Global Tech Excellence Awards

Tuesday, November 5, 2024

Deep Learning Elevates Brain Surgery Precision

Hyperspectral Imaging in Neurosurgery

HSI is an advanced imaging technology that captures a wide spectrum of light from each pixel in an image. This capability allows for the identification of various materials based on their spectral signatures. While HSI is used in fields like agriculture and environmental monitoring, it has significant potential in medical fields, especially in fluorescence-guided surgeries to distinguish tumors from healthy tissues.

For example, fluorescence-guided surgery using 5-aminolevulinic acid (5-ALA) allows the visualization of malignant gliomas. This compound is absorbed by tumor cells and is metabolized into protoporphyrin IX (PpIX), which fluoresces under specific wavelengths of light. However, traditional fluorescence data analysis often struggles with complexities due to tissue heterogeneity and optical artifacts. Therefore, there is a need for novel approaches to enhance the reliability and effectiveness of fluorescence-guided imaging in neurosurgery.

Deep Learning for Hyperspectral Imaging

In this paper, the authors used two deep-learning models to correct and unmix hyperspectral images captured during brain tumor surgeries. The first model, the Attenuation Correction and Unmixing Network (ACU-Net) is a supervised deep-learning architecture designed to process fluorescence spectra and estimate PpIX concentrations.

The second model, Attenuation Correction and Unmixing by a Spectrally-informed Autoencoder (ACU-SA) uses a semi-supervised approach to leverage labeled and unlabeled data. Both models are based on a convolutional neural network (CNN) structure, equipped to handle complex, high-dimensional data typical of HSI in neurosurgery.

The researchers conducted experiments on a large dataset of hyperspectral images from 184 patients, including 891 fluorescence HSI data cubes covering 12 tumor types. These datasets represented all four World Health Organization (WHO) grades and included isocitrate dehydrogenase (IDH) mutant and wild-type samples. Furthermore, training was performed on phantom and pig brain homogenate (PBH) data with known PpIX concentrations to assess the performance of each model.

The ACU-Net model integrates residual connections and convolutional layers to enhance feature extraction, aiming to minimize variance between predicted and actual fluorescence spectra for improved PpIX quantification. In contrast, the ACU-SA model leverages a Siamese architecture to condition the network on known endmember spectra, which helps unmix fluorescence data while also allowing the integration of unlabeled human data.

Impact of Using Deep Learning

The study indicated that both the ACU-Net and ACU-SA models significantly improved the accuracy of PpIX concentration estimation compared to traditional imaging methods in brain tumor surgery. The ACU-Net model achieved Pearson correlation coefficients of 0.997 for phantom data and 0.990 for pig-brain data.

These values represent the close match between known and computed PpIX concentrations. In comparison, traditional methods, such as dual-band normalization followed by non-negative least squares (NNLS) unmixing, yielded lower correlation coefficients of 0.93 and 0.82, respectively.

The semi-supervised ACU-SA model also showed promising performance. It achieved correlation coefficients of 0.98 for phantom data and 0.91 for pig-brain data, suggesting its potential for generalizing to human data. Importantly, the ACU-SA model demonstrated a 36% reduction in false-positive rates for PpIX detection in human samples. This reduction is valuable for minimizing the risk of removing healthy tissue during surgery.

Additionally, the deep learning models exhibited enhanced robustness against common challenges in hyperspectral imaging, such as artifacts and variations in fluorescence signals. The authors highlighted that the ACU-Net model not only improved quantitative outcomes but also provided better visual quality in PpIX concentration maps.

These outcomes indicated that deep learning-based approaches effectively addressed the limitations of traditional imaging techniques and provided a more reliable tool for intraoperative decision-making. Furthermore, both the presented models offered faster processing times, making them suitable for real-time use in surgery.

Key Applications

This research has significant implications for neurosurgery. The enhanced capabilities of the ACU-Net and ACU-SA models can significantly improve the accuracy of tumor detection, supporting more effective surgical interventions.

