Global Tech Excellence Awards

Friday, January 3, 2025

Machine learning reveals how metabolite profiles predict aging and health

Background

Biological aging, distinct from chronological age, reflects molecular and cellular damage influencing health and disease susceptibility. Chronological age alone cannot capture the variability in aging-related physiological states among individuals. However, recent advances in omics technologies, particularly metabolomics, have offered insights into biological aging through molecular profiling.

Metabolites, or small molecules from metabolic pathways, can provide assessments of physiological health and are linked to aging-related outcomes, such as chronic diseases and mortality. Earlier studies have correlated metabolomic data with aging but have been constrained by limited sample sizes and markers.

Recent efforts to derive "aging clocks" using machine learning from omics data have demonstrated significant predictive power for health outcomes. However, there continue to be challenges in optimizing these models for accuracy and interpretability, especially using metabolomics.
The current study

The present study utilized nuclear magnetic resonance (NMR) spectroscopy to analyze plasma metabolite data from the U.K. Biobank, involving 225,212 participants between the ages of 37 and 73 years. The exclusion criteria included pregnancy, data inconsistencies, and extreme metabolite values. The dataset encompassed 168 metabolites representing lipid profiles, amino acids, and glycolysis products.

The researchers applied 17 machine learning algorithms, including linear regression, tree-based models, and ensemble techniques, to the dataset to develop metabolomic aging clocks. They also used a rigorous nested cross-validation approach to ensure robust model evaluation.

Some of the main preprocessing steps included handling outlier metabolite values and correcting age-prediction biases inherent to the models. The predictive models aimed to estimate chronological age using metabolite profiles, and the differences between predicted and actual ages were defined as the "MileAge delta." Statistical corrections were extensively applied to remove systematic biases and enhance prediction accuracy, particularly for younger and older age ranges.

The models were evaluated for predictive accuracy using metrics such as mean absolute error (MAE), root mean square error (RMSE), and correlation coefficients. For example, the Cubist regression model achieved an MAE of 5.31 years, outperforming other models like multivariate adaptive regression splines (MAE = 6.36 years). Further analysis adjusted the predictions to remove systematic biases and improve their alignment with chronological age.

Results

The findings indicated that metabolomic aging clocks developed from plasma metabolite profiles could effectively differentiate biological aging from chronological aging. Of the various models tested in the study, the Cubist rule-based regression model provided the strongest predictive associations with health markers and mortality and outperformed the other algorithms in accuracy and robustness.

Additionally, positive MileAge delta values, which indicated accelerated aging, were linked to frailty, shorter telomeres, higher morbidity, and increased mortality risk. Specifically, a 1-year increase in MileAge delta corresponded to a 4% rise in all-cause mortality risk, with hazard ratios (HR) exceeding 1.5 in extreme cases.

Moreover, the study showed that individuals with accelerated aging were more likely to report poorer self-rated health and experience chronic illnesses. Associations with frailty and telomere attrition were particularly pronounced, with some differences being equivalent to an 18-year disparity in frailty index scores. Interestingly, women exhibited slightly higher MileAge deltas than men across most models.

The study also confirmed the non-linear nature of metabolite-age relationships and emphasized the utility of statistical corrections in enhancing prediction accuracy. Additionally, comparing existing aging markers showed that metabolomic aging clocks captured unique health-relevant signals and often outperformed the simpler predictors. However, the results highlighted that decelerated aging (negative MileAge deltas) did not consistently translate into better health outcomes, underscoring the complexity of biological aging metrics.

Conclusions

Overall, the study demonstrated the utility of metabolomic aging clocks in predicting biological aging and associated health outcomes. By benchmarking multiple machine learning algorithms, the findings also showed the superior performance of the Cubist rule-based model in linking metabolite-derived ages to health markers and mortality.

