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generative ai

Huda Mahmood - Author
Huda Mahmood
| April 8

The modern era of generative AI is now talking about machine unlearning. It is time to understand that unlearning information is as important for machines as for humans to progress in this rapidly advancing world. This blog explores the impact of machine unlearning in improving the results of generative AI.

However, before we dig deeper into the details, let’s understand what is machine unlearning and its benefits.

What is machine unlearning?

As the name indicates, it is the opposite of machine learning. Hence, it refers to the process of getting a trained model to forget information and specific knowledge it has learned during the training phase.

During machine unlearning, an ML model discards previously learned information and or patterns from its knowledge base. The concept is fairly new and still under research in an attempt to improve the overall ML training process.


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A comment on the relevant research

A research paper published by the University of Texas presents machine learning as a paradigm to improve image-to-image generative models. It addresses the gap with a unifying framework focused on implementing machine unlearning to image-specific generative models.

The proposed approach uses encoders in its architecture to enable the model to only unlearn specific information without the need to manipulate the entire model. The research also claims the framework to be generalizable in its application, where the same infrastructure can also be implemented in an encoder-decoder architecture.


A glance at the proposed encoder-only machine unlearning architecture
A glance at the proposed encoder-only machine unlearning architecture – Source: arXiv


The research also highlights that the proposed framework presents negligible performance degradation and produces effective results from their experiments. This highlights the potential of the concept in refining machine-learning processes and generative AI applications.

Benefits of machine unlearning in generative AI

Machine unlearning is a promising aspect for improving generative AI, empowering it to create enhanced results when creating new things like text, images, or music.

Below are some of the key advantages associated with the introduction of the unlearning concept in generative AI.

Ensuring privacy

With a constantly growing digital database, the security and privacy of sensitive information have become a constant point of concern for individuals and organizations. This issue of data privacy also extends to the process of training ML models where the training data might contain some crucial or private data.

In this dilemma, unlearning is a concept that enables an ML model to forget any sensitive information in its database without the need to remove the complete set of knowledge it trained on. Hence, it ensures that the concerns of data privacy are addressed without impacting the integrity of the ML model.


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Enhanced accuracy

In extension, it also results in updating the training data for machine-learning models to remove any sources of error. It ensures that a more accurate dataset is available for the model, improving the overall accuracy of the results.

For instance, if a generative AI model produced images based on any inaccurate information it had learned during the training phase, unlearning can remove that data from its database. Removing that association will ensure that the model outputs are refined and more accurate.

Keeping up-to-date

Another crucial aspect of modern-day information is that it is constantly evolving. Hence, the knowledge is updated and new information comes to light. While it highlights the constant development of data, it also results in producing outdated information.

However, success is ensured in keeping up-to-date with the latest trends of information available in the market. With the machine unlearning concept, these updates can be incorporated into the training data for applications without rebooting the existing training models.


Benefits of machine unlearning
Benefits of machine unlearning


Improved control

Unlearning also allows better control over the training data. It is particularly useful in artistic applications of generative AI. Artists can use the concept to ensure that the AI application unlearns certain styles or influences.

As a result, it offers greater freedom of exploration of artistic expression to create more personalized outputs, promising increased innovation and creativity in the results of generative AI applications.

Controlling misinformation

Generative AI is a powerful tool to spread misinformation through the creation of realistic deepfakes and synthetic data. Machine unlearning provides a potential countermeasure that can be used to identify and remove data linked to known misinformation tactics from generative AI models.

This would make it significantly harder for them to be used to create deceptive content, providing increased control over spreading misinformation on digital channels. It is particularly useful in mitigating biases and stereotypical information in datasets.

Hence, the concept of unlearning opens new horizons of exploration in generative AI, empowering players in the world of AI and technology to reap its benefits.


Here’s a comprehensive guide to build, deploy, and manage ML models


Who can benefit from machine unlearning?

A broad categorization of entities and individuals who can benefit from machine unlearning include:

Privacy advocates

In today’s digital world, individual concern for privacy concern is constantly on the rise. Hence, people are constantly advocating their right to keep personal or crucial information private. These advocates for privacy and data security can benefit from unlearning as it addresses their concerns about data privacy.

Tech companies

Digital progress and development are marked by several regulations like GDPR and CCPA. These standards are set in place to ensure data security and companies must abide by these laws to avoid legal repercussions. Unlearning assists tech companies in abiding by these laws, enhancing their credibility among users as well.

Financial institutions

Financial enterprises and institutions deal with huge amounts of personal information and sensitive data of their users. Unlearning empowers them to remove specific data points from their database without impacting the accuracy and model performance.

AI researchers

AI researchers are frequently facing the impacts of their applications creating biased or inaccurate results. With unlearning, they can target such sources of data points that introduce bias and misinformation into the model results. Hence, enabling them to create more equitable AI systems.


A significant impact of unlearning can come from the work of policymakers. Since the concept opens up new ways to handle information and training datasets, policymakers can develop new regulations to mitigate bias and address privacy concerns. Hence, leading the way for responsible AI development.

Thus, machine unlearning can produce positive changes in the world of generative AI, aiding different players to ensure the development of more responsible and equitable AI systems.


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Future of machine unlearning

To sum it up, machine unlearning is a new concept in the world of generative AI with promising potential for advancement. Unlearning is a powerful tool for developing AI applications and systems but lacks finesse. Researchers are developing ways to target specific information for removal.

For instance, it can assist the development of an improved text-to-image generator to forget a biased stereotype, leading to fairer and more accurate results. Improved techniques allow the isolation and removal of unwanted data points, giving finer control over what the AI forgets.



Overall, unlearning holds immense potential for shaping the future of generative AI. With more targeted techniques and a deeper understanding of these models, unlearning can ensure responsible use of generative AI, promote artistic freedom, and safeguard against the misuse of this powerful technology.

Dua Mahboob
| March 27

If I were to ask you, can Generative AI in education outperform students in competitive assessments like that of Harvard’s or Stanford’s, what would your answer be? Maybe? Let me tell you, the answer is yes.

That’s the exciting world of generative AI, shaking things up everywhere across the globe, be it logical assessments, medical exams, or a thought-provoking essay at the Ivy Leagues.   

Read: Chatbot vs Medical Student Performance on Clinical Reasoning Examinations 

Now, before you imagine robots taking over classrooms, hold on! Generative AI isn’t here to replace humans, it’s more of a super-powered sidekick for education.

From unequal access to education to stressed-out teachers and confused students, the education landscape faces a lot of challenges. Generative AI isn’t here to steal anyone’s job, but maybe, it can help us fix the problems, ushering in a new era of learning and creativity.

Should ChatGPT be banned in schools? 

Role of AI in Education

Here’s how generative AI is reshaping the education landscape: 

Personalized learning

Traditionally, education has relied on a standardized approach. This “one-size-fits-all” method often leaves students behind or bored, failing to cater to their individual learning styles and paces. Generative AI disrupts this model by tailoring the education experience to individual students’ needs.  

With the help of vast amounts of data, it adapts the learning content, pace, and style to suit the strengths, weaknesses, and preferences of each learner, ensuring that no student is left behind.

This personalized approach accommodates different learning styles, such as visual, auditory, reading-writing, or kinesthetic, ensuring that students receive tailored support based on their unique preferences and abilities, while also providing immediate feedback and support. 

AI in Action

For instance, Duolingo leverages generative AI to create personalized learning experiences for young children. The app tailors its content based on a child’s progress, offering interactive activities, games, and even AI-generated stories that reinforce learning. In addition, Khan Academy has launched Khanmigo, an AI tutor that assists young students in various subjects on its platform.

AI in education - within the ed-tech landscape
Popular Generative AI Applications in the EdTech Landscape – Source: Reach Capital

Accessibility and Inclusivity: Breaking Barriers for All

Traditionally, access to quality education has been heavily reliant on individuals’ geographical access and socio-economic background. Generative AI disrupts this norm by delivering high-quality educational resources directly to students, regardless of their backgrounds.

Now, people in remote areas with limited access to knowledge bases, diverse learning environments, and styles, can leverage Generative AI, for personalized tutoring and learning. 

Generative AI further promotes inclusivity and global collaboration by facilitating language learning through the translation of educational content into multiple languages and adapting materials to fit local cultural contexts. It plays a crucial role in developing inclusive and accessible educational content suitable for diverse learner populations. 

Moreover, Generative AI can be personalized to support students with special needs by providing customized learning experiences through assistive functions and communication technologies. This ensures that students with diverse requirements have access to top-quality learning materials.

Curious how generative AI is reshaping the education landscape? Learn what an expert educator has to say!

AI in Action 

For instance, Dreamreader is an AI-powered platform that tailors reading experiences to a student’s reading level and interests. It generates personalized stories with adjustable difficulty, keeping students engaged and motivated to improve their reading skills. 

As technology becomes more accessible, platforms are emerging that enable anyone, even those without coding skills, to create their own “Chat GPT bots,” opening doors of accessibility for all.

Beyond Textbooks: Immersive Learning Adventures

Generative AI has also fostered the emergence of hybrid schools, virtual classrooms, remote learning, and micro-learning, allowing students to access education beyond the confines of a traditional classroom, and opening up a world of limitless learning opportunities. 

Generative AI can transport students to the heart of historical events, conduct virtual experiments in a simulated lab, or even practice a new language with an AI-powered conversation partner. 

AI in Action

Platforms like Historyverse and Hellohistory.AI are prime examples. This AI-powered platform allows students to step into historical simulations, interacting with virtual characters and environments to gain a deeper understanding of the past. 

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Support for Educators: AI as a Partner in Progress

Far from replacing teachers, generative AI is here to empower them. With personalized lesson planning and content creation, AI-assisted evaluation and feedback, intelligent tutoring systems, and virtual teaching assistants, AI can free up valuable teacher time.

This allows educators to focus on what they do best: fostering student engagement, providing personalized instruction, and pursuing professional development. In a future where AI can be a leading source of disseminating information and taking the lead in delivering information, it becomes crucial to reconsider our approach towards education.

Rather than sticking to traditional classrooms, picture a flipped classroom model, a hybrid learning setup where students can engage in remote self-learning and use physical classrooms for interactive group activities and collaborative learning. It’s all about blending the best of both worlds for a more effective and engaging educational experience. 

Generative AI is reshaping the roles and dynamics of the education system, encouraging educators to evolve from knowledge deliverers to facilitators. They need to become mentors who guide and encourage student agency, fostering a collaborative environment built on co-agency and collective intelligence.


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AI in Action

Take a look at GradeScope, a product by Turnitin, a real-world example of generative AI empowering teachers. This platform uses AI to automate the time-consuming task of grading written assignments. Teachers upload student work, and GradeScope utilizes AI to analyze handwriting, identify key concepts, and even provide students with initial grading and personalized feedback.

This frees up valuable teacher time, allowing them to focus on more individualized instruction, like one-on-one conferences or in-depth discussions about student writing. This is the power of generative AI as a partner in education – it empowers teachers to do what they do best: inspire, guide, and unlock the potential in every student

Here’s what every educator must know!

