A recent analysis by SKY ENGINE AI highlights critical trends showing why real-world data is running into limits for modern computer vision applications. In 2025, Vision AI is already tackling advanced perception challenges such as contextual understanding, prediction, and multimodal sensing—areas where traditional image and video datasets fall short.

One major reason is that rare or dangerous events—like atypical human behaviors or critical edge cases—are nearly impossible to capture safely and consistently in real life. For AI systems to learn robustly in scenarios like autonomous driving, robotics, or safety monitoring, datasets must include diverse, controlled, and repeatable scenarios that real-world collection simply cannot provide.

Another accelerating trend is the use of digital twins and virtual sensor replicas—detailed simulations of cameras, LiDAR, radar, and other sensors that mimic real hardware in controlled virtual environments. These simulations allow Vision AI developers to test and train systems in complex scenarios that would be costly or unsafe to collect empirically.

Real-world data collection also struggles with slow cycles and manual labeling bottlenecks, making it difficult for teams to keep pace with fast innovation and evolving requirements from regulators and industry standards.

To address these limitations, the industry is increasingly turning to synthetic data, simulation-augmented validation, and multimodal datasets that provide full ground truth, edge-case coverage, and faster development cycles. Analysts forecast that by 2028, most Vision AI models will rely on multimodal training data that real-world footage alone cannot supply.

The implication is clear: as computer vision evolves from basic detection to contextual understanding and prediction, developers will need to embrace synthetic and simulated data if they want accurate, scalable, and safe AI systems in 2026 and beyond.

A recent study introduces a breakthrough technique designed to address one of AI’s biggest challenges: the opacity of black-box models. While deep learning systems excel at tasks like image recognition, they typically provide little insight into why they make specific decisions. This lack of transparency limits trust, accountability, and safe deployment.

The new approach, developed by researchers at the UNIST Graduate School of Artificial Intelligence and reported by Tech Xplore, shifts the focus from model internals to the training data itself. Instead of probing layers and activations, the method uses large language models to generate detailed, human-readable descriptions of training images. These descriptions are then evaluated for their impact on model predictions using a novel metric called Influence Scores for Texts (IFT).

IFT measures both:

• how strongly each description affects the model’s accuracy

• how well the description aligns with the image content

The result is a clearer, more interpretable map of what AI models actually learn and prioritize.

This technique has the potential to improve AI transparency across domains—from healthcare diagnostics to autonomous systems—by helping developers understand and validate how models use their training data. As the AI community increasingly calls for safer and more accountable systems, human-readable data interpretation represents a promising step toward trustworthy AI.

A new article published by the Financial Times argues that artificial intelligence remains fundamentally incomplete without spatial intelligence—the capability to understand, generate, and interact with three-dimensional worlds.

While large language models have transformed how AI processes text, they lack an understanding of physical space. Spatial intelligence aims to close this gap by enabling AI systems to build realistic 3D environments that mirror how humans perceive and navigate the world.

The article explores how emerging world models, developed by companies such as World Labs, allow users to generate immersive 3D spaces from photos, videos, or imagination. These environments can be explored, modified, and exported, supporting workflows across creative industries and technical fields.

Applications span visual effects and virtual production, architecture and interior design, game development, and robotics. In robotics in particular, spatial world models provide safe and scalable simulation environments, helping machines learn navigation and manipulation before operating in the real world.

A key benefit highlighted is efficiency. By drastically reducing the time required for ideation and iteration, spatial AI tools give creators, developers, and researchers more freedom to explore ideas. Rather than replacing human creativity, these systems are positioned as tools that amplify and accelerate it.

The article also frames spatial intelligence as part of a broader, multi-intelligence future for AI. Just as humans combine linguistic, spatial, and emotional intelligence, next-generation AI systems are expected to integrate language models with world models to better understand and interact with reality.

The overall message is clear: advancing AI beyond words and flat images toward true spatial understanding could redefine creativity, simulation, robotics, and how humans collaborate with intelligent systems.

New research explores and explain why AI-generated images often feel unnatural at a closer look—even when they appear impressive at first glance. Human vision is shaped by biology. Our eyes and brain work together to perceive color, depth, movement, cultural context, and emotional cues, allowing us to interpret meaning and realism in everyday scenes.

AI systems, in contrast, process images computationally. They analyze pixels, edges, textures, and patterns, comparing them with vast image libraries. Instead of understanding context the way humans do, AI relies on statistical patterns learned from training data, much of which comes from high-contrast stock imagery. When AI systems are asked to describe and recreate images, they tend to assume photorealism as a default style. They often overlook color subtleties, depth, and cultural signals, producing visuals that are boxier, more saturated, and more sensational than the original human-made images. Simple scenes may be exaggerated into dramatic, attention-grabbing visuals. These differences help explain why AI images can feel generic or emotionally hollow. While AI vision excels at rapid image labeling, categorization, and large-scale analysis, human-created images remain better at conveying authenticity, nuance, and lived experience. The takeaway is not that one form of vision is superior, but that they serve different purposes. Knowing when to rely on human perception, machine vision, or a combination of both can lead to more effective communication, safer systems, and smarter use of visual AI technologies.

A team of researchers has developed a computer vision–based method that automates the characterization of new electronic materials. Using advanced imaging—hyperspectral and RGB—the invention identifies key properties like band gap and stability with over 96% accuracy. This new approach replaces the traditionally slow, manual processes used to test materials such as semiconductors, delivering results 85 times faster. It was successfully applied to printed samples of perovskites, a promising material for next-generation solar cells.

This innovation could transform how materials are discovered and evaluated. By combining machine learning with optical imaging, the system fits into a fully automated pipeline where materials can be synthesized, analyzed, and optimized with minimal human input. Its creators aim to support faster progress in areas like solar energy, electronics, and sustainable materials by enabling laboratories to run 24/7, accelerating experimentation, reducing bottlenecks, and shortening the path from idea to real-world application.

Computer vision is experiencing a revolution fueled by cutting-edge developments in deep learning and AI. Transformer-based architectures like Vision Transformer (ViT) and Swin Transformer have redefined how machines interpret images, surpassing traditional CNNs in tasks such as object detection and scene segmentation. Meanwhile, self-supervised learning methods like DINO, SimCLR, and MoCo eliminate the need for extensive labeled datasets, unlocking vast potential in unannotated data.

The integration of visual and linguistic modalities through models like CLIP and ALIGN allows machines to reason across images and language, enabling zero-shot classification and complex understanding. Alongside, breakthroughs in 3D modeling—such as Neural Radiance Fields (NeRF)—and edge-optimized models like MobileViT are making real-time, photorealistic, and resource-efficient vision systems a reality. These advancements are shaping the future of AI in fields like robotics, AR/VR, autonomous navigation, and beyond.

The European Commission’s AI Gigafactories initiative is rapidly gaining momentum, receiving 76 expressions of interest covering 60 potential sites across 16 EU member states. This response highlights Europe’s ambition to establish sovereign AI capabilities and reduce reliance on foreign technologies.

AI Gigafactories will be high-performance computational hubs designed to develop and train next-generation AI systems. Unlike traditional data centers, these facilities focus exclusively on AI, offering the infrastructure needed to match or exceed global benchmarks.

While commercial confidentiality prevents the naming of companies, major European telecom firms, data center operators, power companies, and tech giants are all reportedly involved. Combined, these proposals could lead to the deployment of three million GPUs, providing unprecedented training capacity.

What sets these Gigafactories apart is not just scale—but ecosystem integration. They aim to bring together computing infrastructure, energy efficiency, data, and talent under one roof, accelerating innovation in fields such as healthcare, climate modeling, automation, and digital services.

Sustainability is a core focus. Some projects plan to leverage renewable energy sources like hydroelectric power and natural cooling methods, with Northern Sweden being explored as a possible location for a zero-carbon AI factory.

The EuroHPC Joint Undertaking will oversee the next phase, with formal calls for development expected in late 2025. If successful, these Gigafactories could cement Europe’s leadership in ethical, scalable, and sovereign AI development.

A recent exploration into multimodal large language models has revealed that these systems may be developing internal representations of the world that closely resemble how the human brain organizes information. The study focused on how artificial intelligence models categorize and understand natural objects such as animals, plants, and geological forms. Using a test method that involved comparing similarities between sets of three objects, researchers analyzed how different AI systems grouped visual concepts. Remarkably, the resulting patterns of classification aligned strongly with the way humans mentally associate and differentiate between various object categories. These clusters—formed without human intervention—demonstrated that the AI had independently learned semantically meaningful distinctions.

Further comparisons with neural imaging data showed a significant correspondence between the AI models’ internal representations and those found in specific regions of the human brain known to process visual and conceptual information. This suggests that the AI systems are not only mimicking human-like decisions externally but are also developing internal cognitive structures that parallel biological mechanisms. The implications are twofold. On one hand, it offers deeper insight into how the brain reduces complex visual information into structured categories, suggesting a naturally low-dimensional and semantically organized system. On the other, it points toward the potential for more efficient, brain-inspired AI models—capable of processing information in a way that mirrors human perception while reducing computational overhead. As artificial intelligence continues to evolve, findings like these reinforce the growing convergence between human cognitive science and machine learning, highlighting the possibility that machines might eventually reason and understand the world in ways fundamentally similar to the human brain.

A recent breakthrough in artificial intelligence has introduced a powerful pruning method that significantly reduces the size and resource demands of deep learning systems—without affecting performance.

By analyzing how neural networks learn and identifying which parameters are essential, researchers have shown that it's possible to remove up to 90% of parameters in certain layers while maintaining full model accuracy. This makes AI models dramatically lighter, faster, and more energy-efficient.

Unlike earlier approaches that offered limited gains or reduced reliability, this method maintains model performance and enables deployment on smaller, lower-power hardware. It offers a clear path toward more sustainable and scalable AI, especially in mobile devices, embedded systems, or energy-constrained environments.

This development represents a major advancement in the move toward green AI, improving the efficiency and accessibility of intelligent systems across industries. By reducing computational overhead without compromising results, AI can now be smarter, leaner, and more aligned with real-world demands.

An innovative AI system has been developed to detect contamination in construction and demolition wood waste—one of the most challenging and overlooked waste streams in the building industry. This new solution uses advanced deep learning models trained on a large real-world dataset of contaminated wood, achieving 91% accuracy in identifying various contamination types using just standard RGB images.

The system is designed to recognize visible signs of contamination, including paint, metal fragments, adhesives, chemical stains, and other materials that typically make wood unsuitable for recycling. By analyzing images in real time, the AI can help waste processors automatically sort contaminated wood, improving safety, reducing manual labor, and increasing recovery of reusable materials.

Unlike traditional sorting methods, which are time-consuming and inconsistent, this AI-powered approach offers a scalable, reliable alternative. It can be deployed in sorting lines, aerial drones, or handheld scanning devices, allowing on-site decisions to be made faster and with greater accuracy—especially in high-volume or hazardous environments.

This development represents a significant step toward automated resource recovery and contributes directly to circular economy initiatives by enabling more wood to be recycled rather than discarded. It also opens the door for similar AI-driven solutions in other specialized waste streams, where manual sorting is impractical or too costly.

By leveraging image-based learning and focusing on real-world data, this system demonstrates how AI can play a transformative role in sustainable construction, recycling innovation, and global waste reduction efforts.

In a new study, AI agents demonstrated the ability to develop structured language systems similar to human language by interacting in a communication game. Each agent was shown a color and had to communicate it using a meaningless symbol. The receiving agent would then try to identify the correct color based on that symbol. When successful, both agents received a shared reward, encouraging them to refine their symbolic language. Over time, the agents created a system of naming colors that mirrored how humans categorize and name colors in natural languages.