The results suggested that metabolomic aging clocks hold potential for proactive health management and risk stratification and emphasized the need for further validation across diverse populations and longitudinal data for broader clinical application. This study sets a new benchmark for algorithm development, illustrating how metabolomic profiles can offer actionable insights into aging and health.

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Monday, December 30, 2024

Mastering Model Uncertainty: Thresholding Techniques in Deep Learning

In many real-world applications, machine learning models are not designed to make decisions in an all-or-nothing manner. Instead, there are situations where it is more beneficial for the model to flag certain predictions for human review — a process known as human-in-the-loop. This approach is particularly valuable in high-stakes scenarios such as fraud detection, where the cost of false negatives is significant. By allowing humans to intervene when a model is uncertain or encounters complex cases, businesses can ensure more nuanced and accurate decision-making.

In this article, we will explore how thresholding, a technique used to manage model uncertainty, can be implemented within a deep learning setting. Thresholding helps determine when a model is confident enough to make a decision autonomously and when it should defer to human judgment. This will be done using a real-world example to illustrate the potential.

By the end of this article, the hope is to provide both technical teams and business stakeholders with some tips and inspiration for making decisions about modelling, thresholding strategies, and the balance between automation and human oversight.

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Where are we in the evolution of artificial intelligence? Understanding the layers and how to leverage them

Breaking down the levels of AI

The evolution of AI has been classified into levels to better understand its capabilities and applications. Here is a brief description of each of these levels:

Level 1 – Chatbots

At this initial level we find chatbots, tools that respond to specific queries with programmed responses. Their conversational capacity is limited, as they follow predefined rules. A typical example would be a customer service chatbot that answers questions about shop hours or products, following a fixed script and providing direct answers.

Level 2 – Reasoners

At this level, AI goes a step further and can provide answers to more complex questions, helping to solve problems in a practical way. Although some experts insist that AI ‘does not reason’ in the human sense, its ability to analyse data and provide solutions to real problems is very close to this concept. These reasoners can help find quick and accurate answers to problems we face every day

Level 3 – Agents

Agents are more advanced systems that, thanks to prior human training, have the ability to make autonomous decisions and perform complex tasks more independently. Although they act proactively, it is essential to remember that these agents were previously trained by humans to achieve this level of autonomy and precision. They can anticipate user needs and provide solutions before a problem arises.

Chatbots, assistants or agents

We often hear terms such as chatbots, assistants or agents and it can be confusing to differentiate between them, as their concepts are used interchangeably. However, each has a different scope and purpose.

For example, a chatbot is usually a basic tool that responds to specific questions with scripted answers, like an automated menu on a website. An assistant is more advanced and can interact in a more conversational way, helping with tasks such as scheduling appointments or recalling information. Finally, an agent is more sophisticated and autonomous, able to understand more complex contexts and make decisions, such as handling transactions or solving multifaceted problems.

Here is a practical example to help you better understand the difference in their functionalities and their impact. Imagine a customer facing problems with a cancelled flight:Chatbot: The customer asks ‘What time does my flight leave?’ and the chatbot replies, ‘Your flight has been cancelled. Please contact customer service for more details’.

Virtual assistant: The customer says to the assistant, ‘My flight was cancelled, what can I do?’ The assistant understands the situation and offers options such as finding an alternative flight or cancelling the booking for a refund.

Intelligent agent: The customer receives a proactive notification before having to ask: ‘We have detected that your flight has been cancelled. We have already found the best options to rebook you. You can choose between these alternatives or request a direct refund’.

The difference between these levels lies in the responsiveness and the way they anticipate the user’s needs. However, it is important to remember that all of this capability comes from prior training and design by humans.

Conclusion

As we can see, AI continues to advance and there is already talk of a possible level 5, where it could be able to perform even more complex tasks. But this does not mean that AI is entirely self-sufficient; and here the challenge we face is to continue to educate ourselves to have our own critical thinking and to be able to discern when and how we can improve and leverage its value as AI progresses through its different levels.

