Best AI Tools for Resume Screening

Artificial intelligence has made huge inroads into the human resource domain lately. Companies are now deploying AI tools to reduce or eliminate time-consuming tasks in the recruitment process. According to experts, screening resumes is the most critical, albeit, the most labour-intensive and challenging task for HR personnel.

Typically, an HR recruiter of a medium and large corporation gets thousands of job applications. The applications will be all over the map and require hours and hours of screening. And, that’s where AI tools come into play. With the right AI tools, recruiters can sort the applications in a fraction of time.

Here we will list the top eight tools that use artificial intelligence to screen resumes.

Also Read: How AI-Powered Tools Can Shape Your Resume

1. Freshservice

Freshservice, developed by Freshworks, is a comprehensive HR solution that excels in resume screening. Its intuitive user interface and robust integration capabilities make it an ideal choice for quickly assessing resumes. Freshservice acts as a centralized hub for collecting resumes, adding notes, and rating applicants, streamlining the hiring process and enhancing recruiter productivity.

Check Freshwork tool

2. SmartRecruiters

SmartRecruiters is a versatile platform that integrates AI-driven resume screening with a comprehensive applicant tracking system. It enhances candidate-job matching by analyzing resumes against job requirements, ensuring only the most suitable candidates are shortlisted. This tool is particularly beneficial for organizations looking to streamline their recruitment process and improve hiring outcomes.

Check SmartRecruiters tool

3. Manatal

Manatal is renowned for its user-friendly interface and advanced AI capabilities that simplify recruitment. It offers features like social media enrichment and candidate recommendation, which help recruiters identify the best candidates efficiently. Manatal’s AI-driven approach ensures that resumes are assessed with precision, reducing the time spent on manual screening.

Check Manatal tool

4. SkillPool

SkillPool leverages AI to assess resumes by focusing on skills and competencies relevant to job roles. It provides detailed insights into candidates’ abilities, helping recruiters make informed decisions. SkillPool’s emphasis on skills-based assessment makes it a valuable tool for organizations prioritizing competency over traditional qualifications.

Check skillpool tech

5. HireBeat

HireBeat offers a comprehensive suite of features, including an inbuilt applicant tracking system and customizable screening questions. Its AI capabilities enable efficient resume screening and candidate ranking, making it easier for recruiters to identify top talent. HireBeat’s integration with multiple platforms enhances its versatility in various recruitment scenarios

Check hirebeat tool

6. Textkernel

Textkernel specializes in semantic search and matching, providing a sophisticated approach to resume screening. Its AI-driven technology ensures that resumes are matched with job descriptions accurately, improving the quality of shortlisted candidates. Textkernel’s focus on semantic analysis sets it apart from traditional keyword-based tools

Check Textkernel tool

7. Daxtra

Daxtra offers advanced resume parsing and matching capabilities, which are highly beneficial for large-scale recruitment drives. Its AI algorithms efficiently sort and rank resumes, reducing manual effort and improving the speed of the hiring process. Daxtra’s integration with existing HR systems further enhances its utility

Check Daxtra tool

8. Sniper AI

Sniper AI uses machine learning to screen candidates and match their resumes with the job specifications at a blinding speed and stunning accuracy. Backed by Recruitment SMART, a UK-based HR tech startup, Sniper AI can be easily integrated with applicant tracking systems for easy resume screening. Sniper AI comes with 53% internal workforce reduction capability that allows recruiters to spend less time screening the resumes. With clients like Infosys, Vodafone, Capgemini, etc., this tool is quite renowned among the industry and claims to be a game-changer for AI-based recruitment. 

Check out Sniper AI tool.

9. Ideal

Ideal is an AI tool that leverages recruiters’ feedback to build an effective algorithm to shortlist thousands of candidates based on their resumes. Claiming to reduce recruiters’ time by 70%, the AI accurately shortlists candidates for the next round of interview with HR personnel. The AI helps in resume screening for a particular job opening and improves the representation of diverse applicants with 100% bias-free algorithm. It considers external evaluation like assessments, chatbot conversations, and other data points for identifying the right candidate.

Check out the Ideal recruitment tool

10. CVViZ

CVViZ uses its in-house AI algorithm to understand the resume contextually, beyond the keyword or binary search approach. The AI-powered resume screening software reduces manual efforts significantly and ranks candidates in real-time. The model can be customised based on the organisation’s requirement and the type of employees it has worked with before. When an organisation adds a new job opening, the AI automatically finds top talent that may already exist in your recruitment database. The AI makes the recruiting process simpler, intuitive, and efficient by improving the quality of hire.

Check out CVViZ tool

11. Skeeled

Skeeled is an automated applicant tracking system that harnesses AI to screen resumes and assess candidates’ qualifications. It has deployed powerful ranking algorithms to deliver a shortlist based on qualification indicators. Along with their qualification indicator, the system also checks the add-ons like specific driver licence type, work permit, or any other custom criteria that organisations demand. This, in turn, automates and streamlines the first steps of the recruitment process, i.e. resume screening. Additionally, it comes with an advanced search engine and filtering tool that finds the right candidates from organisations’ resume databases.

Check out Skeeled.

12. Hubert

Hubert is a holistic AI recruiting platform that assists HR personnel in doing the hiring process. Trained on millions of reliable data points, the AI has been designed to provide transparent advice to the HR team. Hubert combines data from uploaded documents, personality, logical tests and the desired requirements for the job to pick the right candidate. Its transparent in-house-built algorithm also reduces the impact of unconscious bias to make better hiring decisions. Along with resume parsing, it also comes with features like diversifying the workforce, team compatibility, initial job interview, etc.

Check out Hubert

Also Read: How This Startup Is Revolutionising Tech Hiring By Using NLP & Azure

13. Mosaictrack

A smart recruiting solution, Mosaictrack utilises the cognitive power of artificial intelligence to scan through resumes and social profiles to pick the best talent based on culture fit and skill set. Its predictive analytics capability omits the task of conducting surveys or making questionnaires, where advanced algorithms pre-qualify the applicants to enhance the interview process. The AI-tool has been built with IBM’s Watson technology allowing to match talent with extreme accuracy through the power of machine learning and natural language processing.

Check out Mosaictrack.

14. Vervoe

Vervoe is an AI-based skills assessment tool that uses machine learning algorithms, to screen resumes at scale, allowing recruiters to spend more time with high performing candidates. Its predictive analytics then automatically grades and ranks them based on how well they can do the job. The algorithmic models measure the quality of a candidate’s answer against millions of similar responses. It also processes thousands of responses quickly to look for specific words or sentiments that accurately reflect these values. It then provides a list of candidates ranked in order of their strength and potential businesses need.

Check out Vervoe.

15. XOR

Ranked as a leading artificial intelligence platform for recruiting teams, XOR automates resume screening, interview scheduling, onboarding and more. Its advanced capabilities also allow the platform to manage conversations with candidates through one-on-one messaging that saves even more time for the recruiters. The XOR model is hosted on Microsoft’s Azure that enables automatic mass resume screening for a suitable candidate on more than 100 different languages and algorithms and has been trained on various HR and recruitment data sets.

Check out XOR.

The post 15 Top AI Tools For Resume Screening and Recruitment appeared first on AIM.

Over the past couple of years, the interior designing job role has witnessed significant traction. With the advent of technologies like artificial intelligence (AI), virtual reality (VR), and augmented reality (AR), the domain is just getting bigger and better. The advancements are so significant that we have a slew of mobile apps, web apps and other tools doing the job of an interior designer.

1. REimagine Home

REimagine Home is a powerful AI tool that allows users to redesign their homes with personalized design concepts. It uses generative AI to quickly generate design ideas, making it easier to stage empty rooms or redesign furnished spaces. Users can input their preferences, and the tool takes into account architectural and furniture elements to create a unique design.

Explore REimagine Home designs

2. AI House

AiHouse is considered one of the best tools for home design due to its comprehensive features and user-friendly interface. It offers an all-in-one solution for creating accurate 2D and 3D floor plans, decorating rooms, and customizing furniture with photo-realistic visualizations. The platform is powered by AI, allowing for quick and efficient design processes, including rendering 4K images in minutes. AiHouse also supports immersive 3D walkthroughs and integrates seamlessly with manufacturing systems, making it ideal for both designers and manufacturers looking to streamline their workflows

Explore home designs with AI House

3. Spacely

Spacely AI stands out as a premier tool for home design due to its rapid rendering capabilities and user-friendly interface. It allows interior designers and homeowners to create stunning visualizations in under a minute by simply uploading images of their spaces, sketches, or 3D views. The AI-powered platform offers preset styles and customisation options, facilitating better collaboration during presentations. Its efficiency in generating high-quality designs quickly makes it an invaluable resource for both professionals and DIY enthusiasts looking to visualize their ideas effectively and efficiently.

Explore designs with Spacely AI

4. HomeDesignsAI

HomeDesigns AI is a leading home design tool due to its comprehensive features, including interior, exterior, and landscaping design options. With over 75 design styles and the ability to generate ideas in under 30 seconds, it offers speed and efficiency. The user-friendly interface allows for easy uploads and customization, making it accessible to both professionals and homeowners. Its advanced AI technology provides personalized recommendations, while its cost-effective solutions make it a valuable resource for a wide range of users in the design industry.