Shift towards Metacognitive Continuous Learning

Generative AI is ushering in a new era of “metacognitive continuous learning”. This approach to assessment focuses on students’ ability to understand, monitor, and regulate their cognitive and metacognitive processes, making it an integral part of the learning process.

In metacognitive continuous learning, students not only acquire knowledge but also reflect on their learning strategies and adapt them as needed. They actively engage in self-regulation to optimize their learning experience and become aware of their thinking processes.  

AI systems help students recognize their strengths and weaknesses, suggest strategies for improvement, and promote a deeper understanding of the subject matter. By leveraging AI-supported feedback, students develop essential skills for lifelong learning.

This shift represents a move away from traditional tests that measure memory recall or specific skills and towards a more student-centered and flexible approach to learning, making students self-directed learners.

It recognizes that learning is not just about acquiring knowledge but also about understanding how we think and continuously improving our learning strategies and focusing on personal growth.

Read about the game-changing moments in AI during 2023

Critical Skills to Survive and Thrive in an AI-driven World

While generative AI offers a treasure trove of educational content, it’s crucial to remember that information literacy is essential. Students need to develop the ability to critically evaluate AI-generated content, assessing its accuracy, and biases, leveraging AI to augment their own capabilities rather than blindly relying on it.

Here is a range of key skills that learners need to develop to thrive and adapt. These skills include: 

Critical Thinking: Learners must develop the ability to analyze information, evaluate its credibility, and make informed decisions. Critical thinking allows individuals to effectively navigate the vast amount of data and AI-generated content available. 

Problem-solving: AI presents new challenges and complexities. Learners need to be able to identify and define problems, think creatively, and develop innovative solutions. Problem-solving skills enable individuals to leverage AI technology to address real-world issues. 

Adaptability: The rapid pace of technological change requires learners to be adaptable. They must embrace change, learn new tools and technologies quickly, and be willing to continuously evolve their knowledge and skills. 

Data and AI Literacy: With AI generating vast amounts of data, learners need to develop the ability to understand, interpret, and analyze data so that they can make data-driven decisions and leverage AI technologies effectively. They must also possess AI literacy skills to navigate AI-driven platforms, understand the ethical implications of AI, and effectively use digital tools for learning and work.  

The Human Edge: Fostering Creativity, Emotional Intelligence, and Intuition: While AI excels at crunching numbers and following patterns, certain qualities remain uniquely human and will continue to be valuable in the age of AI. AI can generate content, but it takes human imagination to truly push boundaries and come up with groundbreaking ideas.

Our ability to empathize, build relationships, and navigate complex social situations will remain crucial for success in various fields. In addition, the ability to tap into our intuition and make gut decisions can be a valuable asset, even in the age of data-driven decision-making.

Can AI truly replace humans? Let’s find out now

Effectively Leveraging Generative AI for Education: The PAIR Framework

To equip students with critical thinking and problem-solving skills in the age of AI, the PAIR framework is a very useful tool. This four-step approach integrates generative AI tools into assignments, encouraging students to actively engage with the technology. 

  1. Problem Formulation:

The journey begins with students defining the problem or challenge they want to tackle. This initial step fosters critical thinking and sets the stage for their AI-powered exploration. 

  1. AI Tool Selection:

Students become discerning consumers of technology by learning to explore, compare, and evaluate different generative AI tools. Understanding available features allows them to choose the most appropriate tool for their specific problem. 

  1. Interaction:

Armed with their chosen AI tool, students put their problem-solving skills to the test. They experiment with various inputs and outputs, observing how the tool influences their approach and the outcome. 

  1. Reflection:

The final step involves critical reflection. Students assess their experience with the generative AI tool, reporting on its strengths, weaknesses, and overall impact on their learning process. This reflection solidifies their understanding and helps them become more self-aware learners. 

By incorporating the PAIR framework, students develop the skills necessary to navigate the world of AI, becoming not just passive users, but empowered learners who can leverage technology to enhance their problem-solving abilities.

the PAIR framework model
The PAIR framework model – Source: Harvard Business Publishing

The Road Ahead: Challenges, Considerations, and Responsible Implementation

As with any new technology, generative AI comes with its own set of challenges. Ensuring that AI systems are trained on unbiased data sets is crucial to prevent perpetuating stereotypes or misinformation. Additionally, it’s important to remember that the human element remains irreplaceable in education. 

Academic Dishonesty

AI tools can be misused for plagiarism, with students using them to generate essays or complete assignments without truly understanding the content.

Rather than outright banning these tools, educational institutions need to promote ethical and responsible AI usage. This entails establishing transparent guidelines and policies to deter dishonest or unethical practices.

Accuracy and Bias

Generative AI models are trained on vast amounts of data, which can perpetuate biases or inaccuracies present in that data. They are often trained on datasets that may not adequately represent the cultural and contextual diversity of different regions.

This can lead to a lack of relevance and inclusivity in AI-generated content. Uncritical use of AI-generated content could lead students to faulty information.

In addition, localization efforts are needed to ensure that generative AI systems are sensitive to cultural nuances and reflect diverse perspectives. 

Overdependence on Technology

Overreliance on AI tools for learning can hinder critical thinking and problem-solving skills. Students may become accustomed to having solutions generated for them, rather than developing the ability to think independently.

Educating users about AI’s limitations, potential risks, and responsible usage, becomes extremely important. It is important to promote AI as a tool designed to augment human capabilities rather than holding them back.

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Readiness Disparities

While generative AI offers tremendous potential for improving accessibility and inclusion in education, on some occasions, it can also exacerbate existing disparities.

The integration of generative AI hinges on “technological readiness” – meaning adequate infrastructure, reliable internet access, proper training, and digital literacy.

These factors can vary greatly between regions and countries. Unequal access to these resources could create a situation where generative AI widens, rather than shrinks, the educational gap between developed and developing nations.

These disparities must be addressed to ensure that generative AI reaches all students, regardless of their background, ensuring a more equitable society.  

Way Forward: A Balanced Approach

Market projection of AI in education
Market projection of AI in education – Source: Yahoo Finance

Generative AI undoubtedly holds the potential to reshape the education landscape, by providing personalized learning, improving content, automating tasks, and reducing barriers to education.

To successfully leverage these benefits, a balanced approach is necessary that promotes responsible integration of AI in educational settings, while preserving the human touch. Moreover, it is crucial to empower educators and learners with the relevant skills and competencies to effectively utilize Generative AI while also fostering dialogue and collaboration among stakeholders.

By striking a balance between leveraging its potential benefits and mitigating the associated risks, the equitable integration of Generative AI in education can be achieved, creating a dynamic and adaptive learning environment that empowers students for the future.

Huda Mahmood - Author
Huda Mahmood
| March 15

Covariant AI has emerged in the news with the introduction of its new model called RFM-1. The development has created a new promising avenue of exploration where humans and robots come together. With its progress and successful integration into real-world applications, it can unlock a new generation of AI advancements.

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In this blog, we take a closer look at the company and its new model.

What is Covariant AI?

The company develops AI-powered robots for warehouses and distribution centers. It spun off in 2017 from OpenAI by its ex-research scientists, Peter Chen and Pieter Abbeel. Its robots are powered by a technology called the Covariant Brain, a machine-learning (ML) model to train and improve robots’ functionality in real-world applications.

The company has recently launched a new AL model that takes up one of the major challenges in the development of robots with human-like intelligence. Let’s dig deeper into the problem and its proposed solution.

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What was the challenge?

Today’s digital world is heavily reliant on data to progress. Since generative AI is an important aspect of this arena, data and information form the basis of its development as well. So the development of enhanced functionalities in robots, and the appropriate training requires large volumes of data.

The limited amount of available data poses a great challenge, slowing down the pace of progress. It was a result of this challenge that OpenAI disbanded its robotics team in 2021. The data was insufficient to train the movements and reasoning of robots appropriately.

However, it all changed when Covariant AI introduced its new AI model.


Understanding the Covariant AI model

The company presented the world with RFM-1, its Robotics Foundation Model as a solution and a step ahead in the development of robotics. Integrating the characteristics of large language models (LLMs) with advanced robotic skills, the model is trained on a real-world dataset.

Covariant used its years of data from its AI-powered robots already operational in warehouses. For instance, the item-picking robots working in the warehouses of Crate & Barrel and Bonprix. With these large enough datasets, the challenge of data limitation was addressed, enabling the development of RFM-1.

Since the model leverages real-world data of robots operating within the industry, it is well-suited to train the machines efficiently. It brings together the reasoning of LLMs and the physical dexterity of robots which results in human-like learning of the robots.


An outlook of RFM-1
An outlook of the features and benefits of RFM-1


Unique features of RFM-1

The introduction of the new AI model by Covariant AI has definitely impacted the trajectory of future developments in generative AI. While we still have to see how the journey progresses, let’s take a look at some important features of RFM-1.

Multimodal training capabilities

The RFM-1 is designed to deal with five different types of input: text, images, video, robot instructions, and measurements. Hence, it is more diverse in data processing than a typical LLM that is primarily focused on textual data input.

Integration with the physical world

Unlike your usual LLMs, this AI model engages with the physical world around it through a robot. The multimodal data understanding enables it to understand the surrounding environment in addition to the language input. It enables the robot to interact with the physical world.

Advanced reasoning skills

The advanced AI model not only processes the available information but engages with it critically. Hence, RFM-1 has enhanced reasoning skills that provide the robot with a better understanding of situations and improved prediction skills.


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Benefits of RFM-1

The benefits of the AI model align with its unique features. Some notable advantages of this development are:

Enhanced performance of robots

The multimodal data enables the robots to develop a deeper understanding of their environments. It results in their improved engagement with the physical world, allowing them to perform tasks more efficiently and accurately. It will directly result in increased productivity and accuracy of business operations where the robots operate.

Improved adaptability

Based on the model’s improved reasoning skills, it ensure that the robots are equipped to understand, learn, and reason with new data. Hence, the robots become more versatile and adaptable to their changing environment.

Reduced reliance on programming

RFM-1 is built to constantly engage with and learn from its surroundings. Since it enables the robot to comprehend and reason with the changing input data, the reliance on pre-programmed instructions is reduced. The process of development and deployment becomes simpler and faster.

Hence, the multiple new features of RFM-1 empower it to create useful changes in the world of robotic development. Here’s a short video from Covariant AI, explaining and introducing their new AI model.

The future of RFM-1

The future of RFM-1 looks very promising, especially within the world of robotics. It has opened doors to a completely new possibility of developing a range of flexible and reliable robotic systems.

Covariant AI has taken the first step towards empowering commercial robots with an enhanced understanding of their physical world and language. Moreover, it has also introduced new avenues to integrate LLMs within the arena of generative AI applications.

Read about the top 10 industries that can benefit from LLMs

Huda Mahmood - Author
Huda Mahmood
| March 13

You need the right tools to fully unleash the power of generative AI. A vector embedding model is one such tool that is a critical component of AI applications for creating realistic text, images, and more.

In this blog, we will explore vector embedding models and the various parameters to be on the lookout for when choosing an appropriate model for your AI applications.