Humans do not need ‘levels’ like AI, but it is key that we develop our ability to learn, question and be critical. Understanding how AI can help us, whether as chatbots, assistants or agents, will allow us to make the most of its potential and improve processes in our day-to-day lives. It is about seeing AI as a tool that, when used well, will make us more productive and effective in our work, with us always being responsible for guiding its use and evolution in an ethical and responsible way.

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Saturday, December 21, 2024

Computer Vision: from Image to Artificial Intelligence

Computer vision technology is based on the automated analysis of visual data. Following an interdisciplinary approach, it combines Artificial Intelligence, image processing, and computer science to enable machines to acquire, interpret, and understand images and videos. This technology has evolved a lot in recent years, driven above all by the growing computing power and the availability of large datasets.

Technical limitations in computer vision technology

Despite the opportunities and interest, implementing computer vision systems in embedded devices, such as industrial control systems, robotics, drones, or IoT devices, introduces some complex challenges. First, the limited computational and memory hardware capabilities of embedded devices require careful optimization of computer vision algorithms. Deep neural networks, while highly effective, can also be very expensive in terms of power and memory.

Another aspect to consider is energy efficiency: many embedded systems, such as those used in drones or remote sensors, operate on battery power, so in these cases, it is essential to minimize processor power consumption. Added to this, is the robustness of vision systems, especially in uncontrolled environments. While deep learning models have demonstrated outstanding performance in well-defined contexts and with high-quality datasets, they can be susceptible to sudden changes in environmental conditions, such as changes in lighting, camera angles, or noise, which is particularly problematic in embedded systems used in industrial or outdoor scenarios, where environmental conditions can vary dramatically.

Computer vision-based surveillance devices also raise concerns about the misuse of facial recognition technologies or the invasiveness of visual data collection. It is therefore essential that computer vision system designers incorporate measures to ensure the protection of personal data in compliance with privacy regulations.

Applications and solutions for computer vision

Despite some technical limitations, as mentioned above, the opportunities offered by computer vision are immense. The manufacturing sector is one of the biggest beneficiaries of this technology, where computer vision is used for quality control, process automation, and predictive maintenance. Systems can detect defects in products or anomalies in machinery with greater precision than humans, reducing costs and improving efficiency.

In the healthcare sector, computer vision is transforming medical care, with applications ranging from automated diagnosis of medical images to real-time patient monitoring using video cameras. The automotive industry is also exploiting the potential of computer vision, especially in the development of autonomous vehicles, where computer vision allows vehicles to “see” their surroundings, and recognize obstacles, road signs, and pedestrians.

Analog Devices provides a broad range of computer vision products and solutions specifically designed to support advanced machine vision applications. The products cover various aspects of image processing and accelerate the development of intelligent machine vision systems. With a comprehensive portfolio of advanced technologies, Analog Devices is today a key player in the machine vision market, with integrated and scalable solutions for numerous applications such as industrial and automation, advanced robotics, automotive and autonomous driving, healthcare (medical imaging, telemedicine, diagnostic image analysis), security and consumer.

The company’s key products include integrated solutions for LiDAR and Radar systems, designed for computer vision applications in autonomous vehicles, and ADAS systems that combine different technologies to improve the perception of the surrounding environment, providing detailed, three-dimensional images. There is a growing demand for ADAS solutions to combine efficient power management in smaller footprints, combined with high-speed connectivity, complex interconnections, and data integrity.

Advanced Driver Assistance Systems (ADAS) include technologies designed to assist drivers while driving, improving vehicle safety and efficiency. ADAS features can include obstacle and pedestrian detection, adaptive cruise control, traffic sign recognition, lane keeping, blind spot monitoring, and automatic emergency braking. ADAS uses sensors, cameras, radar, and LiDAR to collect data about the surrounding environment and assist the driver in making correct and safe decisions. In this area, Analog Devices’ radar sensors are particularly appreciated for their accuracy in detecting moving objects.

