#High-Quality Human Expert Data Labeling

[apexcovantage]

Generative AI | High-Quality Human Expert Labeling | Apex Data Sciences

Apex Data Sciences combines cutting-edge generative AI with RLHF for superior data labeling solutions. Get high-quality labeled data for your AI projects.

[macmanx]

To some extent, the significance of humans’ AI ratings is evident in the money pouring into them. One company that hires people to do RLHF and data annotation was valued at more than $7 billion in 2021, and its CEO recently predicted that AI companies will soon spend billions of dollars on RLHF, similar to their investment in computing power. The global market for labeling data used to train these models (such as tagging an image of a cat with the label “cat”), another part of the “ghost work” powering AI, could reach nearly $14 billion by 2030, according to an estimate from April 2022, months before the ChatGPT gold rush began.

All of that money, however, rarely seems to be reaching the actual people doing the ghostly labor. The contours of the work are starting to materialize, and the few public investigations into it are alarming: Workers in Africa are paid as little as $1.50 an hour to check outputs for disturbing content that has reportedly left some of them with PTSD. Some contractors in the U.S. can earn only a couple of dollars above the minimum wage for repetitive, exhausting, and rudderless work. The pattern is similar to that of social-media content moderators, who can be paid a tenth as much as software engineers to scan traumatic content for hours every day. “The poor working conditions directly impact data quality,” Krystal Kauffman, a fellow at the Distributed AI Research Institute and an organizer of raters and data labelers on Amazon Mechanical Turk, a crowdsourcing platform, told me.

Stress, low pay, minimal instructions, inconsistent tasks, and tight deadlines—the sheer volume of data needed to train AI models almost necessitates a rush job—are a recipe for human error, according to Appen raters affiliated with the Alphabet Workers Union-Communications Workers of America and multiple independent experts. Documents obtained by Bloomberg, for instance, show that AI raters at Google have as little as three minutes to complete some tasks, and that they evaluate high-stakes responses, such as how to safely dose medication. Even OpenAI has written, in the technical report accompanying GPT-4, that “undesired behaviors [in AI systems] can arise when instructions to labelers were underspecified” during RLHF.

[auxilifeblog]

Navigating the EU Biocide Products Regulation: A Comprehensive Guide for Manufacturers

The European Union’s Biocide Products Regulation (BPR) ensures that biocidal products used to protect humans, animals, and materials meet strict safety and environmental standards. For manufacturers, navigating this regulatory framework is essential but often complex. This guide will help you understand the key aspects of the BPR and how to streamline compliance with specialized Biocide Products Regulation (BPR) services in Europe.

Understanding the EU Biocide Products Regulation (BPR)

The BPR (Regulation (EU) No 528/2012) governs the approval of biocidal products and their active substances in the EU market. It applies to a wide range of products, from disinfectants and preservatives to pest control solutions. Key objectives include:

Protecting public health and the environment.

Ensuring that only effective, safe products are available on the market.

Harmonizing regulatory processes across EU member states.

Key Steps for Manufacturers to Comply with BPR

Active Substance ApprovalActive substances in your biocidal products must be approved by the European Chemicals Agency (ECHA). This requires submitting robust scientific data on efficacy, safety, and environmental impact.

Product AuthorizationOnce the active substance is approved, the biocidal product itself must be authorized. This can be done at the national level, EU level (Union Authorization), or through mutual recognition.

Data Sharing and Article 95 ComplianceTo avoid redundant testing, manufacturers must adhere to Article 95, which requires data sharing on active substances. This step is crucial for maintaining access to the EU market.

Labeling and Packaging RequirementsProducts must meet specific labeling criteria to ensure proper use and disposal while providing safety information to users.

Post-Market ObligationsManufacturers are responsible for ongoing compliance, including periodic reporting and managing any adverse effects associated with the product.

The Role of Biocide Products Regulation (BPR) Services in Europe

Navigating the regulatory landscape is challenging, especially for small to medium-sized enterprises (SMEs). BPR services in Europe can simplify the process by offering:

Dossier Preparation and Submission: Streamlining the submission process for active substances and products.

Regulatory Strategy Development: Tailored advice to minimize costs and timeframes.

Compliance Monitoring: Ensuring ongoing adherence to BPR requirements post-authorization.

Agriculture Regulatory Compliance Support under BPR

Biocides used in agriculture—such as pesticides, disinfectants, or preservatives—must comply with additional sector-specific regulations. Expert agriculture regulatory compliance support ensures your products align with both BPR and agricultural standards, safeguarding market access.

Key Benefits of Professional Regulatory Support

Time Savings: Faster approval processes with accurate, high-quality dossiers.

Cost Efficiency: Optimized regulatory pathways reduce unnecessary expenses.

Risk Mitigation: Compliance support minimizes the risk of non-compliance penalties.

Final Thoughts

Successfully navigating the EU Biocide Products Regulation requires a clear understanding of its requirements and a strategic approach to compliance. Leveraging professional Biocide Products Regulation (BPR) services in Europe and tailored agriculture regulatory compliance support can help manufacturers stay competitive while ensuring public and environmental safety.

Ready to simplify your regulatory journey? Partner with experts to achieve seamless BPR compliance today!

[medwisdom]

How To Apply Manufacturing License For Medical Device In India?

If you are manufacturer or distributor, looking for manufacturing license for your Medical Device, this guide will help you to simply understand the documents and procedure of obtaining manufacturing license for medical device in India. Manufacturing license is issued by the licensing authority of India. Classification of medical device determines which licensing authority responsible for issuing manufacturing license. State licensing authority is responsible for grant of manufacturing license for class A and B medical device. Central licensing authority is responsible for issuing manufacturing license for class C and D medical device based on the risk it possesses. This guide will help you to figure out your queries for obtaining manufacturing license for medical device in India.

Who Can Apply For Manufacturing License For Medical Device?

Below are some individual who can apply for manufacturing license for medical device in India:

Manufacturer: who is responsible for production and manufacturing of medical device can apply for manufacturing license.

Importer: Person who is looking to Import medical device in India can apply for manufacturing license.

Distributor or Authorized Person: Person who is authorized from manufacturer can apply for license.

