Blog&Events https://serakou.ai en https://serakou.ai/tpost/gof8z3ifs1-there-is-a-first-post-headline https://serakou.ai/tpost/7kk8km52u1-title-of-the-second-sample-post https://serakou.ai/tpost/3y2i1bi6x1-the-third-title-for-the-post MLIS 2025 | November 24–26, Hong Kong https://serakou.ai/tpost/xsg1bfy511-mlis-2025-november-2426-hong-kong https://serakou.ai/tpost/xsg1bfy511-mlis-2025-november-2426-hong-kong?amp=true Thu, 09 Oct 2025 15:12:00 +0300 Events International conference on machine learning and intelligent systems, covering deep learning and automation.

MLIS 2025 | November 24–26, Hong Kong

The 7th International Conference on Machine Learning and Intelligent Systems (MLIS 2025) will be held in Hong Kong, November 24–26, 2025.

This global event brings together experts from academia and industry to explore new developments in intelligent systems and applied AI.

Key topics include:

• Deep learning and pattern recognition

• Reinforcement learning and optimization

• Intelligent automation and robotics

• Ethical and sustainable AI


Format: In-person

Location: Hong Kong

Organizer: MLIS Committee

Website: machinelearningconf.org
]]> AI Summit London 2025 | November 12–13, London, UK https://serakou.ai/tpost/l3paf42oc1-ai-summit-london-2025-november-1213-lond https://serakou.ai/tpost/l3paf42oc1-ai-summit-london-2025-november-1213-lond?amp=true Thu, 09 Oct 2025 15:43:00 +0300 Events Global conference on enterprise AI, innovation, and real-world applications across industries.

AI Summit London 2025 | November 12–13, London, UK

The AI Summit London 2025 will take place on November 12–13, 2025, bringing together business leaders, researchers, and innovators to explore how artificial intelligence is transforming organizations worldwide.

The conference focuses on practical AI adoption, strategy, and governance in business environments.

Key topics include:

• Enterprise-scale AI transformation

• Responsible and ethical AI deployment

• Generative AI in production

• Data-driven decision-making and automation

Format: In-person + hybrid

Location: London, UK

Organizer: Informa Tech / AI Business

Website: aibusiness.com/events/ai-summit-london
]]> MLcon Berlin 2025 | November 25–28, Berlin, Germany https://serakou.ai/tpost/mo1e08is51-mlcon-berlin-2025-november-2528-berlin-g https://serakou.ai/tpost/mo1e08is51-mlcon-berlin-2025-november-2528-berlin-g?amp=true Thu, 09 Oct 2025 15:49:00 +0300 Events Conference bridging theory and practice in machine learning, MLOps, and scalable AI systems.

MLcon Berlin 2025 | November 25–28, Berlin, Germany

The Machine Learning Conference (MLcon Berlin 2025) will be held November 25–28, 2025, uniting ML professionals, engineers, and data scientists to discuss practical implementation of modern AI technologies.

The event emphasizes how to move machine learning from experimentation to production while maintaining scalability, efficiency, and reliability.

Key topics include:

• Building production-ready ML pipelines

• MLOps best practices and tools

• Large-scale model optimization

• Applied AI and data engineering

Format: In-person + online

Location: Berlin, Germany

Organizer: S&S Media Group

Website: mlconference.ai/berlin
]]> AI Summit New York 2025 | December 10–11, New York, USA https://serakou.ai/tpost/gm917yibz1-ai-summit-new-york-2025-december-1011-ne https://serakou.ai/tpost/gm917yibz1-ai-summit-new-york-2025-december-1011-ne?amp=true Thu, 09 Oct 2025 15:57:00 +0300 Events Flagship event for AI professionals exploring business impact, innovation, and emerging technologies.

AI Summit New York 2025 | December 10–11, New York, USA

The AI Summit New York 2025 will take place on December 10–11, 2025, gathering global technology leaders, enterprise executives, and researchers to discuss how AI is driving transformation across sectors.

The event combines keynote sessions, hands-on workshops, and networking opportunities focused on applied AI innovation.

