#High-Quality Human Expert Data Labeling

[apex-seo-work]

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.

[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.

[parvathyseo]

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.

[cubicdesignz]

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

[itexchangeweb]

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.

[gts-ai]

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.

[apex-seo-work]

Data Labeling & Annotation Services | Expert AI & ML Professionals | Apex Data Sciences

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.

Data Labeling & Annotation Services, Expert AI & ML Professionals, Apex Data Sciences, High-Quality Data Annotation, Training Data for AI Models, AI Model Labeling Solutions, Custom Data Annotation Services, Human-Labeled Data for ML, Precision Data Labeling, Scalable Annotation Services, AI & ML Data Preparation, Expert Data Labeling Teams, Human-in-the-Loop Annotation, AI Model Training Data, Automated Labeling with Human Oversight, Quality Assurance in Data Annotation, Machine Learning Data Curation, Custom Dataset Creation for AI

[marketpattern]

Natural Sweetener Candies Market Insights | Anticipating Growth and Advancements by 2031

The "Natural Sweetener Candies Market" is a dynamic and rapidly evolving sector, with significant advancements and growth anticipated by 2031. Comprehensive market research reveals a detailed analysis of market size, share, and trends, providing valuable insights into its expansion. This report delves into segmentation and definition, offering a clear understanding of market components and drivers. Employing SWOT and PESTEL analyses, the study evaluates the market's strengths, weaknesses, opportunities, and threats, alongside political, economic, social, technological, environmental, and legal factors. Expert opinions and recent developments highlight the geographical distribution and forecast the market's trajectory, ensuring a robust foundation for strategic planning and investment.

What is the projected market size & growth rate of the Natural Sweetener Candies Market?

Market Analysis and Insights:

Global Natural Sweetener Candies Market

The natural sweetener candies market is expected to witness market growth at a rate of 11.20% in the forecast period of 2021 to 2028 and is expected to reach USD 29.45 billion by 2028. Data Bridge Market Research report on natural sweetener candies market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecast period while providing their impacts on the market’s growth. The increase in health consciousness among consumers is escalating the growth of natural sweetener market.

Natural sweeteners are known to provide a sweet taste in food and beverages and do not possess any chemical substances. These are popular for food and flavoring because of their nutritive and favorable features. Candy or lollies is defined as a food which is usually made from the main ingredient such as sugar. Natural sweeteners candies are the type of candies which only comprises of natural ingredients.

Increasing consumer awareness regarding consumption of food products containing natural ingredients, rise in consumer awareness regarding healthy and improved lifestyle and high prevalence of health disorders such as type 2 diabetes, heart problems, blood pressure and obesity, especially among younger generation are the major factors driving the natural sweetener candies market.

The growing demand for candies with lower sugar content, rising consumer awareness regarding the ill effects of excessive sugar consumption accelerate the market growth. The rejection of artificial food additives along with serious health concerns about high sugar intake, and the high popularity of the food products due to the ability to lower the calorie intake without compromising on the taste and flavor of the product influence the market. Additionally, increasing consumption of products with natural sweeteners, , increasing efforts by governments and regulatory bodies and high need for food and beverage manufacturers to cut down on the content of added sugar in their products positively affect the natural sweetener candies market. Moreover, rising research and development activities in the market and increasing technological advancements and modernization in the production techniques of gums will further create opportunities to the market players in the forecast period of 2021 to 2028.

However, rising uncertainty in the minds of consumers related to the consumption of natural sweeteners and their ill-effects on human health and adherence to international quality standards and regulations for sweeteners and sweetener-based products are the factors expected to obstruct the market growth, while high cost associated with the product because of higher costs of production and issues with product labeling are projected to challenge the natural sweetener candies market in the forecast period of 2021 to 2028.

This natural sweetener candies market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on natural sweetener candies market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Browse Detailed TOC, Tables and Figures with Charts which is spread across 350 Pages that provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

This research report is the result of an extensive primary and secondary research effort into the Natural Sweetener Candies market. It provides a thorough overview of the market's current and future objectives, along with a competitive analysis of the industry, broken down by application, type and regional trends. It also provides a dashboard overview of the past and present performance of leading companies. A variety of methodologies and analyses are used in the research to ensure accurate and comprehensive information about the Natural Sweetener Candies Market.

