Google El jueves lanzó un nuevo modelo de inteligencia artificial (IA) de la familia Gemini 2.0 que se centra en la visión de futuro. El nuevo modelo de lenguaje grande (LLM), llamado Gemini 2.0 Thinking, aumenta el tiempo de inferencia para permitir que el modelo dedique más tiempo a resolver el problema. El gigante tecnológico con sede en Mountain View afirma que puede resolver tareas complejas de pensamiento, matemáticas y programación. Además, se dice que LLM realiza tareas a mayor velocidad, a pesar del mayor tiempo de procesamiento.
Google lanza un nuevo modelo de inteligencia artificial que se centra en la inferencia
en un correo En Actualmente está disponible en Google AI Studio y los desarrolladores pueden acceder a él a través de la API de Gemini.
Modelo de IA de pensamiento flash Gemini 2.0
Los empleados de Gadgets 360 pudieron probar el modelo de IA y descubrieron que el modelo Gemini, que se centra en el razonamiento avanzado, resuelve con facilidad preguntas complejas que son demasiado difíciles para el modelo 1.5 Flash. En nuestras pruebas, descubrimos que el tiempo de procesamiento típico oscilaba entre tres y siete segundos, lo que supone una mejora significativa con respecto a AbiertoAI o1 cadena que puede tardar hasta 10 segundos en procesar la consulta.
el mellizo Flash Thinking 2.0 también muestra su proceso de pensamiento, donde los usuarios pueden comprobar cómo el modelo de IA llegó al resultado y los pasos que tomó para llegar allí. Descubrimos que LLM pudo encontrar la solución correcta ocho de cada 10 veces. Dado que es un modelo experimental, es de esperar que se produzcan errores.
Aunque Google no reveló detalles sobre la arquitectura del modelo de IA, sí destacó sus limitaciones centradas en los desarrolladores. Publicación de blog. Actualmente, Gemini 2.0 Flash Thinking tiene un límite de entrada de 32.000 tokens. Sólo puede aceptar texto e imágenes como entrada. Solo admite texto como salida y tiene un límite de 8000 tokens. Además, la API no viene con una herramienta integrada como búsqueda o ejecución de código.
Credit: Norman Y. Lono/The New York Times/Redux/eyevine
Israeli US psychologist Daniel Kahneman was sceptical when Amos Tversky — his colleague at the Hebrew University of Jerusalem — gave a lecture on the rational-agent model of economic thinking, which assumes that people typically make rational decisions. This sparked an intense collaboration, and, beginning in the 1970s, the two demonstrated systematic violations of those assumptions. This upended the study of human decision-making, helping to launch the field of behavioural economics and altering how the human agent is viewed across the social sciences and beyond.
Tversky died in 1996, at the age of 59. Kahneman, who has died aged 90, was awarded the 2002 Nobel prize in economic science for their joint work, “having integrated insights from psychological research into economic science, especially concerning human judgement and decision-making under uncertainty”.
Kahneman began his research in visual perception and attention, before shifting to decision-making, judgmental bias and the study of well-being. In his 2011 bestseller, Thinking, Fast and Slow, he outlined two modes for human judgment: an intuitive, effortless process, driven by immediate and emotional impressions, and a more deliberative and analytical one, partly responsible for catching errors made by fast thinking.
Consciousness: what it is, where it comes from — and whether machines can have it
Kahneman, the son of Lithuanian Jews who had emigrated to France in the 1920s, was born in 1934 in Tel Aviv (now Israel) during a family visit. In Nazi-occupied France, forced to wear the yellow star, the family spent years in hiding during the Second World War, living for part of the time in a chicken coop. Kahneman’s father died when he was 10, and, in 1946, his mother took the family to Tel Aviv. Kahneman graduated from the Hebrew University of Jerusalem with a degree in psychology and mathematics in 1954 and received a doctorate in psychology from the University of California, Berkeley, in 1961 before returning to teaching and research in Jerusalem.
Kahneman and Tversky’s early collaboration consisted of long working sessions, often in cafes, peppered with jokes, anecdotes and the search for the perfect demonstrations. They became almost inseparable for a few fervent years, producing several papers, which are now known as the heuristics-and-biases programme. They both left Israel in 1978. Kahneman joined the University of British Columbia in Vancouver, Canada, then Berkeley in 1986, before settling down at Princeton University, New Jersey, in 1993.
Conventional economics holds that people’s decisions are based on anticipated outcomes. By contrast, Kahneman and Tversky’s influential prospect theory focused on gains and losses over final states. In one study, people chose between monetary gambles. One group started with $300 and had to decide between a sure $100 gain or a 50% chance at $200; the other started with $500 and chose between a sure $100 loss or a 50% chance to lose $200. Both groups essentially had to pick between an equal chance at $300 or $500, or $400 for sure. But people preferred a sure gain over a probabilistic gain, and a probabilistic loss over a sure loss, ending up with different outcomes.