Key topics include:

• Scaling AI in enterprise ecosystems

• AI-powered analytics and automation

• The future of generative AI

• Leadership and governance in AI strategy

Format: In-person + hybrid

Location: New York, USA

Organizer: Informa Tech / AI Business

Website: aibusiness.com/events/ai-summit-new-york
]]> Biometric Data: How AI Learns to Recognize Humans https://serakou.ai/tpost/ahndozh121-biometric-data-how-ai-learns-to-recogniz https://serakou.ai/tpost/ahndozh121-biometric-data-how-ai-learns-to-recogniz?amp=true Thu, 09 Oct 2025 16:12:00 +0300 Blog What biometrics are, where they’re used, and how they help train machine learning models for recognition tasks.

Biometric Data: How AI Learns to Recognize Humans

What Are Biometric Data?

Biometric data are unique physical or behavioral characteristics that can be used to verify a person’s identity.
They do not change significantly with age and are unique for every individual. Common examples include fingerprints, iris patterns, facial features, and voice.
These technologies are already part of everyday life. For example, biometric passports contain a microchip that stores not only standard personal information but also the holder’s fingerprints.

Main Types of Biometrics

Fingerprints. One of the most common and reliable identification methods. Each person has a unique fingerprint pattern that can be used in forensics and for everyday authentication, such as unlocking a smartphone.
Facial recognition. Algorithms analyze the geometry of the face — shape, distance between the eyes, and lip contours — to create a digital template used for comparison. This is how Apple’s Face ID works.
Voice recognition. AI systems analyze tone, pitch, and rhythm of speech to identify speakers — for example, in transcription services or contact centers.
Iris recognition. The iris pattern is as unique as a fingerprint. Apple’s Vision Pro headset already uses this method to identify users by eye pattern.
Other biometric identifiers include palm vein patterns, gait, heartbeat (ECG), and even brainwave activity (EEG).

Regulation of Biometric Data

In the European Union, biometric data are regulated under the GDPR (General Data Protection Regulation).
Such data are classified as sensitive personal information, meaning that companies must obtain explicit user consent and ensure enhanced data protection.
If a business operates with EU citizens, these rules are mandatory regardless of location.

Where Biometrics Are Used

Authentication and access control. Unlocking a phone with a fingerprint is faster and more convenient than entering a password. The same principle applies in online banking, tax services, and digital government platforms.
Healthcare. Biometrics prevent patient identification errors. For instance, the Matcher 5 system is used in fertility clinics and donor banks to match patients via fingerprints.
Travel and security. The U.S. and Japan scan fingerprints at border control, while China uses facial recognition at customs to verify travelers’ identities.
Finance. Banks like Citibank have introduced face-based authentication in mobile apps to enhance client security.
Gaming industry. The game Nevermind measures players’ heart rate — the calmer the player, the scarier the story becomes.
Marketing. Research firm Nielsen tracks viewers’ eye movements, EEG, and heart rate to evaluate emotional responses to ads and improve campaign performance.
Industrial safety. The SmartCap system monitors workers’ fatigue levels using EEG sensors embedded in headbands. When attention drops, the system alerts the worker, reducing the risk of accidents.

Biometrics and Artificial Intelligence

For an AI system to recognize a face, voice, or emotion, it must be trained on large datasets containing annotated examples.
These labeled datasets include images, sound recordings, or videos where each element is precisely tagged.
For instance, when developing a model for facial and emotion recognition, our team collected thousands of human images and annotated them using 15 facial key points. This structured approach helps algorithms detect subtle patterns and improve recognition accuracy.
Google also applies biometrics in its Assistant: when a user speaks, their voice is recorded and analyzed by an ML model that splits the audio into signals, recognizes words, and becomes more accurate over time — adapting to accent, tone, and speech rate.

How Biometric Data Are Collected for Machine Learning

The collection method depends on the data type:
  • Images and videos — gathered through crowdsourcing, web scraping, or synthetic data generation.
  • Fingerprints — sourced from open databases or collected in voluntary research. For example, the company Papilon digitized national fingerprint archives in the 2000s, creating an automated identification system.
  • Voice recordings — collected via crowdsourcing platforms or from real contact center calls.
After collection, the data are annotated and converted into formats suitable for model training.

