Amazon has alluded to additional layoffs in the upcoming years in a recent letter to its staff. In a recent letter to his staff, Andy Jassy, the CEO of Amazon, outlined a clear vision for the company’s future.
According to the letter, there will be significant changes in the workforce as a result of the increased emphasis on artificial intelligence (AI), including possible cutbacks in corporate employment responsibilities.
Jassy underlined how AI is used throughout Amazon’s extensive operations, pointing to its use in Alexa, shopping features, and internal operations. He described generative AI as a “once-in-a-lifetime” technical development that may open up new opportunities for businesses and consumers alike.
Underlining the Potential of Generative AI
While showcasing the amazing capabilities of generative AI, Jassy pointed out that such technologies are uncommon; they only come along once in a lifetime and fundamentally alter the possibilities for consumers and companies.
As a result, Amazon is making significant investments, and its progress is clear. It is evident in the way that Amazon is introducing Alexa+, its next-generation Alexa personal assistant, which is significantly smarter and more capable and the first to be able to take important actions for users in addition to intelligently responding to almost any query.
He went on to say that tens of millions of people worldwide use Amazon’s AI shopping assistant to find new products and make better-educated decisions about what to buy.
Jassy continued by concentrating on how Amazon is using generative AI to improve the efficiency of its internal processes. He added that the business is utilising generative AI extensively in all aspects of its internal operations.
Amazon is utilising artificial intelligence (AI) in its fulfilment network to enhance demand forecasting, inventory positioning, and robot efficiency, all of which have increased delivery speed and cost to serve.
With the help of GenAI, it has redesigned its customer service chatbot, offering an even better experience than before. Additionally, it is using GenAI to create product description pages that are more intelligent and captivating.
Infusion of More AI Means Leaner Workforce-Jassy
Jassy made hints about how the workforce will be impacted by the use of AI. He mentioned that Amazon will require fewer individuals to perform certain tasks than it currently does, while a greater number of individuals will be required to perform other categories of jobs.
Although it’s difficult to predict exactly where this will end up, the company anticipates that, as it improves efficiency from implementing AI widely throughout the organisation, it will shrink its overall headcount in the coming years.
As the company undergoes this transition, Jassy advised his staff to be curious about artificial intelligence (AI), educate themselves, attend workshops and trainings, use and experiment with AI whenever possible, take part in team brainstorming sessions.
All these steps are necessary to determine how to innovate for Amazon’s customers more rapidly and extensively, and work more efficiently with more resilient teams.
This article has been attributed to Aditya Pandranki, Founder and CEO, DOQFY
Increased use of AI to streamline automation is driving a significant transformation in contract lifecycle management (CLM).What earlier used to be a manual, labour intensive and error prone domain is now being replace by a more advanced technology.
Research indicates that almost 70% of companies have trouble effectively managing contracts, but only 5% have automated their procedures. This disparity leads to ineffective manual approvals, dependence on several middlemen, and increased non-compliance or legal risks. As a result, CEOs around the world are realising how crucial it is to use AI to streamline contract processes in order to cut processing and review times by as much as 50%.
AI Redefining the Function of Legal Teams
AI has the potential to enhance legal professionals’ skills, not replace them, despite concerns to the contrary. AI gives frees times for Legal professionals to concentrate on more strategic and high-stakes negotiation while AI itself can manage the monotonous and mechanical parts of contract management. As businesses grow and volume of work increases, so does contract volumes, in such a scenario AI is very helpful as it doesn’t get tired and is capable of performing tasks round the clock.
AI assistants can significantly cut down on review time without sacrificing quality in high-volume settings where the average review time per contract can reach 92 minutes. Top Indian companies have started integrating generative AI into their contract management systems to accelerate the contract processes, and ensure that it is complaint with latest laws. This hybrid model of humans working with AI for increased productivity, is quickly becoming the new norm.
Converting Knowledge into Strategic Business Intelligence
AI can convert insights into business intelligence, this is a game changer for businesses looking to put the data into productive use. Now, organisations can keep track of contract renewal dates, which clauses cause disputes, who approved each version, and why some contracts are constantly delayed. Such a technology can also help in general business plans like pricing negotiations, vendor selection, and customer engagement models.
The Function of AI in Contract Management
From contract drafting to performance monitoring, artificial intelligence plays a part at every stage of the contract lifecycle. Automated error detection and notification guarantee that the produced drafts comply with business guidelines and industry standards.
AI tools act as intelligent assistants when examining contracts, they point out any legal red flags, inconsistencies, or risk factors. It can also identify problems like indemnity clauses, inconsistencies in the governing laws, or odd payment terms.
Contract Summaries
AI is also very helpful in contract summaries. Non-legal stakeholders, like finance, HR or operations executives, can now read complicated contracts with ease as AI systems are able to provide customised summaries according to the technical expertise and requirements of the stakeholder.
AI generates contract summaries using a blend of NLP, clause classification, and custom rule engines. The system combines extractive and abstractive summarisation to deliver clear, role-based insights, while flagging deviations from approved standards. Summaries are configurable by business needs, ensuring both legal and non-legal users get tailored, actionable information. Technically, this pipeline integrates OCR, clause segmentation, context-aware NLP models, and custom rule engines, all tuned to the appropriate regulatory requirements and compliance workflows.
Staying compliant and keeping track of clauses and legal updates in an increasingly dynamic regulatory environment is one of the biggest challenges in contract management. AI-powered CLM systems continuously check contracts against changing legal frameworks, and send notifications when terms need to be updated or renegotiated. The end product is a real-time compliance matrix that assists businesses in reducing their legal risk before it becomes more
AI tools reviewing contracts in the Indian regulatory landscape most commonly flag clauses related to termination, indemnity, jurisdiction, dispute resolution, and data protection.
Predictive Analysis
Strong predictive analytics is another benefit of advanced AI. AI models can point out contracts which are at risk of breach, identify common negotiation bottlenecks, or predict the need for renewal by examining past contract data. Decision-makers can now take proactive measures rather than reactive action.
Predictive analytics in contract management relies on a mix of supervised learning models, time-series forecasting, and NLP.
Productive Use of Contract Data by AI
Big businesses frequently handle thousands of contracts. AI makes these documents from mere static files to sources of intelligence. AI searches and compares contracts by leveraging natural language processing (NLP), clause classification, and semantic similarity models. The ability to extract structured insights from unstructured legal text helps legal teams monitor key performance indicators (KPIs) such as contract turnaround time, clause negotiation success rates, renewal cycles, and penalty triggers.