What Is Medical Device? How Many Classes Of Medical Device Are there?

Medical device is any instrument, equipment, implant, or machine that is used in diagnosis, treatment, and mitigation of disease in humans. Medical device are classified into four types based on the risk it possesses:

Class A: Low risk medical device (stethoscope and bandages)

Class B: Intermediate risk (syringe and glucose meter)

 Class C: Moderate high risk ( catheter and implants)

Class D: Very high risk (heart valves)

Documents Required For Obtaining Manufacturing License For Medical Device In India

For the grant of manufacturing license for your medical device, following are some key documents required as per CDSCO guidelines:

Application Form: Form MD-3 is filled for class A and B medical device, form MD-7 for class C and D medical devices.

Plant Master File: Plant master file having information about the manufacturing site, layout of facility, and equipment used.

Device Master File: Detail description of medical device and risk possibilities along with the specifications and design.

Quality Management System: QMS certificate issued under ISO 13485 certification demonstrating high performance quality standard of medical device in compliance with regulatory framework.

Technical Documents: Most important documents including product specifications, clinical report and risk management plan of medical device.

Stability Data: Stability data demonstrating the shelf life of medical device.

Deceleration or Understanding: Understanding from manufacturer, ensuring compliance of medical device with regulatory standards.

Labelling and Packaging: Details of labelling for medical device is required as per Medical Device Rules, 2017.

Procedure To Obtain Manufacturing License For Medical Device

Following are the main steps for the grant of manufacturing license:

Visit Sugam Portal: Register on the CDSCO/sugam portal by creating an account.

Application Submission: Select the Form applicable as per your medical device registration. Fill the form details carefully and submit on the portal.

Fee Payment: Pay the applicable money as per the Medical Device Rules, 2017.

Audit Inspection: Specific regulatory authority will conduct the inspection of product manufacturing site, to ensure the compliance of medical device as per the regulatory standards.

License Issuance: After the successful evaluation of manufacturing site along with your application form documents, Licensing authority will grant the manufacturing license.

How Medwisdom Lifescience Can Assist You With Obtaining Manufacturing License?

Medwisdom offers expert assistance in medical device registration and license, we have secured over 2500 medical device registration so far, we take pride in our exclusive achievement for grant of 500 plus manufacturing license across all classes of medical device (A to D). Our expertise ensure timely registration and cost effective solutions for obtaining manufacturing license.

FAQS

Who Needs Manufacturing License For Medical Device In India?

Any company or individual planning to manufacture medical device for the purpose of sell or marketing, must obtain the manufacturing license for medical device under the Medical Device Rules, 2017.

Who Issue Manufacturing License For Medical Device?

State Licensing Authority is responsible for issuing manufacturing license for class A and class B medical device. Central Licensing Authority

What Is The Validity of Manufacturing License For Medical Device?

Validity of manufacturing license is five years from the date of issue.

What Is The Timeline For Obtaining Manufacturing License?

The approximate time for obtaining manufacturing license for medical device takes 5-6 months, based on the completeness of your application and the response from the regulatory authority.

[tagx01]

Enhance AI Accuracy with Expert Data Annotation Services for Your Business Needs

In today’s data-driven world, businesses rely heavily on artificial intelligence (AI) to improve efficiency, drive innovation, and maintain a competitive edge. However, the accuracy and performance of AI models are only as good as the data they are trained on. This is where data annotation services play a crucial role in the development and enhancement of AI systems.

Data annotation refers to the process of labeling or tagging data to provide context and structure for machine learning (ML) algorithms. It is the foundation of AI training, helping machines interpret and understand information accurately. By utilizing expert data annotation, businesses can improve the quality of their AI models, ensuring that they operate effectively and make more precise decisions.

Why Data Annotation Services Matter for AI Accuracy

Foundation for Machine Learning Models

AI systems, particularly machine learning models, rely on large datasets to learn patterns, make predictions, and provide valuable insights. However, these systems cannot function effectively without properly labeled data. Data annotation is essential because it turns raw data into structured information that machine learning models can understand. Whether it’s image recognition, natural language processing, or speech recognition, accurate annotations are critical for training models to achieve high performance.

Improved AI Accuracy

Data annotation directly impacts the accuracy of AI algorithms. The better the quality of annotations, the more precise the AI model’s predictions will be. Whether you're working with text, images, or videos, accurate annotations ensure that the AI can learn the correct features, leading to better decision-making, reduced errors, and higher precision in results.

Scalability of AI Projects As businesses scale their AI projects, the amount of data they need to process grows exponentially. Without the support of expert, handling vast quantities of data with precision becomes a daunting task. Annotation services provide the necessary infrastructure to manage and label data on a large scale, ensuring that AI systems remain efficient as they expand.

Types of Data Annotation Services

Image Annotation

Bounding Boxes: A rectangular box is drawn around specific objects within an image. This is useful for object detection tasks, such as identifying cars, animals, or faces in images.

Semantic Segmentation: This technique involves labeling each pixel in an image to identify and separate different objects or regions. It’s often used in tasks like autonomous vehicle navigation or medical image analysis.

Keypoint Annotation: Points are annotated on important features in an image (such as facial features or hand gestures) to train models in recognizing human actions, poses, or objects.

Text Annotation

Entity Recognition: Involves labeling specific pieces of text as entities (e.g., names, dates, locations) for applications like chatbots, voice assistants, and content extraction systems.

Sentiment Annotation: Labels are applied to text to indicate the sentiment expressed (positive, negative, or neutral). This is useful for sentiment analysis in customer feedback or social media monitoring.

Part-of-Speech Tagging: Words in a sentence are labeled with their grammatical roles, which is essential for applications like natural language processing (NLP) and text translation.

Audio Annotation

Speech-to-Text Transcription: This involves converting spoken language into written text. It is widely used in virtual assistants, transcription services, and voice recognition systems.

Sound Event Detection: Labels are added to specific sound events in audio files, like identifying the sound of a doorbell or a phone ringing. This is used in surveillance systems or automated monitoring applications.

Video Annotation

Object Tracking: Annotating moving objects across frames in a video to help AI systems track objects over time. This is essential in applications like surveillance, autonomous driving, and sports analytics.

