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With the advent of personal computers and smartphones, the World Wide Web is now literally at our fingertips.

In the last ten years, we’ve seen the proliferation of “smart” technology, from LEDs to smart cars to CCTVs to smart bulbs. Along with this, people have grown accustomed to using automated vehicles and urban areas.

What Is IoT?

The term “Internet of Things” (IoT) refers to a network of “things” that are equipped with electronics, software, and network connectivity so that they may share data with other devices and systems online. These gadgets vary from the commonplace to the highly specialized. IoT has rapidly risen in prominence over the past several years to become one of the most consequential innovations of our time. Now that everything from kitchen appliances to vehicles to thermostats to baby monitors can be connected to the internet via embedded devices, there is no longer any barrier to the flow of information among humans, computers, and the physical world.

By 2024, there will be more than 43 billion devices online, all contributing to the creation, distribution, and utilization of information.

So, here’s a rundown of a few of the most important trends that could influence our approach to these gadgets in the future year.

Read: Alteryx Launches New Alteryx AiDIN Innovations to Fuel Enterprise-wide Adoption of Generative AI

10 Steps Towards AIoT

1. AI and IoT technology enable accurate communication through embedded sensors, allowing robots to quickly adapt to new settings. This streamlines manufacturing and saves money.
2. Wearables, such as fitness trackers, smartwatches, panic buttons, remote monitoring systems, GPS trackers, and music systems, are now prevalent in the AI landscape. These devices are vital to the IoT ecosystem and provide reliable data via smart device IoT apps.
3. A smart city includes smart traffic management, parking, trash management, policing, government, and other issues. The Internet of Things for smart cities transforms how cities run and provide public services like transportation, healthcare, and lighting. Smart cities may be futuristic and have much to cover.
4. IoT AI analyzes constant data streams and finds patterns. Machine learning and AI can also predict operation circumstances and identify parameters that need to be changed for optimal results. Thus, intelligent IoT reveals which procedures are redundant and time-consuming and which can be optimized. Google uses AI and IoT to lower data center cooling costs.
5. IoT and AI enable businesses to quickly process and analyze data to generate new products. Rolls Royce aims to use AI for IoT-enabled aviation engine repair. This method will help identify trends and operational insights.
6. IoT devices include smartphones, high-end computers, and sensors. Low-end sensors in the most typical IoT ecosystem generate massive amounts of data. AI-powered IoT ecosystems review and summarize device data before sharing it. It simplifies massive data sets and connects many IoT devices. This is scalability.
7. Self-driving cars are the greatest AI+IoT system in real life. These autos can predict pedestrian movements and recommend cognitive sensing machine actions. It helps determine the best driving speed, time, and route.
8. AIoT is used in car maintenance and recalls. AIoT can detect part failure and perform service checks by combining data from recalls, warranties, and safety agencies. The manufacturer increases customer trust and loyalty as vehicles become more reliable.
9. Quality healthcare aims to reach all communities. No matter the size or sophistication of healthcare systems, doctors are under more time and task strain and seeing fewer patients. Providing high-quality healthcare while managing administrative burdens is difficult.
10. Retail analytics uses camera and sensor data to track and forecast customer behavior in a physical store, such as checkout times. This helps determine staffing levels and boost cashier productivity, enhancing customer happiness.

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Conclusion

The Internet of Things (IoT) is a popular term this decade that refers to the rapidly expanding systems of interconnected, networked, and communicative physical objects.  AI and IoT enable firms to assess, predict, and automate all types of hazards for quick response. This helps them manage financial loss, personnel safety, and cyber dangers.

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AVPN, the largest network of social investors in Asia, announced the launch of the AI Opportunity Fund: Asia-Pacific, with the support of Google.org and the Asian Development Bank (ADB). The USD 15-million Fund is a three-year programme to equip Asia’s workforce with essential artificial intelligence (AI) knowledge and tools required for the evolving work landscape, thereby ensuring that jobs and roles presented by AI are accessible to more people in the region, specifically those from underserved communities. The Fund will be available through an Open Call to identify and select the non-profit organisations, social enterprises, and workforce associations in Asia-Pacific which meaningfully reach the workers who will be most impacted by the workforce transitions caused by AI. Selected organisations will receive comprehensive support, including guidance, financial support and tailored AI training based on foundational AI courses designed by Google and its external partners.

