On August 20th, 2024, researchers at Meta Fair announced the Self-Taught Evaluator technique, which can train LLM evaluators using synthetic data. This approach is being adopted to reduce the human efforts required to train evaluation models.
The current LLM evaluation method requires human-annotated data, which increases the associated costs and time needed to generate accurate results. The Self-Taught Evaluator will be a big leap in the artificial intelligence domain, significantly improving the scalability and efficiency of LLM evaluators.
Meta’s new evaluation model eliminates the requirement for human-labeled data by introducing the concept of LLM-as-a-judge. In this method, the model is given input, two possible answers, and an evaluation prompt, which the model uses to judge the response with a reasoning chain.
The Self-Taught Evaluator process begins with a base language model and a large pool of unlabeled data, later split into ‘chosen’ and ‘rejected’ categories. The model then trains iteratively, sampling each example and examining traces and judgments.
Meta researchers tested this model using the Llama-70B-Instruct model and the WildChat dataset, containing over 20,000 examples without human involvement. After five iterations, model performance for the RewardBench benchmark increased from 75.4% to 88.7%. Similarly, the performance of the MT-Bench benchmark significantly improved.
This research explored the fine-tuning of LLM evaluation models using automated loops to reduce manual work. This is beneficial, especially for large enterprises, for creating language models and automating the model evaluation. However, there are potential setbacks if the seed model is not thoughtfully considered.
On 20th August 2024, Soumith Chintala, Meta’s AI lead, stormed the social media platform ‘X’ by announcing Metamate, a generative AI assistant, to enhance productivity.
This product will extend the capabilities of artificial intelligence assistant applications, like Perplexity AI, by allowing users to create custom agents using Python scripts. Each agent will cater to the responsibilities of the specific team that has created the bot.
Soumith, along with Zach Rait and Aparna Ramani, developed this product to handle the requirements of large-scale organizations like Meta that operate multiple workflows simultaneously.
According to sources, Metamate offers features for a vast range of applications. Some of its most common benefits include data visualizer, document summarizer, information retrieval, and monitoring work recaps.
Along with these features, Metamate enables Meta employees to generate complex queries and perform advanced mathematical calculations. However, this product is only available for Meta employees, as it is trained on massive volumes of internal company documents.
Esther Crawford, the director of products at Meta, stated, “Any sizable company operating without an internal AI tool is already behind the curve.” Integrating machine learning algorithms with Metamate will further enhance the product.
Data management is critical to modern businesses. It enhances operations’ overall efficiency and drives growth. By implementing the proper data management strategies, you can ensure data availability, integrity, and compliance across your organization.
Enterprise data management (EDM) is a comprehensive process that assists you in managing data strategically. It ensures data is available, consistent, and protected to meet organizational goals and facilitate business continuity.
This article provides an overview of enterprise data management (EDM) and why it is essential for your business. You will also learn how to implement a robust EDM strategy for effective data management.
What is Enterprise Data Management?
Enterprise data management is a systematic approach to managing and governing data. It helps to create assurance and confidence in an organization’s data assets.
EDM involves establishing policies and procedures to ensure data accessibility, consistency, accuracy, security, and compliance with industry standards. It enables you to consolidate data from various sources and store it in a standardized and accessible format to optimize business operations.
Why Is Managing Enterprise Data Critical to Business?
Managing enterprise data is critical to business for several reasons:
Informed Decision-Making
When the data is accurate, you can make informed decisions based on factual figures rather than intuition. Enterprise data management practices ensure your data is correct by allowing you to identify inconsistencies or errors within the data. You can take preventive measures to clean and transform it. This way, your team can identify high-performing products and adjust marketing strategies.
Operational Efficiency
Within the enterprise data management framework, you can define policies and procedures that help maintain data consistency across the organization. Standardizing data across different systems and departments enables you to enhance operational efficiency and improve data accessibility and collaboration.
Customer Insights
EDM allows you to consolidate data from multiple sources, such as marketing platforms, social media, CRM systems, and more, in one place. It helps you gain a comprehensive view of customer data. By analyzing this information, you can identify areas of improvement and enhance customer satisfaction by addressing their concerns about your products and services.
Compliance and Risk Management
Enterprise data management helps you know what regulations to comply with to mitigate data security risks. You can establish protocols for monitoring data usage and access controls, protecting data from unauthorized access, and complying with regulatory standards. The security measures reduce the risk of invasion, compliance penalty risk, and reputational damage.
How to Implement Enterprise Data Management Strategy
Implementing a robust enterprise data management strategy includes several factors. Here is a detailed guide to the factors involved in creating a data management framework for your organization:
Define Data Governance Policies
Data governance policies are a set of regulations, procedures, and guidelines that define how to manage, access, and use data. These regulations include policies for data classification, access permission, quality standards, compliance, integration, security, and retention. You must define all the necessary policies to conduct your business operations efficiently.
Establish Data Ownership
When you establish data ownership, you define the roles and responsibilities of individuals accountable for managing and protecting specific datasets. Clearly define the authority of the data owners to ensure data quality and integrity.
Implement Data Quality Management Processes
Data quality is essential for making accurate and informed decisions. Implementing a data quality management process includes activities like data cleaning, validation, and enrichment, which help identify errors, inconsistencies, and duplicate data.
