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➡️ Start Asking Your Data ‘Why?’ — A Gentle Intro To Causality

Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials. This post is targeted towards anyone making data driven decisions. The main takeaway message is that causality may be possible by […]

Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can, potentially, unveil causal relationships within standard observational data, without having to resort to expensive randomised control trials.

This post is targeted towards anyone making data driven decisions. The main takeaway message is that causality may be possible by understanding that the story behind the data is as important as the data itself.

By introducing Simpson’s and Berkson’s Paradoxes, situations where the outcome of a population is in conflict with that of its cohorts, I shine a light on the importance of using causal reasoning to identify these paradoxes in data and avoid misinterpretation. Specifically I introduce causal graphs as a method to visualise the story behind the data point out that by adding this to your arsenal you are likely to conduct better analyses and experiments.

My ultimate objective is to whet your appetite to explore more on causality, as I believe that by asking data “Why?” you will be able to go beyond correlation calculations and extract more insights, as well as avoid common misjudgement pitfalls.

Note that throughout this gentle intro I do not use equations but demonstrate using accessible intuitive visuals. That said I provide resources for you to take your next step in adding Causal Inference to your statistical toolbox so that you may get more value from your data.

The Era of Data Driven Decision Making

In [Deity] We Trust, All Others Bring Data! — William E. Deming

In this digital age it is common to put a lot of faith in data. But this raises an overlooked question: Should we trust data on its own?

Judea Pearl, who is considered the godfather of Causality, articulated best:

“The collection of information is as important as the information itself “ — Judea Pearl

In other words the story behind the data is as important as the data itself.

Judea Pearl is considered the Godfather of Causality. Credit: Aleksander Molak

This manifests in a growing awareness of the importance of identifying bias in datasets. By the end of this post I hope that you will appreciate that causality pertains the fundamental tools to best express, quantify and attempt to correct for these biases.

In causality introductions it is customary to demonstrate why “correlation does not imply causation” by highlighting limitations of association analysis due to spurious correlations (e.g, shark attacks 🦈 and ice-cream sales 🍦). In an attempt to reduce the length of this post I defer this aspect to an older one of mine. Here I focus on two mind boggling paradoxes 🤯 and their resolution via causal graphs to make a similar point.

Paradoxes in Analysis

To understand the importance of the story behind the data we will examine two counter-intuitive (but nonetheless true) paradoxes which are classical situations of data misinterpretation.

In the first we imagine a clinical trial in which patients are given a treatment and that results in a health score. Our objective is to assess the average impact of increased treatment to the health outcome. For pedagogical purposes in these examples we assume that samples are representative (i.e, the sample size is not an issue) and that variances in measurements are minimal.

Population outcome of imaginary clinical trial. Each dot is one patient and the red line indicates the naïve population trend.

In the figure above we learn that on average increasing the treatment appears to be beneficial since it results in a better outcome.

Now we’ll color code by age and gender groupings and examine how the treatment increases impacts each cohort.

Same data as before where each symbol represents an age-gender cohort.

Track any cohort (e.g, “Girls” representing young females) and you immediately realise that increase in treatment appears adverse.

What is the conclusion of the study? On the one hand increasing the treatment appears to be better for the population at large, but when examining gender-age cohorts it seems disadvantageous. This is Simpson’s Paradox which may be stated:

“Trends can exist in subgroups but reverse for the whole”

Below we will resolve this paradox using causality tools, but beforehand let’s explore another interesting one, which also examines made up data.

Imagine that we quantify for the general population their attractiveness and how talented they are as in this figure:

General population. Source: Wikipedia, created by Cmglee

We find no apparent correlation.

Now we’ll focus on an unusual subset — famous people:

A subset of celebrities. Source: Wikipedia created by Cmglee

Here we clearly see an anti-correlation that doesn’t exist in the general population.

Should we conclude that Talent and Attractiveness are independent variables as per the first plot of the general population or that they are correlated as per that of celebrities?

This is Berkson’s Paradox where one population has a trait trend that another lacks.

Whereas an algorithm would identify these correlations, resolving these paradoxes requires a full understanding of the context which normally is not fed to a computer. In other words without knowing the story behind the data results may be misinterpreted and wrong conclusions may be inferred.