How Biometric Models Are Trained

  1. Data collection. Images, audio, or video are gathered and labeled for the target task.
  2. Training. Algorithms learn unique patterns — the digital “fingerprints” of identity.
  3. Testing. The model’s accuracy is verified on new, unseen data.
  4. Deployment. Once validated, the system is implemented in real environments.
A well-known example is facial recognition at Heathrow Airport (London).
Since 2019, passengers no longer need to show passports or boarding passes — cameras scan faces and verify them in advance, drastically reducing registration and security check times.


Biometrics have already become part of everyday life — from smartphones and banking to healthcare and industrial safety. Combined with machine learning, they make authentication more secure, processes faster, and technologies smarter. However, biometric data remain highly sensitive personal information, requiring careful handling, transparency, and protection.
]]> AI in Medicine: From Diagnosis to Robotic Surgery https://serakou.ai/tpost/yku45z87p1-ai-in-medicine-from-diagnosis-to-robotic https://serakou.ai/tpost/yku45z87p1-ai-in-medicine-from-diagnosis-to-robotic?amp=true Thu, 09 Oct 2025 16:13:00 +0300 Blog Exploring how artificial intelligence is already transforming treatment, research, and medical data processing.

AI in Medicine: From Diagnosis to Robotic Surgery

As a company working with medical data, we see every day how AI is becoming part of real clinical practice. We’ve labeled chest and dental images, annotated histology slides, and worked with MRI and other digital scans. High-quality annotation is the foundation of model training—so we genuinely feel involved in the transformation happening across healthcare.

The scale of change is enormous. Over the past six years, global healthcare spending has grown from $6–7 trillion to $12 trillion; in the U.S., it already accounts for ~17% of GDP. Such rapid cost growth demands not just optimization but a fundamental rethinking of how care is delivered.

Against this backdrop, machine learning and AI are shifting from supporting roles to core instruments for diagnostics, treatment, documentation, and drug development.

Below we review the most promising—and already operational—directions for AI in medicine: from diagnostics and surgery to bioinformatics and automation of day-to-day clinical work.

Diagnostics

AI systems are rapidly entering diagnostic workflows. Platforms such as PathAI, Zebra Medical, Lunit, and PANProfiler detect oncological, cardiovascular, and other diseases with accuracy comparable to—or exceeding—human experts.
  • Example: PANProfiler analyzes breast histology and determines receptor status (ER, PR, HER2) with accuracy up to 87%, which is critical for personalized therapy.
  • Cleerly ISCHEMIA, an AI tool for coronary CT analysis, reaches AUC ≈ 0.91 and has already changed clinical management in 57% of cases.
AI is particularly active in radiology and pathology. Millions of medical images sit unused in local archives. Cloud infrastructure and deep learning now make it possible to process these datasets at scale, flagging pathologies—tumors, hemorrhages, brain lesions—within seconds. Companies like Aidoc already assist radiologists by prioritizing and supporting rapid reads.
This matters in an era of specialist shortages: in large hospitals a radiologist may need to render a diagnosis every 3–4 seconds. AI lightens the workload, reduces error rates, and helps catch rare or hard-to-detect conditions at earlier stages.
In pathology, deep networks analyze digital slides at the pixel level. A Harvard Medical School study showed that algorithms trained on cancer vs. non-cancer images, when combined with human experts, reached up to 99.5% accuracy—boosting reliability and speeding time to diagnosis.
AI also supports prognosis and treatment planning. Microsoft’s InnerEye helps segment tumors on 3D scans, streamlining radiotherapy planning and surgical preparation. Modern algorithms fuse data from multiple modalities (e.g., ultrasound + MRI + CT) and visualize disease in complex anatomical regions, such as the prostate.

Robotic Surgery

Robotic assistants like da Vinci, Mazor X, ROSA, and CyberKnife enable highly complex procedures with minimal invasiveness. They deliver micron-level precision, reducing risk and shortening recovery. AI enhances planning and real-time execution—guiding surgical steps, providing guardrails against lapses in attention, and prompting critical sequences during the operation. The result: better visualization, more precise incisions, optimal suture geometry, less pain, and faster healing.

