#googledeepmind

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Yapay Zekada Devrim: Google DeepMind'in Yeni Araştırması, Büyük Yerine Akıllı Modellerin Yolunu Açıyor

Google DeepMind’in son çalışması, ChatGPT-4 gibi devasa dil modellerini (DDM’ler) iyileştirme konusunda yeni bir perspektif sunuyor. Araştırma, sadece model boyutunu büyütmek yerine, çıkarım esnasındaki hesaplama kaynaklarını optimize etmeye, yani test zamanı hesaplamasına odaklanıyor. Bu yaklaşım, özellikle sınırlı kaynakların olduğu ortamlarda, performanstan ödün vermeden daha verimli ve…

[govindhtech]

Google C2PA Helps Users To Boost New AI Content Availability

How the Google C2PA is helping us increase transparency for new AI content.

It’s contributing to the development of cutting-edge technologies so that users may comprehend how a certain piece of information was made and changed over time. Businesses are committed to assisting consumers in comprehending the creation and evolution of a specific piece of content in order to expand the application of AI to additional goods and services in an effort to boost innovation and productivity. But think it’s critical that people have access to this knowledge, therefore making significant investments in cutting-edge technologies and solutions, like SynthID, to make it available.

As content moves across platforms, but also realize that collaborating with other industry players is crucial to boosting overall transparency online. For this reason, Google became a steering committee member of the Coalition for Content Provenance and Authenticity (Google C2PA) early this year.

Currently providing updates today on its involvement in the development of the newest Google C2PA provenance technology and how it will be incorporated into the products.

Developing current technologies to provide credentials that are more secure

When determining if a shot was captured with a camera, altered with software, or created by generative AI, provenance technology may be helpful. This kind of material promotes media literacy and trust while assisting users in making better educated judgments regarding the images, videos, and sounds they interact with.

As members of the steering committee of the Google C2PA, they have collaborated with other members to enhance and progress the technology that is used to append provenance information to material. Google worked with others on the most recent iteration (2.1) of the technical standard, Content Credentials, during the first part of this year. Because of more stringent technological specifications for verifying the origin of the material, this version is more resistant to manipulation attempts. To assist guarantee that the data connected is not changed or deceptive, stronger defenses against these kinds of assaults are recommended.

Including the Google C2PA standard in It’s offerings

Google will be integrating the most recent iteration of Content Credentials into a couple of Their primary offerings throughout the next few months:

Search: Users will be able to utilize It’s “About this image” function to determine if an image was made or changed using AI techniques if it has Google C2PA information. “About this image” is available in Google photos, Lens, and Circle to Search and helps provide users context for the photos they see online.

Ads: Google C2PA information is beginning to be integrated into Google ad systems. Their intention is to gradually increase this and utilize Google C2PA signals to guide the enforcement of important rules.

Later in the year, they’ll have more details on It’s investigation into how to notify YouTube users with C2PA information when material is recorded using a camera.

In order to enable platforms to verify the material’s provenance, Google will make sure that their implementations evaluate content against the soon-to-be-released Google C2PA Trust list. For instance, the trust list assists in verifying the accuracy of data if it indicates that a certain camera type was used to capture the picture.

These are just a few of the applications for content provenance technology that nous are considering at this moment. It want to add it to many more products in the future.

Maintaining collaborations with other industry players

Determining and indicating the origin of material is still a difficult task that involves many factors depending on the item or service. Even if people are aware that there isn’t a single, universal solution for all online material, collaboration across industry players is essential to the development of long-lasting, cross-platform solutions. For this reason, it’s also urging more hardware and service providers to think about implementing the Google C2PA‘s Content Credentials.

It efforts with the Google C2PA are a direct extension of their larger strategy for openness and ethical AI research. For instance, it’s still adding Google DeepMind‘s SynthID embedded watermarking to more next-generation AI tools for content creation and a wider range of media types, such as text, audio, visual, and video. In addition, Google have established a coalition and the Secure AI Framework (SAIF) and joined a number of other organizations and coalitions devoted to AI safety and research. That are also making progress on the voluntary pledges they made at the White House last year.