Mastering identification and resolution these paradoxes is an important first step to elevating one’s analyses from correlations to causal inference.

Whereas these simple examples may be explained away logically, for the purposes of learning causal tools in the next section I’ll introduce Causal Graphs.

Causal Graphs— Visualising The Story Behind The Data

“[From the Simpson’s and Berkson’s Paradoxes we learn that] certain decisions cannot be made based on the basis of data alone, but instead depend on the story behind the data. … Graph Theory enables these stories to be conveyed” — Judea Pearl

Causal graph models are probabilistic graphical models used to visualise the story behind the data. They are perhaps one of the most powerful tools for analysts that is not taught in most statistics curricula. They are both elegant and highly informative. Hopefully by the end of this post you will appreciate it when Judea Pearl says that this is the missing vocabulary to communicate causality.

To understand causal graph models (or causal graphs for short) we start with the following illustration of an example undirected graph with four nodes/vertices and three edges.

An undirected graph with four nodes/vertices and three edges

Each node is a variable and the edges communicate “who is related to whom?” (i.e, correlations, joint probabilities).A directed graph is one in which we add arrows as in this figure.

A directed graph with four nodes/vertices and five directed edges

A directed edge communicates “who listens to whom?” which is the essence of causation.

In this specific example you can notice a cyclical relationship between the C and D nodes.A useful subset of directed graphs are the directed acyclic graphs (DAG), which have no cycles as in the next figure.

A directed acyclic graph with four nodes/vertices and four directed edges

Here we see that when starting from any node (e.g, A) there isn’t a path that gets back to it.

DAGs are the go-to choice in causality for simplicity as the fact that parameters do not have feedback highly simplifies the flow of information. (For mechanisms that have feedback, e.g temporal systems, one may consider rolling out nodes as a function of time, but that is beyond the scope of this intro.)

Causal graphs are powerful at conveying the cause/effect relationships between the parameter and hence how data was generated (the story behind the data).

From a practical point of view, graphs enable us to understand which parameters are confounders that need to be controlled for, and, as important, which not to control for, because doing so causes spurious correlations. This will be demonstrated below.

The practice of attempting to build a causal graph enables:

  • Design of better experiments.
  • Draw causal conclusions (go beyond correlations by means of representing interventions, counterfactuals and encoding conditional independence relationships; all beyond the scope of this post).

To further motivate the usage of causal graph models we will use them to resolve the Simpson’s and Berkson’s paradoxes introduced above.

💊 Causal Graph Resolution of Simpson’s Paradox

For simplicity we’ll examine Simpson’s paradox focusing on two cohorts, male and female adults.

Outcome of the imaginary therapeutic trial, similar to the previous but focusing on the adults. Each symbol is one patient from the respective age-gender cohort and the red line indicates the naïve population trend.

Examining this data we can make three statements about three variables of interest:

  • Gender is an independent variable (it does not “listen to” the other two)
  • Treatment depends on Gender (as we can see, in this setting the level given depends on Gender — women have been given, for some reason, a higher dosage.)
  • Outcome depends on both Gender and Treatment

According to these we can draw the causal graph as the following:

Simpson’s paradox Graphic Model where Gender is a confounding variable between Treatment and Outcome

Notice how each arrow contributes to communicate the statements above. As important, the lack of an arrow pointing into Gender conveys that it is an independent variable.

We also notice that by having arrows pointing from Gender to Treatment and Outcome it is considered a common cause between them.

The essence of the Simpson’s paradox is that although the Outcome is effected by changes in Treatment, as expected, there is also a backdoor path flow of information via Gender.

As you may have guessed by this stage, the solution to this paradox is that the common cause Gender is a confounding variable that needs to be controlled.

Controlling for a variable, in terms of a causal graph, means eliminating the relationship between Gender and Treatment.

This may be done in two manners:

  • Pre data collection: Setting up a Randomised Control Trial (RCT) in which participants will be given dosage regardless of their Gender.
  • Post data collection: E.g, in this made up scenario the data has already been collected and hence we need to deal with what is referred to as Observational Data.

