Gemini 1.5 Flash is a new addition to Google’s family of models, designed to be faster and more efficient to serve at scale. While lighter-weight than the 1.5 Pro, it retains the ability for multimodal reasoning across vast amounts of information and features the breakthrough long context window of one million tokens.

“1.5 Flash excels at summarisation, chat applications, image and video captioning, data extraction from long documents and tables, and more,” explained Demis Hassabis, CEO of Google DeepMind. “This is because it’s been trained by 1.5 Pro through a process called ‘distillation,’ where the most essential knowledge and skills from a larger model are transferred to a smaller, more efficient model.”

Meanwhile, Google has significantly improved the capabilities of its Gemini 1.5 Pro model, extending its context window to a groundbreaking two million tokens. Enhancements have been made to its code generation, logical reasoning, multi-turn conversation, and audio and image understanding capabilities.

The company has also integrated Gemini 1.5 Pro into Google products, including the Gemini Advanced and Workspace apps. Additionally, Gemini Nano now understands multimodal inputs, expanding beyond text-only to include images.

Google announced its next generation of open models, Gemma 2, designed for breakthrough performance and efficiency. The Gemma family is also expanding with PaliGemma, the company’s first vision-language model inspired by PaLI-3.

Finally, Google shared progress on Project Astra (advanced seeing and talking responsive agent), its vision for the future of AI assistants. The company has developed prototype agents that can process information faster, understand context better, and respond quickly in conversation.

“We’ve always wanted to build a universal agent that will be useful in everyday life. Project Astra, shows multimodal understanding and real-time conversational capabilities,” explained Google CEO Sundar Pichai.

“With technology like this, it’s easy to envision a future where people could have an expert AI assistant by their side, through a phone or glasses.”

Google says that some of these capabilities will be coming to its products later this year. Developers can find all of the Gemini-related announcements they need here.

See also: GPT-4o delivers human-like AI interaction with text, audio, and vision integration

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Linwei Ding, a 38-year-old Chinese national, was arrested on Wednesday in Newark, California, and faces four counts of federal trade secret theft, each punishable by up to 10 years in prison.

The indictment alleges that Ding, who was hired by Google in 2019 to develop software for the company’s supercomputing data centres, began transferring sensitive trade secrets and confidential information to his personal Google Cloud account in 2021.

“Ding continued periodic uploads until May 2, 2023, by which time Ding allegedly uploaded more than 500 unique files containing confidential information,” said the US Department of Justice in a statement.

Prosecutors claim that after stealing the trade secrets, Ding was offered a chief technology officer position at a startup AI company in China and participated in investor meetings for that firm. Additionally, Ding is alleged to have founded and served as CEO of a China-based startup focused on training AI models using supercomputing chips.

“Today’s charges are the latest illustration of the lengths affiliates of companies based in the People’s Republic of China are willing to go to steal American innovation,” said FBI Director Christopher Wray.

“The theft of innovative technology and trade secrets from American companies can cost jobs and have devastating economic and national security consequences.”

If convicted on all counts, Ding faces a maximum penalty of 40 years in prison and a fine of up to $1 million.

The case underscores the ongoing tensions between the US and China over intellectual property theft and the race to dominate emerging technologies like AI.

(Photo by Towfiqu Barbhuiya on Unsplash)

See also: OpenAI: Musk wanted us to merge with Tesla or take ‘full control’

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The controversy arose as users on social media platforms flooded feeds with examples of Gemini generating pictures depicting racially-diverse Nazis, black medieval English kings, and other improbable scenarios.

Google Gemini Image generation model receives criticism for being 'Woke'.

Gemini generated diverse images for historically specific prompts, sparking debates on accuracy versus inclusivity. pic.twitter.com/YKTt2YY265

— Darosham (@Darosham_) February 22, 2024

Meanwhile, critics also pointed out Gemini’s refusal to depict Caucasians, churches in San Francisco out of respect for indigenous sensitivities, and sensitive historical events like Tiananmen Square in 1989.

