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The Future of AI: AI Everywhere



The Future of AI: AI Everywhere

Artificial Intelligence (AI) has become ubiquitous by revolutionizing numerous industries.

AI everywhere is an era that we are rapidly entering whereby the end users are increasingly accustomed to mass personalisation at scale otherwise they will switch to the offerings provided by business rivals who may be using AI to create customised services and user experiences.

The launch of the 5th Gen Intel Xeon Scalable Processor on the 14th Dec 2023 has been announced during an era of rapid advancement in the AI sector due in particular to business and public interest in models such as Generative AI have amassed many users in both the public end user level and also the business community. #IntelAmbassaador


Source: UBS / Yahoo Finance

The chart above shows that ChatGPT from Open AI has taken the world by storm, amassing 100m users in a mere 2 months. Threads from Meta (Instagram) attained 100m users even faster, however, this chart illustrates how rapidly Generative AI is being adopted by users.


Taking a step back and reminding ourselves of the journey and some of the key types of AI are set out below:


Source: Imtiaz Adam

The Rise of Generative AI

Much of the current excitement in AI is being driven by Generative AI, in particular those models applying Transformers with Self-Attention (often combined with Deep Reinforcement Learning). This has allowed the end user and businesses to create content and also for businesses to develop better state-of-the-art virtual agents including chatbots. However, such models are also computationally expensive resulting in high energy consumption and hence a meaningful carbon footprint too.


If we are able to find ways to efficiently scale Generative AI then there are tangible economic benefits. For example, McKinsey estimate that applying Generative AI towards customer care related functions may result in increased productivity ranging from 30% to 45% of existing function costs and in relation to R&D enhance productivity in a range of 10% to 15% of overall costs. Furthermore, the same McKinsey report also estimates that Generative AI may result in higher productivity of the marketing function with a value between 5% and 15% percent of total marketing spending.

Generative AI can assist companies with sentiment analysis, document analysis and summarization and text to image creation. Examples of how AI may transform different sectors of the economy is set out in the following section below.


  • Healthcare: medical imaging, remote monitoring, natural language to analyse electric health records (EHRs), de novo drug discovery and the delivery of personalised medicine;

  • Education: personal tutors to provide customised educational support to meet the needs of the individual student;

  • Marketing: Generative AI for personalised content creation, tailored content targeted towards those more likely to have an interest in the products or services and personalised offers and recommendations;

  • Transportation: navigation for autonomous vehicles, vehicle health checks and monitoring;

  • Construction: Generative AI for design and digital twins;

  • Security: Intruder detection, predictive analytics, crowd control warnings;

  • Cyber Security: Malware threat detection and protection;

  • Manufacturing: Predictive analytics including for unplanned outage detection, automated defective parts analysis;

  • Financial Services & Investment: Fintech solutions for automated credit analysis, equity research, ESG classification, portfolio construction, risk management, factor investing, insurance automated claims management, underwriting risk assessment and pricing;

  • Customer Relationship Management (CRM) and customer experience (CX): chatbots for customer engagement

  • Energy: Drones with computer vision to check for defects in solar panels and wind turbine blades, weather forecasting, renewable energy production forecasting, energy demand forecasting, battery storage optimisation, smart grids

  • Smart Cities: urban traffic planning, smart and intelligent buildings to optimise energy consumption;

  • Retail: personalised recommendations, inventory management, product demand forecasting, supply chain optimisation;

  • Accounting: Fine-tuned LLMs may read and analyse specific documents and spreadsheets, and assist with invoicing documents.

  • Legal sector: natural language for research assistance, case management, invoice management, contract drafting.


A recap of how AI is Transforming the Sectors of the Economy and see AI Everywhere for how Intel 5th Gen Xeon Scalable Processors are advancing this further.


Intel 5th Gen Xeon Scalable Processors Enable Efficient Scaling of AI

A substantial amount of latency would ruin the customer experience (CX) and high computational resources may simply result in Generative AI models proving too costly to adopt at scale. Latency is the time taken for a client device (for example a mobile phone, tablet, laptop or any other internet connected device facing the user) and the time that it takes for a signal (typically containing information) to be sent back from a server device (often a remote cloud-based server where the data and analytics reside). This can result in a poor and unsatisfactory user experience or the even potentially dangerous situations where the response is needed by the client side for key decision making.

The latest offering generation of Intel Xeon Scalable Processors can address this issue and help businesses and the public at large adopt Generative AI models being powered by LLMs on a more efficient basis.


Examples from the Intel in relation to the 5th Gen Xeon Scalable Processors and AI Everywhere are provided below:

Intel Matrix Extensions (Intel AMX) 5th Gen Scalable processors make generative AI more accessible on the CPU   allowing the user to do more before they need to access an accelerator.

