The collaboration brings together the strengths of both companies, integrating NVIDIA’s latest multi-node systems with next-generation GPUs, CPUs, and AI software, along with AWS technologies such as Nitro System advanced virtualisation, Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability.

Key highlights of the expanded collaboration include:

1. Introduction of NVIDIA GH200 Grace Hopper Superchips on AWS:
   • AWS becomes the first cloud provider to offer NVIDIA GH200 Grace Hopper Superchips with new multi-node NVLink technology.
   • The NVIDIA GH200 NVL32 multi-node platform enables joint customers to scale to thousands of GH200 Superchips, providing supercomputer-class performance.
2. Hosting NVIDIA DGX Cloud on AWS:
   • Collaboration to host NVIDIA DGX Cloud, an AI-training-as-a-service, on AWS, featuring GH200 NVL32 for accelerated training of generative AI and large language models.
3. Project Ceiba supercomputer:
   • Collaboration on Project Ceiba, aiming to design the world’s fastest GPU-powered AI supercomputer with 16,384 NVIDIA GH200 Superchips and processing capability of 65 exaflops.
4. Introduction of new Amazon EC2 instances:
   • AWS introduces three new Amazon EC2 instances, including P5e instances powered by NVIDIA H200 Tensor Core GPUs for large-scale generative AI and HPC workloads.
5. Software innovations:
   • NVIDIA introduces software on AWS, such as NeMo Retriever microservice for chatbots and summarisation tools, and BioNeMo to speed up drug discovery for pharmaceutical companies.

This collaboration signifies a joint commitment to advancing the field of generative AI, offering customers access to cutting-edge technologies and resources.

Internally, Amazon robotics and fulfilment teams already employ NVIDIA’s Omniverse platform to optimise warehouses in virtual environments first before real-world deployment.

The integration of NVIDIA and AWS technologies will accelerate the development, training, and inference of large language models and generative AI applications across various industries.

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See also: Inflection-2 beats Google’s PaLM 2 across common benchmarks

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Inflection-2 was trained on over 5,000 NVIDIA GPUs to reach 1.025 quadrillion floating point operations (FLOPs), putting it in the same league as PaLM 2 Large. However, early benchmarks show Inflection-2 outperforming Google’s model on tests of reasoning ability, factual knowledge, and stylistic prowess.

On a range of common academic AI benchmarks, Inflection-2 achieved higher scores than PaLM 2 on most. This included outscoring the search giant’s flagship on the diverse Multi-task Middle-school Language Understanding (MMLU) tests, as well as TriviaQA, HellaSwag, and the Grade School Math (GSM8k) benchmarks:

The startup’s new model will soon power its personal assistant app Pi to enable more natural conversations and useful features.

Inflection said its transition from NVIDIA A100 to H100 GPUs for inference – combined with optimisation work – will increase serving speed and reduce costs despite Inflection-2 being much larger than its predecessor.  

An Inflection spokesperson said this latest model brings them “a big milestone closer” towards fulfilling the mission of providing AI assistants for all. They added the team is “already looking forward” to training even larger models on their 22,000 GPU supercluster.

Safety is said to be a top priority for the researchers, with Inflection being one of the first signatories to the White House’s July 2023 voluntary AI commitments. The company said its safety team continues working to ensure models are rigorously evaluated and rely on best practices for alignment.

With impressive benchmarks and plans to scale further, Inflection’s latest effort poses a serious challenge to tech giants like Google and Microsoft who have so far dominated the field of large language models. The race is on to deliver the next generation of AI.

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See also: Anthropic upsizes Claude 2.1 to 200K tokens, nearly doubling GPT-4

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The release comes on the heels of an expanded partnership with Google that provides Anthropic access to advanced processing hardware, enabling the substantial expansion of Claude’s context-handling capabilities.

With the ability to process lengthy documents like full codebases or novels, Claude 2.1 is positioned to unlock new potential across applications from contract analysis to literary study. 

The 200K token window represents more than just an incremental improvement—early tests indicate Claude 2.1 can accurately grasp information from prompts over 50 percent longer than GPT-4 before the performance begins to degrade.

Anthropic also touted a 50 percent reduction in hallucination rates for Claude 2.1 over version 2.0. Increased accuracy could put the model in closer competition with GPT-4 in responding precisely to complex factual queries.

Additional new features include an API tool for advanced workflow integration and “system prompts” that allow users to define Claude’s tone, goals, and rules at the outset for more personalised, contextually relevant interactions. For instance, a financial analyst could direct Claude to adopt industry terminology when summarising reports.

However, the full 200K token capacity remains exclusive to paying Claude Pro subscribers for now. Free users will continue to be limited to Claude 2.0’s 100K tokens.