Beyond brain surgery, these deep learning techniques could also be adapted for oncology, where accurate tumor margin assessment is crucial for treating different types of cancers. They could also improve diagnostic imaging technology, enhancing patient outcomes.

Conclusion and Future Scopes

In summary, deep learning proved effective in enhancing the accuracy of HSI for medical applications, particularly brain tumor surgery. By addressing optical and geometric variations in fluorescence signals, these models significantly improve tumor margin detection accuracy during brain surgeries. These findings not only support more effective surgical interventions but also suggest that deep learning can advance diagnostic imaging technologies.

Future work should focus on expanding the dataset, incorporating more fluorophores, and further optimizing these models to enhance clinical utility and applicability. Integrating these imaging advancements could transform surgical practices and improve patient care.

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Sunday, November 3, 2024

Machine learning spots single photons to accelerate quantum communication

Single photon sources — physical objects that can emit precisely one quantum of light at a time — are crucial for advancing quantum technologies. Yet, traditional methods for producing these sources are often labor-intensive and time-consuming, hindering their widespread development.

To streamline this process, a research team has leveraged machine learning to help classify photon sources as single- or multi-photon emitters. This innovative approach dramatically speeds up the classification process compared to conventional techniques, paving the way for more efficient implementation of single photon sources in various applications.

“The method we developed has significant practical applications in the fields of quantum communication and quantum computing,” said Seoyoung Paik of the Gwangju Institute of Science and Technology, in an email. “Single-photon emitters are key components in quantum communication, as they are essential for secure information transmission through technologies like quantum key distribution.

“By enabling the fast and accurate identification of single-photon emitters without the need for manual experiments, our method can accelerate the development of quantum communication systems.”

“In quantum computing, single-photon emitters play an important role either as qubits or in creating entanglement between qubits,” Paik continued. “Our method greatly improves the efficiency of identifying and characterizing single-photon emitters, particularly in solid-state systems like diamond and silicon carbide.

“The ability to accurately classify photon emitters across various materials means that our method can be applied to a wide range of solid-state quantum systems.”

Forming and resolving single-photon sources

A source’s ability to produce exactly one photon at a time in response to an external stimulus, such as a laser pulse, is critical for advancing quantum technologies and ensuring secure communication.

This is because the quantum state of a single photon contains encoded information that cannot be intercepted without detection. This is a fundamental principle of quantum mechanics, where if an eavesdropper intercepts and measures — or reads — a photon, the act of measurement changes its state, alerting the communicating parties to the interception.

However, if the source emits multiple photons in the same state, an eavesdropper could intercept one of them and extract the information without altering the state of the remaining photons carrying the same information, compromising the communication without detection.

Ensuring a photon source emits a single photon at a time is therefore necessary, but a challenge. The team therefore set out to try and develop a means of better differentiate and classify multi- and single-photon sources.

As a testbed, they considered diamond that contains nitrogen-vacancy centers, where a nitrogen atom replaces a carbon atom in the diamond crystal lattice. These centers are excellent sources of individual photons because the electrons localized around them occupy specific quantum states, allowing them to emit exactly one photon in response to laser radiation at a wavelength of approximately 500 nanometers, which appears green to the human eye.

However, when these centers are created by bombarding a diamond sample with nitrogen ions using an accelerator, multiple vacancies often form in close proximity to one another. Given that the spacing between them can be on the atomic scale, conventional optical microscopy struggles to resolve them, making it difficult to classify the sources as either single-photon or multi-photon.

A more advanced technique for classifying photon sources known as the Hanbury-Brown-Twiss experiment has helped bridge the gap. This experiment involves directing light emitted from the source through a beam splitter, which sends the light down two separate paths toward two photon detectors. If the source emits only one photon, only one detector will record the photon each time. If the source emits multiple photons, both detectors will sometimes register photons at the same time.

By repeating the experiment multiple times, researchers can measure the coincidence rate — the frequency with which both detectors register photons simultaneously. A high coincidence rate indicates a multi-photon source, while a low rate points to a single-photon emitter.

While the Hanbury-Brown-Twiss experiment is highly reliable, it requires running the experiment on each source repeatedly, making it a time-consuming process when analyzing hundreds or thousands of nitrogen-vacancy centers.