Decades-long bet on consciousness ends — and it’s philosopher 1, neuroscientist 0
Perhaps the most important lesson from prospect theory is that losses loom larger than gains — losing $100 hurts more than winning $100 pleases. Among its myriad implications, this predicts impasses in negotiations, in which what each side renounces weights more than what it stands to gain.
Kahneman coined the term ‘the illusion of validity’ for people’s tendency to form impressions that are markedly less valid than they seem. This relates to systematic overconfidence in one’s judgements, often exacerbated by mental shortcuts that yield misguided intuitions.
Mild and self-effacing, Kahneman was open to the likelihood that he himself was often wrong. He engaged in what he called “adversarial collaborations” — in which researchers with competing theories, often theories in great tension with each other, work together towards some resolution.
Kahneman later studied the difference between ‘experienced’ and ‘remembered’ well-being. The memory of an experience, he concluded, was determined largely by its peak moment and by how it felt towards the end. With Canadian physician Don Redelmeier, he found that, at the end of a colonoscopy, leaving the tube stationary for a moment makes the procedure less unpleasant than removing it straight away. The extra moment of gratuitous but light discomfort left a better memory of the experience and made participants more likely to return for future tests.
In 1978, Kahneman married Anne Treisman, a noted UK cognitive psychologist. In 2013, then US president Barack Obama recognized the achievements of Treisman, through the National Medal of Science, and Kahneman, with the Presidential Medal of Freedom.
Kahneman took more-visible political stances in his last years, particularly regarding developments in Israel. He signed a letter that asked the government to not undermine the independence of the National Library of Israel, supported an international plea to return hostages held by Hamas and spoke at a rally in New York City against the Israeli government’s efforts to overhaul the judiciary.
In his last, co-authored book, Noise (2021), Kahneman focused on noise — the “undesirable variability in judgments of the same problem” — and suggested that organizations should gauge levels of inconsistency in its employees’ professional judgements through audits.
Kahneman’s work has led to the rethinking of decision-making and judgement in areas as diverse as political negotiations, medical treatment, the recruitment of baseball players and the perception of fairness in economic decisions.
Artificial intelligence seems to be on the brink of another significant transformation nearly every week at the moment, and this week is no exception. As developers, businesses and researchers dive deeper into the capabilities of large language models (LLMs) like GPT-4, we’re beginning to see a shift in how these systems tackle complex problems. The human brain operates using two distinct modes of thought, as outlined by Daniel Kahneman in his seminal work, “Thinking, Fast and Slow.” The first, System 1, is quick and intuitive, while System 2 is slower, more deliberate, and logical. Until now, AI has largely mirrored our instinctive System 1 thinking, but that’s changing.
In practical terms, System 2 thinking is what you use when you need to think deeply or critically about something. It’s the kind of thinking that requires you to stop and focus, rather than react on instinct or intuition. For example, when you’re learning a new skill, like playing a musical instrument or speaking a foreign language, you’re primarily using System 2 thinking.
Over time, as you become more proficient, some aspects of these skills may become more automatic and shift to System 1 processing. Understanding the distinction between these two systems is crucial in various fields, including decision-making, behavioral economics, and education, as it helps explain why people make certain choices and how they can be influenced or trained to make better ones.
AI System 2 thinking
Researchers are now striving to imbue AI with System 2 thinking to enable deeper reasoning and more reliable outcomes. The current generation of LLMs can sometimes produce answers that seem correct on the surface but lack a solid foundation of analysis. To address this, new methods are being developed. One such technique is prompt engineering, which nudges LLMs to unpack their thought process step by step. This is evident in the “Chain of Thought” prompting approach. Even more advanced strategies, like “Self-Consistency with Chain of Thought” (SCCT) and “Tree of Thought” (ToT), are being explored to sharpen the logical prowess of these AI models.
The concept of collaboration is also being examined as a way to enhance the problem-solving abilities of LLMs. By constructing systems where multiple AI agents work in concert, we can create a collective System 2 thinking model. These agents, when working together, have the potential to outperform a solitary AI in solving complex issues. This, however, introduces new challenges, such as ensuring the AI agents can communicate and collaborate effectively without human intervention.
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To facilitate the development of these collaborative AI systems, tools like Autogen Studio are emerging. They offer a user-friendly environment for researchers and developers to experiment with AI teamwork. For example, a problem that might have been too challenging for GPT-4 alone could potentially be resolved with the assistance of these communicative agents, leading to solutions that are not only precise but also logically sound.
What will AI be able to accomplish with System 2 thinking?
As we look to the future, we anticipate the arrival of next-generation LLMs, such as the much-anticipated GPT-5. These models are expected to possess even more advanced reasoning skills and a deeper integration of System 2 thinking. Such progress is likely to significantly improve AI’s performance in scenarios that require complex problem-solving.
