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Better Finance. Assemblea ad Atene su finanza sostenibile e protezione degli investitori
Better Finance. Oggi in Grecia la grande realtà europea a tutela dei risparmiatori Better Finance BETTER FINANCE, Federazione europea degli investitori e degli utenti dei servizi finanziari, ha organizzato ad Atene,…
L’articolo Better Finance. Assemblea ad Atene su finanza sostenibile e protezione degli investitori scritto da Paolo Brambilla proviene da Assodigitale.
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A search for bold ideas to drive climate action
Google has been committed to climate action for decades — and during that time, we’ve learned that we can have the biggest impact on our planet by working together. That’s why we’re launching a $30 million Google.org Impact Challenge on Climate Innovation — an open call for ambitious projects from nonprofits and social enterprises that accelerate advances in climate information and action, driven by open data, AI, machine learning and other digital tools.
We’re leading by example at Google by setting a goal to achieve net-zero emissions across all of our operations and value chain, including our consumer hardware products, by 2030. We’re going even further for our data centers and campuses, with a moonshot goal to operate on 24/7 carbon-free energy by the end of the decade. Our work to procure clean energy around the world not only helps us decarbonize our own operations, but also greens the local grids where we’re based, benefitting entire regions.
But when it comes to solving a problem as big and urgent as climate change, we get more done when we partner together. So we’re using our technology to make critical climate data available to everyone. Cities are using our Environmental Insights Explorer to better understand their emissions data, solar potential, air quality and tree canopy coverage. Customers are using innovative new tools in Google Cloud like Carbon Footprint, which helps companies accurately measure the gross carbon footprint of their cloud usage. And Google users can make more sustainable choices with information like the carbon footprint of their travel — whether finding flights with lower carbon emissions or choosing fuel-efficient driving directions in Google Maps.
Drive climate action through data
Through theGoogle.org Impact Challenge on Climate Innovation, we’ll build on this work by supporting nonprofits and social enterprises that demonstrate the power of digital technology in climate innovation. Six projects will receive $5 million each in funding, along with in-kind donations of Google’s products and technical expertise through Google.org Fellowships and more. These funds will speed up the collection of data and development of tools that advocates, policymakers, businesses and individuals need to drive positive impact.
Open data and advanced digital tools, including AI and machine learning, can give way to new climate solutions that simply wouldn’t have been possible in the past. These technologies can reveal patterns and insights that were otherwise hidden in a mountain of data. Since 2018, Google.org has supported a wide range of climate innovators that can help us make better planning decisions by modeling future outcomes — including projects that map emissions on a global scale; show people the most effective places to restore ecosystems; and help small businesses understand their carbon footprint, to name a few. Tools like these make the climate information around us more accessible and useful.
This year’s Impact Challenge builds off the success of Google.org’s Impact Challenge on Climate in Europe in 2020, and a $6 million Google.org Sustainability Seed Fund launched earlier this year for the Asia-Pacific region.
Apply now with your bold ideas
Applications for the Google.org Impact Challenge on Climate Innovation are now open at g.co/climatechallenge. We encourage organizations to apply early, as priority consideration will be given to proposals received by July 29. Selected organizations will be announced on a rolling basis throughout the year, and the application window will remain open until all six projects have been selected.
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#WeArePlay: Meet the people behind your apps and games
Every month, over 2.5 billion people visit Google Play to discover millions of apps and games. Behind each of these apps is an entrepreneur (or two… or three) with a unique story to tell. Some have been programming since childhood, others just learned how to code. Some live in busy cities, others in smaller towns. No matter how different their backgrounds are, these creators all have one thing in common — the passion to turn an idea into a growing business.
#WeArePlay celebrates and shares their stories. Over the next few months, you’ll hear from the people and businesses behind Google Play apps and games, and how they’re making an impact around the world.
Our series kicks off spotlighting Yvonne and Alyssa, the London-based mother and daughter duo who created Frobelles — a dress-up game that helps increase representation of African and Caribbean hair styles in the game industry.
You’ll also meet Hand Talk Translator’s Ronaldo, Carlos and Theadeu from Brazil, DailyArt’s Zuzanna from Poland, and TravelSpend’s world-trotting couple Ina and Jonas from Germany.