Automating Clinical Documentation

How much time do clinicians spend on paperwork? According to Medscape 2023, physicians devote 15.5 hours per week to administrative tasks. For some specialties it’s even higher: 19 hours for physical therapists, 18 for neurologists and oncologists, 17 for family physicians. Time that could be spent with patients is lost to routine documentation.
AI helps. Ambient AI scribes convert physician-patient conversations into structured notes. Studies show they reduce burden and raise documentation quality to the level of top-tier manual notes (PDQI-9).
AI also powers intelligent text processing. NER models such as BioBERT, trained on clinical corpora (e.g., MIMIC-III), automatically extract diagnoses, symptoms, and medications from unstructured notes. This accelerates chart review, improves accuracy, and underpins analytics and clinical decision support.

Genomics and Drug Discovery

AI analyzes genomic and biomedical data to identify disease-linked mutations and tailor personalized treatment. Platforms like Tempus and IBM Watson Health are used in oncology to interpret genomic profiles, forecast outcomes, and choose optimal therapy.
AI is also transforming drug discovery. Models from Insilico Medicine, Atomwise, and especially AlphaFold 3 (DeepMind) predict protein and complex 3D structures, revealing druggable targets and simulating molecular interactions. What once took months can now be done in hours, dramatically improving hit discovery and candidate selection.

Optimizing Clinical Trials

Clinical trials are among the most expensive and time-consuming phases of drug development. Roughly 80% face delays or closures due to recruitment issues, and 37% of sites fail to enroll enough participants. Each day of delay costs sponsors $600,000 to $8 million.
ML offers solutions: automation of both recruitment and data operations. Deep 6 AI analyzes structured data (age, sex, ICD-10 and LOINC codes) and unstructured text (notes, lab reports, imaging descriptions). Using 120+ ontologies, it builds patient graphs to match trial criteria. Researchers can query millions of EHRs—including genetic markers, mutations, and symptoms—through a single interface.
AI also streamlines on-study data management. Medidata Rave Coder+ automates coding of adverse events and symptoms using MedDRA and WHODrug dictionaries, with ML trained on 60+ million examples. At high-confidence thresholds it reaches up to 96% accuracy and cuts coding time from minutes to seconds. The system flags data discrepancies—e.g., between adverse events and medical history—reducing errors and speeding review.

The Road Ahead: Human–AI Partnership

The medical community was slow to adopt AI—not only due to conservatism but because early models had real limitations. Today, machines reliably handle routine tasks, from screening to trial recruitment, freeing clinicians to focus on complex, high-stakes decisions.
Crucially, an AI system’s effectiveness depends not just on algorithms but on clinical robustness. A core limitation remains distributional shift—performance drops in unfamiliar settings. Unlike a clinician, a model does not “know what it doesn’t know” and may confidently apply the wrong logic.
This is why progress favors narrow, task-specific systems (ANI) optimized for concrete use cases—from imaging analysis to therapy selection. Ambitions for AGI that matches physicians across all tasks remain future goals. In healthcare, where the cost of error is high, reliability and transparency are non-negotiable.
The future lies in partnership. AI will not replace clinicians; it will remove the administrative and repetitive load so they can focus on what matters most: clinical reasoning, empathy, and decision-making. The result will be care that is more precise, personalized, and accessible.
]]> Case: Data Annotation for AgriTech https://serakou.ai/tpost/5y36t7bfs1-case-data-annotation-for-agritech https://serakou.ai/tpost/5y36t7bfs1-case-data-annotation-for-agritech?amp=true Thu, 09 Oct 2025 16:18:00 +0300 Blog Implementation of a data labeling project for autonomous machines operating in nut tree orchards.