What are vector embedding models?


vector embedding models
Function of a vector embedding model


These act as data translators that can convert any data into a numerical code, specifically a vector of numbers. The model operates to create vectors that capture the meaning and semantic similarity between data objects. It results in the creation of a map that can be used to study data connections.

Moreover, the embedding models allow better control over the content and style of generated outputs, while dealing with multimodal data. Hence, it can deal with text, images, code, and other forms of data.

While we understand the role and importance of embedding models in the world of vector databases, the selection of right model is crucial for the success of an AI application. Let’s dig deeper into the details of making the relevant choice.


Read more about embeddings as a building block for LLMs


Factors of consideration to make the right choice

Since a vector embedding model forms the basis of your generative AI application, your choice is crucial for its success.


Factors to consider when choosing a vector embedding model
Factors to consider when choosing a vector embedding model


Below are some key factors to consider when exploring your model options.


Use case and desired outcomes

In any choice, your goals and objectives are the most important aspect. The same holds true for your embedding model selection. The use case and outcomes of your generative AI application guide your choice of model.

The type of task you want your app to perform is a crucial factor as different models capture specific aspects of data. The tasks can range from text generation and summarization to code completion and more. You must be clear about your goal before you explore the available options.

Moreover, data characteristics are of equal importance. Your data type – text, code, or image – must be compatible with your data format.


Model characteristics

The particular model characteristics of consideration include its accuracy, latency, and scalability. Accuracy refers to the ability of the model to correctly capture data relationships, including semantic meaning, word order, and linguistic nuances.

Latency is another important property which caters to real-time interactions of the application, improving model’s performance with reduced inference time. The size and complexity of data can impact this characteristic of an embedding model.

Moreover, to keep up with the rapidly advancing AI, it is important to choose a model that supports scalability. It also ensures that the model can cater to your growing dataset needs.


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Practical factors

While app requirements and goals are crucial to your model choice, several practical aspects of the decision must also be considered. These primarily include computational resource requirements and cost of the model. While the former must match your data complexity, the latter should be within your specified budget.

Moreover, the available level of technical expertise also dictates your model choice. Since some vector embedding models require high technical expertise while others are more user-friendly, your strength of technical knowledge will determine your ease-of-use.


Here’s your guide to top vector databases in the market


While these considerations address the various aspects of your organization-level goals and application requirements, you must consider some additional benchmarks and evaluation factors. Considering these benchmarks completes the highly important multifaceted approach of model selection.


Benchmarks for evaluating vector embedding models

Here’s a breakdown of some key benchmarks you can leverage:


Internal evaluation

These benchmarks focus on the quality of the embeddings for all tasks. Some common metrics of this evaluation include semantic relationships between words, word similarity in the embedding space, and word clustering. All these metrics collectively determine the quality of connections between embeddings.


External evaluation

It keeps track of the performance of embeddings in a specific task. Follwoing is a list of some of the metrics used for external evaluation:

ROUGE Score: It is called the Recall-Oriented Understudy for Gisting Evaluation. It deals with the performance of text summarization tasks, evaluating the overlap between generated and reference summaries.

BLEU Score: The Bilingual Evaluation Understudy, also called human evaluation measures the coherence and quality of outputs. This metric is particularly useful to track the quality of dialog generation.

MRR: It stands for Mean Reciprocal Rank. As the name suggests, it ranks the documents in the retrieved results based on their relevance.


MRR explained
A visual explanation of MRR – Source: Evidently AI


Benchmark Suites

The benchmark suites work by providing a standardized set of tasks and datasets to assess the models’ performance. It helps in making informed decisions as they highlight the strengths and weaknesses of of each model across a variety of tasks. Some common benchmark suites include:

BEIR (Benchmark for Evaluating Retrieval with BERT)

It focuses on information retrieval tasks by using a reference set that includes diverse information retrieval tasks such as question-answering, fact-checking, and entity retrieval. It provides datasets for retrieving relevant documents or passages based on a query, allowing for a comprehensive evaluation of a model’s capabilities.

MTEB (Massive Text Embedding Benchmark)


Outlook of the MTEB
An outlook of the MTEB – Source: Hugging Face


The MTEB leaderboard is available on Hugging Face. It expands on BEIR’s foundation with 58 datasets and covering 112 languages. It enables evaluation of models against a wide range of linguistic contexts and use cases.

Its metrics and databases are suitable for tasks like text summarization, information retrieval, and semantic textual similarity, allowing you to see model performance on a broad range of tasks.


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Hence, the different factors, benchmark suites, evaluation models, and metrics collectively present a multi-faceted approach towards selecting a relevant vector embedding model. However, alongside these quantitative metrics, it is important to incorporate human judgment into the process.



The final word

In navigating the performance of your generative AI applications, the journey starts with choosing an appropriate vector embedding model. Since the model forms the basis of your app performance, you must consider all the relevant factors in making a decision.

While you explore the various evaluation metrics and benchmarks, you must also carefully analyze the instances of your application’s poor performance. It will help in understanding the embedding model’s weaknesses, enabling you to choose the most appropriate one that ensures high-quality outputs.

Huda Mahmood - Author
Huda Mahmood
| March 1

In today’s rapidly evolving technological world, the economic potential of generative AI and other cutting-edge industrial developments is more pronounced than ever before. AI and the chip industry are pivotal in modern-day innovations and growth.

It is important to navigate the impact and economic potential of generative AI in the chip design industry as it maps out the technological progress and innovation in the digital world. The economic insights can highlight new investment avenues by informing policymakers and business leaders of the changing economic landscape timely.

As per McKinsey’s research, generative AI is set to potentially unlock 10 to 15 percent of the overall R&D costs in productivity value, raising its stakes in the economic impact. Since the economic potential of generative AI can create staggering changes and unprecedented opportunities, let’s explore it.


Major players in the economic landscape of AI and chip industry


While generative AI is here to leave a lasting impact on the technological world, it is important to recognize the major players in the industry. As trends, ideas, and innovation are the focus of leading names within the chip industry, following their progress provides insights into the economic potential of generative AI.


Major Players in the AI Chip Industry
Major players in the AI chip industry


Some of the common industry giants of generative AI within the chip industry include:




It is one of the well-established tech giants, holding a dominant position within the AI chip industry. It is estimated to hold almost 80% of the global market for GPUs (Graphics Processing Units). Its robust software ecosystem includes frameworks like CUDA and TensorRT, simplifying generative AI development.

However, the rise of the production of specialized chips has led to an evolving landscape for generative AI. NVIDIA must adapt and innovate within the changing demands of the AI chip industry to maintain its position as a leading player.




While Intel has been a long-standing name in the semiconductor industry, it is a new player within the AI chip industry. Some of its strategic initiatives as an AI chip industry player include the acquisition of Habana Labs which provided them expertise in the AI chip technology.

They used the labs to design a Gaudi series of AI processors that specialize in the training of large language models (LLMs). Compared to established giants like NVIDIA, Intel is a fairly new player in the AI chip industry. However, with the right innovations, it can contribute to the economic potential of generative AI.


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Microsoft holds a unique position where it is one of the leading consumers of the AI chip industry while aiming to become a potential contributor. Since the generative AI projects rely on chips from companies like NVIDIA, Microsoft has shown potential to create custom AI chips.

Within the economic potential of generative AI in the chip industry, Microsoft describes its goal to tailor and produce everything ‘from silicon to service‘ to meet the AI demands of the evolving industry.


Google AI


Like Microsoft, Google AI is also both a consumer and producer of AI chips. At the forefront, the development of its generative AI models is leading to innovation and growth. While these projects lead to the consumption of AI chips from companies like NVIDIA, Google AI contributes to the development of AI chips through research and collaboration.

Unlike other manufacturers focused on developing the new chips for businesses, Google AI plays a more collaborative role. It partners with these manufacturers to contribute through research and model development.




Groq has emerged as a new prominent player within the AI chip industry. Its optimized chips for generative AI applications are different from the generally developed GPUs. Groq is focused on creating LPUs (Liquid Programmable Units).

LPUs are designed to handle specific high-performance generative AI tasks like inferencing LLMs or generating images. With its new approach, Groq can boost the economic potential of generative AI within the chip industry. altering the landscape altogether.


Each of these players brings a unique perspective to the economic landscape of generative AI within the AI chip industry. The varying stages of chip development and innovation promise a competitive environment for these companies that is conducive to growth.

Now that we recognize some leading players focused on exploring the economic potential of generative AI in the chip industry, it is time to understand some of the major types of AI chip products.


Types of AI chips within the industry


The rapidly evolving technological landscape of the AI chip industry has promoted an era of innovation among competitors. It has led to the development of several types of chips that are available for use today.


The economic potential of generative AI within the chip design industry | Data Science Dojo
Major Types of Chip Designs


Let’s dig deeper into some of the major types of AI chips.


GPUs – Graphics Processing Units


These are designed to handle high-performance graphics processing. Some of its capabilities include massively parallel processing and handling large matrix multiplications. NVIDIA is a major provider of GPUs, like NVIDIA Tesla and NVIDIA A100.


ASICs – Application-Specific Integrated Circuits


As the name indicates, these are customized chips that are built for any specified task. Companies usually build ASICs to cater to the particular demands of the application development process. Google and Amazon rely on ASICs built specifically to handle their specific AI needs.

While the specificity offers enhanced performance and efficiency, it also diminishes the flexibility of an AI chip. The lack of versatility prevents it from performing a wide variety of tasks or applications.


NPUs – Neural Processing Units


These are custom-built AI chips that specialize in handling neural network computations, like image recognition and NLP. The differentiation ensures better performance and efficiency of the chips. The parallel processing architecture enables the AI chips to process multiple operations simultaneously.

Like ASICs, NPUs also lack versatility due to their custom-built design. Moreover, these chips are also expensive, incurring high costs to the users, making their adoption within the industry limited.


FPGAs – Field-Programmable Gate Arrays


FPGAs are an improvement to custom-built chip design. Its programmability makes them versatile as the chips can be reprogrammed after each specific use. It makes them more flexible to handle various types of AI workloads. They are useful for rapid prototyping and development.


LPUs – Liquid Programmable Units


Also called linear processing units, these are a specific chip design developed by Groq. These are designed to handle specific generative AI tasks, like training LLMs and generating images. Groq claims its superior performance due to the custom architecture and hardware-software co-design.

While LPUs are still in their early stage of development, they have the potential to redefine the economic landscape of the AI chip industry. The performance of LPUs in further developmental stages can greatly influence the future and economic potential of generative AI in the chip industry.


Learn to build LLM applications


Among these several chip designs available and under development, the choice within the market relies on multiple factors. Primarily, the choice is dictated by the needs of the AI application and its developmental stage. While a GPU might be ideal for early-stage processing, ASICs are more useful for later stages.

Moreover, the development of new AI chip designs has increased the variety of options for consumers. The manufacturers of these chips must keep these factors in mind during their research and development phases so the designed chips are relevant in the market, ensuring a positive impact on the economic landscape.


What is the economic potential of generative AI in chip design?