Ensure Data Compliance
Data compliance is essential to protecting your organization’s critical data assets, mitigating risks related to legal obligations, and ensuring data credibility. Identify the relevant regulatory compliance specific to your industry to conduct regular audits and comply with regulatory obligations such as HIPAA, GDPR, PCI DSS, etc.
Design a Scalable and Flexible Data Architecture
A well-designed data architecture allows you to scale data and be flexible with data management, enhancing your business’s operational efficiency. A sturdy data architecture must have the following capabilities:
Smooth data integration
Scalable data storage
Efficient data processing
Data Analytics
Define Data Storage Solutions
You must analyze your organization’s requirements before choosing a data storage solution to implement an enterprise data management framework. Some of the specifications can include:
The type of database, relational or non-relational
Scalability
Data compression for storage efficiency
Data indexing and partitioning are needed for better data accessibility.
Implement Efficient ETL Workflows
ETL workflows use integration tools, such as Airbyte, to extract data from different sources, transform it, and load it into the target system for further analysis. By implementing the ELT process, you can automate your workflows and increase data performance and reliability.
Implement MDM Processes for Data Consolidation
Master data management (MDM) is a process for centralizing and governing critical organization data. By establishing data governance controls and quality rules within the MDM environment, you can ensure consistency and synchronize master data across your organization.
Define Data Quality Metrics
Data quality measures are essential for managing enterprise data with consistency and timeliness. Within the quality measures, you must define the metrics and KPIs to help you evaluate data quality across the organization.
Implement Robust Data Security
Nowadays, data security is the most critical concern for organizations. A comprehensive data security strategy should include encryption methods, access controls, and authentication mechanisms. By implementing data security, you can conduct regular assessments to detect and prevent intrusion and allow role-based access to protect sensitive information.
Master Data Management Vs. Enterprise Data Management
Enterprise and master data management are related but have distinct scopes and functions. EDM is a broader concept that covers the entire lifecycle of your data across the organization. In contrast, MDM focuses on managing the master data (critical data entities within the business environment). Let’s look at some of the essential differences between MDM and EDM.
Basis of Difference
Master Data Management (MDM)
Enterprise Data Management (EDM)
Definition
It is the process of creating uniform data related to a single entity, such as a product, customer, or supplier, across different departments.
It is managing, storing, and governing data within an organization.
Purpose
To make data more consistent for operational and analytics use.
To oversee the entire data lifecycle and ensure effective data management and governance.
Scope
Limited for managing and maintaining the master data.
It has various attributes and manages all the data within the organization, including the data types and sources.
Functionality
MDM centralizes the management of crucial data entities of your business by integrating in one place and synchronizing the workflows
There are many aspects of EDM, including data management, governance, quality management, integration, security, architecture, and compliance
Example
A retail company can use MDM to create a single view of customer data, including interactions with products, website visits, products bought, and feedback. This will help the company better understand the customer and create targeted campaigns to improve performance efficiency.
A finance company can implement EDM to govern data from various sources, including payment gateways, account information, and daily transactions. This helps identify unusual activity and optimize investment strategies to provide a personalized customer experience.
Few Enterprise Data Management Tools
Enterprise data management tools are vital in establishing, monitoring, and optimizing organizational data practices. These tools facilitate data quality management, ensuring the data is accurate, complete, and consistent for developing and implementing strategic business decisions.
Let’s look at some of the enterprise data management tools:
Tableau: Tableau is a data visualization tool that simplifies raw data and helps you present it in an understandable format. It allows you to create interactive dashboards and reports, providing a clear view of your enterprise data and resources.
Dell Boomi: This enterprise-grade platform is designed to provide high productivity by enabling you to synchronize data within a centralized hub. It lets you connect various systems and applications to streamline data flow, ensuring the information is updated across the organization.
SAP Master Data Governance: This tool focuses on managing the master data entities within your organization. It integrates with both SAP and non-SAP systems. SAP Master Data Governancegives you a unified view of your data and helps you meet industry standards for better compliance.
IBM InfoSphere QualityStage: This data management tool specializes in quality management through data profiling, cleaning, and standardization. It helps you identify duplicate values and reduce redundancy, enhancing data quality.
Key Takeaways
Enterprise data management (EDM) is a strategic practice that helps you manage your enterprise data through data quality, governance, and security measures. By implementing EDM, you can make informed decisions, foster a data-driven culture, mitigate risks related to security and compliance, and increase operational efficiency. You can also use third-party tools to apply EDM to streamline workflows within your organization.
FAQs
What are the examples of enterprise data?
Examples of enterprise data include:
Operational data, such as transactions, inventory levels, customer orders, accounting, and HR statistics.
Strategic data that includes reports, CRM platform data, market trends, and opportunity analysis.
Application-specific data like GPS for transportation.
Network alerts for maintaining IT infrastructure.
What is the EDM framework?
An enterprise data management framework is a set of practices implemented within your organization’s environment to manage the data effectively.
Which team is responsible for EDM?
The enterprise data managers, including database and IT administrators or project managers, manage enterprise data.