“AI presents tremendous opportunities for the Asia-Pacific region, but it’s crucial to equip people with the skills needed to thrive. This new AI Opportunity Fund in Asia Pacific will help to empower underserved communities and ensure everyone benefits from the transformative power of AI,” said Scott Beaumont, President of Google Asia-Pacific.

Read: 10 AI ML In Data Storage Trends To Look Out For In 2024

Why Is It Important?

Employers in Asia-Pacific are confident in AI’s potential to boost productivity. Over the next five years, 93% expect to use generative AI tools, with some even offering salary bumps of up to 44% for workers with AI skills^[1]. While AI is perceived positively by employees, 16% of those surveyed by PwC believed AI could replace their jobs^[2]. The work uncertainty compounds for those already disadvantaged, where a third of the population does not use the internet and where high job informality persists^[3].

Must Read: What is Experience Management (XM)?

Naina Subberwal Batra, CEO of AVPN, said “Building an AI-ready workforce is an essential social mandate for businesses and governments, and one that can unlock Asia’s full workforce potential, considering this region will have 165 million working-age people by 2030. There is urgency for such an enablement initiative, as the new world work realities unfold, and we start to see the socioeconomic impact of AI work transition on workers who have limited support to adapt and catch up. AVPN is optimistic about leveraging the knowledge and reach of impact organisations that are already actively working on the ground to bridge the gaps for an AI Workplace Just Transition, and to ensure that the benefits of AI work opportunities reach more of our workers.”

Week’s Top Read Insight:10 AI ML In Supply Chain Management Trends To Look Out For In 2024

“Asia’s evolving work landscape, shaped by the increasing influence of AI, highlights the pressing need to bridge critical skills and knowledge gaps in the underserved communities. The AI Opportunity Fund: Asia-Pacific will support organisations to upskill and reskill workers, ensuring they can access and benefit from the evolving opportunities in the age of AI,” said Jason Rush, Principal Regional Cooperation Specialist, ADB.

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What is The News About?

Envision this: you’ve set aside some time to relax and have decided to whip up a handmade pizza. You can hardly wait to dig into your pie once you put it in the oven after assembling it. Unfortunately, the cheese slips off as soon as you’re about to bite into your greasy masterpiece. Feeling frustrated, you decide to search Google for an answer.

“Use some glue,” is the response that Google returns. Combine the sauce with approximately 1/8 cup of Elmer’s glue. Any glue that isn’t harmful will do.

Read: 10 AI ML In Data Storage Trends To Look Out For In 2024

Yeah, that’s not a good idea. But at the time of this writing, that is the recommendation from Google’s new AI Overviews tool. Although not all queries trigger it, it does search the web and return an AI-generated response. Someone named “fucksmith” commented on a Reddit topic that’s almost a decade old—and they’re joking—because that’s where the pizza glue query got its response.

Why Is It Important?

The new feature that Google released to the public earlier this month had numerous errors, like this one. It goes on to say that a dog has played in the NBA, NFL, and NHL, that Batman is a police officer, and that previous US President James Madison graduated from the University of Wisconsin not once, but twenty-one times.

The errors were caused by “generally very uncommon queries, and aren’t representative of most people’s experiences,” according to Google spokesperson Meghann Farnsworth. Using these “isolated examples” to further improve the product, she claimed, the corporation has taken action against policy violators.

Businesses in the AI industry are like parents dealing with a misbehaving child: they quickly distance themselves from responsibility for their systems. They say they have no say because they can’t foresee what the AI will come up with.

However, that poses an issue for the users. Earlier this year, Google declared AI to be search’s guiding light. What good is it if the search turns out to be even less intelligent than before? In the quest to integrate AI into everything, our search results are currently tainted by Reddit posts from a decade ago. According to many idealists, these problems are just the beginning, who think we are about to experience something amazing. I hope they’re accurate. The internet makes it inevitable that we will soon see someone slathering pizza with adhesive.

Must Read: What is Experience Management (XM)?

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Introduction

Together, the Google for Startups Cloud Program and the NVIDIA Inception program will increase startups’ access to cloud credits, go-to-market support, and technical expertise, allowing them to speed up the delivery of value to customers. This announcement was made today at Google Cloud Next ’24 in Las Vegas. A new partnership between NVIDIA and Google Cloud will aid startups globally in developing generative AI apps and services more quickly.

