Mistral AI stated that Mistral Large outscored most major LLMs except for OpenAI’s recently launched GPT-4 in tests of language understanding. It also performed strongly in maths and coding assessments.

Co-founder and Chief Scientist Guillaume Lample said Mistral Large represents a major advance over earlier Mistral models. The company also launched a chatbot interface named Le Chat to allow users to interact with the system, similar to ChatGPT.  

The proprietary model boasts fluency in English, French, Spanish, German, and Italian, with a vocabulary exceeding 20,000 words. While Mistral’s first model was open-source, Mistral Large’s code remains closed like systems from OpenAI and other firms.  

Mistral AI received nearly $500 million in funding late last year from backers such as Nvidia and Andreessen Horowitz. It also recently partnered with Microsoft to provide access to Mistral Large through Azure cloud services.  

Microsoft’s investment of €15 million into Mistral AI is set to face scrutiny from European Union regulators who are already analysing the tech giant’s ties to OpenAI, maker of market-leading models like GPT-3 and GPT-4. The European Commission said Tuesday it will review Microsoft’s deal with Mistral, which could lead to a formal probe jeopardising the partnership.

Microsoft has focused most of its AI efforts on OpenAI, having invested around $13 billion into the California company. Those links are now also under review in both the EU and UK for potential anti-competitive concerns. 

Pricing for the Mistral Large model starts at $8 per million tokens of input and $24 per million output tokens. The system will leverage Azure’s computing infrastructure for training and deployment needs as Mistral AI and Microsoft partner on AI research as well.

While third-party rankings have yet to fully assess Mistral Large, the firm’s earlier Mistral Medium ranked 6th out of over 60 language models. With the latest release, Mistral AI appears positioned to challenge dominant players in the increasingly crowded AI space.

(Photo by Joshua Golde on Unsplash)

See also: Stability AI previews Stable Diffusion 3 text-to-image model

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Leveraging the power of NVIDIA’s AI foundry service on Microsoft Azure, Amdocs aims to meet the escalating demand for data processing and analysis in the telecoms sector.

The telecoms industry processes hundreds of petabytes of data daily. With the anticipation of global data transactions surpassing 180 zettabytes by 2025, telcos are turning to generative AI to enhance efficiency and productivity.

NVIDIA’s AI foundry service – comprising the NVIDIA AI Foundation Models, NeMo framework, and DGX Cloud AI supercomputing – provides an end-to-end solution for creating and optimising custom generative AI models.

Amdocs will utilise the AI foundry service to develop enterprise-grade LLMs tailored for the telco and media industries, facilitating the deployment of generative AI use cases across various business domains.

This collaboration builds on the existing Amdocs-Microsoft partnership, ensuring the adoption of applications in secure, trusted environments, both on-premises and in the cloud.

Enterprises are increasingly focusing on developing custom models to perform industry-specific tasks. Amdocs serves over 350 of the world’s leading telecom and media companies across 90 countries. This partnership with NVIDIA opens avenues for exploring generative AI use cases, with initial applications focusing on customer care and network operations.

In customer care, the collaboration aims to accelerate the resolution of inquiries by leveraging information from across company data. In network operations, the companies are exploring solutions to address configuration, coverage, or performance issues in real-time.

This move by Amdocs positions the company at the forefront of ushering in a new era for the telecoms industry by harnessing the capabilities of custom generative AI models.

(Photo by Danist Soh on Unsplash)

See also: Wolfram Research: Injecting reliability into generative AI

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The integration of Azure ND H100 v5 virtual machines (VMs) with NVIDIA H100 Tensor Core GPUs and Quantum-2 InfiniBand networking promises seamless scaling of generative AI and high-performance computing applications, all at the click of a button.

This cutting-edge collaboration comes at a pivotal moment when developers and researchers are actively exploring the potential of large language models (LLMs) and accelerated computing to unlock novel consumer and business use cases.

