The new capability allows Gemini 1.5 to process extremely long text passages – up to one million characters – to understand context and meaning. This dwarfs previous AI systems like Claude 2.1 and GPT-4 Turbo, which max out at 200,000 and 128,000 tokens respectively:

“Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra’s state-of-the-art performance across a broad set of benchmarks,” said Google researchers in a technical paper (PDF).

The efficiency of Google’s latest model is attributed to its innovative Mixture-of-Experts (MoE) architecture.

“While a traditional Transformer functions as one large neural network, MoE models are divided into smaller ‘expert’ neural networks,” explained Demis Hassabis, CEO of Google DeepMind.

“Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialisation massively enhances the model’s efficiency.”

To demonstrate the power of the 1M token context window, Google showed how Gemini 1.5 could ingest the entire 326,914-token Apollo 11 flight transcript and then accurately answer specific questions about it. It also summarised key details from a 684,000-token silent film when prompted.

Google is initially providing developers and enterprises free access to a limited Gemini 1.5 preview with a one million token context window. A 128,000 token general release for the public will come later, along with pricing details.

For now, the one million token capability remains experimental. But if it lives up to its early promise, Gemini 1.5 could set a new standard for AI’s ability to understand complex, real-world text.

Developers interested in testing Gemini 1.5 Pro can sign up in AI Studio. Google says that enterprise customers can reach out to their Vertex AI account team.

(Image Credit: Google)

See also: Amazon trains 980M parameter LLM with ’emergent abilities’

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Available for Vertex AI customers on the allowlist, Imagen 2 enables users to craft and deploy photorealistic images using intuitive tooling and fully-managed infrastructure. 

Developed with Google DeepMind technology, Imagen 2 offers improved image quality and a range of functionalities tailored for specific use cases.

Key features of Imagen 2 include:

• Diverse image generation: Imagen 2 excels in creating high-resolution images from natural language prompts that cater to various user requirements.

• Text rendering in multiple languages: Overcoming common challenges, Imagen 2 supports accurate text rendering in multiple languages.

• Logo generation: Businesses can leverage Imagen 2 to create a variety of creative and realistic logos—with the option to overlay them on products, clothing, business cards, and more.

• Captions and question-answering: Imagen 2’s advanced image understanding capabilities facilitate the creation of descriptive captions and provide detailed answers to questions about image elements.

• Multi-language support: Imagen 2 introduces support for six additional languages in preview, with plans for more in early 2024. This includes the ability to translate between prompt and output.

• Safety measures: Imagen 2 incorporates built-in safety precautions, aligning with Google’s Responsible AI principles. It features safety filters and integrates with a digital watermarking service to ensure responsible use.

Enterprise-ready capabilities

Imagen 2 on Vertex AI is designed to meet enterprise standards, offering reliability and governance akin to its predecessor. With new features such as high-quality image rendering, improved text rendering, logo generation, and safety measures, Imagen 2 aims to provide organisations with a comprehensive tool for creative image generation.

Leading companies like Snap, Shutterstock, and Canva have already embraced Imagen for creative purposes.

Chris Loy, Director of AI Services at Shutterstock, commented: “We exist to empower the world to tell their stories by bridging the gap between idea and execution.

“Variety is critical for the creative process, which is why we continue to integrate the latest and greatest technology into our image generator and editing features—as long as it is built on responsibly sourced data,”

Danny Wu, Head of AI at Canva, added: “We’re continuing to use generative AI to innovate the design process and augment imagination.

“With Imagen, our 170M+ monthly users can benefit from the image quality improvements to uplevel their content creation at scale.”

As Imagen 2 makes waves in the creative industry, organisations are encouraged to explore its potential. Google Cloud anticipates users will harness the new features to elevate their creative endeavours and build on the success achieved with Imagen.

(Photo by G on Unsplash)

See also: Microsoft unveils 2.7B parameter language model Phi-2

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The post Google Cloud announces Imagen 2 text-to-image generator appeared first on AI News.

AI Booster is the result of 18 months of development and is built upon Google’s Vertex AI and integrates with Vodafone’s Neuron platform.

Vertex AI, among other Google technologies, had not been officially announced when Vodafone started development on AI Booster.

Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone, said:

“To maximise business value at pace and scale, our vision was to enable fast creation and horizontal/vertical scaling of use cases in an automated, standardised manner. To do this, 18 months ago we set out to build a next-generation AI/ML platform based on new Google technology, some of which hadn’t even been announced yet.

We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned.

Together, we’ve used the best possible ML Ops tools and created Vodafone’s AI Booster Platform to make data scientists’ lives easier, maximise value, and take co-creation and scaling of use cases globally to another level.”

Google’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform.

Ashish Vijayvargia, Analytics Product Lead at Vodafone, commented:

“As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability, and security.

The result is we’re delivering more value from data science while embedding reliability engineering principles throughout.”

Vodafone highlighted four key features of AI Booster:

1. Automated ML lifecycle compliance activities (drift/skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services.
2. Embedded security by design.
3. Capitalise on Google-native ML tooling using BQML, AutoML, Vertex AI and others.
4. Boost adoption through standardised and embedded ML templates.

The new platform reportedly enables Vodafone’s data scientists and developers to slash the time of going from proof-of-concept to production from five months to just four weeks.

Vodafone and Google have forged a very close relationship in recent years. In May 2021, the two companies extended their relationship to build a global data platform.

“Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company,” said Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone.

“It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science.”

(Image Credit: Vodafone)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Vertex was announced during this year’s virtual I/O developer conference and somewhat breaks from Google’s tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers.

Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms.

Bradley Shimmin, Chief Analyst for AI Platforms, Analytics, and Data Management at Omdia, said:

“Data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order.

It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”

Vertex brings together Google Cloud’s AI solutions into a single environment where models can go from experimentation all the way to production.

Andrew Moore, VP and GM of Cloud AI and Industry Solutions at Google Cloud, said:

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Vertex provides access to Google’s MLOps toolkit which the company uses internally for workloads involving computer vision, conversation, and language.

Other MLOps features supported by Vertex include Vizier, which increases the rate of experimentation; Feature Store to help practitioners serve, share, and reuse ML features; and Experiments to accelerate the deployment of models into production with faster model selection.

Some high-profile companies were given early access to Vertex. Among them is ModiFace, a part of L’Oréal that focuses on the use of AR and AI to revolutionise the beauty industry.

Jeff Houghton, COO at ModiFace, said:

“We provide an immersive and personalized experience for people to purchase with confidence whether it’s a virtual try-on at web check out, or helping to understand what brand product is right for each individual.

With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life.”

ModiFace uses Vertex to train AI models for all of its new services. For example, the company’s skin diagnostic service is trained on thousands of images from L’Oréal’s Research & Innovation arm and is combined with ModiFace’s AI algorithm to create tailor-made skincare routines.

Another firm that is benefiting from Vertex’s capabilities is Essence, a media agency that is part of London-based global advertising and communications giant WPP.

With Vertex AI, Essence’s developers and data analysts are able to regularly update models to keep pace with the rapidly-changing world of human behaviours and channel content.

Those are just two examples of companies whose operations are already being greatly enhanced through the use of Vertex. Now the floodgates have been opened, we’re sure there’ll be many more stories over the coming years and we can’t wait to hear about them.

You can learn how to get started with Vertex AI here.

(Photo by John Baker on Unsplash)

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