Joining NVIDIA Inception is a great way for members of the Google for Startups Cloud Program to get access to a wealth of resources, including technical knowledge, training credits from the NVIDIA Deep Learning Institute, and NVIDIA products and tools. Participation in NVIDIA Inception Capital Connect, a network that introduces entrepreneurs to venture capital firms interested in the field, is also available to eligible participants of the Google for Entrepreneurs Cloud Program. A new partnership between NVIDIA and Google Cloud will aid startups globally in developing generative AI apps and services more quickly.

Read: AITHORITY Weekly Roundup – AI News That Went Viral This Week

Together, the Google for Startups Cloud Program and the NVIDIA Inception program will increase startups’ access to cloud credits, go-to-market support, and technical expertise, allowing them to speed up the delivery of value to customers. This announcement was made today at Google Cloud Next ’24 in Las Vegas. Google Cloud credits—up to $350,000 for AI-focused firms—will be available to qualified members of NVIDIA Inception, a worldwide program that supports over 18,000 startups. This will allow them to leverage Google Cloud infrastructure more quickly.

Why Does This News Matter?

Joining NVIDIA Inception is a great way for members of the Google for Startups Cloud Program to get access to a wealth of resources, including technical knowledge, training credits from the NVIDIA Deep Learning Institute, and NVIDIA products and tools. Participation in NVIDIA Inception Capital Connect, a network that introduces entrepreneurs to venture capital firms interested in the field, is also available to eligible participants of the Google for Entrepreneurs Cloud Program. Co-marketing, product acceleration, and expedited onboarding to Google Cloud Marketplace are additional benefits for high-growth emerging software producers in both programs.

The two companies have previously announced several initiatives meant to lower the barrier to entry for businesses of all kinds when it comes to creating generative AI applications, and this partnership is the most recent in that vein. The hefty price tags of artificial intelligence initiatives are a major hurdle for new businesses. Google DeepMind introduced a suite of cutting-edge open models called Gemma in February. Optimizements across all NVIDIA AI platforms for Gemma were recently announced by NVIDIA and Google, assisting in lowering client expenses and accelerating innovative work for domain-specific use cases.

Companies’ teams collaborated closely to improve the speed of Gemma, which is based on the same research and technology as Google DeepMind’s most powerful model to date, Gemini when executed on NVIDIA GPUs using NVIDIA TensorRT-LLM, an open-source library for optimizing large language model inference. A simplified way to build AI-powered applications and release optimized AI models into production is provided by the combination of NVIDIA NIM microservices, which are part of the NVIDIA AI Enterprise software platform, and Google Kubernetes Engine (GKE). Utilizing inference engines such as TensorRT-LLM and NVIDIA Triton Inference Server, NIM expedites the implementation of generative AI in organizations by providing AI inferencing that is both smooth and scalable, and it supports a diverse set of top-tier models.

Another convenient approach for users to get NVIDIA NeMo and other AI development frameworks is through Google Cloud Marketplace. Nemo is part of NVIDIA AI Enterprise. Along with the announcement that A3 Mega will be available to the general public next month, Google Cloud is further expanding access to generative AI computing that is accelerated by NVIDIA. These instances are a new addition to its A3 family of virtual machines, which are driven by NVIDIA H100 Tensor Core graphics processing units. A3 VMs’ GPU-to-GPU network capacity will be doubled by the new instances.

Support for confidential computing is now available in Google Cloud’s new Confidential VMs on A3. This will allow customers to secure their sensitive data, apps, and AI workloads while they train and infer, all without having to modify any code to take advantage of H100 GPU acceleration. Inside Preview this year, you’ll find these Confidential VMs powered by GPUs.

Read: 10 AI News that Broke the Internet Last Week: Top Headlines

Benefits

1. Enhanced Cloud Access: Startups benefit from increased cloud credits and technical expertise, accelerating value delivery to customers.
2. Accelerated AI Development: Global startups can rapidly develop generative AI apps and services with NVIDIA and Google Cloud partnerships.
3. Resource Accessibility: Members of both programs gain access to training credits, technical knowledge, and NVIDIA products/tools for AI development.
4. Venture Capital Opportunities: Eligible participants can connect with venture capital firms through NVIDIA Inception Capital Connect.
5. Co-Marketing and Product Acceleration: High-growth software producers receive support for marketing, product acceleration, and streamlined onboarding to Google Cloud Marketplace.
6. Lower Barrier to AI Entry: Initiatives like Gemma models and simplified AI deployment reduce costs and accelerate innovation for new businesses.

