.

Synbio experiments rely on advanced robotic platforms to repetitively move a large number of samples. They also use machine learning to analyze the results of large-scale experiments.

These, in turn, generate large amounts of digital data. This process is known as “digitalization,” where digital technologies are used to transform traditional methods and ways of working.

Some of the key objectives of automating and digitalizing scientific processes are to scale up the science that can be done while saving researchers time to focus on what they would consider more “valuable” work.

Paradoxical Result

However, in our study, scientists were not released from repetitive, manual, or boring tasks as one might expect. Instead, the use of robotic platforms amplified and diversified the kinds of tasks researchers had to perform. There are several reasons for this.

Among them is the fact that the number of hypotheses (the scientific term for a testable explanation for some observed phenomenon) and experiments that needed to be performed increased. With automated methods, the possibilities are amplified.

Scientists said it allowed them to evaluate a greater number of hypotheses, along with the number of ways that scientists could make subtle changes to the experimental set-up. This had the effect of boosting the volume of data that needed checking, standardizing, and sharing.

Also, robots needed to be “trained” in performing experiments previously carried out manually. Humans, too, needed to develop new skills for preparing, repairing, and supervising robots. This was done to ensure there were no errors in the scientific process.

Scientific work is often judged on output such as peer-reviewed publications and grants. However, the time taken to clean, troubleshoot, and supervise automated systems competes with the tasks traditionally rewarded in science. These less valued tasks may also be largely invisible—particularly because managers are the ones who would be unaware of mundane work due to not spending as much time in the lab.

The synbio scientists carrying out these responsibilities were not better paid or more autonomous than their managers. They also assessed their own workload as being higher than those above them in the job hierarchy.

Wider Lessons

It’s possible these lessons might apply to other areas of work too. ChatGPT is an AI-powered chatbot that “learns” from information available on the web. When prompted by questions from online users, the chatbot offers answers that appear well-crafted and convincing.

According to Time magazine, in order for ChatGPT to avoid returning answers that were racist, sexist, or offensive in other ways, workers in Kenya were hired to filter toxic content delivered by the bot.

There are many often invisible work practices needed for the development and maintenance of digital infrastructure. This phenomenon could be described as a “digitalization paradox.” It challenges the assumption that everyone involved or affected by digitalization becomes more productive or has more free time when parts of their workflow are automated.

Concerns over a decline in productivity are a key motivation behind organizational and political efforts to automate and digitalize everyday work. But we should not take promises of gains in productivity at face value.

Instead, we should challenge the ways we measure productivity by considering the invisible types of tasks humans can accomplish, beyond the more visible work that is usually rewarded.

We also need to consider how to design and manage these processes so that technology can more positively add to human capabilities.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Gerd Altmann from Pixabay

Fuente:

Ribeiro, B. (2023, 23 marzo). AI Could Make More Work for Us, Instead of Simplifying Our Lives. Singularity Hub. https://singularityhub.com/2023/03/24/ai-could-make-more-work-for-us-instead-of-simplifying-our-lives/

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