In both pre- and post- data collection the elimination of the Treatment dependency of Gender (i.e, controlling for the Gender) may be done by modifying the graph such that the arrow between them is removed as in the following:

A modified version of the Simpson’s paradox Graphic Model. The dark node means we control for Gender.

Applying this “graphical surgery” means that the last two statements need to be modified (for convenience I’ll write all three):

  • Gender is an independent variable
  • Treatment is an independent variable
  • Outcome depends on Gender and Treatment (but with no backdoor path).

This enables obtaining the causal relationship of interest : we can assess the direct impact of modification Treatment on the Outcome.

The process of controlling for a confounder, i.e manipulation of the data generation process, is formally referred to as applying an intervention. That is to say we are no longer passive observers of the data, but we are taking an active role in modification it to assess the causal impact.

How is this manifested in practice?

In the case of RCTs the researcher needs to control for important confounding variables. Here we limit the discussion to Gender (but in real world settings you can imagine other variables such as Age, Social Status and anything else that might be relevant to one’s health).

RCTs are considered the golden standard for causal analysis in many experimental settings thanks to its practice of confounding variables. That said, it has many setbacks:

  • It may be expensive to recruit individuals and may be complicated logistically
  • The intervention under investigation may not be physically possible or ethical to conduct (e.g, one can’t ask randomly selected people to smoke or not for ten years)
  • Artificial setting of a laboratory — not a true natural habitat of the population.

Observational data on the other hand is much more readily available in the industry and academia and hence much cheaper and could be more representative of actual habits of the individuals. But as illustrated in the Simpson’s diagram it may have confounding variables that need to be controlled.

This is where ingenious solutions developed in the causal community in the past few decades are making headway. Detailing them are beyond the scope of this post, but I briefly mention how to learn more at the end.

To resolve for this Simpson’s paradox with the given observational data one

  1. Calculates for each cohort the impact of the change of the treatment on the outcome
  2. Calculates a weighted average contribution of each cohort on the population.

Here we will focus on intuition, but in a future post we will describe the maths behind this solution.

I am sure that many analysts, just like myself, have noticed Simpson’s at some stage in their data and hopefully have corrected for it. Now you know the name of this effect and hopefully start to appreciate how causal tools are useful.

That said … being confused at this stage is OK 😕

I’ll be the first to admit that I struggled to understand this concept and it took me three weekends of deep diving into examples to internalised it. This was the gateway drug to causality for me. Part of my process to understanding statistics is playing with data. For this purpose I created an interactive web application hosted in Streamlit which I call Simpson’s Calculator 🧮. I’ll write a separate post for this in the future.

Even if you are confused the main takeaways of Simpson’s paradox is that:

  • It is a situation where trends can exist in subgroups but reverse for the whole.
  • It may be resolved by identifying confounding variables between the treatment and the outcome variables and controlling for them.

This raises the question — should we just control for all variables except for the treatment and outcome? Let’s keep this in mind when resolving for the Berkson’s paradox.

🦚 Causal Graph Resolution of Berkson’s Paradox

As in the previous section we are going to make clear statements about how we believe the data was generated and then draw these in a causal graph.

Let’s examine the case of the general population, for convenience I’m copying the image from above:

General population. Source: Wikipedia, created by Cmglee

Here we understand that:

  • Talent is an independent variable
  • Attractiveness is an independent variable

A causal graph for this is quite simple, two nodes without an edge.

In the general population ones Talent and Attractiveness are independent

Let’s examine the plot of the celebrity subset.

A subset of celebrities. Source: Wikipedia created by Cmglee

The cheeky insight from this mock data is that the more likely one is attractive the less they need to be talented to be a celebrity. Hence we can deduce that:

  • Talent is an independent variable
  • Attractiveness is an independent variable
  • Celebrity variable depends on both Talent and Attractiveness variables. (Imagine this variable is boolean as in: true for celebrities or false for not).

Hence we can draw the causal graph as:

Being a celebrity depends on Talent and Attractiveness

By having arrows pointing into it Celebrity is a collider node between Talent and Attractiveness.

Berkson’s paradox is the fact that when controlling for celebrities we see an interesting trend (anti correlation between Attractiveness and Talent) not seen in the general population.