The fast-paced technological world of today is marked by developments in generative AI. According to Statista Market Insights, the generative AI market size is predicted to reach $70 billion in 2030. Hence, it is crucial to understand the role and impact of AI in the modern economy.

From our knowledge of different players and the types of chip designs, we can conclude that both factors are important in determining the economic potential of generative AI in chip design. Each factor adds to the competitiveness of the market, fostering growth and innovation.

Thus, the impact of generative AI is expected to grow in the future, subsequently leading to the growth of AI chip designs. The increased innovation will also enhance its impact on the economic landscape.

Mariyam Arshad
| February 20

People operations are an integral part of any organization. Disruptive technologies tend to spark equal parts interest and fear in those related to operations, as they are directly affected by them.


Impact of generative AI on people operations


Generative AI (artificial intelligence) has had similar effects, where its accessibility and a vast variety of use cases have created a buzz that has led to a profound impact on jobs of every nature. Within HR (human resources), it can help automate and optimize repetitive tasks customized at an employee level.


Very basic use cases include generating interview questions, creating job postings, and assisting in writing performance reviews. It can also help personalize each employee’s experience at the company by building custom onboarding paths, learning plans, and performance reviews.


Impact of AI on people operations
Impact of generative AI on people operations


This takes a bit off the HR team’s plate, leaving more time for strategic thinking and decision-making. On a metric level, AI can help in hiring decisions by calculating turnover, attrition, and performance.


Since AI is revolutionizing the way processes are organized in companies, HR processes automated by generative AI can feel more personalized and thus drive engagement. We will particularly investigate the impact and potential changes in the landscape of learning and development of organizations.


Development benefits for employees


Now, more than ever, companies are investing in and reaping from the benefits of L&D, leading to better employee experiences, lower turnover, higher productivity, and higher performance at work. In an ever-changing technological environment, upskilling employees has taken center stage.


As technology reshapes industries, skill requirements have shifted, demanding continuous adaptation. Amid the proliferation of automation, AI, and digitalization, investing in learning ensures individuals remain relevant and competitive.


Moreover, fostering a culture of continuous development within organizations enhances employee satisfaction and engagement, driving innovation and propelling businesses forward in an era where staying ahead is synonymous with staying educated. In addition to that, younger employees are attracted to learning opportunities and value career growth based on skill development.


Large language model bootcamp


Catering to more personalized learning and teaching needs


A particular way that generative AI impacts and influences learning and development is through greater personalization in learning. Using datasets and algorithms, AI can help generate adaptable educational content based on analyzing each learner’s learning patterns, strengths, and areas of improvement.


AI can help craft learning paths that cater to everyone’s learning needs and can be tailored according to their cognitive preferences. Since L&D professionals spend a lot of time generating content for training and workshops, AI can help not only generate this content for them but also, based on the learning styles, comprehension speed, and complexity of the material, determine the best pedagogy.


For trainers creating teaching material, Generative AI lightens the workload of educators by producing assessments, quizzes, and study materials. AI can swiftly create a range of evaluation tools tailored to specific learning outcomes, granting educators more time to focus on analyzing results and adapting their teaching strategies accordingly.


One of the important ways that training is designed is through immersive experiences and simulations. These are often difficult to create and take lengthy hours. Using generative AI, professionals can create scenarios, characters, and environments close to real life, enhancing the experience of experiential learning.


Learning skills that are elevated risk, for example, medical procedures or hazardous industrial tasks, learners can now be exposed to such situations without risk on a secure platform using a simulation generated through AI. In addition to being able to learn in an experiential simulation, which can lead to skill mastery.

Such simulations can also generate personalized feedback for each learner, which can lead to a better employee experience. Due to the adaptability of these simulations, they can be customized according to the learner’s pace and style.


AI can help spark creativity by generating unexpected ideas or suggestions, prompting educators to think outside the box and explore innovative teaching approaches. Generative AI optimizes content creation processes, offering educators time-saving tools while preserving the need for human guidance and creativity to ensure optimal educational outcomes.


Is AI the ultimate replacement for people?


Is AI a replacement for humans?
Is AI a replacement for humans? – Source: eWEEK


Although AI can help speed up the process of creating training content, this is an area where human expertise is always needed to verify accuracy and quality. It is necessary to review and refine AI-generated content, contextualizing it based on relevance, and adding a personal touch to make it relatable for learners.


This constructive interaction ensures that the advantages of AI are leveraged while ensuring speed. As with other AI-generated content, there are certain ethical considerations that L&D professionals must consider when using it to create content.


Transparency in communications


Educators must ensure that AI-generated materials respect intellectual property and provide accurate attributions to original sources. Transparent communication about AI involvement is crucial to maintaining trust and authenticity in educational settings. We have discussed at length how AI is useful in generating customizable learning experiences.


However, AI relies on user data for personalization, requiring strict measures to protect sensitive information. It is also extremely important to ensure transparency when using AI to generate content for training, where learners must be able to distinguish between AI-generated and human-created materials. L&D professionals also need to address any biases that might inadvertently seep into AI-generated content.


AI has proven to be proficient in helping make processes quicker and more streamlined, however, its inability to understand complex human emotions limits its capacity to understand culture and context. When dealing with sensitive issues in learning and development, L&D professionals should be wary of the lack of emotional intelligence in AI-generated content, which is required for sensitive subjects, interpersonal interactions, and certain creative endeavors.


Hence, human intervention remains essential for content that necessitates a deep understanding of human complexities.


Learn to build LLM applications


The solution lies in finding the right balance


Assuming that with time there will be greater involvement of AI in people operations for the need of automation, HR leaders will have to ensure that the human element is not lost during it. This should be seen as an opportunity by HR professionals to reduce the number of administrative tasks, automate the menial work, and focus more on strategic decision-making.


Learning and development can be aided by AI, which empowers educators with efficient tools. Also, learners can engage with simulations, fostering experiential learning. However, the symbiotic relationship between AI and human involvement remains crucial for a balanced and effective educational landscape.



With an increase in the importance of learning and development at companies, generative AI is a revolutionizing tool helping people strategize by enabling dynamic content creation, adaptive learning experiences, and enhanced engagement.


Next step for operations in organizations

Yet, as AI advances, educators and stakeholders must collaborate to ensure ethical content generation, transparency, bias mitigation, and data privacy. AI’s potential can be harnessed to augment human expertise, elevate education while upholding ethical standards, and preserve the indispensable role of human guidance.

Huda Mahmood - Author
Huda Mahmood
| February 19

Are you confused about where to start working on your large language model? It all starts with an understanding of a typical LLM project lifecycle. As part of the generative AI world, LLMs have led to innovation in machine-learning tasks.


Let’s take a look at the steps that make up an LLM project lifecycle and their impact on the process.


Roadmap to understanding an LLM project lifecycle


Within the realm of generative AI, a project involving large language models can be a daunting task. It demands proper coordination and skills to execute a task successfully. In order to create an ease of understanding, we have broken down a typical LLM project lifecycle into multiple steps.


A roadmap of an LLM project lifecycle
A roadmap of an LLM project lifecycle


In this section, we will delve deeper into the various stages of the process.


Defining the scope of the project


It is paramount to begin your LLM project lifecycle by understanding its scope. It begins with a comprehension of the problem you aim to solve. Market research and stakeholder interviews are a good place to start at this stage. You must also review the available technological possibilities.


LLMs are multifunctional but the size and architecture of the model determine its ability, ranging from long-form text generation and text summarization to language translation. Based on your research, you can determine the specifics of your LLM project and hence the scope of it.


The next part of this step is to explore the feasibility of a solution in generative AI. You must use this to set clear and measurable objectives as they would define the roadmap for your LLM project lifecycle.


Data preprocessing and relevant considerations


Now that you have defined your problem, the next step is to look for relevant data. Data collection can encompass various sources, depending on your problem. Once you have the data, you need to clean and preprocess it. The goal is to make the data usable for model training.


Moreover, it is important in your LLM project lifecycle to consider all the ethical and legal considerations when dealing with data. You must have the clearance to use data, including protection laws, anonymization, and user consent. Moreover, you must ensure the prevention of potential biases through the diversity of perspectives in the data.


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Selecting a relevant model


When it comes to model selection, you have two choices. Either use an existing base model or pre-train your own from scratch. Based on your project demands, you can start by exploring the available models to check if any aligns with your requirements.


Models like GPT-4 and PalM2 are powerful model options. Moreover, you can also explore FLAN-T5 – a hugging face model, offering enhanced Text-to-Text Transfer Transformer features. However, you need to consider license and certification details before choosing an open-source base model.


In case none of the existing models fulfill your demands, you need to pre-train a model from scratch to begin your LLM project lifecycle. It requires machine-learning expertise, computational resources, and time. The large investment in pre-training results in a highly customized model for your project.


  • What is pre-training? It is a compute-intensive phase of unsupervised learning tasks. In an LLM project lifecycle, the objective primarily focuses on text generation or next-token prediction. During this complex process, the model is trained and the transformer architecture is decided. It results in the creation of Formation Models.


Training the model


The next step in the LLM project lifecycle is to adapt and train the foundation model. The goal is to refine your LLM model with your project requirements. Let’s look at some common techniques for the model training process.


  • Prompt engineering: As the name suggests, this method relies on prompt generation. You must structure prompts carefully for your LLM model to get accurate results. It requires you to have a proper understanding of your model and the project goals.

For a typical LLM model, a prompt is provided to the model for it to generate a text. This complete process is called inference. It is the simplest phase in an LLM project lifecycle that aims to refine your model responses and enhance its performance.


  • Fine-tuning: At this point, you focus on customizing your model to your specific project needs. The fine-tuning process enables you to convert a generic model into a tailored one by using domain-specific data, resulting in its optimized performance for particular tasks. It is a supervised learning task that adds weights to the foundation model, making it more efficient in the process.


  • Caching: It is one of the less-renowned but important techniques in the training process. It involves the frequent storage of prompts and responses to speed up your model’s performance. Caching high-dimensional vectors results in faster retrieval of information and generation of more efficient results.


Reinforcement learning


Reinforcement learning happens from human or AI feedback, where the former is called RLHF and the latter is RLAIF. RLHF is aimed at aligning the LLM model with human values, expectations, and standards. The human evaluators review, rate, and provide feedback on the model performance.


reinforcement learning-LLM project lifecycle
A visual representation of reinforcement learning – Source: Medium


It is an iterative process completed using rewards against each successful model output which results in the creation of a rewards model. Then the RLAIF is used to scale human feedback that ensures the model is completely aligned with the human values.


Learn to build LLM applications


Evaluating the model


It involves the validation and testing of your LLM model. The model is tested using unseen data (also referred to as test data). The output is evaluated against a set of metrics. Some common LLM evaluation metrics include BLEU (Bilingual Evaluation Understudy), GLUE (General Language Understanding Evaluation), and HELM (Holistic Evaluation of Language Models).


Along with the set metrics, the results are also analyzed for adherence to ethical standards and the absence of biases. This ensures that your model for the LLM project lifecycle is efficient and relevant to your goals.