The study also explored how language evolves across generations. New agents were introduced and allowed to learn from the communication patterns of previous generations before developing the language further. This combination of shared learning and reinforcement through use led to more effective and human-like language development. The results highlight how structured communication can emerge when learning is tied to real tasks and passed on across generations—offering insights into both AI behavior and the evolution of human language.

Researchers at MIT and the Woods Hole Oceanographic Institution have developed SeaSplat, an innovative AI-powered tool that removes the distorting effects of water to reveal the true colors of underwater scenes in stunning 3D. By combining a color-correcting algorithm with advanced 3D Gaussian splatting technology, SeaSplat reconstructs underwater images into immersive, true-color 3D models that can be explored virtually from any angle. This breakthrough overcomes common underwater imaging challenges such as light scattering and color fading caused by water and suspended particles, allowing scientists to see marine environments as if the water had been drained away.

SeaSplat’s ability to produce accurate, high-quality 3D representations is especially valuable for marine biologists monitoring coral reefs and other delicate ecosystems. For example, it enhances the detection of coral bleaching, which can be difficult to spot due to color distortion in underwater photos. While the current system requires powerful computing resources, it holds great promise for tethered underwater vehicles and remote scientific exploration, enabling researchers to virtually dive into ocean scenes and study marine biodiversity with unprecedented clarity and precision

Researchers at the University of Michigan have introduced H-CAST, an innovative AI model that classifies images by creating a hierarchical tree—from broad categories like “bird” to very specific ones such as “bald eagle.” Unlike previous models that treat coarse and fine classifications as separate tasks, H-CAST aligns these levels through intra-image segmentation, focusing attention on the same object at multiple detail layers. This leads to more accurate and consistent predictions. Traditional models often struggle with imperfect or unclear images, but H-CAST’s hierarchical approach enables it to provide reliable classifications at varying levels of detail. Tested on four benchmark datasets, H-CAST outperformed existing state-of-the-art models by up to 11% in accuracy, while also reducing inconsistencies between coarse and fine predictions.

This technology shows strong potential for real-world applications such as wildlife monitoring—where it can identify species when possible or fallback to broader categories—and autonomous vehicles, which can use coarser predictions to safely interpret occluded or distant objects. By mimicking human flexible reasoning, H-CAST marks a significant advancement in building AI systems that are both more interpretable and robust in complex visual environments.

A novel mathematical model is now enabling more accurate predictions of how well transfer learning will work in neural networks—especially when only limited data is available. This advancement is critical in domains like medical diagnostics, where collecting large labeled datasets is often impractical. By allowing AI systems to reuse knowledge from models trained on larger datasets, the method improves generalization and reduces the risk of overfitting in new, data-scarce tasks.

The model combines two powerful analytical techniques: Kernel Renormalization and the Franz-Parisi formalism from spin glass theory. This innovative approach directly applies to real-world datasets and accurately estimates how a target network will perform when trained with transferred knowledge. It marks a significant step toward making AI more effective and reliable in specialized fields with limited training data.

A recent article explores the critical question of whether AI is more rational than humans, rather than simply smarter. The piece argues that while advanced AI systems like GPT-4 demonstrate impressive cognitive abilities, they often outperform human reasoning in certain contexts but fail to fully replicate the complexities of human decision-making.

AI’s potential lies in its ability to enhance rationality, not just intelligence. By understanding and incorporating human decision-making patterns, AI systems can become more effective, complementary tools for solving real-world problems.

The article underscores the importance of focusing on the rationality of AI, suggesting that this approach could lead to more intuitive and adaptive systems, ultimately improving their integration into everyday applications.

Edge AI brings artificial intelligence processing directly onto local devices like smartphones, IoT systems, and industrial equipment, enabling real-time data analysis with minimal latency. By eliminating constant cloud communication, edge AI offers major benefits such as faster response times, enhanced data privacy, and reduced network costs. Key industries adopting edge AI include healthcare (portable diagnostics), autonomous vehicles (instant decision-making), retail (inventory management), and smart cities (traffic optimization). Organizations are encouraged to start with a clear business use case, select optimized AI models, and design a scalable edge architecture tailored to their operational needs.

While edge AI offers transformational potential, it also presents challenges. Deploying AI models on resource-constrained devices demands careful model optimization and specialized technical expertise. Initial infrastructure costs, security concerns, and balancing power consumption with performance are key considerations. However, rapid advances in both hardware and software are making edge AI more accessible, promising an expanding range of business applications in the near future.

A new method known as Self-Disciplined Autoregressive Sampling (SASA), developed by researchers at MIT and IBM, introduces an efficient way for large language models (LLMs) to control and detoxify their own language generation without the need for retraining. SASA works during the inference phase by analyzing the internal representation of the LLM to distinguish between toxic and non-toxic language. It builds a classifier in the model's embedding space to define a boundary between harmful and acceptable content. By re-weighting the sampling probabilities of each potential next token based on its proximity to this boundary, SASA guides the generation toward safer and more appropriate responses while preserving fluency.

This method offers a lightweight and effective alternative to traditional detoxification approaches that rely on costly retraining or complex reward models. Evaluations across different LLMs and datasets, such as RealToxicityPrompts, BOLD, and AttaQ, show that SASA significantly reduces toxicity and mitigates biases, such as gender-based differences in output. It performs comparably to state-of-the-art models in detoxification while maintaining a lower computational burden. Moreover, SASA can be extended to incorporate multiple human values like fairness, helpfulness, and truthfulness, making it a promising framework for generating ethically aligned AI communication.

Large Language Models (LLMs) such as GPT-4 have gained widespread attention for their ability to generate human-like text, but experts caution that these models don’t actually “know” anything. Trained on static datasets, LLMs lack real-time awareness and can produce inaccurate or fabricated responses—a phenomenon known as hallucination. Despite growing investments in larger models, these challenges persist, especially in high-stakes domains where accuracy and reliability are critical.

To address this, a new approach called Retrieval-Augmented Generation (RAG) is gaining traction. RAG enhances LLMs by integrating them with external data sources, enabling models to retrieve and reference real-time, domain-specific information when generating responses. This method not only improves the accuracy and trustworthiness of AI outputs but also supports applications in industries requiring up-to-date and verifiable content. With RAG, the AI landscape is moving from hype toward more grounded, actionable intelligence.

A new framework sheds light on how neural networks learn and organize information internally, offering a unified theory behind several core deep learning behaviors. Known as the Canonical Representation Hypothesis (CRH), this theory proposes that during training, neural networks naturally align their internal components—representations, weights, and gradients—within each layer. This inherent alignment leads to the development of compact and efficient internal structures, improving both the interpretability and performance of models. When this alignment is disrupted, the Polynomial Alignment Hypothesis (PAH) comes into play, suggesting that these elements evolve into polynomial relationships, revealing distinct learning phases within the network.

This dual-hypothesis approach provides a potential explanation for previously observed phenomena in deep learning, such as neural collapse and feature alignment patterns. Importantly, it also opens new avenues for optimizing model behavior by intentionally injecting noise into gradients to shape the network's representations. These findings not only enhance our understanding of deep learning mechanisms but also hint at parallels with how biological brains process information, making it a promising step toward more interpretable and biologically inspired AI systems.

A recent study from NYU Tandon introduces a groundbreaking approach to enhancing privacy in Large Language Models (LLMs) by leveraging entropy-guided attention. As AI systems increasingly rely on cloud-hosted models, user data privacy becomes a critical concern. Private Inference (PI) offers a solution by enabling AI models to operate on encrypted data, but it comes with high computational costs, leading to increased latency and energy consumption. This study aims to address these challenges by rethinking the fundamental design of AI architectures.

The researchers found that removing nonlinearities—a key component in deep learning—leads to two major failure modes: entropy collapse in deeper layers, which destabilizes training, and entropic overload in early layers, reducing model efficiency. To counter this, they developed Entropy Regularization to control information flow and PI-Friendly Normalization to stabilize training without compromising privacy. Their findings establish entropy as a crucial design principle for building efficient, privacy-preserving AI. By bridging information theory and neural architecture, this work paves the way for secure AI models that maintain both accuracy and efficiency. The team has open-sourced their implementation, inviting further research into the future of private AI.

As AI becomes a critical business asset, organizations must establish dedicated AI innovation units to maximize its potential. These units serve as a hub for AI development, deployment, and governance, ensuring that AI initiatives align with business goals while fostering a culture of innovation. By consolidating AI expertise, companies can accelerate digital transformation, improve efficiency, and create new revenue opportunities. A well-structured AI innovation unit helps mitigate risks, maintain ethical AI practices, and drive measurable organizational value by integrating AI into products, services, and decision-making processes.

Leadership plays a crucial role in the success of an AI innovation unit. A Chief AI Officer (CAIO) or senior executive should lead the team, ensuring collaboration across departments such as HR, marketing, finance, and legal. A multidisciplinary team, including data scientists, AI ethicists, and engineers, is essential for diverse and effective AI solutions. Reporting directly to the C-suite ensures AI remains a strategic priority, securing resources for scalable projects. By fostering collaboration with academic institutions and technology startups, businesses can stay ahead in AI advancements while maintaining governance frameworks to uphold transparency and ethical standards.

As AI, regulatory shifts, and geopolitical forces reshape the tech landscape, businesses must rethink models, modernize data, and navigate compliance to stay competitive. Generative AI (GenAI) and IoT are driving innovation, helping companies unlock new value streams and streamline operations. However, data monetization remains a challenge, with 46% of TMT companies struggling to extract value. AI-driven investments, partnerships, and ecosystem-based business models are proving more profitable than standalone products, with margins reaching 50-60%. Additionally, AI-powered automation is optimizing workflows, with 45% of tech leaders expecting GenAI to drive significant cost savings.

At the same time, regulatory and geopolitical risks add complexity to AI adoption. Tightening AI laws and US-EU regulatory divergence are forcing companies into regional strategies, while restrictions on AI investments in China highlight global competition concerns. To thrive in 2025, businesses must prioritize AI-driven innovation, invest in data modernization, and build resilience through strategic partnerships. Those that embrace AI responsibly and adapt to evolving regulations will gain a competitive edge in an increasingly digital and dynamic world

A novel AI training method has been developed to tackle spurious correlations, a common issue where models make decisions based on misleading patterns rather than meaningful features. Traditional approaches require identifying these spurious correlations manually, which is often impractical when their exact cause is unknown.

The new technique addresses this by removing a small subset of complex and ambiguous training data. By eliminating these difficult-to-interpret samples, AI models can avoid relying on irrelevant details, leading to more accurate and reliable predictions. Unlike previous methods that depend on manual intervention, this approach works even when the source of the spurious correlation is unidentified.

Experimental results show significant improvements in model performance, making this a promising advancement in AI training methodologies. The findings will be showcased at an upcoming international AI research conference.

As businesses expand across international markets, success in 2025 will be defined by how effectively they harness AI, hyperautomation, and ecosystem collaboration. AI is no longer just an emerging trend—it has become a cornerstone of operational efficiency, enabling real-time content adaptation, personalized customer engagement, and seamless multilingual communication. However, companies must move beyond mere automation, integrating human expertise and linguistic precision to ensure AI-driven outputs remain brand-aligned and culturally relevant.

The future of global business growth lies in hyperautomation, where AI seamlessly integrates with real-time data and operational workflows to optimize processes, enhance decision-making, and deliver scalable, personalized experiences. Companies that adopt a strategic AI approach, combining automation with strong ecosystem partnerships, will gain a significant competitive edge. The real question for businesses today is not whether to adopt AI, but how to deploy it effectively to drive innovation, efficiency, and long-term market leadership.