Case: Data Annotation for AgriTech

Computer Vision in Agriculture: Why It’s Essential

Agriculture is one of the key industries ensuring global food security, livestock feed, and biofuel production.
By 2050, the world’s population is expected to reach 9 billion people, meaning agricultural output must double to meet demand. To achieve this, crop yields must increase by at least 25%, while efficiency across all processes must grow dramatically.
Computer vision is transforming agriculture by automating tasks that were previously manual or impossible using traditional methods. It enables machines to see beyond human limitations, analyzing every frame in milliseconds and delivering instant insights — accelerating decision-making and improving operational precision.
Moreover, computer vision reduces dependency on human labor. In a time of workforce shortages, AI-powered systems can automate complex operations — from crop health monitoring to autonomous vehicle control. Combined with precision farming devices, drones, and robotics, these technologies make agriculture more resilient to workforce challenges and external factors like weather.
In 2024, the global AI in agriculture market was valued at $2.08 billion and is projected to reach $5.76 billion by 2029, growing at a CAGR of 22.55%.
Automated systems using computer vision help agricultural enterprises boost yields, minimize losses, and reduce labor costs. Such technologies are already being used for crop monitoring, automated harvesting, weed detection, soil condition analysis, and livestock management.

The Importance of Data Quality

The performance of any computer vision model depends on the quality of its training data.
The more precise and detailed the annotation, the better the algorithm can analyze the environment, detect objects, and adapt to challenging conditions such as dust, rain, or low visibility.
Errors during the data preparation stage can drastically reduce model accuracy — which is critical for autonomous agricultural machinery.
With this in mind, our team developed a high-quality dataset to train a computer vision model for agricultural automation. We ensured that all environmental complexities were carefully annotated, enabling the model to perform reliably in real-world conditions.

Project Goals and Challenges

Our team partnered with an agritech company to develop an autonomous control system for machines operating in walnut orchards.
A key requirement was high annotation precision for elements captured by specialized cameras.
This accuracy was essential for the model to successfully perform the following tasks:
  • Determine vehicle position for precise navigation within orchard rows.
  • Recognize tree age and condition (young, mature, dead, or leaning).
  • Detect and avoid obstacles such as people and irrigation systems.
  • Predict the start and end of rows to adjust navigation routes and prevent damage or crop loss.
The project involved multiple annotation types — Polygon Segmentation, Point Annotation, Polyline Annotation, and Tag Annotation — and required strict logical consistency across numerous object classes.

Our Methodology

1. Iterative Process
Data was delivered in batches, each representing a new model stage under different environmental conditions. This allowed continuous adaptation and model improvement.
2. Technical Specification Development
A detailed technical brief was created, including case analysis and edge conditions, to minimize annotation errors.
3. Continuous Communication
All annotation updates and instruction revisions were promptly shared with annotators via a team chat to ensure fast workflow adaptation.
4. Quality Control
  • 100% validation coverage ensured full accuracy compliance.
  • Automated checks: a custom script was implemented to identify logical inconsistencies, speeding up QA and reducing rework cycles.

Results

  • High annotation quality — our team successfully delivered a complex dataset with consistent, high-accuracy labeling across all iterations.
  • Model performance improvement — the client received a robust system capable of reliable object detection, confident navigation, and seasonal adaptation.
Our experience confirmed that data annotation is not just a preparatory step — it’s the foundation of every successful AI model.
In harsh environments with rain, dust, or limited visibility, autonomous systems powered by computer vision enable precise navigation and safe operations.
If you’re working on similar challenges and want your technology to perform flawlessly in complex real-world conditions — we’re here to help! 🚀
]]> Speaking the Language of AI: Glossary of Key Terms https://serakou.ai/tpost/p090vxflp1-speaking-the-language-of-ai-glossary-of https://serakou.ai/tpost/p090vxflp1-speaking-the-language-of-ai-glossary-of?amp=true Thu, 09 Oct 2025 16:19:00 +0300 Blog 30 essential AI, ML, and data preparation terms for everyone working with artificial intelligence technologies.

Speaking the Language of AI: Glossary of Key Terms

30 essential concepts from the world of artificial intelligence — for specialists, project managers, students, and everyone involved in building and applying AI systems.

The glossary is organized by sections: from core concepts and model training to data preparation and quality control.