Explore interior and exterior designs with homedesingai

5. Arch – AI Home Design

Arch – AI Home Design is considered one of the best tools for home design due to its user-friendly features and advanced AI capabilities. It allows users to instantly transform their spaces by simply taking a photo, offering a diverse selection of over 10 interior design styles. The app provides detailed recommendations on furniture, decor, and color schemes tailored to each style, enabling users to create cohesive environments easily. Additionally, it allows users to save and share their designs, facilitating collaboration and feedback, making it a versatile tool for both personal and professional use.

Find Arch AI home design in google play store

6. IKEA Place

One of the leading furniture brands, IKEA boasts of a platform that helps users design interior — IKEA Place. The platform is built on ARCore and is built in such a way that it virtually places the company products in spaces that you scan using your phone camera. The platform also has an intelligent feature that lets users scan any existing item and enables the users to know which products from IKEA match their product.

Another best thing about the app is that it provides accurate measurement of the furniture that would fit at the scanned space, which helps users pick the perfect fit.

Find IKEA Place in play store

7. Leaperr

Leaperr is an Artificial Intelligence (AI) system that does the job of an interior designer. The Leaperr system is a combination of deep-Learning, image-processing and advanced algorithms that can automatically produce interior designs. The system is designed in such a way that it simplifies the entire process of designing. All you need to do is take a picture of your room, fill out a short questionnaire or choose photos of designs you like and the AI system will come up with various photos tailor-made to your space. And one of the best parts about Leaperr is the fact that it generates interior designs that look like real photos.

Explore designs in Leaperr

8. LexSet.ai

LexSet.ai is a New York-based AI startup that is changing the way how furniture used to be purchased. The company’s AI analyses a room and identifies different furniture and essentials in the room and also comes up with item recommendations (for e.g. furniture) that the user would like to have in the room.

Explore interior designs with Lexset.ai

9. Planner 5D

Available for Web, iOS, Android, Mac OS and Windows, Planner 5D is another best-in-class AI-powered home designing tool.  Powered by AI, VR and AR, this popular home design tool enables anyone to easily create floor plans and interior designs. The popularity of this tool has reached such a level that it has 40 million users and these users have designed more than 80 million projects without any special design or software skills. Another standout functionality is that the app turns a 2D blueprint into a 3D version. Its intelligent neural-network instantly digitise floor plans. All you need to do is just upload a floor plan and the AI will do its job.

Explore planner 5d designs

Homestory AR (Discontinued)

Available on iOS, Homestory AR is an AI-powered interior designing app that shows different types of furniture and other essentials that would look good. The app is designed in such a way that all a user needs to do is scan the area in the house and the AI functionality will measure the room’s size and shape and would recommend products based on that. Other features of this platform include: one can share images and designs with others and also, one uses the app’s 3D virtual room view to take a look at it from different angles.

Find Homestory AR iOS store

The above top AI tools for home design are ideal for a diverse range of users. Professional interior designers can leverage AI to enhance creativity and streamline their workflow, allowing for more efficient and innovative design processes. Homeowners and DIY enthusiasts benefit from AI tools by visualising and experimenting with design ideas without needing extensive expertise. Real estate professionals can use AI for virtual staging, making properties more appealing to potential buyers. Additionally, architects and design firms can integrate AI to improve customisation and efficiency in their projects. Overall, AI tools democratise design, making it accessible to both professionals and amateurs alike.

The post Best AI Tools for Home And Interior Designing appeared first on AIM.

“Where’s the delete and undo button in LLM?” asked furious Anshu Sharma, co-founder and CEO of Skyflow, who was introducing the concept of LLM vaults in a recent interaction with AIM. 

“LLM vaults are built on top of the proprietary detect engine that detects sensitive data from the training datasets used to build LLMs, ensuring that this data is not inadvertently included in the models themselves,” he added, saying the company has built a proprietary algorithm to detect sensitive information in unstructured data that is being stored in the vault. 

The Need for LLM Vault 

Sharma has a stronger reason to believe so. “While storing data in the cloud with encryption will safeguard your data from obvious risks, in reality, we need layers. The same data can not be given to everyone. Instead, you can have an LLM vault that can identify sensitive data while inference and only share non-sensitive versions of the information with LLM,” said Sharma, suggesting why the LLM vault matters. 

The vice president of Amazon Web Services, Jeff Barr, also mentioned that “The vault protects PII with support for use cases that span analytics, marketing, support, AI/ML, and so forth. For example, you can use it to redact sensitive data before passing it to an LLM”. 

Gokul Ramrajan, a tech investor, explained the importance of LLM vaults, saying, “If you think protecting private data was hard with databases, LLMs make it even harder. “No rows, no columns, no delete.” What is needed is a data privacy vault to protect PII, one that polymorphically encrypts and tokenises sensitive data before passing it to a LLM”. 

A few weeks ago, when Slack started training on user data, Sama Carlos Samame, the co-founder of BoxyHQ, raised a similar concern for organisations that are using AI tools and why they should have LLM vaults to safeguard their sensitive data. 

Going Beyond LLM Vault 

The likes of OpenAI, Anthropic and Cohere are also coming up with innovative methods and features to handle the data of a user and enterprise. For instance, if you are using OpenAI API, then your data won’t be used to train their model. Also, you can opt out of data sharing to ChatGPT. Privacy options like these somewhat eliminated the need for LLM Vaults.

Anthropic, on the other hand, have also incorporated strict policies on how they use user data to train their model and unless a user volunteers to do so or a specific scenario comes in where they collect user data. 

Meanwhile, Cohere has collaborated with AI security company Lakera to protect against LLM data leakage by defining new LLM security standards. Together, it has created the LLM Security Playbook and the Prompt Injection Attacks Cheatsheet to address prevalent LLM cybersecurity threats. 

There are other techniques like Fully Homomorphic Encryption (FHE) which allows computations to be performed directly on encrypted data without the need to decrypt it first. This means the data remains encrypted throughout the entire computation process, and the result is also encrypted.

The post Why Isn’t There a Delete or Undo Button in LLMs? appeared first on AIM.

This month, Target will launch its official generative AI chatbot, dubbed the Store Companion,which will be deployed across Target’s 2,000 stores spread across the US, acting as a helper for their store employees. 

According to the retailer, the Store Companion will be able to “answer on-the-job process questions, coach new team members, support store operations management and more.” 

With the release of the bot, Target has become the first major retailer to provide generative AI capabilities to its service employees. However, the Companion is just one of several generative AI initiatives that the US retailer has undertaken. 

With a dedicated team of technologists, data scientists and researchers, 1,700 of which are based in Bengaluru, the retailer has been working on several generative AI initiatives over the last year.

Speaking to AIM, Brad Thompson, Target’s senior vice president of technology, said that the retailer has spent the last year working on as many as 20 generative AI use cases that they’re hoping to scale from proof of concepts, with the Companion as part of this initiative.

“Generative AI presents us with new tools and techniques to create experiences that were not possible before at scale with traditional AI. But it’s very difficult to go from a proof of concept to scaling it to the size of what our operations are. So that’s where we’ve been focused for the last year, scaling them so that it can be used by 400,000 team members on the field or by hundreds of millions of guests in the US,” he said.

Interestingly, the way Thompson explains the adaptability of the Companion and other generative AI integrations seems to reflect on the overall consensus of how retail-oriented chatbots will be dealt with across the board.

How Are Retailers Using Proprietary Models?

The Companion leverages GPT-4o in its functioning, with the retailer having already partnered with Google, Microsoft and OpenAI, as well as others in the AI field. Thanks to this, Thompson said that they’re able to cater to whatever generative AI needs come up.

“We have partnerships with all the big players. We are willing to use Google’s LLMs, OpenAI’s LLMs or open source LLMs that we can host on our infrastructure. They all have different strengths and weaknesses, so depending on the kind of experience we’re trying to build, we will experiment with multiple and pick the right one,” he said.

This also seems to be the case for many retailers and other enterprises, wherein the general consensus seems to be partnerships with multiple AI companies, rather than just one, to leverage their capabilities.

Potential For Leaks?

Thanks to this, cybersecurity is a major focus. Thompson said that while there was no way that the retailer would build their own LLMs, moderation was something that was focused on heavily, especially when it comes to integrating a third-party LLM.

“We will use proprietary LLMs extensively, I would be surprised if we built our own LLMs. We benefit from their investment, but we also add our own moderation services on top.There are very clear guidelines about what a retailer has to do and an LLM provider, like Google or OpenAI, doesn’t care about that. So we create our own moderation services and do lots of testing,” he said.

This involves a cybersecurity team who internally red team their systems, or hire contractors to do so, with Target actively investing in these capabilities. This is similar to AIM’s recent coverage of how jailbreaking is turning into a profitable industry, as well as cybersecurity startups coming up to deal with potential threats due to generative AI integration in enterprises. However, Thompson admitted that there would always be a risk.

“We take that risk very seriously, but that doesn’t mean there won’t ever be a situation. We think it can and will happen. It happened to Amazon, and I’m sure that’s something they’re looking at and learning from. But we will need to invest and test, test, test. The risk is not zero, but we think there are some common sense precautions to reduce those risks,” he concluded.

The post Target to Launch First Retail Chatbot for Store Employees This Month appeared first on AIM.

Are AI agents the next big thing? The co-founders of Sarvam AI definitely think so. One of the startup’s theses is that consumers of AI will use generative AI models not just as a chatbot, but to perform tasks and achieve goals and that too, through a voice interface rather than text.

At an event held in Bengaluru on August 13th, Sarvam AI announced Sarvam Agents. While the startup, which is backed by Lightspeed, Peak XV, and Khosla Ventures, is not the only company building AI agents, what stood out was the pricing.