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Which are the driving factors of the Natural Sweetener Candies market?

The driving factors of the Natural Sweetener Candies market include technological advancements that enhance product efficiency and user experience, increasing consumer demand driven by changing lifestyle preferences, and favorable government regulations and policies that support market growth. Additionally, rising investment in research and development and the expanding application scope of Natural Sweetener Candies across various industries further propel market expansion.

Natural Sweetener Candies Market - Competitive and Segmentation Analysis:

Global Natural Sweetener Candies Market, By Product Type (Chocolate Candy, Non- Chocolate Candy), Distribution Channel (Supermarkets and Hypermarkets, Convenience Stores, Retailers, Online Retail, Others), End-User (Food and Beverages, Direct Sales, Other End-Use Sectors), Country (U.S., Canada, Mexico, Germany, Sweden, Poland, Denmark, Italy, U.K., France, Spain, Netherland, Belgium, Switzerland, Turkey, Russia, Rest of Europe, Japan, China, India, South Korea, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Brazil, Argentina, Rest of South America, UAE, Saudi Arabia, Oman, Qatar, Kuwait, South Africa, Rest of Middle East and Africa) Industry Trends and Forecast to 2028

How do you determine the list of the key players included in the report?

With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale, but also the regional small and medium-sized companies that play key roles and have plenty of potential growth.

Which are the top companies operating in the Natural Sweetener Candies market?

The major players covered in the natural sweetener candies market report are Nana's Cookie Company, YummyEarth, Inc., JJ's Sweets, HailMerry, Ice Chips Candy LLC, Wholesome Sweeteners Inc., Amore Di Mona, Orkla, HailMerry, Nutiva Inc. and Dr. John's Healthy Sweets LLC and among other domestic and global players.

Short Description About Natural Sweetener Candies Market:

The Global Natural Sweetener Candies market is anticipated to rise at a considerable rate during the forecast period, between 2024 and 2031. In 2023, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

North America, especially The United States, will still play an important role which can not be ignored. Any changes from United States might affect the development trend of Natural Sweetener Candies. The market in North America is expected to grow considerably during the forecast period. The high adoption of advanced technology and the presence of large players in this region are likely to create ample growth opportunities for the market.

Europe also play important roles in global market, with a magnificent growth in CAGR During the Forecast period 2024-2031.

Natural Sweetener Candies Market size is projected to reach Multimillion USD by 2031, In comparison to 2024, at unexpected CAGR during 2024-2031.

Despite the presence of intense competition, due to the global recovery trend is clear, investors are still optimistic about this area, and it will still be more new investments entering the field in the future.

This report focuses on the Natural Sweetener Candies in global market, especially in North America, Europe and Asia-Pacific, South America, Middle East and Africa. This report categorizes the market based on manufacturers, regions, type and application.

Get a Sample Copy of the Natural Sweetener Candies Report 2024

What are your main data sources?

Both Primary and Secondary data sources are being used while compiling the report. Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users. Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.

Geographically, the detailed analysis of consumption, revenue, market share and growth rate, historical data and forecast (2024-2031) of the following regions are covered in Chapters

What are the key regions in the global Natural Sweetener Candies market?

North America (United States, Canada and Mexico)

Europe (Germany, UK, France, Italy, Russia and Turkey etc.)

Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)

South America (Brazil, Argentina, Columbia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

This Natural Sweetener Candies Market Research/Analysis Report Contains Answers to your following Questions

What are the global trends in the Natural Sweetener Candies market?

Would the market witness an increase or decline in the demand in the coming years?

What is the estimated demand for different types of products in Natural Sweetener Candies?

What are the upcoming industry applications and trends for Natural Sweetener Candies market?

What Are Projections of Global Natural Sweetener Candies Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export?

Where will the strategic developments take the industry in the mid to long-term?

What are the factors contributing to the final price of Natural Sweetener Candies?

What are the raw materials used for Natural Sweetener Candies manufacturing?

How big is the opportunity for the Natural Sweetener Candies market?

How will the increasing adoption of Natural Sweetener Candies for mining impact the growth rate of the overall market?

How much is the global Natural Sweetener Candies market worth? What was the value of the market In 2020?

Who are the major players operating in the Natural Sweetener Candies market? Which companies are the front runners?

Which are the recent industry trends that can be implemented to generate additional revenue streams?

