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Se han revelado informes de revisión por pares de artículos de Turing, Watson y Crick

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Me imagino a la profesora Dorothy Mary Hodgkin trabajando en su oficina en los años sesenta.

La reseña de Dorothy Hodgkin (en la foto alrededor de 1960) se encuentra entre una colección de informes publicados por la Royal Society. Crédito: Archivos del Daily Herald/Museo Nacional de Ciencias y Medios/SSPL vía Getty

La Royal Society del Reino Unido ha descubierto más de 1.600 informes revisados ​​por pares que datan de 1949 a 1954, incluida la revisión de Dorothy Hodgkin del manuscrito de Watson y Crick sobre la estructura del ADN y comentarios contradictorios de dos revisores sobre un artículo de 1951 de Alan Turing (Todos parecen ser ignorado.) Informes Brinde una idea de cómo era el proceso de revisión por pares antes de que se formalizara en la década de 1970.. “En el siglo XIX y principios del XX ya se entendía que la revisión por pares es un debate real”, dice Louisienne Verlier, directora de recursos digitales de la sociedad. “Entonces, se convierte en una forma de gestionar el flujo de artículos de la revista”.

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Geofísicos y arqueólogos colaboraron para descubrirlo Un tranquilo lugar de entierro debajo del elaborado edificio del tesoro excavado en la roca de Petra. Después de que un examen no invasivo revelara atractivos vacíos similares a tumbas, las excavaciones confirmaron la presencia de 12 restos óseos y un grupo de artefactos funerarios. Los investigadores estiman que los restos pueden ser de las alturas de la comunidad nabatea, que construyó la fortaleza montañosa de Petra en lo que hoy es Jordania.

New York Times | 4 minutos de lectura

Animales en el fondo del mar. Puede prosperar en cavidades subterráneas y pasajes cerca de respiraderos hidrotermales. – Una pista de cómo las criaturas colonizan los respiraderos recién erupcionados. Los biólogos marinos utilizaron una palanca (unida a un sofisticado submarino robótico) para levantar un trozo de basalto del fondo del Océano Pacífico. Así, se descubrió un tipo de gusano tubícola (Oasis de Alvina) y sus larvas, así como almejas y poliquetos. Los investigadores habían pensado que el líquido debajo del fondo del mar estaría demasiado caliente para sustentar la vida, pero la temperatura del agua era de unos agradables 18 grados centígrados. Los resultados sugieren que las larvas pueden viajar a través de un “sistema subterráneo” de canales entre rocas, que podrían ser destruidos por la minería en aguas profundas.

Ciencia | lectura de 5 minutos

referencia: Comunicaciones de la naturaleza papel

Características y opinión

Sin embargo, publicar software de biología para que cualquiera pueda usarlo no es tan fácil como parece. Hay maneras de hacer el camino más fácil. Esto incluye tener en cuenta el tiempo de mantenimiento, simplificar la instalación y automatizar las pruebas. Los científicos dicen que el resultado vale la pena. “El presente y el futuro de la comprensión biológica dependen del software”, afirma el bioinformático Titus Brown.

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“¿Por qué no existe una cultura de resultados? “Porque hablar es divertido, los resultados son difíciles y la gente odia la rendición de cuentas”, escribió el líder mundial de la salud, Peter Singer, quien fue asesor especial durante seis años del director general de la OMS. más Objetivos, como los contenidos en la Carta para el Futuro recientemente aprobada, Las Naciones Unidas deberían centrarse en las soluciones existentes que no se han cumplido. Se trata de aquellos que entran dentro de los objetivos de desarrollo sostenible, de los cuales sólo el 17% van por buen camino. Al reflexionar sobre sus propios esfuerzos por intentar convertir a la OMS en una organización basada en resultados, Singer pide a la ONU que dé un paso atrás. “Creo que los planes de la ONU necesitan otro acrónimo: GSD, que significa Get Stuff Done”.

Naturaleza | lectura de 5 minutos

Cada vez hay más conciencia de que los pájaros hacen algo más que chirriar sin rumbo fijo. Algunas especies susurran entre sí, cantan a sus huevos aún no eclosionados y desarrollan “familias” de sonidos compartidos por una unidad familiar. Impulsado por el deseo de comprender Si los sonidos de los pájaros pueden considerarse un “lenguaje” (y para ayudar con la mayor parte de la observación de aves que implica simplemente escuchar), los ornitólogos e investigadores de inteligencia artificial están trabajando para descifrar el significado.