Data has evolved over the years from simple paper records to complex digital formats, encompassing various forms, including numbers, text, and electronic bits. As the data volumes and the complexity of handling data continue to increase, the need for structured data management has become more imperative.
Data structures are specialized formats for efficiently organizing, storing, and accessing data. With the help of these data structures, you can classify and categorize data into organized formats. This categorization enhances retrieval speed and processing efficiency.
This article explores data structure fundamentals, types, and uses. It also examines how you can apply data structures in real-world applications to enhance computational efficiency.
What is a Data Structure?
Data structures offer a systematic way to organize, manage, and process data. They extend beyond primitive data types such as integers or floating-point numbers, providing a more sophisticated organization of varied data.
A data structure encompasses both the logical format of the data and its implementation in a program. This systematic approach enhances both computer and human understanding and usage of the data.
For example, a list of objects can be used to manage employee details. Each object represents an employee and stores attributes like name, position, and department. This allows you to store and retrieve information about any employee by quickly iterating through the list or accessing specific entries.
Classification of Data Structures
Data structures can be broadly classified into two types:
Linear Data Structure: In a linear data structure, the data elements are organized sequentially, and each element is connected to its previous and next elements. Some examples of these structures include arrays, linked lists, stacks, and queues.
Non-Linear Data Structure: The data elements in non-linear data structures are not organized sequentially. They are either connected in a hierarchical order or a network-like fashion. Some examples of these structures include trees and graphs.
Basic Terminologies Related to Data Structure
Here are some of the key terms related to data structures:
Data: It is a piece of information that consists of basic values or a collection of values. For example, an employee’s name, ID, position, and salary are pieces of information about that employee.
Data Item: A data item represents a single piece of data within the data. For instance, a person’s first name is a data item.
Group Item: This is a collection of related data items. For example, an employee’s name might include first, middle, and last names.
Elementary Items: The data items that can not be divided further. For example, an employee’s ID is an elementary item because it is a single value.
Entity and Attribute: An entity is a category of objects within the dataset, such as an employee. On the other hand, an attribute is characteristic of that entity, such as ID, gender, and job title.
File: A file is a component that includes a collection of records of the same entity type. For example, a file containing records for 100 employees would include data for each employee.
Need for Data Structures
A data structure offers a formal model that outlines how to logically arrange data elements within your application or organization’s storage system. These structures serve as building blocks for developing complex applications.
Factors to Consider While Choosing a Data Structure
When selecting a data structure for a program or an application, you should consider the following factors:
Required Operations: Determine the specific functions and operations your program or application will perform. For example, if your program frequently needs to search the data elements, a data structure with fast search capabilities would be beneficial. Options like a binary tree or a hash table might be appropriate for this purpose.
Processing Time: Evaluate the time complexity associated with each data structure for your needed operations. Processing time will impact how quickly an algorithm can complete a task as the size of the data increases. For example, simple arrays or linked lists might take linear time, which is manageable for smaller datasets but slow for large ones.
Ease of Use: Some data structures, like arrays and linked lists, are simple, while others, like graphs and trees, might require more complex implementations. The complexity of the data structure should match the requirements of your application.
Type of Data Structure
Different data structures are good for other tasks. The type of data structure used in a particular situation depends on what you need to do with that data and the methods you want to use.
For example, some data structures are better for finding specific items, while others are better for adding and removing items. Choosing the proper data structure helps you program data more efficiently.
Here are some of the commonly used data structures:
Array
An array data structure allows you to store multiple elements of the same type in a single variable. It can store a collection of data elements, such as numbers or strings.
Each component of an array is identified by an index, which is a unique number indicating its position within the array. The indexing makes accessing, modifying, and managing the data easy for an array.
Types of an Array
One- Dimensional Array: Stores elements in a single row and are accessed using a single index number.
Two-Dimensional Array: This array consists of rows and columns resembling a matrix, and the values inside it are accessed using two indices.
Multi-Dimensional Array: It is an array of arrays with multiple indices and dimensions.
Linked Lists
A linked list consists of elements, called nodes, stored in a sequence and connected using pointers. Each node contains two fields: one stores the data, and the other includes the address of the next node. The pointer of the last node is a null pointer, which indicates that there are no more nodes to follow, signifying the end of the list.
Types of Linked Lists
Singly Linked List: A linked list where you can move in only one direction. It consists of data and a pointer field referencing the next node.
Doubly Linked List: A doubly linked list consists of one data field and two pointer fields. One pointer field refers to a node before, and the other field refers to the node after. This list allows you to go in both directions, backward and forward.
Circular Linked List: Acircular linked list is similar to a singly linked list. The key difference is that, in a circular linked list, the pointer field of the last node consists of the address of the first made, creating a circular structure.
Stacks
Stack is a data structure that manages the data elements using ‘Last in, First Out’ (LIFO) to manage the data elements. The LIFO method implies that the last element added will be the first to be removed.
Key Operations You Can Perform on a Stack
Push: It is the operation to add an element to the stack.
Pop: Pop is an operation that removes an element from the stack.
Queues
A queue is a data structure that follows the ‘First In, First Out’ (FIFO) principle. It implies that the first element added to the queue will be the first to be removed.