As the AI landscape shifts, Claude 2.1’s enhanced precision and adaptability promise to be a game changer—presenting new options for businesses exploring how to strategically leverage AI capabilities.

With its substantial context expansion and rigorous accuracy improvements, Anthropic’s latest offering signals its determination to compete head-to-head with leading models like GPT-4.

(Image Credit: Anthropic)

See also: Paul O’Sullivan, Salesforce: Transforming work in the GenAI era

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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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According to Reuters, the tech giant is pouring millions into this project to create a model with a staggering two trillion parameters. OpenAI’s GPT-4, for comparison, is estimated to have around one trillion parameters.

This move puts Amazon in direct competition with OpenAI, Meta, Anthropic, Google, and others. The team behind Amazon’s initiative is led by Rohit Prasad, former head of Alexa, who now reports directly to CEO Andy Jassy.

Prasad, as the head scientist of artificial general intelligence (AGI) at Amazon, has unified AI efforts across the company. He brought in researchers from the Alexa AI team and Amazon’s science division to collaborate on training models, aligning Amazon’s resources towards this ambitious goal.

Amazon’s decision to invest in developing homegrown models stems from the belief that having their own LLMs could enhance the attractiveness of their offerings, particularly on Amazon Web Services (AWS).

Enterprises on AWS are constantly seeking top-performing models and Amazon’s move aims to cater to the growing demand for advanced AI technologies.

While Amazon has not provided a specific timeline for the release of the Olympus model, insiders suggest that the company’s focus on training larger AI models underscores its commitment to remaining at the forefront of AI research and development.

Training such massive AI models is a costly endeavour, primarily due to the significant computing power required.

Amazon’s decision to invest heavily in LLMs is part of its broader strategy, as revealed in an earnings call in April. During the call, Amazon executives announced increased investments in LLMs and generative AI while reducing expenditures on retail fulfillment and transportation.

Amazon’s move signals a new chapter in the race for AI supremacy, with major players vying to push the boundaries of the technology.

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See also: OpenAI introduces GPT-4 Turbo, platform enhancements, and reduced pricing

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This momentous agreement – signed in a ceremony at SK Seorin Building, Seoul – marks the culmination of discussions initiated by the Global Telco AI Alliance, a consortium launched in July 2023 by SK Telecom, Deutsche Telekom, E&, and Singtel.

This innovative partnership aims to create a telco-specific LLM that empowers global telcos to effortlessly and rapidly construct generative AI models. With a focus on multilingual capabilities (including German, English, and Korean), this LLM is designed to enhance customer services—particularly in areas like AI-powered contact centres.

Claudia Nemat, Member of the Board of Management for Technology and Innovation at Deutsche Telekom, said:

“AI shows impressive potential to significantly enhance human problem-solving capabilities.

To maximise its use, especially in customer service, we need to adapt existing large language models and train them with our unique data. This will elevate our generative AI tools.”

The collaboration also involves key AI industry players, such as Anthropic (Claude 2) and Meta (Llama2), enabling the co-development of a sophisticated LLM.

Anticipated to debut in the first quarter of 2024, the new telco-focused LLM will offer a deeper understanding of telecommunication service-related areas and customer intentions that surpass the capabilities of general LLMs.

One of the primary objectives of this collaboration is to assist telcos worldwide in developing flexible generative AI services, including AI agents. By streamlining the process of building AI-driven solutions like contact centres, telcos can save time and costs and open new avenues for business growth and innovation.

Ryu Young-sang, CEO of SK Telecom, commented:

“Through our partnership with Deutsche Telekom, we have secured a strong opportunity and momentum to gain global AI leadership and drive new growth.

By combining the strengths and capabilities of the two companies in AI technology, platform, and infrastructure, we expect to empower enterprises in many different industries to deliver new and higher value to their customers.”

This collaboration signifies a proactive response to the escalating demand for AI solutions within the telco industry, promising a paradigm shift in the traditional telecommunications landscape. The announcement follows SK Telecom’s $100 million investment in Anthropic in August.

See also: UMG files landmark lawsuit against AI developer Anthropic

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The v3.1 iteration of the benchmark suite has seen record participation, boasting over 13,500 performance results and delivering up to a 40 percent improvement in performance. 

What sets this achievement apart is the diverse pool of 26 different submitters and over 2,000 power results, demonstrating the broad spectrum of industry players investing in AI innovation.

Among the list of submitters are tech giants like Google, Intel, and NVIDIA, as well as newcomers Connect Tech, Nutanix, Oracle, and TTA, who are participating in the MLPerf Inference benchmark for the first time.