This can be visualised in the causal graph that by confounding for the Celebrity parameter we are creating a spurious correlation between the otherwise independent variables Talent and Attractiveness. We can draw this as the following:

Berkson’s paradox Graphic Model. The dark node means we control for Celebrity. Controlling this collider variable generates a spurious correlation (dashed line) between Talent and Attractiveness.

The solution of this Berkson’s paradox should be apparent here: Talent and Attractiveness are independent variables in general, but by controlling for the collider Celebrity node causes a spurious correlation in the data.

Let’s compare the resolution of both paradoxes:

  • Resolving Simpson’s Paradox is by controlling for common cause (Gender)
  • Resolving Berkson’s Paradox is by not controlling for the collider (Celebrity)

The next figure combines both insights in the form of their causal graphs:

Graph models show how to resolve the paradoxes. Dark nodes are controlled for. Left: Modified graph to resolve Simpson’s paradox by controlling for Gender. Right: To resolve for Berkson’s paradox the collider should not be controlled.

The main takeaway from the resolution of these paradoxes is that controlling for parameters requires a justification. Common causes should be controlled for but colliders should not.

Even though this is common knowledge for those who study causality (e.g, Economics majors), it is unfortunate that most analysts and machine learning practitioners are not aware of this (including myself in 2020 after over 15 years of analysis and predictive modelling experience).

Oddly, statisticians both over- and underrate the importance of confounders — Judea Pearl

Summary

The main takeaway from this post is that the story behind the data is as important as the data itself.

Appreciating this will help you avoid result misinterpretation as spurious correlations and, as demonstrated here, in Simpson’s and Berskon’s paradoxes.

Causal Graphs are an essential tool to visualise the story behind the data. By using them to solve for the paradoxes we learnt that controlling for variables requires justification (common causes ✅, colliders ⛔️).

For those interested in taking the next step in their causal journey I highly suggest mastering Simpson’s paradox. One great way is by playing with data. Feel free to do so with my interactive “Simpson-calculator” 🧮.

Loved this post? 💌 Join me on LinkedIn or ☕ Buy me a coffee!

Credits

Unless otherwise noted, all images were created by the author.

Many thanks to Jim Parr, Will Reynolds, Hedva Kazin and Betty Kazin for their useful comments.

Wondering what your next step should be in your causal journey? Check out my new article on mastering Simpson’s Paradox — you will never look at data the same way. 🔎

Useful Resources

Here I provide resources that I find useful as well as a shopping list of topics for beginners to learn.

📚 Books

Credit: Gaelle Marcel
  • The Book of Why — popular science reading (NY Times level)
  • Causal Inference in Statistics A Primer — excellent short technical book (site)
  • Causal Inference and Discovery in Python by Aleksander Molak (Packt, github) — clearly explained with python applications 🐍.
  • What If? — a cohesive presentation of concepts of, and methods for, causal inference (site, github)
  • Causal Inference The Mixtape — Social Science focused using Python, R and Strata (site, resources, mooc)
  • Counterfactuals and Causal Inference — Methods and Principles (Social Science focused)

This list is far from comprehensive, but I’m glad to add to it if anyone has suggestions (please mention why the book stands out from the pack).

🔏 Courses

Credit: Austrian National Library

There are probably a few courses online. I love the 🆓 one of Brady Neil bradyneal.com/causal-inference-course.

  • Clearly explained
  • Covers many aspects
  • Thorough
  • Provides memorable examples
  • F.R.E.E

One paid course 💰 that is targeted to practitioners is Altdeep.

💾 Software

Credit: Artturi Jalli

This list is far from comprehensive because the space is rapidly growing:

Causal Wizard app also have an article about Causal Diagram tools.

🐾 Suggested Next Steps In The Causal Journey

Here I highlight a list of topics which I would have found useful when I started my learnings in the field. If I’m missing anything I’d be more than glad to get feedback and adding. I bold face the ones which were briefly discussed here.