The cost of these agents starts at just one rupee per minute. According to co-founder Vivek Raghavan, enterprises can integrate these agents into their workflow without much hassle.

“These are going to be voice-based, multilingual agents designed to solve specific business problems. They will be available in three channels – telephony, WhatsApp, or inside an app,” Raghavan told AIM in an interaction prior to the event.

These agents could be integrated into contact centres and various applications across multiple industries, including insurance, food and grocery delivery, e-commerce, ride-hailing services, and even banking and payment apps.

For example, they could streamline customer service operations in insurance by handling policy inquiries, make reservations, assist with financial transactions, facilitate order tracking and customer support in food delivery, and manage ride requests and driver communications in ride-hailing apps.

Enabling AI Agents 

A technology that offers this capability at just a rupee per minute could be transformative. AI adoption could see substantial growth with AI agents, and Sarvam AI’s mission is to make this a reality.

Meta, which owns WhatsApp and other major social media platforms like Facebook and Instagram, introduced Meta AI to all these platforms. 

Meta AI can be summoned in group chats for planning and suggestions, it can make restaurant recommendations, trip planning assistance, and also provide general information.

However, Sarvam AI claims their generative AI stack could help AI scale in India compared to others. Their models perform better in Indic languages than the Llama models, which is powering Meta AI. During the event, the startup demoed their models, which managed to outperform certain models in Indic language tasks.

The startup is currently making its agents available in Hindi, Tamil, Telugu, Malayalam, Punjabi, Odia, Gujarati, Marathi, Kannada, and Bengali, and plans to add more languages soon.

Interestingly, given the backgrounds of the co-founders, especially Raghavan, who has helped Aadhaar scale significantly in India, the startup is well-positioned to drive widespread AI adoption and impact.

Raghavan served as the chief product officer at the Unique Identification Authority of India (UIDAI) for over nine years. As of September 29, 2023, over 138.08 crore Aadhaar numbers were issued to the residents of India.

As part of the interaction, Raghavan highlighted his experience in scaling technology to benefit humanity. He also mentioned that the startup is already in talks with several companies interested in utilising Sarvam agents. At the event, the startup revealed that their agent is already being integrated into the Sri Mandir app. 

(Vivek Raghavan & Pratyush Kumar, co-founders at Sarvam AI)

Models Powering Sarvam Agents

Raghavan said there are multiple models that form the backbone of these AI agents. The first is a speech-to-text model called Saaras which translates spoken Indian languages into English with high accuracy, surpassing traditional ASR systems. 

The second model, called Bulbul, is text-to-speech, offering diverse voices in multiple languages with consistent or varied options depending on preference.

The third is a parsing model designed for high-quality document extraction. This model addresses common issues with complex data, aiming to improve accuracy in parsing financial statements and other intricate documents.

Notably, these models are closed-source and available to customers as AI. However, the startup also launched an open-source, two billion-parameter foundational model trained on four trillion tokens and completely from scratch.

Less Dramatic but Good Demo

At the event, the startup also demoed what their agents could do. The demo, which was pre-recorded, showcased how a Sarvam agent could comprehend a person’s health condition, assist in finding the right doctor, and even book an appointment.

A pre-recorded demo may not appeal to everyone, but from the startup’s perspective, it’s a safe bet and completely understandable. Live demos carry inherent risks; for instance, at the Made by Google event, one Googler’s attempt to showcase Google Gemini’s capabilities live saw them fail twice before succeeding.

Sarvam AI’s demo was also reminiscent of OpenAI’s showcase of their latest model, GPT-4o, earlier this year. While Sarvam AI’s demo was less dramatic and also not at all controversial, it effectively demonstrated that their agents could understand the context as well as various Indian languages and dialects.

“These agents can also be very contextual. For example, when you’re on a particular page, you press a button seeking more information about a particular item. The agent will be context-aware, so it knows where you’re asking from. In contrast, when you call a number, it starts from scratch without that context,” Raghavan said.

The startup revealed it trained its models using NVIDIA DGX, leveraging Yotta’s infrastructure. Other notable collaborators include Exotel, Bhashini, AI4Bharat, EkStep Foundation and People+ai.

The post AI Agents at INR 1 Per Min Could Really Help Scale AI Adoption in India appeared first on AIM.

The IT industry seems to love its employees a little too much. It is not some kind of major breakthrough that the salaries offered by these Indian tech giants are way too low, but Cognizant has set a new record this time and is being heavily trolled for it. 

“My driver makes way more than that…,” commented one user on X when they came across the news that Cognizant is offering a salary of Rs 2.52 lakh per annum for a third year undergraduate, which roughly boils down to Rs 20,000 per month. 

The saddest part about this is that it’s the same package that was offered in 2002. Nothing has changed. “No house, no free commutation, no free food. All this to be managed in just 18 to 19K rupees after PF deduction in metro cities,” added the user.

The salary is barely peanuts. “Degrees have become useless in India,” said a user, which is similar to a discussion a while back about developers in India being more likely to be unemployed if they are educated.

Expanding Base and Capabilities, But Not Pay

Adding to all of this, Cognizant is expanding its operations across the country. Most recently, it opened its first centre in Indore, Madhya Pradesh, a move set to create over 1,500 jobs with the potential to grow to 20,000 in the future. Cognizant’s expansion in Indore adds to its existing presence in cities like Bengaluru, Bhubaneswar, Chennai, and others across India.

Right now, Hyderabad has become Cognizant’s biggest centre globally, replacing Chennai from the top spot with around 57,000 employees.

Cognizant also went on a spree of partnering with companies for generative AI capabilities aimed at its employees. Just this year, it partnered with Microsoft and ServiceNow to integrate generative AI for employees, making a generative AI-powered digital workspace. 

The IT giant was also actively hiring in FY23 but the numbers for FY24 are yet to be revealed. Cognizant hired around 60,000 freshers, unlike TCS, Infosys, or Wipro. But it seems like this comes at the cost of not offering good enough pay to its employees. 

“2.52 LPA is very generous. What will the graduates do with so much money?” remarked a user on X, while another added that this is why the younger generation is content with making reels on Instagram.

This year, Cognizant also gave poor salary hikes, with developers on Reddit pointing out this was despite the fact that the company has the highest paid CEO earning Rs 186 crore per year. “Why can’t they have minimum wage criteria in all the sectors,” asked a user on X. Another said that they get more money with cashbacks than this.

The truth is that there are people who are ready to take any job they get and this salary is more than appealing to them. As Debarghya ‘Deedy’ Das puts it, “It’s simple. If you think it’s too low, find another job. If you want 186 cr, start the next Cognizant,” he said. 

The Same With All of the Indian IT

Some people make the argument that the companies, though they’re offering less salaries, are also upskilling them with generative AI to make them ready for the future market, and possibly making them prepare for a business.

If the wage is too low, “Cognizant should get 0 employees, then they will realise they raise the salary. If they got 10,000 employees doing this, then you are wrong and it’s not too low,” Das replied to another post. 

To explain this point, TCS recently also had 80,000 job openings that they could not fill citing a skill gap as the reason. The company is still looking for people for the roles of Ninja, Digital, and Prime. Interestingly, the Ninja category offers a package of Rs 3.3 LPA for various roles, while the other two roles offer a range between Rs 9 and Rs 11.5 LPA.

Most of the IT giants have stopped hiring, but people would still love to work there to start somewhere. Moreover, there is almost no hike at TCS. To put inflationary pressures into perspective, Jio and Airtel have hiked their prices by 25%, but TCS gave only around a 1% hike to its employees. 

There is an oversupply of engineers in India, which makes it clear that all these job openings can get easily filled. This makes Cognizant’s risky bet not so risky as the backlash will die soon as soon as the seats are filled. 

But this also puts the perspective of why graduates are shying away from IT companies. They get better salaries at startups and even GCCs in India. Indian IT giants are not even trying to lure new joinees anymore.

The post Cognizant is Offering a Very Generous Salary of INR 2.5 LPA for Freshers appeared first on AIM.

A few days back, a developer named Jason revealed that 99% of front end development as we know it today will be fully automated within 3 years. “The 1% will be branding and curating. 2-3 individuals will do as much as 20 does today,” he said in a post on X.

Jason isn’t wrong. HTML, CSS and JavaScript are still considered as the foundation of frontend development. HTML arranged both structure and content. This material is styled using CSS. JavaScript has the ability to provide the content an incredible level of interaction. 

Source: X

However, the frontend development landscape of today is incredibly vast, with bundlers like Webpack and Rollup, automated task runners like Gulp and Grunt, and CSS preprocessors like Sass and Less that let developers modularise CSS code and work with control flow commands and other utilities in it.

There are also powerful testing tools like Puppeteer and Cypress, and UI frameworks and libraries like Vue.js, Angular, and React.

Source: X

Additionally, web developers have become quite outdated! These days, it’s unlikely that a neighbourhood bakery, dentist, or artist will hire a developer and pay them tens of thousands of dollars to create a website from the ground up. They will visit website building and hosting softwares, select a preferred template, and pay as low as $20 per month.

The Threat of Generative AI

AI tools are also taking up some frontend development tasks. A tool like Anima lets you convert Figma designs to React code. Sketch2Code converts wireframe sketches into HTML pages. These AI coding tools can increase developer productivity, allowing them to build products faster.