What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Natural Sweetener Candies Industry?

Customization of the Report

Can I modify the scope of the report and customize it to suit my requirements? Yes. Customized requirements of multi-dimensional, deep-level and high-quality can help our customers precisely grasp market opportunities, effortlessly confront market challenges, properly formulate market strategies and act promptly, thus to win them sufficient time and space for market competition.

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Detailed TOC of Global Natural Sweetener Candies Market Insights and Forecast to 2031

Introduction

Market Segmentation

Executive Summary

Premium Insights

Market Overview

Natural Sweetener Candies Market By Type

Natural Sweetener Candies Market By Function

Natural Sweetener Candies Market By Material

Natural Sweetener Candies Market By End User

Natural Sweetener Candies Market By Region

Natural Sweetener Candies Market: Company Landscape

SWOT Analysis

Company Profiles

Continued...

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Data Bridge Market Research:

Today's trends are a great way to predict future events!

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Global Natural Sweetener Candies Market – Industry Trends and Forecast to 2028

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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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Looking for a specific action in a video? This AI-based method can find it for you

New Post has been published on https://sunalei.org/news/looking-for-a-specific-action-in-a-video-this-ai-based-method-can-find-it-for-you/

Looking for a specific action in a video? This AI-based method can find it for you

The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.

But pinpointing when and where a particular action happens in a long video can be tedious. To streamline the process, scientists are trying to teach computers to perform this task. Ideally, a user could just describe the action they’re looking for, and an AI model would skip to its location in the video.

However, teaching machine-learning models to do this usually requires a great deal of expensive video data that have been painstakingly hand-labeled.

A new, more efficient approach from researchers at MIT and the MIT-IBM Watson AI Lab trains a model to perform this task, known as spatio-temporal grounding, using only videos and their automatically generated transcripts.

The researchers teach a model to understand an unlabeled video in two distinct ways: by looking at small details to figure out where objects are located (spatial information) and looking at the bigger picture to understand when the action occurs (temporal information).

Compared to other AI approaches, their method more accurately identifies actions in longer videos with multiple activities. Interestingly, they found that simultaneously training on spatial and temporal information makes a model better at identifying each individually.

In addition to streamlining online learning and virtual training processes, this technique could also be useful in health care settings by rapidly finding key moments in videos of diagnostic procedures, for example.

“We disentangle the challenge of trying to encode spatial and temporal information all at once and instead think about it like two experts working on their own, which turns out to be a more explicit way to encode the information. Our model, which combines these two separate branches, leads to the best performance,” says Brian Chen, lead author of a paper on this technique.

Chen, a 2023 graduate of Columbia University who conducted this research while a visiting student at the MIT-IBM Watson AI Lab, is joined on the paper by James Glass, senior research scientist, member of the MIT-IBM Watson AI Lab, and head of the Spoken Language Systems Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Hilde Kuehne, a member of the MIT-IBM Watson AI Lab who is also affiliated with Goethe University Frankfurt; and others at MIT, Goethe University, the MIT-IBM Watson AI Lab, and Quality Match GmbH. The research will be presented at the Conference on Computer Vision and Pattern Recognition.

Global and local learning

Researchers usually teach models to perform spatio-temporal grounding using videos in which humans have annotated the start and end times of particular tasks.

Not only is generating these data expensive, but it can be difficult for humans to figure out exactly what to label. If the action is “cooking a pancake,” does that action start when the chef begins mixing the batter or when she pours it into the pan?

“This time, the task may be about cooking, but next time, it might be about fixing a car. There are so many different domains for people to annotate. But if we can learn everything without labels, it is a more general solution,” Chen says.

For their approach, the researchers use unlabeled instructional videos and accompanying text transcripts from a website like YouTube as training data. These don’t need any special preparation.

They split the training process into two pieces. For one, they teach a machine-learning model to look at the entire video to understand what actions happen at certain times. This high-level information is called a global representation.

For the second, they teach the model to focus on a specific region in parts of the video where action is happening. In a large kitchen, for instance, the model might only need to focus on the wooden spoon a chef is using to mix pancake batter, rather than the entire counter. This fine-grained information is called a local representation.

The researchers incorporate an additional component into their framework to mitigate misalignments that occur between narration and video. Perhaps the chef talks about cooking the pancake first and performs the action later.

