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We’ve been here before: AI promised humanlike machines in 1958 – Japan Today

A room-size computer equipped with a new type of circuitry, the Perceptron, was introduced to the world in 1958 in a brief news story buried deep in The New York Times. The story cited the U.S. Navy as saying that the Perceptron would lead to machines that will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

More than six decades later, similar claims are being made about current artificial intelligence. So, whats changed in the intervening years? In some ways, not much.

The field of artificial intelligence has been running through a boom-and-bust cycle since its early days. Now, as the field is in yet another boom, many proponents of the technology seem to have forgotten the failures of the past and the reasons for them. While optimism drives progress, its worth paying attention to the history.

The Perceptron, invented by Frank Rosenblatt, arguably laid the foundations for AI. The electronic analog computer was a learning machine designed to predict whether an image belonged in one of two categories. This revolutionary machine was filled with wires that physically connected different components together. Modern day artificial neural networks that underpin familiar AI like ChatGPT and DALL-E are software versions of the Perceptron, except with substantially more layers, nodes and connections.

Much like modern-day machine learning, if the Perceptron returned the wrong answer, it would alter its connections so that it could make a better prediction of what comes next the next time around. Familiar modern AI systems work in much the same way. Using a prediction-based format, large language models, or LLMs, are able to produce impressive long-form text-based responses and associate images with text to produce new images based on prompts. These systems get better and better as they interact more with users.

AI boom and bust

In the decade or so after Rosenblatt unveiled the Mark I Perceptron, experts like Marvin Minsky claimed that the world would have a machine with the general intelligence of an average human being by the mid- to late-1970s. But despite some success, humanlike intelligence was nowhere to be found.

It quickly became apparent that the AI systems knew nothing about their subject matter. Without the appropriate background and contextual knowledge, its nearly impossible to accurately resolve ambiguities present in everyday language a task humans perform effortlessly. The first AI winter, or period of disillusionment, hit in 1974 following the perceived failure of the Perceptron.

However, by 1980, AI was back in business, and the first official AI boom was in full swing. There were new expert systems, AIs designed to solve problems in specific areas of knowledge, that could identify objects and diagnose diseases from observable data. There were programs that could make complex inferences from simple stories, the first driverless car was ready to hit the road, and robots that could read and play music were playing for live audiences.

But it wasnt long before the same problems stifled excitement once again. In 1987, the second AI winter hit. Expert systems were failing because they couldnt handle novel information.

The 1990s changed the way experts approached problems in AI. Although the eventual thaw of the second winter didnt lead to an official boom, AI underwent substantial changes. Researchers were tackling the problem of knowledge acquisition with data-driven approaches to machine learning that changed how AI acquired knowledge.

This time also marked a return to the neural-network-style perceptron, but this version was far more complex, dynamic and, most importantly, digital. The return to the neural network, along with the invention of the web browser and an increase in computing power, made it easier to collect images, mine for data and distribute datasets for machine learning tasks.

Familiar refrains

Fast forward to today and confidence in AI progress has begun once again to echo promises made nearly 60 years ago. The term artificial general intelligence is used to describe the activities of LLMs like those powering AI chatbots like ChatGPT. Artificial general intelligence, or AGI, describes a machine that has intelligence equal to humans, meaning the machine would be self-aware, able to solve problems, learn, plan for the future and possibly be conscious.

Just as Rosenblatt thought his Perceptron was a foundation for a conscious, humanlike machine, so do some contemporary AI theorists about todays artificial neural networks. In 2023, Microsoft published a paper saying that GPT-4s performance is strikingly close to human-level performance.

But before claiming that LLMs are exhibiting human-level intelligence, it might help to reflect on the cyclical nature of AI progress. Many of the same problems that haunted earlier iterations of AI are still present today. The difference is how those problems manifest.

For example, the knowledge problem persists to this day. ChatGPT continually struggles to respond to idioms, metaphors, rhetorical questions and sarcasm unique forms of language that go beyond grammatical connections and instead require inferring the meaning of the words based on context.

Artificial neural networks can, with impressive accuracy, pick out objects in complex scenes. But give an AI a picture of a school bus lying on its side and it will very confidently say its a snowplow 97% of the time.