ADI’s next-generation ADAS architectures combine AI and machine learning with computer vision to improve object recognition, scene understanding, and video analytics, while also enabling faster time to market. ADAS systems, including precision sensing, intelligent power management, high-speed connectivity, and data integrity, enable efficient design with a small footprint of external components. All these ADAS capabilities are enabled by a set of sensors distributed throughout the car, networked to I/O modules, actuators, and controllers. Driver monitoring systems, parking and autonomous vehicle cameras, acoustic warning systems for electric vehicles, and emergency vehicle detection complete the portfolio.

The flexibility and scalability of next-generation ADAS systems aim to enable efficient and precise operations, reduce design complexity, and accelerate development time. ADI provides precision sensing, intelligent power management, and connectivity, which support sensor fusion and processing from cameras, radar, and LIDAR systems.

ADI also provides image sensors optimized for capturing high-resolution images, with applications ranging from machine vision to video acquisition. The sensors support capabilities such as low-light image processing and high dynamic range and are widely used in:Industrial automation and robotics
Medical imaging devices

ADAS and autonomous vehicles

There are also advanced processing platforms with low-power embedded vision capabilities and hardware accelerators for image processing. All Analog Devices embedded vision solutions offer a combination of advanced sensors and high-performance processing hardware, suitable for industrial, automotive, and healthcare contexts.

For example, the range of products such as the Blackfin Embedded Vision Processor, is designed to provide optimized processing power for vision applications. ADI provides processors and processing solutions to handle the data flow from image sensors, accelerating the process of inference and visual analysis; these include digital signal processors (DSPs) optimized for machine vision and deep learning.

The Blackfin ADSP-BF609 processor is optimized for embedded vision and video analytics applications using a dual-core fixed-point DSP processor with a unique pipelined vision processor (PVP). The PVP is a set of functional blocks alongside the Blackfin cores designed to accelerate image processing algorithms and reduce overall bandwidth requirements. Other processor specifications include an advanced high-performance infrastructure, large on-chip memory, and a feature-rich peripheral set with extensive connectivity options. The ADSP-BF609 processor is ideal for many embedded vision applications such as automotive advanced driver assistance systems (ADAS), machine vision and robotics for manufacturing, security and surveillance analytics, and barcode scanners.

The ADSD3500 is a time-of-flight (ToF) depth image signal processor for Analog Devices ToF products such as the ADTF3175 and ADSD3030. The ADSD3500 supports full depth, active brightness, and confidence calculation for 640×480 resolution and partial depth calculation (pre-phase unwrap) for 1024×1024 resolution. The data flow and processing are controlled via the integrated ARM Cortex-M33. The calculation is performed using dedicated hardware and memory, enabling a low-power ToF depth ISP solution.

The ADSD3500 also controls the booting of the image sensor module, loading of calibration data, and triggering of frames. Designed for an operating temperature range of -25°C to +85°C, it addresses the following application fields: augmented reality (AR) systems, robotics, building automation, and machine vision systems. The ADSD3500 is available in a 3.47mm x 3.47mm WLCSP package.

ADI also provides a range of high-quality video acquisition and transmission solutions, including high-speed video interfaces, encoders, decoders, and transceivers.

Computer vision on low-cost platforms

Object detection is one of the main applications of Artificial Intelligence, which is used both at the Machine Learning and Deep Learning levels. The well-known single-board computer brand, Raspberry Pi, has had a significant impact on the field of embedded computer vision. Thanks to its compact and powerful boards, Raspberry Pi can run artificial vision algorithms even on low-cost devices, such as the Pi Camera module, which integrates perfectly with the platform for embedded vision projects, making it today the preferred tool for hobbyists, academic researchers and developers of prototypes and real applications.