By 2028, one-third of enterprise software applications will feature embedded agentic AI, a significant rise from just 1% in 2024, according to Gartner. This exponential growth is driven by AI’s ability to boost efficiency, cut costs, and enhance customer experiences across industries like healthcare, retail, and finance.

Why Agentic AI?

Smarter Customer Interactions – AI systems can learn from text, video, and data to deliver more context-aware responses.

Operational Efficiency – Automates tasks like invoice processing, content summarization, and recruitment, saving time and costs.

Enhanced Decision-Making – AI-driven insights help businesses optimize strategies and workflows.

5 Key Steps for Adoption

Modernize Infrastructure – Ensure systems can support AI’s power, storage & security needs.

Train the Workforce – Bridge AI skill gaps through upskilling programs.

Launch Pilot Programs – Test AI in small-scale real-world scenarios before full deployment.

Embrace Cultural Change – Foster trust in automated decision-making.

Strengthen Data Practices – Implement robust governance & ethical data policies.

The Future is Now!

Agentic AI is reshaping industries—but preparation is key. Companies that invest in technology, people, and culture today will lead the AI-powered future.

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Hugging Face has introduced Pi0, a groundbreaking AI model that enables robots to interpret and execute natural language commands. Developed by Physical Intelligence and integrated into Hugging Face’s LeRobot platform, Pi0 is a vision-language-action model designed to make robotics more autonomous and accessible. Unlike traditional robots that require detailed programming, Pi0 can be trained to perform complex tasks such as folding laundry, waiting tables, and packing groceries. It was trained on seven robotic platforms and 68 advanced tasks, demonstrating its adaptability. The model can also be fine-tuned with custom datasets, making it valuable across industries.

Experts see Pi0 as a game-changer in AI-driven robotics. According to Shawn DuBravac, CEO of Avrio Institute, the model reduces development costs, allows non-programmers to direct robots using natural language, and enables multi-purpose robots instead of single-use machines. Additionally, Pio-Fast, an enhanced version, introduces faster training and better adaptability. By combining images, text, and actions, Pi0 brings AI-driven automation closer to real-world applications. Available on Hugging Face’s open-source ecosystem, it represents a major step toward artificial physical intelligence.

AI #Robotics #HuggingFace #Automation #MachineLearning

A groundbreaking AI algorithm, Torque Clustering, is transforming unsupervised learning by enabling AI systems to independently identify patterns in data without human intervention. This innovation marks a significant step toward AI that learns more like natural intelligence. Unlike traditional supervised learning, which relies on extensive labeled datasets, Torque Clustering autonomously detects structures within complex datasets, making it highly effective for applications in biology, finance, medicine, and astronomy. Inspired by the physical concept of torque and gravitational interactions, the algorithm seamlessly adapts to diverse data types and processes vast amounts of information with exceptional efficiency. "This breakthrough brings us closer to AI that can learn the way animals do—by observing and exploring—without predefined instructions," said Distinguished Professor CT Lin. "By eliminating the need for labeled data, Torque Clustering enables more scalable and efficient AI applications across industries." With its fully autonomous, parameter-free approach, Torque Clustering has the potential to accelerate progress toward general artificial intelligence (AGI), particularly in robotics and autonomous systems. Its open-source availability is expected to drive further innovation, positioning it as a transformative tool in AI research and development.

AI’s growing role in various industries raises concerns about reliability, bias, misinformation, and privacy risks. Guardrails AI, co-founded by Shreya Rajpal, addresses these challenges by providing a real-time validation layer for AI models, ensuring safer and more predictable outputs. This open-source platform consists of two key components: Guardrails Hub – A collection of 65+ safeguards detecting hallucinations, security risks, and bias. Orchestration Engine – Allows users to configure guardrails, ensuring minimal performance impact. Supporting major AI providers like OpenAI, Google, and Anthropic, Guardrails AI integrates seamlessly with Python-based applications and OpenTelemetry for enhanced monitoring. Its standout feature is low-latency, high-accuracy failure detection, responding in under 100 milliseconds. To further improve AI safety, the team is developing the Guardrails Index, a benchmarking tool to compare various AI risk mitigation strategies. The company is also expanding its approach beyond input-output validation. As AI adoption accelerates, Guardrails AI plays a crucial role in ensuring trustworthy and responsible AI deployment. Developers can contribute via GitHub, LinkedIn, and Discord, helping shape the future of AI safety and reliability.

Transformer2 is a self-adaptive LLM designed to dynamically adjust its weights when faced with new tasks, eliminating the need for traditional fine-tuning. Unlike conventional models, which require parameter adjustments followed by retraining, Transformer2 adapts on the fly as it encounters unfamiliar tasks.

The model works in two stages. First, it analyzes the incoming task to determine the necessary response. Then, it adjusts its internal weight system using Singular Value Decomposition (SVD) and reinforcement learning, helping it prioritize the most relevant information for the task at hand.

During inference, Transformer2 employs three main strategies: prompt-based, classifier-based, and a few-shot adaptation method. This flexibility allows the model to perform well on both familiar tasks and new, challenging queries, making it more versatile than traditional LLMs. The result is an AI that can adjust to different types of tasks dynamically, offering improved efficiency and flexibility.

Multiverse Computing and Kinesis Network have teamed up to address the growing demands of AI while reducing energy consumption and optimizing resource usage. By harnessing the power of quantum-inspired algorithms, Multiverse's CompactifAI software enhances AI model performance while minimizing computational requirements, leading to significant energy savings without compromising accuracy.

Kinesis Network’s expertise in distributed computing helps organizations make better use of idle resources, dynamically allocating workloads to reduce waste and lower infrastructure costs. This partnership aims to streamline AI workloads, making them more efficient and sustainable.

As AI continues to push the limits of current infrastructure, this collaboration offers an innovative solution by integrating Multiverse’s AI model optimization with Kinesis’s compute management platform. Together, they aim to empower AI companies to expand their capabilities without the burden of excessive energy usage.

The partnership also aligns with efforts to address the environmental impact of AI, with both companies prioritizing sustainable technology practices. Multiverse’s CEO, Enrique Lizaso Olmos, highlighted that the collaboration would open new possibilities for AI models that were previously constrained by power and compute limitations.

With both companies connected to Amazon Web Services (AWS), this collaboration sets a powerful example for future AI innovations that prioritize both performance and sustainability.

AI #Sustainability #QuantumComputing #TechInnovation #DistributedComputing #EnergyEfficiency #LLM #AIOptimization #GreenTech #AIandSustainability

Fine-tuning a pre-trained AI model involves adapting it to a specific task, preserving its existing knowledge while teaching it new skills. Hyperparameters, which control the model's learning process, play a crucial role in fine-tuning. These include:

Learning Rate: Controls how much the model adjusts after each data batch.

Batch Size: Determines the number of samples processed at once, balancing speed and detail.

Epochs: The number of passes through the dataset; too many can lead to overfitting, too few can limit learning.

Dropout Rate: Randomly disables parts of the model during training to avoid overfitting.

Weight Decay: Prevents overfitting by discouraging excessive reliance on specific features.

Learning Rate Schedules: Gradually reduces the learning rate to fine-tune the model's performance.

Freezing/Unfreezing Layers: Freezes parts of the model’s layers to preserve learned knowledge while unfreezing others to adapt to new tasks.

Challenges include overfitting due to small datasets, high computational costs, and the need for task-specific adjustments. To succeed in fine-tuning, start with default settings, adjust based on task similarity, monitor validation performance, and test with smaller datasets first.

Fine-tuning, though requiring trial and error, helps tailor models for specialized tasks, improving their overall performance.

A team from the Indian Institute of Science (IISc) and University College London has developed a machine learning model that predicts material properties even with limited data. This breakthrough aims to address the challenge of limited data in material science, as testing materials for specific properties can be costly and time-consuming.

The researchers utilized transfer learning, where a model is pre-trained on large datasets and then fine-tuned with smaller, target-specific datasets. This approach improves the model's ability to predict material properties like electronic band gaps, formation energies, and mechanical characteristics. By employing Graph Neural Networks (GNNs), the model processes data in the form of atomic structures and chemical bonds.

Their Multi-property Pre-Training (MPT) framework trained the model on multiple material properties simultaneously, significantly enhancing its predictive power. The model not only predicted properties it was specifically trained on but also successfully forecast the band gap value for materials it hadn’t seen before.

This model is being applied to predict the movement of ions in battery electrodes, which could lead to more efficient energy storage devices. Additionally, it could help improve semiconductor manufacturing by predicting tendencies for point defects in materials, aligning with India's efforts to boost its semiconductor industry.

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with the Technical University of Munich, has unveiled Contextual Self-Supervised Learning (ContextSSL), a breakthrough approach in machine learning designed to enhance adaptability without requiring model retraining.

Traditional self-supervised learning often relies on predefined data augmentations to enforce invariance or equivariance, which may not generalize across tasks. ContextSSL addresses this limitation by dynamically tailoring data representations to task-specific contexts using transformer modules and world models. This innovation enables AI systems to flexibly adapt to diverse tasks by selectively applying invariance or equivariance based on contextual needs.

Extensive testing on benchmark datasets such as CIFAR-10 and MIMIC-III has demonstrated ContextSSL's superior performance in domains like computer vision and medical diagnostics. The model enhances predictive accuracy while improving fairness metrics, marking a significant advancement in AI’s ability to balance task-specific sensitivity and generalizability.

This development represents a major step toward creating flexible, general-purpose AI systems capable of seamlessly adapting to complex real-world environments.

"Machine Psychology" Could Unlock Human-Level AI, Says Researcher from Linköping University . Artificial intelligence may soon achieve human-level intelligence, thanks to the emerging field of "Machine Psychology." This groundbreaking concept, developed by Robert Johansson in his Ph.D. research at Linköping University, combines psychological learning models with adaptive AI systems to approach the long-standing challenge of Artificial General Intelligence (AGI).

AGI, which could perform intellectual tasks like humans, has been a key goal of AI research since the 1950s. Johansson believes AGI could become a reality within the next five years, revolutionizing society by enabling AI to act as virtual researchers, psychologists, and more. Unlike current AI, AGI would embody a completely new type of intelligence.

Using the Non-Axiomatic Reasoning System (NARS), Johansson integrates psychological principles to mimic human-like learning and reasoning. This system operates with incomplete data and limited computational power, offering flexibility for real-world applications. Johansson describes AGI as transformative but emphasizes careful ethical and societal considerations as its development progresses.

Johansson’s approach has garnered attention from leading AI organizations like Google DeepMind. While AGI presents risks, such as societal misuse, Johansson envisions it as a tool for fostering understanding and countering global challenges.

Psychology will play a crucial role in unlocking human-level intelligence in AI, Johansson says, urging responsible development of this powerful technology.

Researchers at the University of Waterloo's Institute for Quantum Computing (IQC) have discovered that quantum algorithms can help speed up the creation and usage of generative AI. A recent study, titled "Gibbs Sampling of Continuous Potentials on a Quantum Computer," explores how quantum computing can alleviate bottlenecks in generative AI, particularly for problems involving periodic patterns, like molecular dynamics. While generative AI in computer vision and speech may not see significant improvements, quantum algorithms can be transformative in fields such as pharmacology, where the folding of large molecules like proteins plays a crucial role.