El neoyorquino | 22 minutos de lectura

cita del dia

Para las parejas de científicos, es importante evaluar la relación científica para detectar cualquier desequilibrio que pueda ser necesario discutir para evitar conflictos, dice el investigador del cáncer Jim Allison, casado con el inmunooncólogo Padmani Sharma. (Los caminos de la naturaleza | lectura de 9 minutos)

Hoy estoy pensando en trajes espaciales prada Qué usarán los astronautas de la NASA en la misión Artemis a la luna planificada para 2026. Advertencia justa: se parecen mucho a los trajes espaciales normales, tal como suelen ser los trajes espaciales normales. Trajes de Pierre Cardin Que será utilizado por los astronautas de la ESA en sus entrenamientos. Pero la apariencia no lo es todo, afirma el astronauta Matthias Maurer. “Se nota la diferencia si lo acaban de hacer ingenieros o si además hay alguien que sabe cómo funciona el tejido”, dijo mientras se probaba un traje Cardin. “Siempre tuve puntos de presión y ahora me siento muy bien por dentro”.

Déjame saber qué te gustaría ver para una apariencia de diseñador, así como cualquier otro comentario en este boletín, en informació[email protected].

Gracias por leer,

Flora Graham es editora senior de Nature Briving

Con contribuciones de Jacob Smith

¿Quieres más? Suscríbase a nuestros otros boletines gratuitos de Resumen de la Naturaleza:

• Informe sobre la naturaleza: empleos – Información, consejos y periodismo galardonado para ayudarle a mejorar su vida laboral

• Informe sobre la naturaleza: microbiología – Los organismos más abundantes en nuestro planeta -los microorganismos- y el papel que desempeñan en la salud, el medio ambiente y los sistemas alimentarios

• Informe sobre la naturaleza: el antropoceno — Cambio climático, biodiversidad, sostenibilidad y geoingeniería

• Nature Briefing: Inteligencia artificial y robótica – 100% escrito por humanos, por supuesto.

• Resumen de la naturaleza: cáncer – Un boletín semanal escrito pensando en los investigadores del cáncer.

• Naturaleza del resumen: investigación traslacional – Cubre biotecnología, descubrimiento de fármacos y productos farmacéuticos.

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Learn about generative AI for free with the Alan Turing Institute

Generative AI explained by the Alan Turing Institute

If you would like to learn more about the field of generative artificial intelligence (AI) that is rapidly transforming how we interact with technology. This area of study, which focuses on the creation of new content such as text, images, and audio from existing data, is becoming increasingly relevant in our daily lives. Technologies like ChatGPT and DallE 3 are prime examples of how generative AI can innovate and automate tasks, showcasing its ability to influence our digital experiences.

Generative AI has been around for a while, subtly shaping the way we use technology. Early versions of AI, like Google Translate and Siri, have set the stage for more advanced systems such as GPT-4. These technologies have evolved from simple automated responses to generating complex, human-like text and realistic images, making them more and more a part of our everyday digital interactions.

At its core, generative AI works by mimicking the human brain through language modeling and neural networks. This allows the AI to learn from vast amounts of data on the web, recognizing patterns and associations that enable it to produce content that is both relevant and engaging. However, creating a generative AI model is just the first step. Fine-tuning these models is crucial to ensure that they can perform specific tasks accurately and reliably.

What is generative AI?

One of the most remarkable aspects of generative AI is its ability to improve itself through self-supervised learning. This means that the AI can analyze additional data, identify its own errors, and correct them without human intervention, much like how we learn from our own experiences.

Here are some other articles you may find of interest on the subject of  generative AI

As AI models become larger and more complex, they can produce outputs that are increasingly nuanced and sophisticated. But scaling up these models comes with its own set of challenges, such as managing the computational demands and the potential for errors that can arise.

Generative AI is not without its flaws. Issues such as bias, misinformation, and the generation of irrelevant or nonsensical content—sometimes referred to as “hallucinations”—can lead to distorted outputs that may be unreliable or even harmful. Addressing these challenges is essential for the ethical use of AI.

The impact of generative AI extends beyond the technology itself. There are environmental considerations to take into account, as well as the potential effects on job markets. As AI becomes more prevalent in society, it’s important to ensure that its development aligns with societal values and ethical practices.

Looking ahead, the future of generative AI is likely to involve more efficient system architectures and the need for careful regulation. Despite its progress, AI still faces difficulties in understanding the physical world and human emotions, which highlights the importance of ongoing research and development.

The recent Turing Institute lecture stressed the importance of human involvement in guiding the evolution of AI. As AI continues to advance, it’s crucial to ensure that it serves beneficial purposes, reduces biases, and reflects societal values.

Generative AI is a powerful tool that has the potential to reshape various industries. Understanding its capabilities, limitations, and impact on society is key to harnessing its power responsibly. As we look to the future, it’s clear that generative AI will continue to play a significant role in how we interact with technology, and it’s up to us to steer its development in a direction that benefits everyone.