Google Rising Artists Series has 24 brand-new Chrome themes

Six up-and-coming artists from various backgrounds were asked to create new themes for the Chrome browser.Image Credit To Google

September marks the beginning of a season of change: a new school year, a new you, and matching Chrome themes.

Google started the Chrome-sponsored Artist Series a few years ago to honor the talent of artists worldwide and provide their creations as unique Chrome themes. They commissioned six brilliant up-and-coming artists from various backgrounds to present their work in Chrome for the newest collection, which is available beginning today: Melcher Oosterman, DIRTYPOTE, Kaitlin Brito, Kanioko, Kate Dehler, and Martha Olivia.Image Credit To Google

Check out the Rising Artists Series by visiting the Google Chrome Web Store. Select a theme that inspires you, click “Add to Chrome,” and take in the eye-catching hues and upbeat patterns. To see and use themes from this collection, you may alternatively create a new Chrome tab and click the “Customize Chrome” icon in the bottom right corner.

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DeepMind introduces a groundbreaking method to generate synchronized audio for silent videos, using deep learning.

This technology, named "AudioLM," can create realistic and coherent sounds matching the visual activities in videos without using any textual input or pre-existing audio templates.

This advancement not only enhances the accessibility of videos for hearing-impaired viewers but also opens new avenues for content creation in the film and video industry. . . .

For more AI related updates, follow @trillionstech.ai

[mysocial8one]

Dive into our latest article exploring Design2Code, an AI model developed by Microsoft and Google DeepMind. Learn how it’s revolutionizing front-end engineering by transforming visual designs into functional code, and matching the abilities of commercial models like Gemini Pro Vision.

[trcoffeebyefe]

Yapay Zekayı kimler inşa ediyor?

Herkese merhabalar, yeni bir yayına daha hoş geldiniz. Son yıllarda herkesin en çok konuştuğu konulardan bir tanesi yapay zeka oldu. Artık neredeyse her yerde karşımıza çıkan yapay zekayı kimlerin inşa ettiğini ve bu işe nasıl başladıklarını sizde benim gibi merak eden biriyseniz; daha fazla beklemeden buyrun hemen konunun detaylarına geçelim.

Bu arada yaptığım yayınları beğeniyor ve yeni yayınları kaçırmak istemiyorsanız dinlediğiniz platformlardan abone olarak tüm yayınlara anında ulaşabilir veya [patreon] üzerinden bana destek olabilirsiniz.

Time dergisi, AI100 ana başlığı ile yayınladıkları son sayıda bu konuya oldukça büyük bir yer verdi. Ben tabiki bu yapay zekayla alakalı kurulmuş olan 100 adet girişimin her birinden bu yayında bahsetmeyeceğim. Bunun yerine en çok ilgi çeken ve merak uyandıran girişimleri sizlerle paylaşacağım. Eğer bu listedeki 100 adet işletmenin hepsine göz atmak isterseniz ben yine bu araştırmanın linkini sizler için bıraktım. Gelin artık listemize artık göz atmaya başlayalım.

Google DeepMind, Demis Hassabis

Son zamanlarda benim en fazla ilgimi çeken yapay zeka platformu Google’un yakın zaman önce tanıttığı Google DeepMind oldu. Google bu yapay zekayı Gemini olarak adlandırdı ve yayınladıkları videoda yapay zekanın gördüğü her şeyi anında tanımlaması ve konuyla alakalı müzikler, oyunlar ve bir takım sesleri hiç beklemeden yaratması; daha önce başka hiç bir yapay zeka platformunda benim denk gelmediğim bir türdü.