For example, it is possible to implement a real-time automatic object detection and identification application on Raspberry Pi through TensorFlow, an open-source platform for Machine Learning designed to facilitate the construction, training, and deployment of Machine Learning and Artificial Intelligence models. To do this, all you need is a common Raspberry Pi 3, a camera for image acquisition, and an SD memory card.

Additionally, the neural network can be trained to detect specific classes of objects within the same image, turning the Raspberry Pi into a highly customized detection system for computing applications. Even a low-cost embedded platform with performance that cannot match specialized AI hardware can run an object recognition model with acceptable results. With its versatility and the support of a large development community, the Raspberry Pi provides a solid foundation for embedded vision applications, allowing you to integrate cameras, sensors, and hardware accelerators into your designs.

Conclusions and Development Prospects

The field of computer vision represents today one of the most dynamic frontiers of modern technology. Designers of computer vision systems must achieve a good compromise between the management of computational resources for image and video processing, robustness of algorithms, and precision of expected results, without losing sight of energy savings. Thanks to innovations by companies in the sector, the development and implementation of powerful hardware platforms are now more accessible and open the doors to new sectors and increasingly intelligent and performing solutions, even in advanced applications and extreme conditions.

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Friday, November 22, 2024

AI Inference Server solution enhances AI-assisted machine vision processes

New AI server solution reduces the time and resource needed for quality and defect analysis with the removal of elementary inspection tasks.

Embedded systems and display solutions provider, Review Display Systems (RDS) has announced the introduction of a new AAEON AI Inference Server. The MAXER-2100 is a 2U Rackmount server powered by the Intel Core i9-13900 Processor, which is designed to meet high-performance computing needs.

The MAXER-2100 also supports both 12th and 13th Generation Intel Core LGA 1700 socket-type CPUs and features an integrated NVIDIA GeForce RTX 4080 SUPER GPU. The default MAXER-2100 server features the NVIDIA GeForce RTX 4080 SUPER and is also compatible with a NVIDIA-certified Edge System for both the NVIDIA L4 Tensor Core and NVIDIA RTX 6000 Ada GPUs.

Equipped with both a high-performance CPU and industry-leading GPU, a key feature of the MAXER- is its ability to execute complex AI algorithms and datasets, process multiple high-definition video streams simultaneously, and use machine learning to refine large language models (LLMs) and inferencing models.

Providing latency-free operation, the MAXER-2100 offers up to 128GB of DDR5 system memory through dual-channel SODIMM slots. For storage, it includes an M.2 2280 M-Key for NVMe and two hot-swappable 2.5” SATA SSD bays with RAID support. The system also provides extensive functional expansion options, including one PCIe x16 slot, an M.2 2230 E-Key for Wi-Fi, and an M.2 3042/3052 B-Key with a micro SIM slot.

For peripheral connectivity, the server boasts a total of four RJ-45 ports, two running at 2.5GbE and two at 1GbE speed, along with four USB 3.2 Gen 2 ports running at 10Gbps. For industrial communications, the MAXER-2100 implements RS-232/422/485 via a DB-9 port. Multiple display interfaces are supported with HDMI 2.0, DP 1.4, and VGA ports, which employ the graphic capability of the integrated NVIDIA GeForce RTX 4080 SUPER GPU.

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Thursday, November 21, 2024

Machine learning and supercomputer simulations predict interactions between gold nanoparticles and blood proteins

Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein interactions can be predicted from machine learning models that are trained from atom-scale molecular dynamics simulations. The new methodology opens ways to simulate the efficacy of gold nanoparticles as targeted drug delivery systems in precision nanomedicine.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano–bio interface.

Modeling the properties of the nano-bio interface is demanding since the important processes such as electronic charge transfer, chemical reactions or restructuring of the biomolecule surface can take place in a wide range of length and time scales, and the atomistic simulations need to be run in the appropriate aqueous environment.

Machine learning helps to study interactions at the atomic level

Recently, researchers at the University of Jyväskylä demonstrated that it is possible to significantly speed up atomistic simulations of interactions between metal nanoparticles and blood proteins.