Pearl’s Causal Hierarchy of seeing, doing, imagining and their applications. This is an approved modification of the original illustration by Maayan Harel from MaayanVisuals.com in The Book of Why.
  • Pearl’s Causal Hierarchy of seeing, doing and imagining (figure above)
  • Observational data vs. Randomised Control Trials
  • d-separation, common causes, colliders, mediators, instrumental variables
  • Causal Graphs
  • Structural Causal Models
  • Assumptions: Ignorability, SUTVA, Consistency, Positivity
  • “Do” Algebra — assessing impact on cohorts by intervention
  • Counterfactuals — assessing impact on individuals by comparing real outcomes to potential ones
  • The fundamental problem of causality
  • Estimand, Estimator, Estimate, Identifiability — relating causal definitions to observable statistics (e.g, conditional probabilities)
  • Causal Discovery — finding causal graphs with data (e.g, Markov Equivalence)
  • Causal Machine Learning (e.g, Double Machine Learning)

For completeness it is useful to know that there are different streams of causality. Although there is a lot of overlap you may find that methods differ in naming convention due to development in different fields of research: Computer Science, Social Sciences, Health, Economics

Here I used definitions mostly from the Pearlian perspective (as developed in the field of computer science).

The Story Behind This Post

This narrative is a result of two study groups that I have conducted in a previous role to get myself and colleagues to learn about causality, which I felt missing in my skill set. If there is any interest I’m glad to write a post about the study group experience.

This intro was created as the one I felt that I needed when I started my journey in causality.

In the first iteration of this post I wrote and presented the limitations of spurious correlations and Simpson’s paradox. The main reason for this revision to focus on two paradoxes is that, whereas most causality intros focus on the limitations of correlations, I feel that understanding the concept of justification of confounders is important for all analysts and machine learning practitioners to be aware of.

On September 5th 2024 I have presented this content in a contributed talk at the Royal Statistical Society Annual Conference in Brighton, England (abstract link).

Unfortunately there is no recording but there are of previous talks of mine:

The slides are available at bit.ly/start-ask-why. Presenting this material for the first time at PyData Global 2021

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IBM laying foundation for mainframe as ultimate AI server

“It will truly change what customers are able to do with AI,” Stowell said. IBM’s mainframe processors The next generation of processors is expected to continue a long history of generation-to-generation improvements, IBM stated in a new white paper on AI and the mainframe. “They are projected to clock in at 5.5 GHz. and include ten 36 MB level 2 caches. They’ll feature built-in low-latency data processing for accelerated I/O as well as a completely redesigned cache and chip-interconnection infrastructure for more on-chip cache and compute capacity,” IBM wrote.  Today’s mainframes also have extensions and accelerators that integrate with the core systems. These specialized add-ons are designed to enable the adoption of technologies such as Java, cloud and AI by accelerating computing paradigms that are essential for high-volume, low-latency transaction processing, IBM wrote.  “The next crop of AI accelerators are expected to be significantly enhanced—with each accelerator designed to deliver 4 times more compute power, reaching 24 trillion operations per second (TOPS),” IBM wrote. “The I/O and cache improvements will enable even faster processing and analysis of large amounts of data and consolidation of workloads running across multiple servers, for savings in data center space and power costs. And the new accelerators will provide increased capacity to enable additional transaction clock time to perform enhanced in-transaction AI inferencing.” In addition, the next generation of the accelerator architecture is expected to be more efficient for AI tasks. “Unlike standard CPUs, the chip architecture will have a simpler layout, designed to send data directly from one compute engine, and use a range of lower- precision numeric formats. These enhancements are expected to make running AI models more energy efficient and far less memory intensive. As a result, mainframe users can leverage much more complex AI models and perform AI inferencing at a greater scale

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VergeIO enhances VergeFabric network virtualization offering