Google Deepmind’ın başındaki isim ise Demis Hassabis, kendisi Gemini’ın chatGPT ‘nin çok daha ilerisinde işler başarabileceğini savunuyor. Gemini, multi-model denilen bir özelliğe sahip bu özelliği türkçeleştirecek olursak; girdi ve çıktı özelliğine sahip olunması denebilir ve bu model sadece yazı girdilerini değil resim, ses ve video girdilerinide okuyup buna uygun sonuçlar yani çıktılar verebiliyor.

Şu an için pek çok yapay zeka platformuna bir soru sormanız, bir yazı girdisi vermeniz yada görsel bir içeriği sizin sunmanız gerekiyor. Fakat Gemini ekranda gördüğü her şeye bir soru gelmesine yani bir girdi sunulmasına ihtiyaç duyulmadan gördüğü her şeyi anında anlamlandırabiliyor.

Ama öte yandan Google dünyadaki en maymun iştahlı firmalardanda biri olma etiketinide taşıyor. Google’un yarattığı sosyal medya platformu Google+, online oyun platformu Google Stadia, Google Glass, Google’un mesajlaşma platformu Google Hangouts, hepsi tarih olmuş durumda. Hatta Google’un öldürdüğü bu girişimleri listeyen bir websitesi dahi var. Bu listeye göz atınca Gemini’ında bu mezarlığa gelecekte katılıp katılmayacağını düşünmeden edemedim.

xAI, Elon Musk

Listemizde devam edecek olursak; artık neredeyse her konuda karşımıza çıkan isim haline gelen Elon Musk, bu konuda da boy göstermeyi başardı. 2010’ların başında yapay zekanın insanlığa ne gibi katkıları olacağı ile alakalı açıklamalar yapan Musk, Google’un yapay zeka girişimi DeepMind’a ilk yatırım yapan isimlerden biri oldu, çoğu kişinin haberi olmasada Sam Altam ile kurucu ortak olup 2015 yılında OpenAI’ın hayata geçmesinde yer aldı.

Fakat bunun ardından OpenAI’a sadece kârı artırmayı amaçlayan cehennemden gelen bir şeytan” gibi ağır sözler ederek yönetim kurulundan ayrıldı. Bundan sonra TruthGPT adında kendi yapay zeka platformunu kuracağını söyledi ama bu platform hayata geçirilmedi. Şu an itibariyle xAI platformu Elon Musk’un en son karar kıldığı ve üzerinde çalıştığı yapay zeka platformu olarak piyasa çıktı. Bu yapay zekaya ise Grok adı verilmiş. Ben bu websitesini ziyaret ettim ama şu an aktif bir platform değil eğer Musk son olarak bundan da vazgeçmez ise yakın zamanda bu yapay zekayada sahip olacağız gibi duruyor.

Anthropic, Dario ve Daniela Amodei

Dünyanın önde gelen yapay zeka labaratuarlarından biri olan Anthropic, iki kardeş Dario ve Daniela Amodei tarafından hayata geçirildi. Sürekli olarak Dünya devi firmaların yapay zeka işlerine el attığına şahit olsakta iki kardeşin hayata geçirdiği böyle bir girişimde bu sektörde var olmayı başardı. Ve Amazondan tam 4 milyar dolarlık yatırım aldı.

Dario ve Daniela bu işe ilk başladıklarında yapay zekanın insani değerlere uyumlu olmasını amaçlayan kurallara öncelikli olarak odaklanmışlar. Diğer yapay zeka platformlarından farklı olarak Anthropic yapay zekanın zararsız ve çok daha arkadaş canlısı olmasını sağlayan bir deneyimi kullanıcılarına sunuyor. 2021 yılında hayata geçirilen Anthropic Claude 2 adını verdikleri bu yapay zekayı ücretsiz olarak herkese sunuyor. Etik kurallara çok daha fazla önem veren bu yapay zeka bu platformu benim oldukça ilgimi çekti.