Leveraging machine learning

To address the inefficiency of traditional methods, the research team turned to machine learning. Rather than conducting the Hanbury-Brown-Twiss experiments on every single photon source within the diamond crystal lattice, they proposed using deep learning to classify photon sources based on image data instead. Specifically, they trained the algorithm on images produced by the light emitted from nitrogen-vacancy centers.

The team generated these images by allowing the light emitted from nitrogen-vacancy centers to strike a screen. The resulting image patterns differ depending on whether the source is single- or multi-photon. By feeding the algorithm images from sources that had already been classified using the Hanbury-Brown-Twiss experiment, the machine learning model learned to differentiate between the two types of sources based on their image characteristics.

Once trained, the algorithm could then classify new sources with remarkable accuracy. In tests, the machine learning model correctly identified the type of photon source in 98% of cases. This high accuracy suggests that machine learning could significantly reduce the need for repetitive Hanbury-Brown-Twiss experiments, dramatically speeding up the process of identifying single-photon sources.

“This marks the ﬁrst attempt to identify single photon emitters using deep learning, bypassing the need for [Hanbury-Brown-Twiss] experiments and signiﬁcantly improving efficiency,” the scientists wrote.

Challenges and future directions

While the study produced promising results, the team acknowledges one significant limitation: these models are often difficult to interpret, meaning researchers can’t always pinpoint exactly how the algorithm arrives at its decisions. This opacity can be a problem when trying to apply the algorithm to new types of photon sources.

“Despite the successful application of the [machine learning] model for single-emitter classiﬁcation, further improvements in understanding the underlying mechanisms are needed for extension to broader applications,” the scientists wrote in their paper.

“The ‘black box’ nature of deep learning is a well-recognized challenge across various domains. We plan to continue our effort to unravel these complexities. A deeper understanding of the decision-making process could reveal key features necessary for accurate classiﬁcation and enhance the model’s robustness and adaptability across different setups.”

However, despite all these difficulties, the authors of the study believe that the technique they developed will find wide application in the rapidly developing field of quantum technologies, significantly reducing the time and labor costs of producing photon sources.

“Our method has significant potential for applications across various quantum systems,” concluded Sang-Yun Lee of the Gwangju Institute of Science and Technology. “While our current research focuses primarily on nitrogen-vacancy centers in diamond, this approach can be applied to other materials as well. For instance, it can be extended to silicon vacancies in silicon carbide, and single-photon emitters observed in two-dimensional materials, such as transition metal dichalcogenides or hexagonal boron nitride.

“These materials are promising for quantum technologies due to their unique optical and electronic properties, and our classification method could help efficiently identify single-photon emitters in these systems.”

Moreover, the deep learning approach used by the researchers has potential applications beyond this study. It could be applied to other physical systems where isolating and identifying individual quantum objects is essential. For instance, in scanning tunneling microscopy — where quantum states of atoms or molecules are imaged — this method could be employed to automatically identify and classify quantum states, significantly reducing the need for manual analysis.

This technique holds promise for any system requiring the differentiation of individual quantum states of atoms or molecules.

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Saturday, November 2, 2024

Artificial Intelligence in practice

The world has known the term artificial intelligence for decades. Until recently, discussion of this technology was prospective; experts merely developed theories about what AI might be able to do in the future. Today, integrating AI into your workflow isn’t hypothetical, it’s MANDATORY. No matter what market you operate in, AI is critical to keeping your business competitive. When considering how to work AI into your existing business practices and what solution to use, you must determine whether your goal is to develop, deploy, or consume AI technology.

Developing AI

When most people think about artificial intelligence, they likely imagine a coder hunched over their workstation developing AI models. In the past, creating a new AI model required data scientists to custom-build systems from a frustrating parade of moving parts, but Z by HP has made it easy with tools like Data Science Stack Manager and AI Studio. With those tools involved, users can build new AI models on relatively low-powered machines, saving heavy-duty units for the compute-intensive process of model training.