Model optimization and deployment


Model optimization is a prerequisite to the deployment process. You must ensure that the model is efficiently designed for your application environment. The process primarily includes the reduction of model size, enhancement of inference speed, and efficient operation of the model in real-world scenarios. It ensures faster inference using less memory.


Some common optimization techniques include:


  • Distillation – it teaches a smaller model (called the student model) from a larger model (called the teacher model)


  • Post-training quantization – it aims to reduce the precision of model weights


  • Pruning – it focuses on removing the model weights that have negligible impact


This stage of the LLM project lifecycle concludes with seamless integration of workflows, existing systems, and architectures. It ensures smooth accessibility and operation of the model.


Model monitoring and building LLM applications


The LLM project lifecycle does not end at deployment. It is crucial to monitor the model’s performance in real-world situations and ensure its adaptability to evolving requirements. It also focuses on addressing any issues that arise and regularly updating the model parameters.


Finally, your model is ready for building robust LLM applications. These platforms can cater to diverse goals, including automated content creation, advanced predictive analysis, and other solutions to complex problems.



Summarizing the LLM project lifecycle

Hence, the roadmap to completing an LLM project lifecycle is a complex trajectory involving multiple stages. Each stage caters to a unique aspect of the model development process. The final goal is to create a customized and efficient machine-learning model to deploy and build innovative LLM applications.

Huda Mahmood - Author
Huda Mahmood
| February 16

After DALL-E 3 and GPT-4, OpenAI has now introduced Sora as it steps into the realm of video generation with artificial intelligence. Let’s take a look at what we know about the platform so far and what it has to offer.


What is Sora?


It is a new generative AI Text-to-Video model that can create minute-long videos from a textual prompt. It can convert the text in a prompt into complex and detailed visual scenes, owing to its understanding of the text and the physical existence of objects in a video. Moreover, the model can express emotions in its visual characters.


Source: OpenAI


The above video was generated by using the following textual prompt on Sora:


Several giant wooly mammoths approach, treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds; and a sun high in the distance creates a warm glow, The low camera view is stunning, capturing the large furry mammal with beautiful photography, depth of field.


While it is a text-to-video generative model, OpenAI highlights that Sora can work with a diverse range of prompts, including existing images and videos. It enables the model to perform varying image and video editing tasks. It can create perfect looping videos, extend videos forward or backward, and animate static images.


Moreover, the model can also support image generation and interpolation between different videos. The interpolation results in smooth transitions between different scenes.


What is the current state of Sora?


Currently, OpenAI has only provided limited availability of Sora, primarily to graphic designers, filmmakers, and visual artists. The goal is to have people outside of the organization use the model and provide feedback. The human-interaction feedback will be crucial in improving the model’s overall performance.


Moreover, OpenAI has also highlighted that Sora has some weaknesses in its present model. It makes errors in comprehending and simulating the physics of complex scenes. Moreover, it produces confusing results regarding spatial details and has trouble understanding instances of cause and effect in videos.


Now, that we have an introduction to OpenAI’s new Text-to-Video model, let’s dig deeper into it.


OpenAI’s methodology to train generative models of videos


As explained in a research article by OpenAI, the generative models of videos are inspired by large language models (LLMs). The inspiration comes from the capability of LLMs to unite diverse modes of textual data, like codes, math, and multiple languages.


While LLMs use tokens to generate results, Sora uses visual patches. These patches are representations used to train generative models on varying videos and images. They are scalable and effective in the model-training process.


Compression of visual data to create patches


We need to understand how visual patches are created that Sora relies on to create complex and high-quality videos. OpenAI uses an AI-trained network to reduce the dimensionality of visual data. It is a process where a video input is initially compressed into a lower-dimensional latent space.


It results in a latent representation that is compressed both temporally and spatially, called patches. Sora operates within the same temporal space to generate videos. OpenAI simultaneously trains a decoder model to map the generated latent representations back to pixel space.


Generation of spacetime latent patches


When the Text-to-Video model is presented with a compressed video input, the AI model extracts from it a series of spacetime patches. These patches act as transformer tokens that are used to create a patch-based representation. It enables the model to train on videos and images of different resolutions, durations, and aspect ratios. It also enables control over the size of generated videos by arranging patches in a specific grid size.


What is Sora, architecturally?


Sora is a diffusion transformer that takes in noisy patches from the visual inputs and predicts the cleaner original patches. Like a typical diffusion transformer that produces effective results for various domains, it also ensures effective scaling of videos. The sample quality improves with an increase in training computation.


Below is an example from OpenAI’s research article that explains the reliance of quality outputs on training compute.


Source: OpenAI

This is the output produced with base compute. As you can see, the video results are not coherent and highly defined.


Let’s take a look at the same video with a higher compute.


Source: OpenAI


The same video with 4x compute produces a highly-improved result where the video characters can hold their shape and their movements are not as fuzzy. Moreover, you can also see that the video includes greater detail.


What happens when the computation times are increased even further?


Source: OpenAI


The results above were produced with 16x compute. As you can see, the video is in higher definition, where the background and characters include more details. Moreover, the movement of characters is more defined as well.


It shows that Sora’s operation as a diffusion transformer ensures higher quality results with increased training compute.


The future holds…


Sora is a step ahead in video generation models. While the model currently exhibits some inconsistencies, the demonstrated capabilities promise further development of video generation models. OpenAI talks about a promising future of the simulation of physical and digital worlds. Now, we must wait and see how Sora develops in the coming days of generative AI.

Huda Mahmood - Author
Huda Mahmood
| February 1

Retrieval augmented generation (RAG) has improved the function of large language models (LLM). It empowers generative AI to create more coherent and contextually relevant content. Let’s take a deeper look into understanding RAG.


What is retrieval augmented generation?


It is an AI framework and a type of natural language processing (NLP) model that enables the retrieval of information from an external knowledge base. It integrates retrieval-based and generation-based approaches to provide a robust database for LLMs.


A retrieval augmented generation model accesses a large pre-existing pool of knowledge to improve the quality of LLM-generated responses. It ensures that the information is more accurate and up-to-date by combining factual data with contextually relevant information.


By combining vector databases and LLM, the retrieval model has set up a standard for the search and navigation of data for generative AI. It has become one of the most used techniques for LLM.


retrieval augmented generation
An example illustrating retrieval augmentation – Source: LinkedIn


Benefits of RAG

While retrieval augmented generation improves LLM responses, it offers multiple benefits to the generative AI efforts of an organization.

Explore RAG and its benefits, trade-offs, use cases, and enterprise adoption, in detail with our podcast! 

Improved contextual awareness


The retrieval component allows access to a large knowledge base, enabling the model to generate contextually relevant information. Due to improved awareness of the context, the output generated is more coherent and appropriate.


Enhanced accuracy


An LLM using a retrieval model can produce accurate results with proper attribution, including citations of relevant sources. Access to a large and accurate database ensures that factually correct results are generated.


Adaptability to dynamic knowledge


The knowledge base of a retrieval model is regularly updated to ensure access to the latest information. The system integrates new information without retraining the entire program, ensuring quick adaptability. It enables the generative models to access the latest statistics and research.


Resource efficiency


Retrieval mechanisms enable the model to retrieve information from a large information base. The contextual relevance of the data enhances the accuracy of the results, making the process resource-efficient. It makes handling of large data volumes easier and makes the system cost-efficient.


Increased developer control


Developers use a retrieval augmented generation model to control the information base of a LLM. They can adapt the data to the changing needs of the user. Moreover, they can also restrict the accessibility of the knowledge base, giving them control of data authorization.


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Frameworks for retrieval augmented generation


A RAG system combines a retrieval model with a generation model. Developers use frameworks and libraries available online to implement the required retrieval system. Let’s take a look at some of the common resources used for it.


Hugging face transformers


It is a popular library of pre-trained models for different tasks. It includes retrieval models like Dense Passage Retrieval (DPR) and generation models like GPT. The transformer allows the integration of these systems to generate a unified retrieval augmented generation model.


Facebook AI similarity search (FAISS)


FAISS is used for similarity search and clustering dense vectors. It plays a crucial role in building retrieval components of a system. Its use is preferred in models where vector similarity is crucial for the system.


PyTorch and TensorFlow


These are commonly used deep learning frameworks that offer immense flexibility in building RAG models. They enable the developers to create retrieval and generation models separately. Both models can then be integrated into a larger framework to develop a RAG model.




It is a Python framework that is built on Elasticsearch. It is suitable to build end-to-end conversational AI systems. The components of the framework are used for storage of information, retrieval models, and generation models.


Learn to build LLM applications


Use cases of RAG


Some common use cases and real-world applications are listed below.

Content creation


It primarily deals with writing articles and blogs. It is one of the most common uses of LLM where the retrieval models are used to generate coherent and relevant content. It can lead to personalized results for users that include real-time trends and relevant contextual information.


Real-time commentary


A retriever uses APIs to connect real-time information updates with an LLM. It is used to create a virtual commentator which can be integrated further to create text-to-speech models. IBM used this mechanism during the US Open 2023 for live commentary.


Question answering system


question answering through retrieval augmented generation
Question answering through retrieval augmented generation – Source: Medium


The ability of LLMs to generate contextually relevant content enables the retrieval model to function as a question-answering machine. It can retrieve factual information from an extensive knowledge base to create a comprehensive answer.


Language translation


Translation is a tricky process. A retrieval model can detect the context of phrases and words, enabling the generation of relevant translations. Access to external databases ensures the results are accurate and fluent for the users. The extensive information on available idioms and phrases in multiple languages ensures this use case of the retrieval model.


Educational assistance


The application of a retrieval model in the educational arena is an extension of question answering systems. It uses the said system, particularly for educational queries of users. In answering questions and generating academic content, the system can create more comprehensive results with contextually relevant information.



Future of RAG


The integration of retrieval and generation models in LLM is expected to grow in the future. The current trends indicate their increasing use in technological applications. Some common areas of future development of RAG include:


  • Improved architecture – the development of retrieval and generation models will result in the innovation of neural network architectures


  • Enhanced conversational agents – improved adaptation of knowledge base into retrieval model databases will result in more sophisticated conversational agents that can adapt to domain-specific information in an improved manner


  • Integration with multimodal information – including different types of information, including images and audio, can result in contextually rich responses that encompass a diverse range of media


  • Increased focus on ethical concerns – since data privacy and ethics are becoming increasingly important in today’s digital world, the retrieval models will also focus more on mitigating biases and ethical concerns from the development systems



Hence, retrieval augmented generation is an important aspect of large language models within the arena of generative AI. It has improved the overall content processing and promises an improved architecture of LLMs in the future.

Huda Mahmood - Author
Huda Mahmood
| January 25

Vector embeddings refer to numerical representations of data in a continuous vector space. The data points in the three-dimensional space can capture the semantic relationships and contextual information associated with them.  

With the advent of generative AI, the complexity of data makes vector embeddings a crucial aspect of modern-day processing and handling of information. They ensure efficient representation of multi-dimensional databases that are easier for AI algorithms to process. 



vector embeddings - chunk text
Vector embeddings create three-dimensional data representation – Source: robkerr.ai


Key roles of vector embeddings in generative AI 

Generative AI relies on vector embeddings to understand the structure and semantics of input data. Let’s look at some key roles of embedded vectors in generative AI to ensure their functionality. 