Signal, Meredith Whittaker

Yapay zekanın etik kuralları takip edip etmeyeceği endişesi farklı Signal firmasının da gündeminde yer alıyor. Signal uygulamasının başındaki isim olan Meredith Whittaker, yapay zeka ve daha pek çok teknoloji firmasının, kendi kurdukları sistemleri eğitmek için büyük miktarda veriyi çoğunlukla izinsiz olarak topladıklanırı söylüyor.

Profil resimlerimiz, bir işletmeye yazdığımız yorumumuz, gönderdiğimiz tweet’ler ve bunun gibi internette bıraktığımız pek çok ayak izimiz bu firmalar tarafından izinsiz şekilde toplanıyor. Daha doğrusu; bu uygulamalara kayıt olurken, kabul etmek zorunda olduğumuz ve okumadan geçtiğimiz pek çok sözleşmede verilerimizin toplanılmasına izin vermiş oluyoruz.

2022 yılında 500 milyon Whatsapp kullanıcısının verisi internete sızıp başka firmalara satılmıştı bunun üzerine de Signal uygulaması biranda popüler hale geldi. Signal uygulamasının başkanı Meredith Whittaker, signalin her zaman ücretsiz, reklamsız ve güvenli bir ortam olacağını söylüyor. Ayrıca yapay zeka firmalarının, insanların verilerini elde edebilmek için yarıştığı bu çağda kendileri gibi daha pek çok firmanın var olması gerektiğinide Times dergisine verdiği demeçte dile getirmiş.

Hugging Face, Clément Delangue

Paris merkezli bir girişim olan Hugging Face, Clément Delangue tarafından hayata geçirilmiş. Bu işletme, makine öğrenmesini, açık kaynaklı şekilde herkese sunan bir girişim. Pek çoğumuzu büyüleyen yapay zekanın marifetlerinin nasıl tasarlandığını merak ediyor ve bu alanda yazılımsal bilginizi genişletmek istiyorsanız Hugging Face tam size göre bir platform.

Fakat yazılımcılar ve bilgisayar biliminde faliyet gösteren profesyoneller, böyle websitelerinden büyük faydalar sağlayacaktır. Eğer ki bu alanda çalışmak istiyor ve kariyerinizi bu alana yöneltmek istiyorsanızda; benim şahsen en faydalı bulduğum ücretsiz kurs Harvard Üniversitesinin sunduğu CS50: Introduction to Computer Science. Bu eğitime kesinlikle göz atmaya değer ayrıca Türkçe altyazı desteğide bu kursta mevcut. Ben programlamayı öğrenirken freecodecamp websitesini kullanmıştım; şahsen web design kısmı oldukça öğretici ve güzel tasarlanmıştı ama Javascript gibi daha ağır konularda ben bu platformu biraz yetersiz bulmuştum.

AI4All, FEI-FEI LI

Standford Üniversitenide Araştırmacı olan Fei-Fei Li, günümüzde şu an faliyet gösteren görüntü tanıma sistemlerinin temellerinin atılmasına ve yapay zekanın sağlık hizmetlerinde kullanılmasına çok büyük faydalar sağlamış. Yapay zekaya olan katılımı ve çeşitliliği arttırmak adına da kâr amacı gütmeyen bir işletme olan AI4ALL şirketini hayata geçirmiş.

Kendisi Haziran 2023’te Amerikan Başkanı Biden ile Amerikada yapay zekanın kamu yararına kullanılması için çalışmalarına devam ettiğini paylaştı. Tabi bir de kendisi Amerika Ulusal Yapay Zeka Araştırma Kaynağı adı verilen bir organizasyonun da bir üyesi.

Time dergisine verdiği röportajda Oppenheimer filmini izlemeye çocukları ile beraber gittiğinden bahsediyordu. Filmden çıktıktan sonra kendisi bilim insanlarının sahip oldukları sorumluluk duygusunun, ne kadar ağır olduğunu bir defa daha anladığını bu röportajda paylaşmış. Ben bu websitesine bir göz attım ve şu an özellikle eğitim hayatına devam eden gençlere özel kurslar düzenliyorlar ve pek çok ücretsiz eğitimde bu platformda barındırıyorlar.