In response to the backlash, Jack Krawczyk, the product lead for Google’s Gemini Experiences, acknowledged the issue and pledged to rectify it. Krawczyk took to social media platform X to reassure users:

For now, Google says it is pausing the image generation of people:

While acknowledging the need to address diversity in AI-generated content, some argue that Google’s response has been an overcorrection.

Marc Andreessen, the co-founder of Netscape and a16z, recently created an “outrageously safe” parody AI model called Goody-2 LLM that refuses to answer questions deemed problematic. Andreessen warns of a broader trend towards censorship and bias in commercial AI systems, emphasising the potential consequences of such developments.

Addressing the broader implications, experts highlight the centralisation of AI models under a few major corporations and advocate for the development of open-source AI models to promote diversity and mitigate bias.

Yann LeCun, Meta’s chief AI scientist, has stressed the importance of fostering a diverse ecosystem of AI models akin to the need for a free and diverse press:

Bindu Reddy, CEO of Abacus.AI, has similar concerns about the concentration of power without a healthy ecosystem of open-source models:

As discussions around the ethical and practical implications of AI continue, the need for transparent and inclusive AI development frameworks becomes increasingly apparent.

(Photo by Matt Artz on Unsplash)

See also: Reddit is reportedly selling data for AI training

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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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Unveiled in December, Bard was touted as a competitor to chatbots like ChatGPT but failed to impress in demos. Google staff even called the launch “botched” and slammed CEO Sundar Pichai.

Now rebranded as Gemini, Google says it represents the company’s “most capable family of models” for natural conversations. Two experiences are being launched: Gemini Advanced and a mobile app.

Gemini Advanced grants access to Ultra 1.0, billed by Google as its “largest and most capable state-of-the-art AI model.” In blind evaluations, third-party raters preferred Gemini Advanced with Ultra 1.0 over alternatives in complex tasks like coding, logical reasoning, and creative collaboration.  

The AI can serve as a tutor by creating personalised lessons and quizzes. Developers are aided for trickier coding problems. Gemini Advanced is designed to spark ideas and strategise ways that creators can grow their audiences.

Google plans to expand Gemini Advanced’s capabilities over time with exclusive features like expanded multimodal interactions, interactive coding, deeper data analysis, and more. The service already supports over 150 countries in English and will add more languages soon.  

Access to Gemini Advanced is granted through a new $19.99 (£18.99) per month Google One AI Premium Plan, including a free two-month trial. Subscribers get the latest Google AI advancements plus 2TB of storage from the existing Premium plan.  

Google claims Gemini Advanced underwent extensive trust and safety checks before its launch, including external reviews, to mitigate issues around unsafe content and bias. More details are available in an updated technical report (PDF).

Lastly, Google launched new mobile apps on Android and iOS to access basic Gemini features on-the-go. Users can ask for help with images, tasks, and more while out-and-about. Over time, the plan is for Gemini to become a true personal AI assistant.

The Gemini mobile apps are now available in the US as a dedicated app on Android and in the Google app on iOS, supporting English conversations initially. Next week, the apps expand to Japan and Korea, followed by more countries and languages thereafter.

(Image Credit: Google)

See also: DeepMind framework offers breakthrough in LLMs’ reasoning

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Their new ‘SELF-DISCOVER’ prompting framework – published this week on arXiV and Hugging Face – represents a significant leap beyond existing techniques, potentially revolutionising the performance of leading models such as OpenAI’s GPT-4 and Google’s PaLM 2.

The framework promises substantial enhancements in tackling challenging reasoning tasks. It demonstrates remarkable improvements, boasting up to a 32% performance increase compared to traditional methods like Chain of Thought (CoT). This novel approach revolves around LLMs autonomously uncovering task-intrinsic reasoning structures to navigate complex problems.