Action Recognition: Identifying and tagging actions or activities in video clips, such as walking, running, or waving. This technique is commonly used in security surveillance, human-computer interaction, and sports analytics.

How Data Annotation Services Enhance AI Models

Increased Efficiency in AI Training

Annotating data efficiently and accurately saves time during the AI training process. High-quality annotated datasets allow machine learning algorithms to train faster and more effectively, producing models that are ready for deployment in a shorter time.

Customization for Business-Specific Needs

Expert provide customized solutions based on the unique needs of your business. Whether you are developing a healthcare application, a retail recommendation system, or a fraud detection system, annotated data tailored to your specific use case helps create more accurate models that are optimized for your business objectives.

Better Accuracy in Predictions and Decisions

Accurate data annotations ensure that AI models make better predictions, leading to smarter decisions. For instance, in an e-commerce application, annotated data can help the AI understand user preferences, which can then be used to personalize product recommendations, enhancing customer experience and boosting sales.

Improved Model Validation

Data annotation also help in validating AI models by providing clear examples of correct and incorrect outputs. These examples allow businesses to fine-tune their AI models, ensuring that they operate at peak performance. Regular validation and re-annotation can help maintain the effectiveness of AI systems over time.

How to Choose the Right Data Annotation Services

Industry Expertise

Whether it’s healthcare, retail, finance, or autonomous driving, choosing a provider with domain-specific knowledge ensures that the annotations are highly relevant and accurate for your AI needs.

Quality Assurance

Ensure the service provider offers stringent quality control processes to maintain the accuracy and consistency of annotations. This includes double-checking annotations, using experienced annotators, and having mechanisms in place for error detection and correction.

Scalability and Speed

As your business grows, the amount of data you need to annotate will likely increase. Choose a provider that can scale their services and maintain speed without sacrificing quality. A reliable data annotation service will be able to handle large datasets efficiently and meet tight deadlines.

Cost-Effectiveness

While quality should never be compromised, it's also essential to consider the cost-effectiveness of the data annotation services. Look for a provider that offers competitive pricing while ensuring top-notch results.

Conclusion

In conclusion, data annotation services are crucial for enhancing the accuracy of AI models. By turning raw data into structured, labeled information, businesses can develop AI systems that deliver more accurate predictions, better customer experiences, and smarter decision-making. Whether you're building a computer vision model, natural language processing system, or audio recognition tool, accurate data annotation ensures that your AI solutions are capable of performing at their best.

To ensure the best possible outcomes for your AI projects, partner with a trusted data annotation service provider that understands your industry needs and delivers high-quality annotations efficiently. Start enhancing the accuracy of your AI models today—contact a professional data annotation service provider and take your AI initiatives to the next level.

Visit Us, https://www.tagxdata.com/

[alexanderkylesworld]

Sam Altman on AGI: Engineering Challenges and Opportunities for 2025

Artificial General Intelligence (AGI) is a pivotal goal in the field of artificial intelligence. It refers to the capability of machines to perform any intellectual task that a human can do. Sam Altman, the CEO of Open AI, has been a leading voice in the discussion surrounding AGI. His insights into the engineering challenges and opportunities that lie ahead, particularly looking towards 2025, are crucial for understanding the future of AI technology. This article explores Altman's perspective on AGI and the implications for the coming years.

Current State of AGI Development

As of now, the development of AGI is still in its infancy. Most AI systems today are designed for narrow applications, excelling in specific tasks such as language processing, image recognition, and data analysis. Altman acknowledges that while significant advancements have been made in these areas, achieving true AGI remains a complex challenge. The current AI technologies lack the generalization and adaptability that characterize human intelligence, making the journey to AGI a formidable task.

Engineering Challenges in AGI Development

One of the primary engineering challenges in developing AGI is the complexity of human cognition. Understanding how humans think, learn, and make decisions is a monumental task. Altman emphasizes that replicating these processes in machines requires advanced algorithms and a deeper understanding of neuroscience. Additionally, the integration of various AI systems to create a cohesive AGI framework presents significant technical hurdles. These challenges necessitate innovative engineering solutions and interdisciplinary collaboration.

Data Requirements for AGI

Data plays a crucial role in training AI systems. For AGI to function effectively, it requires vast amounts of diverse and high-quality data. Altman points out that the data used to train AGI models must encompass a wide range of human experiences and knowledge. This includes not only factual information but also emotional and contextual understanding. The challenge lies in curating and processing this data to ensure that AGI systems can learn and adapt in a manner similar to humans.

The Role of Machine Learning in AGI

Machine learning is at the core of AGI development. Altman highlights the importance of advancing machine learning techniques to enable AGI systems to learn from their experiences. Current machine learning models, while powerful, often require extensive supervision and labeled data. To achieve AGI, researchers must develop unsupervised and reinforcement learning methods that allow machines to learn autonomously. This shift in approach is essential for creating systems that can adapt and evolve over time.

Opportunities for Innovation in AGI

Despite the challenges, Altman sees numerous opportunities for innovation in AGI development. The next few years are critical for advancing the technology and addressing the engineering hurdles. Altman believes that breakthroughs in hardware, such as more powerful processors and specialized AI chips, will significantly enhance the capabilities of AGI systems. Additionally, advancements in algorithms and training techniques will pave the way for more efficient and effective learning processes.

Collaboration and Interdisciplinary Approaches

Altman emphasizes the importance of collaboration in overcoming the engineering challenges associated with AGI. He advocates for interdisciplinary approaches that bring together experts from various fields, including computer science, neuroscience, psychology, and ethics. By fostering collaboration, researchers can share insights and develop comprehensive solutions to the complex problems posed by AGI. OpenAI's commitment to transparency and open research is a testament to this collaborative spirit.

Ethical Considerations in AGI Engineering

As engineering challenges are addressed, ethical considerations must also be at the forefront of AGI development. Altman stresses the need for ethical guidelines to govern the creation and deployment of AGI systems. Ensuring that AGI aligns with human values and prioritizes societal well-being is paramount. The potential risks associated with AGI, including biases and unintended consequences, necessitate a careful and responsible approach to its engineering.