Lessons to heed

In fact, it turns out that AI is quite easy to fool in ways that humans would immediately identify. I think its a consideration worth taking seriously in light of how things have gone in the past.

The AI of today looks quite different than AI once did, but the problems of the past remain. As the saying goes: History may not repeat itself, but it often rhymes.

Danielle Williams is a **Postdoctoral Fellow in Philosophy ofScience,Arts&Sciencesat WashingtonUniversityin StLouis**.

The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.

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EIA Enters Agreement Tied to Collection of Cryptocurrency Mining Data – American Public Power Association

The U.S. Energy Information Administration has entered into an agreement that stems from recent litigation related to EIAs plan to collect data tied to the electricity consumption associated with cryptocurrency mining activity.

EIA on Feb. 1 detailed its plans to focus on evaluating the electricity consumption associated with cryptocurrency mining activity. Given the dynamic and rapid growth of cryptocurrency mining activity in the United States, we will be conducting a mandatory survey focused on systematically evaluating the electricity consumption associated with cryptocurrency mining activity, which is required to better inform planning decisions and educate the public, EIA said inan analysis posted on its website.

The Texas Blockchain Council alongside one of its members, Riot Platforms, on Feb. 22initiated legal proceedingsagainst EIA, challenging an alleged unprecedented and illegal data collection demand against thebitcoinmining industry.

A Texas judge on Feb. 23 granted a temporary restraining order in a proceeding involving the Energy Information Administrations recently announced plan to collect data tied to the electricity consumption associated with cryptocurrency mining activity.

Under the March 1 agreement in principle, EIA has agree to destroy any information that it has already received in response to the EIA-862 Emergency Survey. If EIA receives additional information in response to the EIA-862 Emergency Survey, EIA will destroy that data. EIA will sequester and keep confidential any information it has received or will receive in response to the EIA-862 Emergency Survey until it is destroyed.

EIA will also publish in the Federal Register a new notice of a proposed collection of information that will withdraw and replace a February 9 Notice.

EIA will allow for submission of comments for 60 days, beginning on the date of publication of the new Federal Register notice.

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Alibabas cloud computing unit slashes prices by up to 55% on 100 core products – South China Morning Post

Alibaba Group Holdings cloud computing unit has cut prices by up to 55 per cent on more than 100 core products the largest discount offering in the companys history to attract more enterprises and software developers as users in mainland China, as the adoption of artificial intelligence (AI) picks up steam across various industries. This new campaign, which offers an average 20 per cent reduction in prices, took effect on Thursday and includes the units elastic compute service (ECS), object storage service (OSS) and database product categories, Alibaba Cloud said in a statement. Hangzhou-based parent Alibaba owns the South China Morning Post.

The initiative seeks to lower the threshold of cloud services for more enterprises and developers, according to Liu Weiguang, the president of public cloud business at Alibaba Cloud Intelligence.

With the rapidly increasing amount of data in China, businesses will need robust, high-performance and cost-effective computing power to help handle and analyse the data before turning them into actionable intelligence, Liu said. This is where we can help, as we aim to become the most open cloud [platform] and help our customers to turn AI into productivity.

Prices of Alibaba Clouds ECS, which provides users with virtual cloud servers, has been cut up to 36 per cent, while those for OSS a service to store and access any amount of data from anywhere was slashed up to 55 per cent. For database product categories, prices were reduced up to 40 per cent. Both existing and new customers can avail of the discounted prices, according to Alibaba Cloud.

Alibabas cloud unit now serves 80% of Chinese tech companies

Lowering prices, however, could also trigger a price war among Chinas major cloud services providers, as competition intensifies among vendors to support the development and deployment of innovative AI services in the country.

Alibaba seeks growth from AI co-development programme, cloud price cuts

At present, Alibaba Cloud operates 89 availability zones where it operates data centres in 30 regions globally, supporting more than 4 million customers worldwide.