Based on extensive molecular dynamics simulation data of gold nanoparticle—protein systems in water, graph theory and neural networks were used to create a methodology that can predict the most favorable binding sites of the nanoparticles to five common human blood proteins (serum albumin, apolipoprotein E, immunoglobulin E, immunoglobulin G and fibrinogen). The machine learning results were successfully validated by long-timescale atomistic simulations.

"In recent months, we also published a computational study which showed that it is possible to selectively target over-expressed proteins at a cancer cell surface by functionalized gold nanoparticles carrying peptides and cancer drugs, says professor of computational nanoscience," says Hannu Häkkinen.

"With the new machine learning methodology, we can now extend our work to investigate how drug-carrying nanoparticles interact with blood proteins and how those interactions change the efficacy of the drug carriers."

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Wednesday, November 20, 2024

How Generative AI is Shaping the Next Wave of Innovation

Generative AI – An Introduction

Innovation for change and growth in various sectors around the globe is determined and motivated by the speed in using digital technologies. Generative AI is the most revolutionary technological innovation that has occurred in the last few years. Generative AI gives output that is different from the existing information while operating in a similar way to the previous types of AI and working by a set of rules and algorithms. Generative AI learns from the patterns of the provided data to create new content and this ability to have original output is creating new horizons, bringing organisations and institutions in every sector to the next phase of advancements.

The Power of Generative AI: A Brief Overview

In its essence, Generative AI uses basic machine learning as well as deep learning and neural networks to learn from the quantity of information to generate information.

GPT (Generative Pretrained Transformers), DALL·E, and GANs (Generative Adversarial Networks) paved the way for content generation through AI systems that are able to produce highly creative content.

As per a recent survey, around 74% of Business executives claimed that using Generative AI completely revolutionised their approach towards the business operations. Generative AI’s capability of providing organisations with unprecedented opportunities across all fields, such as manufacturing, healthcare, finance, entertainment, and marketing.

Generative AI – Its Role in the Manufacturing Sector

Generative AI has brought a revolutionary change in the manufacturing industry that was originally based on mechanistic production processes. AI is becoming an increasingly important factor of production in various industries and one of the most noticeable areas is the product designing domain.

Generative design software provides innovative project solutions depending on the defined parameters such as weight, material, and cost amongst others.

At the same time, there is the growth of additive manufacturing, that is 3D printing, with generative design to advance the possibility of constructing specific, delicate structures, which had never been achievable by conventional construction methods.

Generative AI: Forging A New Paradigm in Healthcare

Healthcare, a field that always requires advances, is improvising with the help of the applications of Generative AI. The healthcare sector is using data produced by AI in drug discovery, and personalised medicine to drive progress that can ultimately lead to saving lives.

In drug discovery, for instance, Generative AI is being applied to estimate the chemical configuration of new drugs. It used to take much time to identify a viable drug component from the identification of the target all the way to the development of compound screening and testing.

Now, with AI systems, a company can model millions of ‘molecule conversations’ in a few hours and spit out compounds that are then synthesised in the lab. Generative AI has the effect of greatly accelerating the process of arriving at new drugs while decreasing expenses.

Moreover, deep learning-derived synthesised data considerably bridges the gaps in shrinking patient datasets for medical researchers. AI in this case is useful in developing artificial but highly accurate patient data sets that simulate real-life situations. Generative AI is being used in training machine learning models, conducting diagnoses, and performing forecast analysis.

Generative AI is also being applied for the optimization of diagnostic image quality, in tumour detection and prediction of patient outcomes from existing databases. Special diagnostic applications based on artificial intelligence are expanding the existing set of tools used by doctors, which helps to diagnose the disease and develop an individual treatment plan more quickly and accurately.