Key Operations You Can Perform On a Queue
Enqueue: Enqueue allows you to add an element to the rear (end of the queue from where you add an element) of the queue.
Dequeue: Dequeue allows you to remove an element from the front (end of the queue where the element is removed) of the queue.
Trees
A tree is a nonlinear data structure that helps you organize data hierarchically. It consists of nodes connected by edges, with a single node called the root. A node can have child nodes, creating a parent-child relationship.
Types of Trees
Binary Tree: In this data structure, a parent node can have at most two children, a left node and a right node.
Binary Search Tree: A BST is a binary tree in which each node has a specific order of its elements. The value of each left child node is smaller than that of the parent node, and the value of each right node is larger than that of the parent node.
AVL Tree: An AVL tree is a special BST particular that automatically balances itself, maintaining a roughly even shape. Each node in the AVL tree has a balance factor; if the balance becomes uneven, the tree performs rotation to rebalance itself.
Graphs
A graph is a nonlinear data structure consisting of vertices (nodes) and edges (connections between nodes). Graphs can represent various real-world systems, such as social and communication networks.
Depending on the structure and properties, graphs can be categorized into several types: directed, undirected, weighted, unweighted, null, trivial, simple, and more. Each type of graph serves a different purpose based on the nature of the connection and the data it represents.
Operations on Data Structures
Operations on data structures are fundamental actions performed to manipulate and manage the data stored within them. Let’s look at some operations that apply to the above data structures.
Insertion: Insertion is adding a new element to a data structure. For example, you can use an insert operation to add details of a new employee, such as name, ID, or position, to a linked list.
Deletion: It involves the removal of a data element from a data structure. For example, you can delete the record of an employee who just left the company.
Searching: You can search operations for a specific element within the data structure. This involves checking each element to locate a value, such as searching for a node in a binary search tree.
Sorting: Arranging elements in a specific order, such as ascending or descending, allows you to organize the data efficiently. For instance, sorting a list of contacts alphabetically by last name in a contact management app will enable you to find and access specific contacts quickly.
Merging: Merging is combining two data structures into one. This could involve merging two sorted arrays into a single sorted array or combining two linked lists. For instance, a company with separate online and in-store transaction databases can merge their datasets into a single comprehensive database.
Splitting: Splitting involves dividing a data structure into smaller parts, such as splitting a large array into multiple sub-arrays or partitioning a graph into separate components.
Updating involves modifying an existing element’s value in the data structure, such as updating an employee’s salary in an employee management system.
Applications and Use Cases of Data Structures
Data structures are essential for efficiently managing and processing data in various real-world applications. Here are some key uses of data structures:
Linked lists are useful for managing collections of items that don’t need to be ordered, such as playlists in a music app or collections of bookmarks in a web browser. This allows you to add or remove a data element without worrying about the order.
Queues are suited for collections needing first-in and first-out orders, like print queues, which ensure jobs are processed in the order they are received.
Graphs can be used to analyze connectivity and relationships. For example, you can utilize them for map routes in transportation networks by representing locations as vertices and routes as edges. It allows for the calculation of shortest paths and the optimization of travel routes.
Key Takeaways
Data structures are fundamental for structuring data to allow for efficient processing, storage, and retrieval. You can broadly classify data structure into two types: linear (array, linked lists, stacks, queues) and non-linear (trees, graphs) structures, each suited to different tasks. Data structures support various operations, including insertion, deletion, searching, sorting, merging, splitting, and updation. Understanding their properties and applications helps you make informed data management and programming decisions.
The Indian government has continuously invested in cutting-edge technologies to promote robotics and enhance industrial productivity. In 2021, the sales of industrial robots in India surged by 54% and reached a new record, with 4,945 units installed. As a result, the World Robotics Report, released by the International Federation of Robotics (IFR), ranked India 10th in annual robot installations in 2022.
In addition to industries, robots are used extensively in many other sectors, such as medicine, education, agriculture, and hospitality. Let us look at some of the top robots that are changing the picture of robotics in India in 2024.
Top Robots in India in 2024
Here is a list of top 10 robots in India in 2024:
1. IRIS
IRIS is India’s first AI-powered humanoid teacher robot. Developed by Makerlabs, it was first deployed at a school in Kerala in March 2024. The robot is created using generative AI technologies and delivers educational content from preschool to high school through an Android app.
IRIS has a 4-wheel chassis and 5 degrees of freedom, allowing it to move freely and demonstrate various learning activities. It uses visual aids, games, and quizzes to make learning more interactive. It also has a voice assistant that can comprehensively answer questions asked by students.
2. KARMI-Bot
KARMI-Bot is a robot built by Asimov, a Kerala-based robotics company. It is used extensively in the healthcare sector to protect healthcare workers from viral infections. The robot can navigate in isolation wards independently to deliver food and medical kits to infected patients. This minimizes direct interaction between medical staff and patients.
KARMI-Bot has a transportation speed of 1m/sec and can be monitored remotely through video streaming. It was used in some government hospitals in India during the COVID-19 outbreak. Asimov also developed a variant of the KARMI-Bot called KARMI-CLEAN to facilitate the disinfection of large areas using UV light.