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Cloud Native Computing Foundation Announces Falco Graduation – PR Newswire

The cloud native runtime security tool is used by more than 30 public adopters, including Booz Allen Hamilton, GitLab, Shopify

SAN FRANCISCO, Feb. 29, 2024 /PRNewswire/ --TheCloud Native Computing Foundation (CNCF), which builds sustainable ecosystems for cloud native software, today announced the graduation of Falco, a cloud native security tool designed for Linux systems and the de facto Kubernetes threat detection engine.

Falco was created and open sourced in 2016 by Sysdig and became the first runtime security project accepted into the CNCF Sandbox in 2018 and, subsequently, the Incubator in April 2020. Since then, Falco has added maintainers from Amazon, Apple, IBM, Red Hat, and more. The project has also seen a 400% increase in active contributors since moving to incubation and now has hundreds active code contributors.

The project has over 30 public, self-declared adopters, including organizations like Cisco, Shopify, Skyscanner, and Vinted. Since moving to incubation, it has seen a 526% increase in total downloads, with a 135% increase in average monthly downloads.

"Real time visibility into the security of cloud native deployments is invaluable at scale," Chris Aniszczyk, CTO of CNCF. "Falco is helping to push advancements in the open source cloud native runtime security space with eBPF, and we look forward to seeing the progress in this area as the project continues to grow."

Falco employs custom rules on kernel events to provide real-time alerts and helps users gain visibility into abnormal behavior, potential security threats, and compliance violations, contributing to comprehensive runtime security. In the past few years, maintainers have dedicated time to improving engineering processes and refactoring the Falco code base, including improved test suites and a new Kernet testing framework, increased quality checks, and new features like a new eBPF probe and integration with new first-party data sources.

"The conclusion that led to Falco's development and contribution to CNCF is that runtime security must be widely accessible and seamlessly integrated across cloud native infrastructure you need prevention in the cloud, but threat detection is just as important," said Loris Degioanni, Creator of Falco and CTO and Founder of Sysdig. "The support Falco has received underscores the reality that you can't prevent everything, security teams need defense in depth, even in the cloud. I am grateful for the incredible Falco community and for surpassing this milestone within CNCF, but the Falco community has never seen graduation as the end goal rather, just the beginning of expanding Falco use cases through its plugin system."

To officially graduate from incubating status, the Falco project underwent a due diligence process with the CNCF Technical Oversight Committee (TOC), completed a third-party security audit, and supported the process of allowing CNCF projects to include GPL-licensed Linux kernel modules alongside the eBPF code. Graduation validates Falco's growth, maturity, and future outlook and cements the project's leadership in the runtime security space.

End User Support

"We needed a real-time solution that simultaneously met our application security needs and open source commitments Falco delivered both, providing immediate visibility across environments and prompt detection of and alerting on potential issues," said Aurimas Rudinskis, Security Engineering Manager at Vinted. "Falco offers an open source answer to the question of incident response in the cloud, and we're pleased to see its successful CNCF graduation."

"Congratulations to Falco for achieving CNCF graduated project status," said Ayoub Elaassal, Cybersecurity Director at Qonto. "In a world where ensuring robust security strategies relies on a multi-layered defense approach, Falco's runtime detection plays a pivotal role as an indispensable component within that framework. At Qonto we rely on Falco to get extreme visibility on low-level interactions on the system to, not only harden existing containers but also identify any suspicious or unexpected activity. Falco with its runtime security, is and should be an essential layer of any decent defense in depth strategy."

Learn more about Falco

About Cloud Native Computing Foundation

Cloud native computing empowers organizations to build and run scalable applications with an open source software stack in public, private, and hybrid clouds. The Cloud Native Computing Foundation (CNCF) hosts critical components of the global technology infrastructure, including Kubernetes, Prometheus, and Envoy. CNCF brings together the industry's top developers, end users, and vendors and runs the largest open source developer conferences in the world. Supported by more than 800 members, including the world's largest cloud computing and software companies, as well as over 200 innovative startups, CNCF is part of the nonprofit Linux Foundation. For more information, please visit http://www.cncf.io.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page. Linux is a registered trademark of Linus Torvalds.