NVIDIA’s H100 GPU achieves supercomputing-class performance through an array of architectural innovations. These include fourth-generation Tensor Cores, a new Transformer Engine for enhanced LLM acceleration, and NVLink technology that propels inter-GPU communication to unprecedented speeds of 900GB/sec.

The integration of the NVIDIA Quantum-2 CX7 InfiniBand – boasting 3,200 Gbps cross-node bandwidth – ensures flawless performance across GPUs, even at massive scales. This capability positions the technology on par with the computational capabilities of the world’s most advanced supercomputers.

The newly introduced ND H100 v5 VMs hold immense potential for training and inferring increasingly intricate LLMs and computer vision models. These neural networks power the most complex and compute-intensive generative AI applications, spanning from question answering and code generation to audio, video, image synthesis, and speech recognition.

A standout feature of the ND H100 v5 VMs is their ability to achieve up to a 2x speedup in LLM inference, notably demonstrated by the BLOOM 175B model when compared to previous generation instances. This performance boost underscores their capacity to optimise AI applications further, fueling innovation across industries.

The synergy between NVIDIA H100 Tensor Core GPUs and Microsoft Azure empowers enterprises with unparalleled AI training and inference capabilities. This partnership also streamlines the development and deployment of production AI, bolstered by the integration of the NVIDIA AI Enterprise software suite and Azure Machine Learning for MLOps.

The combined efforts have led to groundbreaking AI performance, as validated by industry-standard MLPerf benchmarks:

The integration of the NVIDIA Omniverse platform with Azure extends the reach of this collaboration further, providing users with everything they need for industrial digitalisation and AI supercomputing.

(Image Credit: Uwe Hoh from Pixabay)

See also: Gcore partners with UbiOps and Graphcore to empower AI teams

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Currently, Azure OpenAI Service provides access to some of the most powerful AI models in the world—including Codex and DALL-E 2.

A “fine-tuned” version of GPT-3.5 will also be available through Azure OpenAI Service soon.

Azure OpenAI Service was unveiled in November 2021. However, until now the service was not generally available.

In the months since its unveiling, Microsoft and OpenAI have demonstrated more of the models’ capabilities.

In June 2021, Microsoft-owned GitHub launched ‘Copilot’—a controversial AI programmer that can help developers write and improve their code.

Copilot has continued to see regular enhancements. Just this week, GitHub Next unveiled a project called Code Brushes which uses machine learning to update code “like painting with Photoshop”.

In October 2022, Microsoft announced that the impressive text-to-image generative AI model DALL-E 2 would be integrated with the new Designer app and Bing Image Creator.

DALL-E 2, alongside others like Midjourney and Stable Diffusion, also stirred controversy and spurred protests from artists.

Beyond integrating DALL-E 2 in the Bing Image Creator, Microsoft is rumoured to be preparing to use ChatGPT to enhance Bing’s search capability and challenge Google’s dominance.

While the AI models have caused their fair share of concerns and raised important questions around everything from copyright to the wider societal impact, Microsoft and OpenAI have shown how powerful the models are.

Azure OpenAI Service has the potential to enhance our content production in several ways, including summarization and translation, selection of topics, AI tagging, content extraction, and style guide rule application,” said Jason McCartney, Vice President of Engineering at Al Jazeera.

“We are excited to see this service go to general availability so it can help us further contextualize our reporting by conveying the opinion and the other opinion.”

By making Azure OpenAI Service generally available, the duo are enabling more businesses to join others in accessing tools which can improve their operations.

“At Moveworks, we see Azure OpenAI Service as an important component of our machine learning architecture. It enables us to solve several novel use cases, such as identifying gaps in our customer’s internal knowledge bases and automatically drafting new knowledge articles based on those gaps,” commented Vaibhav Nivargi, CTO and Founder at Moveworks.