Deploying AI

Many modern AI systems are capable of leveraging machine-to-machine connections to automate data ingestion and initiate responsive activity. In some cases, the data ingestion comes from cameras or recording devices connected to the model. In other cases, the model might scan and process open-source data. This process, where both input and output of the model are automated, is known as AI deployment.

Some examples of AI deployment are:Self-driving technology
Medical imaging and augmented diagnosis
Production facility monitoring and quality control
Dynamic pricing
Personalized product recommendations

Consuming AI

Consuming AI refers to the practice of incorporating existing artificial intelligence into various aspects of daily human life, such as work, entertainment, and personal relationships. It involves using AI-powered tools and technologies to automate tasks, make decisions, and enhance experiences, with the goal of improving efficiency, productivity, and overall quality of life.

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Tuesday, October 29, 2024

How AI Improves Quality Control with Computer Vision in Manufacturing

Artificial Intelligence (AI), especially computer vision, has brought advancements that help improve quality control in manufacturing. This combination enables the real-time inspection of items during production, increasing efficiency, accuracy, and consistency.

What is AI-Powered Quality Control?

AI-based quality control often relies on machine vision. This branch of AI processes visual information by utilizing cameras and smart algorithms. Machine vision analyzes images of products as they’re produced, detecting defects that human inspectors may miss. These AI models are trained using large image sets, allowing them to spot problems like surface flaws, incorrect sizes, or packaging errors. With this, manufacturers can ensure only high-standard products leave the factory.

A major advantage of AI-powered systems, compared to manual or automated ones, is their ability to learn and adapt. The more data these AI systems process, the better they become at spotting defects, cutting down on both false alarms and missed errors. This learning process helps manufacturers maintain quality across their products.

For those looking to enhance their role in this field, gaining the Certified Artificial Intelligence (AI) Expert™ credential can deepen your understanding and impact on production quality.

AI-Powered Automated Inspection

One of the areas seeing big changes from AI is automated optical inspection (AOI). Manufacturers have long depended on AOI systems to catch flaws, but older systems struggled with adapting to different production conditions. They followed preset rules and would fail when there were changes in the production process. AI has made these systems much more adaptable by allowing them to learn and adjust on the go.

These AI-based AOI systems use deep learning to evaluate large amounts of visual data, spotting even the tiniest flaws with precision. They get smarter after every inspection, cutting the need for manual checks and reducing errors. This results in smoother production lines and better product quality. For instance, Nissan’s assembly plant in Tennessee saw an improvement in defect detection rates by almost 7% using AI-powered inspections.

Real-Time Monitoring and Feedback

AI’s ability to monitor production lines in real-time brings another benefit. Traditional quality checks were done at intervals, but AI-driven systems constantly monitor operations. This immediate oversight helps catch mistakes as they occur, lowering the chances of defects going unnoticed. These systems don’t just detect problems; they also analyze production data, offering insights to optimize future processes. This helps reduce costly rework and downtime.

For example, Bosch uses AI to monitor data from vehicle parts during assembly. AI identifies potential problems early on and fixes them before they escalate, helping keep the assembly line running without hiccups.

Enhanced Visual Inspection with Computer Vision

AI has taken on a bigger role in visual quality inspections. Visual checks are crucial in fields like electronics, automotive, and aerospace, where detail is everything. Yet, manual inspections can take a lot of time and aren’t always accurate. AI-driven computer vision now automates this process, quickly detecting issues in surfaces, materials, and textures more efficiently than human inspectors.

In 2024, the BMW Group adopted AI-powered image recognition to inspect parts in real-time. AI compared images of components with thousands of samples to identify deviations, ensuring all parts meet quality standards before moving further down the line.

Edge computing has further enhanced these systems. With edge AI, data is processed directly on devices such as cameras or sensors. This speeds up feedback and lowers dependency on centralized servers, making operations more efficient, even in areas where network coverage might be unreliable.