Looking Ahead to 2025

As we look towards 2025, the landscape of AGI development is poised for significant transformation. Altman believes that by addressing the engineering challenges and seizing the opportunities for innovation, we can make substantial progress towards achieving AGI. The next few years will be critical in shaping the future of AI technology and determining how AGI will impact society. Altman's vision for AGI is one of hope and potential, emphasizing the importance of responsible development and collaboration.

Conclusion: The Future of AGI

Sam Altman's insights into the engineering challenges and opportunities for AGI provide a roadmap for the future of artificial intelligence. While the path to AGI is fraught with obstacles, the potential benefits are immense. By fostering collaboration, prioritizing ethical considerations, and embracing innovation, we can navigate the complexities of AGI development. The journey towards AGI is not just about technology; it is about creating a future that enhances human capabilities and addresses the challenges facing our world

[sunaleisocial]

Can robots learn from machine dreams?

New Post has been published on https://sunalei.org/news/can-robots-learn-from-machine-dreams/

Can robots learn from machine dreams?

For roboticists, one challenge towers above all others: generalization — the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery. This process traditionally requires human oversight, with operators carefully challenging robots to expand their abilities. As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans’ ability to provide it.

Now, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers has developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments. The new system, called “LucidSim,” uses recent advances in generative AI and physics simulators to create diverse and realistic virtual training environments, helping robots achieve expert-level performance in difficult tasks without any real-world data.

Play video

LucidSim: Can Robots Learn from Machine Dreams? Video: MIT CSAIL

LucidSim combines physics simulation with generative AI models, addressing one of the most persistent challenges in robotics: transferring skills learned in simulation to the real world. “A fundamental challenge in robot learning has long been the ‘sim-to-real gap’ — the disparity between simulated training environments and the complex, unpredictable real world,” says MIT CSAIL postdoc Ge Yang, a lead researcher on LucidSim. “Previous approaches often relied on depth sensors, which simplified the problem but missed crucial real-world complexities.”

The multipronged system is a blend of different technologies. At its core, LucidSim uses large language models to generate various structured descriptions of environments. These descriptions are then transformed into images using generative models. To ensure that these images reflect real-world physics, an underlying physics simulator is used to guide the generation process.

The birth of an idea: From burritos to breakthroughs

The inspiration for LucidSim came from an unexpected place: a conversation outside Beantown Taqueria in Cambridge, Massachusetts. ​​“We wanted to teach vision-equipped robots how to improve using human feedback. But then, we realized we didn’t have a pure vision-based policy to begin with,” says Alan Yu, an undergraduate student in electrical engineering and computer science (EECS) at MIT and co-lead author on LucidSim. “We kept talking about it as we walked down the street, and then we stopped outside the taqueria for about half-an-hour. That’s where we had our moment.”

To cook up their data, the team generated realistic images by extracting depth maps, which provide geometric information, and semantic masks, which label different parts of an image, from the simulated scene. They quickly realized, however, that with tight control on the composition of the image content, the model would produce similar images that weren’t different from each other using the same prompt. So, they devised a way to source diverse text prompts from ChatGPT.

This approach, however, only resulted in a single image. To make short, coherent videos that serve as little “experiences” for the robot, the scientists hacked together some image magic into another novel technique the team created, called “Dreams In Motion.” The system computes the movements of each pixel between frames, to warp a single generated image into a short, multi-frame video. Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.

“We outperform domain randomization, a method developed in 2017 that applies random colors and patterns to objects in the environment, which is still considered the go-to method these days,” says Yu. “While this technique generates diverse data, it lacks realism. LucidSim addresses both diversity and realism problems. It’s exciting that even without seeing the real world during training, the robot can recognize and navigate obstacles in real environments.”

The team is particularly excited about the potential of applying LucidSim to domains outside quadruped locomotion and parkour, their main test bed. One example is mobile manipulation, where a mobile robot is tasked to handle objects in an open area; also, color perception is critical. “Today, these robots still learn from real-world demonstrations,” says Yang. “Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene. We hope to make this easier, thus qualitatively more scalable, by moving data collection into a virtual environment.”

Who’s the real expert?

The team put LucidSim to the test against an alternative, where an expert teacher demonstrates the skill for the robot to learn from. The results were surprising: Robots trained by the expert struggled, succeeding only 15 percent of the time — and even quadrupling the amount of expert training data barely moved the needle. But when robots collected their own training data through LucidSim, the story changed dramatically. Just doubling the dataset size catapulted success rates to 88 percent. “And giving our robot more data monotonically improves its performance — eventually, the student becomes the expert,” says Yang.

“One of the main challenges in sim-to-real transfer for robotics is achieving visual realism in simulated environments,” says Stanford University assistant professor of electrical engineering Shuran Song, who wasn’t involved in the research. “The LucidSim framework provides an elegant solution by using generative models to create diverse, highly realistic visual data for any simulation. This work could significantly accelerate the deployment of robots trained in virtual environments to real-world tasks.”

From the streets of Cambridge to the cutting edge of robotics research, LucidSim is paving the way toward a new generation of intelligent, adaptable machines — ones that learn to navigate our complex world without ever setting foot in it.

Yu and Yang wrote the paper with four fellow CSAIL affiliates: Ran Choi, an MIT postdoc in mechanical engineering; Yajvan Ravan, an MIT undergraduate in EECS; John Leonard, the Samuel C. Collins Professor of Mechanical and Ocean Engineering in the MIT Department of Mechanical Engineering; and Phillip Isola, an MIT associate professor in EECS. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. The researchers presented their work at the Conference on Robot Learning (CoRL) in early November.

[jcmarchi]

Can robots learn from machine dreams?

New Post has been published on https://thedigitalinsider.com/can-robots-learn-from-machine-dreams/

Can robots learn from machine dreams?

For roboticists, one challenge towers above all others: generalization — the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery. This process traditionally requires human oversight, with operators carefully challenging robots to expand their abilities. As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans’ ability to provide it.

Now, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers has developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments. The new system, called “LucidSim,” uses recent advances in generative AI and physics simulators to create diverse and realistic virtual training environments, helping robots achieve expert-level performance in difficult tasks without any real-world data.