Source: Mckinsey

Debugging AI-generated code can be difficult, though. You may end up taking up even more time than you’d have if you’d written the code yourself.

Too Complex

This is similar to what AIM had said earlier that overuse of such tools had made frontend development more complex. Many developers on Reddit have found web development frustrating. 

One developer went on to say that, “Web development is f**** stupid”, while another wrote, “I started off learning video games and desktop apps, but by the time I finished college, I realised most of software engineering is web apps, which I despise.”

Developers say that the dependency management with npm and Yarn (JavaScript package managers) is a nightmare and “frameworks like React, Redux, and Next.js are constantly changing for no reason, making the entire process unnecessarily complicated”.

The hate for web development is not new. A simple search on Google, X, Quora, Reddit, or any community platform throws thousands complaining about the constantly changing paradigm of web development for more than a decade.

The threat of low-code and no-code tools

Even large corporations have some low- and no-code use cases. For instance, Upwork uses Webflow to add new pages to its website and change it in real time without consulting the IT team. 

As a result, the engineering team can concentrate on the final product instead of making quick tweaks or upgrades that the marketing team may do on their own. 

Without a doubt, low-code tools may help front-end developers with some jobs. For instance, they can create templates that can be used to generate designs rather than having to start from scratch. 

Too Early To Call It Quit

However, not everyone shares this negative view. Some see the challenges of web development as opportunities for interesting projects. “There are so many interesting projects in web development. People think it’s just building simple websites, but web apps can have so many interesting challenges and depth,” a user noted.

Moreover, simply using AI tools won’t help you build a website.  According to the McKinsey study “Unleashing developer productivity with generative AI,” AI code tools produced “incorrect coding recommendations and even introduced errors in the code.”

The project or organisation’s context is unknown to the tools. You have to provide the context as a developer, meaning you need to be great at prompt engineering.

AI code generation tools are also more suitable for simpler tasks, like generating code snippets. They do not generate helpful code in more complex use cases.

The post Generative AI is a Threat to Frontend Developers appeared first on AIM.

The Technology Innovation Institute (TII), the applied research arm of Abu Dhabi’s Advanced Technology Research Council (ATRC), has launched the Falcon Mamba 7B, a groundbreaking addition to its Falcon series of LLMs. Open-source State Space Language Model (SSLM), Falcon Mamba 7B has been independently verified by Hugging Face to outshine all competitors.

Marking a significant departure from previous Falcon models, which relied on transformer-based architecture, the Falcon Mamba 7B introduces SSLM technology to the Falcon lineup. This model not only outperforms Meta’s Llama 3.1 8B, Llama 3 8B, and Mistral’s 7B in new benchmarks but also claims the top spot on Hugging Face’s tougher benchmark leaderboard.

Source: Falcon

SSLMs excel at processing complex, time-evolving information, making them ideal for tasks like book-length comprehension, estimation, forecasting, and control tasks. Falcon Mamba 7B demonstrates superior capabilities in Natural Language Processing, machine translation, text summarisation, computer vision, and audio processing, with significantly lower memory requirements compared to traditional transformer models.

H.E. Faisal Al Bannai, Secretary General of ATRC and Adviser to the UAE President for Strategic Research and Advanced Technology Affairs “The Falcon Mamba 7B marks TII’s fourth consecutive top-ranked AI model, reinforcing Abu Dhabi as a global hub for AI research and development. This achievement highlights the UAE’s unwavering commitment to innovation.”

Source: Falcon

TII Continues Growth with SLMs

In a focussed shift to building small language models, Hakim Hacid, executive director and acting chief researcher at Technology Innovation Institute (TII), had discussed the same with AIM in an exclusive interaction, earlier this year. 

“We were asking at some point the question, as to how big should we go? I think now the question is how small we could go by keeping a small model,” said Hacid, saying that they are exploring that path. 

Further, he said that they are making models smaller because, again, “if we want the pillar of the deployment to succeed, we need to actually have models that can run in devices, and in infrastructure that is not highly demanding.”

With over 45 million downloads of Falcon LLMs to date, the Falcon Mamba 7B continues TII’s tradition of pioneering research and open-source contributions. The model will be released under the TII Falcon License 2.0, a permissive software licence based on Apache 2.0, emphasising the responsible use of AI.

TII continues to build on the open-source culture and believes not everyone will be able to sustain it. “You need a lot of funding to sustain open-source and we believe that not everyone will be able to do it,” said Hacid. 

The post TII Unveils Falcon Mamba 7B, Outperforming Llama 3.18B and Other SLMs  appeared first on AIM.

Acer recently launched its latest Chromebook Plus models, the Acer Chromebook Plus 14 and 15 laptops, in India. The all-new Chromebook Plus with the latest built-in Google Gemini AI features offering robust performance, enhanced productivity features, and a sleek, professional design.

These advanced laptops are specifically designed to cater to the demands of the enterprise and education sector.

Acer Chromebook Plus offers built-in Google apps and powerful AI capabilities. It also offers Google Photos Magic Eraser, File Sync, Wallpaper generation, AI-created video backgrounds, and Adobe Photoshop on the web to help consumers boost their productivity.

Powered by a range of Powerful Intel® & AMD® processor variants, these Chromebooks ensure robust performance for multitasking and running demanding applications. The Chromebook Plus 14 has two variants. One with Intel® Core i3-N305 processor and another with AMD® Ryzen® 7000 Series Processor, while the Chromebook Plus 15 offers Up to Intel® 13^th Gen Core i7-1355U processor.

All the models support up to 16GB LPDDR5X SDRAM and Storage up to 512 GB PCIe NVMe SSD, ensuring fast data access and ample space for important files and applications.

“With powerful Intel® & AMD® processors, vibrant displays, Powerful AI capabilities, and robust security features, we believe these Chromebooks will significantly enhance productivity and learning experiences. Our goal is to offer solutions that empower professionals and students to achieve more, and the Chromebook Plus 14 and 15 embody this vision perfectly,” Sudhir Goel, Chief Business Officer at Acer India, said.

Designed for durability and reliability, these Chromebooks have undergone rigorous military-grade reliability tests, including mechanical shock, transit drop, vibration, and resistance to sand, dust, humidity, and extreme temperatures.

In a previous interaction with AIM, Goel said, “At Computex 2024, we have showcased a lot of new products, which we are thrilled to introduce to the Indian market in the coming year.”

PC makers are hoping AI could help pull the market from the stalemate that it was last year. Research firm Canalys predicts that the PC market will see an 8% annual growth in 2024 as more AI PCs hit the market. Canalys also predicts AI PCs will capture 60% of the market by 2027.

The post Acer Launches New AI-Powered Chromebook Laptops in India appeared first on AIM.

Cognizant has expanded its presence in India by opening its first centre in Indore, Madhya Pradesh, a move set to create over 1,500 jobs with potential growth to 20,000 in the future.

The new facility, spanning 46,000 square feet, was inaugurated by Madhya Pradesh Chief Minister Mohan Yadav, who emphasised the importance of intellectual property in the 21st century. “The 21st century is the century of intellectual property, which will make its mark in the world on the basis of information technology and artificial intelligence (AI),” Yadav stated during the ceremony.

Ravi Kumar S, CEO of Cognizant, posted his excitement about the new launch on LinkedIn. “This is the 2nd one after our announcement early this year of a new center in Bhubaneswar. We love the enthusiasm amongst our Cognizant associates about this strategy of expansion into smaller cities in India. We are the largest IT employer in Coimbatore and our aspirations are to be the largest in Indore and Bhubaneswar.”

Surya Gummadi, EVP of Cognizant and President of Cognizant Americas, highlighted the strategic importance of the Indore center, saying, “Indore will seamlessly integrate into our existing delivery network across India, focus on innovative solutions for our global clients, create new opportunities for local talent, and bring our offices closer to where our associates live.”

Located at Brilliant Titanium in the heart of Indore, the centre has a seating capacity for 500 and can accommodate up to 1,250 associates in a hybrid work model. Cognizant’s expansion in Indore adds to its existing presence in cities like Bengaluru, Bhubaneswar, Chennai, and others across India.

Madhya Pradesh has secured investment proposals worth INR 3,200 crore from Google, NVIDIA, and Microsoft, in a single day during an interactive session held in Bengaluru on August 7-8, 2024.

The post Cognizant Expands to Indore, Creating 1,500 Jobs appeared first on AIM.

This year has been particularly tough for IT employees with companies declining to hire freshers. Well, those working at the organisation are also not having the best time of their lives.

The entire IT sector witnessed a sharp decline in hike growth in 2023, dropping to 9.1% from 10.3% in 2022. Excluding products, the IT/ITeS sector saw the lowest average salary hike at just 8.4%.

In 2021, the average salary hike was 8.8%, which increased to 9.7% in 2022. Last year, the sector saw average increments between 8.5% and 9.1%. 

IT companies are offering single-digit salary increments to employees who lack AI-related skills. Further, there is an abundance of entry-level talent who are now equipping themselves with the latest tech and tools. Additionally, rising business costs, coupled with layoffs are making this trend continue this year too. 

IT Giants Follow Single Digit Hike Trend 

Earlier in May, a Dehradun-based engineer, Akshay Saini, took social media by storm with his critique of the corporate appraisal system in India.

Saini claimed that appraisals are a joke and further urged employees feeling underpaid to switch jobs.

He is not all wrong. 

Infosys, which offered an average salary hike of 14.6% in FY22, came down to 9% in FY24. For TCS, the average hike ranged between 7-9% in 2023-24, compared to 10.5% in 2021-22. 