Indian enterprises typically handle large volumes of unstructured contract data annually. Large and mid-sized companies often process thousands to tens of thousands of contracts each year across various departments such as legal, HR, procurement, sales and compliance. In cities like Mumbai and Bengaluru, there is a rapid expansion of data centres and digital infrastructure which is also driving contract volumes related to IT services, cloud, and outsourcing. Large Indian enterprises manage around 5,000 to 20,000 contracts annually across various different departments, this shows the complexity and scale of their business operations and regulatory environment.
Conclusion
AI in legal operations in India is set to evolve rapidly from task automation to helping in strategy. Over the next few years, contract tools will also progress from doing simple contract drafting and clause tagging to making more advanced predictive analysis, offering insights on risk and litigation likelihood. With laws like the DPDP Act and increasing regulatory scrutiny from bodies like RBI and IRDAI, AI models will also become more compliance-aware and provide real-time updates.
Legal tech platforms will also consolidate into unified systems integrating contracts, litigation, compliance, and IP, offering intelligent dashboards for decision-making. Natural language interfaces and AI-powered legal assistants will democratise access, allowing business users to interact with legal systems via chat or voice.
This is a great time to be alive – a great time to learn and then, earn! So, in this piece – we shall talk about the free ones. The surge in AI tools like ChatGPT has sparked a growing interest in AI education, and luckily, there are plenty of free AI certification courses available. These top-notch programmes offer a fantastic opportunity to learn about generative AI, machine learning, and other cutting-edge technologies that are shaping our future. Let’s talk about some of the best AI courses for beginners and seasoned professionals alike.
Google’s AI for Everyone course offers a comprehensive introduction to artificial intelligence, cutting through the hype to deliver practical knowledge. These free AI certification programs are designed for individuals without a background in computer science or mathematics, making it accessible to anyone curious about AI and machine learning.
Key Topics Covered
The course delves into fundamental AI concepts, including neural networks, types of machine learning (supervised, unsupervised, and reinforcement), and real-world applications like recommender systems and computer vision. Participants gain hands-on experience with data, learning how to teach computers to recognise images and sounds.
Learning Outcomes
By the end of the course, learners will have a solid understanding of AI terminology, ethics, and fairness in AI applications. They’ll also gain insight into AI programming and its practical applications. This knowledge equips participants to engage in informed discussions about AI and machine learning in both personal and professional settings, providing a strong foundation for further exploration of these emerging technologies.
IBM AI Foundations for Everyone
IBM – Best Free AI Course with Certificate
This specialisation consists of three self-paced courses, each designed to be completed in about four weeks with 2-4 hours of study per week. The entire programme can be finished in 2-3 months at a relaxed pace or in just one month with more intensive study. The courses cover a range of topics, from AI basics to generative AI and chatbot creation, providing a comprehensive introduction to AI without requiring any coding skills.
Skills Gained
Participants will gain a solid understanding of AI fundamentals, including machine learning and neural networks. They’ll learn about generative AI, its real-world applications, and prompt engineering techniques. The course also teaches how to create AI-powered chatbots without programming, using IBM Watson’s Natural Language Processing capabilities. These skills are highly relevant in today’s job market, where AI literacy is becoming increasingly important across all industries.
Certification Details
Upon completing the specialisation, learners receive a verified digital credential from IBM, which can be shared on LinkedIn or added to a CV. This certification demonstrates expertise in AI fundamentals, generative AI applications, and chatbot development, providing a valuable asset for career advancement in the rapidly evolving field of artificial intelligence.
AWS Skill Builder offers a robust range of AI courses, catering to various skill levels and interests. From introductory courses on generative AI to advanced topics like building language models, there’s something for everyone. The platform provides free and low-cost options, making it accessible to learners worldwide.
Learning Resources
Learners can access a wealth of resources, including interactive labs, video tutorials, and hands-on experiences. These tools are designed to help individuals build practical skills in AI and machine learning. The courses cover topics such as prompt engineering, low-code machine learning, and using AI-powered tools like Amazon CodeWhisperer.
Industry Recognition
AWS certifications are highly regarded in the tech industry. The platform offers exam preparation materials for AI-related certifications, including the AWS Certified AI Practitioner and AWS Certified Machine Learning Engineer – Associate. These credentials demonstrate expertise in AI fundamentals and applications, providing a valuable asset for career advancement in the rapidly evolving corridors of AI as we call it.
DeepLearning.AI’s AI Specialisations
DeepLearning.AI – Best Free AI Course with Certificate
DeepLearning.AI offers a range of top AI courses for beginners and professionals alike. Their Deep Learning Specialisation, led by AI pioneer Andrew Ng, has attracted over 1 million learners. This comprehensive programme covers neural networks, machine learning, and practical AI applications. The Machine Learning Specialisation provides a foundational understanding of AI concepts through an intuitive visual approach.
Instructor Expertise
The courses are taught by industry experts, including Andrew Ng, who co-founded Google Brain and Coursera. Their instructors bring real-world experience and cutting-edge knowledge to the programmes, ensuring learners gain practical skills in AI tools and generative AI.
Practical Applications
DeepLearning.AI’s courses focus on hands-on learning, allowing students to build and train neural networks, implement machine learning algorithms, and develop AI applications. Learners gain experience with popular frameworks like TensorFlow and explore areas such as computer vision and natural language processing.
Harvard’s Introduction to AI with Python
Harvard – Best Free AI Certification Courses
This course builds on CS50’s foundation, requiring basic programming knowledge. Familiarity with Python is beneficial, as is a grasp of statistics and probability concepts. The course is designed to accommodate learners with varying levels of experience in AI and machine learning.
Curriculum Highlights
The curriculum delves into core AI concepts, including graph search algorithms, classification, optimisation, and reinforcement learning. Students explore cutting-edge topics like game-playing engines, handwriting recognition, and machine translation. The course emphasises practical applications, enabling learners to incorporate AI principles into their own Python programmes.
Project-Based Learning
Hands-on projects form a crucial part of the learning experience. Students gain practical skills by working with machine learning libraries and designing intelligent systems. This approach allows learners to apply theoretical knowledge to real-world problems, preparing them for careers in AI and data science.