Play video

LucidSim: Can Robots Learn from Machine Dreams? Video: MIT CSAIL

LucidSim combines physics simulation with generative AI models, addressing one of the most persistent challenges in robotics: transferring skills learned in simulation to the real world. “A fundamental challenge in robot learning has long been the ‘sim-to-real gap’ — the disparity between simulated training environments and the complex, unpredictable real world,” says MIT CSAIL postdoc Ge Yang, a lead researcher on LucidSim. “Previous approaches often relied on depth sensors, which simplified the problem but missed crucial real-world complexities.”

The multipronged system is a blend of different technologies. At its core, LucidSim uses large language models to generate various structured descriptions of environments. These descriptions are then transformed into images using generative models. To ensure that these images reflect real-world physics, an underlying physics simulator is used to guide the generation process.

The birth of an idea: From burritos to breakthroughs

The inspiration for LucidSim came from an unexpected place: a conversation outside Beantown Taqueria in Cambridge, Massachusetts. ​​“We wanted to teach vision-equipped robots how to improve using human feedback. But then, we realized we didn’t have a pure vision-based policy to begin with,” says Alan Yu, an undergraduate student in electrical engineering and computer science (EECS) at MIT and co-lead author on LucidSim. “We kept talking about it as we walked down the street, and then we stopped outside the taqueria for about half-an-hour. That’s where we had our moment.”

To cook up their data, the team generated realistic images by extracting depth maps, which provide geometric information, and semantic masks, which label different parts of an image, from the simulated scene. They quickly realized, however, that with tight control on the composition of the image content, the model would produce similar images that weren’t different from each other using the same prompt. So, they devised a way to source diverse text prompts from ChatGPT.

This approach, however, only resulted in a single image. To make short, coherent videos that serve as little “experiences” for the robot, the scientists hacked together some image magic into another novel technique the team created, called “Dreams In Motion.” The system computes the movements of each pixel between frames, to warp a single generated image into a short, multi-frame video. Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.

“We outperform domain randomization, a method developed in 2017 that applies random colors and patterns to objects in the environment, which is still considered the go-to method these days,” says Yu. “While this technique generates diverse data, it lacks realism. LucidSim addresses both diversity and realism problems. It’s exciting that even without seeing the real world during training, the robot can recognize and navigate obstacles in real environments.”

The team is particularly excited about the potential of applying LucidSim to domains outside quadruped locomotion and parkour, their main test bed. One example is mobile manipulation, where a mobile robot is tasked to handle objects in an open area; also, color perception is critical. “Today, these robots still learn from real-world demonstrations,” says Yang. “Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene. We hope to make this easier, thus qualitatively more scalable, by moving data collection into a virtual environment.”

Who’s the real expert?

The team put LucidSim to the test against an alternative, where an expert teacher demonstrates the skill for the robot to learn from. The results were surprising: Robots trained by the expert struggled, succeeding only 15 percent of the time — and even quadrupling the amount of expert training data barely moved the needle. But when robots collected their own training data through LucidSim, the story changed dramatically. Just doubling the dataset size catapulted success rates to 88 percent. “And giving our robot more data monotonically improves its performance — eventually, the student becomes the expert,” says Yang.

“One of the main challenges in sim-to-real transfer for robotics is achieving visual realism in simulated environments,” says Stanford University assistant professor of electrical engineering Shuran Song, who wasn’t involved in the research. “The LucidSim framework provides an elegant solution by using generative models to create diverse, highly realistic visual data for any simulation. This work could significantly accelerate the deployment of robots trained in virtual environments to real-world tasks.”

From the streets of Cambridge to the cutting edge of robotics research, LucidSim is paving the way toward a new generation of intelligent, adaptable machines — ones that learn to navigate our complex world without ever setting foot in it.

Yu and Yang wrote the paper with four fellow CSAIL affiliates: Ran Choi, an MIT postdoc in mechanical engineering; Yajvan Ravan, an MIT undergraduate in EECS; John Leonard, the Samuel C. Collins Professor of Mechanical and Ocean Engineering in the MIT Department of Mechanical Engineering; and Phillip Isola, an MIT associate professor in EECS. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. The researchers presented their work at the Conference on Robot Learning (CoRL) in early November.

[aiforbusinessuk]

AI for Business : Geospatial

The Convergence of Geospatial Data and Artificial Intelligence

In recent years, the intersection of geospatial data and artificial intelligence has opened up new frontiers in data analysis and decision-making across various industries. This convergence is revolutionizing how we understand and interact with our world, from urban planning to environmental conservation.

Understanding Geospatial Data

Geospatial data encompasses information that identifies the geographic location and characteristics of natural or constructed features on Earth. This data comes in various formats, from simple map coordinates to complex satellite imagery, and is collected through methods ranging from aerial flyovers to UAVs and small drones.

The evolution of geospatial data mirrors technological advancement. What began as basic mapping and location services has transformed into intricate layers of information, including real-time traffic data and detailed environmental attributes. Advancements in satellite imagery resolution and the increasing affordability of consumer-grade drones have made high-quality geospatial data more accessible than ever before.

Applications Across Industries

Geospatial data finds applications in numerous fields:

Urban Planning: Designing smarter, more efficient cities

Environmental Monitoring: Tracking climate change and managing natural resources

Transportation: Optimizing routes and managing traffic

Business: Conducting market analysis and identifying prime locations for expansion

The AI Revolution in Geospatial Analysis

Traditionally, analyzing geospatial data was labor-intensive, often relying on manual labeling or specialized software that required extensive expertise. However, the parallel growth of geospatial data availability and AI capabilities has transformed this landscape.

Early AI applications in this field focused on specific tasks. For instance, Microsoft's open-source projects demonstrated AI's potential in automatically identifying damage to buildings in disaster-affected areas and mapping new solar farms using basic deep learning architectures.

Recent advancements have expanded both the scale and scope of AI in geospatial analysis. A prime example is the watsonx.ai geospatial foundation model from IBM and NASA, which leverages 250,000 terabytes of NASA's satellite data, including hyperspectral imagery. This state-of-the-art model can be fine-tuned for various tasks such as land use identification and vegetation type classification.