  • Improved data representation 
    Vector embeddings present a three-dimensional representation of data, making it more meaningful and compact. Similar data items are presented by similar vector representations, creating greater coherence in outputs that leverage semantic relationships in the data. They are also used to capture latent representations in input data.
  • Multimodal data handling 
    Vector space allows multimodal creativity since generative AI is not restricted to a single form of data. Vector embeddings are representative of different data types, including text, image, audio, and time. Hence, generative AI can generate creative outputs in different forms using of embedded vectors.
  • Contextual representation

    contextual representation in vector embeddings
    Vector embeddings enable contextual representation of data

    Generative AI uses vector embeddings to control the style and content of outputs. The vector representations in latent spaces are manipulated to produce specific outputs that are representative of the contextual information in the input data. It ensures the production of more relevant and coherent data output for AI algorithms.


  • Transfer learning 
    Transfer learning in vector embeddings enable their training on large datasets. These pre-trained embeddings are then transferred to specific generative tasks. It allows AI algorithms to leverage existing knowledge to improve their performance.
  • Noise tolerance and generalizability 
    Data is often marked by noise and missing information. In three-dimensional vector spaces, the continuous space can generate meaningful outputs even with incomplete information. Encoding vector embeddings cater to the noise in data, leading to the building of robust models. It enables generalizability when dealing with uncertain data to generate diverse and meaningful outputs. 


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Use cases of vector embeddings in generative AI 

There are different applications of vector embeddings in generative AI. While their use encompasses several domains, following are some important use cases of embedded vectors: 


Image generation 

It involves Generative Adversarial Networks (GANs) that use embedded vectors to generate realistic images. They can manipulate the style, color, and content of images. Vector embeddings also ensure easy transfer of artistic style from one image to the other. 

Following are some common image embeddings: 

  • CNNs
    They are known as Convolutional Neural Networks (CNNs) that extract image embeddings for different tasks like object detection and image classification. The dense vector embeddings are passed through CNN layers to create a hierarchical visual feature from images.
  • Autoencoders 
    These are trained neural network models that are used to generate vector embeddings. It uses these embeddings to encode and decode images. 


Data augmentation 

Vector embeddings integrate different types of data that can generate more robust and contextually relevant AI models. A common use of augmentation is the combination of image and text embeddings. These are primarily used in chatbots and content creation tools as they engage with multimedia content that requires enhanced creativity. 


Music composition 

Musical notes and patterns are represented by vector embeddings that the models can use to create new melodies. The audio embeddings allow the numerical representation of the acoustic features of any instrument for differentiation in the music composition process. 

Some commonly used audio embeddings include: 

  • MFCCs 
    It stands for Mel Frequency Cepstral Coefficients. It creates vector embeddings using the calculation of spectral features of an audio. It uses these embeddings to represent the sound content.
  • CRNNs 
    These are Convolutional Recurrent Neural Networks. As the name suggests, they deal with the convolutional and recurrent layers of neural networks. CRNNs allow the integration of the two layers to focus on spectral features and contextual sequencing of the audio representations produced. 


Natural language processing (NLP) 


word embeddig
NLP integrates word embeddings with sentiment to produce more coherent results – Source: mdpi.com


NLP uses vector embeddings in language models to generate coherent and contextual text. The embeddings are also capable of. Detecting the underlying sentiment of words and phrases and ensuring the final output is representative of it. They can capture the semantic meaning of words and their relationship within a language. 

Some common text embeddings used in NLP include: 

  • Word2Vec
    It represents words as a dense vector representation that trains a neural network to capture the semantic relationship of words. Using the distributional hypothesis enables the network to predict words in a context.
  • GloVe 
    It stands for Global Vectors for Word Representation. It integrates global and local contextual information to improve NLP tasks. It particularly assists in sentiment analysis and machine translation.
  • BERT 
    It means Bidirectional Encoder Representations from Transformers. They are used to pre-train transformer models to predict words in sentences. It is used to create context-rich embeddings. 


Video game development 

Another important use of vector embeddings is in video game development. Generative AI uses embeddings to create game environments, characters, and other assets. These embedded vectors also help ensure that the various elements are linked to the game’s theme and context. 


Learn to build LLM applications


Challenges and considerations in vector embeddings for generative AI 

Vector embeddings are crucial in improving the capabilities of generative AI. However, it is important to understand the challenges associated with their use and relevant considerations to minimize the difficulties. Here are some of the major challenges and considerations: 

  • Data quality and quantity
    The quality and quantity of data used to learn the vector embeddings and train models determine the performance of generative AI. Missing or incomplete data can negatively impact the trained models and final outputs.
    It is crucial to carefully preprocess the data for any outliers or missing information to ensure the embedded vectors are learned efficiently. Moreover, the dataset must represent various scenarios to provide comprehensive results.
  • Ethical concerns and data biases 
    Since vector embeddings encode the available information, any biases in training data are included and represented in the generative models, producing unfair results that can lead to ethical issues.
    It is essential to be careful in data collection and model training processes. The use of fairness-aware embeddings can remove data bias. Regular audits of model outputs can also ensure fair results.
  • Computation-intensive processing 
    Model training with vector embeddings can be a computation-intensive process. The computational demand is particularly high for large or high-dimensional embeddings. Hence. It is important to consider the available resources and use distributed training techniques to fast processing. 


Future of vector embeddings in generative AI 

In the coming future, the link between vector embeddings and generative AI is expected to strengthen. The reliance on three-dimensional data representations can cater to the growing complexity of generative AI. As AI technology progresses, efficient data representations through vector embeddings will also become necessary for smooth operation. 

Moreover, vector embeddings offer improved interpretability of information by integrating human-readable data with computational algorithms. The features of these embeddings offer enhanced visualization that ensures a better understanding of complex information and relationships in data, enhancing representation, processing, and analysis. 



Hence, the future of generative AI puts vector embeddings at the center of its progress and development. 

Fiza Author image
Fiza Fatima
| January 18

Historically, technological revolutions have significantly affected jobs, often eliminating certain roles while creating new ones in unpredictable areas.

This pattern has been observed for centuries, from the introduction of the horse collar in Europe, through the Industrial Revolution, and up to the current digital age.

With each technological advance, fears arise about job losses, but history suggests that technology is, in the long run, a net creator of jobs.

The agricultural revolution, for example, led to a decline in farming jobs but gave rise to an increase in manufacturing roles.

Similarly, the rise of the automobile industry in the early 20th century led to the creation of multiple supplementary industries, such as filling stations and automobile repair, despite eliminating jobs in the horse-carriage industry.

The introduction of personal computers and the internet also followed a similar pattern, with an estimated net gain of 15.8 million jobs in the U.S. over the last few decades.

Now, with generative AI and robots with us, we are entering the fourth industrial revolution. Here are some stats to show you the seriousness of the situation:

  1. Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across 63 use cases analyzed. Read more
  2. Current generative AI technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today, which is a significant increase from the previous estimate that technology has the potential to automate half of the time employees spend working.

This bang of generative AI’s impact will be heard in almost all of the industries globally, with the biggest impact seen in banking, high-tech, and life sciences.

This means that lots of people will be losing jobs. We can see companies laying off jobs already. Read more

But what’s more concerning is the fact that different communities will face this impact differently.

How will generative AI affect the jobs of the black communities

Regarding the annual wealth generation from generative AI, it’s estimated to produce around $7 trillion worldwide, with nearly $2 trillion of that projected to benefit the United States.

US household wealth captures about 30 percent of US GDP, suggesting the United States could gain nearly $500 billion in household wealth from gen AI value creation. This would translate to an average of $3,400 in new wealth for each of the projected 143.4 million US households in 2045.

However, black Americans capture only about 38 cents of every dollar of new household wealth despite representing 13 percent of the US population. If this trend continues, by 2045, the racially disparate distribution of new wealth created by generative AI could increase the wealth gap between black and White households by $43 billion annually.

Generative AI impact on black communities
Source: McKinsey and Company


Generative AI revolutionizing jobs for success

Higher employment of black community in high mobility jobs

Mobility jobs are those that provide livable wages and the potential for upward career development over time without requiring a four-year college degree.

They have two tiers including target jobs and gateway jobs.

  1. Gateway jobs are positions that do not require a four-year college degree and are based on experience. They offer a salary of more than $42,000 per year and can unlock a trajectory for career upward mobility.An example of a gateway job could be a role in customer support, where an individual has significant experience in client interaction and problem-solving.
  2. Target jobs represent the next level up for people without degrees. These are attractive occupations in terms of risk and income, offering generally higher annual salaries and stable positions.An example of a target job might be a production supervision role, where a worker oversees manufacturing processes and manages a team on the production floor.

Generative AI may significantly affect these occupations, as many of the tasks associated with them—including customer support, production supervision, and office support—are precisely what generative AI can do well.

For black workers, this is particularly relevant. Seventy-four percent of black workers do not have college degrees, yet in the past five years, one in every eight has moved to a gateway or target job.

However, gen AI may be able to perform about half of these gateway or target jobs that many workers without degrees have pursued between 2030 and 2060. This could close a pathway to upward mobility that many black workers have relied on.

Generative AI - high mobility jobs
Source: McKinsey and Company

Furthermore, coding bootcamps and training, which have risen in popularity and have unlocked access to high-paying jobs for many workers without college degrees, are also at risk of disruption as gen AI–enabled programming has the potential to automate many entry-level coding positions.

These shifts could potentially widen the racial wealth gap and increase inequality if not managed thoughtfully and proactively.

Therefore, it is crucial for initiatives to be put in place to support black workers through this transition, such as reskilling programs and the development of “future-proof skills”.

These skills include socioemotional abilities, physical presence skills, and the ability to engage in nuanced problem-solving in specific contexts. Focusing efforts on developing non-automatable skills will better position black workers for the rapid changes that gen AI will bring.

Large language model bootcamp

How can generative AI be utilized to close the racial wealth gap in the United States?


Despite all the foreseeable downsides of Generative AI, it has the potential to close the racial wealth gap in the United States by leveraging its capabilities across various sectors that influence economic mobility for black communities.

In healthcare, generative AI can improve access to care and outcomes for black Americans, addressing issues such as preterm births and enabling providers to identify risk factors earlier.

In financial inclusion, gen AI can enhance access to banking services, helping black consumers connect with traditional banking and save on fees associated with nonbank financial services.

Additionally,  AI can be applied to the eight pillars of black economic mobility, including credit and ecosystem development for small businesses, health, workforce and jobs, pre–K–12 education, the digital divide, affordable housing, and public infrastructure.

Thoughtful application of gen AI can generate personalized financial plans and marketing, support the creation of long-term financial plans, and enhance compliance monitoring to ensure equitable access to financial products.

However, to truly close the racial wealth gap, generative AI must be deployed with an equity lens. This involves reskilling workers, ensuring that AI is used in contexts where it can make fair decisions, and establishing guardrails to protect black and marginalized communities from potential negative impacts of the technology.