Generative AI in the Finance Sector: Unlocking New Possibilities

Generative AI is a new breakthrough that has found usage in the financial services industry, as this industry has been very receptive to new technologies. Algorithmic trading is one of the most promising fields in which generative AI can be applied.

AI systems are able to develop very efficient trading algorithms based on past stock exchange data and market factors, calculated after simulating numerous market conditions. Such strategies are usually better as compared to manual-based strategies, thereby ensuring the concerned financial institution has a competitive advantage in the market.

In addition, generative AI is revolutionising the insurance industry since firms are now capable of generating bespoke insurance policies for their clients. Through Generative AI, the risk and behaviour of the customers can be evaluated to come up with specific insurance solutions that will make customers satisfied and companies likely to avoid any losses due to claims.

Fraud detection is one of the domains where AI is being developed very actively. AI systems are also being used to create a synthetic transaction dataset that will help in fraud detection in real time.

Marketing and Generative AI: A New Frontier

Generative AI is changing the way brands create and deliver content in the field of marketing. With the rise of AI-driven copywriting tools such as Jasper as well as Copy.ai marketers can now generate creative content at scale, from blog posts to social media updates.

The best part about having AI generate content is how personalised it can be, and as promised, how fast it can generate content and data. AI analyses customer data to produce personally relevant messages to attract a given customer segment thereby creating engagement and conversion. Data-driven insights also can be generated by AI marketers to help them better optimise their campaigns in real-time.

Additionally, AI generative is being applied to create synthetic media, like AI influencers and avatars, to interact with social media platforms. Users can have real-time conversations with these AI-generated personae about personalised recommendations, answering queries, entertaining the users, and collecting valuable data for marketers.

Ethical Consideration and Challenges When Using Generative AI – What Are They

Generative AI has the potential to accomplish a wide range of tasks, but there are challenges to its adoption. As synthetic data creation, deep fakes, and AI-produced content come to the fore, ethical issues related to intellectual property, misinformation, and privacy are the main challenges that arise while using generative AI.

Deepfake technology — enabled by Generative AI — is causing concern because it can produce incredibly realistic but entirely fabricated videos. In other words, this carries the risk of political manipulation, identity theft, and the spread of misinformation.

The second big challenge also comes with potential job displacement due to the automation of the creative process. As AI gets better at tasks that humans have historically handled, industries must find the right balance to let AI create jobs while saving the workforce/employees from losing work.

In addition, generative AI models consume more natural resources, thereby raising concerns about the ecological impact. With that in mind, companies must think about how they might be able to implement AI solutions sustainably and ethically.

Conclusion: The Future of Generative AI

One thing for certain: generative AI has changed the game by introducing new technologies and completely changing the working environment of sectors across industries. Generative AI can revolutionise healthcare and manufacturing, and reframe creativity in the hospitality and marketing sectors.

However, as technologies like Generative AI move forward and are adopted and integrated by industries, there will arise issues pertaining to ethics and environmental concerns.

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Tuesday, November 19, 2024

How AI Is Turning DNA Secrets Into Lifesaving Medical Insights

Revolutionary AI Model for Disease Research

To better understand DNA’s role in disease, scientists at Los Alamos National Laboratory have developed EPBDxDNABERT-2, a pioneering multimodal deep learning model. This model is designed to precisely identify interactions between transcription factors—proteins that regulate gene activity—and DNA. EPBDxDNABERT-2 uses a process known as “DNA breathing,” where the DNA double-helix spontaneously opens and closes, allowing the model to capture these subtle dynamics. This capability has the potential to enhance drug design for diseases rooted in gene activity.

“There are many types of transcription factors, and the human genome is incomprehensibly large,” explained Anowarul Kabir, a researcher at Los Alamos and lead author of the study. “So, it is necessary to find out which transcription factor binds to which location on the incredibly long DNA structure. We tried to solve that problem with artificial intelligence, particularly deep-learning algorithms.”