A big thank you to all the apps and games businesses that are part of our Google Play community. Dive into some of their stories today and stay tuned for more.
How AI creates photorealistic images from text

Have you ever seen a puppy in a nest emerging from a cracked egg? What about a photo that’s overlooking a steampunk city with airships? Or a picture of two robots having a romantic evening at the movies? These might sound far-fetched, but a novel type of machine learning technology called text-to-image generation makes them possible. These models can generate high-quality, photorealistic images from a simple text prompt.
Within Google Research, our scientists and engineers have been exploring text-to-image generation using a variety of AI techniques. After a lot of testing we recently announced two new text-to-image models — Imagen and Parti. Both have the ability to generate photorealistic images but use different approaches. We want to share a little more about how these models work and their potential.
How text-to-image models work
With text-to-image models, people provide a text description and the models produce images matching the description as closely as possible. This can be something as simple as “an apple” or “a cat sitting on a couch” to more complex details, interactions and descriptive indicators like “a cute sloth holding a small treasure chest. A bright golden glow is coming from the chest.”

In the past few years, ML models have been trained on large image datasets with corresponding textual descriptions, resulting in higher quality images and a broader range of descriptions. This has sparked major breakthroughs in this area, including Open AI’s DALL-E 2.
How Imagen and Parti work
Imagen and Parti build on previous models. Transformer models are able to process words in relationship to one another in a sentence. They are foundational to how we represent text in our text-to-image models. Both models also use a new technique that helps generate images that more closely match the text description. While Imagen and Parti use similar technology, they pursue different, but complementary strategies.
Imagen is a Diffusion model, which learns to convert a pattern of random dots to images. These images first start as low resolution and then progressively increase in resolution. Recently, Diffusion models have seen success in both image and audio tasks like enhancing image resolution, recoloring black and white photos, editing regions of an image, uncropping images, and text-to-speech synthesis.
Parti’s approach first converts a collection of images into a sequence of code entries, similar to puzzle pieces. A given text prompt is then translated into these code entries and a new image is created. This approach takes advantage of existing research and infrastructure for large language models such as PaLM and is critical for handling long, complex text prompts and producing high-quality images.
These models have many limitations. For example, neither can reliably produce specific counts of objects (e.g. “ten apples”), nor place them correctly based on specific spatial descriptions (e.g. “a red sphere to the left of a blue block with a yellow triangle on it”). Also, as prompts become more complex, the models begin to falter, either missing details or introducing details that were not provided in the prompt. These behaviors are a result of several shortcomings, including lack of explicit training material, limited data representation, and lack of 3D awareness. We hope to address these gaps through broader representations and more effective integration into the text-to-image generation process.
Taking a responsible approach to Imagen and Parti
Text-to-image models are exciting tools for inspiration and creativity. They also come with risks related to disinformation, bias and safety. We’re having discussions around Responsible AI practices and the necessary steps to safely pursue this technology. As an initial step, we’re using easily identifiable watermarks to ensure people can always recognize an Imagen- or Parti-generated image. We’re also conducting experiments to better understand biases of the models, like how they represent people and cultures, while exploring possible mitigations. The Imagen and Parti papers provide extensive discussion of these issues.
What’s next for text-to-image models at Google
We will push on new ideas that combine the best of both models, and expand to related tasks such as adding the ability to interactively generate and edit images through text. We’re also continuing to conduct in-depth comparisons and evaluations to align with our Responsible AI Principles. Our goal is to bring user experiences based on these models to the world in a safe, responsible way that will inspire creativity.