3. Manav
Manav is India’s first 3D-printed humanoid robot, developed by A-SET Training and Research Institute, Delhi. It is a two-foot-tall humanoid designed primarily for research purposes. The outer body of Manav is designed using the 3D printing technique and is made up of acrylonitrile butadiene styrene (ABS).
Manav has two degrees of freedom in the head and neck to facilitate sideways and up-and-down movement. It supports WiFi and Bluetooth connectivity and uses binocular vision processing to gain an in-depth perspective. It can also walk, talk, and dance in response to human voice commands.
4. Athena
Athena is a surveillance robot developed by an Indian company named Kody Technolabs. It is highly vigilant and can be used for round-the-clock security of spaces such as shops, malls, or industries. The robot has powerful vision because its ultra-high-definition cameras enable it to see even in low-light conditions.
Athena has cognitive security capabilities allow it to differentiate threats from harmless instances by analyzing its surroundings. It has facial recognition technology and can identify suspects or offenders. It also has an instant alert mechanism with a two-way intercom system that helps prevent crime before it can be committed.
5. DRDO Daksh
Daksh is a remote-controlled robot created by the Defence Research and Development Organization (DRDO). It can safely track, handle, and destroy hazardous objects such as bombs. Daksh is fully automated and can navigate through staircases, steep slopes, and narrow corridors to reach the target object.
After reaching its target, it lifts the suspicious object and scans it using a portable X-ray device. If it is a bomb, Daksh defuses it with its water jet disrupter. It also has a shotgun that can break locked doors and scan explosives in vehicles. With such a high functionality, Daksh can be dubbed an anti-terror robot that can help our nation fight terrorism.
6. Mitra
Mitra is a humanoid robot created by Invento Robotics, a Bengaluru-based startup. It was launched in 2017 at the Global Entrepreneurship Summit. The robot became famous as it greeted Indian PM Narendra Modi and former US President Donald Trump at this summit. Mitra is five feet tall and was created with high-performance capacity within a year.
Since its appearance, Mitra has found applications in various areas, such as banks, movie theatres, malls, airports, and hospitals. After its success, Invento Robotics also launched upgraded versions, Mitra 2 and Mirta 3. These variants have more robust facial recognition features and can interact effectively with humans.
7. RADA
RADA is an AI-powered robot developed by Vistara Airlines, a joint venture of Tata Sons and Singapore Airlines, to assist airport passengers. It was first deployed at Delhi’s Indira Gandhi International Airport in 2018. RADA can scan boarding passes and provide passengers with information about the weather conditions of the destination city and real-time flight status.
RADA can rotate 360 degrees as it is built on a chassis of four wheels. It also has three built-in cameras that enable it to interact cognitively with the air passengers using its effective voice technology.
8. SSi Mantra
SSi Mantra is a robotic surgeon developed by SS Innovations. It was designed to make surgeries efficient and cost-effective. The first telesurgery performed by SSi Mantra was a robotic cholecystectomy conducted over a distance of five kilometers.
Since its launch, SSI Mantra has assisted surgeons in several surgeries and has become a popular healthcare robotic system in other countries. SSI Innovations recently unveiled SSI Mantra 3, an upgraded version of its predecessors. It has a 3D HD headset and 4K vision to enable surgeons to conduct and monitor surgeries efficiently from a distance.
9. IRA
IRA (Interactive Robotic Assistant) is a humanoid robot developed by Asimov Robotics. HDFC Bank first deployed it to assist bank staff in serving customers. The robot can greet customers and guide them to the relevant counters to perform their desired banking operations.
The bank later launched IRA 2.0, an upgraded version of IRA in collaboration with Invento Markerspaces and Senseforth Technologies. It can answer banking FAQs and has voice-based navigation capabilities to guide customers through various counters. It also recognizes customers using its facial recognition algorithm.
10. PuduBot
PuduBot is a robot developed by Pudu Robotics, a service robotics company. It is used for smart delivery services in the hospitality sector, makes intelligent voice announcements while marketing, and provides essential amenities to patients in the healthcare sector.
PuduBots can immensely improve the operational efficiency of any organization by taking care of goods and services that are delivered. This allows workers to direct their efforts on product development and marketing. They are durable bots as they can work 24 hours with just four hours of battery charging. Thus, PuduoBot is a highly reliable and durable solution that can enhance the operability of any industry.
11. BRABO
BRABO, a short name for ‘Brave Robot,’ is an articulated robot developed by Tata Group with MSMEs as the focus domain. It was designed by TAL, styled by TAL Elxsi, manufactured by Tata AutoComp, and financed by Tata Capital. BRABO was launched in 2017 and is the first ‘Made in India’ robot.
Articulated robots have rotary joints and can mimic human arm movements. Thus, BRABO can perform various industrial tasks, such as sorting with a vision system, press and machine tending, picking, packing, sealing, and welding. It is used extensively in electronics, logistics, food packaging, and the pharmaceutical industry.
12. Milagrow Robots
Milagrow Humantech, a robotics company in India, manufactures Milagrow Robots at affordable prices. It provides a wide range of products, namely Milagrow iMap, Window Seagull, and RoboTiger, for various domestic purposes, such as floor cleaning, lawn mowing, and pool cleaning.