Predictive Maintenance and AI’s Role

AI has also boosted quality control through predictive maintenance. Sensors fitted in production machines collect data like temperature and vibrations. AI analyzes this data to predict possible machine breakdowns, allowing operators to carry out repairs before they lead to defects or halts in production. Predictive maintenance is especially valuable in sectors like automotive and aerospace, where downtime is costly.

Siemens is an example of a company that uses AI for predictive maintenance. Their system monitors machinery and alerts staff when parts need attention, lowering the risk of unexpected downtime and saving on repair costs.

Applications Across Different Industries

Many manufacturers, from various industries, have embraced AI for quality control. The results are clear. Ford, for instance, uses AI in vehicle production, quickly identifying and fixing potential problems. This has cut rework costs and helped keep their standards high.

Another example is Teledyne e2v, which introduced an AI-powered imaging module in 2024. This new module offers more detailed inspections, including 3D depth data, which is essential for inspecting complex or layered products.

Challenges in AI Implementation

Although AI’s benefits in quality control are obvious, there are some challenges:Initial Costs: The investment in AI systems can be high, especially for hardware like high-quality cameras and AI software models.
Integration Issues: Incorporating AI into existing manufacturing lines isn’t always smooth. It often requires working with tech experts and giving workers special training.
Data Quality: AI relies on high-quality data to be effective. Poor or mislabeled data can lead to incorrect defect detection. Manufacturers need to put in place solid data collection and management systems to get the most from AI.

Conclusion

AI and computer vision have become crucial tools for improving quality control in manufacturing. They enable real-time defect detection, improve inspection accuracy, and offer insights that help manufacturers refine their processes. The growing shift towards edge computing also allows faster feedback and fewer issues related to connectivity. As more companies implement AI-powered solutions, quality control will continue to see improvements in reliability, waste reduction, and overall customer satisfaction.

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Computers normally can't see optical illusions — but a scientist combined AI with quantum mechanics to make it happen

A new artificial intelligence (AI) system can mimic how people interpret complex optical illusions for the first time, thanks to principles borrowed from the laws of quantum mechanics.

Optical illusions, such as the Necker Cube and Rubin's Vase, trick the brain into seeing one interpretation first and then another, as the image is studied. The human brain effectively switches between two or more different versions of what is possible, despite the image remaining static.

Computer vision, however, cannot simulate the psychological and neurological aspects of human vision and struggles to mimic our naturally evolved pattern recognition capabilities. The most advanced AI agents today, therefore, struggle to see optical illusions the way humans do.

But a new study published Aug. 22 in the journal APL Machine Learning demonstrated a technique that lets an AI imitate the way a human brain interprets an optical illusion, by utilizing the physical phenomenon of "quantum tunneling."

The AI system is dubbed a "quantum-tunneling deep neural network" and combines neural networks with quantum tunneling. A deep neural network is a collection of machine learning algorithms inspired by the structure and function of the brain — with multiple layers of nodes between the input and output. It can model complex non-linear relationships and, unlike conventional neural networks (which include a single layer between input and output) deep neural networks include many hidden layers.

Quantum tunneling, meanwhile, occurs when a subatomic particle, such as an electron or photon (particle of light), effectively passes through an impenetrable barrier. Because a subatomic particle like light can also behave as a wave — when it is not directly observed it is not in any fixed location — it has a small but finite probability of being on the other side of the barrier. When sufficient subatomic particles are present, some will "tunnel" through the barrier.

After the data representing the optical illusion passes through the quantum tunneling stage, the slightly altered image is processed by a deep neural network.

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Saturday, October 26, 2024

A Game-Changer for AI: The Tsetlin Machine’s Role in Reducing Energy Consumption

The rapid rise of Artificial Intelligence (AI) has transformed numerous sectors, from healthcare and finance to energy management and beyond. However, this growth in AI adoption has resulted in a significant issue of energy consumption. Modern AI models, particularly those based on deep learning and neural networks, are incredibly power-hungry. Training a single large-scale model can use as much energy as multiple households consume yearly, leading to significant environmental impact. As AI becomes more embedded in our daily lives, finding ways to reduce its energy usage is not just a technical challenge; it's an environmental priority.