This beginner-friendly programme offers a thorough exploration of Python AI programming. Learners gain hands-on experience with essential tools like NumPy, Pandas, Matplotlib, and PyTorch. The curriculum covers Python fundamentals, data structures, and object-oriented programming, providing a solid foundation for AI development.
Nanodegree Programme
The Nanodegree programme spans several months, offering in-depth learning through practical projects. Students apply their skills to real-world scenarios, such as creating image classifiers and working with neural networks. This approach ensures learners gain practical experience with AI tools and concepts, preparing them for careers in the field.
Career Support
Udacity provides robust career services to help students transition into AI careers. These include personalised project reviews, access to industry experts, and guidance on building a professional portfolio. The programme also offers insights into the latest AI trends, helping learners stay current in this ever-growing field.
Coursera’s AI for Everyone by Andrew Ng
Coursera – Best Free AI Certification Courses
This course aims to demystify AI for non-technical professionals, making it accessible to everyone. It provides a 360-degree overview of AI technologies, applications, and societal impact. Learners gain insights into AI’s potential and limitations, helping them identify opportunities within their own organisations.
Module Breakdown
The course covers AI fundamentals, machine learning concepts, and real-world applications. It explores topics such as data science workflows, AI project management, and the roles within AI teams. Learners also examine ethical considerations and potential pitfalls in AI implementation.
Real-World Examples
The course showcases practical Artificial Intelligence applications across various industries, including virtual assistants, self-driving cars, and healthcare. These examples illustrate how AI is transforming businesses and society, providing learners with a realistic view of AI’s capabilities and limitations.
Microsoft’s AI Fundamentals
Microsoft – Best Free AI Certification Courses
Microsoft‘s AI Fundamentals course offers a special introduction to artificial intelligence concepts and Azure AI services. The programme is designed for both technical and non-technical learners, making it one of the best AI courses for beginners. It combines instructor-led training with online materials on the Microsoft Learn platform, providing a blended learning experience.
Azure AI Services
The course covers a range of Azure AI services, including Azure AI Vision, Azure AI Face detection, and Azure AI Language. Learners gain hands-on experience with these tools, exploring their capabilities in computer vision, natural language processing, and generative AI. This practical approach helps students understand how AI tools can be applied to real-world scenarios.
Certification Exam
Upon completing the course, learners can take the AI-900 Microsoft Azure AI Fundamentals exam. This certification verifies knowledge of AI concepts and Azure services, covering topics such as machine learning, computer vision, and natural language processing. The exam consists of 40-60 questions and has a passing score of 700 out of 1000, making it an achievable goal for those new to AI.
Intel Edge AI Certification – Best Free AI Certification Courses
Intel offers a range of AI courses, including Network Transformation and AI Fundamentals. These programmes cover topics such as SDN, NFV, and network acceleration technologies. The AI Essentials course provides a foundation for AI discovery conversations, making it one of the best AI courses for beginners.
Industry Applications
Intel’s courses explore AI applications across various sectors, including healthcare, transportation, and retail. Learners gain insights into how AI tools and generative AI are transforming industries. The courses also cover Intel’s network technology solutions for cloud, network, and edge applications.
Hands-on Labs
Practical experience is a key component of Intel’s AI courses. Students work with tools like the Intel Distribution of OpenVINO toolkit, learning to deploy computer vision capabilities in edge applications. The courses also include exercises on using Windows Machine Learning and optimising deep learning inference on edge devices.
Stanford University
Stanford University – Best Free AI Certification Courses
The Stanford AI Graduate Certificate Program offers a structured learning experience with core and elective courses. Students start with a core course—either Artificial Intelligence: Principles and Techniques or Machine Learning—covering AI foundations, deep learning, and optimization. They can then choose electives like NLP with Deep Learning for chatbot and language models, Robotics for autonomous systems, Computer Vision for image recognition, and Deep Reinforcement Learning for AI-driven decision-making.
Real-World Examples
These courses have real-world applications in Google Translate, ChatGPT, Tesla’s self-driving cars, Boston Dynamics robots, facial recognition, medical imaging, and financial trading models. The program provides hands-on learning to apply AI in healthcare, finance, automation, and beyond.
End Note
The world of artificial intelligence is changing significantly, offering exciting opportunities for learning and growth. These free AI certification courses provide a solid foundation to understand and harness the power of AI technologies. So, there’s something for everyone looking to expand their knowledge in this field. These programmes not only equip learners with valuable skills but also have an influence on their career prospects in an increasingly AI-driven job market.
As you get onto your AI learning journey, remember that continuous learning is key in this field. To stay updated with the latest AI trends and insights, don’t forget to check out other AI-related articles at StartupTalky and follow the Instagram for quality content shared by the team. Finally, no matter who you are – a beginner or a seasoned professional, these courses offer a stepping stone to explore the fascinating world of artificial intelligence, helping you to stay ahead in an era where AI is causing a revolution in nearly every industry.
FAQ
How to get AI certified for free?
You can get AI certified for free by enrolling in online courses from platforms like :
IBM
AWS
DeepLearning.AI
Harvard
Udacity
Coursera
Microsoft
Intel’s
Stanford
Is a certificate in AI worth it?
Yes, a certificate in AI can be worth it as it validates your skills, enhances your resume, and opens up opportunities in AI-driven fields. It can also increase your credibility and competitiveness in the job market.
Can I learn AI in 3 months?
Yes, you can complete AI certification courses in 3 months, as many programs are designed for short-term learning. However, achieving proficiency in AI may require ongoing practice beyond the course duration.
What are free AI courses with certificate?
Here are some free AI courses with certificates:
AI For Everyone – Coursera (DeepLearning.AI)
Machine Learning Crash Course – Google
AI Fundamentals – IBM SkillsBuild
AI For Beginners – Microsoft
Generative AI Learning Path – Google Cloud
These courses cover AI basics, ML, NLP, and deep learning.
Jio Platforms Limited and Confluent, a data streaming business based in California, have teamed together to promote the growth of real-time and GenAI use cases in India. Jio Cloud Services will offer Confluent Cloud as part of the deal, enabling companies in India to begin using data streaming without any problems. As data streaming is essential to enabling real-time analytics and GenAI developments, this partnership is expected to strengthen India’s digital infrastructure, an official statement said. Data streaming is essential for companies to keep ahead of consumer trends, including advances in artificial intelligence, as Confluent is poised for rapid transformation in India, according to Kiran Thomas, president and CEO of Jio Platforms Limited.
How the Collaboration will Operate?