At its core, the framework empowers LLMs to self-discover and utilise various atomic reasoning modules – such as critical thinking and step-by-step analysis – to construct explicit reasoning structures.

By mimicking human problem-solving strategies, the framework operates in two stages:

• Stage one involves composing a coherent reasoning structure intrinsic to the task, leveraging a set of atomic reasoning modules and task examples.
• During decoding, LLMs then follow this self-discovered structure to arrive at the final solution.

In extensive testing across various reasoning tasks – including Big-Bench Hard, Thinking for Doing, and Math – the self-discover approach consistently outperformed traditional methods. Notably, it achieved an accuracy of 81%, 85%, and 73% across the three tasks with GPT-4, surpassing chain-of-thought and plan-and-solve techniques.

However, the implications of this research extend far beyond mere performance gains.

By equipping LLMs with enhanced reasoning capabilities, the framework paves the way for tackling more challenging problems and brings AI closer to achieving general intelligence. Transferability studies conducted by the researchers further highlight the universal applicability of the composed reasoning structures, aligning with human reasoning patterns.

As the landscape evolves, breakthroughs like the SELF-DISCOVER prompting framework represent crucial milestones in advancing the capabilities of language models and offering a glimpse into the future of AI.

(Photo by Victor on Unsplash)

See also: The UK is outpacing the US for AI hiring

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The 33-acre site in Waltham Cross, Hertfordshire will bring much-needed compute capacity to businesses, supporting AI innovation and ensuring reliable digital services for Google Cloud customers and general consumers relying on products like Search, Maps, and YouTube.

Ruth Porat, Alphabet’s president and chief financial officer, said the data centre “represents our latest investment in the UK and the wider digital economy.” She added that it builds on previous investments like Saint Giles and Kings Cross offices, a multi-year research deal with Cambridge, and the Grace Hopper subsea cable connecting the UK with the US and Spain.

Porat said the facility will “help meet growing demand for our AI and cloud services and bring crucial compute capacity to businesses across the UK while creating construction and technical jobs for the local community.”

As a pioneer in computing infrastructure, Google runs some of the most efficient data centres in the world and has committed to powering them entirely on carbon-free energy around the clock by 2030.

Last year, Google signed a deal with ENGIE for offshore wind energy from Scotland’s Moray West farm which will provide 100MW of energy and put UK operations on track for 90 percent clean energy by 2025.

The new data centre will recover heat for local homes and businesses while also deploying an air-cooling system.

Porat called the new data centre the “latest in a series of investments that support Brits and the wider economy” and evidence of its “continued commitment to the UK.” Other investments include $1 billion for its Central Saint Giles office space, developing the one million sq ft King’s Cross campus, and an Accessibility Discovery Centre spurring accessible technology.

Beyond offices, data centres, and subsea cables, Google has also provided digital skills training for over a million Brits and expanded its AI-focussed Digital Garage curriculum to capitalise on demand for the technology.  

Google’s announcement follows Microsoft confirming a £2.5 billion data centre in the UK last November after overcoming regulatory hurdles for its £55 billion Activision Blizzard acquisition.

“This is the single largest investment in its 40-year history in the country which will see Microsoft grow its UK AI infrastructure across sites in London and Cardiff and potential expansion into northern England, helping to meet the exploding demand for efficient, scalable, and sustainable AI specific compute power,” explained HM Treasury.

“Data centres process, host, and store the massive amounts of digital information that is critical for developing AI models.”

Microsoft is supplying its UK data centre with more than 20,000 advanced GPUs for machine learning and the development of new AI models.

Chancellor of the Exchequer Jeremy Hunt said: “The UK is the tech hub of Europe with an ecosystem worth more than that of Germany and France combined – and this investment is another vote of confidence in us as a science superpower.”