The Tsetlin Machine offers a promising solution. Unlike traditional neural networks, which rely on complex mathematical computations and massive datasets, Tsetlin Machines employ a more straightforward, rule-based approach. This unique methodology makes them easier to interpret and significantly reduces energy consumption.

Understanding the Tsetlin Machine

The Tsetlin Machine is an AI model that reimagines learning and decision-making. Unlike neural networks, which rely on layers of neurons and complex computations, Tsetlin Machines use a rule-based approach driven by simple Boolean logic. We can think of Tsetlin Machines as machines that learn by creating rules to represent data patterns. They operate using binary operations, conjunctions, disjunctions, and negations, making them inherently simpler and less computationally intensive than traditional models.

TMs operate on the principle of reinforcement learning, using Tsetlin Automata to adjust their internal states based on feedback from the environment. These automata function as state machines that learn to make decisions by flipping bits. As the machine processes more data, it refines its decision-making rules to improve accuracy.

One main feature that differentiates Tsetlin Machines from neural networks is that they are easier to understand. Neural networks often work like “black boxes,” giving results without explaining how they got there. In contrast, Tsetlin Machines create clear, human-readable rules as they learn. This transparency makes Tsetlin Machines easier to use and simplifies the process of fixing and improving them.

Recent advancements have made Tsetlin Machines even more efficient. One essential improvement is deterministic state jumps, which means the machine no longer relies on random number generation to make decisions. In the past, Tsetlin Machines used random changes to adjust their internal states, which was only sometimes efficient. By switching to a more predictable, step-by-step approach, Tsetlin Machines now learn faster, respond more quickly, and use less energy.

The Current Energy Challenge in AI

The rapid growth of AI has led to a massive increase in energy use. The main reason is the training and deployment of deep learning models. These models, which power systems like image recognition, language processing, and recommendation systems, need vast amounts of data and complex math operations. For example, training a language model like GPT-4 involves processing billions of parameters and can take days or weeks on powerful, energy-hungry hardware like GPUs.

A study from the University of Massachusetts Amherst shows the significant impact of AI's high energy consumption. Researchers found that training a single AI model can emit over 626,000 pounds of CO₂, about the same as the emissions from five cars over their lifetimes. This large carbon footprint is due to the extensive computational power needed, often using GPUs for days or weeks. Furthermore, the data centers hosting these AI models consume a lot of electricity, usually sourced from non-renewable energy. As AI use becomes more widespread, the environmental cost of running these power-hungry models is becoming a significant concern. This situation emphasizes the need for more energy-efficient AI models, like the Tsetlin Machine, which aims to balance strong performance with sustainability.

There is also the financial side to consider. High energy use means higher costs, making AI solutions less affordable, especially for smaller businesses. This situation shows why we urgently need more energy-efficient AI models that deliver strong performance without harming the environment. This is where the Tsetlin Machine comes in as a promising alternative.

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Friday, October 25, 2024

The mainframe’s future in the age of AI

If there’s any doubt that mainframes will have a place in the AI future, many organizations running the hardware are already planning for it.

While the 60-year-old mainframe platform wasn’t created to run AI workloads, 86% of business and IT leaders surveyed by Kyndryl say they are deploying, or plan to deploy, AI tools or applications on their mainframes. Moreover, in the near term, 71% say they are already using AI-driven insights to assist with their mainframe modernization efforts.

Running AI on mainframes as a trend is still in its infancy, but the survey suggests many companies do not plan to give up their mainframes even as AI creates new computing needs, says Petra Goude, global practice leader for core enterprise and zCloud at global managed IT services company Kyndryl.

Many Kyndryl customers seem to be thinking about how to merge the mission-critical data on their mainframes with AI tools, she says. In addition to using AI with modernization efforts, almost half of those surveyed plan to use generative AI to unlock critical mainframe data and transform it into actionable insights.

“You either move the data to the [AI] model that typically runs in cloud today, or you move the models to the machine where the data runs,” she adds. “I believe you’re going to see both.”

Meanwhile, AI can also help companies modernize their mainframe strategies, whether it be assisting with moving workloads to the cloud, converting old mainframe code, or training workers in mainframe-related technologies, Goude says.