Confluent will handle all of the essential components of streaming services on Jio Cloud Services, including connecting, processing, controlling, and streaming data. Offering enterprise-grade security and governance to safely handle massive amounts of data, Confluent Platform will be made available as a managed service to Indian consumers and businesses, including the public sector.
Indian Prime Minister Narendra Modi recently urged international cooperation to create governance and standards that preserve common values at the first AI Action Summit, which was held in Paris. Given its diversity, India is creating its own large language model (LLM).
We are creating AI apps for the general public’s benefit. The PM asserted, “We have the largest pool of AI talent in the world.” Ashwini Vaishnaw, the union minister, recently announced that India intends to develop its own fundamental AI model over the next ten months, revealing the country’s attempt to create its own LLM.
Government Offering a Massive Support to Develop AI
Vaishnaw added that in the coming days, the government will provide entities nationwide with 18,000 top-notch GPU-based computing facilities for AI development, allowing the creation of the AI model. While the nation’s leader emphasised the advantages AI can offer humanity, the Indian Economic Survey 2024–25 emphasised the enormous threat AI poses to the nation’s labour market in terms of employment disruption. According to the Economic Survey, which cited studies from the International Monetary Fund, the International Labour Organisation, Goldman Sachs, and other organisations, AI-led automation may present difficulties for the Indian labour market and economy.
About Confluent
The data streaming platform Confluent is leading the way in a radically new class of data infrastructure that moves data. The core platform for data in motion is Confluent’s cloud-native solution, which is intended to act as the intelligent connective tissue that allows real-time data from many sources to continuously flow throughout the company. Confluent helps businesses achieve the new business imperative of moving to complex, real-time, software-driven backend operations while providing rich, digital front-end customer experiences.
Seven Indian businesses that are experts in generative artificial intelligence have been chosen to participate in the Global Generative AI Accelerator program offered by Amazon Web Services (AWS).
These seven Indian startups—Convrse, House of Models, Neural Garage, Orbo.ai, Phot.ai, Unscript AI, and Zocket—represent some of the most promising AI-driven enterprises out of the 80 companies that were selected from around the world about artificial intelligence. India’s batch highlights the nation’s expanding importance in the artificial intelligence business. Notably, it also has the most number of startups picked from any Asia-Pacific region.
Fostering and Providing Resources for Development
Startups that are chosen to participate in the accelerator programme will be eligible to receive up to one million dollars in Amazon Web Services credits. The startups will be able to design, train, test, and deploy their generative artificial intelligence solutions with the assistance of these credits by utilising tools like as Amazon SageMaker and Amazon Bedrock. In addition, Amazon Web Services (AWS) will make available access to additional services, such as computing, storage, and database technologies, as well as specialised artificial intelligence chips, such as AWS Trainium and AWS Inferentia2.
Through the use of AWS’s infrastructure and expertise, the programme will assist these entrepreneurs in scaling their artificial intelligence solutions on a worldwide scale.
According to Amitabh Nagpal, who is the Head of Startup Business Development at Amazon Web Services India, “Our commitment of $230 million and global expansion of the generative AI accelerator reflects our continued focus on supporting startups to develop, build, and scale their unique ideas using generative AI.”
AWS Continues to Support Generative AI Startups
Through initiatives like as AWS GenAI Loft, which is a collaborative display space that was recently hosted in Bangalore to stimulate innovation in artificial intelligence, Amazon Web Services (AWS) continues to provide assistance to generative artificial intelligence (AI) businesses in India. It provided visitors with a one-of-a-kind platform to gather insights and investigate real-world uses of generative artificial intelligence across a variety of companies.
As of the year 2024, PitchBook Data, Inc. reports that 1,813 artificial intelligence and machine learning firms have brought in capital in India, totalling over 82 billion dollars in investments. Nevertheless, only 35% of generative AI companies worldwide have offices in locations other than their headquarters country, indicating that there is still room for improvement in the support of entrepreneurs in achieving their growth aspirations.
According to Brendan Burke, Senior Analyst, Emerging Technology Research at PitchBook, Asia-Pacific-based generative artificial intelligence startups have raised more than $2.5 billion in 2024, which is more than had been raised in the three years prior combined. In order to train personalised models based on one-of-a-kind cultural and linguistic data, developers and businesses are making the most of the chance.
Furthermore, renowned researchers in the field of artificial intelligence are currently working on a wide variety of transformer models in multimodal domains such as synthetic voice, interactive media, and three-dimensional graphics. AI applications of the future will be dependent on specialist model architectures, and in order for startups to realise their vision, they will require cloud infrastructure of the highest possible quality.
Rufus, a new conversational shopping assistant driven by generative artificial intelligence, has been introduced to the Indian market by the eCommerce retail giant Amazon.
On the Amazon India mobile app, the assistant will initially be made accessible in beta mode to a restricted group of consumers. In the following weeks, it will be made available to a wider audience.
The purpose of Rufus is to improve the overall experience of shopping online by providing personalized product recommendations, shopping list assistance, comparisons of product categories, and insights gleaned from consumer evaluations.
Characteristics and Powers of Rufus
Rufus is well-versed in Amazon’s enormous product catalog and can draw information from various sources on the internet to provide answers to a wide variety of questions posed by customers.
Users can receive assistance from the AI assistant at a variety of points throughout their purchasing journey. As an illustration, it can be of use in conducting general research such as “things to consider when purchasing a washing machine” or in conducting particular product comparisons such as “Should I get a fitness band or a smartwatch?”
In addition, it provides recommendations that are geared to specific queries, such as “What are the best dinosaur toys for a child of five years old?” or “What are the best gaming laptops?”
Engagement With Users and Their Experiences
Customers can communicate with Rufus by using a chat conversation box that is situated in the lower right-hand corner of the Amazon mobile client.
After being engaged, Rufus gives users the ability to investigate questions that have been suggested, ask follow-up questions, and obtain thorough responses.
Additionally, the assistant can support particular product-related inquiries while perusing product detail pages. This makes it simpler for customers to obtain complete information without having to leave the shopping interface. Customers can dismiss Rufus and return to the conventional search results by swiping down the chat box if they require it.
Clicking on “What do customers say?” on a product description page provides a handy summary of consumer reviews. For example, when a customer is looking at a product’s information page, they can ask Rufus questions like “Is this jacket machine washable?” or “Is this cordless drill easy to hold?” and get instant replies. Using information from listings, reviews, and community Q&As, Rufus will produce replies.