Enhancing Drug Development With DNA Dynamics

DNA, consisting of an equivalent of 3 billion English letters in each human cell, acts as a blueprint for growth and function. Transcription factors bind to DNA regions, regulating gene expression—how genes guide cell development and function. This regulation plays a role in diseases, such as cancer, so accurately predicting transcription factor binding locations could have a significant impact on drug development.

The foundational model used by the research team was trained on DNA sequences. The team built a DNA simulation program that captures numerous DNA dynamics and integrated it with the genomic foundation model, resulting in EPBDxDNABERT-2, capable of processing genome sequences across chromosomes and incorporating corresponding DNA dynamics as input. One such input, DNA breathing, or the local and spontaneous opening and closing of the DNA double-helix structure, correlates with transcriptional activity, such as transcription factor binding.

“The integration of the DNA breathing features with the DNABERT-2 foundational model greatly enhanced transcription factor-binding predictions,” said Los Alamos researcher Manish Bhattarai. “We give sections of DNA code as input to the model and ask the model whether it binds to a transcription factor, or not, across many cell lines. The results improved the predictive probability of binding specific gene locations with many transcription factors.”

Leveraging Supercomputers for Genomic Analysis

The team ran their deep-learning model on the Laboratory’s newest supercomputer, Venado, which combines a central processing unit with a graphics processing unit to drive artificial intelligence capabilities. A deep-learning model works in ways similar to the brain’s neural networks, incorporating images and text and uncovering complex patterns to generate predictions and insights.

To train the model, the team used gene sequencing data from 690 experimental results, encompassing 161 distinct transcription factors and 91 human cell types. They found that EPBDxDNABERT-2 significantly improves — by 9.6% in one key metric — the prediction of the binding of over 660 transcription factors. Further experiments on in vitro datasets, drawn from experiments in a controlled environment, complemented the in nature datasets, or the data drawn directly from research with living organisms, such as mice.

The Promise of Multimodal Computational Genomics

The team found that while DNA breathing alone can estimate transcriptional activity almost accurately, the multimodal model can extract binding motifs, the specific DNA sequences to which transcription factors bind — a crucial element for explaining transcription processes.

“As demonstrated by its performance across multiple, diverse datasets, our multimodal foundational model exhibits versatility, robustness, and efficacy,” Bhattarai said. “This model signifies a substantial advancement in computational genomics, providing a sophisticated tool for analyzing complex biological mechanisms.”

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Computer Simulation Models Neuron Growth

Scientists developed a computer simulation that models neuron growth in the brain, which could support advancements in neurode generative disease research. The simulation accurately replicated real neuron growth patterns in the hippocampus, a brain region key to memory.

Built using BioDynaMo software, the model uses Approximate Bayesian Computation to closely match real-life neuron data, improving its precision. Though the simulation has shown success with specific neuron types, it may need further adjustments for broader applications.

Researchers hope this technology can lead to breakthroughs in understanding and treating conditions like Alzheimer’s. The model’s success points to the potential of digital simulations in enhancing brain research.

Key FactsThe simulation accurately mimicked neuron growth in the hippocampus.
The model uses Approximate Bayesian Computation for fine-tuning realism.
Built on BioDynaMo software, the tool aids in diverse biological simulations.

A new computer simulation of how our brains develop and grow neurons has been built by scientists from the University of Surrey.

Along with improving our understanding of how the brain works, researchers hope that the models will contribute to neurodegenerative disease research and, someday, stem cell research that helps regenerate brain tissue.

The research team used a technique called Approximate Bayesian Computation (ABC), which helps fine-tune the model by comparing the simulation with real neuron growth. This process ensures that the artificial brain accurately reflects how neurons grow and form connections in real life.

The simulation was tested using neurons from the hippocampus—a critical region of the brain involved in memory retention. The team found that their system successfully mimicked the growth patterns of real hippocampal neurons, showing the potential of this technology to simulate brain development in fine detail.

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