#AlphaGo

[macmanx]

brilliant

[zzedar2]

Was reading about AlphaGo's match against Fan Hui (the European champion). This was a much less powerful version than the one that beat Lee Sedol and apparently there was debate among professional go players in China about whether (based on those games) it was as good as a professional player. Really shows the gap between the Big Three go-playing countries and the west that a computer who beat the champion of an entire continent 5-0 was arguably not good enough to even be considered a professional player in Asia.

[filosofiadelbuenvivir]

Alphago: revolución en el juego de go

ICCSI El juego de mesa Go ha sido considerado uno de los desafíos más difíciles para la inteligencia artificial debido a su complejidad y a la dificultad de programarlo. Sin embargo, el programa de computadora AlphaGo logró una victoria histórica sobre el jugador profesional Lee Sedol, lo que marcó un hito en la historia de la inteligencia artificial y la ingeniería informática. Aunque AlphaGo…

[pensarenmusaranyes]

[inspirednews]

Professor de Stanford afirma que já superamos a inteligência artificial geral

A inteligência artificial geral (AGI), que se refere a máquinas com capacidade de realizar qualquer tarefa humana, é frequentemente considerada o objetivo mais ambicioso da pesquisa em tecnologia. Apesar das divergências entre especialistas sobre a possibilidade de alcançá-la, Michal Kosinski, professor de comportamento organizacional na Graduate School of Business da Universidade de Stanford, está convencido de que já ultrapassamos essa barreira. Em uma apresentação realizada no evento Brazil at Silicon Valley, que reuniu cerca de 600 participantes no Google Event Center em Sunnyvale, Califórnia, Kosinski afirmou: “Nós já ultrapassamos a inteligência artificial geral”.(...)

Leia a noticia completa no link abaixo:

https://www.inspirednews.com.br/professor-de-stanford-afirma-que-ja-superamos-a-inteligencia-artificial-geral

[gogoigo]

https://www.instagram.com/share/reel/BASwD9rSJd

[qhsetools2022]

The evolution of AI: From AlphaGo to AI agents, physical AI, and beyond

Experience the latest in AI innovation. Join Microsoft at the NVIDIA GTC AI Conference. Learn more and register. The critical moment of ChatGPT The release of ChatGPT by OpenAI in November 2022 marked another significant milestone in the evolution of AI. ChatGPT, a large language model capable of generating human-like text, demonstrated the potential of AI to understand and generate natural…

[anselmolucio]

Pequeños y grandes pasos hacia el imperio de la inteligencia artificial

Fuente: Open Tech Traducción de la infografía: 1943 – McCullock y Pitts publican un artículo titulado Un cálculo lógico de ideas inmanentes en la actividad nerviosa, en el que proponen las bases para las redes neuronales. 1950 – Turing publica Computing Machinery and Intelligence, proponiendo el Test de Turing como forma de medir la capacidad de una máquina. 1951 – Marvin Minsky y Dean…

[dataexpertise18]

Advanced Techniques in Deep Learning: Transfer Learning and Reinforcement Learning

Deep learning has made remarkable strides in artificial intelligence, enabling machines to perform tasks that were once thought to be the exclusive domain of human intelligence. Neural networks, which lie at the heart of deep learning, emulate the human brain’s structure and function to process large volumes of data, identify patterns, and make informed decisions.

While traditional deep learning models have proven to be highly effective, advanced techniques like transfer learning and reinforcement learning are setting new benchmarks, expanding the potential of AI even further. This article explores these cutting-edge techniques, shedding light on their functionalities, advantages, practical applications, and real-world case studies.

Understanding Transfer Learning

Transfer learning is a powerful machine learning method where a model trained on one problem is repurposed to solve a different, but related, problem. This technique leverages knowledge from a previously solved task to tackle new challenges, much like how humans apply past experiences to new situations. Here's a breakdown of how transfer learning works and its benefits:

Use of Pre-Trained Models: In essence, transfer learning involves using pre-trained models like VGG, ResNet, or BERT. These models are initially trained on large datasets such as ImageNet for visual tasks or extensive text corpora for natural language processing (NLP). This pre-training equips them with a broad understanding of patterns and features.

Fine-Tuning for Specific Tasks: Once a pre-trained model is selected, it undergoes a fine-tuning process. This typically involves modifying the model's architecture:

Freezing Layers: Some layers of the model are frozen to retain the learned features.

Adapting or Replacing Layers: Other layers are adapted or replaced to tailor the model to the specific needs of a new, often smaller, dataset. This customization ensures that the model is optimized for the specific task at hand.

Reduced Training Time and Resources: One of the major benefits of transfer learning is that it significantly reduces the time and computational power required to train a new model. Since the model has already learned essential features from the initial training, it requires less data and fewer resources to fine-tune for new tasks.