1. Core Concepts

Artificial Intelligence (AI) — a branch of computer science focused on enabling machines to perform tasks that require human-like intelligence, such as reasoning, learning, and adaptation.
Machine Learning (ML) — a field of AI that develops algorithms capable of improving their performance through experience, without explicit programming of every rule.
Deep Learning — a subset of ML that uses neural networks with multiple hidden layers. Thanks to advances in hardware and algorithms, it has become the foundation of modern systems for speech recognition, image understanding, and autonomous decision-making.
Big Data — large and complex datasets that exceed the capabilities of traditional processing methods. Big Data enables user behavior analytics, recommendation systems, predictive modeling, and real-time monitoring.
Computer Vision — an area of AI that gives computers the ability to interpret images and video with human-level understanding. Deep learning has significantly advanced its accuracy and speed.
Natural Language Processing (NLP) — the study of how computers can analyze, understand, and generate human language. It combines linguistics and ML for translation, sentiment analysis, chatbots, and speech recognition.
Generative AI — AI systems that create new content based on learned patterns. They can generate text, images, code, or audio and are used in creative industries, automation, and design (e.g., ChatGPT, Midjourney, GitHub Copilot).
Large Language Model (LLM) — a neural network trained on massive text datasets to perform language-related tasks like text generation, translation, and summarization. LLMs are based on transformer architectures (GPT, BERT) and power most modern chatbots.
Vision-Language Model (VLM) — a model that combines image and text understanding. It can describe images, answer questions about them, or locate visual objects based on text input. These models are applied in medicine, robotics, and multimodal AI systems.

2. Model Training

Model — the trained output of an AI algorithm that encodes learned patterns and can be reused or adapted to new data.
Pretrained Model — a model already trained on a large dataset and ready for use or fine-tuning on a related task, saving time and computational resources.
Training — the iterative process in which a model processes input data, compares predictions with expected results, and adjusts its parameters to improve performance.
Supervised Learning — training with labeled data, where each input has a known correct output, enabling the model to learn mappings and make predictions on new data.
Unsupervised Learning — training on unlabeled data to identify hidden structures, clusters, or anomalies.
Reinforcement Learning — a method where an agent interacts with its environment and learns to optimize actions by maximizing rewards over time.
Fine-Tuning — retraining a pretrained model on a smaller, domain-specific dataset to improve relevance and accuracy.
Ensemble Learning — combining multiple models to produce more stable and accurate results through voting or averaging mechanisms.
Generative Adversarial Network (GAN) — a neural architecture with two competing networks: a generator that creates data and a discriminator that distinguishes it from real examples. GANs are used in image synthesis, style transfer, and simulation.
Neural Network — a mathematical model inspired by the human brain, consisting of layers of artificial neurons that process data and learn complex patterns.
Human-in-the-Loop (HITL) — an approach where humans actively participate in training or validating models to ensure higher accuracy and real-world alignment.
Reinforcement Learning with Human Feedback (RLHF) — a form of reinforcement learning where human judgment guides model optimization, widely used for LLM fine-tuning.
Active Learning — a strategy where the model identifies uncertain examples and requests human annotation, reducing labeling costs and focusing on valuable data.

3. Data Preparation

Annotation — the process of labeling raw data for use in supervised learning. In computer vision, this may include bounding boxes, segmentation masks, or keypoints; in NLP — tagging entities, relations, or intents.
Optical Character Recognition (OCR) — technology that extracts text from images or scanned documents and converts it into machine-readable format.
Augmentation — techniques that artificially expand a dataset by applying transformations like rotation, scaling, cropping, or brightness adjustment, improving model generalization.
Synthetic Data — artificially generated data that mimics real-world information, created through simulations or generative models when real data is scarce or sensitive.
Ground Truth — verified and accurate data used as a benchmark for model training and validation.
Data Collection — the process of gathering information from sensors, cameras, databases, APIs, or manual input. Effective collection ensures data diversity, relevance, and representativeness.
Quality Control (QC) — procedures to verify the accuracy, completeness, and consistency of data or annotations using manual review, validation tools, or automated metrics.
Consensus Annotation — validation method where multiple annotators label the same data, and the final label is determined through agreement or majority voting to ensure reliability.


We didn’t aim to cover every term in the industry — only those most commonly used in real-world projects.
Would you add something to this list? Share your ideas — this glossary will keep evolving.
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