“Given that so much of the modern enterprise relies on language to get work done, the possibilities are endless—and we look forward to continued collaboration and partnership with Azure OpenAI Service.”

You can find out more about Azure OpenAI Service here.

(Image Credit: Microsoft)

Related: OpenAI opens waitlist for paid version of ChatGPT

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Space is an unforgiving environment and equipment failures can be catastrophic. Gloves are particularly prone to wear and tear as they’re used for just about everything, including repairing equipment and installing new equipment.

Currently, astronauts will send back images of their gloves to Earth to be manually examined by NASA analysts.

“This process gets the job done with the ISS’s low orbit distance of about 250 miles from Earth, but things will be different when NASA once again sends people to the moon, and then to Mars – 140 million miles away from Earth,” explains Tom Keane, Corporate Vice President of Mission Engineering at Microsoft, in a blog post.

Harnessing the power of HPE’s Spaceborne Computer-2, the teams from the three companies are developing an AI system that can quickly detect even small signs of wear and tear on astronauts’ gloves that could end up compromising their safety.

Astronauts’ gloves are built to be robust and have five layers. The outer layer features a rubber coating for grip and acts as the first defense. Next up is the Vectran® layer, a cut-resistant material. The final three layers maintain pressure and protect against the extreme temperatures of space.

However, space does its best to do all it can to get through these defenses and problems can occur when the Vectran® layer is reached. Aside from the usual day-to-day wear that happens even from using gloves here on Earth, astronauts’ gloves have to deal with a variety of additional hazards.

Micrometeorites, for example, create numerous sharp edges on handrails and other components. On arrival to locations like the moon and Mars, the lack of natural erosion means rock particles are more like broken glass than sand.

To create the glove analyser, the project’s team first started with images of new, undamaged gloves and those which featured wear and tear from spacewalk and terrestrial training. NASA engineers went through the images and tagged specific types of wear through Azure Cognitive Services’ Custom Vision.

A cloud-based AI system was trained using the data and the results were comparable to NASA’s own actual damage reports. The tool generates a probability score of damage to areas of each glove.

In space, images would be taken of astronauts’ gloves while they remove their equipment in the airlock. These images would then be analysed locally using HPE’s Spaceborne Computer-2 for signs of damage and, if any is detected, a message will be sent to Earth with areas highlighted for additional human review by NASA engineers.

“What we demonstrated is that we can perform AI and edge processing on the ISS and analyse gloves in real-time,” said Ryan Campbell, senior software engineer at Microsoft Azure Space. 

“Because we’re literally next to the astronaut when we’re processing, we can run our tests faster than the images can be sent to the ground.”

The project serves as a great example of the power of AI combined with edge computing, in areas with as limited connectivity as space.

Going forward, the project could extend to detecting early damage to other areas like docking hatches before they become a serious problem. Microsoft even envisions that a device like HoloLens 2 or a successor could be used to enable astronauts to visually scan for damage in real-time.

“Bringing cloud computing power to the ultimate edge through projects like this allows us to think about and prepare for what we can safely do next – as we expect longer-range human spaceflights in the future and as we collectively begin pushing that edge further out,” concludes Jennifer Ott, Data and AI Specialist at Microsoft. 

(Photo by NASA on Unsplash)

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Researchers from Microsoft explained their machine learning model in a paper on preprint repository arXiv.

The model uses VIsual VOcabulary pre-training (VIVO) which leverages large amounts of paired image-tag data to learn a visual vocabulary.

A second dataset of properly captioned images is then used to help teach the AI how to best describe the pictures.

“Ideally, everyone would include alt text for all images in documents, on the web, in social media – as this enables people who are blind to access the content and participate in the conversation. But, alas, people don’t,” said Saqib Shaikh, a software engineering manager with Microsoft’s AI platform group.

Overall, the researchers expect the AI to deliver twice the performance of Microsoft’s existing captioning system.