Rufus helps consumers make smarter purchases by generating responses based on pertinent information from all around the web and Amazon.in. Generative AI is in its infancy, hence it may not always produce accurate results. To make Rufus more useful over time, Amazon’s tech team will refine responses and enhance its AI models. In addition to the more traditional “thumbs up” or “thumbs down” ratings, customers also can submit more detailed, free-form comments.
As part of its full-stack Generative AI (GenAI) platform, Sarvam AI, an AI startup based in Bengaluru, released a suite of products recently. These solutions cater to both enterprise usage and open source communities.
With the backing of investors like Lightspeed and Peak XV (formerly Sequoia India), the company said that its upcoming products will support 10 Indian languages—Hindi, Tamil, Telugu, Malayalam, Punjabi, Odia, Gujarati, Marathi, Kannada, and Bengali—and be voice-enabled to run a variety of jobs.
During an interview with a media outlet, Sarvam cofounder Vivek Raghavan said that the full-stack GenAI platform has been developed and deployed in collaboration with prominent industrial and technological partners. The product mix unveiled includes A1, Sarvam Models, Sarvam Agents, Shuka1.0, Sarvam 2B, and Sarvam Models.
Products and their usage
With their multilingual speech capabilities, the initial product, Sarvam Agents, will provide clients with the ability to communicate with agents through phone calls, WhatsApp, or in-app chat. Additionally, they will have the capability to act and make judgements in response to customer inputs. Businesses in industries such as banking, law, consumer products, telecommunications, media, and technology will be able to take advantage of the voice agents for as little as one rupee a minute.
Another offering is an open source large-language model (LLM) named Sarvam 2B. According to Sarvam, the LLM can efficiently carry out targeted tasks in ten Indian languages thanks to its training on an internal dataset consisting of four trillion tokens.
Meta, a digital giant, has an open source Llama 8B language model; their third product, Shuka1.0, will be an audio extension that supports Indian language usage. This product will also be available as open source.
Additionally, a product titled “Sarvam Models” will be made accessible, which contains the Indic language models utilised in the development of Sarvam Agents. Application programming interfaces (APIs) will now be made available for these models. As part of Sarvam’s developer API platform, developers will have access to models for document parsing, speech synthesis, translation, and speech recognition.
With tools like regulatory chat, document creation, redaction, and data extraction, the fifth product, ‘A1’, is a generative AI workbench made for solicitors to improve their skills.
Enhancing the growth with partnership
Yotta, Nvidia, Exotel, Microsoft Azure, and Google Cloud Platform (GCP) are some of the companies that Sarvam will team up with to power these offerings. During an event, Vishal Dhupar, Nvidia’s managing director for South Asia, said that the Sarvam stack will be powered by Nvidia’s DGX infrastructure.
The Beckn Foundation and the financial technology company Pine Labs will be Sarvam’s partners. Open Network for Digital Commerce (ONDC), a government-backed e-commerce network, is powered by the Beckn Protocol.
A rival to Sarvam, Krutrim AI, which has backing from Matrix, has recently launched a number of offerings, including GPU-as-a-service, cloud hosting for large language models, and access to other open source models. In May, Krutrim—which is part of the Ola group headed by Bhavish Aggarwal—also released consumer-facing smartphone applications for the Krutrim AI assistant.
This article has been contributed by Dr. Ravi Changle, Director, AI & Emerging Technologies, Compunnel.
Generative AI and Microsoft Copilot are all but set to turn around the way businesses work across a range of industries, fostering innovation and increasing efficiency. The worldwide market for artificial intelligence (AI) is estimated to reach $1.81 trillion by 2030 from $136.6 billion in 2024, indicating the escalating adoption of AI technology that supports business transformation.
It has been predicted by Gartner that about 50% of all content will be created through AI by 2025, emphasizing how instrumental this technology has become in artistic and day-to-day operations. In sectors such as manufacturing generative AI is already revolutionizing industries with product designs optimised and time-to-market reduced by almost 30 percent.
In addition to enhancing productivity and collaboration within an organization, Microsoft Copilot embedded in Microsoft 365 helps to generate text for drafting documents, helping to create presentation materials, and analyzing data.
The Current State of Generative AI and Microsoft Copilot
There have been substantial improvements in generative AI thereby providing powerful capabilities across different domains. For example, GPT models can be used for content creation resulting in high-quality texts, images, and videos which play crucial roles such as marketing, media, and entertainment hence reducing manual inputs. Generative AI is the best in predictive analytics as it provides insights that can be acted upon and predicts market trends and results. Still, it employs AI-driven chatbots to perform repetitive tasks such as data entry and customer service hence increasing efficiency and enabling human resource teams to concentrate on strategic actions.
Microsoft Copilot is integrated into Microsoft 365 thus upgrading productivity and collaboration by using artificial intelligence features. With natural language processing, it writes emails, reports, and documents, thus reducing time spent on writing and editing. In Excel, large data sets are processed by Copilot for real-time insights needed for data-oriented decision-making. Moreover, it also makes creating presentations easier by summing up main points in order to generate PowerPoint slides which are followed by preparing final drafts of a report or an email. During meetings agendas are generated, notes taken down while discussions are summarized thereby saving time.
Additionally, when certain activities need to be done automatically like scheduling meetings or setting reminders for someone’s phone number so that he/she does not forget some important information then this is done through Copilot thereby reducing administrative burdens while at the same time increasing overall productivity.
Research Gaps and Present Limitations of Generative AI and Microsoft Copilot
Generative AI and Microsoft Copilot have significantly transformed business operations, but there are still a number of research gaps and limitations. Therefore, understanding these spaces will be important for driving future advancements as well as fully exploring the potential provided by these technologies.
Generative AI
Contextual Understanding: There are times when AI fails to maintain context over long interactions leading to irrelevant outputs. We need a greater capacity for contextual awareness.
Bias and Fairness: Biases can be passed on to AI through training data with the consequence that its outputs become unfair. We need ways to detect such biases better and minimize their effects.
Creativity and Originality: Most of the time AI lacks true originality, it may reproduce existing works. What we must do is improve creative capabilities in artificial intelligence.
Understanding Nuance: It may lack nuances like sarcasm or cultural references thereby producing inappropriate responses. So, improving upon AI’s grasp of nuanced language is key here.