VergeIO is not, however, using an off-the-shelf version of KVM. Rather, it is using what Crump referred to as a heavily modified KVM hypervisor base, with significant proprietary enhancements while still maintaining connections to the open-source community. VergeIO’s deployment profile is currently 70% on premises and about 30% via bare-metal service providers, with a particularly strong following among cloud service providers that host applications for their customers. The software requires direct hardware access due to its low-level integration with physical resources. “Since November of 2023, the normal number one customer we’re attracting right now is guys that have had a heart attack when they got their VMware renewal license,” Crump said. “The more of the stack you own, the better our story becomes.” A 2024 report from Data Center Intelligence Group (DCIG) identified VergeOS as one of the top 5 alternatives to VMware. “VergeIO starts by installing VergeOS on bare metal servers,” the report stated. “It then brings the servers’ hardware resources under its management, catalogs these resources, and makes them available to VMs. By directly accessing and managing the server’s hardware resources, it optimizes them in ways other hypervisors often cannot.” Advanced networking features in VergeFabric VergeFabric is the networking component within the VergeOS ecosystem, providing software-defined networking capabilities as an integrated service rather than as a separate virtual machine or application.

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Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

The modular data center industry is undergoing a seismic shift in the age of AI, and few are as deeply embedded in this transformation as Andrew Lindsey, Co-Founder and CEO of Flexnode. In a recent episode of the Data Center Frontier Show podcast, Lindsey joined Editor-in-Chief Matt Vincent and Senior Editor David Chernicoff to discuss the evolution of modular data centers, the growing demand for high-density liquid-cooled solutions, and the industry factors driving this momentum. A Background Rooted in Innovation Lindsey’s career has been defined by the intersection of technology and the built environment. Prior to launching Flexnode, he worked at Alpha Corporation, a top 100 engineering and construction management firm founded by his father in 1979. His early career involved spearheading technology adoption within the firm, with a focus on high-security infrastructure for both government and private clients. Recognizing a massive opportunity in the data center space, Lindsey saw a need for an innovative approach to infrastructure deployment. “The construction industry is relatively uninnovative,” he explained, citing a McKinsey study that ranked construction as the second least-digitized industry—just above fishing and wildlife, which remains deliberately undigitized. Given the billions of square feet of data center infrastructure required in a relatively short timeframe, Lindsey set out to streamline and modernize the process. Founded four years ago, Flexnode delivers modular data centers with a fully integrated approach, handling everything from site selection to design, engineering, manufacturing, deployment, operations, and even end-of-life decommissioning. Their core mission is to provide an “easy button” for high-density computing solutions, including cloud and dedicated GPU infrastructure, allowing faster and more efficient deployment of modular data centers. The Rising Momentum for Modular Data Centers As Vincent noted, Data Center Frontier has closely tracked the increasing traction of modular infrastructure. Lindsey has been at the forefront of this

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Last Energy to Deploy 30 Microreactors in Texas for Data Centers

As the demand for data center power surges in Texas, nuclear startup Last Energy has now announced plans to build 30 microreactors in the state’s Haskell County near the Dallas-Fort Worth Metroplex. The reactors will serve a growing customer base of data center operators in the region looking for reliable, carbon-free energy. The plan marks Last Energy’s largest project to date and a significant step in advancing modular nuclear power as a viable solution for high-density computing infrastructure. Meeting the Looming Power Demands of Texas Data Centers Texas is already home to over 340 data centers, with significant expansion underway. Google is increasing its data center footprint in Dallas, while OpenAI’s Stargate has announced plans for a new facility in Abilene, just an hour south of Last Energy’s planned site. The company notes the Dallas-Fort Worth metro area alone is projected to require an additional 43 gigawatts of power in the coming years, far surpassing current grid capacity. To help remediate, Last Energy has secured a 200+ acre site in Haskell County, approximately three and a half hours west of Dallas. The company has also filed for a grid connection with ERCOT, with plans to deliver power via a mix of private wire and grid transmission. Additionally, Last Energy has begun pre-application engagement with the U.S. Nuclear Regulatory Commission (NRC) for an Early Site Permit, a key step in securing regulatory approval. According to Last Energy CEO Bret Kugelmass, the company’s modular approach is designed to bring nuclear energy online faster than traditional projects. “Nuclear power is the most effective way to meet Texas’ growing energy demand, but it needs to be deployed faster and at scale,” Kugelmass said. “Our microreactors are designed to be plug-and-play, enabling data center operators to bypass the constraints of an overloaded grid.” Scaling Nuclear for

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Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting.  Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ;  Dallas, TX;  Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

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Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland.  With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116,  a bill

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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