In order to benchmark the performance of their new AI, the researchers entered it into the ‘nocaps’ challenge. As of writing, Microsoft’s AI now ranks first on its leaderboard.

“The nocaps challenge is really how are you able to describe those novel objects that you haven’t seen in your training data?” commented Lijuan Wang, a principal research manager in Microsoft’s research lab.

Developers wanting to get started with building apps using Microsoft’s auto-captioning AI can already do so as it’s available in Azure Cognitive Services’ Computer Vision package.

Microsoft’s impressive SeeingAI application – which uses computer vision to describe an individual’s surroundings for people suffering from vision loss – will be updated with features using the new AI.

“Image captioning is one of the core computer vision capabilities that can enable a broad range of services,” said Xuedong Huang, Microsoft CTO of Azure AI Cognitive Services.

“We’re taking this AI breakthrough to Azure as a platform to serve a broader set of customers,” Huang continued. “It is not just a breakthrough on the research; the time it took to turn that breakthrough into production on Azure is also a breakthrough.”

The improved auto-captioning feature is also expected to be available in Outlook, Word, and PowerPoint later this year.

(Photo by K8 on Unsplash)

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The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs (yes, it can probably even run Crysis.)

OpenAI is a non-profit that was founded by one Elon Musk to promote the ethical development of artificial intelligence technologies. Musk, however, departed OpenAI following disagreements over the company’s direction.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open,” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Microsoft invested $1 billion in OpenAI last year and it seems we’re just beginning to see the fruits of that relationship. While most AIs today focus on doing single tasks well, the next wave of research is focusing on performing multiple at once.

“The exciting thing about these models is the breadth of things they’re going to enable,” said Microsoft Chief Technical Officer Kevin Scott.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now.”

So-called Artificial General Intelligence (AGI) is the ultimate goal for AI research; the point when a machine can understand or learn any task just like the human brain.

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.”

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

AGI will, of course, require tremendous amounts of processing power.

Microsoft and OpenAI claim their new supercomputer would rank in the top five but do not give any specific power measurements. To rank in the top five, a supercomputer would currently require more than 23,000 teraflops of performance. The current leader, the IBM Summit, reaches over 148,000 teraflops.

“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said Altman. “And then Microsoft was able to build it.”

Unfortunately, for now at least, the supercomputer is built exclusively for OpenAI.

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If you’ve spent your life binging on sci-fi flicks and shows, today’s AI is far from the likes of HAL and Jarvis. For those kinds of experiences, AGI is needed.

Sam Altman, CEO of OpenAI, said:

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity. Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.

We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

Whereas AI is essentially pre-programmed to perform set tasks, the promise of AGI is to act more like a human brain. Indeed, this is where the excitement of assistants like the aforementioned turn into concerns of a Skynet-like AI as portrayed in Terminator.

Elon Musk founded OpenAI in part due to his concerns that AGI could pose an existential risk if developed carelessly. OpenAI most recently made headlines for developing essentially a fake news generator that, for obvious reasons, it deemed too dangerous to release.

Musk departed OpenAI in February last year over disagreement’s with the company’s development, but the San Francisco-based firm continues to pursue AGI.

Microsoft is clearly on-board with OpenAI’s mission. The $1 billion investment ensures Microsoft becomes OpenAI’s exclusive provider of cloud computing services to OpenAI, and the pair will build new Azure AI supercomputing technologies.

Satya Nadella, CEO of Microsoft, commented:

“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges.

By bringing together OpenAI’s breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratise AI — while always keeping AI safety front and centre — so everyone can benefit.”

Both companies promise developments will adhere to their ‘shared principles on ethics and trust,’ and will ‘create the foundation for advancements in AI to be implemented in a safe, secure, and trustworthy way’.

The goal of Microsoft and OpenAI is a lofty one that may not even be achieved in many of our lifetimes. In a recent survey of some leading experts in the field, the average estimate was there is a 50 percent chance AGI will be developed by 2099.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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