Tech Mahindra and HCLTech offered average hikes between 5-7%. Wipro and LTIMindtree are yet to finalise their decisions on the same. 

Meanwhile, Accenture has opted not to provide salary hikes for its India-based employees this time.

Well, the story of startups is no different. 

Can GenAI Skill Help?

As per the Amazon Web Services (AWS) report, Indian employees with AI skills and knowledge may see salary hikes of more than 54% and those in IT and research and development enjoying the highest pay increases. 

Major Indian IT companies, despite substantial generative AI training initiatives, are offering lower starting salaries of around INR 3-4 lakh. 

AIM noted that AI engineers with generative AI skills saw a significant 50% increase in their salaries. At companies like Accenture, a generative developer can earn about INR 8.5 lakh per annum, compared to the INR 5-6 lakh that regular software engineers make. 

AIM reached out to an employee who works with generative AI at TCS. He said that the company’s pay scale won’t change just for GenAI resources, and the hike is also largely based on the company’s financial performance and business unit budget allocation.

“I believe we may get some good hikes in the range of 25-30% after switching to another company,” the employee added.

Hiring Freeze

Indian IT companies have all recently highlighted their commitment to integrate generative AI into their operations. Top firms like TCS, Infosys, and Wipro have been leveraging technology-enabled training for their employees.

As the companies are offering training programs, they seem to be following Charles Darwin’s survival of the fittest theory to retain employees. So, if an employee falls behind in the race, he is laid off. 

During the calendar year 2023, India’s IT sector laid off around 20,000 techies in a manner known as a “silent layoff,” according to All India IT & ITeS Employees’ Union (AIITEU) data. 

On the other end, companies have delayed the hiring process. Indian IT companies like Wipro, TCS, and Infosys have delayed the onboarding of 10,000 freshers, and refused to provide a joining date. 

As per the Nasscom data, the IT sector will create only 60,000 new jobs in FY24 compared to 2,70,000 jobs that were created by the sector in the previous fiscal year.

Ultimately, the choice is yours – either stay with the company, undergo training, and earn an average salary. Or get laid off, switch to another company, and wait for the same cycle down the road. 

The post Salary Hikes in IT Companies are Less than Cash Back appeared first on AIM.

While the initial launch date for Tesla’s self-driving cab Robotaxi has been pushed, Tesla chief Elon Musk remains optimistic. Tesla owners will be able to transform their vehicles into Robotaxis, allowing their cars to generate income, much like an “Airbnb on wheels.”

Robotaxi Vision 

The Robotaxi service aims to make Tesla drivers add their vehicles into the cab fleet, a little different from other autonomous cab providers such as Waymo or Baidu. 

“We’ll have a fleet that’s on the order of 7 million that are capable of autonomy. In the years to come it will be over 10 million and 20 million. This is immense,” said Tesla chief Elon Musk. “The car is able to operate 24/7 unlike the human drivers,” he said.  

However, Tesla’s concept of Robotaxi has been recently questioned by Uber CEO Dara Khosrowshahi, in a recent interview. “It’s not clear to me that the average person,Tesla owner, or owner of any other car is going to want to have that car be ridden in by a complete stranger,” he said. 

Shared Revenue

Similar to your third-party cab provider, such as Uber, Tesla is also looking to have a shared revenue format with the Robotaxi owners. Musk also highlighted how Robotaxi will give the luxury to the users to choose the hours and schedule the hours of operation accordingly, thereby giving Robotaxi owners the choice to use it as both a personal and a commercial vehicle. 

Interestingly, Khosrowshahi even questioned the supply angle for this particular format of ride. “It just so happens that probably the times at which you’re going to want your Tesla are probably going to be the same times that ridership is going to be at a peak.” Thereby, hinting at how the demand and supply will not be met. 

Furthermore, he is also sceptical about the whole autonomous feature in vehicles.  “We’re seeing that when one of our customers is offered an autonomous ride, about half the time they say, yeah that would be really cool, and half the time they say, no thank you, I’d rather have a human I think that’s going to improve over a period of time,” he said.  

Even then, the Uber CEO has not denied a likely partnership with Tesla in the future. “Hopefully, Tesla will be one of those partners. You never know.” 

With numerous autonomous vehicles on the market, the key distinction lies in the different approaches each company has taken toward autonomous capabilities.

LiDAR vs Vision

Tesla uses computer vision (Tesla Vision) rather than the conventional LiDAR (Light Detection and Ranging) tech for autonomous vehicles. 

Musk has always been vocal about using vision-only methods for autonomous capabilities as opposed to Waymo and other self-driving cars that heavily rely on LiDAR. 

Previously, Musk had even called out LiDAR as a “fool’s errand” and that anyone relying on it is “doomed.” He even referred to Waymo’s robotaxi services as limited and fragile, and claims Tesla’s systems to work anywhere in the world, not limited by geography. 

The cost of LiDAR has been a major deciding factor for adopting sensors in autonomous vehicles. Musk considers LiDAR to be expensive sensors that are unnecessary. He believes that cameras, which Tesla banks on, will help them navigate through adverse weather conditions. 

Kilian Weinberger, professor of Computer Science at Cornell University, had earlier said that cameras are dirt cheap compared to lidar. “By doing this they can put this technology into all the cars they’re selling. If they sell 500,000 cars, all of these cars are driving around collecting data for them,” he said. 

While Tesla is heavily backing vision tech for AV, LiDAR is not completely out of the picture. Recently, Tesla purchased over $2 million worth of lidar sensors from Luminar. The company revealed that Tesla was its largest LiDAR customer in Q1. 

“At some point Tesla will pivot and adopt LiDAR. I think it is not an if question, but rather when,” a user speculated on Reddit. 

Self-Driving Cars on the Rise

Driverless car services are witnessing a huge growth. Google’s parent Alphabet, recently announced an investment of $5 billion on its self-driving subsidiary Waymo. Currently, they operate in San Francisco, Phoenix, and Los Angeles and they will soon test them on the freeways of the San Francisco Bay Area. It was reported that Waymo is currently delivering 50,000 paid rides per week. 

Zoox, a subsidiary of Amazon, is also developing autonomous vehicles and is operational in certain cities in the US. 

Baidu’s autonomous fleet which is already running 6000 driverless rides per day in Wuhan (China) adopts a mix of technologies. As of April 2024, the cumulative test mileage of Apollo L4 has exceeded 100 million kilometres.

“I think we are all at L4 today; and with the government regulation, it’s not possible to do L5. Another thing is that, I think, all of us providing this technology haven’t been tested in all the scenarios. We wouldn’t have this confidence to claim that we have the L5 capability,” said Helen K. Pan, general manager and board of directors for Baidu Apollo, California, in an earlier interaction with AIM.

While Waymo and Baidu are at L4 level of autonomous capability, Tesla is still between L2 and L3. 

The Robotaxi unveiling event is currently planned for October 10. Musk is also positive of expanding Tesla’s self-driving technology to a wide market in the U.S. and internationally.

The post Elon Musk’s Robotaxis are Built for Riders, Not for Drivers appeared first on AIM.

Mumbai-based legal tech company LexLegis has set itself apart as “India’s answer to Harvey AI,” having opened for access as of this week.

LexLegis AI has been trained on one crore legal documents aggregated over 25 years. The AI tool is aimed at legal professionals, offering detailed analyses for legal research.

“It is to help simplify and demystify the legal complexities for everyone and to save time on the vast amounts of time that we’re spending on legal research. The tool enables users to efficiently navigate through thousands of pages and extract meaningful, actionable information,” said co-founder and managing director Saakar S Yadav.

The legal research company, which was founded in 1998, has reinvented itself this year with the goal of building an LLM for Indian law. The company was founded by the late S C Yadav, who served as the Chief Commissioner of Income Tax, and his son Saakar S Yadav.

Over the years, the company worked on several legal tech solutions. Shortly after its founding, the company developed and launched a search engine catered specifically towards legal professionals and tax consultants, to help in the understanding of the taxation domain.

Additionally, they also built the largest database of judgments in India in 2004, followed a decade later by the development of the National Judicial Reference System (NJRS), which is the world’s largest repository of appeals for the Income Tax Department.

With LexLegis AI, the company has leveraged its 25 years of experience within the industry to offer an overarching tool to help legal professionals, businesses and researchers cut down on the time used to research and find citations for relevant cases.

Speaking on the tool, Yadav stated that while the tool currently focuses on tax law, it aims to inculcate all fields of law for use.

While previously AIM has covered tools to assist legal professionals, this is one of the first Indian-made LLMs for law, focusing solely on the Indian legal system.

The post This Mumbai-Based Startup Has Released India’s Very Own Harvey AI appeared first on AIM.

Bengaluru is synonymous with traffic. If you happened to be stuck in your car or bike in places like Silk Board, K.R. Puram, Marathalli, or Outer Ring Road, you are surely to blame BMTC and KSRTC buses for occupying the entire road and blocking your way. 

Source: Bangalore Insider

“Public transport occupies a much lesser space compared to any personal mobility vehicle, car, two-wheeler. Plus public transport emits a much lesser tailpipe emission per passenger they carry,” said Dr Ashish Verma, Convenor, IISc Sustainable Transportation Lab, in a conversation with AIM.

Read more: ChatGPT Craves Human Expertise

Well, this is true when calculated with a full bus, usually public transportation, especially eco-oriented buses that have lesser emissions than two-wheelers or cars. In the same respect, a bus which occupies the space of 2 cars can carry up to 10 cars worth of people. It is noted that these are the calculations done with a full bus, which is not always the case. 