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Optimizations for Gemma on AI Platforms

Gemma, Google’s new lightweight 2 billion- and 7 billion-parameter large language models, can be run anywhere, reducing costs and speeding up innovative work for domain-specific use cases. NVIDIA and Google launched optimizations across all NVIDIA AI platforms for Gemma.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

Using NVIDIA TensorRT-LLM, an open-source library for optimizing large language model inference, coupled with NVIDIA GPUs in the data center, the cloud, and locally on workstations with NVIDIA RTX GPUs or PCs with GeForce RTX GPUs, the companies’ teams worked closely to speed up Gemma’s performance. Gemma is built from the same research and technology as the Gemini models. Because of this, developers may aim their products at the more than 100 million high-performance AI PCs around the world that have NVIDIA RTX GPUs installed.

Read: 10 AI In Energy Management Trends To Look Out For In 2024

NVIDIA and Google Supercharge AI Platforms for Gemma

On top of that, developers can use Gemma on NVIDIA GPUs in the cloud, such as the A3 instances on Google Cloud that are built on the H100 Tensor Core GPU and the soon-to-be-deployed H200 Tensor Core GPUs from NVIDIA, which come with 141GB of HBM3e memory and 4.8 terabytes per second. To further enhance Gemma and implement the optimized model in their production applications, enterprise developers can leverage NVIDIA’s extensive ecosystem of technologies, which includes NVIDIA AI Enterprise with the NeMo framework and TensorRT-LLM.

Read: Intel’s Automotive Innovation At CES 2024

Find out how TensorRT-LLM is boosting Gemma’s inference and other details for developers. All of the model versions, including the FP8-quantized one and the many Gemma checkpoints, were optimized with TensorRT-LLM.

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Google’s Lightweight Open Source Llama Challenge

Building on the work of the Gemini models, Google has introduced the Gemma family of open models. With two sizes to choose from, the Gemma 2B and 7B open models come with pre-trained and instruction-tuned options, respectively.
Across all of the benchmarks, Gemma outperforms Meta’s LLM, Llama 2. For example, Gemma’s 7 billion parameter model outperforms Lama 2 in reasoning, math, and other categories, boasting a general accuracy of 64.3%.

With the free tier for Colab notebooks and free access in Kaggle, users can begin working with Gemma now. Furthermore, $300 in credits are available to first-time users of Google Cloud. Google Cloud credits, which researchers can request for in amounts up to $500,000, can also be used to speed up studies.
Two variants of Gemma will be available: Gemma 2B with 2 billion parameters and Gemma 7B with 7 billion parameters. There are pre-trained and instruction-tuned variations for each size that are released. In addition, Gemma now comes with a new Responsible Generative AI Toolkit that gives you all the tools you need to make AI apps that are safer to use.

Read: 10 AI In Energy Management Trends To Look Out For In 2024

Among the other features are:

Inference and supervised fine-tuning (SFT) toolchains for native Keras 3.0 in all major frameworks: JAX, PyTorch, and TensorFlow.
Gemma’s integration with popular tools like Hugging Face, MaxText, Nvidia NeMo, and TensorRT-LLM, as well as its ready-to-use Colab and Kaggle notebooks, making it easy to get started.
With simple deployment on Vertex AI and Google Kubernetes Engine (GKE), you can execute pre-trained and instruction-tuned Gemma models on your laptop, desktop, or Google Cloud.
The best performance in the market is guaranteed by optimizing across many AI hardware platforms, such as Google Cloud TPUs and Nvidia GPUs.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

All organizations, regardless of size, are allowed responsible commercial usage and distribution per the terms of use.
Several benchmarks, such as MMLU, HellaSwag, and HumanEval, show that Gemma performs better than Llama 2.Gemma is natively Keras 3.0 compliant, thus it can be used with TensorFlow, PyTorch, and JAX, which will help it gain widespread use. In this edition, you may get pre-built notebooks for Kaggle and Colab, as well as integration with widely used tools like Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM.

