microsoft.com
7 Ways AI Is Forcing a Transformation of Tech Infrastructure
Curated by
cdteliot
5 min read
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As reported by EY Ireland, artificial intelligence is poised to revolutionize the infrastructure sector, requiring a fundamental shift in mindset, skillset, and toolset to unlock its full potential across the asset lifecycle.
AI Infrastructure Revolution
Artificial Intelligence (AI) is undoubtedly remaking how individuals optimize their life processes and businesses build their infrastructures. While AI drives massive change, it's also requiring a technical revolution that is already seeing substantial upgrades to handle its needs
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. From the world of content creation to workforce automation to chatbots that work to handle the needs of everyday humans, AI is set to revolutionize the way we live and work3
. In the following paragraphs, we'll explore how AI is transforming technology infrastructures as we know them:3 sources
1. High-Performance Computing Is Becoming a Must
cnbc.com
AI models need extensive amounts of computing resources to successfully process data and AI algorithms. This includes both machine learning and deep learning resources, and CPU-based infrastructures aren't normally robust enough to handle AI algorithms. To handle all the needs of AI, GPUs (graphic processing units) or newly developed AI chips are utilized. As AI technologies continue to evolve, the consensus is that GPUs have the power to handle their needs best.
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This hasn't stopped brands like Nvidia and Google from developing special AI chips that may become the standard-bearers within the world of AI. They are working around the clock to innovate technologies that make processing flawless while lessening the tech burden that growing organizations must take on.2
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2. Data Storage Needs Are Skyrocketing
solutionsreview.com
AI applications are increasingly requiring more storage and larger infrastructures, which is leading to the creation of "data lakes" and warehouses. Additionally, the storage infrastructures that are being built must be scalable and fast enough to keep up with AI processing. The biggest challenge for all the players in the market, however, isn't just about storage capacity. Their growing technologies must focus on speed, quick access to data, and AI models that can look at data, analyze it, and output in real time.
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This is where cloud storage is a must, putting AWS, Microsoft Azure, and other cloud providers in the position of working day and night to capitalize on the opportunity and better their offerings. This means optimizing their platforms for AI workflows while growing their feature sets to include things like AI-focused data pipelines, machine learning tools directly integrated to their product offerings, and faster data transfer speeds.3
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The message is clear for the cloud computing players: either meet the growing demands of AI or risk being left behind.4 sources
3. Cloud Platforms Must Evolve Quickly
As mentioned above, cloud providers are focusing on their growth and innovation plans. This includes cloud providers offering AI-as-a-service via platforms like SageMaker, Google AI, and others.
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Cloud providers are also developing extremely powerful virtual machines that are optimized specifically for AI and extensive workloads. This helps growing players in the space reduce their costs and make them more efficient.3
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4. Edge Computing Is Changing Everything
lannerinc.com
In recent years, the tech industry has seen the writing on the wall: massive data centers may become obsolete sooner than later. For projects like autonomous vehicles, loT devices, and more, there must be a new paradigm that we can all depend on. This is where edge processing comes into the picture. It allows for data to live locally, leading to faster decision-making for AI projects and far less load on the cloud
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. This allows for data to be delivered and processed quickly, especially for projects that need split-second decisions to be made, and even a slight delay can have major consequences for both the humans using tools and those who have developed them3
. As mentioned above, the example of autonomous vehicles depends on numerous factors to make sure they are efficient and safe. They include several sensors, cameras, and radars that are placed around the car to deliver data to be processed4
. A tiny data lag or delay can cause deadly accidents. Edge computing can help solve these issues, allowing for data to live locally rather than in some distant data farm5
. As more innovations come to market, they too will rely on this technology for efficiency and safety6
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5. Networking Must Shift
The process of moving sizable datasets between data centers means upping the ante where networking is concerned. Now that 5G is increasingly becoming the standard, data can be sent and received faster than ever.
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Plus, network optimization is helping to make networks stronger and more efficient, and AI algorithms are helping to improve traffic flow, find flaws, and more.2
The advancements in network optimization strategies are also helping to optimize bandwidth usage and help automate tasks that will help make data transfers more seamless and boost the efficiency of data-focused environments.3
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6. Automation in IT Operations (AIOps)
ninjaone.com
AI is not only changing the hardware and infrastructure, it's also requiring that IT operations evolve and run. This has led to the development of new technologies for IT ops, including AIOps. AIOps refers to AI working to automate and enhance operations. Now AI can do everything from monitor system health to pinpointing issues that can arise, to helping out an end to putting an end to downtime
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. It also allows teams to streamline the way they work in real time and deal with issues before they become huge headaches3
. The secret to success with AIOps is that it allows teams to identify both trends and patterns within a massive dataset. Next, they can forecast issues such as security vulnerabilities, system bottlenecks, and other inefficiencies that could negatively impact the way AI works4
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. Then, it can solve the issue itself or help team members solve issues more seamlessly. This leads to both increased system availability and overall resilience6
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7. The Face of Security Is Being Reinvented
datasciencedojo.com
As AI progresses, so do new vulnerabilities, threats, and their potential solutions. As AI becomes further integrated into innovative technologies, it becomes more likely there will be increased exploitation issues that need to be closely looked at. Thankfully, AI models can do wonders for detecting dangerous cyber threats and helping to find solutions. It makes the process far faster and far more efficient
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. More specifically, AI models can analyze huge amounts of data quickly, and as mentioned above can identify abnormal patterns and activities that are related to security breaches far faster than humans can3
. Plus, AI can adapt from threats that it has dealt with in the recent past, such as data breaches, phishing attempts, and ransomware and improve the way it defends against these issues4
. In many tests and real-world scenarios alike, AI has been found to neutralize threats before they happen, reducing chances for damage and data loss5
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Closing Thoughts
As AI continues to rapidly evolve, it is not only bringing innovations but opening the doors to a myriad of vulnerabilities and challenges. In its race against time, the world of tech must be diligent in its quest for knowledge and growth.
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Artificial Intelligence is not just a new tech advancement, it's the beginning of an entirely new technological infrastructure that will impact data centers and cloud computing setups around the world. This includes investments in high-performance computing, ramping up data storage infrastructures, and getting comfortable with the concept of edge computing and other similar technologies3
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As we stand on the cusp of the IA era, the tech industry must be proactive. By fostering collaboration, prioritizing security, and committing to continuous learning and adaptation, we can harness the full potential of AI.5
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This will not only revolutionize how we live and work but also pave the way for a future where technology and humanity advance hand in hand.7
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Related
What are the most common vulnerabilities in modern AI systems
How can organizations prioritize AI security in their tech infrastructure
What are the key differences between AI-specific vulnerabilities and traditional cybersecurity risks
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