Quantum computing's potential in areas like cryptography demonstrates its unique capabilities, offering solutions beyond just security concerns. The study suggests that quantum algorithms can advance simulations of molecular structures, leading to better materials and life-saving drugs, opening up significant economic and scientific opportunities. Researchers emphasize the importance of developing useful quantum algorithms to optimize the design of quantum computers for real-world applications.

The newly developed image generation framework by researchers from Seoul National University of Science and Technology addresses the challenge of controlling the content in generated images, a common limitation of traditional generative models like GANs. While GANs often produce random synthetic images, the new framework allows users to specify desired content by using a reference image. This content-preserving process is achieved through advanced encoding techniques, which involve two main components: the frequency encoding module and the content fusion module.

The frequency encoding module captures key features and structures from the reference image, focusing on specific frequency components, while the content fusion module creates a guiding vector that encapsulates the desired content features. These content-guiding vectors are then fused with projected noise vectors during the image generation process, ensuring that the output maintains the content of the reference image but introduces stylistic variations.

In tests, the framework outperformed conventional GAN models by generating images that better aligned with the reference image, retaining about 85% of its key attributes. This innovative framework shows promise not only for generating highly tailored datasets to train computer vision models but also for creative fields, such as design, where precise control over generated content is crucial. The ability to create images that meet specific expectations opens new possibilities for AI-assisted content creation and design, offering a significant improvement over current generative models.

Researchers at the University of Texas at Austin have developed a groundbreaking AI model that can convert audio recordings into accurate street-view images. The team trained this model using paired audio and visual data from various urban and rural locations across North America, Asia, and Europe. They used 10-second audio clips from YouTube videos of different cities and corresponding image stills to train the model, which was then able to generate high-resolution images from new audio inputs.

The AI-generated images showed strong correlations with real-world images in terms of the proportions of key elements like sky, greenery, and buildings. Human judges were able to match the generated images to their corresponding audio clips with an accuracy rate of 80%. The model also maintained architectural styles and the spatial arrangement of objects, reflecting the time of day based on environmental sounds like traffic noise or insect chirping.

This research demonstrates that AI can replicate a human-like ability to associate sound with visual environments, highlighting the potential for machines to approximate the sensory experiences humans have when interacting with their surroundings. The study not only advances our understanding of AI’s capability to recognize and replicate physical environments but also offers new insights into how multisensory factors influence our perception of place.

Mamba, a novel mechanism based on State Space Models (SSMs), is gaining attention as a promising alternative to the traditional Attention mechanism in large language models (LLMs). It is designed to overcome some of the critical challenges associated with Attention, such as high computational and memory requirements, by offering a more efficient approach. Mamba excels in processing long sequences and extended contexts, making it highly scalable and well-suited for tasks that require handling lengthy documents or complex dependencies. This efficiency also enables the development of smaller, more compact LLMs, addressing the increasing resource demands of modern AI models.

Despite its potential, Mamba is still in its early stages, with a relatively nascent ecosystem and limited adoption compared to the well-established Attention framework. Early benchmarks indicate comparable or even superior performance for long-context tasks, but its real-world application and robustness are still under evaluation. As more LLMs experiment with Mamba, such as those emerging on platforms like HuggingFace, it could revolutionize how sequence processing is approached in AI, paving the way for innovative and resource-efficient models.

Researchers at Tokyo University of Science, led by Professor Takayuki Kawahara, have made a breakthrough in Ising machine technology, improving its efficiency for solving complex optimization problems. Ising machines, which rely on magnetic spin principles to find optimal solutions, have traditionally struggled with scalability due to the large number of interactions required in fully connected systems. Kawahara's team has found a way to halve the interaction matrix, reducing the physical interactions necessary while maintaining performance.

Their new approach involves dividing the interaction matrix into four sections, halving each one individually, and then restructuring the matrix into a more efficient rectangular layout. This innovation allows the implementation of two fully connected Ising machines, each with 384 spins, on just eight FPGA chips. This method improves scalability and reduces the required circuitry for large-scale systems.

In their experiments, the researchers successfully solved two classic optimization problems—the Max-Cut and Four-Color problems—simultaneously using their enhanced Ising machine, showing a performance boost of about 400 times compared to simulating one Ising machine on a standard CPU. The results highlight the potential of Ising machines in solving combinatorial optimization problems much faster than conventional computers.

These advancements pave the way for scalable Ising machines to be used in real-world applications, such as accelerating molecular simulations for drug discovery and optimizing the efficiency of data centers and electrical grids. As the technology continues to evolve, Ising machines could become indispensable tools for solving some of the most challenging optimization problems across industries

In a breakthrough for Generative AI (GenAI), GraphRAG, a cutting-edge retrieval-augmented generation tool, is transforming large language model (LLM) accuracy by integrating knowledge graphs for advanced contextual understanding. Traditional LLMs, though powerful, often struggle with niche or complex queries and are prone to generating "hallucinations"—plausible but incorrect information. GraphRAG tackles these issues by retrieving data from knowledge graphs, structured networks of interconnected entities, instead of typical text databases. This graph-based approach allows LLMs to better handle specialized queries, reduce errors, and improve contextual relevance, making it particularly valuable for high-stakes fields like healthcare, finance, and education. GraphRAG's combination of LLMs with knowledge graphs marks a promising step forward in AI, delivering precise, contextually-aware insights that enhance the reliability of generative models across industries.

A recent study led by Lee Schwamm at Yale School of Medicine examined how well peer reviewers can distinguish between essays written by humans and those generated by large language models (LLMs) like ChatGPT. The research, published in the journal Stroke, found that reviewers accurately identified authorship only 50% of the time, akin to flipping a coin. Interestingly, AI-generated essays were rated higher for quality compared to human submissions. However, when reviewers believed an essay was AI-written, they rated it as the best only 4% of the time, indicating a bias against machine-generated content. Schwamm emphasizes the need for policies regarding AI's role in scientific writing, noting that as LLMs become more advanced, their detection by reviewers may diminish. He advocates for viewing AI as a tool to enhance scientific communication, particularly benefiting non-native English speakers, rather than as a shortcut that undermines academic integrity. The findings suggest that the scientific community must rethink its approach to AI in writing to embrace its potential benefits while ensuring quality and accountability.

A study led by Professor Giuseppe Riva at Università Cattolica del Sacro Cuore introduces "System 0," an external cognitive layer enabled by AI, which supplements traditional human thinking systems (System 1 and System 2). Dubbed a revolutionary step, System 0 enhances human cognition by processing vast data sets without intrinsic meaning. This "external thinking" assists humans but requires interpretation, emphasizing the need for transparency and critical interaction. The study calls for ethical guidelines to prevent over-reliance on AI and to protect cognitive autonomy, warning that unchecked AI could disrupt independent human thought.

A team of researchers from Peking University and Kuaishou Technology has unveiled Pyramid Flow, a groundbreaking AI model that generates high-resolution (768p) video content. Available as open-source software, Pyramid Flow allows developers to create applications at no cost. The model efficiently produces videos in multiple low-resolution stages, generating a five-second clip in just 56 seconds while using less computing power.

Silicon and rare earth metals are foundational to the AI technology stack. Silicon, derived from quartz sand, is essential for semiconductor chip production, powering the AI models we use today. Rare earth metals, such as lithium, cobalt, and nickel, are critical for advanced semiconductors and batteries that support AI hardware.

The extraction of these materials raises environmental and geopolitical concerns, especially as demand for AI technology increases. Ensuring a stable supply of silicon and rare earth metals is crucial for the continued growth of AI while addressing the sustainability challenges in mining practices.

Recent research published in the International Journal of Market Research reveals that AI can accurately detect specific human emotions in social media posts, particularly on X (formerly Twitter). By using a transformer transfer learning model, the study analyzed over 3.6 million sentences to identify emotions like joy, anger, sadness, and disgust. The findings show that emotions expressed in tweets significantly influence donation behavior toward non-profit organizations, with sadness driving donations to the Fred Hollows Foundation and anger increasing contributions to the University of Auckland.

While this capability has implications for marketing and decision-making, it raises ethical concerns about privacy and responsible usage as AI continues to evolve in understanding human emotions.

Meta's new Llama 3.2 models bring powerful multimodal capabilities, combining text and image processing for tasks like image captioning and visual question answering. The 90B Vision Model is designed for enterprise-level reasoning, while the 11B Vision Model is more compact, ideal for content creation. Smaller 1B and 3B Text Models focus on tasks like summarization and rewriting, optimized for local use. Built on Llama 3.1, the new models add image understanding through a vision tower and image adapter for visual-textual reasoning. Meta’s evaluations show Llama 3.2's competitiveness with top AI models.

Researchers from EPFL have discovered that large language models (LLMs), such as GPT-4, exhibit an "Arrow of Time" effect, showing greater accuracy in predicting the next word in a sentence than the previous one. This fundamental asymmetry, observed across various LLM architectures, suggests that while both forward and backward predictions should theoretically be equally challenging, LLMs are consistently a few percent less accurate when predicting backwards. The findings connect to Claude Shannon's work on information theory and imply deeper insights into language structure, intelligence, and even the nature of time. The study originated from a collaboration with a theater school to create a chatbot for improv, leading to unexpected revelations about language processing and causality.

We are glad to announce that AiTecServ will be participating in the Portugal Smart Cities Summit 2024, held from 8-10 October at FIL - Nations Park, Lisbon. This event is a major platform for exploring the future of urban development, bringing together global leaders in technology, sustainability, and urban planning. The summit will focus on smart solutions for improving mobility, infrastructure, and quality of life in cities. We look forward to connecting with industry experts and exploring new opportunities in the growing smart cities ecosystem and to present our solution AiAction.

AI is revolutionizing marketing by enhancing audience targeting, personalizing campaigns, and automating processes. It helps marketers analyze consumer behavior, predict trends, and create data-driven, customized marketing strategies. AI-powered tools assist in developing proposals, optimizing budgets, and automating tasks like content creation and campaign management. Personalized marketing, driven by AI insights, strengthens customer engagement and loyalty. However, challenges like data security and bias must be managed. Overall, AI enables businesses to increase efficiency, reduce costs, and achieve better results in marketing efforts.

Cold Spring Harbor Laboratory's Associate Professor Saket Navlakha has developed a new algorithm inspired by the nervous system to improve bipartite matching, a fundamental computer science problem used in ridesharing, organ donation, and other fields. In animals, neurons compete to connect with muscle fibers, and the system prunes excess connections over time. Navlakha adapted this biological process into a simple, two-equation algorithm that efficiently matches pairs, such as drivers and riders or donors and recipients. The algorithm has shown near-optimal performance while preserving privacy, and could have widespread applications in various matching systems.

The landscape of LLM-based agents is rapidly evolving beyond the limitations of earlier ReAct models, which struggled with high abstraction and poor usability. The new generation of agents embraces more structured architectures, using defined pathways and frameworks like LangGraph and LlamaIndex Workflows to enhance reliability and effectiveness. These frameworks help developers build agents that are more adaptable and efficient, addressing complex tasks with greater precision. This shift marks a significant advancement in AI, paving the way for more practical and intelligent systems in various applications, from customer service to automation.

Model collapse is a concern in the AI community, where over-reliance on AI-generated content for training future AI models could lead to a decline in quality and diversity of AI outputs. As more AI-generated data fills the internet, these systems risk becoming less accurate and more prone to errors, akin to the problem of inbreeding. This could result in models generating increasingly inaccurate or homogeneous responses, reducing the richness of cultural and social perspectives. To avoid this, the continued reliance on high-quality human-generated data is crucial, as completely filtering out AI-made content is impractical and financially demanding.