For most users, mainframe modernization means keeping some mission-critical workloads on premises while shifting other workloads to the cloud, Goude says. A huge majority of survey respondents plan to move some workloads off the mainframe, but nearly as many say they consider mainframes important to their business strategies.

Goude sees more business and IT leaders embracing a hybrid IT environment now than in past years, when many organizations were taking an all-or-nothing approach.

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Tuesday, October 22, 2024

Why Python is the language of choice for AI

The widespread adoption of AI is creating a paradigm shift in the software engineering world. Python has quickly become the programming language of choice for AI development due to its usability, mature ecosystem, and ability to meet the data-driven needs of AI and machine learning (ML) workflows. As AI expands to new industries and use cases, and Python’s functionality evolves, the demand for developers versed in the language will balloon. Python developers who invest in their AI and ML knowledge will be well-positioned to thrive in the era of AI.

Python is the most popular programming language, according to the TIOBE Programming Community Index. Python took its first lead over the other languages in 2021 and continued to explode in popularity as the growth of other languages largely remained stagnant. Meanwhile, nearly 30% of the searches for programming language tutorials on Google were for Python, nearly double the percentage for Java, which is ranked second, according to the PYPL Index, which is based on data from Google Trends. It’s no wonder that the popularity of Python has extended to AI workflows too.

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Monday, October 21, 2024

Implementing cross-validation for datasets with spatial autocorrelation using scikit-learn

A typical and useful assumption for statistical inference is that the data is independently and identically distributed (IID). We can take a random subset of patients and predict their likelihood for diabetes with a normal train-test split, no problem. In practice, however, there are some types of datasets where this assumption doesn’t hold, and a typical train-test split can introduce data leakage. When the distribution of the variable of interest is not random, the data is said to be autocorrelated — and this has implications on machine learning models.

We can find spatial autocorrelation on many datasets with a geospatial component. Consider the maps below:

If data were IID, it would look like the map on the right. But in real life, we have maps like on the left where we can easily observe patterns. The first law of geography states that nearer things are more related to each other than distant things. Attributes usually aren’t randomly distributed across a location – it’s more likely that an area is very similar to its neighbors. In the example above, the population level of a single area is likely to be similar of an adjacent area, as opposed to a distant one.
When do we need spatial cross-validation?

When data is autocorrelated, we might want to be extra wary about overfitting. In this case, if we use random samples for train-test splits or cross-validation, we violate the IID assumption since the samples are not statistically independent. Area A could be in the training set, but an Area Z in the validation set happens to be only a kilometer away from Area A while also sharing very similar features. The model would have a more accurate prediction for Area Z since it saw a very similar example in the training set. To fix this, grouping the data by area would prevent the model from peeking into data it shouldn’t be seeing. Here’s how spatial cross-validation would look like:

A good question to ask here: do we always want to prevent overfitting? Intuitively, yes. But as with most machine learning techniques, it depends. If it fits your use case, overfitting may even be beneficial!

Let’s say we had a randomly sampled national survey on wealth. We have wealth values of a distributed set of households across the country, and we’d like to infer the wealth levels for unsurveyed areas to get complete wealth data for the entire country. Here, the goal is only to fill in spatial gaps. Training with the data of the nearest areas would certainly help fill in the gaps more accurately!

It’s a different story if we were trying to build a generalizable model — say, one that we would apply to another country altogether. [2] In this case, exploiting the spatial autocorrelation property during training will likely inflate the accuracy of a potentially poor model. This is especially concerning if we use this seemingly-good model on an area where there is no ground truth for verifying.
Spatial cross-validation implementation on scikit-learn

To address this, we’d have to split areas between training and testing. If this were a normal train-test split, we could easily filter out a few areas out for our test data. In other cases, however, we would want to utilize all of the available data by using cross-validation. Unfortunately, scikit-learn’s built-in CV functions split the data randomly or by target variable, not by chosen columns. A workaround can be implemented, taking into consideration that our dataset includes geocoded elements.

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