Enhanced Performance: By reusing existing models, transfer learning brings valuable pre-learned features and insights, which can lead to higher accuracy in new tasks. This pre-existing knowledge provides a solid foundation, allowing the model to perform better than models trained from scratch.

Effectiveness with Limited Data: Transfer learning is particularly beneficial when labeled data is scarce. This is a common scenario in specialized fields such as medical imaging, where collecting and labeling data can be costly and time-consuming. By leveraging a pre-trained model, researchers can achieve high performance even with a limited dataset.

Transfer learning’s ability to save time, resources, and enhance performance makes it a popular choice across various domains, from image classification to natural language processing and healthcare diagnostics.

Practical Applications of Transfer Learning

Transfer learning has demonstrated its effectiveness across various domains by adapting pre-trained models to solve specific tasks with high accuracy. Below are some key applications:

Image Classification: One of the most common uses of transfer learning is in image classification. For instance, Google’s Inception model, which was pre-trained on the ImageNet dataset, has been successfully adapted for various image recognition tasks. Researchers have fine-tuned the Inception model to detect plant diseases, classify wildlife species, and identify objects in satellite imagery. These applications have achieved high accuracy, even with relatively small amounts of training data.

Natural Language Processing (NLP): Transfer learning has revolutionized how models handle language-related tasks. A prominent example is BERT (Bidirectional Encoder Representations from Transformers), a model pre-trained on vast amounts of text data. BERT has been fine-tuned for a variety of NLP tasks, such as:

Sentiment Analysis: Understanding and categorizing emotions in text, such as product reviews or social media posts.

Question Answering: Powering systems that can provide accurate answers to user queries.

Language Translation: Improving the quality of automated translations between different languages. Companies have also utilized BERT to develop customer service bots capable of understanding and responding to inquiries, which significantly enhances user experience and operational efficiency.

Healthcare: The healthcare industry has seen significant benefits from transfer learning, particularly in medical imaging. Pre-trained models have been fine-tuned to analyze images like X-rays and MRIs, allowing for early detection of diseases. Examples include:

Pneumonia Detection: Models fine-tuned on medical image datasets to identify signs of pneumonia from chest X-rays.

Brain Tumor Identification: Using pre-trained models to detect abnormalities in MRI scans.

Cancer Detection: Developing models that can accurately identify cancerous lesions in radiology scans, thereby assisting doctors in making timely diagnoses and improving patient outcomes.

Performance Improvements: Studies have shown that transfer learning can significantly enhance model performance. According to research published in the journal Nature, using transfer learning reduced error rates in image classification tasks by 40% compared to models trained from scratch. In the field of NLP, a survey by Google AI reported that transfer learning improved accuracy metrics by up to 10% over traditional deep learning methods.

These examples illustrate how transfer learning not only saves time and resources but also drives significant improvements in accuracy and efficiency across various fields, from agriculture and wildlife conservation to customer service and healthcare diagnostics.

Exploring Reinforcement Learning

Reinforcement learning (RL) offers a unique approach compared to other machine learning techniques. Unlike supervised learning, which relies on labeled data, RL focuses on training an agent to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. This trial-and-error method enables the agent to learn optimal strategies that maximize cumulative rewards over time.

How Reinforcement Learning Works:

Agent and Environment Interaction: In RL, an agent (the decision-maker) perceives its environment, makes decisions, and performs actions that alter its state. The environment then provides feedback, which could be a reward (positive feedback) or a penalty (negative feedback), based on the action taken.

Key Components of RL:

Agent: The learner or decision-maker that interacts with the environment.

Environment: The system or scenario within which the agent operates and makes decisions.

Actions: The set of possible moves or decisions the agent can make.

States: Different configurations or situations that the environment can be in.

Rewards: Feedback received by the agent after taking an action, which is used to evaluate the success of that action.

Policy: The strategy or set of rules that define the actions the agent should take based on the current state.

Adaptive Learning and Real-Time Decision-Making:

The adaptive nature of reinforcement learning makes it particularly effective in dynamic environments where conditions are constantly changing. This adaptability allows systems to learn autonomously, without requiring explicit instructions, making RL suitable for real-time applications where quick, autonomous decision-making is crucial. Examples include robotics, where robots learn to navigate different terrains, and self-driving cars that must respond to unpredictable road conditions.

Statistics and Real-World Impact:

Success in Gaming: One of the most prominent examples of RL’s success is in the field of gaming. DeepMind’s AlphaGo, powered by reinforcement learning, famously defeated the world champion in the complex game of Go. This achievement demonstrated RL's capability for strategic thinking and complex decision-making. AlphaGo's RL-based approach achieved a win rate of 99.8% against other AI systems and professional human players.

Robotic Efficiency: Research by OpenAI has shown that using reinforcement learning can improve the efficiency of robotic grasping tasks by 30%. This increase in efficiency leads to more reliable and faster robotic operations, highlighting RL’s potential in industrial automation and logistics.