There have been so many instances where buses with one or two passengers making the rounds in the city, occupy more space, leading to more traffic.

Source: Deccan Herald

So, how is BMTC addressing this concern, alongside optimising its buses and routes? 

BMTC recently introduced Namma BMTC, a passenger information app for phones, under the Government of India’s Nirbhaya scheme. Developed by MCT Cards & Technology and Amnex Infotechnologies, the app can be used in both Kannada and English and offers comprehensive details about bus schedules, routes, stops, and other relevant details for public transportation.

Buses: The Unsung Lifeline Transportation System

“We have a footfall of around 30 lakh passengers on a daily basis in buses which is almost six times than the metros, and we can proudly say that it is the lifeline of Bangalore where people, mostly low-income household bank on buses for the daily commute,” said A.V. Surya Sen, Director, IT, BMTC. 

In Bangalore, there are currently more than one crore private vehicles on the road, but they are hardly able to transport another 30 lakh passengers. In comparison, there are only 6800 buses operating in Bangalore, making the bus population nowhere near the vehicle population in terms of providing transportation services. So according to Sen, blaming the volume of traffic solely on the number of buses is unfair. 

Nonetheless, even with the availability of various modes of transportation, buses retain a crucial function. They are not only vital for connectivity and convenience but also serve as a means of providing essential services to the public. Buses can complement the higher-level public transport system while also offering distinct advantages. 

“Additionally, buses are a cost-effective and affordable option, especially for individuals from low-income households. Hence, buses contribute to equitable transportation accessibility,” added Verma.

Read more: Open Source Opens Funds for Semiconductor in India

AI is Solving Traffic

The Namma BMTC app is an ode to the successful implementation of AI and ML algorithms.  “The use of ML and AI is instrumental in providing effective solutions. The availability of extensive historical and legacy data allows us to make accurate predictions about bus arrival times and create algorithms for determining bus schedules and locations,” added Sen.  

Having a comprehensive mobile application that offers a range of functions, including real-time tracking, trip planning, identifying nearby bus stops, and suggesting optimal travel routes, is an important aspect of an information system. However, it is essential to understand that the app itself does not provide a complete solution. It is just one part of a larger set of actions and initiatives that need to be taken.

According to Verma, the app in the future will also function as an Automated Fare Collection (AFC) system, enabling mobile ticketing, mobile passes, and digital wallets, revamping the process of purchasing tickets and travelling on buses by introducing digital payment methods. 

Compared to other metropolitan cities like Mumbai, Chennai, and Delhi, BMTC is extremely expensive. 

However, the government is taking steps to address the issue of affordability, and it is expected that improvements will be made in this regard.

“It is important to note that service quality and affordability are two separate aspects. Urban public transportation should be viewed as a service rather than a profit-oriented business, and efforts should be made to enhance service conditions while making it more affordable,” commented Verma. 

Read more: Don’t Call Us Back to the Office

Moving Towards a Greener City

According to the NASSCOM report of 2021, Bangalore is the top destination for IT professionals in India with over 1 million people moving to the city each year in search of work. Now, with employees returning to the office as remote work is ending, buses are now in higher demand along with places to rent. 

Public transportation promotes sustainability by reducing emissions, improving air quality, alleviating traffic congestion, and fostering social equity. It efficiently carries more passengers, lowers greenhouse gas emissions, reduces pollutants, saves time and money, and provides access to opportunities for those without cars. Furthermore, in terms of carrying a single passenger, public transport emits significantly lower levels of tailpipe emissions per passenger. 

“Public transportation is a viable solution to combat global warming and carbon emissions. Prioritising public transportation and reducing reliance on private vehicles leads to a cleaner environment and improved quality of life,” concluded Verma.

The headline of the story has been updated to show that BMTC is not the reason behind Bengaluru’s traffic.

The post Stuck in Bengaluru Traffic? Don’t Blame BMTC Buses appeared first on AIM.

Back in 2018, India was one of the first countries to develop a national AI strategy, as part of which an AI task force was formed specifically for defence-related projects, resulting in the formation of the Defence AI Council (DAIC) and the Defence AI Project Agency. As part of this, as many as 40 generative AI products have apparently been developed as part of the task force.

In March this year, Army Chief General Manoj Pande stated that natural language processing, vehicle tracking, facial recognition and other autonomous systems were things that the Indian Army was currently working on as part of their generative AI capabilities.

Similarly, during the SPACE (SPace, Aerospace and defenCE) Conference 2024, Aeronautical Defense Agency (ADA) director Jitendra Jadav said that research on generative AI use for military aircraft was ongoing, though there were still several challenges to tackle.

Challenges For GenAI Integration?

Apart from computational demands and software limitations, Jadav said that AI was good at knowledge-based and statistical reasoning, but still lacked when it comes to contextual reasoning. This is something that becomes a problem when talking about completely autonomous piloting.

“We did a lot of simulations and it is a very major challenge when there is an automated manoeuvring decision. It works in some scenarios, but it is very difficult to predict all the scenarios and make it work. So that is why fine tuning algorithms is one of the major challenges engineers face,” said Jadav.

“But when it comes to contextual reasoning, we found that human pilots perform significantly better than AI. A lot more work has to be done. Autonomous combat is very difficult because it cannot replace the skills of the pilot. Autonomous combat is totally different,” he added.

Despite this, generative AI has proven to be more cost-effective and efficient when it comes to the defence sector. From assessing vast swaths of data to providing real-time monitoring, the use of AI in general has found a permanent place in the sector.

Currently, Jadav said that the agency has already integrated the use of AI within several operations, including maintenance, optionally manned aircraft and manned-unmanned operations.

However, the use of generative AI will soon see the light of day, with the agency already working on inculcating this into their maintenance operations.

GenAI’s Place in Defence

Currently, the agency itself hopes to make use of AI as a preventative mechanism. In terms of vehicle management, the agency has largely made use of the data provided by their legacy aircraft to predict when functional aircraft begin to face fatigue-related issues, including structural and thermal fatigue.

“We are trying to do a complete documentation of the pilot flight manual. The whole thing will be used for maintenance and documentation of these systems. It will be like a chatbot that is given to the maintenance person on the ground. They can ask questions and receive answers on where the problem is and how to address them,” said Jadav.

Adding to that, Jadav said that they are planning to get out of the fatigue related issues by introducing integrated vehicle environment algorithms, where the fatigue cracks and all that thing will be well predicted, based on sensors. “And based on that, we can give a remaining estimate of the usefulness of each of the systems,” he said. 

However, this will largely make use of data and physics-based algorithms, rather than generative AI.

But, as mentioned above, the agency is already experimenting with generative AI capabilities, like the use of a chatbot for maintenance issues. Similarly, echoing the initiatives of several other Indian defence agencies, the use of generative AI in virtual assistance and training is being explored, allowing for real-time assistance and training for pilots and maintenance workers.

When it comes to combat capabilities, while Jadav specifically mentioned that human pilots still manage to outperform AI pilots, the use of GenAI to augment judgments is actively being explored.

“Synergetic man-machine cooperation is very important while designing the architecture of artificial intelligence. So what we learn from ground systems and ground simulation of hardware, software, architecture, and standardisation is very important to get this artificial intelligence going on in the platform.

“We plan to use it initially as an advisory role and, after adequate training, push it into maintenance,” he said.

So it seems that the use of generative AI has already found a foothold in the Indian defence sector and will continue to expand. Specifically in terms of augmenting operations to make the sector more efficient, as well as proving to be more cost-effective and reducing the loss of human life.

“This is what sixth-generation fighters are planned to do, but we will do them phase-wise. But we are very confident in making use of AI to do so,” Jadav concluded.

The post How is India Using GenAI in Defence? appeared first on AIM.

CoRover.ai, the company behind BharatGPT, announced its inclusion in NVIDIA Inception, a program designed to support startups advancing industries through technological innovation.

CoRover.ai, known for its human-centric conversational AI platform, has developed BharatGPT, India’s first indigenous generative AI platform. The platform is accessible across various channels, formats, and languages, serving 1.3 billion users. 

CoRover’s solutions include virtual assistants like chatbots, voicebots, and videobots, which are deployed across multiple sectors, including government and private organizations like IRCTC, LIC, and the Indian Navy.

The NVIDIA Inception membership will provide CoRover with access to NVIDIA’s resources, including GPUs, compute power, and software support. This partnership is expected to accelerate CoRover’s development of AI-driven customer engagement solutions.

 “As we are committed to addressing real business use cases in a B2B2C landscape, having access to NVIDIA’s technological know-how and resources through NVIDIA Inception will help CoRover effectively handle large language models and domain-specific models, automating conversational AI use cases,” said Ankush Sabharwal, CEO of CoRover.

NVIDIA Inception supports startups with benefits such as NVIDIA Deep Learning Institute credits, preferred pricing on hardware and software, and ongoing technological assistance, aiding in product development, prototyping, and deployment.

CoRover.ai recently announced a strategic partnership with AI auditing firm EthosAI.one to advance the development of responsible AI. 

The partnership aims to ensure the reliability, fairness, and accuracy of BharatGPT, reinforcing it as a trustworthy AI solution. EthosAI.one will continuously audit and enhance BharatGPT models, aligning them with the highest ethical standards.

The post CoRover.ai Joins NVIDIA Inception to Accelerate BharatGPT  appeared first on AIM.