To develop a more realistic solution, the researchers focused on uncut videos that are several minutes long. In contrast, most AI techniques train using few-second clips that someone trimmed to show only one action.

A new benchmark

But when they came to evaluate their approach, the researchers couldn’t find an effective benchmark for testing a model on these longer, uncut videos — so they created one.

To build their benchmark dataset, the researchers devised a new annotation technique that works well for identifying multistep actions. They had users mark the intersection of objects, like the point where a knife edge cuts a tomato, rather than drawing a box around important objects.

“This is more clearly defined and speeds up the annotation process, which reduces the human labor and cost,” Chen says.

Plus, having multiple people do point annotation on the same video can better capture actions that occur over time, like the flow of milk being poured. All annotators won’t mark the exact same point in the flow of liquid.

When they used this benchmark to test their approach, the researchers found that it was more accurate at pinpointing actions than other AI techniques.

Their method was also better at focusing on human-object interactions. For instance, if the action is “serving a pancake,” many other approaches might focus only on key objects, like a stack of pancakes sitting on a counter. Instead, their method focuses on the actual moment when the chef flips a pancake onto a plate.

Next, the researchers plan to enhance their approach so models can automatically detect when text and narration are not aligned, and switch focus from one modality to the other. They also want to extend their framework to audio data, since there are usually strong correlations between actions and the sounds objects make.

This research is funded, in part, by the MIT-IBM Watson AI Lab.

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Your guide to LLMOps

New Post has been published on https://thedigitalinsider.com/your-guide-to-llmops/

Your guide to LLMOps

Navigating the field of large language model operations (LLMOps) is more important than ever as businesses and technology sectors intensify utilizing these advanced tools. 

LLMOps is a niche technical domain and a fundamental aspect of modern artificial intelligence frameworks, influencing everything from model design to deployment. 

Whether you’re a seasoned data scientist, a machine learning engineer, or an IT professional, understanding the multifaceted landscape of LLMOps is essential for harnessing the full potential of large language models in today’s digital world. 

In this guide, we’ll cover:

What is LLMOps?

How does LLMOps work?

What are the benefits of LLMOps?

LLMOps best practices

What is LLMOps?

Large language model operations, or LLMOps, are techniques, practices, and tools that are used in operating and managing LLMs throughout their entire lifecycle.

These operations comprise language model training, fine-tuning, monitoring, and deployment, as well as data preparation.  

What is the current LLMops landscape?

LLMs. What opened the way for LLMOps.

Custom LLM stack. A wider array of tools that can fine-tune and implement proprietary solutions from open-source regulations.

LLM-as-a-Service. The most popular way of delivering closed-based models, it offers LLMs as an API through its infrastructure.

Prompt execution tools. By managing prompt templates and creating chain-like sequences of relevant prompts, they help to improve and optimize model output.

Prompt engineering tech. Instead of the more expensive fine-tuning, these technologies allow for in-context learning, which doesn’t use sensitive data.

Vector databases. These retrieve contextually relevant data for specific commands.

The fall of centralized data and the future of LLMs

Gregory Allen, Co-Founder and CEO at Datasent, gave this presentation at our Generative AI Summit in Austin in 2024.

What are the key LLMOps components?

Architectural selection and design

Choosing the right model architecture. Involving data, domain, model performance, and computing resources.

Personalizing models for tasks. Pre-trained models can be customized for lower costs and time efficiency. 

Hyperparameter optimization. This optimizes model performance as it finds the best combination. For example, you can use random search, grid research, and Bayesian optimization.

Tweaking and preparation. Unsupervised pre-training and transfer learning lower training time and enhance model performance. 

Model assessment and benchmarking. It’s always good practice to benchmark models against industry standards. 

Data management

Organization, storing, and versioning data. The right database and storage solutions simplify data storage, retrieval, and modification during the LLM lifecycle.

Data gathering and processing. As LLMs run on diverse, high-quality data, models might need data from various domains, sources, and languages. Data needs to be cleaned and pre-processed before being fed into LLMs.

Data labeling and annotation. Supervised learning needs consistent and reliable labeled data; when domain-specific or complex instances need expert judgment, human-in-the-loop techniques are beneficial.

Data privacy and control. Involves pseudonymization, anonymization techniques, data access control, model security considerations, and compliance with GDPR and CCPA.