Son Söz

Konuyu toparlayacak olursak, yapay zekaya olan ilgi ve bu alanda kurulan işletmeler her geçen gün inanılmaz bir hızla artmaya devam ediyor. Kimi firma yapay zekayı daha güçlü hale getirmeye odaklanırken kimi firmada etik kuralların belirlenmesi için çabalıyor. Bu yarışın sonunda da sahip olacağımız yeni nesil teknolojilerin, bizlere ne gibi yarar ve zarar getireceğini tahmin etmek ise şu an için oldukça zor gibi duruyor.

Bu bölümde yapay zekanın arkaplanında kimlerin var olduğunu ve ne gibi girişimler ile bunu başardıklarını inceledik. Umarım sizlere faydalı bilgiler sunabilmişimdir. En kısa sürede yeni yayınlarda görüşmek üzere, kendinize çok iyi bakın hoşçakalın.

Referanslar

Jacobs, S. (2023, September 7). How we chose the time100 most influential people in ai. Time. https://time.com/6311323/how-we-chose-time100-ai/

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[adngold]

Dans un monde où la science-fiction devient réalité

🤖🔒 Dans un monde où la science-fiction devient réalité, Google DeepMind s’inspire des lois de la robotique d’Isaac Asimov pour créer une “constitution du robot” qui assure la sécurité humaine. Une “constitution du robot” de Google pour ne pas faire de mal aux humains Voici quelques points clés de cette avancée majeure : – 🧠 **AutoRT**: Un système innovant pour former les robots, assurant une…

[incredefy]

[soniarandhawa]

@GoogleDeepMind makes drug discovery with #AlphaFold which is able to predict protein structures. https://bloomberg.com/news/articles/2024-05-08/deepmind-ceo-targets-100-billion-plus-ai-drug-discovery-business-with-alphafold…

#ArtificialIntelligence #AI #HealthTech #Medicine

[toptipsai]