AI Consulting in Geospatial Applications

AI consulting companies are at the forefront of applying these technologies to real-world challenges. For example:

Processing orthomosaic drone imagery to determine rock particle sizes in quarry blasts, improving blasting practices and reducing CO2 emissions

Developing state-of-the-art AI models for automated labeling of peatlands, significantly reducing the time investment required from human experts in land conservation and restoration projects

AI developers specializing in geospatial applications are continually pushing the boundaries of what's possible, creating custom solutions that transform raw data into actionable insights.

The Future of Geospatial AI

As we move forward, the synergy between geospatial data and AI promises to unlock even more potential. AI consultants are playing a crucial role in this transformation, applying their expertise to convert complex geospatial data into valuable, actionable intelligence across various sectors.

The future of geospatial AI lies in more sophisticated models, integration of diverse data sources, and increasingly automated analysis processes. As these technologies continue to evolve, they will undoubtedly shape how we understand and interact with our world, driving innovation and informed decision-making in countless fields.

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Challenges and Limitations of Natural Language Processing (NLP)

Natural Language Processing (NLP) has made tremendous strides in recent years, transforming how we interact with technology and leveraging vast amounts of textual data for various applications. However, NLP still faces several challenges and limitations that researchers and practitioners continue to address. This following are some of the key challenges in NLP and discusses ongoing efforts to overcome these obstacles.

1. Ambiguity and Polysemy

Human language is fundamentally ambiguous and context-dependent. Words and phrases may have numerous meanings depending on the context in which they are used. For example, the term "bank" might apply to a financial organization or the banks of a river. Resolving this ambiguity accurately remains a substantial issue for NLP systems, especially in tasks like word sense disambiguation and semantic parsing.

2. Lack of Data and Data Quality

NLP models often require large amounts of annotated data for training, fine-tuning, and evaluation. Acquiring high-quality labeled datasets can be expensive, time-consuming, and may not always be available for all languages or domains. Moreover, the quality and representativeness of the data can impact the performance and generalizability of NLP models, leading to biases and limitations in real-world applications.

3. Handling Informal Language

Informal language, which includes slang, dialects, colloquialisms, and emoticons, presents difficulties for NLP systems intended primarily for regular formal language. Understanding and accurately processing informal language is a research topic, particularly in applications such as social media, consumer evaluations, and user-generated content.

4. Contextual Understanding

While NLP models have improved in understanding syntactic and semantic structures of language, they still struggle with deep contextual understanding. Tasks requiring detailed comprehension, such as sarcasm detection, metaphor interpretation, and understanding cultural references, are particularly challenging for current NLP systems.

5. Domain Adaptation and Transfer Learning

NLP models trained on specific datasets frequently struggle to generalize to new domains or tasks with little or varied training data. Domain adaptation techniques and transfer learning approaches try to address this issue by utilizing knowledge from related domains or pre-trained models, but obtaining robust performance across several domains remains an important research field.

6. Bias and Fairness

NLP systems can inherit biases present in the training data, leading to unfair or discriminatory outcomes in applications such as hiring processes, sentiment analysis, and automated decision-making. Addressing bias and ensuring fairness in NLP models and applications is a critical ethical consideration that requires ongoing research and development of bias detection and mitigation techniques.

7. Computational Resources and Efficiency

Training and implementing large-scale NLP models, such as transformer-based systems, necessitates enormous computational resources and energy usage. Improving the efficiency of NLP models while retaining performance is critical for scaling NLP applications and lowering environmental impact.

Future Directions and Solutions

Addressing these challenges requires interdisciplinary collaboration among linguists, computer scientists, ethicists, and domain experts. Future research in NLP is focused on developing more robust and interpretable models, advancing techniques for handling ambiguity and informal language, improving data diversity and quality, and ensuring ethical considerations are integrated into NLP design and deployment.

Conclusion

In conclusion, while NLP has made remarkable progress, navigating its challenges and limitations is essential for unlocking its full potential in applications ranging from healthcare and finance to education and beyond. By addressing these challenges through innovative research and ethical practices, NLP can continue to evolve as a powerful tool for understanding and interacting with human language in diverse and meaningful ways.

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Unlock the power of AI with high-quality data labeling from Apex Data Sciences. Our expert team ensures flawless training data for your machine learning models.

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Unlocking Instagram Marketing Secrets for Explosive Brand Growth in 2024

Instagram-the visual playground where creativity meets community continues to be a powerhouse for brands and marketers. With over 2 billion monthly active users, it’s not just a photo-sharing app; it’s a dynamic platform that can propel your brand to new heights. Let’s dive into the secrets and strategies that will make your Instagram presence shine in 2024:

Know Your Audience Inside Out Understanding your audience is the foundation of any successful Instagram strategy. Who are they? What do they love? Where do they hang out? Dive deep into demographics, interests, and behaviors. Use Instagram Insights to uncover valuable data. Remember, it’s not just about followers; it’s about building a community of engaged fans.

Create Thumb-Stopping Content In the scroll-happy world of Instagram, your content needs to stop thumbs mid-swipe. Here’s how:

High-Quality Visuals: Invest in eye-catching photos and videos. Use filters consistently to maintain your brand’s aesthetic. Stories: Leverage Stories for behind-the-scenes glimpses, polls, and interactive content. Add stickers, GIFs, and music to spice things up. Reels: Jump on the Reels bandwagon! These short, entertaining videos are Instagram’s answer to TikTok. Get creative, showcase your brand personality, and entertain your audience.

Hashtags: The Magic Key Hashtags are your passport to discovery. Research relevant and trending hashtags. Mix broad ones with niche tags. Create a branded hashtag unique to your business. And don’t forget to engage with hashtag communities—like-minded users who share your interests.

Collaborate with Influencers Influencer marketing isn’t going anywhere. But in 2024, it’s not just about mega-influencers. Micro-influencers (with smaller but highly engaged followings) can be gold. Their authenticity resonates with niche audiences. Partner with them for genuine endorsements.

Shop Till You Drop (Literally) Instagram’s shopping features are a game-changer. Set up your Instagram Shop, tag products in posts, and use Shopping Stickers in Stories. Make the buying process seamless. Remember, people come to Instagram to discover and shop—so give them what they want!