Democratized access to generative AI and the cultivation of diverse tech talent is also critical to ensure that the benefits of gen AI are equitably distributed.


Embracing the Future: Ensuring Equity in the Generative AI Era


In conclusion, the advent of generative AI presents a complex and multifaceted challenge, particularly for the black community.

While it offers immense potential for economic growth and innovation, it also poses a significant risk of exacerbating existing inequalities and widening the racial wealth gap. To harness the benefits of this technological revolution while mitigating its risks, it is crucial to implement inclusive strategies.

These should focus on reskilling programs, equitable access to technology, and the development of non-automatable skills. By doing so, we can ensure that generative AI becomes a tool for promoting economic mobility and reducing disparities, rather than an instrument that deepens them.

The future of work in the era of generative AI demands not only technological advancement but also a commitment to social justice and equality.

Data Science Dojo
Ayesha Saleem
| January 13

In the rapidly evolving landscape of technology, small businesses are continually looking for tools that can give them a competitive edge. One such tool that has garnered significant attention is ChatGPT Team by OpenAI.

Designed to cater to small and medium-sized businesses (SMBs), ChatGPT Team offers a range of functionalities that can transform various aspects of business operations. Here are three compelling reasons why your small business should consider signing up for ChatGPT Team, along with real-world use cases and the value it adds.


Read more about how to boost your business with ChatGPT


They promise not to use your business data for training purposes, which is a big plus for privacy. You also get to work together on custom GPT projects and have a handy admin panel to keep everything organized. On top of that, you get access to some pretty advanced tools like DALL·E, Browsing, and GPT-4, all with a generous 32k context window to work with.

The best part? It’s only $25 for each person in your team. Considering it’s like having an extra helping hand for each employee, that’s a pretty sweet deal!


Large language model bootcamp


The official announcement explains:

“Integrating AI into everyday organizational workflows can make your team more productive.

In a recent study by the Harvard Business School, employees at Boston Consulting Group who were given access to GPT-4 reported completing tasks 25% faster and achieved a 40% higher quality in their work as compared to their peers who did not have access.”

Learn more about ChatGPT team

Features of ChatGPT Team

ChatGPT Team, a recent offering from OpenAI, is specifically tailored for small and medium-sized team collaborations. Here’s a detailed look at its features:

  1. Advanced AI Models Access: ChatGPT Team provides access to OpenAI’s advanced models like GPT-4 and DALL·E 3, ensuring state-of-the-art AI capabilities for various tasks.
  2. Dedicated Workspace for Collaboration: It offers a dedicated workspace for up to 149 team members, facilitating seamless collaboration on AI-related tasks.
  3. Administration Tools: The subscription includes administrative tools for team management, allowing for efficient control and organization of team activities.
  4. Advanced Data Analysis Tools: ChatGPT Team includes tools for advanced data analysis, aiding in processing and interpreting large volumes of data effectively.
  5. Enhanced Context Window: The service features a 32K context window for conversations, providing a broader range of data for AI to reference and work with, leading to more coherent and extensive interactions.
  6. Affordability for SMEs: Aimed at small and medium enterprises, the plan offers an affordable subscription model, making it accessible for smaller teams with budget constraints.
  7. Collaboration on Threads & Prompts: Team members can collaborate on threads and prompts, enhancing the ideation and creative process.
  8. Usage-Based Charging: Teams are charged based on usage, which can be a cost-effective approach for businesses that have fluctuating AI usage needs.
  9. Public Sharing of Conversations: There is an option to publicly share ChatGPT conversations, which can be beneficial for transparency or marketing purposes.
  10. Similar Features to ChatGPT Enterprise: Despite being targeted at smaller teams, ChatGPT Team still retains many features found in the more expansive ChatGPT Enterprise version.

These features collectively make ChatGPT Team an adaptable and powerful tool for small to medium-sized teams, enhancing their AI capabilities while providing a platform for efficient collaboration.


Learn to build LLM applications



Enhanced Customer Service and Support

One of the most immediate benefits of ChatGPT Team is its ability to revolutionize customer service. By leveraging AI-driven chatbots, small businesses can provide instant, 24/7 support to their customers. This not only improves customer satisfaction but also frees up human resources to focus on more complex tasks.


Real Use Case:

A retail company implemented ChatGPT Team to manage their customer inquiries. The AI chatbot efficiently handled common questions about product availability, shipping, and returns. This led to a 40% reduction in customer wait times and a significant increase in customer satisfaction scores.


Value for Small Businesses:

  • Reduces response times for customer inquiries.
  • Frees up human customer service agents to handle more complex issues.
  • Provides round-the-clock support without additional staffing costs.

Streamlining Content Creation and Digital Marketing

In the digital age, content is king. ChatGPT Team can assist small businesses in generating creative and engaging content for their digital marketing campaigns. From blog posts to social media updates, the tool can help generate ideas, create drafts, and even suggest SEO-friendly keywords.

Real Use Case:

A boutique marketing agency used ChatGPT Team to generate content ideas and draft blog posts for their clients. This not only improved the efficiency of their content creation process but also enhanced the quality of the content, resulting in better engagement rates for their clients.

Value for Small Businesses:

  • Accelerates the content creation process.
  • Helps in generating creative and relevant content ideas.
  • Assists in SEO optimization to improve online visibility.

Automation of Repetitive Tasks and Data Analysis

Small businesses often struggle with the resource-intensive nature of repetitive tasks and data analysis. ChatGPT Team can automate these processes, enabling businesses to focus on strategic growth and innovation. This includes tasks like data entry, scheduling, and even analyzing customer feedback or market trends.

Real Use Case:

A small e-commerce store utilized ChatGPT Team to analyze customer feedback and market trends. This provided them with actionable insights, which they used to optimize their product offerings and marketing strategies. As a result, they saw a 30% increase in sales over six months.

Value for Small Businesses:

  • Automates time-consuming, repetitive tasks.
  • Provides valuable insights through data analysis.
  • Enables better decision-making and strategy development.


For small businesses looking to stay ahead in a competitive market, ChatGPT Team offers a range of solutions that enhance efficiency, creativity, and customer engagement. By embracing this AI-driven tool, small businesses can not only streamline their operations but also unlock new opportunities for growth and innovation.

Fiza Author image
Fiza Fatima
| January 11

The emergence of Large language models such as GPT-4 has been a transformative development in AI. These models have significantly advanced capabilities across various sectors, most notably in areas like content creation, code generation, and language translation, marking a new era in AI’s practical applications.

However, the deployment of these models is not without its challenges. LLMs demand extensive computational resources, consume a considerable amount of energy, and require substantial memory capacity.

These requirements can render LLMs impractical for certain applications, especially those with limited processing power or in environments where energy efficiency is a priority.

In response to these limitations, there has been a growing interest in the development of small language models (SLMs). These models are designed to be more compact and efficient, addressing the need for AI solutions that are viable in resource-constrained environments.

Let’s explore these models in greater detail and the rationale behind them.

What are small language models?

Small Language Models (SLMs) represent an intriguing segment of AI. Unlike their larger counterparts, GPT-4 and LlaMa 2, which boast billions, and sometimes trillions of parameters, SLMs operate on a much smaller scale, typically encompassing thousands to a few million parameters.

This relatively modest size translates into lower computational demands, making lesser-sized language models accessible and feasible for organizations or researchers who might not have the resources to handle the more substantial computational load required by larger models. Read more


Benefits of Small Language Models SLMs


However, since the race behind AI has taken its pace, companies have been engaged in a cut-throat competition of who’s going to make the bigger language model. Because bigger language models translated to be the better language models.

Given this, how do SLMs fit into this equation, let alone outperform large language models?

How can small language models function well with fewer parameters?


There are several reasons why lesser-sized language models fit into the equation of language models.

The answer lies in the training methods. Different techniques like transfer learning allow smaller models to leverage pre-existing knowledge, making them more adaptable and efficient for specific tasks. For instance, distilling knowledge from LLMs into SLMs can result in models that perform similarly but require a fraction of the computational resources.

Secondly, compact models can be more domain-specific. By training them on specific datasets, these models can be tailored to handle specific tasks or cater to particular industries, making them more effective in certain scenarios.

For example, a healthcare-specific SLM might outperform a general-purpose LLM in understanding medical terminology and making accurate diagnoses.

Despite these advantages, it’s essential to remember that the effectiveness of an SLM largely depends on its training and fine-tuning process, as well as the specific task it’s designed to handle. Thus, while lesser-sized language models can outperform LLMs in certain scenarios, they may not always be the best choice for every application.

Collaborative advancements in small language models


Hugging Face, along with other organizations, is playing a pivotal role in advancing the development and deployment of SLMs. The company has created a platform known as Transformers, which offers a range of pre-trained SLMs and tools for fine-tuning and deploying these models. This platform serves as a hub for researchers and developers, enabling collaboration and knowledge sharing. It expedites the advancement of lesser-sized language models by providing necessary tools and resources, thereby fostering innovation in this field.

Similarly, Google has contributed to the progress of lesser-sized language models by creating TensorFlow, a platform that provides extensive resources and tools for the development and deployment of these models. Both Hugging Face’s Transformers and Google’s TensorFlow facilitate the ongoing improvements in SLMs, thereby catalyzing their adoption and versatility in various applications.

Moreover, smaller teams and independent developers are also contributing to the progress of lesser-sized language models. For example, “TinyLlama” is a small, efficient open-source language model developed by a team of developers, and despite its size, it outperforms similar models in various tasks. The model’s code and checkpoints are available on GitHub, enabling the wider AI community to learn from, improve upon, and incorporate this model into their projects.

These collaborative efforts within the AI community not only enhance the effectiveness of SLMs but also greatly contribute to the overall progress in the field of AI.

Phi-2: Microsoft’s small language model with 2.7 billion parameters

What are the potential implications of SLMs in our personal lives?

Potential Applications of SLMs in Technology and Services

Small Language Models have the potential to significantly enhance various facets of our personal lives, from smartphones to home automation. Here’s an expanded look at the areas where they could be integrated:


1.       Smartphones:

SLMs are well-suited for the limited hardware of smartphones, supporting on-device processing that quickens response times, enhances privacy and security, and aligns with the trend of edge computing in mobile technology.

This integration paves the way for advanced personal assistants capable of understanding complex tasks and providing personalized interactions based on user habits and preferences.

Additionally, SLMs in smartphones could lead to more sophisticated, cloud-independent applications, improved energy efficiency, and enhanced data privacy.

They also hold the potential to make technology more accessible, particularly for individuals with disabilities, through features like real-time language translation and improved voice recognition.

The deployment of lesser-sized language models in mobile technology could significantly impact various industries, leading to more intuitive, efficient, and user-focused applications and services.

2.       Smart Home Devices:


Voice-Activated Controls: SLMs can be embedded in smart home devices like thermostats, lights, and security systems for voice-activated control, making home automation more intuitive and user-friendly.

Personalized Settings: They can learn individual preferences for things like temperature and lighting, adjusting settings automatically for different times of day or specific occasions.