Synthetic data is revolutionizing AI by addressing the challenges of real-world data scarcity and accessibility. Unlike fake data, which is often overly simplistic and lacks realistic complexity, synthetic data is generated through AI models that accurately replicate the statistical properties of real datasets. This makes it a valuable resource for various applications, including software testing and data science. Synthetic data enables rigorous testing of applications under realistic conditions, provides a scalable solution for industries with restricted data access, and supports effective data analysis and model development. By emulating real-world patterns and behaviors, synthetic data offers a significant advantage over traditional fake data, ensuring more accurate and reliable outcomes in AI-driven projects.

Small Language Models (SLMs) offer a strategic advantage over large language models (LLMs) by addressing their limitations, such as high costs, inefficiencies, and privacy concerns. SLMs, designed for specific domains, provide targeted, precise insights and enhance data security by using proprietary datasets, thereby minimizing the risk of data exposure. They are more cost-effective and operationally efficient than LLMs and techniques like Retrieval-Augmented Generation (RAG) or LLM fine-tuning. By integrating SLMs, organizations can achieve better relevance, accuracy, and overall business value, making them a valuable tool for optimizing data usage and operational efficiency.

Researchers at Doshisha University have developed advanced adversarial techniques to improve automatic image cropping models. By introducing imperceptible noisy perturbations, these methods ensure that crucial parts of images, like copyright information, are preserved and not inadvertently cropped.

Their study, published in IEEE Access, features two approaches: a white-box method using internal model gradients and a black-box method employing Bayesian optimization. Both methods enhance cropping accuracy and fairness, addressing biases and legal risks associated with automatic image cropping. This research marks a significant step towards more reliable and transparent AI systems.

The Mixture of Experts (MoE) architecture in AI models, like Mistral AI's Mixtral and OpenAI's GPT-4, enhances performance by employing specialized sub-models (experts) for specific tasks. This approach, akin to having specialized doctors in a hospital, allows MoE models to handle complex data efficiently and accurately by dynamically activating relevant experts for each input. The gating network directs tasks to the most suitable experts, optimizing computational resources and improving scalability. While MoE models offer significant benefits in efficiency, flexibility, and specialization, they also face challenges like increased complexity and training instability. As AI continues to evolve, MoE's ability to manage large-scale problems with precision promises further advancements in the field.

Exploring the cutting edge of machine learning, the article uncovers breakthrough strategies for tackling out-of-distribution (OOD) generalization in graph-based data. It introduces three innovative approaches: invariant learning, which uses Exploration-Extrapolation Risk Minimization (EERM) to focus on consistent features across various environments; causal intervention, which employs variational context adjustment to decipher the true causal relationships between inputs and outputs; and divergence fields, which integrate diffusion processes with causal techniques to manage both explicit and implicit graph structures. By comparing these methods and their practical applications, the article provides insights into enhancing model robustness and paves the way for future advancements in generalization and OOD detection.

MIT researchers have introduced IF-COMP, a novel approach utilizing the minimum description length principle (MDL) to enhance uncertainty estimates in machine-learning models. Unlike traditional methods, IF-COMP efficiently calculates stochastic data complexity using influence functions and temperature-scaling, ensuring well-calibrated predictions across large-scale deep-learning models. This advancement addresses crucial reliability concerns in high-stakes applications like medical diagnostics, offering users better insights into model confidence and potential mislabeling of data points. IF-COMP’s versatility and accuracy make it a promising tool for various real-world scenarios, emphasizing the importance of robust uncertainty quantification in machine-learning deployments.

In John Lee's comprehensive exploration, predictive failure analytics (PFA) emerges as a pivotal force in enhancing automotive safety and reliability through AI. The article details how advanced AI techniques leverage extensive sensor data to predict and prevent failures in complex vehicle systems, surpassing traditional methods. It introduces a hybrid modeling approach that promises higher accuracy, illustrating its potential to optimize maintenance schedules and uphold rigorous safety standards across the automotive industry.

Expert systems harness AI to mimic human reasoning within specific domains, offering invaluable applications across healthcare, finance, engineering, and more. These systems use knowledge bases, inference engines, and user interfaces to make rule-based decisions, aiding in diagnosis, risk assessment, and process optimization. While they excel in specialized tasks, their limitations include narrow scope and the need for constant updates to stay relevant. Advances in AI and machine learning continue to enhance their capabilities, paving the way for more intelligent applications. Explore the future of AI with expert systems! #AI #ExpertSystems #ArtificialIntelligence #MachineLearning

Researchers at North Carolina State University have developed Multi-View Attentive Contextualization (MvACon), a technique that enhances AI's ability to map 3D spaces using 2D images from multiple cameras. MvACon improves the performance of vision transformers by refining how they utilize existing data without increasing computational demands. Testing with leading vision transformers showed significant enhancements in object location, speed, and orientation detection. This advancement holds promise for better navigation in autonomous vehicles

A team of researchers from FAIR at Meta, INRIA, Université Paris Saclay, and Google have developed an automated technique for data curation that enhances self-supervised pre-training of AI datasets. This new method involves a three-step process: using a feature-extraction model to embed data points, applying successive k-means clustering to group similar data points, and employing multi-step hierarchical k-means clustering to ensure balanced data clusters. Testing with vision models showed that their technique performed as well as, or better than, manual curation, highlighting its potential for improving AI training efficiency and accuracy. Further testing is needed to evaluate its effectiveness on real-world data and various AI systems.

AI revolutionizes retail by optimizing product presentation through enhanced images and descriptions, managing risks with fraud detection algorithms, ensuring quality control, and offering personalized experiences. These advancements drive business growth and enhance customer satisfaction, fostering loyalty and trust. From streamlining operations to improving customer interactions, AI is reshaping the retail landscape, making it more efficient, effective, and consumer-centric than ever before.

Researchers from the University of Manchester and Pragmatic Semiconductor have developed a novel method to generate tiny classifier circuits for the classification of tabular data using an evolutionary algorithm. Unlike traditional approaches, their method does not map to predefined machine learning models or hardware circuits. These tiny classifiers, composed of only a few hundred logic gates, achieve prediction accuracies comparable to state-of-the-art machine learning classifiers while using significantly fewer hardware resources and power. In simulations and real low-cost integrated circuits, these classifiers demonstrated 8-75 times less area and 4-75 times less power consumption compared to conventional methods. Potential applications include smart packaging, monitoring systems, and low-cost near-sensor computing.

Nuro introduces LAMBDA, their Multimodal Large Language Model (MLLM), designed to facilitate real-time question answering within The Nuro Driver’s™ autonomy stack. LAMBDA demonstrates exceptional reasoning capabilities, allowing it to comprehend diverse road scenarios and inform autonomous vehicle decision-making. By harnessing LAMBDA's advanced reasoning and common sense abilities, Nuro aims to enhance their autonomy systems' adaptability and interaction capabilities, ultimately advancing the deployment of Level 4 driverless technology.

Researchers at the University of Illinois Urbana-Champaign have introduced KnowHalu, a groundbreaking framework designed to detect hallucinations in text generated by Large Language Models (LLMs). These hallucinations, often stemming from inaccuracies or irrelevance in LLM responses, pose a significant obstacle to their real-world applications. KnowHalu employs a two-phase process, including the identification of non-fabrication hallucinations and a comprehensive fact-checking procedure. By leveraging multi-form knowledge and sophisticated algorithms, KnowHalu outperforms existing methods and offers valuable insights into LLM behavior. This innovative framework could pave the way for more reliable and accurate LLM applications across various domains. #AI #GenerativeAI #TextGeneration

The rapid expansion of AI technology in various sectors has brought about immense innovation potential along with ethical challenges. As AI systems gain autonomy in decision-making, integrating ethics into AI practices becomes crucial to avoid bias, reputational damage, and legal issues. In response, the European Parliament has passed the EU AI Act, the first comprehensive legal framework aimed at safeguarding human rights in AI usage. Companies are urged to establish robust governance and controls for AI, with experts like Douglas Dick from KPMG advocating for dedicated AI ethics and governance teams to ensure compliance and ethical deployment of AI technologies. Additionally, fostering a culture that prioritizes ethical considerations in AI operations is essential, as is educating employees on the evolving role of AI in their work environments.

Researchers at Aalto University have developed a groundbreaking machine-learning model that simulates human typing behaviors to improve smartphone keyboard designs. This model, which incorporates virtual "eyes and fingers" along with working memory, accurately replicates the typing dynamics of different user groups, including variations between one-handed and two-handed typing and differences across age groups. Collaborating with Google, the team aims to use this model to streamline the typically costly and time-consuming process of keyboard testing. Set to present their findings at the CHI Conference on human-computer interaction, this research could significantly enhance user interface designs, making daily interactions with technology smoother and more intuitive.

Ensuring data integrity in adopting Large Language Models (LLMs) is paramount. Robust LLMOps models safeguard data privacy, supported by stringent protocols such as classification, anonymization, and encryption. Ethical AI practices ensure transparency and accountability throughout the process. By prioritizing safety and adhering to industry best practices, organizations can effectively harness the transformative potential of LLMs while fortifying against risks and maintaining ethical standards. This comprehensive approach not only mitigates potential misuse but also fosters trust and confidence in the utilization of LLMs for driving innovation and growth in various business sectors.

Europe's 'Year of AI' has opened up new horizons for innovation and growth across industries. With AI poised to unlock €600 billion for the economy by 2030, the focus now turns to closing the digital skills gap to ensure everyone can participate in this digital revolution. In 2023, businesses embracing AI saw a 32% increase, leading to improved revenues, productivity, and innovation. Examples like NeuroPro using AI for faster medical diagnoses and Growy optimizing vertical farming show the transformative power of AI. Let's equip ourselves with the right skills to shape Europe's digital future together.

In a study published in Nature Communications, Yale University researchers investigated how data leakage affects machine learning models. Data leakage, where information from the testing dataset influences model training, can distort results. They found that leakage can inflate the model's prediction performance, particularly through "feature selection" and "repeated subject" leakage types. This inflation can mislead researchers into believing the model performs well when it struggles with truly unseen data. Furthermore, leakage effects are more pronounced in smaller sample sizes. To mitigate this, researchers advocate for transparency, sharing code, and maintaining a healthy skepticism about results. By avoiding data leakage, the reliability and reproducibility of machine learning models can be ensured.

The European AI Office, a linchpin of AI governance, implements the pioneering AI Act, safeguarding individuals' rights and ensuring legal clarity for businesses across the EU. It enforces regulations for general-purpose AI models, conducts evaluations, and collaborates to establish codes of practice. Championing trustworthy AI, it fosters innovation, advises on best practices, and promotes AI literacy. Internationally, it advocates the EU's approach to AI governance, fostering global cooperation. Collaborating with diverse stakeholders, including the scientific community and industry, it ensures comprehensive insights for informed decision-making. Job opportunities and collaboration initiatives further support its mission to advance trustworthy AI development and utilization in the EU.

In the quest for high-performance data systems, the rise of the ELT (Extract, Load, Transform) model offers scalability and flexibility. Despite its benefits, choosing the right provider and target presents challenges. The proliferation of EL technology prompts organizations to evaluate options between building and buying software. An introduction to the Analytic Hierarchy Process aids in quantitative decision-making. Open data lakes emerge as the preferred EL destination, accommodating both structured and unstructured data. Leveraging cloud storage, modern data lakehouses offer scalability and advanced features. While open table formats gain popularity, challenges remain in achieving compatibility and standardization. The future heralds high-throughput, low-latency open tables, promising a unified approach to data management.