Data version control. LLM iteration and performance improvement are simpler with a clear data history; you’ll find errors early by versioning models and thoroughly testing them.

Deployment platforms and strategies

Model maintenance. Showcases issues like model drift and flaws.

Optimizing scalability and performance. Models might need to be horizontally scaled with more instances or vertically scaled with additional resources within high-traffic settings.

On-premises or cloud deployment. Cloud deployment is flexible, easy to use, and scalable, while on-premises deployment could improve data control and security. 

LLMOps vs. MLOps: What’s the difference?

Machine learning operations, or MLOps, are practices that simplify and automate machine learning workflows and deployments. MLOps are essential for releasing new machine learning models with both data and code changes at the same time.

There are a few key principles of MLOps:

1. Model governance

Managing all aspects of machine learning to increase efficiency, governance is vital to institute a structured process for reviewing, validating, and approving models before launch. This also includes considering ethical, fairness, and ethical concerns.

2. Version control

Tracking changes in machine learning assets allows you to copy results and roll back to older versions when needed. Code reviews are part of all machine learning training models and code, and each is versioned for ease of auditing and reproduction.

3. Continuous X

Tests and code deployments are run continuously across machine learning pipelines. Within MLOps, ‘continuous’ relates to four activities that happen simultaneously whenever anything is changed in the system:

Continuous integration

Continuous delivery

Continuous training

Continuous monitoring 

4. Automation

Through automation, there can be consistency, repeatability, and scalability within machine learning pipelines. Factors like model training code changes, messaging, and application code changes can initiate automated model training and deployment.

MLOps have a few key benefits:

Improved productivity. Deployments can be standardized for speed by reusing machine learning models across various applications.

Faster time to market. Model creation and deployment can be automated, resulting in faster go-to-market times and reduced operational costs.

Efficient model deployment. Continuous delivery (CI/CD) pipelines limit model performance degradation and help to retain quality. 

LLMOps are MLOps with technology and process upgrades tuned to the individual needs of LLMs. LLMs change machine learning workflows and requirements in distinct ways:

1. Performance metrics

When evaluating LLMs, there are several different standard scoring and benchmarks to take into account, like recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU).

2. Cost savings

Hyperparameter tuning in LLMs is vital to cutting the computational power and cost needs of both inference and training. LLMs start with a foundational model before being fine-tuned with new data for domain-specific refinements, allowing them to deliver higher performance with fewer costs.

3. Human feedback

LLM operations are typically open-ended, meaning human feedback from end users is essential to evaluate performance. Having these feedback loops in KKMOps pipelines streamlines assessment and provides data for future fine-tuning cycles.

4. Prompt engineering

Models that follow instructions can use complicated prompts or instructions, which are important to receive consistent and correct responses from LLMs. Through prompt engineering, you can lower the risk of prompt hacking and model hallucination.

5. Transfer learning

LLM models start with a foundational model and are then fine-tuned with new data, allowing for cutting-edge performance for specific applications with fewer computational resources.

6. LLM pipelines

These pipelines integrate various LLM calls to other systems like web searches, allowing LLMs to conduct sophisticated activities like a knowledge base Q&A. LLM application development tends to focus on creating pipelines, not new ones. 

3 learnings from bringing AI to market

Drawing from experience at Salesforce, Mike Kolman shares three essential learnings to help you confidently navigate the AI landscape.

How does LLMOps work?

LLMOps involve a few important steps:

1.  Selection of foundation model

Foundation models, which are LLMs pre-trained on big datasets, are used for downstream operations. Training models from scratch can be very expensive and time-consuming; big companies often develop proprietary foundation models, which are larger and have better performance than open-source ones. They do, however, have more expensive APIs and lower adaptability.

Proprietary model vendors:

OpenAI (GPT-3, GPT-4)

AI21 Labs (Jurassic-2)

Anthropic (Claude)

Open-source models:

LLaMA

Stable Diffusion

Flan-T5

2. Downstream task adaptation

After selecting the foundation model, you can use LLM APIs, which don’t always say what input leads to what output. It might take iterations to get the LLM API output you need, and LLMs can hallucinate if they don’t have the right data. Model A/B testing or LLM-specific evaluation is often used to test performance.