Read: Intel’s Automotive Innovation At CES 2024

When optimized for NVIDIA GPUs and Google Cloud TPUs, Gemma models achieve industry-leading performance. However, they can run on a variety of platforms, including workstations, laptops, and Google Cloud. Following Google’s recent introduction of Gemini 1.5, which boasts the largest ever seen in NLP models—a 1 million token context window—this advancement follows suit. The context windows of GPT-4 Turbo and Claude 2.1 are 128K and 200K, respectively.

The post Introducing Gemma: Google’s Lightweight Open Source Llama Challenge appeared first on AiThority.

The Delicate Balance Between Innovation and Responsibility

Google committed yet another embarrassing AI faux pas: an algorithm for creating photographs that ludicrously increased their diversity without considering their historical context. Since then, Google has apologized—or nearly did. Despite how obvious the core issue is, Google c***** the model is “becoming” too sensitive. No, the model didn’t magically materialize. Gemini, the company’s conversational AI platform, will ask for a version of the Imagen 2 model whenever you ask it to take photos.

Until recently, though, people didn’t realize that it produced ridiculous results when asked to visualize particular persons or events from history. Even though it is now known that many of the Founding Fathers owned slaves, they were depicted as a diverse group that included people of color. This embarrassing and easily replicated problem was quickly parodied by online critics. Commentators used it as evidence that the already liberal IT industry was being infected even more by the woke mind virus, and it was also dragged into the continuing issue about diversity, equity, and inclusion, which is currently having a negative impact on its reputation locally. But this problem was generated by a completely reasonable solution for systematic bias in training data, as Google points out in its pretty sad little apology-adjacent essay today, and as anyone knowledgeable with the technology might tell you.

Read OpenAI Open-Source ASR Model Launched- Whisper 3

The Generative Model

For example, suppose you’re planning to use Gemini to create ten photos depicting “a person walking a dog in a park” for a promotional campaign. The dealer gets to choose the type of person, dog, or park to use; the generative model only gives the dealer what it knows is best. In addition, that is usually due to biases in the training data rather than actual findings. Which kinds of people, dogs, and parks stand out most in the thousands of relevant photos that the model has seen? When you don’t tell the model to display a specific race, it will likely default to white people because of the disproportionate representation of white people in many picture collections (stock photos, rights-free photography, etc.).

Read Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

Although Google acknowledges that “because our users come from all over the world, we want it to work well for everyone,” the issue is actually related to the training data. When requesting a photo of a football player or someone walking a dog, it can be wise to provide a diverse range of subjects. Images of people that are uniformly one race (or gender) are probably not something you’re looking for. Photographing a white guy in a suburban park with a golden retriever is quite OK. But what if, when you summon them, every one of the ten is a white man strolling a golden retriever in a suburban park? Do you really call Morocco home? It seems like every corner has a distinct character, from the people to the pets to the parks. Obviously, you would prefer that not to occur. When a feature is not specified, the model should prioritize diversity over homogeneity, even though its training data may be biased. This is a problem that every form of generative media has. Furthermore, a simple solution does not exist. In common, delicate, or both scenarios, however, companies like Google, OpenAI, Anthropic, etc. discreetly add more model instructions.

LLM ecosystem

This kind of implicit instruction is quite common, and I can’t stress it enough. The whole LLM ecosystem is built upon implicit instructions, or system prompts as they are commonly known. Guidelines such as “don’t swear,” “be concise,” and others are provided to the model before each talk. The model has been taught to avoid uttering racist jokes, much like the rest of us, so even though it has eaten millions of jokes, it will not deliver one if you ask it for one. Despite the need for greater openness, this is infrastructure and not a clandestine mission. Problematically, Google’s model did not provide any implicit direction for cases where the past played a pivotal role. So, while “the person is of a random gender and ethnicity” or whatever else they insert helps prompts like “a person walking a dog in a park,” substituting it with “the U.S. Founding Fathers signing the Constitution” obviously does not.

Read the Latest blog from us: AI And Cloud- The Perfect Match

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Google has come up with an interesting blog on digital security. The world is fixating on AI’s potential, and both governments and businesses are trying to figure out how to regulate it so that it’s safe and secure. Many see AI as a turning moment in the fight for digital security. Their plight is shared. At this weekend’s Munich Security Conference, attendees will hear about artificial intelligence’s potential to improve security, an issue that more than 40% of people rank as AI’s top application.