The concept of System 2 thinking, as applied to AI and large language models (LLMs), involves the development of AI systems that can engage in more deliberate, logical, and reasoned processing, akin to human System 2 thinking. This advancement would represent a significant leap in AI capabilities, moving beyond quick, pattern-based responses to more thoughtful, analytical problem-solving. Here’s what such an advancement could entail:
Enhanced Reasoning and Problem Solving: AI with System 2 capabilities would be better at logical reasoning, understanding complex concepts, and solving problems that require careful thought and consideration. This could include anything from advanced mathematical problem-solving to more nuanced ethical reasoning.
Improved Understanding of Context and Nuance: Current LLMs can struggle with understanding context and nuance, especially in complex or ambiguous situations. System 2 thinking would enable AI to better grasp the subtleties of human language and the complexities of real-world scenarios.
Reduced Bias and Error: While System 1 thinking is fast, it’s also more prone to biases and errors. By incorporating System 2 thinking, AI systems could potentially reduce these biases, leading to more fair and accurate outcomes.
Better Decision Making: In fields like business or medicine, where decisions often have significant consequences, AI with System 2 thinking could analyze vast amounts of data, weigh different options, and suggest decisions based on logical reasoning and evidence.
Enhanced Learning and Adaptation: System 2 thinking in AI could lead to improved learning capabilities, allowing AI to not just learn from data, but to understand and apply abstract concepts, principles, and strategies in various situations.
More Effective Human-AI Collaboration: With System 2 thinking, AI could better understand and anticipate human needs and behaviors, leading to more effective and intuitive human-AI interactions and collaborations.
It’s important to note that achieving true System 2 thinking in AI is a significant challenge. It requires advancements in AI’s ability to not just process information, but to understand and reason about it in a deeply contextual and nuanced way. This involves not only improvements in algorithmic approaches and computational power but also a better understanding of human cognition and reasoning processes. As of now, AI, including advanced LLMs, primarily operates in a way that’s more akin to human System 1 thinking, relying on pattern recognition and rapid response generation rather than deep, logical reasoning.
The journey toward integrating System 2 thinking into LLMs marks a pivotal moment in the evolution of AI. While there are hurdles to overcome, the research and development efforts in this field are laying the groundwork for more sophisticated and dependable AI solutions. The ongoing dialogue about these methods invites further investigation and debate on the most effective ways to advance System 2 thinking within artificial intelligence.
Filed Under: Technology News, Top News
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Having a pool in your backyard is an exciting outlook and can make your backyard a haven of relaxation and pleasure that your family and friends will love. But before you take a plunge, there is a lot to know before building a pool, as you want to ensure the type you envision best fits your needs and property. One popular pool type that combines durability, easy maintenance and stylish design is the fibreglass pool. Before diving into your adventure, here is a closer look at everything you should know about fibreglass pools to build your perfect backyard oasis.
Fibreglass pools are made from fibreglass and reinforced plastic. The finished pool shells have a smooth, non-porous surface that is stain-resistant and more durable than other materials. The composite material is made with extremely fine strands of glass combined with a plastic resin, creating a robust material that can be moulded into complex shapes.
Fibreglass pools have become the top choice for swimming pools among homeowners due to their durability. Unlike concrete pools, which are likely to crack and deteriorate over time, with proper care and maintenance, fibreglass pools can last for many years as the material does not corrode or degrade from sun exposure, making it a wise investment in the value of your property.
Choosing a fibreglass pool means great enjoyment and little maintenance, as the smooth and non-porous surface prevents algae growth. Thus, your pool does not require heavy cleaning and scrubbing and far less chemical treatment than concrete, making it an environmentally friendly choice.
Another benefit of fibreglass pools is their quick installation time. Unlike concrete pools, which are built on-site and require extended construction time and many staff, the moulds of fibreglass pools are transported pre-fabricated to your home and can be installed on the premises in as little as 3-5 days.
Fibreglass pools can be designed to create custom pool shapes and sizes to suit your individual preferences and backyard layout. Options include freeform designs or a more classic and elegant geometrical shape to complement your vision of an aesthetic backyard oasis. Consider incorporating features such as built-in steps and tanning ledges or mosaic tiles to enhance the visual and functional appeal of your new fibreglass pool even more.
Fibreglass pools have high insulating capabilities and can hold heat for an extended amount of time. Thus, you can extend the swimming season without worrying about your utility bills due to their low energy consumption. The shorter installation time translates to lower labour costs than concrete pools, and their low maintenance requirements make fibreglass pools a cost-effective investment.
To keep your fibreglass pool looking fresh for decades to come, ask your professional pool builder to inspect the mechanical systems annually. All you need to do yourself is sweep the walls and floor weekly with a non-abrasive brush, test and balance the chemicals according to usage and clean the filter monthly. By following these simple maintenance steps, your fibreglass pool will provide beauty, enjoyment and convenience for your backyard leisure for years to come.
While fibreglass pools come with a multitude of benefits, the key to ensuring your pool stands the test of time is a professional installation. Thus, working with a reputable pool builder is crucial. They have the expertise to prepare your site correctly, handle the delicate transportation and installation process, and provide guidance on pool maintenance and care. Reach out to a trusted pool builder to discuss your options and start creating your own slice of paradise right in your backyard today.