European Parliament committees have provisionally agreed on the EU AI Act, marking a significant step towards regulating artificial intelligence. The Act aims to establish rules for AI systems, including high-risk applications like ChatGPT, and to curb governments' use of biometric surveillance. Passage is expected in April, with implementation slated between 2024 and 2027. Despite overwhelming support, dissenters argue the Act lacks adequate safeguards, especially regarding privacy concerns and potential mass surveillance. Margrethe Vestager underscores the urgency of AI regulation in light of recent incidents, while others suggest the Act may set a global precedent for responsible AI governance, although its impact remains uncertain compared to existing models in the US and China.

A comprehensive study conducted by Strand Partners for AWS unveils the profound impact of AI on Portugal's economy, forecasting a potential €61 billion by 2030 with a 35% adoption rate. While digital investments surged by 61% since 2022, challenges persist, including fostering innovation, bridging digital skill gaps, and democratizing technology access. Despite 87% awareness, only 35% of companies are well-versed in AI. Moreover, 71% struggle with hiring digital-savvy HR. Although AI adoption primarily benefits larger corporations, 64% of Portuguese companies anticipate sector transformation. AWS pledges over €21 billion investment in Europe, positioning Portugal for unprecedented growth opportunities, according to AWS country leader Suzana Curic.

"Data Quality: Crucial for informed decisions and operational efficiency. Understanding data context, handling missing values intelligently, and ensuring consistency in format and language are paramount. Detecting and managing duplicates, outliers, and processing errors are essential for accurate analyses. Defining variables accurately and complying with legal and ethical standards are imperative. Our solutions offer comprehensive data quality optimization, addressing these challenges effectively to avoid costly mistakes. By prioritizing data quality, organizations can enhance decision-making, drive business processes, and maintain customer satisfaction, ultimately leading to improved performance and competitive advantage in today's data-driven landscape. Explore our tailored solutions to optimize data quality and maximize the value of your data assets."

Researchers at Stanislav Fort delve into the alarming realm of multi-attacks on image classification systems, uncovering vulnerabilities beyond current defense strategies. Their innovative methodology employs standard optimization techniques, rooted in a toy model theory, utilizing the Adam optimizer in machine learning. The approach not only executes successful multi-attacks but also delves into understanding the pixel space landscape for optimal manipulation. Results emphasize the complexity of class decision boundaries and raise concerns about AI training practices. The study underscores the growing importance of fortifying AI against adversarial threats as its integration across industries accelerates, urging advancements in image classification models' security.

Delve into the symbiotic relationship between data science and deepfake technology. Fueled by sophisticated AI algorithms, data science plays a pivotal role in crafting hyper-realistic forgeries through deep learning, presenting ethical concerns. Data scientists bear the responsibility of developing guidelines for ethical use, ensuring transparency, and advocating for policies to prevent misuse. In the realm of detection, data science pioneers advanced algorithms, facing an ongoing 'arms race' with evolving deepfake techniques. Collaborative efforts across sectors and innovative approaches, such as blockchain for verification, underscore the dynamic landscape. The journey navigates ethical challenges, advances detection, and shapes the future of AI-generated content.

Real-time vector search, a fusion of generative AI and vector embeddings, is reshaping industries. With the ability to process rapid data, it transcends creative domains, impacting finance through instant fraud detection and dynamic risk assessment. In logistics, it optimizes route planning and enhances agility. Defense applications benefit from swift threat identification using vector embeddings. From retail to energy, real-time vector search elevates analytics, offering unparalleled insights and efficiency. The shift to real-time vector databases, exemplified by innovations like NVIDIA's GPU framework, underscores this transformative trend. #AI #Innovation #RealTimeAnalytics

Explore the evolution of AI communication in "Prompt Engineering 101." This comprehensive guide navigates from foundational principles to cutting-edge techniques like Chain-of-Thought. The article delves into mastering Language Models, offering insights for both beginners and experienced AI enthusiasts. From understanding prompt optimization to advanced strategies and collaborative tips, it provides a robust foundation. Dive deep into individual techniques like Chain-of-Thought, Skeleton-of-Thought, and Thought Propagation. Supplementary resources and e-books enhance the learning journey, ensuring proficiency in crafting high-performance prompts for effective AI interactions.

Exciting times for Android users! Microsoft quietly launched the Copilot app on Google Play, offering a ChatGPT-like experience. Seamlessly draft emails, generate images using DALL-E 3, and harness powerful chatbot capabilities. Notably, it provides free access to OpenAI's latest GPT-4 model, a feature requiring payment with ChatGPT. The Copilot app marks a strategic move, transforming the Bing Chat experience into a standalone app, accessible at copilot.microsoft.com. While Android users have enjoyed this for almost a week, an iOS version is yet to arrive. This expansion aligns with Microsoft's broader AI integration into daily applications. Stay tuned for further developments and dive into the future of AI on your mobile device! #MicrosoftCopilot #AIInnovation

In a scientific breakthrough, the AI-driven system 'Coscientist' autonomously mastered Nobel Prize-winning chemical reactions, including palladium-catalysed cross couplings. Developed by Carnegie Mellon University, the system utilizes large language models, akin to GPT-4, for experimental planning, execution, and analysis. This marks the first instance of non-organic intelligence successfully navigating and performing complex reactions invented by humans, pointing towards AI's transformative role in accelerating scientific discoveries. The integration of AI in research processes showcases its potential to enhance productivity, replicability, and reliability in experimental results.

In the ongoing debate about whether AI will replace humanity, Vidhi Chugh explores the probabilities and complexities surrounding the issue. Intelligence, defined by Stanford and Gartner, involves learning, problem-solving, and goal achievement. While AI excels at pattern recognition, it lacks human-like consciousness, emotions, and a sense of past, present, and future. Tests like the Turing test evaluate human-like responses, but AI's abilities remain within statistical patterns. Concerns about job loss due to automation are rising, with McKinsey predicting up to 30% of US jobs could be automated by 2030. While AI can be a valuable assistant, it lacks empathy, compassion, and a moral compass, highlighting the irreplaceable human qualities.

Generative AI plays a crucial role in testing by automating the generation of test scripts, derived from well-written User Stories and acceptance criteria. While AI can efficiently create scripts, manual review is essential to correct any errors. Starting with manual steps aids in clarifying user intent. Once validated, these steps can be input into a prompt to generate automated test scripts in the desired language. The resulting scripts facilitate pre-commitment testing and can be seamlessly integrated into CICD pipelines. Additionally, Generative AI simplifies release note creation by generating summaries directly from User Stories, streamlining documentation efforts. Despite its potential, ongoing review, consideration of security and privacy, and acknowledgment of AI's current developmental stage are vital.

Modern data quality practices utilize advanced technologies, automation, and machine learning to manage diverse data sources, prioritize governance, and ensure real-time processing. Contrasting with traditional methods, modern approaches encompass a broader range of data types, including unstructured and external data. They address the challenges of processing massive data volumes through technologies like cloud computing, emphasizing real-time processing over batch mode. Collaboration across stakeholders, treating data quality as a continuous process, distinguishes modern practices, ensuring accurate, reliable data for informed decision-making and business success.

The Density Kernel Depth (DKD) method emerges as a crucial tool for outlier detection in functional data, addressing the challenges posed by complex curves and surfaces. Unlike traditional approaches suited for scalar or multivariate data, functional data, represented by continuous curves, requires advanced techniques. DKD compares the density of each curve to the overall dataset density using non-parametric kernel methods. This yields a depth value for each curve, facilitating efficient outlier detection. DKD's flexibility, interpretability, and computational efficiency make it suitable for diverse functional data structures. In practical scenarios, such as analyzing patient heart rate curves, DKD offers a holistic perspective, detecting entire abnormal curves for more comprehensive insights. As data complexity grows, DKD exemplifies a promising tool for navigating intricate functional data landscapes, paving the way for sophisticated analysis techniques.

The UK, US, EU, Australia, and China have signed the Bletchley declaration, acknowledging the potentially catastrophic risks posed by artificial intelligence (AI). Twenty-eight governments agreed to collaborate on AI safety research during the AI safety summit hosted by the British government. The declaration recognizes the potential harm stemming from advanced AI systems and emphasizes the need for collective global efforts in addressing these risks. UK Technology Secretary Michelle Donelan highlighted the importance of collectively examining risks around frontier AI. The diplomatic success marks a step toward global unity, with South Korea and France set to host future AI summits. However, international consensus on global AI regulations remains elusive.

The evolving landscape of transaction monitoring in the realm of financial institutions calls for a shift from traditional, wide-net approaches to a more effective and efficient focus on identifying suspicious activities. Technological advancements, including AI and machine learning, are key in this transition, complementing rules-based detection methods. The future of transaction monitoring involves expanding rule-based detection, making previously unseen connections, fostering cross-industry AML collaboration, and continuous optimization of detection processes. Organizational transformation will see a reduction in false positives handling, an increase in alert complexity, a demand for senior-level investigators, and improved compliance accuracy. Advanced analytics are set to revolutionize the fight against financial crime.

Data engineering and machine learning pipelines serve distinct yet interconnected roles in data-driven organizations. Data pipelines focus on collecting, cleaning, integrating, and storing data, forming the foundation for informed decision-making. Machine learning pipelines, in contrast, encompass data cleaning, feature engineering, model training, evaluation, deployment, and monitoring, enabling automation of ML model development. While data pipelines follow a linear path from data source to storage, ML pipelines operate in a circular fashion, iterating from data to model deployment. Both require adaptable computational resources to handle variable workloads. Understanding these differences is crucial for building and maintaining effective pipelines, ensuring timely, accurate data for informed decisions and improved performance.

To accelerate analytics and achieve faster insights from data, IT leaders are turning to modern data pipelines with flexible infrastructure. Automation and orchestration play a key role in breaking down data silos and ensuring efficient data flow. The choice of infrastructure, like Pure Storage, with advanced data persistence and storage capabilities, is crucial. Such solutions support real-time analytics, scalability, and agility, reducing energy consumption. IT leaders are urged to modernize both infrastructure and applications to fully harness the potential of data-driven decision-making. The combination of modern data pipelines and the right storage solution enables businesses to break through silos and gain real-time insights, fueling digital transformation.

In this article, the author explores the limitations of modern artificial intelligence (AI) and introduces the concept of "living AI." They argue that current neural networks lack the ability to mimic the dynamic and adaptable nature of biological neural networks. The article highlights recent scientific findings about the role of transmembrane proteins in neural plasticity, suggesting that neurons are not just signal conductors but can change their response to incoming signals in real time.

To address this limitation, the author proposes a new mathematical neuron with a variable dynamic position function, akin to the axon initial segment in biological neurons. This innovation would enable AI to create its own attitude towards sensory information, make mistakes, and learn, gradually forming its own character and preference matrix. The author envisions AI built around a neural matrix that behaves like a living organism, offering potential applications in creating personalized AI with human-like characteristics.

The article concludes that AI based on matrix neural networks could evolve into a digital form of life, actively participating in the real world.

Artificial intelligence (AI) has revolutionized digital advertising, offering a data-driven approach that empowers advertisers. AI's generative capabilities streamline content creation, while its analytical prowess dissects consumer behavior for precise targeting. Real-time optimization ensures campaigns are fine-tuned on the fly, reducing ad spend waste. Additionally, AI guides strategic decision-making by forecasting trends and offering market insights. The human-AI partnership is the future, with AI serving as a co-pilot to optimize campaigns while retaining the human touch in marketing. In the ever-evolving digital landscape, AI in advertising brings efficiency, personalization, and strategic advantage, ultimately driving success in the industry.