Reinforcement Learning in Game Playing: Beyond AlphaGo

The groundbreaking success of AlphaGo marked a turning point in the field of AI, particularly in game-playing. Since then, Reinforcement Learning (RL) has evolved, transcending traditional board games to revolutionize the realm of video games and beyond. This article delves into the advancements post-AlphaGo, offering practical examples, code snippets, and insights into the role of RL in modern game-playing AI. Section 1: The Evolution of RL in Board Games - DeepMind's AlphaZero: - AlphaZero, a successor to AlphaGo, mastered chess, shogi, and Go, learning entirely from self-play without human data. - Practical Example: AlphaZero's unconventional and creative chess strategies defied centuries of established human play. - Code Snippet: Basic MCTS for AlphaZero-Style AI: def monte_carlo_tree_search(root): while not root.is_terminal(): leaf = traverse_nodes(root) # Traverse to a leaf node simulation_result = rollout(leaf) # Simulate a random game backpropagate(leaf, simulation_result) # Propagate results return best_child(root) - Insight: The success of AlphaZero emphasizes the importance of exploration and creativity in AI strategies. Section 2: Reinforcement Learning in Chess Engines - RL-Based Chess Engines: - Modern chess engines like Leela Chess Zero (LCZero) employ neural networks and RL for continuous learning. - Code Snippet: Policy Network Training for Chess: def update_policy_network(board, move, reward): board_state = board_to_state(board) predicted_move = policy_network.predict(board_state) loss = calculate_loss(predicted_move, move, reward) policy_network.fit(board_state, move, loss) - Actionable Insight: Combining deep learning with RL can enhance strategic depth in games traditionally dominated by brute-force methods. https://www.youtube.com/watch?v=WXuK6gekU1Y&ab_channel=GoogleDeepMind Section 3: RL in Complex Video Games - OpenAI Five in Dota 2: - OpenAI Five demonstrated how RL could navigate the complexities of multiplayer online games, involving team dynamics and real-time decision-making. - Practical Example: OpenAI Five's ability to coordinate as a team and adapt strategies against human players. - Challenges: Training AI in such environments requires handling a vast action space and managing real-time reactions. Section 4: RL in Non-Deterministic Games - AI in Games with Random Elements: - RL is being applied in games where uncertainty and randomness play a significant role, like card games. - Insight: RL agents in these games must learn to adapt strategies based on probabilistic outcomes. - Future Direction: Develop a more robust AI capable of handling uncertainty and adapting strategies dynamically. https://www.youtube.com/watch?v=tXlM99xPQC8&ab_channel=GoogleDeepMind Section 5: Policy Gradient Methods in Game AI - Policy Gradient Techniques: - Methods like REINFORCE allow for more nuanced decision-making in games, especially with high-dimensional action spaces. - Code Snippet: Implementing REINFORCE for Game Playing: def reinforce_update(states, actions, rewards, policy_network, gamma): for state, action, reward in zip(states, actions, rewards): action_prob = policy_network.predict(state) policy_network.gradient_ascent(state, action, reward * gamma) - Application: Suitable for continuous action spaces like those found in real-time strategy games. Section 6: Transfer Learning in Game AI - Applying Transfer Learning: - Transfer learning in RL enables knowledge acquired in one game to be applied to others, enhancing learning efficiency. - Practical Example: An AI trained in one variant of a card game adapting its strategies to different variants. - Code Snippet: Applying Transfer Learning: target_game_model = source_game_model.clone() target_game_model.retrain(new_game_data) Section 7: Multi-Agent Reinforcement Learning in Games - Complex Dynamics of Multi-Agent Systems: - In games involving multiple agents, RL must account for varied interactions, cooperation, and competition. - Example: Competitive games where AI agents must both cooperate with teammates and compete against adversaries. - Insight: Training multi-agent systems requires not just mastering individual strategies but also understanding team dynamics. Section 8: Overcoming the Challenges of Sparse Rewards - Sparse Rewards in Gaming: - In many games, significant rewards or feedback are not frequently available, making it challenging to train RL agents effectively. - Strategies: Using reward shaping techniques or auxiliary tasks to provide more frequent learning signals . - Example: In adventure games, creating intermediate rewards for subgoals to aid in learning. Section 9: Human-like AI Opponents - Creating Human-like AI: - The ultimate challenge is developing AI opponents that exhibit human-like behaviors and unpredictability. - Application: Enhancing player experience in games by providing realistic and unpredictable AI opponents. - Future Prospect: Developing AI that not only plays optimally but also emulates human play styles and errors. https://www.youtube.com/watch?v=0g9SlVdv1PY&ab_channel=ChessNetwork Section 10: Ethical Considerations in AI Game Playing - Fair Play and AI Transparency: - Ensuring AI plays fairly and transparently, especially when competing against human players. - Actionable Insight: Regularly reviewing AI strategies for ethical considerations, especially in competitive gaming. - Balance in AI Design: Maintaining a balance between challenging and enjoyable AI opponents. Conclusion The advancements in Reinforcement Learning since AlphaGo has not only pushed the boundaries of AI capabilities in games but also opened avenues for exploration in other complex, real-world scenarios. As RL continues to evolve, its applications in game-playing AI are likely to become more sophisticated, realistic, and engaging, promising an exciting future for both AI development and the gaming industry. Read the full article

[chicagochinesenews]

Google旗下DeepMind新AI工具 預測有害基因突變

Google旗下人工智慧公司DeepMind推出一種工具，可預測基因突變是否會造成傷害，對罕見疾病的研究或許有所幫助。（圖取自twitter.com/GoogleDeepMind） （中央社華盛頓19日綜合外電報導）Google（谷歌）旗下人工智慧（AI）公司DeepMind今天推出一種工具，可預測基因突變是否會造成傷害，對罕見疾病的研究或許有所幫助。 Continue reading Untitled

[tonin-terets]

Visualising AI - Energy Efficiency from Linus Zoll on Vimeo.