Milagrow also manufactures educational robots that enhance students’ learning experiences by helping them learn STEM concepts and cognitive skills. Its body massaging robots facilitate health care.
Future of Robotics in India
With the emergence of artificial intelligence, the Indian robotics industry is expected to be dominated by AI-powered robots in 2024. Generative AI, a subset of AI, is used globally to program robots. It will help developers focus more on research and development instead of investing much time in coding.
Predictive AI helps analyze robot performance trajectory, saving time and resources required for infrastructure maintenance. In the coming time, you will also see increased human-robot collaboration through cobots that assist humans in performing repetitive or hazardous tasks in industries.
Mobile manipulator, or MoMa, is a combination of mobile elements of robotic systems such as wheels and robotic arms as manipulators. It excels in operations and infrastructure maintenance in heavy industries.
Another significant trend that will see growth is the assimilation of humanoid robots into daily life. According to research by Goldman Sachs, the humanoid robot market may increase to $38 million by 2035. They have cited rapid enhancement in AI and the affordability of robotic components as the main reasons behind this positive trend.
Way Forward
India’s robotic landscape is growing rapidly across various sectors, reflecting the country’s increasing aptitude for innovation. The twelve robots featured in this article highlight the numerous ways in which robots can be integrated into our lives.
From defense to medicine to education, robots are becoming drivers of human and industrial development in India. With continuous AI and machine learning advancements, India will incorporate even more sophisticated robotic systems in different facets of life and industry.
OpenAI partners with the U.S. AI Safety Institute to provide early access to its next-generation AI model. This collaboration aims to address safety risks with AI by prioritizing responsible practices in AI development.
OpenAI has taken a significant step to improve AI safety by providing the U.S. AI Safety Institute early access to its next AI model. OpenAI CEO Sam Altman recently announced on X that the organization will work with the government’s executive body for better safety evaluation.
In May, OpenAI faced criticisms over its internal security system protocols. The organization dissolved its safety team, which made headlines because lawmakers raised concerns about it.
Reports said that the company prioritized launching new features over safety. These incidents led to the resignations of important OpenAI employees Jan Leike and Ilya Sutskever.
In response to all these criticisms and allegations, OpenAI said it would remove the clauses from the company’s guidelines that forbade employees from speaking. The company also plans to set up a security commission.
The company had previously committed to dedicating 20% of its funds to security, but it has not been fulfilled. Sam Altman has pledged to complete this commitment and stated that restrictive terms were removed for all new and old employees.
OpenAI increased its budget spending on government policies and legislatures compared to the previous year. The previous whole year’s budget was $260,000. In 2024, the half-year budget was $800,000.
The news of the partnership came when the new proposed bill, “The Future of Innovation Act,” was passed. This bill makes the U.S. AI Security Institute responsible for making rules and regulations for the safety of AI. The executive body under the Commerce Department will now work with OpenAI to improve AI security in the future.
This collaboration marks a significant step in addressing AI safety concerns. It highlights the commitment of both OpenAI and the U.S. AI Safety Institute to promote responsible AI development presently and also in the future.
Vectara and Ofer Mendelevitch collaborate to offer a short course on enhancing search relevance using advanced embedding techniques.
Semantic search is a technology that enables search engines to understand the underlying intent behind the queries. It is quickly transforming information retrieval by delivering more relevant and accurate search results.
However, conventional semantic search often falls short in language learning model (LLM) applications, which rely on a single embedding model. This approach retrieves results that resemble the question rather than relevant answers.
To circumvent this, Vectara and Ofer Mendelevitch have partnered to offer a short course, Embedding Models: From Architecture to Implementation, for all data science enthusiasts.
The course teaches building, training, and deploying dual encoder models using separate embedding models for questions and answers. This significantly improves matching questions with appropriate answers, enhancing overall search relevance.
The course also covers the concept of word embeddings and their evolution to BERT, where embeddings consider the surrounding context of each word. Learners will also gain hands-on experience using contrastive loss to build a dual encoder model with one encoder trained to embed questions and the other responses.
Lastly, the participants will learn to analyze the impact of dual encoders on search relevance and compare it to retrieval processes using single encoders. This course provides a valuable opportunity for anyone looking to advance their understanding of embedding models and their application in modern search systems. The best part is that applicants can enroll in this course for free. Here’s the link to apply now!
Announced at the SIGGRAPH conference, NVIDIA introduces cutting-edge technology for creating high-fidelity real-time 3D modeling for autonomous systems and climate research.
At SIGGRAPH 2024, NVIDIA introduced fVDB, an advanced deep-learning framework designed to construct incredibly detailed and expansive AI-ready virtual representations of the real world. Built upon the foundation of OpenVDB, an industry-standard library for simulating and rendering complex volumetric data, fVDB has taken a significant leap forward in 3D generative modeling.
This innovation has opened new doors for industries relying on accurate digital models to train their generative physical AI for spatial intelligence. fVDB effectively converts raw environmental data collected by LiDAR and neural radiance fields (NeRFs) into large-scale virtual replicas that can be rendered in real-time.
Introducing fVDB, a deep learning framework for large-scale, high-performance spatial intelligence.