Engage, Engage, Engage Don’t be a silent observer. Respond to comments, engage with Stories, and participate in conversations. Show your human side. Host Q&A sessions, go live, and build relationships. Remember, social media is about being social!

Track, Analyze, Optimize Use Instagram Insights to track performance. Which posts resonate? When is your audience most active? Adjust your strategy accordingly. Test different content formats, posting times, and calls-to-action. Be agile and adapt.

Be Authentic and Transparent Authenticity wins hearts. Share your brand story, values, and the faces behind your business. Transparency builds trust. If you’re running ads, label them clearly. Your audience appreciates honesty.

Remember, Instagram is a dynamic canvas. Paint it with your brand’s colors, tell your story, and connect with your tribe. Whether you’re a fashion brand, a local bakery, or a tech startup, Instagram has a spot for you. So go ahead—create, engage, and conquer! And if you need expert guidance, reach out to us at Cubic Designz Digital Marketing Agency in Chennai.

And hey, if you need those 15 creative Instagram post templates, grab them from Hootsuite—they’re like sprinkles on your content cupcake! 🧁📸

Sources:

Hootsuite: Instagram Marketing Strategy Guide

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10 Tips for Successful AI Development Projects

Artificial Intelligence (AI) is revolutionizing industries by enabling machines to perform tasks that typically require human intelligence. From healthcare to finance, AI development projects are driving innovation and efficiency. However, developing AI solutions is a complex process that requires careful planning and execution. Here are ten essential tips for ensuring the success of your AI development projects.

1. Define Clear Objectives

Before embarking on any Artificial Intelligence development project, it is crucial to define clear and measurable objectives. Understand what you aim to achieve with the AI solution. Are you looking to automate processes, enhance customer experience, or gain insights from data? Clear objectives help in setting the right direction and evaluating the project's success.

2. Understand the Problem Domain

A deep understanding of the problem domain is essential for developing effective AI solutions. Collaborate with domain experts to gain insights into the specific challenges and requirements of the industry. This collaboration ensures that the AI solution is tailored to address the real-world problems effectively.

3. Assemble a Skilled Team

AI development requires a diverse set of skills, including data science, machine learning, software engineering, and domain expertise. Assemble a team of skilled professionals who can work collaboratively. Ensure continuous learning and skill development to keep the team updated with the latest advancements in AI technology.

4. Data Quality and Quantity

Data is the backbone of any AI development project. Ensure that you have access to high-quality and relevant data. The data should be clean, well-labeled, and representative of the problem you are trying to solve. Sometimes, obtaining sufficient data might require investing in data collection and annotation processes.

5. Choose the Right Tools and Technologies

Selecting the appropriate tools and technologies is critical for the success of your AI development project. Evaluate different AI frameworks, libraries, and platforms to find the ones that best suit your project needs. Popular choices include TensorFlow, PyTorch, and Scikit-learn. The right tools can significantly streamline the development process.

6. Start with a Prototype

Starting with a prototype allows you to test your ideas quickly and get feedback before committing to full-scale development. Build a minimum viable product (MVP) that demonstrates the core functionality of your AI solution. This approach helps in identifying potential issues early and making necessary adjustments.

7. Focus on Model Interpretability

In many applications, it is important to understand how the AI model makes decisions. Focus on developing interpretable models, especially in critical domains like healthcare and finance. Techniques such as feature importance analysis and model-agnostic interpretability methods can help in explaining the model's behavior.

8. Implement Robust Evaluation Metrics

Evaluating the performance of your AI model is crucial. Implement robust evaluation metrics that align with your project objectives. Common metrics include accuracy, precision, recall, F1 score, and area under the curve (AUC). For more complex tasks, custom metrics might be necessary to capture the nuances of the problem.

9. Ensure Scalability and Integration

Consider scalability and integration from the beginning of the project. Ensure that your AI solution can handle increasing amounts of data and users without compromising performance. Integration with existing systems and workflows should be seamless to maximize the impact of the AI solution.

10. Continuous Monitoring and Maintenance

AI models require continuous monitoring and maintenance to remain effective over time. Implement monitoring systems to track the performance of your AI solution in real-world conditions. Regularly update the model with new data and retrain it to adapt to changing patterns and behaviors.

Conclusion

Successful AI development projects require a strategic approach that encompasses clear objectives, a skilled team, quality data, appropriate tools, and continuous evaluation and maintenance. By following these ten tips, you can enhance the likelihood of developing effective and impactful AI solutions. The field of AI development is dynamic and rapidly evolving, making it essential to stay informed about the latest trends and advancements to maintain a competitive edge. As AI continues to transform industries, a thoughtful and well-executed approach to AI development will be key to harnessing its full potential.

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Unlock the potential of your NLP and speech recognition models with our high-quality text and audio annotation services. GTS offer precise transcription, sentiment analysis, entity recognition, and more. Our expert annotators ensure that your data is accurately labeled, helping your AI understand and process human language better. Enhance your chatbots, virtual assistants, and other language-based applications with our reliable and comprehensive annotation solutions.

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Enhancing AI Accuracy: The Role of a Data Labeling Company

In the realm of artificial intelligence (AI), the accuracy and effectiveness of machine learning models hinge significantly on the quality of labeled data they are trained on. This crucial task of data labeling, however, is often a labor-intensive and time-consuming process. This is where a specialised entity, known as a data labeling company, steps in to streamline and optimise the data annotation process.

A data labeling company serves as a dedicated partner to organizations seeking to enhance their AI capabilities. By leveraging a combination of human expertise and cutting-edge technology, these companies meticulously label large datasets, ensuring that the data is accurately annotated according to specific requirements and standards. This process is essential for training AI algorithms across various industries, including healthcare, finance, automotive, and more.

One of the key advantages of partnering with a data labeling company is the scalability it offers. These companies are equipped to handle large volumes of data, allowing organisations to accelerate their AI development initiatives without compromising on quality. Moreover, by outsourcing data labeling tasks to a specialised provider, organisations can free up their internal resources to focus on core business activities.