Bengaluru-based AI startup Sarvam AI recently announced the launch of India’s first open-source foundational model, built completely from scratch.

The startup, which raised $41 million last year from the likes of Lightspeed, Peak XV Partners and Khosla Ventures, believes in the concept of sovereign AI- creating AI models tailored to address the specific needs and unique use cases of their country.

The model, called Sarvam 2B, is trained on 4 trillion tokens of data. It can take instructions in 10 Indic languages, including Hindi, Tamil, Telugu, Malayalam, Punjabi, Odia, Gujarati, Marathi, Kannada, and Bengali.

According to Vivek Raghavan, Sarvam 2B is among a class of Small Language Models (SLMs) that includes Microsoft’s Phi series models, Llama 3 8 billion, and Google’s Gemma models.

“This is the first open-source foundational model trained on an internal dataset of 4 trillion tokens by an Indian company, with compute in India, with efficient representation for 10 Indian languages,” Raghavan told AIM in an interaction prior to the announcement.

The model, which will be available on Hugging Face, is well suited for Indic language tasks such as translation, summarisation and understanding colloquial statements. The startup is open-sourcing the model to facilitate further research and development and to support the creation of applications built on it.

Previously, Tech Mahindra introduced its Project Indus foundational model, while Krutrim also developed its own foundational model from scratch. However, neither of these models is open-source.

India’s First Open-Source AudioLM

The startup, which Raghavan co-founded with Pratyush Kumar, also believes that in India, consumers will use generative AI through voice mode rather than text. At an event held in ITC Gardenia, Bengaluru, on August 13th, the startup announced Shuka 1.0–India’s first open-source audio language model.

The model is an audio extension of the Llama 8B model to support Indian language voice in and text out, which is more accurate than frontier models. 

“The audio serves as the input to the LLM, with audio tokens being the key component here. This approach is notably unique. It’s somewhat similar to what GPT-4o introduced by OpenAI a couple of months ago,” Raghavan said.

According to the startup, the model is 6x more faster than Whisper + Llama 3. At the same time, its accuracy across the 10 languages is higher compared to Whisper+ Llama 3.

Previously, the startup has hinted extensively at developing a voice-enabled generative AI model. Startups and businesses aiming to incorporate voice experiences into their services can leverage this tool, particularly for Indian languages.

Raghavan also said that its aim is to make the model sound more human-like in the coming months. 

Sarvam Agents are Here

Another interesting development announced by the startup is Sarvam Agents. Raghavan believes that AI’s real use case is not in the form of chatbots but in AI doing things on one’s behalf. 

“Sarvam Agents are going to be voice-based, multilingual agents designed to solve specific business problems. They will be available in three channels– they can be available via telephony, it can be available via WhatsApp, and it can be available inside an app,” Raghavan said.

These agents are also available in 10 Indian languages, and the cost of these voice agents starts at a minimal cost of just INR 1/min.  These AI agents can be deployed by contact centres or by sales teams of different enterprises, etc.

While these agents may sound like existing conversational AI products available in the market, Raghavan said their architecture, which uses multiple in-house developed LLMs, makes them fundamentally different.

“These agents can also be very contextual. For example, when you’re on a particular page, you press a button seeking more information about a particular item. The agent will be context-aware, so it knows where you’re asking from. In contrast, when you call a number, it starts from scratch without that context,” he said.

Sarvam Models APIs

While both Sarvam 2B and Shuka 1.0 are open-source models, Sarvam.ai is making available a bunch of close-sourced Indic models used in the creation of Sarvam agents ready to be consumed as APIs.

“These include five sets of models. I will tell you about the three important ones. Our first model, a speech-to-text model, translates spoken Indian languages into English with high accuracy, surpassing traditional ASR systems. The second model is a text-to-speech model which converts text into speech, offering diverse voices in multiple languages, with consistent or varied options depending on preference,” Raghavan said. 

The third model is a parsing model designed for high-quality document extraction. This model addresses common issues with complex data, aiming to improve accuracy in parsing financial statements and other intricate documents. 

Other announcements made by the startup include a generative AI workbench designed for law practitioners to enhance their capabilities with features such as regulatory chat, document drafting, redaction and data extraction.

The post Sarvam AI Launches India’s First Open Source Foundational Model in 10 Indic Languages appeared first on AIM.

The semiconductor industry in India is fabless, not fabulous, meaning that while designs are conceived within the country, the actual manufacturing of chips occurs overseas. While PSMC and Tata Electronic have their roadmap for building a fab in India by 2026, till then, Indian companies rely on TSMC and others for building semiconductor chips that were designed in India. Which creates a security issue within the supply chain of the semiconductor industry.

To explain the possible risks in the semiconductor supply chain and how to protect from them, Shashwath TR, the CEO and founder of Mindgrove Technologies spoke with AIM. “When I send our design to a foundry, I am trusting them with the intellectual property,” said Shashwath.

Founded in 2021, Mindgrove Technologies is a Chennai based semiconductor company focusing on the design and production of Systems on Chips (SoCs). The fabless startup secured $2.32 million in seed funding last year from investors led by Sequoia Capital India (now Peak XV Partners).

In May, the company unveiled India’s inaugural commercial high-performance SoC (system on chip) dubbed Secure IoT, which would be in production by next year. “When it does go into production, that is the revenue generating thing for us,” added Shashwath. 

Secure IoT’s production is based on MPW (Multi-Project Wafer). This enables cost-effective prototyping and low-volume production, reducing the cost of a full prototyping wafer run to 10% or even 5% of the initial price.

Moreover, the company also has plans for releasing a prototype for its Vision SoC by next year, or another 18 months. 

But There is a Bigger Issue

When it comes to manufacturing chips outside India, trust is foundational. But it is also the first point of vulnerability in the supply chain. Foundries like TSMC assure clients that they will not misuse or replicate the designs, but the risk cannot be entirely eliminated.

Two primary risks emerge when outsourcing semiconductor manufacturing: design theft and malicious tampering. While the former requires advanced state-level capabilities, the latter could be more subtle, involving the insertion of backdoors or other vulnerabilities into the chip design.

“As far as stealing the design is concerned, it’s really hard to do—it takes a state actor,” Shashwath emphasised, underscoring the sophisticated level of expertise required to extract a chip design post-manufacture. 

Even with the guarantees provided by foundries like TSMC, the potential for tampering remains. This concern is mitigated through rigorous verification processes, including non-functional verification, silicon validation, and extensive testing. A more subtle risk lies in the potential for unauthorised modifications during the manufacturing process. 

“Somebody in the middle of the supply chain could add something into the design, making it insecure,” Shashwath said. To mitigate this, companies must implement rigorous verification processes, including functional and non-functional tests, to ensure that the final product aligns with the original design and is free of tampering. “We can measure the power output from different things in simulation and then see if they match in real life,” Shashwath noted, highlighting the meticulous checks in place to ensure chip integrity.

Despite these precautions, the semiconductor industry has yet to experience a proven attack at the silicon level. However, the potential for such an attack exists. “There are usually three or four papers a year talking about these possibilities,” Shashwath remarked, highlighting the ongoing research into theoretical vulnerabilities.

The risk does not end at the foundry

Once a chip is manufactured, the One-Time Programmable (OTP) memory, which stores critical cryptographic keys, remains unprogrammed until the packaging stage. This introduces another layer of vulnerability—if compromised, it could render the entire chip insecure. 

Shashwath explained, “An insecure root key can be provisioned on that chip, which makes the entire chip insecure.”

To counter this, companies like Mindgrove Technologies employ multiple strategies, including the ability to disable compromised keys via updates. “We have space for four root keys inside the chip. If any root key is known to be compromised, we can write an OT update to the chip, which will disable that key,” Shashwath shared, illustrating a proactive approach to potential threats.

In such a complex landscape, the role of government regulations and industry standards becomes crucial. Agencies like the RBI or Ministry of External Affairs or Ministry of Defense in India often prescribe specific security measures for chips used in sensitive applications. These measures are not only about protecting the chip itself but ensuring that the entire supply chain remains secure. 

“There is a long consultation process of what kind of regulations you need,” Shashwath mentioned, acknowledging the collaborative effort required across the ecosystem.

Read: What’s Stopping India’s Semiconductor Mission

Much More is Needed

While companies like Mindgrove Technologies are pioneering in securing their products, Shashwath admits that the broader challenge lies in creating a consensus across the industry. “The ecosystem has to agree on what to do,” he said, reflecting the shared responsibility of all stakeholders in the semiconductor supply chain.

“Everybody who’s making systems on chip will be working on this, especially if they have some kind of relationship with the government or security-focused applications,” Shashwath explained that every company such as Netrasemi, Sima, or even AMD, Intel, Qualcomm, everyone is concerned about this. 

As India looks to the future, the establishment of a domestic fab by PSMC and Tata Electronics is a significant step toward reducing reliance on foreign foundries. “When it comes, it’ll be great. We hope to be able to use a wholly indigenous supply chain,” Shashwath remarked, expressing optimism about the potential for a more secure and self-reliant semiconductor industry in India.

However, he remains realistic about India’s current capabilities when asked about building something like NVIDIA. “We have to learn to walk before we can run, before we can fly,” Shashwath observed, highlighting the importance of focusing on consumer appliances, electronics, and smart devices before venturing into more complex areas like supercomputing.

The post India’s Fabless Semiconductor Supply Chain is Far from Fabulous appeared first on AIM.