Ethical and Privacy Concerns: The use of AI in sensitive spheres raises ethical dilemmas along with privacy issues. Therefore, robust ethical frameworks should be put in place alongside privacy-preserving techniques.
Microsoft Copilot
Complex Decision-Making: While being extraordinary at routine tasks, Microsoft Copilot has difficulty dealing with complex decisions that require deep expertise.
Customization: Copilot may not fully adapt to specific workflows. Developing more flexible AI solutions that can adapt to diverse needs is crucial.
Industry-Specific Contexts: Copilot might not understand industry-specific jargon, limiting effectiveness. Training AI on diverse datasets will enhance applicability.
Integration with Legacy Systems: Integrating Copilot with legacy systems can be challenging. Improving interoperability is essential.
Real-Time Collaboration: Copilot’s real-time feedback and adjustment capabilities are limited. Advancing real-time collaboration features will improve productivity.
Filling Research Gaps in Generative AI and Copilots
Moving forward, future developments will aim to make AI more context-aware, equal, imaginative, and ethical. To keep coherence in extended communications memory mechanisms enhanced models and contextual learning would be useful for AI.
Advanced bias detection and mitigation techniques will ensure that fairness prevails in the outputs of AI systems. Enhanced NLP models could understand subtleties like sarcasm and cultural allusions better thus generating responses that are more contextual. Robust frameworks for ethical considerations as well as privacy-preserving technologies such as federated learning and differential privacy will be employed.
Microsoft Copilot is expected to incorporate advanced decision support algorithms, increased customization, industry-specific training datasets, improved interoperability protocols, and real-time collaboration features in its future developments. These transformations are expected to enhance the sophistication of generative AI and copilots that can be used by common people thereby leading to unprecedented productivity levels, especially with regard to creativity and innovation.
Next-Gen AI: The Transformative Potential of Generative Physical AI in Industry 5.0
Generative physical AI will play a key role in Industry 5.0, revolutionizing industries with advanced material design, hyper-personalization, and integration with 3D printing. AI-driven algorithms will develop superior materials, optimize production processes, and create custom products, especially in healthcare. Autonomous systems will self-optimize and self-repair, reducing downtime and maintenance costs. This integration of human creativity and advanced AI technology will drive personalized, sustainable, and human-centric production, transforming industries with increased efficiency, creativity, and functionality.
In summary, generative AI and Microsoft Copilot are poised to transform business operations, driving innovation and efficiency. Addressing current limitations and advancing these technologies will unlock their full potential, shaping the future of work and industry. Get ready for a smarter, more efficient future!
This article has been contributed by Rangarajan Vasudevan, Chief Data Officer, Lentra.
India’s financial sector, the driving force behind the nation’s economic progress, is dancing on a knife’s edge. Increasing regulatory scrutiny, geopolitical tensions, a volatile global economy, and the ever-looming specter of cyberattacks intertwine to form a complex and constantly shifting risk landscape. The gravity of this scenario is starkly illustrated by the fact that fraud in banking operations alone surged tenfold in 2021-22 compared to a decade ago, reaching an alarming INR 45,598 crore, as reported by the RBI.
Effective risk management is the cornerstone of financial stability. It entails proactively identifying, assessing, and mitigating potential economic losses. Indian financial institutions (FIs) face a multitude of risks. Credit risk, a persistent concern, is exacerbated by the growth of the microfinance sector and the rising non-performing assets in Indian banks. Market risk, brought on by fluctuations in interest rates and stock prices, presents another challenge, as seen in the recent volatility in the Indian market. Operational risk, arising from internal failures like human error, technology glitches, or cyberattacks, is also a growing concern, especially in the digital age.
The Reserve Bank of India (RBI) emphasises the need for strong cybersecurity measures to address this threat. In fact, the apex bank recently started placing limitations on various lenders citing concerns related to IT infrastructure and information security practices. Therefore, a failure to adhere to evolving regulatory standards necessitates continuous adaptation by FIs.
Traditionally, risk management in Indian FIs heavily relied on manual processes and historical data analysis. While this approach was sufficient in a less volatile environment, its inadequacies are now glaring. An ever-changing regulatory landscape in India, with new laws and regulations being introduced regularly, plus the growing interconnectedness of the global financial system, with transactions and investments spanning multiple countries, has added another layer of complexity.
In addition, the unpredictable nature of natural disasters and pandemics, which can have far-reaching economic implications, further complicates risk management. Legacy systems cannot keep pace with the dynamic nature of risk today.
The limitations of traditional risk management have created fertile ground for AI’s transformative power. AI, alongside automation and cloud technologies, is poised to accelerate digital transformation in financial services. AI’s secret weapon? Its ability to sift through vast amounts of data from unconventional sources. Financial statements, market trends, social media sentiment, and even weather patterns all contribute to AI’s analytical capabilities. This allows for a far more comprehensive and nuanced understanding of risk, identifying potential threats that human analysts, reliant solely on historical data, might miss.
Recent advancements in generative AI further emphasise this urgency. According to EY, modernising core functions and platforms is a top priority for banks aiming to expedite digital transformation, with 58% focusing on this area. Furthermore, 78% of Chief Risk Officers (CROs) prioritise AI implementation—a sign of the industry’s growing appetite for this technology.
The benefits extend far beyond individual institutions. A joint study by National Business Research Institute and Narrative Science reveals that 32% of Indian financial service providers already leverage AI for tasks like voice recognition and predictive analytics. Major banks in India are actively employing AI to streamline operations, and a report by Accenture indicates that 83% of Indian bankers believe AI will collaborate with humans in the near future. JP Morgan Chase has developed the Contract Intelligence (COiN) platform, which can analyse legal documents in seconds, extracting key data points – a task that would take humans hundreds of thousands of hours. Not only is AI faster, but it is also demonstrably less prone to errors.
AI’s reach is not limited to financial institutions. Regulatory bodies like the Reserve Bank of India can leverage AI to identify systemic risks within the Indian economic system. Real-time risk identification empowers regulators to take preventive measures, like adjusting interest rates, to enhance financial stability.
Initiative Taken to Manage Implementation Risks of Generative AI By Organisations Worldwide as of 2024
How AI Can Revolutionise Risk Management
AI’s impact on financial risk management goes beyond static risk estimation. By analysing historical data, AI can recommend dynamic portfolio diversification, proactively identify emerging threats, and adjust allocations to mitigate market risk. It can even simulate various economic and market scenarios to stress-test loan portfolios, helping institutions develop more resilient lending policies.