Application mapping, or application dependency mapping, is a critical process in managing complex big data ecosystems. It involves visualizing the intricate relationships and dependencies between various software applications and the underlying IT infrastructure. Application mapping enhances data lineage, performance optimization, collaboration, and risk management in big data environments. By offering a holistic view of system architecture, it facilitates decision-making, troubleshooting, and compliance efforts. Automated discovery tools and manual annotation are used for mapping, with hierarchical mapping helping to understand dependencies. Best practices include starting at a high level, drilling down for details, documenting data flows, incorporating metadata, and identifying single points of failure. In sum, effective application mapping is essential for managing and optimizing big data environments.

Data Governance and Data Management are often used interchangeably but have distinct roles in handling an organization's data. Data Management encompasses data capture, storage, transformation, and usage, aligning with the business strategy. It involves various roles collaborating to ensure data quality and compliance. Data Governance, a subset of Data Management, coordinates people, technology, and activities to integrate and ensure legal compliance. While both focus on data quality, integration, and standards, Data Management includes technical details, handled by IT experts, while Data Governance emphasizes high-level decision-making by business stakeholders.

Combining these approaches can lead to more effective data management and governance, preventing miscommunication and scope creep.

WebSummit 2022 in Lisbon welcomed 71.033 attendees from 160 countries. Most of the presented companies are "start ups", companies in the initial stage of development and looking for investors. This is one of the central objectives of the event: to help put in contact, on the one hand, those who have projects and ideas, but need investment, and, on the other hand, those who have money to invest and are looking for good opportunities to monetize that capital.

In the spotlight, as always, were multiple conferences on different stages and formats, total around 1.000 speakers, including Brad Smith - the president of Microsoft, and Changpeng Zhao - the Chinese-Canadian entrepreneur who founded and leads Binance, the cryptocurrency giant.

Among the Portuguese speakers were several political leaders, such as Marcelo Rebelo de Sousa, António Costa, Carlos Moedas, Fernando Medina, in addition to other figures from different sectors, from the outset leaders of large companies such as the executive president of TAP, the president of GALP, the CEO of EDP.

WebSummit’s educational content, informative masterclasses, and innovative roundtables are giving valuable skills to attendees. WebSummit “attracts a diverse list of world-class speakers and facilitates content that inspires and empowers attendees.”

Tickets for WebSummit 2023 are already available on the official website of summit.

We are participating at Portugal Smart Cities Summit. Come and meet us at FIL - Parque das Nações - October 11-13, Pavilion 04 - Stand 4A04.

The “Smart Cities” Concept encompasses more than mobility, digital platforms or sustainability. The fundamental objective of a Smart City is the incorporation of all these areas in order to improve the lives of citizens in the world. PORTUGAL SMART CITIES SUMMIT is the place of convergence and the physical marketplace for creating opportunities for the national and international market.

Opportunities linked to research and education that provide business creation in the most diverse sectors. Opportunities to reflect on the future of organizing cities in the world.

Today’s businesses need a ton of information, analysis, and market studies to make the best possible decisions.

The most crucial skills for the labor market of today are digital ones, which require constant reskilling. By 2022, the World Economic Forum estimates that at least 54% of all workers will need to upgrade their digital skills.

To enable data analysis and predict the future for efficient decision-making techniques, organizations use AI tools on a bigger scale. A PwC study found that 86% of people consider AI to be a common technology. This is due to AI tools have developed into a driving force for technical advancements including Machine Learning (ML), Predictive Analytics, Natural Language Processing (NLP), Data Labeling, and many more. (see the concepts in our library/Artificial Intelligence – General Description)

Every firm can benefit from using AI tools to increase revenue; for instance, it can assist organizations to find their weak points and make precise forecasts about competitors, market trends, and prospects. AI decision-making is superior to human decision-making in that it is devoid of bias and emotion.

Scientists from Tokyo Institute of Technology instead of predicting the smell from molecular data, developed the method which predicts molecular features based on the odor impression. This is achieved using standard mass spectrum data and machine learning models. ML (see concept in our library/Machine Learning – General Description)

The mass spectra of odor mixtures is obtained by a linear combination of the mass spectra of single components. This simple method allows for the quick preparation of the predicted spectra of odor mixtures and can also predict the required mixing ratio, an important part of the recipe for new odor preparation.

This novel method described in this study can provide highly accurate predictions of the physicochemical properties of odor mixtures, as well as the mixing ratios required to prepare them, thereby opening the door to endless tailor-made fragrances.

It is also interesting for the food, health, beauty, and wellness industries, where odors and fragrances are of keen interest.

The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson’s from their nocturnal breathing — i.e., breathing patterns that occur while sleeping.

Over the years, researchers have investigated the potential of detecting Parkinson’s using cerebrospinal fluid and neuroimaging, but such methods are invasive, costly, and require access to specialized medical centers, making them unsuitable for frequent testing that could otherwise provide early diagnosis or continuous tracking of disease progression.

The researchers demonstrated that the artificial intelligence assessment of Parkinson's can be done every night at home while the person is asleep and without touching their body.

This study is likely one of the largest sleep studies ever conducted on Parkinson’s.

Datawarehouses are widely used by organizations of all sizes. Nowadays one of the big decisions for an organization that's looking to deploy a datawarehouse is whether to put it on premises or in the cloud. Both ways have their advantages and disadvantages.

An enterprise datawarehouse stores data from all of an organization's business operations in a single, centralized platform, which is handling by in-house administrator and IT-team.

A big challenge for on-premises datawarehouses is the need to deploy a hardware and software computing environment.

One of the benefits of a cloud data warehouse is that the vendor assumes responsibility for the underlying architecture.

The cost of deploying and supporting a data warehouse system in an on-premises data center usually will be much higher than renting one from a cloud provider with usage-based payments.

But cloud data warehouses can also pose some challenges to organizations and require changes in IT processes.

Here in the article, there are more details on the pluses and minuses of two approaches in some key areas.

AI/ML use cases in the supply chain help retailers, suppliers and distributors drive transformational changes that are so much needed today. Machine Learning delivers unprecedented value to supply chain operations: from cost savings through reduced operational overhead and risk mitigation, to enhanced supply chain forecasting, speedy deliveries, and improved customer service, to name a few. Machine learning is also providing insights into where automation can deliver the most significant scale advantages.

Some use cases of AI in SCM:

-Predicting customer’s behaviour

-Predictive capabilities are helping demand forecasting

-Avoiding charge-back risks

-Smart warehouses

-Identifying quality issues in line production at the early stages, etc.

The average cost of a food recall due to bacterial or microbial contamination is US$10 million. AI can help optimize the current systems used in the food processing industry.

The power of AI and machine learning is the predictive ability; it can help the food industry take steps earlier to reduce the occurrence of foodborne disease in their operations.

The idea is to get machines to think like the human brain. So that the current errors and inefficiencies from human intervention—which may at times compromise food safety—can be predicted by AI and ultimately decreased or eliminated.

Another promising application is using AI to analyze the behavioral data of human workers in the processing line. In practice, this could mean workers fill in a quick, online weekly survey about their workplace culture. Natural language processing would then be used to analyze their answers and create a quantitative measure of food safety culture. The company could predict the likelihood of food safety issues by looking at the trends in food safety culture and combining them with the other food safety related data, and take timely steps to reduce the chance of an outbreak.

With AI advancing at an exponential rate, it’s become possible for companies to use artificial intelligence to gain valuable insight into their customers. In particular, advances in artificial intelligence are leading to increased efficiency in customer service throughout different industry vertices.

Machine learning and AI-based interactive voice response systems have created a new paradigm for what customers and customer service agents can expect from these technologies. When applied correctly, artificial intelligence will enhance the customer experience in various ways, from identifying their interests through sentiment analysis to gathering data about their preferences.

In a short term AiTecServ will release AIsense solution based on this technology.

NLP (see concept in our library/artificial intelligence – Natural Language Processing/Common Tasks)

Two of the most innovative and disruptive technologies in use today are data science and blockchain technology. Data science examines and explains the unprocessed data to comprehend how a system functions. A cutting-edge method of recording transactions and storing financial data is blockchain technology. These two ideas have been used to create amazing advances in a variety of fields, including banking and software development.

Possibilities of Blockchain important for data science:

-Enables data tracing

-The capability of real-time analysis

-Ensures the data’s accuracy

-Lessens the difficulty of data exchange

-Builds trust

-Improving data integrity

Data is the foundation of blockchain technology.

The new platform reportedly enables Vodafone’s data scientists and developers to slash the time of going from proof-of-concept to production from five months to just four weeks.

Vodafone highlighted four key features of AI Booster:

-Automated ML lifecycle compliance activities (drift/skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services.

-Embedded security by design.

-Capitalise on Google-native ML tooling using BQML, AutoML, Vertex AI and others.

-Boost adoption through standardised and embedded ML templates.

The AI system consists of a sensor in the ball that relays its position on the field 500 times a second, and 12 tracking cameras mounted underneath the roof of stadiums, which use machine learning to track 29 points in players’ bodies.

Software will combine this data to generate automated alerts when players commit offside offenses. Alerts will be sent to officials in a nearby control room, who will validate the decision and tell referees on the field what call to make.

FIFA claims this process will happen “within a few seconds and means that offside decisions can be made faster and more accurately.” The data generated by the cameras and ball will also be used to create automated animations, which can be played on screens in the stadium and in TV broadcasts “to inform all spectators in the clearest possible way” of why the call was made.

The 2022 World Cup will take place in Qatar, making it the first World Cup to ever be hosted in an Arabic country. To offset the heat of Qatar, the tournament will be held from November to December instead of in the summer, as is tradition.

The universities have created a tool that measures the electricity usage of any machine-learning program. With it, Azure users building new models can view the total electricity consumed by graphics processing units (GPUs)—computer chips specialized for running calculations in parallel—during every phase of their project, from selecting a model to training it and putting it to use. It’s the first major cloud provider to give users access to information about the energy impact of their machine-learning programs.

The new Azure tool, which debuted in October, currently reports energy use, not emissions. So now researchers figured out how to map energy use to emissions, and they presented a companion paper on that work at FAccT, a major computer science conference, in late June. Researchers used a service called Watttime to estimate emissions based on the zip codes of cloud servers running 11 machine-learning models.

They found that emissions can be significantly reduced if researchers use servers in specific geographic locations and at certain times of day. Emissions from training small machine-learning models can be reduced up to 80% if the training starts at times when more renewable electricity is available on the grid, while emissions from large models can be reduced over 20% if the training work is paused when renewable electricity is scarce and restarted when it’s more plentiful.

Here are some of the best deep learning tools. Though this list is by no means exhaustive! There are more deep learning tools in the market but these are the ones that are used the most.

1. H2O.ai

H2O is a deep learning tool that was built from scratch in Java and follows a seamless integration with other open source products like Apache Hadoop and Spark. It has an easy to use Web UI and is massively scalable in big data analysis.

2. TensorFlow

TensorFlow is a deep learning tool that was written in highly-optimized C++ and CUDA (Nvidia’s language for programming GPUs) and provides an interface to languages like Python, Java, Go. It is an open-source library that was developed by the tech giant, Google, for the smooth running of deep learning applications.

TensorFlow makes it fairly easy for beginners and even experts to create machine learning models for mobile, web, desktop, and cloud.

3. Keras

Keras is a high-level neural network API that is capable of running on top of TensorFlow or Theano. It is written in Python and was developed mainly on enabling faster experimentation. Keras deep learning library allows the user for easier and faster prototyping with the use of modularity, minimalism and easy extensibility.

4. Caffe

Caffe is a deep learning tool whose framework is built with expression, speed and modularity in consideration. Developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors, it is also an open source deep learning tool.