Media Contact

Katie Meinders The Linux Foundation [emailprotected]

SOURCE Cloud Native Computing Foundation

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With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel … – Nature.com

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Why cloud evolution needs a cohesive approach to succeed – CIO

Many organisations in India are migrating to the cloud, and there is no shortage of cloud providers. But if you want cloud to revolutionise your business, it wont help to get stuck with a basic cloud configuration that works by default but doesnt keep pace with your evolving goals.

This is what Mobicule Technologies, an independent software vendor (ISV) in fintech, realised as they expanded their client base in Indias financial services industry.

Large banks have tens of thousands of loan customers. Managing these accounts is an operational burden, as it involves constant follow-ups on monthly instalments, account maintenance and timely collections, especially when customers default on payments.

Mobicule has developed a comprehensive, cloud-based platform that automates the end-to-end management of various loan types, including consumer, vehicle, home and business loans. Their clients transfer the full management lifecycle of their loan accounts to this platform, which blends a range of digital functionality with a customer-focused call centre to streamline debt collection and resolution.

While other loan-management software vendors typically charge a fixed monthly fee per loan account, Mobicule only bills their clients once instalments have been recovered. This allows banks to minimise the risks associated with their loan accounts in a flexible, cost-effective way.

Before Mobicule started working with NTT DATA, they had already sourced cloud services from a large hyperscaler and were doing development in the cloud.

However, they lacked a sense of ownership of their cloud environment, and they found themselves having to fit square pegs in round holes while demand was rising for their services. They needed a provider who could tailor a solution to their needs, with an emphasis on cost efficiency because they deliver their software-as-a-service (SaaS) offering in a hypercompetitive market.

In financial services, security and compliance are as important as reliability and responsiveness. Mobicule needed help with their security information and event management (SIEM) approach: combining security information management (collecting, analysing and reporting on log data generated from all their technology infrastructure) and security event management (monitoring, correlating and analysing security events generated by hardware and software, in real time).

This level of reliability and security had to be scalable across multiple clients, each of which needed a guarantee that, despite being part of a cloud-native, multitenant environment, their data and infrastructure would remain private and protected.

As a pioneer in a competitive market, we need to be nimble in order to maintain our early-mover advantage. To achieve this, we needed a cloud partner who could assume the role of a trusted adviser and deliver a cloud landscape that would enable us to create a robust, secure and cost-efficient cloud landscape for our SaaS offerings, says Siddharth Agarwal, Founder and MD of Mobicule.

NTT DATA went the extra mile to help Mobicule, starting with cloud discovery and analysis sprints to define clear objectives. Working with the Mobicule team, we selected our SimpliCloud public-cloud platform as the optimal execution venue for their application landscape.

With SimpliCloud, Mobicule can deploy infrastructure-as-a-service and platform-as-a-service solutions, containers and microservices, and connect to other hybrid or multicloud platforms as needed.

The combination of SimpliCloud (an on-demand enterprise public cloud) and SimplyVPC (an agile and secure hosted private cloud) allows us to offer a seamless hybrid and multicloud solution for organisations navigating the complexities of SaaS delivery.

Mobicule now consumes cloud capacity in an on-demand, pay-as-you-go model, with access to a range of cloud-native microservices, all managed around the clock by NTT DATA. In this way, they have realised savings in the form of a 40% cloud cost optimisation compared with their previous public-cloud setup.

Combined with our portfolio of managed services, which span everything from the application layer to people, tools and processes, this cloud solution is a compelling proposition not only for Mobicule and financial service providers but also for organisations in other industries.

NTT DATAs cloud platform has been instrumental in enabling us to be efficient and agile. Their approach to cloud transformation allows us to focus on our core ISV offerings rather than worry about our cloud landscape, says Agarwal.

We want to help our clients grow because their success is our success. This sets us apart in the cloud space, and we hope to help many more innovative organisations like Mobicule advance their digital transformation.

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Why teams must collaborate in the complex world of cloud security – SC Media

In an era where the cloud reigns supreme, one might assume that by now, we'd already have a straightforward process for ensuring the security of cloud environments. After all, with the vast amount of time and resources invested in cloud technology, wed expect a well-defined process for controlling the security posture of these environments. However, managing cloud security has become increasingly complex, involving multiple teams from various organizations.