With applications spanning autonomous vehicles, optimizing urban city infrastructure, and disaster management, fVDB has a crucial role in transforming robotics and advanced scientific research.
NVIDIA’s research team put in tremendous effort to develop fVDB. This framework is already being used to power high-precision models of complex real-world environments for NVIDIA Research, DRIVE, and Omniverse projects.
fVDB facilitates high-performance deep learning applications by integrating NVIDIA-powered AI operators, including convolution, pooling, and meshing into NanoVDB, a GPU-optimized data structure for 3D simulations. This enables the development of sophisticated neural networks tailored for spatial intelligence tasks, such as large-scale point cloud reconstruction and 3D generative modeling.
Key features of fVDB include:
Larger Scale: It can handle four times larger environments than previous frameworks.
Faster Performance: fVDB achieves 3.5 times faster processing speeds than its predecessors.
Interoperability: The framework seamlessly handles massive real-world datasets, converting VDB files into full-sized 3D environments.
Enhanced Functionality: With ten times more operators than previous frameworks, fVDB simplifies processes that once required multiple deep-learning libraries.
NVIDIA is committed to making fVDB accessible to a wide range of users. The framework will soon be available as NVIDIA NIM inference microservices, enabling seamless integration into OpenUSD workflows and the NVIDIA Omniverse platform.
The upcoming microservices include:
fVDB Mesh Generation NIM: For generating digital 3D environments of the real world.
fVDB NeRF-XL NIM: To create large-scale NeRFs within the OpenUSD framework using Omniverse Cloud APIs.
fVDB Physics Super-Res NIM: It will be useful in performing super-resolution to create high-resolution physics simulations using OpenUSD.
These microservices will be crucial for generating AI-compatible OpenUSD geometry within the NVIDIA Omniverse platform, which is designed for industrial digitalization and generative physical AI applications.
NVIDIA’s commitment to advancing the OpenVDB is evident through its efforts to enhance this open-source library. In 2020, the company introduced NanoVDB and provided GPU support to OpenVDB, boosting performance and simplifying development. This paved the way for real-time simulation and rendering.
In 2022, NVIDIA launched NeuralVDB, which expanded NanoVDB’s capabilities by incorporating ML to compress the memory footprint of VDB volumes by up to 100 times. The addition allowed developers, creators, and other users to interact comfortably with extremely large datasets.
NVIDIA is making fVDB available through an early access program for its PyTorch extension. It will also be integrated into the OpenVDB GitHub repository, ensuring easy accessibility to this unique technology. To better understand fVDB and its potential impact, watch NVIDIA’s founder and CEO, Jensen Huang’s fireside chats at SIGGRAPH. These videos provide further insights into how accelerated computing and generative AI drive innovation and create new opportunities across industries.
Google upgraded Gemini by launching 1.5 Flash, which includes a wide array of languages, faster performance, and quick responses. This upgrade will prove advantageous for teenagers and mobile app users.
A few months back, Google announced the release of Gemini 1.5 Flash. This chatbot’s latest iteration promises significant improvements in speed and performance. This feature upgrade is designed to enhance user experience by providing quicker responses.
Although Gemini 1.5 Flash is a lighter version than Gemini 1.5 Pro, it became a notable upgrade because of its text summarization, image processing, and real-time analytics capabilities.
With these new features, Gemini now helps users work with diverse data types, such as images, voice, text, PDFs, and others, within a single framework. It supports the handling of images through visual recognition, voice through audio-to-text conversion, and other data types with advanced capabilities.
The most important feature of 1.5 Flash is its accessibility. It is now accessible to users in more than 240 countries and supports more than 40 languages, ensuring that many users can benefit from its advancements.
Besides these linguistic and global reach innovations, Gemini 1.5 Flash is also accessible to teenagers. Earlier, as per Google’s policies, there were age restrictions for specific users. Now, teens can also use this version for their school/college subjects and projects.
Google designed Gemini with a strong focus on responsibility and user safety. It has also introduced security policies to ensure the safe use of AI by teenagers and set policies to handle sensitive topics appropriately.
Google’s Gemini 1.5 Flash offers significant benefits in the current digital age. Offering a 1.5 Flash model to free users shows Google’s vision of making AI accessible to maximum people. This move supports innovation and boosts efficiency across various fields.
The enhanced features, such as accessibility and performance of Gemini 1.5 Flash, will ensure that Google is ready to set new standards in the field of AI.
DeepMind took a major leap forward in AI innovation by launching a 2 billion-parameter model for its Gemma 2 family.
As the demand for advanced AI grows, there is a need for models that balance high performance with accessibility across various platforms. Many existing models are too resource-intensive for widespread use, limiting their application to high-end infrastructure.
To address this gap, Google DeepMind has introduced the Gemma 2 2B model to deliver outsized results. This article highlights the significance of the new addition to the Gemma 2 model family.
Inside Google DeepMind: A Short Glimpse into the Future of Technology
Google DeepMind, a subsidiary of Google, is a cutting-edge AI research lab renowned for deep learning and reinforcement learning tasks. It gained global recognition in 2016 when its AlphaGo program defeated world champions in the Go game. Following this notable achievement, DeepMind has continued to innovate with a series of AI language models, including Gato, Sparrow, Chinchilla, Gemini, and more.