5. DeepLearningKit

DeepLearningKit is an open source deep learning tool for Apple’s iOS, OS X, tvOS, etc. The main idea behind its creation was to support pre-trained models on all the Apple devices that have a GPU. This deep learning tool is developed in Swift and can be used on device GPU to perform low-latency deep learning calculations. DeepLearningKit supports Convolutional Neural Networks too. Its vision is to support other deep learning tools like Torch and TensorFlow.

6. Torch

Torch is an efficient open source program that uses a LuaJIT scripting language and C/CUDA implementation. If you are using this deep learning tool, you will be able to take advantage of its powerful features like: multiple routines for indexing, transposing, slicing, amazing interface to C via LuaJIT, neural network. It offers fast, efficient GPU support and is easily embeddable that makes it easier to work with iOS, Android, FPGA.

7. Theano

Theano is a Python library that is used to evaluate mathematical operations including multi-dimensional arrays. It is mainly used in building deep learning projects and is way faster on a GPU than a CPU. With its use, it is possible to attain speed that rivals self-made C implementations for problems that involve large amounts of data.

AutoML (Automated Machine Learning) automates certain key components of the machine learning pipeline. Some aspects of the machine learning pipeline are automated using AutoML (TPOT, Auto-Sklearn, AutoViML, AutoKeras and many more).

Advantages of AutoML are Accessibility, Efficiency, Fewer Errors, Cost savings, Meet Industry Demands.

But data science jobs would not be eliminated by a lot of reasons.

1.While AutoMLs are capable of selecting models in the majority of cases, they are still unable of doing the majority of a Data Scientist’s labour. We still require data scientists/analysts to apply their domain expertise to develop more meaningful features and information that affect the intended outcome (Feature Engineering).

2.AutoML will not replace most data science professions; rather, it will assist experts in completing their assignments more quickly.

3.Machines aren’t smart enough, and algorithms frequently fail to generalize and comprehend the context of an issue.

4.While AutoML can assist us in finding a good model for a specific problem, it cannot create a novel approach, which is frequently necessary for emergent real-world challenges.

AutoML will continue to grow in popularity. Certain duties will almost certainly be mechanised, and certain responsibilities will almost certainly be eliminated. Because a deep learning model has many parameters, the AutoML framework may speed up a data scientist’s work and deliver the optimal deep learning model in a shorter amount of time.

As businesses do cloud migration, they are moving away from their old on-premises data warehousing technologies, to these new cloud data platforms. The transformation is a huge shift in data management and has profound implications for businesses.

Businesses require a strong knowledge of cloud architectures, data lakes, storage options, and cloud subscription models to make technology choices.

Cloud data warehouse solution providers have integrated data science and analytic tools hosted directly inside of their data warehouse offerings. Companies shifting towards the cloud must consider these trends:

-iPaaS (Integration Platform-as-a-Service) for Integration.

-NoSQL for Big Data.

-Data Marts For Quick Data Access.

-Column-Storage For Advanced Analytical Query.

-DW Automation for Deploying Code.

The cloud migration of data warehouses brings all the benefits of cloud-like agility, cost, effectiveness, scalability, and performance. With cloud data warehouse automation, your team can quickly keep up with new infrastructure and migration projects and increase development and operations capabilities. This is the best way to maximize your organization’s return on cloud investment.

New software based on Machine Learning concept that helps drivers find parking spaces will also incentivize drivers to cooperate with local councils in their quest to keep pollution within safe limits in busy urban centers, as part of a far-reaching program designed to reduce toxic air in city centers.

The new project will use the latest AI technology to create services that allow local authorities to analyze vast amounts of data on driver behavior and to better control local travel patterns.

Imagine you are traveling into town on a Thursday morning and without you knowing it, your car is the one engine that triggers the town to go over the allowed pollution level, resulting in a big fine for the local government. Now imagine that instead of this happening, you receive a suggestion to park in another, better place, and you are issued a free parking space. You're also shown a low-traffic route to your free parking space. The whole service would be tailored to your individual needs while also helping towards net-zero objectives.

It will form an intrinsic part of the software development strategy for the future, allowing to expand capabilities and more importantly reduce pollution and manage mobility supply and demand in real-time.

These is a list of books made at Marketing AI Institute to understand how artificial intelligence is changing life, business, and the world.

The books are listed according to a "reading tracks" depending on what you may be looking to achieve with AI.

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

Prediction Machines: The Simple Economics of Artificial Intelligence

You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place

Marketing Artificial Intelligence: AI, Marketing, and the Future of Business

The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You

The AI Marketing Canvas: A Five Stage Roadmap to Implementing Artificial Intelligence in Marketing

The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

Applied Artificial Intelligence: A Handbook for Business Leaders

Voice Strategy: Creating Useful & Usable Voice Experiences

Human + Machine: Reimagining Work in the Age of AI

Artificial Intelligence: A Modern Approach

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

How to Create a Mind: The Secret of Human Thought Revealed

Deep Learning

Homo Deus: A Brief History of Tomorrow

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

AI Superpowers: China, Silicon Valley, and the New World Order

Superintelligence: Paths, Dangers, Strategies

The system, uses deep learning (see concept in our library/artificial intelligence – Computer vision/Some Concepts), reads live camera footage and adapts the lights to compensate, keeping the traffic flowing and reducing congestion.

In testing, the system significantly outperformed all other methods, which typically rely on manually-designed phase transitions.

Because the AI "sees" high traffic volume before the cars have gone through the lights and makes its decision then, it is more responsive and can react more quickly.

The program can be set up to view any traffic junction—real or simulated—and will start learning autonomously. The reward system can be manipulated, for example to encourage the program to let emergency vehicles through quickly. But the program always teaches itself, rather than being programmed with specific instructions.

The researchers hope to begin testing their system on real roads this year.

Machine learning is making great strides in healthcare to make sure patients get the best services. Now we could talk about 9 main areas:

-Precision medicine and personalisation of healthcare.

-Categorisation applications.

-Analysing imaging.

-Claims and payment administration.

-Other administrative processes.

-Prediction and health policy.

-Electronic health records.

-Diagnosis and treatment.

-Drug development.

Long-term outcomes could include better quality of care and a more efficient and cost-effective healthcare system, which can only benefit patients, providers, insurers, regulators, and policymakers.

Supercomputers help to study the causes and effects—usually in that order—of complex phenomena. However, scientists occasionally need to deduce the origins of scientific phenomena based on observable results. These so-called inverse problems are notoriously difficult to solve, especially when the amount of data that must be analyzed outgrows traditional machine-learning tools.

To reduce coordinator-worker communication, which often involves repeating the same requests multiple times, the team introduced a response cache that stores the metadata from each request. The first new strategy was this caching approach, which allows to immediately recognize and automatically calculate familiar requests.

The second new technique involves grouping the mathematical operations of multiple DNN models, which streamlines tasks and improves scaling efficiency—the total number of images processed per training step—by taking advantage of the similarities in each model's calculations. This process leads to significant improvements in power usage as well.

By strategically grouping these models, the team aims to eventually train a single model on multiple GPUs and achieve the same efficiency obtained when training one model per GPU.

Many companies don’t know how to get started on their AI journeys. But now AI industry makes the quality of data a priority. AI cannot thrive when it’s not trusted. Explainable AI (XAI) is a partial solution to the problem. XAI is an artificial intelligence in which the results of the solution can be understood by humans.

So, what is expected:

-a large number of acquisitions of startups specializing in synthetic data training;

-a lot of companies will smoothly incorporate AI in their work;

-the migration of AI from the cloud to the edge will accelerate;

-human input to lead to more useful AI decisions;

-enterprises will favor AI solutions that avoid lock-in;

-chatbots will get much closer to offering a human-like experience.

Deep Learning algorithms to accurately predict how changes in measurements could indicate forthcoming earthquakes — a task that has confounded experts for centuries.

Deep Learning algorithms are particularly useful for earthquake monitoring because they can take the burden off human seismologists.

Deep Learning could make the prediction process quicker, and more accurate, by helping to cut through large volumes of data. The model uncovers quakes that would previously have been dismissed as human-generated vibrations.

In the Machine Learning in Manufacturing Market study from 2022, the industry landscape is covered from driving factors to upstream markets and the overall state of the market. An in-depth analysis of the overall growth prospects for the global and regional market was provided which was based on an in-depth analysis of key industry players, primary and secondary data.

The report contains analysis of companies Intel, IBM, Siemens, GE, Google, Microsoft, Micron Technology, Amazon Web Services (AWS), Nvidia, Sight Machine, with 114 Pages Report.

Machine Learning in manufacturing can be used in visual quality control. Harnessing the power of image recognition and deep learning may significantly reduce the cost of visual quality control while also boosting overall process efficiency.

Analysts presently use artificial intelligence to distinguish the environmental and epidemiological examples that lead up to scourges to foresee flare-ups before they occur. Numerical models and AI can investigate enormous measures of information to get hints about a probable wellspring of contamination and help in the prevention of the spreading of disease through AI.

Extraordinary difficulties require incredible devices. Furthermore, in reality, as we know it, where an infection, for example, SARS-CoV-2 has had an enormous worldwide effect is simply a matter of months, its intricacy and speed require new strategies for tracking down arrangements. Progressive types of man-made consciousness (man-made intelligence) are as of now assuming a basic part in our pandemic-battling endeavours, and they’re supposed to assume an inexorably fundamental part in the conflict against irresistible infection later on.

We see simulated intelligence propelling our irresistible sickness battling abilities across the accompanying aspects:

• Determination

• Wellbeing

• Treatment

• Relief

The world’s first print and digital publication focused on AI, Big Data and Analytics ranks top 10 Machine Learning books for business leaders. The Analytics Insight gathered extensive guides, academic studies, quick read works and even ones with economic and political mentions, such as the rise of China’s tech giants like Alibaba. There are also marketing insights and real-life examples from successful entrepreneurs.

*.1 Applied Artificial Intelligence: A Handbook for Business Leaders

.2 Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data-Driven Technologies

.3 AI Superpowers: China, Silicon Valley, and the New World Order

.4 Artificial Intelligence: The Insights You Need from Harvard Business Review

.5 The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

.6 Artificial Intelligence and Machine Learning for Business

.7 AI for People and Business

.8 Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

.9 Artificial Intelligence for Marketing: Practical Applications

.10 The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You

In the article, the author explains the origins, details and exemplifies the process of one Deep Learning application: convolutional neural network (CNN) which helps computers detect objects. In addition, he briefly covers its variations, such as Region-based Convolutional Neural Network (R-CNN), Fast R-CNN and Faster R-CNN. In conclusion, the specialist mentions the latest study about it: You Only Look Once (YOLO).

In this article published in bdtechtalks.com, Deep Learning pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun discuss a future in which deep learning models can learn with little or no help from humans. Deep learning can be described as artificial neural networks in which it mimics brain funcionality. The specialists highlighted a few subsets such as Supervised, Reinforcement, Contrastive and System 2 learning, alongside to pros and cons. The considerations were published this month in the paper “Deep Learning for AI”, in the Communications of the ACM journal.

As timing is critical when it comes to delivering vaccines, carriers and airlines are using AI to overcome logistical difficulties of COVID-19 vaccine distribution. The author gathers great examples of how AI has helped to optimize vaccine transportation during the Covid-19 pandemic. For example, it was used to increase fuel efficiency by tracking drivers' behaviours, predict land or air traffic using historical data to estimate how long it will arrive in specific locations. Besides, it allowed the temperature of the vaccine to be monitored throughout the delivery journey, using Bluetooth intelligent mesh technology.