David Kanter, Executive Director of MLCommons, highlighted the significance of this achievement:

“Submitting to MLPerf is not trivial. It’s a significant accomplishment, as this is not a simple point-and-click benchmark. It requires real engineering work and is a testament to our submitters’ commitment to AI, to their customers, and to ML.”

MLPerf Inference is a critical benchmark suite that measures the speed at which AI systems can execute models in various deployment scenarios. These scenarios span from the latest generative AI chatbots to the safety-enhancing features in vehicles, such as automatic lane-keeping and speech-to-text interfaces.

The spotlight of MLPerf Inference v3.1 shines on the introduction of two new benchmarks:

• An LLM utilising the GPT-J reference model to summarise CNN news articles garnered submissions from 15 different participants, showcasing the rapid adoption of generative AI.

• An updated recommender benchmark – refined to align more closely with industry practices – employs the DLRM-DCNv2 reference model and larger datasets, attracting nine submissions. These new benchmarks are designed to push the boundaries of AI and ensure that industry-standard benchmarks remain aligned with the latest trends in AI adoption, serving as a valuable guide for customers, vendors, and researchers alike.

Mitchelle Rasquinha, co-chair of the MLPerf Inference Working Group, commented: “The submissions for MLPerf Inference v3.1 are indicative of a wide range of accelerators being developed to serve ML workloads.

“The current benchmark suite has broad coverage among ML domains, and the most recent addition of GPT-J is a welcome contribution to the generative AI space. The results should be very helpful to users when selecting the best accelerators for their respective domains.”

MLPerf Inference benchmarks primarily focus on datacenter and edge systems. The v3.1 submissions showcase various processors and accelerators across use cases in computer vision, recommender systems, and language processing.

The benchmark suite encompasses both open and closed submissions in the performance, power, and networking categories. Closed submissions employ the same reference model to ensure a level playing field across systems, while participants in the open division are permitted to submit a variety of models.

As AI continues to permeate various aspects of our lives, MLPerf’s benchmarks serve as vital tools for evaluating and shaping the future of AI technology.

Find the detailed results of MLPerf Inference v3.1 here.

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See also: GitLab: Developers view AI as ‘essential’ despite concerns

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The alert comes as concerns rise over the practice of “prompt injection” attacks, where individuals deliberately create input or prompts designed to manipulate the behaviour of language models that underpin chatbots.

Chatbots have become integral in various applications such as online banking and shopping due to their capacity to handle simple requests. Large language models (LLMs) – including those powering OpenAI’s ChatGPT and Google’s AI chatbot Bard – have been trained extensively on datasets that enable them to generate human-like responses to user prompts.

The NCSC has highlighted the escalating risks associated with malicious prompt injection, as chatbots often facilitate the exchange of data with third-party applications and services.

“Organisations building services that use LLMs need to be careful, in the same way they would be if they were using a product or code library that was in beta,” the NCSC explained.

“They might not let that product be involved in making transactions on the customer’s behalf, and hopefully wouldn’t fully trust it. Similar caution should apply to LLMs.”

If users input unfamiliar statements or exploit word combinations to override a model’s original script, the model can execute unintended actions. This could potentially lead to the generation of offensive content, unauthorised access to confidential information, or even data breaches.

Oseloka Obiora, CTO at RiverSafe, said: “The race to embrace AI will have disastrous consequences if businesses fail to implement basic necessary due diligence checks. 

“Chatbots have already been proven to be susceptible to manipulation and hijacking for rogue commands, a fact which could lead to a sharp rise in fraud, illegal transactions, and data breaches.”

Microsoft’s release of a new version of its Bing search engine and conversational bot drew attention to these risks.

A Stanford University student, Kevin Liu, successfully employed prompt injection to expose Bing Chat’s initial prompt. Additionally, security researcher Johann Rehberger discovered that ChatGPT could be manipulated to respond to prompts from unintended sources, opening up possibilities for indirect prompt injection vulnerabilities.

The NCSC advises that while prompt injection attacks can be challenging to detect and mitigate, a holistic system design that considers the risks associated with machine learning components can help prevent the exploitation of vulnerabilities.

A rules-based system is suggested to be implemented alongside the machine learning model to counteract potentially damaging actions. By fortifying the entire system’s security architecture, it becomes possible to thwart malicious prompt injections.

The NCSC emphasises that mitigating cyberattacks stemming from machine learning vulnerabilities necessitates understanding the techniques used by attackers and prioritising security in the design process.

Jake Moore, Global Cybersecurity Advisor at ESET, commented: “When developing applications with security in mind and understanding the methods attackers use to take advantage of the weaknesses in machine learning algorithms, it’s possible to reduce the impact of cyberattacks stemming from AI and machine learning.