3.       Wearable Technology:


Health Monitoring: In devices like smartwatches or fitness trackers, lesser-sized language models can provide personalized health tips and reminders based on the user’s activity levels, sleep patterns, and health data.

Real-Time Translation: Wearables equipped with SLMs could offer real-time translation services, making international travel and communication more accessible.

4.       Automotive Systems:


Enhanced Navigation and Assistance: In cars, lesser-sized language models can offer advanced navigation assistance, integrating real-time traffic updates, and suggesting optimal routes.

Voice Commands: They can enhance the functionality of in-car voice command systems, allowing drivers to control music, make calls, or send messages without taking their hands off the wheel.

5.       Educational Tools:


Personalized Learning: Educational apps powered by SLMs can adapt to individual learning styles and paces, providing personalized guidance and support to students.

Language Learning: They can be particularly effective in language learning applications, offering interactive and conversational practice.

6.       Entertainment Systems:


Smart TVs and Gaming Consoles: SLMs can be used in smart TVs and gaming consoles for voice-controlled operation and personalized content recommendations based on viewing or gaming history.

The integration of lesser-sized language models across these domains, including smartphones, promises not only convenience and efficiency but also a more personalized and accessible experience in our daily interactions with technology. As these models continue to evolve, their potential applications in enhancing personal life are vast and ever-growing.

Do SLMs pose any challenges?

Small Language Models do present several challenges despite their promising capabilities

  1. Limited Context Comprehension: Due to the lower number of parameters, SLMs may have less accurate and nuanced responses compared to larger models, especially in complex or ambiguous situations.
  2. Need for Specific Training Data: The effectiveness of these models heavily relies on the quality and relevance of their training data. Optimizing these models for specific tasks or applications requires expertise and can be complex.
  3. Local CPU Implementation Challenges: Running a compact language model on local CPUs involves considerations like optimizing memory usage and scaling options. Regular saving of checkpoints during training is necessary to prevent data loss.
  4. Understanding Model Limitations: Predicting the performance and potential applications of lesser-sized language models can be challenging, especially in extrapolating findings from smaller models to their larger counterparts.

Embracing the future with small language models

The journey through the landscape of SLMs underscores a pivotal shift in the field of artificial intelligence. As we have explored, lesser-sized language models emerge as a critical innovation, addressing the need for more tailored, efficient, and sustainable AI solutions. Their ability to provide domain-specific expertise, coupled with reduced computational demands, opens up new frontiers in various industries, from healthcare and finance to transportation and customer service.

The rise of platforms like Hugging Face’s Transformers and Google’s TensorFlow has democratized access to these powerful tools, enabling even smaller teams and independent developers to make significant contributions. The case of “Tiny Llama” exemplifies how a compact, open-source language model can punch above its weight, challenging the notion that bigger always means better.

As the AI community continues to collaborate and innovate, the future of lesser-sized language models is bright and promising. Their versatility and adaptability make them well-suited to a world where efficiency and specificity are increasingly valued. However, it’s crucial to navigate their limitations wisely, acknowledging the challenges in training, deployment, and context comprehension.

In conclusion, compact language models stand not just as a testament to human ingenuity in AI development but also as a beacon guiding us toward a more efficient, specialized, and sustainable future in artificial intelligence.

Fiza Author image
Fiza Fatima
| January 9

Have you ever wondered what it would be like if computers could see the world just like we do? Think about it – a machine that can look at a photo and understand everything in it, just like you would.

This isn’t science fiction anymore; it’s what’s happening right now with Large Vision Models (LVMs).

Large vision models are a type of AI technology that deal with visual data like images and videos. Essentially, they are like big digital brains that can understand and create visuals.

They are trained on extensive datasets of images and videos, enabling them to recognize patterns, objects, and scenes within visual content.

LVMs can perform a variety of tasks such as image classification, object detection, image generation, and even complex image editing, by understanding and manipulating visual elements in a way that mimics human visual perception.

How large vision models differ from large language models

Large Vision Models and Large Language Models both handle large data volumes but differ in their data types. LLMs process text data from the internet, helping them understand and generate text, and even translate languages.

In contrast, LVMs focus on visual data, working to comprehend and create images and videos. However, they face a challenge: the visual data in practical applications, like medical or industrial images, often differs significantly from general internet imagery.

Internet-based visuals tend to be diverse but not necessarily representative of specialized fields. For example, the type of images used in medical diagnostics, such as MRI scans or X-rays, are vastly different from everyday photographs shared online.

Similarly, visuals in industrial settings, like manufacturing or quality control, involve specific elements that general internet images do not cover.

This discrepancy necessitates “domain specificity” in large vision models, meaning they need tailored training to effectively handle specific types of visual data relevant to particular industries.

Importance of domain-specific large vision models

Domain specificity refers to tailoring an LVM to interact effectively with a particular set of images unique to a specific application domain.

For instance, images used in healthcare, manufacturing, or any industry-specific applications might not resemble those found on the Internet.

Accordingly, an LVM trained with general Internet images may struggle to identify relevant features in these industry-specific images.

By making these models domain-specific, they can be better adapted to handle these unique visual tasks, offering more accurate performance when dealing with images different from those usually found on the internet.

For instance, a domain-specific LVM trained in medical imaging would have a better understanding of anatomical structures and be more adept at identifying abnormalities than a generic model trained in standard internet images.

This specialization is crucial for applications where precision is paramount, such as in detecting early signs of diseases or in the intricate inspection processes in manufacturing.

In contrast, LLMs are not concerned with domain-specificity as much, as internet text tends to cover a vast array of domains making them less dependent on industry-specific training data.

Performance of domain-specific LVMs compared with generic LVMs

Comparing the performance of domain-specific Large Vision Models and generic LVMs reveals a significant edge for the former in identifying relevant features in specific domain images.

In several experiments conducted by experts from Landing AI, domain-specific LVMs – adapted to specific domains like pathology or semiconductor wafer inspection – significantly outperformed generic LVMs in finding relevant features in images of these domains.

Large Vision Models
Source: DeepLearning.AI

Domain-specific LVMs were created with around 100,000 unlabeled images from the specific domain, corroborating the idea that larger, more specialized datasets would lead to even better models.

Additionally, when used alongside a small labeled dataset to tackle a supervised learning task, a domain-specific LVM requires significantly less labeled data (around 10% to 30% as much) to achieve performance comparable to using a generic LVM.

Training methods for LVMs

The training methods being explored for domain-specific Large Vision Models involve, primarily, the use of extensive and diverse domain-specific image datasets.

There is also an increasing interest in using methods developed for Large Language Models and applying them within the visual domain, as with the sequential modeling approach introduced for learning an LVM without linguistic data.

Sequential Modeling Approach for Training LVMs


This approach adapts the way LLMs process sequences of text to the way LVMs handle visual data. Here’s a simplified explanation:

Large Vision Models - LVMs - Sequential Modeling
Sequential Modeling Approach for Training LVMs

This approach adapts the way LLMs process sequences of text to the way LVMs handle visual data. Here’s a simplified explanation:

  1. Breaking Down Images into Sequences: Just like sentences in a text are made up of a sequence of words, images can also be broken down into a sequence of smaller, meaningful pieces. These pieces could be patches of the image or specific features within the image.
  2. Using a Visual Tokenizer: To convert the image into a sequence, a process called ‘visual tokenization’ is used. This is similar to how words are tokenized in text. The image is divided into several tokens, each representing a part of the image.
  3. Training the Model: Once the images are converted into sequences of tokens, the LVM is trained using these sequences.
    The training process involves the model learning to predict parts of the image, similar to how an LLM learns to predict the next word in a sentence. This is usually done using a type of neural network known as a transformer, which is effective at handling sequences.
  4. Learning from Context: Just like LLMs learn the context of words in a sentence, LVMs learn the context of different parts of an image. This helps the model understand how different parts of an image relate to each other, improving its ability to recognize patterns and details.
  5. Applications: This approach can enhance an LVM’s ability to perform tasks like image classification, object detection, and even image generation, as it gets better at understanding and predicting visual elements and their relationships.

The emerging vision of large vision models

Large Vision Models are advanced AI systems designed to process and understand visual data, such as images and videos. Unlike Large Language Models that deal with text, LVMs are adept at visual tasks like image classification, object detection, and image generation.

A key aspect of LVMs is domain specificity, where they are tailored to recognize and interpret images specific to certain fields, such as medical diagnostics or manufacturing. This specialization allows for more accurate performance compared to generic image processing.

LVMs are trained using innovative methods, including the Sequential Modeling Approach, which enhances their ability to understand the context within images.

As LVMs continue to evolve, they’re set to transform various industries, bridging the gap between human and machine visual perception.

Data Science Dojo
Amna Zafar
| January 4
(LLMs) and generative AI are revolutionizing the finance industry by bringing advanced Natural Language Processing (NLP) capabilities to various financial tasks. They are trained on vast amounts of data and can be fine-tuned to understand and generate industry-specific content.

In finance, LLMs contribute by automating mundane tasks, improving efficiency, and aiding decision-making processes. These models can analyze bank data, interpret complex financial regulations, and even generate reports or summaries from large datasets.

They offer the promise of cutting coding time by as much as fifty percent, which is a boon for developing financial software solutions. Furthermore, LLMs are aiding in creating more personalized customer experiences and providing more accurate financial advice, which is particularly important in an industry that thrives on trust and personalized service.

As the financial sector continues to integrate AI, LLMs stand out as a transformative force, driving innovation, efficiency, and improved service delivery.

Generative AI’s impact on tax and accounting 

Finance, tax, and accounting have always been fields where accuracy and compliance are non-negotiable. In recent times, however, these industries have been witnessing a remarkable transformation thanks to the emergence of generative AI, and I couldn’t be more excited to share this news. 

Leading the charge are the “Big Four” accounting firms. PwC, for instance, is investing $1 billion to ramp up its AI capabilities, while Deloitte has taken the leap by establishing an AI research center. Their goal? To seamlessly integrate AI into their services and support clients’ evolving needs.

But what does generative AI bring to the table? Well, it’s not just about automating everyday tasks; it’s about redefining how the industry operates. With regulations becoming increasingly stringent, AI is stepping up to ensure that transparency, accurate financial reporting, and industry-specific compliance are met. 


Read more about large language models in finance industry


The role of generative AI in accounting innovation

One of the most remarkable aspects of generative AI is its ability to create synthetic data. Imagine dealing with situations where data is scarce or highly confidential. It’s like having an expert at your disposal who can generate authentic financial statements, invoices, and expense reports. However, with great power comes great responsibility.

While some generative AI tools, like ChatGPT, are accessible to the public, it’s imperative to approach their integration with caution. Strong data governance and ethical considerations are crucial to ensuring data integrity, eliminating biases, and adhering to data protection regulations. 



In this verge, the finance and accounting world also faces a workforce challenge. Deloitte reports that 82% of hiring managers in finance and accounting departments are struggling to retain their talented professionals. But AI is riding to the rescue. Automation is streamlining tedious, repetitive tasks, freeing up professionals to focus on strategic endeavors like financial analysis, forecasting, and decision-making.