With AI acceleration in every core, 5th Gen Intel® Xeon® processors are ready to handle demanding AI workloads—including inference and fine tuning on models up to 20 billion parameters —before the need to add discrete accelerators.

The SLAs enable real-time UX with less than 100ms token latency on LLMs under 20 billion parameters.


The specific performance enhancements entail: Up to 13% average first token speedup and up to 22% average second token speedup on GPT-J vs 4th Gen Intel Xeon processors.

Up to 2.3X average first token speedup and up to 64% average second token speedup on GPT-J relative to 3rd Gen Intel processors.

Up to 12% speeding up for first token latency and

Up to 2.1x speedup for first token latency and up to 48% speedup for second token latency on LLaMA-2 13B vs. 3rd Gen Intel Xeon processors.

Recommendations in Real-Time

5th Gen Intel Xeon Scalable Processors deliver fast, personalized product or content recommendations that don’t slow down the user experience with a Deep Learning-based recommender system that accounts for real-time user behavior signals and context features, such as time and location. 5th Gen Intel® Xeon® Scalable processors feature Intel® Advanced Matrix Extensions (Intel® AMX), a built-in accelerator that speeds up Deep Learning inference and accelerates small model training on the CPU. Performance improvements include:

Natural Language Processing

Smoother experiences with faster responses


Enable more responsive smart assistants, chatbots, predictive text, language translation, and more with a performance leap in natural language processing (NLP) inference. 5th Gen Intel® Xeon® Scalable Processors.

  • Up to 9.9x higher real-time inference performance on BERT-Large vs. 3rd Gen Intel Xeon processors with FP32.

  • Up to 7x higher real-time inference performance on DistilBERT vs. 3rd Gen Intel Xeon processors with FP32.

With Intel® oneAPI Deep Neural Network Library (oneDNN) software optimizations already integrated into the mainstream distributions of TensorFlow and PyTorch, developers can more easily access the benefits of built-in AI acceleration. Intel® Software Development Tools give developers the freedom to migrate code across different hardware architectures and vendors with comparable performance, increasing productivity and future-readiness without costly and time-consuming challenges.

For more intensive AI needs, add purpose-built Intel® Gaudi® AI accelerators to expand your CPU-based foundation.

  • Up to 1.19x (BF16) and 1.23x (INT8) vs. 4th Gen and up to 9.9x (BF16) and 9.2x (INT8) vs. 3rd Gen Intel® Xeon® processors. See A19 at 5th Gen Intel Xeon Scalable processors. Results may vary.

  • Up to 1.41x (BF16) and 1.35x (INT8) vs. 4th Gen and up to 7x (BF16) and 2.9x (INT8) vs. 3rd Gen Intel® Xeon® processors. See A24 at 5th Gen Intel Xeon Scalable processors. Results may vary.

With Intel® oneAPI Deep Neural Network Library (oneDNN) software optimizations already integrated into the mainstream distributions of TensorFlow and PyTorch, developers can access the benefits of built-in AI acceleration. 

  • Up to 2.34x vs. AMD EPYC 9654 and up to 1.9x vs. AMD EYPC 9754. See A208 at 5th Gen Intel Xeon Scalable processors. Results may vary.

Up to 1.24x (BF16) and 1.24x (INT8) vs. 4th Gen and up to 8.7x (BF16) and 5.5x (INT8) vs. 3rd Gen Intel® Xeon® processors. See A20 at 5th Gen Intel Xeon Scalable processors. Results may vary.

Machine Learning

The 5th Generation Intel Xeon Scalable Processor enables high speed Machine Learning on a CPU.

Classical Machine Learning plays a crucial role in high-performance computing (HPC) and AI applications, from life sciences to finance to academic research. With large memory, fast cores, and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), 5th Gen Intel® Xeon® Scalable processors deliver excellent Machine Learning training and inference performance.


With the Intel® AI software portfolio, developers can accelerate end-to-end Machine Learning and data science pipelines. These tools include optimized frameworks, a model repository, Intel® Extension for Scikit-learn and Intel® Optimization for XGBoost for machine learning, accelerated data analytics through the Intel® Distribution of Modin, optimized core Python libraries, and samples for end-to-end workloads.

Furthermore, Intel claim that the 5th Gen Xeon Scalable Processor offers a wider range for the entire AI pipeline compared to NVIDIA whereby the user may:

  • Navigate the entire AI pipeline with Intel® Xeon® processors that excel at a wider range of AI tasks than NVIDIA GPUs, from data preprocessing to inference.

  • Train small and medium deep learning models on a CPU in just minutes. With Intel® Advanced Matrix Extensions (Intel® AMX), the user receives a built-in matrix multiplication engine that offers discrete accelerator performance without the added hardware and complexity of a GPU.