Autonomous Driving: In the automotive industry, reinforcement learning is used to train autonomous vehicles for tasks such as lane changing, obstacle avoidance, and route optimization. By continually learning from the environment, RL helps improve the safety and efficiency of self-driving cars. For instance, companies like Waymo and Tesla use RL techniques to enhance their vehicle's decision-making capabilities in real-time driving scenarios.

Reinforcement learning's ability to adapt and learn from interactions makes it a powerful tool in developing intelligent systems that can operate in complex and unpredictable environments. Its applications across various fields, from gaming to robotics and autonomous vehicles, demonstrate its potential to revolutionize how machines learn and make decisions.

Practical Applications of Reinforcement Learning

One of the most prominent applications of reinforcement learning is in robotics. RL is employed to train robots for tasks such as walking, grasping objects, and navigating complex environments. Companies like Boston Dynamics use reinforcement learning to develop robots that can adapt to varying terrains and obstacles, enhancing their functionality and reliability in real-world scenarios.

Reinforcement learning has also made headlines in the gaming industry. DeepMind’s AlphaGo, powered by reinforcement learning, famously defeated a world champion in the ancient board game Go, demonstrating RL's capacity for strategic thinking and complex decision-making. The success of AlphaGo, which achieved a 99.8% win rate against other AI systems and professional human players, showcased the potential of RL in mastering sophisticated tasks.

In the automotive industry, reinforcement learning is used to train self-driving cars to make real-time decisions. Autonomous vehicles rely on RL to handle tasks such as lane changing, obstacle avoidance, and route optimization. Companies like Tesla and Waymo utilize reinforcement learning to improve the safety and efficiency of their autonomous driving systems, pushing the boundaries of what AI can achieve in real-world driving conditions.

Comparing Transfer Learning and Reinforcement Learning

While both transfer learning and reinforcement learning are advanced techniques that enhance deep learning capabilities, they serve different purposes and excel in different scenarios. Transfer learning is ideal for tasks where a pre-trained model can be adapted to a new but related problem, making it highly effective in domains like image and language processing. It is less resource-intensive and quicker to implement compared to reinforcement learning.

Reinforcement learning, on the other hand, is better suited for scenarios requiring real-time decision-making and adaptation to dynamic environments. Its complexity and need for extensive simulations make it more resource-demanding, but its potential to achieve breakthroughs in fields like robotics, gaming, and autonomous systems is unparalleled.

Conclusion

Transfer learning and reinforcement learning represent significant advancements in the field of deep learning, each offering unique benefits that can be harnessed to solve complex problems. By repurposing existing knowledge, transfer learning allows for efficient and effective solutions, especially when data is scarce. Reinforcement learning, with its ability to learn and adapt through interaction with the environment, opens up new possibilities in areas requiring autonomous decision-making and adaptability.

As AI continues to evolve, these techniques will play a crucial role in developing intelligent, adaptable, and efficient systems. Staying informed about these advanced methodologies and exploring their applications will be key to leveraging the full potential of AI in various industries. Whether it's enhancing healthcare diagnostics, enabling self-driving cars, or creating intelligent customer service bots, transfer learning and reinforcement learning are paving the way for a smarter, more automated future.

[1day1movie]

AlphaGo (2017) Greg Kohs.

[sifytech]

All You Need to Know about Gemini, Google's Response to ChatGPT

As Google releases its new generative AI model called Gemini, Adarsh takes you through everything we know about it so far. Read More. https://www.sify.com/ai-analytics/all-you-need-to-know-about-gemini-googles-response-to-chatgpt/

[hungwy]

labatut's description of the lee sedol-alphago match is making me cry. we won, once, by a single divine bolt of genius... thank you lee sedol

[villainessbian]

It has to be stated as a defiant position because despite there being "no need to inflict that boredom on other people - other artists," the boredom of a few re: actually doing art or respecting others' work was and still is inflicted on everyone through AI.

And for clarification:

"People who think the lack of autonomy is an interesting artistic statement"? Not when making art they don't. The statement can be about a lack of of autonomy, or about making things themselves despite constraints (which is how most forms of poetry function). Not having autonomy and not making something in the first place is not a statement, it's a lack of statement. Silence isn't speech. Definitionally.

"People who are physically disabled in a way that prevents them from engaging with 'traditional' art" is very exactly no one who would artistically benefit from the plagiarism machine. Watching, hearing, smelling, touching, reading, existing in, just knowing any piece of art in any shape or form is engaging with it. If they can't do that with the rest they can't do it with dall-e. You mean people who physically can't create things but somehow are still able to communicate something to the machine.

And to that:

The robot isn't making them able, it's literally a third party copying people who were able.