The teams responsible for cloud infrastructure security typically include R&D, infrastructure, security, and compliance. Each team brings its unique expertise and perspective, making collaboration essential for effective security management. While R&D focuses on developing innovative cloud applications, infrastructure teams handle the deployment and maintenance of cloud resources. Security teams play a crucial role in assessing and mitigating security risks, while compliance teams ensure that cloud deployments adhere to industry regulations and standards.

This comes to a point where the teams I mentioned have different objectives and key performance indicators (KPIs), use different software and technologies, and speak different languages. The tension and friction between them turn into non-productive communication and are possibly the root cause of the security incident.

To effectively manage cloud security in this complex landscape, teams need to address several important tasks:

Developing secure cloud applications is the cornerstone of cloud security. R&D teams must prioritize security throughout the entire CI/CD lifecycle, incorporating security best practices and robust authentication mechanisms to mitigate potential vulnerabilities. It starts with the code written, but then we need to bring into consideration the data were fetching, how we request and grant access (and to whom), APIs, and third-parties we integrate with.

Risk assessments are essential for identifying and prioritizing security risks across all layers of the cloud. The infrastructure teams must conduct thorough assessments to identify potential vulnerabilities in network configurations, storage solutions, and server instances. The gateways between these different services usually begin with good identity and access management (IAM) configuration, the most commonly used access mechanism today. We must always remember that identity is not only for users, but also for non-human or machine identities. Next, we need to check access to data and the way it gets stored and encrypted. And lastly, how are we connected to the outside world?

Establishing clear security policies and implementing guardrails has become crucial for maintaining a secure cloud environment. Security teams should regularly review and update security policies to align with evolving threats and industry best practices. Automated guardrails can help enforce compliance with security policies, preventing unauthorized access and data breaches. I like comparing this part to the work of a very professional DevOps engineer. When a good process and pipeline gets built, for the most part, it will operate smoothly and the engineer will only have to make tweaks and changes along the way. The same goes for the assessment process. Teams should always do it continuously, not just before an audit. This way, we'll find fixing issues a routine and ongoing process. Place guardrails not only based on the different compliance frameworks, but also based on the organizations unique business, applications, and appetite for risk.

Think of cloud security as an ongoing process that requires continuous monitoring and remediation. Security teams must promptly address security incidents and vulnerabilities as they arise, implementing remediation measures to mitigate risks. Conduct regular audits and assessments to ensure compliance with security standards and regulations. AI technology came to the rescue and today we can save a lot of time by correctly prioritizing the different security risks, based on the impact they create on our organization. Moreover, using the right technology can assist us in quicker remediation cycles. First by building customized remediation, based on our applications and infrastructure, and second, by automating enforcement processes.

If we acknowledge the importance of effective collaboration in driving efficient security processes across the organization, the subsequent step involves identifying a platform to facilitate this collaboration. Recognizing that various teams have different objectives in mind (code security versus IAM), it's essential to note that each product offers a unique set of capabilities. Rather than feeling overwhelmed by the multitude of acronyms, focus on the specific challenges the team aims to address and the goals it wants to achieve. Then, explore opportunities for cross-team collaboration to attain collective objectives while ensuring a secure and compliant environment. This entails implementing least-privileged access, safeguarding data, and configuring systems to enhance speed and efficacy in delivery.

Shira Shamban, co-founder and CEO, Solvo

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Why teams must collaborate in the complex world of cloud security - SC Media

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Juggling 5G networking with cloud and security concerns – SiliconANGLE News

Connectivity is the backbone of todays digital world. Thus, advancements such as 5G networking serve to bolster that backbone and advance the telecom sector with existing enterprise requirements in areas such as speed, bandwidth and latency.

The future of telecom, cloud computing and security is a complex landscape filled with challenges and opportunities, and the transition to 5G represents an unprecedented ground-up overhaul. The implications of doing so are only now becoming apparent.

When you look at 5G and you move it into the cloud,all of a sudden you go blind, saidBruce Kelley (pictured, left), senior vice president and chief technology officer of NetScout Systems Inc. That means youve got microservicestalking to each other inside the cloud, east, west, where,youre not seeing that traffic.Observability is critical because youve got gaps.