Unlike traditional, static policies based on limited factors, AI and ML models can analyse every possible combination of variables, creating a powerful tool for credit managers. This allows them to simulate different policy settings and see the predicted impact on loan approvals. This data-driven approach empowers them to optimise conversion rates while minimising risk.
The benefits extend beyond credit risk. Enterprises are leveraging generative AI as a virtual regulatory and policy expert. Trained on vast datasets of regulations, company policies, and guidelines, it can answer questions, identify compliance gaps in code, and automate regulatory checks – even providing alerts for potential breaches.
However, with these advancements come policy considerations. Financial institutions must ensure the transparency of AI models’ decision-making processes to comply with regulations and maintain trust. Additionally, robust data governance practices are crucial to ensure the quality and security of the extensive datasets that power these robust AI systems.
Hurdles to AI Implementation
The potential of AI in financial risk management is undeniable. However, we need to address some key challenges to fully unlock this potential. One challenge is the constant game of catch-up regulators face. AI is evolving rapidly, and regulations often struggle to keep pace. This creates uncertainty for financial institutions and discourages broader adoption of AI in risk management.
Another hurdle is the lack of standardised practices for developing and deploying AI systems. This inconsistency makes it difficult to ensure fairness, avoid bias, and most importantly, understand how AI reaches its conclusions. Without this transparency, trust is difficult to build.
The financial sector itself faces its own set of challenges. The cost of acquiring, implementing, and maintaining sophisticated AI systems can be significant, especially for smaller institutions. Additionally, the effectiveness of AI hinges on high-quality data. Fragmented datasets and data privacy concerns can create significant roadblocks for institutions looking to leverage AI for risk management.
The Future of Risk Management
Despite these challenges and the ever-increasing volume of data, which presents a challenge for human analysis, it also creates a unique opportunity for AI’s implementation in India’s financial sector.
Collaboration is crucial. Industry and regulators must work together to establish clear frameworks for responsible AI development and use in risk management. These frameworks should prioritise best practices, data governance, and Explainable AI (XAI) – tools that help us understand how AI models reach conclusions. This fosters trust and ensures ethical implementation.
Despite AI’s automation capabilities, human expertise remains essential for interpreting results, making final decisions, and ensuring ethical applications. Therefore, upskilling the workforce becomes critical. Financial institutions must invest in training existing employees and attracting talent with AI, data science, and risk management expertise. This fosters a culture of human-AI collaboration, where AI amplifies human expertise while human oversight ensures responsible AI use.
By embracing AI as a transformative tool in risk management, the Indian financial sector can navigate the complexities of modern finance with greater confidence. This shift paves the way for a more stable and secure financial future for all stakeholders – from individual depositors to credit-seeking businesses and the broader Indian economy.
People who know how to write and write daily, be it for fun, as a hobby, or for a living, always feel that there must be something to put your innate thoughts into words. They write letters, articles, records and minutes of meetings, and all these kinds of letters— joining letters, resignation letters, offer letters, letters to fathers, sons and wives and mothers, and my favourite, letters to pen pals. Those who write often understand something called writer’s block. It is a moment while writing when you can’t finish a thought and can’t find words to describe a particular type of thought that seems to linger around some corner of your mind. And this is where I think ChatGPT can help.
For people who do not know what ChatGPT is— it is a generative AI (Artificial Intelligence) Chatbot model developed by OpenAI. The company has been involved in Artificial Intelligence, ML, and deep learning research and development for many years from now. OpenAI’s ChatGPT is a conversational AI model based on the Generative Pretrained Transformer 3 (GPT-3) architecture.
Around 2017, a bunch of scientists who had previously worked at Google Brain developed the Transformer architecture, which was a big success in the field of generative AI. In 2018, OpenAI launched the first Generative Pretrained Transformer (GPT-1), and then in 2019 came GPT-2, further succeeded by the launch of GPT-3 in 2020. Recently, in March 2023, OpenAI launched its most ambitious project and the most advanced GPT model, the GPT-4.
ChatGPT has been trained on a diverse range of internet text, allowing it to generate human-like text in response to the given prompts. It is built on top of OpenAI’s GPT-3.5 and GPT-4 (ChatGPT Plus) foundational large language models (LLMs) and has been fine-tuned using both— supervised and reinforcement learning techniques, which allows it to process natural language more efficiently and converse better with users. It can interact conversationally, answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
What Are The Ways ChatGPT Can Be Used For Content Creation?
ChatGPT is a generative AI and works on user input or prompts. Although it can understand human language and keep in mind the context of the conversation, you, as a user, must know what kind of content you want. You are to give it instructions to produce content and generate the sort of text you want. On the surface, ChatGPT can help with brainstorming content outlines. It acts like an editor, giving you direction and structure for your ideas, which can be especially useful when starting a new piece of content and needing guidance for organizing your thoughts. Ultimately, you have to come up with the original idea of where your content should be heading and what information you would like to use.
Let’s see how ChatGPT can help with content creation;
Creating Content For Websites And Blogs Using ChatGPT
When it comes to content writing for websites and blogs, there are a couple of things you need to keep in mind:
Researching facts and details about the topic
Flows of your blogs/article
Creativity
Add something new
ChatGPT is a powerful tool that can help you write a well-researched and succinct article or blog post. If used wisely, it can also create a wireframe of your article, with headings and subheadings, and create an intro, body, and conclusion as per your command.
Even if you put the title of your blog or article under quotes and ask it to write a blog, it will most certainly do that and will create a blog post for you.
Let me show you what I mean:
When I asked ChatGPT to write a blog on “why one should not write only using ChatGPT.”
It highlights that while ChatGPT has opened many possibilities in various applications, one should not solely rely on ChatGPT or any other language models as it lacks creativity and originality. The content produced might not always be factual, and you might have to fact-check yourself. The content might also lack emotional intelligence, and context will surely hamper your writing style.
ChatGPT response on Why one should not write only using ChatGPTChatGPT response on Why one should not write only using ChatGPT
So, what should be done instead? How to use ChatGPT for creating content? Well, the answer is simple as it said: treat it like an assistant and not a replacement.
Here’s a list of things to follow:
You can ask ChatGPT for some background on the topic
Ask ChatGPT to create a wireframe for your blog post, and if you don’t like it, ask it to be creative.
You can write an intro for your blog, put a different perspective, and if you want, you can ask it to rephrase some of the lines.