Created for Google DeepMind's Visual AI Project.

About Visualising AI Visualising AI aims to open up conversations around AI. By commissioning a wide range of artists to create open-source imagery, the project seeks to make AI more accessible. It explores the roles and responsibilities of the technology, weighing up concerns and societal benefits in a highly original collection of artist works.

The ongoing project invites cutting-edge artists to spend time with scientists, engineers, researchers, ethicists and more, discussing key themes in AI, before using those conversations as a catalyst to create new artworks. Crucially, each artist is given complete creative freedom, explicitly briefed to interpret the shared knowledge in whichever way they choose.

Download on Unsplash: unsplash.com/@googledeepmind Download on Pexels: pexels.com/@googledeepmind/

[govindhtech]

Getting Started With Imagen 3 On Vertex AI For Developers

A step-by-step guide for application developers to get started with Imagen 3 on Vertex AI

Early adopters tested Imagen 3 on Vertex AI over the course of the last few months and provided with insightful feedback. Users obviously want an AI model that powers your practical creative applications and produces visually attractive content. Based on their input, Google Cloud has determined three recurring themes:

Demand for unmatched excellence in a variety of artistic mediums and styles

Strong swift adherence and quick image creation are desired.

Advanced safety filters and SynthID watermarking are controls that safeguard and foster confidence.

We’ll go over each of these ideas in detail throughout this essay. To help you get the most of Imagen 3, we will also include some code examples and best prompt practices.

Unwavering versatility and quality

A new benchmark for quality and control over your created photographs is set with Imagen 3. This text-to-image model creates remarkably composed, crisp, color-accurate, and high-resolution photorealistic images. You can experiment with a greater range of artistic formats and styles with Imagen 3. With the model’s wider variety of styles and forms, you may create anything from amusing claymation scenarios to photorealistic masterpieces, giving you the freedom to express your own artistic vision.

Let’s go through an example of making image mockups for a new cookbook cover to show off these photorealistic capabilities. The resulting image, which was created with the prompt below, has amazing detail, composition, and photorealism.

import vertexai from vertexai.preview.vision_models import ImageGenerationModel

TODO(developer): Update and un-comment below lines

project_id = “PROJECT_ID”

vertexai.init(project=PROJECT_ID, location=”us-central1″)

generation_model = ImageGenerationModel.from_pretrained(“imagen-3.0-generate-001”)

prompt = “”” A photorealistic image of a cookbook laying on a wooden kitchen table, the cover facing forward featuring a smiling family sitting at a similar table, soft overhead lighting illuminating the scene, the cookbook is the main focus of the image. “””

image = generation_model.generate_images( prompt=prompt, number_of_images=1, aspect_ratio=”1:1″, safety_filter_level=”block_some”, person_generation=”allow_all”, )

OPTIONAL: View the generated image in a notebook

image[0].show()Image credit to Google Cloud

Text Rendering

Regarding text rendering inside images, Imagen 3 also opens up new possibilities. Creating pictures of greeting cards, posters, and social media posts with captions in different fonts and colors is a great way to experiment with this tool. To use this function, simply write a brief written description of what you would like to see in the prompt.

Nearer to your purpose

No matter how complex your natural language descriptions are, Imagen 3’s rapid comprehension converts them into precisely matched pictures. In your description, you can detail everything from particular camera angles to different kinds of lenses to image compositions. Imagen 3 closely follows the cue, assisting in bridging the mental image and the final image. Simple subject-action-setting instructions or complex, multi-layered descriptions can be given to the model; it will adjust to your creative process to support a wide variety of styles.