“Unfortunately, speed to launch or cost savings can typically overwrite standard and future-proofing security programming, leaving people and their data at risk of unknown attacks. It is vital that people are aware that what they input into chatbots is not always protected.”

As chatbots continue to play an integral role in various online interactions and transactions, the NCSC’s warning serves as a timely reminder of the imperative to guard against evolving cybersecurity threats.

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See also: OpenAI launches ChatGPT Enterprise to accelerate business operations

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The Llama 2 models range in size from 7-70 billion parameters, making them a formidable force in the AI landscape.

According to Meta’s claims, these models “outperform open source chat models on most benchmarks we tested.”

The release of Llama 2 marks a turning point in the LLM (large language model) market and has already caught the attention of industry experts and enthusiasts alike.

The new language models offered by Llama 2 come in two variants – pretrained and fine-tuned:

• The pretrained models are trained on a whopping two trillion tokens and have a context window of 4,096 tokens, enabling them to process vast amounts of content at once.
• The fine-tuned models, designed for chat applications like ChatGPT, have been trained on “over one million human annotations,” further enhancing their language processing capabilities.

While Llama 2’s performance may not yet rival OpenAI’s GPT-4, it shows remarkable promise for an open-source model.

The Llama 2 journey started with its predecessor, LLaMA, which Meta released as open source with a non-commercial license in February.

However, someone leaked LLaMA’s weights to torrent sites, leading to a surge in its usage within the AI community. This laid the foundation for a fast-growing underground LLM development scene.

Open-source AI models like Llama 2 come with their share of advantages and concerns.

On the positive side, they encourage transparency in terms of training data, foster economic competition, promote free speech, and democratise access to AI. However, critics point out potential risks, such as misuse in synthetic biology, spam generation, or disinformation.

To address such concerns, Meta released a statement in support of its open innovation approach, emphasising that responsible and open innovation encourages transparency and trust in AI technologies.

Despite the benefits of open-source models, some critics remain sceptical, especially regarding the lack of transparency in the training data used for LLMs. While Meta claims to have made efforts to remove data containing personal information, the specific sources of training data remain undisclosed, raising concerns about privacy and ethical considerations.

With the combination of open-source development and commercial licensing, Llama 2 promises to bring exciting advancements and opportunities to the AI community while simultaneously navigating the challenges of data privacy and responsible usage.

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See also: Anthropic launches ChatGPT rival Claude 2

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Claude 2 is designed to simulate conversations with a helpful colleague or personal assistant. The latest version has been fine-tuned to deliver an improved user experience, with enhanced conversational abilities, clearer explanations, reduced production of harmful outputs, and extended memory.

In coding proficiency, Claude 2 outperforms its predecessor and achieves a higher score on the Codex HumanEval Python programming test. Its proficiency in solving grade-school math problems, evaluated through GSM8k, has also seen a notable improvement.

“When it comes to AI coding, devs need fast and reliable access to context about their unique codebase and a powerful LLM with a large context window and strong general reasoning capabilities,” says Quinn Slack, CEO & Co-founder of Sourcegraph.

“The slowest and most frustrating parts of the dev workflow are becoming faster and more enjoyable. Thanks to Claude 2, Cody’s helping more devs build more software that pushes the world forward.”

Claude 2 introduces expanded input and output length capabilities, allowing it to process prompts of up to 100,000 tokens. This enhancement enables the model to analyse lengthy documents such as technical guides or entire books, and generate longer compositions as outputs.

“We are really happy to be among the first to offer Claude 2 to our customers, bringing enhanced semantics, up-to-date knowledge training, improved reasoning for complex prompts, and the ability to effortlessly remix existing content with a 3X larger context window,” said Greg Larson, VP of Engineering at Jasper.

“We are proud to help our customers stay ahead of the curve through partnerships like this one with Anthropic.”

Anthropic has focused on minimising the generation of harmful or offensive outputs by Claude 2. While measuring such qualities is challenging, an internal evaluation showed that Claude 2 was twice as effective at providing harmless responses compared to its predecessor, Claude 1.3.

Anthropic acknowledges that while Claude 2 can analyse complex works, it is vital to recognise the limitations of language models. Users should exercise caution and not rely on them as factual references. Instead, Claude 2 should be utilised to process data provided by users who are already knowledgeable about the subject matter and can validate the results.

As users leverage Claude 2’s capabilities, it is crucial to understand its limitations and use it responsibly for tasks that align with its strengths, such as information summarisation and organisation.

Users can explore Claude 2 for free here.

(Image Credit: Anthropic)

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