(Image Credit: Google)

See also: DeepMind AlphaGeometry solves complex geometry problems

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In a new paper in Nature, DeepMind revealed that AlphaGeometry was able to solve 25 out of 30 benchmark geometry problems from past International Mathematical Olympiad (IMO) competitions within the standard time limits. This nearly matches the average score of 26 problems solved by human gold medalists on the same tests.

The AI system combines a neural language model with a rule-bound deduction engine, providing a synergy that enables the system to find solutions to complex geometry theorems.

AlphaGeometry took a revolutionary approach to synthetic data generation by creating one billion random diagrams of geometric objects and deriving relationships between points and lines in each diagram. This process – termed “symbolic deduction and traceback” – resulted in a final training dataset of 100 million unique examples, providing a rich source for training the AI system.

According to DeepMind, AlphaGeometry represents a breakthrough in mathematical reasoning for AI, bringing it closer to the level of human mathematicians. Developing these skills is seen as essential for advancing artificial general intelligence.

Evan Chen, a maths coach and former Olympiad gold medalist, evaluated a sample of AlphaGeometry’s solutions. He said its output was not just correct, but also clean, human-readable proofs using standard geometry techniques—unlike the messy numerical solutions often produced when AI systems brute force maths problems.

While AlphaGeometry only handles the geometry portions of Olympiad tests so far, its skills alone would have been enough to earn a bronze medal on some past exams. DeepMind hopes to continue improving its maths reasoning abilities to the point it could pass the entire multi-subject Olympiad.

Advancing AI’s understanding of mathematics and logic is a key goal for DeepMind and Google. The researchers believe mastering Olympiad problems brings them one step closer towards more generalised artificial intelligence that can automatically discover new knowledge.

(Photo by Dustin Humes on Unsplash)

See also: Stability AI releases Stable Code 3B for enhanced coding assistance

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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

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The collaboration between Google and Anthropic dates back to the founding of Anthropic in 2021. The two companies have closely collaborated, with Anthropic building one of the largest Google Kubernetes Engine (GKE) clusters in the industry.

“Our longstanding partnership with Google is founded on a shared commitment to develop AI responsibly and deploy it in a way that benefits society,” said Dario Amodei, co-founder and CEO of Anthropic.

“We look forward to our continued collaboration as we work to make steerable, reliable and interpretable AI systems available to more businesses around the world.”

Anthropic utilises Google’s AlloyDB, a fully managed PostgreSQL-compatible database, for handling transactional data with high performance and reliability. Additionally, Google’s BigQuery data warehouse is employed to analyse vast datasets, extracting valuable insights for Anthropic’s operations.

As part of the expanded partnership, Anthropic will leverage Google’s latest generation Cloud TPU v5e chips for AI inference. Anthropic will use the chips to efficiently scale its powerful Claude large language model, which ranks only behind GPT-4 in many benchmarks.

The announcement comes on the heels of both companies participating in the inaugural AI Safety Summit (AISS) at Bletchley Park, hosted by the UK government. The summit brought together government officials, technology leaders, and experts to address concerns around frontier AI.

Google and Anthropic are also engaged in collaborative efforts with the Frontier Model Forum and MLCommons, contributing to the development of robust measures for AI safety.

To enhance security for organisations deploying Anthropic’s models on Google Cloud, Anthropic is now utilising Google Cloud’s security services. This includes Chronicle Security Operations, Secure Enterprise Browsing, and Security Command Center, providing visibility, threat detection, and access control.

“Anthropic and Google Cloud share the same values when it comes to developing AI–it needs to be done in both a bold and responsible way,” commented Thomas Kurian, CEO of Google Cloud. 

“This expanded partnership with Anthropic – built on years of working together – will bring AI to more people safely and securely, and provides another example of how the most innovative and fastest growing AI startups are building on Google Cloud.”

Google and Anthropic’s expanded partnership promises to be a critical step in advancing AI safety standards and fostering responsible development.

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See also: Amazon is building a LLM to rival OpenAI and Google

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