Another critical aspect of data labeling companies is their ability to ensure the quality and consistency of labeled data. Through rigorous quality control measures and the use of sophisticated annotation tools, these companies can minimise errors and discrepancies in the labeled datasets, thereby improving the overall performance of AI models.

Furthermore, data labelling companies play a pivotal role in addressing the ethical considerations associated with AI development. By adhering to strict privacy guidelines and data protection regulations, these companies help mitigate the risk of bias and ensure that AI algorithms are developed ethically and responsibly.

In conclusion, a data labeling company serves as a strategic partner for organisations looking to harness the power of AI. By providing scalable, high-quality data labelling services, these companies enable organisations to unlock new opportunities and drive innovation across various industries. As AI continues to reshape the future of technology, the role of data labelling companies in enhancing AI accuracy and efficiency will only become more pronounced.

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Human In the Loop for Machine Learning

The majority of machine learning models rely on human-created data. But the interaction between humans and machines does not end there; the most powerful systems are designed to allow both sides to interact continuously via a mechanism known as “Human in the loop” (HITL).

HUMAN-IN-THE-LOOP (HITL) machine learning necessitates human inspecting, validating, or changing some aspect of the AI development process. This philosophy extends to those who collect, label and perform quality control (QC) on data for machine learning.

We are confident that AI will not fire its most trusted employees anytime soon. In reality, AI systems supplement and augment human capabilities rather than replace them. The nature of our work may change in the coming years as a result of AI. The fundamental principle, however, is the elimination of mundane tasks and increased efficiency for tasks that require human input.

Recent advancements in the field of artificial intelligence (AI) have given rise to techniques such as active learning and cooperative learning. Data is the foundation of any machine learning algorithm, and these datasets are typically unlabeled (e.g. Images). During the training stage, a human must manually label this dataset (the output, such as a cat or dog).

This data is then used to train the machine learning model, which is known as supervised learning. The algorithms in this technique learn from labeled data to predict previously unseen cases. Using what we already know, we can go deeper and develop more sophisticated techniques to uncover other insights and features in the training dataset, resulting in more accurate and automated results.

Human and machine expertise are combined during the testing and evaluation phase by allowing the human to correct any incorrect results that have been produced. In this case, the human will specifically correct the labels that the machine was unable to detect with high accuracy (i.e. classified a dog for a cat). When the machine is overly confident about a wrong prediction, the human takes the same approach. 

The algorithm’s performance will improve with each iteration, paving the way for automated lifelong learning by reducing the need for future human intervention. When such work is completed, the results are forwarded to a domain expert who makes decisions that have a greater impact.

Machine learning with a human-in-the-loop 

When you have a large enough dataset, an algorithm can make accurate decisions based on it. However, the machine must first learn how to properly identify relevant criteria and thus arrive at the correct conclusion. Here is where human intelligence comes into play: Machine learning with human-in-the-loop (HITL) combines human and machine intelligence to form a continuous circle in which the algorithm is trained, tested, and tuned. With each loop, the machine becomes smarter, more confident, and more accurate.

Machine learning can’t function without human input. The algorithm cannot learn everything necessary to reach the correct conclusion on its own. For example, without human explanation, a model does not understand what is shown in an image. This means that, especially in the case of unstructured data, data labeling must be the first step toward developing a reliable algorithm.

The algorithm is unable to comprehend unstructured data that has not been properly labeled, such as images, audio, video, and social media posts. As a result, along the way, the human-in-the-loop approach is required. Specific instructions must be followed when labeling the data sets.

What benefit does HITL offer to Machine Learning applications?

1. Many times data are incomplete and unambiguous. Humans annotate/label raw data to provide meaningful context so that machine learning models can learn to produce desired results, identify patterns, and make correct decisions.

2. Humans check the models for over-fitting. They teach the model about extreme cases or unexpected scenarios.

3. Humans evaluate if the algorithm is overconfident or low in confidence to determine correct decisions. If the accuracy is low, the machine goes through an active learning cycle wherein humans give feedback for the machine to reach the correct result and increase its predictability.

4. It offers a significant enhancement in transparency as application no longer appears as a Black box with humans involved in each and every step in the process.

5. It incorporates human judgment in the most effective ways and shifts pressure away from building “100% machine perfect ” algorithms to optimal models offering maximum business benefit. This in turn offers more powerful and useful applications.

At the end of the day, AI systems are built to help humans. The value of such systems lies not solely in efficiency or correctness, but also in human preference and agency. The Humans-in-the-loop system puts humans in the decision loop.

Three Stages of Human-in-the-Loop Machine Learning

Training – Data is frequently incomplete or jumbled. Labels are added to raw data by humans to provide meaningful context for machine learning models to learn to produce desired results, identify patterns, and make correct decisions. Data labeling is an important step in the development of AI models because properly labeled datasets provide a foundation for further application and development.

Tuning – At this stage, humans inspect the data for overfitting. While data labeling lays the groundwork for accurate output, overfitting occurs when the model trains the data too well. When the model memorizes the training dataset, it may generalize, rendering it unable to perform against new data. It allows for a margin of error to allow for unpredictability in real-world scenarios.

It is also during the tuning stage that humans teach the model about edge cases or unexpected scenarios. For example, facial recognition provides convenience but is vulnerable to gender and ethnicity bias when datasets are misrepresented.

Testing – Finally, humans assess whether the algorithm is overly confident or lacking in making an incorrect decision. If the accuracy rate is low, the machine enters an active learning cycle in which humans provide feedback to the machine in order for the machine to reach the correct result or increase its predictability.

Final Thoughts 

As people’s interest in artificial intelligence and machine learning grows, it’s important to remember that people still play an important role in the process of creating algorithms. The human-in-the-loop concept is one of today’s most valuable. While this implies that you will need to hire people to do some work (which may appear to be the polar opposite of process automation), it is still impossible to obtain a high-performing, sophisticated, and accurate ML model otherwise.

TagX stands out in the fast-paced, tech-dominated industry with its people-first culture. We offer data collection, annotation, and evaluation services to power the most cutting-edge AI solutions. We can handle complex, large-scale data labeling projects whether you’re developing computer vision or natural language processing (NLP) applications.

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Original source , https://tagxdata1.blogspot.com/2024/04/human-in-loop-for-machine-learning.html