Read Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

At this critical juncture, AI stands before lawmakers, security experts, and members of civil society with an opportunity to shift the cybersecurity power dynamic away from cybercriminals and toward cyberdefenders. At a time when bad actors are playing around with AI, decisive and prompt action is needed to influence the future of this technology. The fact that cybercriminals just require a single effective, new threat to circumvent the most robust defenses has been a major problem in the field for many years and continues to be so today. Meanwhile, there is little tolerance for error as defenders must constantly deploy top-tier defenses across ever-evolving digital landscapes. We call this situation the “Defender’s Dilemma,” because up until now, no one has found a foolproof solution.

We are certain that AI has the potential to change this dynamic because of their expertise with large-scale AI deployments. With the use of AI, defenders and security experts may speed up their processes for identifying threats, analyzing malware, finding vulnerabilities, correcting vulnerabilities, and responding to incidents. If they want to make a difference online, they should open up new AI developments to government agencies and companies of all sizes and in all sectors. Our Vertex AI platform and other extensive generative AI capabilities will be supported by more than five billion euros invested in data centers across Europe between 2019 and the end of 2024. This investment will help ensure the continued availability of a wide variety of digital services.

Read the Latest blog from us: AI And Cloud- The Perfect Match

Today’s AI governance choices have the potential to change the cyber landscape in ways no one has anticipated. To prevent a future where thugs can develop while protectors can’t, their communities need a moderate approach to AI regulation. In order for enterprises to harness the full potential of AI while preventing its misuse by competitors, they require strategic investments, collaborations between businesses and governments, and efficient regulatory strategies. To back this up, they’re announcing $2 million in research grants and strategic partnerships to bolster AI-based cybersecurity research initiatives. These will help with things like developing larger language models with better resilience to threats, better understanding of how AI can aid with cyber offense and defense countermeasures, and improving code verification.

Researchers at Stanford, Carnegie Mellon, and the University of Chicago are receiving the funds. Gmail, Drive, and secure Browsing are just a few of the products that use Magika to help keep users secure online. The VirusTotal team also uses it to make the internet a better place. With a 30% improvement in overall accuracy and a 95% improvement in precision on notoriously difficult-to-identify but potentially harmful information like VBA, JavaScript, and Powershell, Magika surpasses previous file recognition methods.

Read OpenAI Open-Source ASR Model Launched- Whisper 3

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This is our AI Daily Roundup. We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities in artificial intelligence (AI), Machine Learning, Robotic Process Automation, Fintech, and human-system interactions.

We cover the role of AI Daily Roundup and its application in various industries and daily lives.

The Future of AI: A 2024 Vision for Customer Service and Beyond

With the rapid advancement of both open and closed-source AI models, unparalleled investments, and immediate adoption by major tech companies, AI in all its forms is unavoidable from both an operational and strategic perspective. But the dizzying speed of development has left business leaders with glimpses of amazing benefits, shrouded by a thick fog of questions and uncertainty.

Google and Gemini: Conversations for Eternity

You can disable future chats with Gemini from being stored to a Google Account for review by going to the My Activity dashboard and turning off Gemini Apps Activity (which is enabled by default). This will ensure that the three-year window does not apply. Meanwhile, the Gemini Apps Activity screen allows you to eliminate individual prompts and discussions.

Meet ChatGPU: Inference.ai Launches Generative AI-Powered Bot to Eliminate Guesswork in Buying GPU

Inference.ai, a leading provider of GPU’s (Graphics Processing Unit) for the AI revolution, announces the launch of ChatGPU, a new tool to help bring clarity around purchasing GPUs for AI training and inferencing.

Nasuni Launches Nasuni IQ to Unlock Data Silos for AI Services

Nasuni, a leading hybrid cloud storage solution, announced Nasuni IQ: data intelligence capabilities to help enterprises manage, assess, and prepare their unstructured data environment for artificial intelligence (AI). With Nasuni IQ, businesses can quickly monitor usage patterns, make proactive data management decisions, and better enable the delivery of intelligent insights.

Lightning AI Signs Strategic Collaboration Agreement with AWS

Lightning AI, the creator of PyTorch Lightning and Lightning Studios, announced it has signed a Strategic Collaboration Agreement (SCA) with Amazon Web Services, Inc. (AWS). The SCA allows Lightning AI to leverage AWS compute services to power generative artificial intelligence (AI) services and to provide first-class support for Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, powered by AWS Trainium accelerators, directly within the platform.

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