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The future of generative AI from the Turing Institute Lecture

The future of generative AI from the Turing Institute

The field of artificial intelligence (AI) is undergoing a significant transformation, and the Turing Institute is at the forefront of this exciting era. Named after the legendary AI pioneer Alan Turing, the institute has become a beacon of innovation, turning theoretical concepts into practical applications that are beginning to reshape our world.

Since the mid-2000s, AI has experienced a surge in growth, driven by breakthroughs in machine learning. The effectiveness of these systems is largely dependent on the quality of the training data they receive. This process, known as supervised learning, allows AI to learn from examples. One of the most critical developments in this area has been the creation of neural networks. These networks, inspired by the human brain, enable machines to process and interpret vast amounts of data.

The future of generative AI

Among the most notable advancements in AI is the creation of sophisticated language models, such as GPT-3. These models have the ability to generate text that is so similar to human writing that it can be difficult to distinguish between the two. The versatility of these models is remarkable, and they are being used in a variety of applications. However, they are not without their flaws. These AI systems can sometimes produce errors, demonstrate biases, and raise concerns about issues such as toxicity and compliance with laws like the General Data Protection Regulation (GDPR).

Despite the impressive capabilities of current AI systems, they still fall short in certain areas. For instance, AI does not yet fully understand context, nor does it possess consciousness or reasoning abilities. This distinction highlights the gap between what AI can do and the full spectrum of human intelligence, which encompasses more than just language skills and pattern recognition.

The pursuit of General AI, which aims to replicate the full range of human intellectual abilities, raises profound philosophical and ethical questions. As AI-generated content becomes more prevalent online, we must consider the responsibilities associated with this content and the potential impact of AI on society, including the feedback loops it may create.

To address some of these challenges, researchers are exploring new approaches that combine symbolic AI, which operates based on a set of rules, with the data-driven methods used by large AI systems. This combination is expected to yield more robust and capable AI technologies. Additionally, the development of multimodal AI, which can process and understand various types of data such as text, images, and videos, is set to expand the possibilities of what AI can achieve.

The Turing Institute is playing a critical role in pushing the boundaries of AI while also addressing the ethical considerations that accompany these technological advances. As AI continues to progress, the goal is not to replace human capabilities but to augment them, creating tools that enhance our abilities and contribute positively to society. The future of generative AI is not only about technological innovation but also about navigating the complex landscape of societal implications that come with it.

Image Credit: Turing Institute

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The Turing Lectures discuss the future of generative AI

The Turing Lectures discuss the future of generative AI

In a recent series of lectures at the Turing Institute, the UK’s hub for data science and artificial intelligence research, experts gathered to discuss the future of generative AI. This technology, which can independently generate new content, has been making significant strides, from crafting written text to creating visual art and complex legal documents. The series culminated with a session that paid homage to Alan Turing, a pioneer in computing, and delved into the exciting trajectory of generative AI.

Artificial intelligence has been on a remarkable journey, marked by steady progress and recent breakthroughs in machine learning that have propelled AI capabilities forward. Central to these advancements is the training data that AI systems use to learn. Typically, AI is fed labeled data in a process known as supervised learning, which it then uses to predict outcomes or generate new content.

During the lecture, the focus was on neural networks, which are complex structures modeled after the human brain. These networks are crucial for AI’s ability to recognize patterns and make decisions. The speakers highlighted how the combination of large data sets, affordable computing power, and scientific discoveries in deep learning have expanded what AI can achieve.

The future of generative AI

Here are some other articles you may find of interest on the subject of generative AI :

One of the most significant milestones in AI was the introduction of the Transformer architecture and large language models like GPT-3. These have greatly improved AI’s text generation capabilities, making it more realistic and pushing the boundaries of machine creativity. However, the lecture also pointed out the challenges that come with such powerful technology, including errors, biases, toxicity, and copyright issues, as well as the need to comply with GDPR to protect privacy and data.

AI’s understanding is still not perfect and is often constrained by the contexts of its training data. This limitation has led to philosophical and ethical discussions about the potential for general artificial intelligence and the concept of machine consciousness. The Turing Test, a historical benchmark for AI’s ability to mimic human intelligence, was reconsidered in light of these new developments.

Looking to the future, the lecture suggested that AI could soon develop multimodal capabilities, combining text, images, sound, and video to create personalized content. This could change the way we interact with technology, making it more intuitive and responsive to individual preferences.

The Turing Lecture series has shed light on the significant impact of generative AI and the ethical considerations and limitations that come with its use and development. As AI continues to advance, it is poised to redefine content creation and many other areas, leading to a future where it may become increasingly difficult to tell apart content created by humans from that generated by machines.

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