Given that Imagen 3 performs well with complex prompts, giving strong details typically results in outcomes that are higher quality and more accurate. Some choices to think about when creating your prompts are listed below:

Arrangement: Set the tone for the scene by indicating the locations of the subjects.

Lighting: Adjust the lighting’s focus and direction to create a mood. Use strong or soft lighting.

Camera angles and lens selections can be used to create depth and perspective.

Styles: Create digital art, cinematic, vintage, minimalist, and more go beyond photorealism.

Decreased latency

Imagen 3 Fast

Google Cloud provide Imagen 3 Fast, which is geared for generation speed, in addition to Imagen 3, which is their highest quality model to date. For producing photographs with greater contrast and brightness, Imagen 3 Fast is appropriate. You can observe a 40% reduction in latency when compared to Imagen 2. You can use the same prompt to create two photos that illustrate these two models.

Take care of your creations and preserve them

With built-in security features, Imagen 3 allows you to maintain control while concentrating on your creative ideas. Imagen 3 uses a technique called SynthID, which is partnered with Google DeepMind and embeds an undetectable watermark at the pixel level. All photos created by Imagen 3 have a digital watermark attached to them by default, but you may specifically enable this functionality by using the add_watermark argument. The API can also be used to confirm if an image was created with Imagen. By confirming the legitimacy of your AI-generated photos, this promotes transparency and helps protect your creations from exploitation.

You have more control over the kinds of images that are created to ensure that they align with your brand’s values or ideals by using Imagen 3’s sophisticated safety filters. Change the safety_filter_level to set safety filter thresholds for generated photos. You can select “block_most,” “block_some,” or “block_few” as the safety level. Person_generation can be modified to “allow_all,” “allow_adult,” or “dont_allow” in order to alter the safety setting that determines who gets generated.

Imagen 3 image generation

image = generation_model.generate_images( prompt=prompt, number_of_images=1, aspect_ratio=”1:1″, safety_filter_level=”block_some”, person_generation=”allow_all”, add_watermark=True, )

What comes next?

Imagen 3 with an allow list is now publicly accessible. Currently, developers at companies with clear use cases get priority access to Imagen 3 on Vertex AI.

Read more on govindhech.com

[teragames]

AlphaFold es una IA que predice la estructura de las proteínas

AlphaFold es un sistema de IA desarrollado por @GoogleDeepMind que predice la estructura 3D de una proteína a partir de su secuencia de aminoácidos, regularmente logra una precisión competitiva con el experimento.

Las proteínas son las moléculas más importantes de la vida, ya que realizan funciones esenciales para el funcionamiento de las células, los tejidos y los organismos. Sin embargo, conocer la estructura y el comportamiento de las proteínas es un reto científico enorme, que requiere de técnicas experimentales complejas y costosas. Por eso, la inteligencia artificial (IA) se ha convertido en una…

[hackernewsrobot]

Google SIMA

https://twitter.com/GoogleDeepMind/status/1767918515585994818

[denebola42-blog]

Fungal zombie movies remind me if seeing furry poop covered with mushrooms, from animals. I think from animals. And I've read scientists wanna transfer meds via bacterium or even other organisms and parasites that can get past our defenses. And gene therapy via horizontal gene transfer means you don't need crispr. You can take away or give insanity, not giving permission for the latter, just saying it's possible in case there's mad scientists out there. Spores, tardigrades etc and even electrolytes, vitamins, minerals etc, hormones, plasma etc anything that can get past the human body defenses and likely how aids etc can get past and be cured. Or other deadly diseases like ebola. Perhaps the Google AI that solved protein folding can work on that? #TeamPixel #googledeepmind and what if a computer was hooked up to your body and live support and cures, via another dimension? Quantum medicine. And communication? Tracking devices too for children and elderly etc. Families. #citizenscientist #citizenscience #godmatters #godisfamily #familymatters #weareallfamily (at North Ogden, Utah) https://www.instagram.com/p/CM-furbBThg/?igshid=1d02tefp3a1l7