  • And it is noted that the majority of data center AI inference deployments today run on Intel® Xeon® processors indicting the level of trust.

  • Furthermore, Intel also claim that with large memory, fast cores, and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), Intel Xeon processors deliver better machine learning training and inference performance than NVIDIA GPUs.

Furthermore, it is anticipated that the edge (in particular the Internet of Things, IoT)  will continue to grow and scale during 2024 and hence hardware resources that allow for AI decision making on the device will be key. The next article in this series will take a closer look at the edge and the potential of AI and the IoT to assist in sustainability and the fight against climate change. However, it may be noted that the Intel 5th Gen Xeon Scalable Processor may yield up to 24% higher real-time image classification inferencing with Intel Advanced Matrix Solutions.


In summary the 5th Gen Intel Xeon Scalable Processor provides businesses and end-users with the potential to fully access and scale the vast potential offered by Generative AI and AI models in general in an efficient manner including the computational and energy costs (hence the carbon footprint) with significant performance increases relative to the prior generation of Intel Xeon Processors.

NB all claims on performance are taken from the following source and results may vary:

AI Everywhere


5th Gen Intel Xeon Processors #5thGenIntelXeon

And data contained in this article is provided on a non-reliance basis and no representations, warranties or guarantees, whether express or implied are provided in relation to the content in this article. up-to-

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Why Malia Obama Received Major Criticism Over A Secret Facebook Page Dissing Trump



Why Malia Obama Received Major Criticism Over A Secret Facebook Page Dissing Trump

Given the divisive nature of both the Obama and Trump administrations, it’s unsurprising that reactions to Malia Obama’s alleged secret Facebook account would be emotional. Many online users were quick to jump to former President Donald Trump’s defense, with one user writing: “Dear Malia: Do you really think that anyone cares whether you and/or your family likes your father’s successor? We’re all trying to forget you and your family.”

Others pointed out the double standard held by those who condemn Trump for hateful rhetoric but praise people like Malia who speak out against her father’s successor in what they believe to be hateful rhetoric. Some users seemed bent on criticizing Malia simply because they don’t like her or her father, proving that the eldest Obama daughter couldn’t win for losing regarding the public’s perception of her or her online presence. 

The secret Facebook situation is not all that dissimilar to critics who went after Malia for her professional name at the 2024 Sundance Film Festival. In this instance, people ironically accused Malia of using her family’s name to get into the competitive festival while also condemning her for opting not to use her surname, going by Malia Ann instead.

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Best Practices for Data Center Decommissioning and IT Asset Disposition




Best Practices for Data Center Decommissioning and IT Asset Disposition

Data center decommissioning is a complicated process that requires careful planning and experienced professionals.

If you’re considering shutting down or moving your data center, here are some best practices to keep in mind:

Decommissioning a Data Center is More than Just Taking Down Physical Equipment


Decommissioning a data center is more than just taking down physical equipment. It involves properly disposing of data center assets, including servers and other IT assets that can contain sensitive information. The process also requires a team with the right skills and experience to ensure that all data has been properly wiped from storage media before they’re disposed of.

Data Centers Can be Decommissioned in Phases, Which Allows For More Flexibility

When you begin your data center decommissioning process, it’s important to understand that it’s not an event. Instead, it’s a process that takes place over time and in phases. This flexibility allows you to adapt as circumstances change and make adjustments based on your unique situation. For example:

  • You may start by shutting down parts of the facility (or all) while keeping others running until they are no longer needed or cost-effective to keep running.

  • When you’re ready for full shutdown, there could be some equipment still in use at other locations within the company (such as remote offices). These can be moved back into storage until needed again.

Data Center Decommissioning is Subject to Compliance Guidelines

Data center decommissioning is subject to compliance guidelines. Compliance guidelines may change, but they are always in place to ensure that your organization is following industry standards and best practices.

  • Local, state and federal regulations: You should check local ordinances regarding the disposal of any hazardous materials that were used in your data center (such as lead-based paint), as well as any other applicable laws related to environmental impact or safety issues. If you’re unsure about how these might affect your plans for a decommissioned facility, consult an attorney who specializes in this area of law before proceeding with any activities related to IT asset disposition or building demolition.

  • Industry standards: There are many industry associations dedicated specifically toward helping businesses stay compliant with legal requirements when moving forward with projects such as data center decommissioning.

  • Internal policies & procedures: Make sure everyone on staff understands how important it is not just from a regulatory standpoint but also from an ethical one; nobody wants their name associated with anything inappropriate!