If you doubt the facts presented, you can ask it to show resources and check that out yourself to ensure accuracy.
When I’m creating content, I like to delve deep into research and explore various angles and perspectives. It’s important to consider different viewpoints and opinions in order to present a well-rounded argument. This is where ChatGPT can be an invaluable tool. It can present arguments from the opposing side of your viewpoint, which can help you strengthen your argument by addressing potential counterarguments. Additionally, ChatGPT can assist in refining your message by presenting alternative ways of expressing your ideas for different audiences. Overall, ChatGPT is a powerful resource for creating comprehensive and compelling content.
Brainstorming Content Outlines
After identifying the key points of your content, you may find it helpful to use ChatGPT as a tool to shape and generate content outlines. In my experience, this is one of the prime benefits of ChatGPT – it operates as a virtual editor, providing you with direction and form for your ideas. By utilizing the AI-powered assistance of ChatGPT, you can effectively structure and organize your content in a clear and concise way for your intended audience.
Summarizing Content
If you are looking for ways to make your research and planning phase easier while creating something, then ChatGPT can be of great help. One way to utilize ChatGPT’s abilities is by asking it to summarize lengthy blog posts, books, or video transcripts. I have found ChatGPT to be highly efficient in summarizing content. To request ChatGPT to summarize a blog post for you, you can simply say, “Can you please summarize the key concepts of the following blog post in a few paragraphs:” and provide the post details. While you don’t necessarily need to use the word “please,” it can be a polite way to request ChatGPT’s assistance.
ChatGPT is an amazing tool that can assist you in your research and planning tasks when creating content. However, it can also help you write complete content drafts for various purposes, such as blog posts, social media captions, video scripts, emails, and more. One of the best features of ChatGPT is that it can help you overcome writer’s block by providing you with a good starting point. All you need to do is give it an idea of what you want to write, and it will do the rest. For instance, if your boss has asked you to write an out-of-office email, you can rely on ChatGPT to help you compose a message that is both informative and playful. Thanks to its advanced programming, ChatGPT can suggest the right words and phrases to use, making the process of writing more efficient and less stressful.
Repurposing Content
I have discovered that ChatGPT is not only capable of generating content from scratch, but it can also be incredibly helpful in repurposing the content that I have created across various platforms. One notable example is the way ChatGPT can take a TikTok script that I have previously recorded and transform it into a highly effective post on LinkedIn. This capability has been a game-changer for me, as it allows me to maximize the impact of my content without having to start from scratch every time I want to post on a new platform.
Writing Proposals for Projects
One of the ways in which I find ChatGPT to be exceptionally useful is in creating proposals and documents for different projects that I am working on. Specifically, I have found that using ChatGPT for content writing is an excellent starting point for a wide range of internal processes or ad hoc tasks. With its impressive capabilities, ChatGPT has proven to be an invaluable tool that helps me to streamline my work and produce high-quality deliverables in a timely manner.
ChatGPT can help here as well. It can help you generate research you can use in SEO-focused content, saving you time and effort in gathering information for your content. In addition to writing and research, it can also perform specific SEO tasks to support your content creation. It can help with keyword research or content clustering, which is very useful in optimizing your content for search engines and improving its visibility. Another way ChatGPT can help with content creation is by creating embeddable elements and enhancements within your content. This can make your articles more engaging and linkable, thus improving their performance on search engines.
Mastering ChatGPT for SEO Content
Apart from that, ChatGPT can help you create other kinds of content as well. You can ask it to write letters to your boss on a particular matter or a letter to your mother, which you will have to edit personally, but it can get you started nonetheless. And sometimes a start is a good thing. Most people who write often struggle with where to start, and ChatGPT has been a big help.
ChatGPT can also help you with emails, speeches, codes, and creative writing like short stories and poetry, which is especially helpful for school-going kids and college students.
ChatGPT is a large language model or a conversational AI or chatbot trained to be informative and comprehensive, and it performs its job wonderfully— most of the time. It is introduced to a massive amount of text data (approx. 570GB in size and 6B parameters) and can communicate and generate human-like text output in response to a wide range of user prompts and questions. You can ask it to summarize a historical event; it will do just that. When it comes to content creation, it can help to improve the quality of content by providing writers with access to a more expansive range of information and perspectives. It can generate drafts that can further be edited by the writers themselves, speeding up the content creation process. However, despite its vast data set and training, ChatGPT still produces subpar content. It lacks the context, emotional intelligence, and sophistication of a human writer. Furthermore, its training dataset is limited to 2021, meaning it is stuck in 2021 while we are in 2023. So, most of the time, when it should, it fails to bring up new events that just happened. This is a problem, and if you’re a news writer, it matters even more. So, use it wisely and ethically.
Below are some helpful resources to learn more about ChatGPT and its effective usage:
The OpenAI website: This is ChatGPT’s official website. Here, you can find information about the tool, including its capabilities and limitations, as well as tutorials and examples of how to use it.
The OpenAI GitHub repository: This is an open-source community for ChatGPT developers that provides code examples and implementation details.
The OpenAI community forum: This platform enables developers and users to exchange insights and experiences regarding ChatGPT.
The OpenAI API documentation: This guide provides a detailed explanation of how to access and utilize the ChatGPT API to generate text.
The OpenAI blog: This website provides valuable information and best practices for utilizing ChatGPT.
FAQs
Can I use ChatGPT for content writing?
Yes. ChatGPT is an excellent tool for content writing, offering impressive capabilities to streamline the writing process and produce high-quality content efficiently.
How to use ChatGPT for content creation?
Here’s a list of things to follow while creating content through ChatGPT:
You can ask ChatGPT to create a wireframe for your blog post, and if you are not satisfied with it, ask it to come up with something more creative.
If you need help writing an introduction for your blog, ChatGPT can provide a different perspective, and you can ask it to rephrase some lines.
If you have doubts about the facts presented in your blog, you can ask ChatGPT to show you resources to check for yourself and ensure accuracy.
Finally, if you need help concluding your blog post, you can ask ChatGPT to provide an ending.
How does ChatGPT help in creating SEO-friendly content?
ChatGPT can help to generate research that can be used in SEO-focused content, saving time and effort in gathering information for the content. In addition to writing and research, it can also perform specific SEO tasks to support content creation. It can help with keyword research or content clustering, which is very useful in optimizing content for search engines and improving its visibility.