It's less involved than ordering at a subway, which doesn't make you a "sandwich maker" even if you decided what to put in it. Just another customer. The process is still handled by someone else, your options are still limited by outside forces, and you still only asked for the ingredients.

It all relies on the assumption that the skills displayed are irrelevant to the end product - that a flawless monochrome is equal in value to a click with the paint bucket tool, since they're the same production. There's a reason why art is considered a creative process, not an end result.

Ultimately, this line of thought about "making art accessible" is about the supposed tragedy of someone having a vision without the skills to realise it. But that was always a solved issue. If you can develop these skills, develop them. If you can't or don't want to, commission someone. They're the only ways for you to actually be involved in the creation. Tweaking a machine until it's "yeah, close enough" isn't involvement. It's boredom. It's not caring about what is there. And for some reason that only applies to a few types of art, hm? If I tweak an android to run faster than Usain Bolt it doesn't make me an athlete. If I input a recipe setting in my Thermomix it doesn't make me a competent cook. Installing an autopilot doesn't make me a great pilot. And with my body I can't be any of these things.... and they're all damn closer to accessibility than midjourney is. You want to know what disabled people need? If I need something fetched - e.g. at the pharmacy - and my joint issues prevent me, then a small, fast robot that knows the way would be great. My eyes aren't good enough to visually check for a number of important things in the kitchen and my brain doesn't process time normally, so an automatic timer for cooking times with things that are already checked everywhere saves me a lot of time and food and health issues. Not a single time have I needed openai to make something. If I draw something, maybe my poor vision shows and I get the colours wrong. I don't have a robot colour-pick for me from the top 10 reposted painters online. It looks the same to me but not to you, and that's a much stronger statement about lack of autonomy than you not seeing it or me not making it. If I write it'll be my author's voice, not predictive text with a non-confrontational, PC-according-to-Silicon-Valley-execs tone. If I decide to try composing it will never be "an epic tune in the style of <insert currently-viral group>". And that's the difference between inspiration and botting.

As gen-AI becomes more normalized (Chappell Roan encouraging it, grifters on the rise, young artists using it), I wanna express how I will never turn to it because it fundamentally bores me to my core. There is no reason for me to want to use gen-AI because I will never want to give up my autonomy in creating art. I never want to become reliant on an inhuman object for expression, least of all if that object is created and controlled by tech companies. I draw not because I want a drawing but because I love the process of drawing. So even in a future where everyone’s accepted it, I’m never gonna sway on this.

[el-candelabro]

"Go: Dominando el Antiguo Juego de Inteligencia y Táctica"

[14dyh]

list of my saved youtube videos that Hange would watch:

A/N: someone watch this nerdy stuff with me pls, i'll go insane. need a hange for myself :') currently watching these videos to feed my nerdy hange delusions :D [i marked my faves with an (*) hehe]

short videos (10-30 minutes)

The Nightmares of Eduardo Valdés-Hevia

The Creatures of Codex Inversus

Nietzsche's Most Dangerous Idea | The Übermensch

Don't fear intelligent machines. Work with them | Garry Kasparov

* Decomposing Bodies to Solve Cold Case Murders

Glow-in-the-dark sharks and other stunning sea creatures | David Gruber

* You Will Never Do Anything Remarkable

* The Cognitive Tradeoff Hypothesis

* Inspiring the next generation of female engineers | Debbie Sterling | TEDxPSU

The Disturbing Paintings of Hieronymus Bosch

Roko's Basilisk: The Most Terrifying Thought Experiment

The 5 Most Dangerous Chemicals on Earth

Depth Charge Explosion Soaks Dr. Tatiana In Water

Monster Surgeon: The Lost Work of Dr. Spencer Black

The Biology of Giants Explained | The Science of Giants

I Made an Ecosystem With a Mini Pond Inside, Here’s How!

CSI Special Insects Unit: Forensic Entomology

not-so-short but under 1 hr (31-59 minutes)

* The unpredictable tale of The Dead Man's Story by J. Hain Friswell

Planets: The Search for a New World | Space Science | Episode 4 | Free Documentary

* Let's Visit the World of the Future [tw: might be a bit disturbing, it's an interesting scifi horror though]

The Mystery of Matter: “INTO THE ATOM” (Documentary)

* Australia's Deadliest Coast (Full Episode) | When Sharks Attack: There Will Be Blood

* How Leonardo da Vinci Changed the World

long videos (over 1 hr)

Demystifying the Higgs Boson with Leonard Susskind

* The complete FUN TO IMAGINE with Richard Feynman

The Brain That Wouldn't Die (1962) Colorized | Sci-Fi Horror | Cult Classic | Full Movie

* AlphaGo - The Movie | Full award-winning documentary

Particle Fever - Documentary

* Exploring The Underwater World | 4K UHD | Blue Planet II | BBC Earth

What was the Earth like in the Age of Giant Prehistoric Creatures? | Documentary Earth History