Companies Should Consider Safety and Security During the Decommissioning Process

Data center decommissioning is a complex process that involves several steps. Companies need to consider the risks associated with each step of the process, and they should have a plan in place to mitigate these risks. The first step of data center decommissioning is identifying all assets and determining which ones will be reused or repurposed. At this point, you should also determine how long it will take for each asset to be repurposed or recycled so that you can estimate how much money it will cost for this part of your project (this can be done through an estimate based on previous experience).

The second step involves removing any hazardous materials from electronic equipment before it’s sent off site for recycling; this includes chemicals used in manufacturing processes like lead-free solder paste adhesives used on circuit boards made from tin-based alloys containing up 80% pure tin ingots stamped out into flat sheets called “pucks”. Once these chemicals have been removed from whatever device needs them taken off their surfaces then those devices can safely go through any other necessary processes such as grinding away excess plastic housing material using high pressure water jets until only its bare frame remains intact without any cracks where moisture might collect inside later causing corrosion damage over time due too much moisture exposure.

With Proper Planning and an Effective Team, You’ll Help Protect Your Company’s Future

Data center decommissioning is a complex process that should be handled by a team of experts with extensive experience in the field. With proper planning, you can ensure a smooth transition from your current data center environment to the next one.

The first step toward a successful data center decommissioning project is to create a plan for removing hardware and software assets from the building, as well as documenting how these assets were originally installed in the facility. This will allow you or another team member who may inherit some of these assets later on down the line to easily find out where they need to go when it’s time for them to be moved again (or disposed).

Use Professional Data Center Decommissioning Companies

In order to ensure that you get the most out of your data center decommissioning project, it’s important to use a professional data center decommissioning company. A professional data center decommissioning company has experience with IT asset disposition and can help you avoid mistakes in the process. They also have the tools and expertise needed to efficiently perform all aspects of your project, from pre-planning through finalizing documentation.

Proper Planning Will Help Minimize the Risks of Data Center Decommissioning


Proper planning is the key to success when it comes to the data center decommissioning process. It’s important that you don’t wait until the last minute and rush through this process, as it can lead to mistakes and wasted time. Proper planning will help minimize any risks associated with shutting down or moving a data center, keeping your company safe from harm and ensuring that all necessary steps are taken before shutdown takes place.


To Sum Up

The key to a successful ITAD program is planning ahead. The best way to avoid unexpected costs and delays is to plan your ITAD project carefully before you start. The best practices described in this article will help you understand what it takes to decommission an entire data center or other large facility, as well as how to dispose of their assets in an environmentally responsible manner.

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Massive Volatility Reported – Google Search Ranking Algorithm Update



Google Logo Exploding Cracking

I am seeing some massive volatility being reported today after seeing a spike in chatter within the SEO community on Friday. I have not seen the third-party Google tracking tools show this much volatility in a long time. I will say the tracking tools are way more heated than the chatter I am seeing, so something might be off here.

Again, I saw some initial chatter from within the SEO forums and on this site starting on Friday. I decided not to cover it on Friday because the chatter was not at the levels that would warrant me posting something. Plus, while some of the tools started to show a lift in volatility, most of the tools did not yet.

To be clear, Google has not confirmed any update is officially going on.

Well, that changed today, and the tools are all superheated today.

Google Tracking Tools:

Let’s start with what the tools are showing:










Advanced Web Rankings:











Cognitive SEO:




So most of these tools are incredibly heated, signaling that they are showing massive changes in the search result positions in the past couple of days.

SEO Chatter

Here is some of the chatter from various comments on this site and on WebmasterWorld since Friday:


Speaking of, is anyone seeing some major shuffling going on in the SERPs today? It’s a Friday so of course Google is playing around again.

Something is going on.

Pages are still randomly dropping out of the index for 8-36h at a time. Extremely annoying.

Speaking of, is anyone seeing some major shuffling going on in the SERPs today? It’s a Friday so of course Google is playing around again

In SerpRobot I’m seeing a steady increase in positions in February, for UK desktop and mobile, reaching almost the ranks from the end of Sep 2023. Ahrefs shows a slight increase in overall keywords and ranks.

In the real world, nothing seems to happen.

yep, traffic has nearly come to a stop. But exactly the same situation happened to us last Friday as well.

USA traffic continues to be whacked…starting -70% today.

In my case, US traffic is almost zero (15 % from 80%) and the rest is kind of the same I guess. Traffic has dropped from 4K a day to barely scrapping 1K now. But a lot is just bots since payment-wise, the real traffic seems to be about 400-500. And … that’s how a 90% reduction looks like.

Something is happening now. Google algo is going crazy again. Is anyone else noticing?

Since every Saturday at 12 noon the Google traffic completely disappears until Sunday, everything looks normal to me.

This update looks like a weird one and no, Google has not confirmed any update is going on.

What are you all noticing?

Forum discussion at WebmasterWorld.

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