Kelley andDarren Anstee (right), chief technology officer for security at NetScout, spoke with theCUBE Research analystsDave VellanteandJohn Furrierat MWC Barcelona, during an exclusive broadcast on theCUBE, SiliconANGLE Medias livestreaming studio. They discussed the integration of 5G, the nuances of cloud-native architectures and the imperative of security, shedding light on critical aspects shaping the industrys trajectory.(* Disclosure below.)

As telecom operatorsprovide enterprise services powered by 5G, observability and security are even more crucial. Enterprises are demanding stringent service-level agreements, so operators must guarantee robust security in addition to high performance.

[Telcos] aregoing to have to be able to see [and] improve the SLAs, Kelley said. At the same time, theyre going to have to offer clean slicesto these banks and enterprises.

Observability, which involves gaining visibility into microservices and network traffic, is now a critical tool in assuring SLAs and identifying potential vulnerabilities. This is especially more pressing as critical industries, such as healthcare and finance, begin to rely on 5G, Kelley added.

The enterprise is going to want guarantees.Theyre not going to just sign up for 5Gand say, Well, I hope it works,' he said.Theyre going to hold them to a certain latency,a certain throughput thats promised. [With]the service theyre signing up for,theyre going to want to guarantee it all the time. It could be life or death, it could be loss of revenue andit could be brand reputation.

Unlike previous generational upgrades that primarily focused on speed improvements, the transition to 5G represents a fundamental restructuring of telecommunications infrastructure. Moving toward cloud-native architectures means abandoning traditional physical networks in favor of cloud-based, encrypted systems. This transition not only introduces new levels of complexity, but also necessitates a diverse skill set encompassing cloud technologies alongside traditional telecommunications expertise, according to Anstee.

One of the key thingsthat weve realized recently is thatthe data set that we build for observabilityto help our customers assure performanceand all of those kinds of things within their networks,within the services that they deliver to their enterprises,that data set can also drivea lot of different security value propositions, Anstee said. We announced a partnership yesterdaywith Palo Alto where were going to feedsome of our data sets into their solutionsso that they can make more use of themto enrich their capabilityand the overall service that the mobile operatorcan offer to the enterprise.

Also important in 5G networking is slicing, which promises to revolutionize how operators deliver services to enterprises. By partitioning the 5G infrastructure into virtual networks, or slices, telcos can cater to diverse enterprise needs with customized offerings. These slices not only guarantee specific performance metrics, but also enable telcos to monetize their expertise and infrastructure, Anstee added.

Heres the complete video interview, part of SiliconANGLEs and theCUBE Researchs coverage of MWC Barcelona:

(* Disclosure: TheCUBE is a paid media partner for the MWC Barcelona event. No sponsors have editorial control over content on theCUBE or SiliconANGLE.)

THANK YOU

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Alibaba halves prices triggering cost war in China cloud computing – Verdict

Chinese cloud computing company Alibaba has cut the prices of its cloud software by up to 55%, striking the first blow in a cost war between Chinese cloud companies.

Alibabas price cuts averaged around a 20% decrease on more than 100 of its services.

Competitor JD.com also reduced its prices less than 24 hours after Alibaba.

In its statement posted on a WeChat account, a popular social media site in China, JD.com said that the entirety of its products would continue to be cheaper than its competitors.

This price comparison activity is targeted at specific cloud service providers, it stated.

In its 2024 thematic intelligence report into cloud computing, research and analysis company GlobalData reported that cloud computing will now be competing within the AI market.

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Each cloud provider, forecasts GlobalData, will be in a race to provide the markets best AI platforms and tools.

The global rise of GenAI has catalysed this competition and as every major cloud company releases its own AI products, cost will be a dominant factor used to compete for buyer attention.

GlobalDatas report stated that cloud companies had long been investing in AI chips to support growing workloads, seeking out better performance than using standard GPUs.

According to GlobalData forecasts, the total cloud computing market will be worth $1.4trn in 2027, achieving a compound annual growth rate of 17% between 2022 and 2027.

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Microsoft hits AI snag amid weak cloud forecast, OpenAI probe – Henry Herald

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