You can adapt foundation models to downstream activities:

Model assessment

Prompt engineering

Using embeddings

Fine-tuning pre-trained models

Using external data for contextual information

3. Model deployment and monitoring

LLM-powered apps must closely monitor API model changes, as LLM deployment can change significantly across different versions.

What are the benefits of LLMOps?

Scalability

You can achieve more streamlined management and scalability of data, which is vital when overseeing, managing, controlling, or monitoring thousands of models for continuous deployment, integration, and delivery.

LLMOps does this by enhancing model latency for more responsiveness in user experience. Model monitoring with a continuous integration, deployment, and delivery environment can simplify scalability.

LLM pipelines often encourage collaboration and reduce speed release cycles, being easy to reproduce and leading to better collaboration across data teams. This leads to reduced conflict and increased release speed.

LLMOps can manage large amounts of requests simultaneously, which is important in enterprise applications.

Efficiency

LLMOps allow for streamlined collaboration between machine learning engineers, data scientists, stakeholders, and DevOps – this leads to a more unified platform for knowledge sharing and communication, as well as model development and employment, which allows for faster delivery.

You can also cut down on computational costs by optimizing model training. This includes choosing suitable architectures and using model pruning and quantization techniques, for example.

With LLMOps, you can also access more suitable hardware resources like GPUs, allowing for efficient monitoring, fine-tuning, and resource usage optimization. Data management is also simplified, as LLMOps facilitate strong data management practices for high-quality dataset sourcing, cleaning, and usage in training.

With model performance able to be improved through high-quality and domain-relevant training data, LLMOps guarantees peak performance. Hyperparameters can also be improved, and DaraOps integration can ease a smooth data flow. 

You can also speed up iteration and feedback loops through task automation and fast experimentation. 

3. Risk reduction

Advanced, enterprise-grade LLMOps can be used to enhance privacy and security as they prioritize protecting sensitive information. 

With transparency and faster responses to regulatory requests, you’ll be able to comply with organization and industry policies much more easily.

Other LLMOps benefits

Data labeling and annotation 

GPU acceleration for REST API model endpoints

Prompt analytics, logging, and testing

Model inference and serving

Data preparation

Model review and governance

Superintelligent language models: A new era of artificial cognition

The rise of large language models (LLMs) is pushing the boundaries of AI, sparking new debates on the future and ethics of artificial general intelligence.

LLMOps best practices

These practices are a set of guidelines to help you manage and deploy LLMs efficiently and effectively. They cover several aspects of the LLMOps life cycle:

Exploratory Data Analysis (EDA)

Involves iteratively sharing, exploring, and preparing data for the machine learning lifecycle in order to produce editable, repeatable, and shareable datasets, visualizations, and tables.

Stay up-to-date with the latest practices and advancements by engaging with the open-source community.

Data management

Appropriate software that can handle large volumes of data allows for efficient data recovery throughout the LLM lifecycle. Making sure to track changes with versioning is essential for seamless transitions between versions. Data must also be protected with access controls and transit encryption.

Data deployment

Tailor pre-trained models to conduct specific tasks for a more cost-effective approach.

Continuous model maintenance and monitoring

Dedicated monitoring tools are able to detect drift in model performance. Real-world feedback for model outputs can also help to refine and re-train the models.

Ethical model development

Discovering, anticipating, and correcting biases within training model outputs to avoid distortion.

Privacy and compliance

Ensure that operations follow regulations like CCPA and GDPR by having regular compliance checks.

Model fine-tuning, monitoring, and training

A responsive user experience relies on optimized model latency. Having tracking mechanisms for both pipeline and model lineage helps efficient lifecycle management. Distributed training helps to manage vast amounts of data and parameters in LLMs.

Model security

Conduct regular security tests and audits, checking for vulnerabilities.

Prompt engineering

Make sure to set prompt templates correctly for reliable and accurate responses. This also minimizes the probability of prompt hacking and model hallucinations.

LLM pipelines or chains

You can link several LLM external system interactions or calls to allow for complex tasks.

Computational resource management

Specialized GPUs help with extensive calculations on large datasets, allowing for faster and more data-parallel operations.

Disaster redundancy and recovery

Ensure that data, models, and configurations are regularly backed up. Redundancy allows you to handle system failures without any impact on model availability. 

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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.

Visit us , https://www.tagxdata.com/human-in-the-loop-for-machine-learning

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