Gemma: A Game Changer in AI-Language Models
On February 21st, 2024, DeepMind’s Gemma launched with a 7 billion parameter size suitable for desktop computers and small servers. Gemma is a family of lightweight, open-source large language models built on the same research and technology used to create Google Gemini. It is a text-to-text, decoder-only AI model available in English and comes with open weights for instruction-tuned and pre-trained versions.
Gemma’s second generation, Gemma 2, was released in June. It includes two sizes: 9 billion (9B) parameters for higher-end desktop PCs and 27 billion (27B) parameters for large servers or server clusters.
To mark a leap forward in AI innovation, DeepMind announced a new 2 billion (2B) parameter version of the Gemma 2 model on July 31st, 2024. The Gemma 2 series’ 2B parameter model is designed for CPU usage and on-device applications. It has a more compact parameter size than the 9B and 27B versions. Still, it can deliver best-in-class performance for various text generation tasks, including question answering, summarization, and reasoning.
We’re welcoming a new 2 billion parameter model to the Gemma 2 family. 🛠️
It offers best-in-class performance for its size and can run efficiently on a wide range of hardware.
Top-Tier Performance of 2B Gemma 2 Parameter Model
The 2B Gemma 2 parameter model offers powerful capabilities for the generative AI field. Here are some key highlights:
Flexible: The Gemma 2 model, with its 2 billion parameters, can run efficiently on a wide range of hardware platforms. These include data centers, local workstations, laptops, edge computing devices, and cloud platforms with Vertex AI and Google Kubernetes Engine (GKE).
Integration for Streamlined Development: Gemma 2 2B allows you to integrate seamlessly with Keras, Hugging Face, NVIDIA Nemo, Ollama, and Gemma.cpp. It will soon support MediaPipe.
Exceptional Performance: The company claims that Gemma 2 2B outperforms all GPT-3.5 models on the LMSYS Chatbot Arena leaderboard, a benchmark for evaluating AI chatbot performance.
Open Standard: The 2B model is available under commercial-friendly Gemma terms for commercial and research use.
Easily Accessible: The 2B Gemma 2 model’s lightweight design allows it to operate on the free tier of the NVIDIA T4 deep learning accelerator in Google Colab. This makes advanced AI accessible for experimentation and development without requiring high-end hardware.
Improved Efficiency: Gemma 2 2B has been optimized using NVIDIA’s TensorRT-LLM library to improve efficiency and speed during inference.
Continuous Learning through Distillation: The 2B model leverages knowledge distillation, learning from larger models by mimicking their behavior. This allows the new parameter model to achieve impressive performance despite its smaller size.
A Quick Look At Gemma 2B Model Training, Preprocessing, and Evaluation
The dataset for training Gemma 2 models includes web documents, mathematical text, code, and more. The 2B parameter model was trained on 2 trillion tokens using Tensor Processing Unit (TPU) hardware, JAX, and ML Pathways. To ensure quality, rigid preprocessing methods, such as CSAM filtering and sensitive data filtering, were applied.
The 2B model was evaluated based on text generation benchmarks, such as MMLU, BoolQ, MATH, HumanEval, and more. It was also assessed for ethics and safety using structured evaluations and internal red-teaming testing methods.
Gemma 2B Model Intended Usage
Text Generation: The 2B model helps in creating various types of content, including poems, scripts, code, marketing materials, email drafts, and so on.
Text Summarization: The 2B Gemma 2 model can produce concise summaries for research papers, articles, text corpus, or reports.
Chatbots and Conversational AI: Enhance conversational interfaces for customer service, virtual assistants, and interactive applications.
NLP Research: The 2B model provides a foundation for researchers to test Natural Language Processing (NLP) techniques, develop algorithms, and advance the field.
Language Learning Tools: The model facilitates interactive language learning, including grammar correct and writing practice.
Knowledge Exploration: The Gemma 2B model enables researchers to analyze large text collections and generate summaries or answer specific questions.
New Additions to Gemma 2 Model
DeepMind is adding two new models to the Gemma 2 family. Let’s take a brief look at them:
ShieldGemma: It consists of safety classifiers designed to identify and manage harmful content in AI model inputs and outputs. ShieldGemma is available in various sizes; it targets hate speech, harassment, sexually explicit material, and dangerous content.
Gemma Scope: Gemma Scope is focused on transparency. It features a collection of sparse autoencoders (SAEs), specialized neural networks that clarify the complex inner workings of the Gemma 2 models. These SAEs help users understand how the models process information and make decisions. There are more than 400 freely scalable SAEs covering all layers of the Gemma 2 2B model.
How to Get Started?
To get started, download Gemma 2 2B from Kaggle, Hugging Face, and Vertex AI Model Garden, or try its features through Google AI Studio.
Key Takeaways
Google DeepMind has upgraded the Gemma 2 model with a new 2 billion parameter version. Released on July 31st, 2024, this model is designed for on-device applications, offering efficient performance in tasks like text generation, summarization, and reasoning. It operates well on diverse hardware platforms, including local workstations and cloud services. The Gemma 2 2B model is optimized with NVIDIA’s TensorRT-LLM library and utilizes model distillation for improving performance.