Japanese AI startup, Sakana AI, has released, ‘The AI Scientist,’ which introduces the first comprehensive system for fully automatic scientific discovery, enabling foundation models such as LLMs to perform research independently.

Curated in collaboration with the Foerster Lab for AI Research at the University of Oxford, and Jeff Clune and Cong Lu at the University of British Columbia, this AI system is capable of conducting independent scientific research and communicating its findings.

The AI Scientist harnesses frontier LLMs to generate research ideas, write code, execute experiments, visualise results, and draft scientific papers. It is also dubbed as an innovative framework, representing a significant step toward fully automatic scientific discovery.

To ensure the quality of its work, the system runs a simulated review process for evaluation, mimicking the human scientific community’s peer-review process.

Remarkably, each idea generated by the AI was developed into a full paper at a cost of less than $15 per paper and to evaluate the generated papers, researchers have designed and validated an automated reviewer. 

https://x.com/SakanaAILabs/status/1823178623513239992Sakana AI also recently introduced EvoSDXL-JP, an image generation model developed through Evolutionary Model Merge where it generates Japanese styles 10x faster and is available on HuggingFace with a demo for research and education.

The post Sakana AI Releases AI Scientist which Writes Scientific Papers for $15 appeared first on AIM.

Generative AI has made AI much more relevant in our day-to-day lives. Whether you need your blog proofread, a piece of music produced, or an artwork made within seconds, it’s likely that generative AI already plays a part in it somehow. 

Moreover, generative AI has managed to pervade every part of one’s professional life, with companies investing in integrating AI left, right and centre. 

With AI practically taking over our lives, the task of understanding it might seem daunting. But it doesn’t have to be. Thankfully, there is no dearth of courses and learning material to make sense of it all. 

So, without further ado, here are some of the best courses out there to help you out:

Generative AI Crash Course

This course, by the Association of Data Scientists (ADaSci), is more hands-on and specific to generative AI. It’s designed for non-tech professionals looking to expand their horizons using AI. At the end of the course, participants can expect to have a strong grasp of generative AI fundamentals and tools that they can leverage in their day-to-day lives. 

Additionally, the course expands on how GenAI can be used in various contexts and within various industries. The five-day virtual course will kick off from August 19 onwards, with a total of ten hours of learning and practical projects.

More details here.

AI for Non-Technical People: A Hands-On Beginner’s Course

This course introduces the fundamentals of artificial intelligence to non-technical individuals through simple lessons, real-world examples, and practical activities. Participants learn about AI’s impact on industries like healthcare and finance and explore ethical issues such as privacy and bias. 

The course includes a hands-on project to build AI models without needing technical skills, providing a clear understanding of AI concepts, applications, and implications.

More details here.

Generative AI for Beginners

While less hands-on, Microsoft also offers a short-term course on generative AI catering to beginners. The course is focused on those looking to build generative AI applications with little to no technical background. The 18-lesson course includes a short video introduction, as well as a written lesson alongside coding samples and links to external resources so that you aren’t left in the lurch after completing the course. 

One of the downsides is that the course requires access to the OpenAI API or Azure’s OpenAI service. However, it offers a comprehensive rundown on how to build your own generative AI applications, as well as how to deal with the risks and ethics involved.

More details here.

AI For Everyone

Of course, everyone’s favourite AI professor has a beginner-friendly course for AI too. Andrew Ng’s DeepLearning.ai offers an AI For Everyone course, specifically for non-technical professionals. 

While it is an overarching course about AI, it also takes into consideration the use of generative AI, and gives you a list of common AI terminologies and what they mean. Additionally, since the course is meant for professionals, Ng recommends it to engineers as well, especially those who want to understand the business side of AI.

More details here.

Introduction to Generative AI

Google Cloud also offers a similar generative AI course on Coursera, though it is part of a larger course on their introduction to generative AI learning path specialisation. While short, the hour-long course gives an effective and generalised explanation of what generative AI is, the types of models currently used, how they work and where they can be used. 

The overall course offers insights from industry experts and also contributes to developing your professional GenAI skills.

More details here.

Generative AI Automation Specialisation

Vanderbilt University offers a more specialised course in generative AI, with a focus on how to use LLM-based chatbots, like ChatGPT, to optimise your workflow. The month-long course offers a ten-hour workload per week and requires no prior experience or knowledge of generative AI. 

The course is unique in offering specific skills in leveraging LLMs within your professional life, with modules on prompt engineering, data analysis, and identifying trustworthy generative AI.

More details here.

Introduction to Generative AI – Art of the Possible

AWS offers a short course with a focus on generative AI concepts and use cases, as well as the importance of generative AI in business. The course is perfect for a short introductory foray into how generative AI works, how it fits in with machine learning and why it’s important in the grand scheme of things, specifically for business purposes. 

Additionally, it gives participants a good idea of the risks and benefits associated with generative AI and where it can be leveraged.

More details here.

The post Top 7 GenAI Courses for Non-Techies appeared first on AIM.

The race for building a team of AI software engineers doesn’t stop. After Cognition’s Devin, Cosine, the human reasoning lab, has introduced Genie, hailed as the most capable AI software engineering model globally, achieving 30.08% on SWE-Bench evaluations. 

Genie is designed to emulate the cognitive processes of human engineers, enabling it to solve complex problems with remarkable accuracy and efficiency. “We believe that if you want a model to behave like a software engineer, it has to be shown how a human software engineer works,” said Alistair Pullen, the founder of Cosine.

Moreover, this UK-based AI startup Cosine has secured $2.5 million in funding from SOMA and Uphonest, with additional investment from Lakestar and Focal and is part of the YC-W23 batch. 

As the first AI Software Engineering colleague, Genie is trained on data that mirrors the logic, workflow, and cognitive processes of human engineers. 

This allows it to overcome the limitations of existing AI tools, which are often extensions of foundational models with added features like web browsers or code interpreters. Unlike these, Genie can tackle unseen problems, iteratively test solutions, and proceed logically, akin to a human engineer.

Genie has set a new standard on SWE-Bench, achieving a score of 30.08%, a 57% improvement over the previous best scores held by Amazon’s Q and Code Factory. 

This milestone not only represents the highest score ever recorded but also the largest single increase in the benchmark’s history. Genie’s enhanced reasoning and planning capabilities extend beyond software engineering, positioning it as a versatile tool for various domains.

In its development, Genie was evaluated using SWE-Bench and HumanEval, with a strong focus on its ability to solve software engineering problems and retrieve the correct code for tasks. 

Genie scored 64.27% in retrieving necessary code lines, identifying 91,475 out of 142,338 required lines. This marks significant progress, though Cosine acknowledges room for improvement in this area.

Genie’s development involved overcoming challenges related to training models with limited context windows. Early efforts using smaller models highlighted the need for a larger context model, leading to Genie’s training on billions of tokens. The training mix was carefully selected to ensure proficiency in the programming languages most relevant to users.

Cosine’s innovative approach to Genie’s development included the use of self-improvement techniques, where the model was exposed to imperfect scenarios and learned to correct its mistakes. This iterative process significantly strengthened Genie’s problem-solving abilities.

Looking ahead, Cosine plans to continue refining Genie, expanding its capabilities across more programming languages and frameworks. The company aims to develop smaller models for simpler tasks and larger ones for complex challenges, leveraging their unique dataset. Exciting future developments include fine-tuning Genie on specific codebases, enabling it to understand large, legacy systems even in less common languages.

The post Cosine Unveils Genie, the AI Software Engineer that Beats Cognition’s Devin appeared first on AIM.

Runway, the US-based AI startup, has taken another significant step in the rapidly evolving field of AI-generated video. The company announced today that its Gen-3 Alpha Image to Video tool now supports using an image as either the first or last frame of video generation, a feature that could dramatically improve creative control for filmmakers, marketers, and content creators.

The startup was founded in 2018 by Cristóbal Valenzuela, Alejandro Matamala, and Anastasis Germanidis.

Furthermore, this update comes after the startup officially released Gen-3 Alpha, highlighting the company’s aggressive push to stay ahead in the competitive AI video generation market. 

The new capabilities of the model allows users to anchor their AI-generated videos with specific imagery, potentially solving one of the key challenges in AI video creation; consistency and predictability.

By allowing users to generate high-quality, ultra-realistic scenes that are up to 10 seconds long—with various camera movements—using only text prompts, still imagery, or pre-recorded footage, this model has set a new benchmark in video creation.

“The ability to create unusual transitions has been one of the most fun and surprising ways we’ve been using Gen-3 Alpha internally,” said Runway co-founder and CTO Anastasis Germanidis.

Back in February 2023, Runway released Gen-1 and Gen-2, the first commercial and publicly available foundational video-to-video and text-to-video generation models accessible via an easy-to-use website. Now the Gen-3 update takes it to the next level.

The Power of First and Last Frames

“Gen-3 Alpha update now supports using an image as either the first or last frame of your video generation. This feature can be used on its own or combined with a text prompt for additional guidance,” Runway announced on X. 

The impact of this feature was immediately recognised by users. Justin Ryan, a digital artist, posted in response: “This is such a big deal! I’m hoping this means we are closer to the First and final frame like Luma Labs offers.”

This development puts Runway in direct competition with other players in the space, such as Luma Labs, Pika, OpenAI’s much-anticipated Sora, and the Bengaluru-based startup Unscript, which is generating videos using single images.

However, Runway’s public availability gives it a significant edge over Sora, which remains in closed testing.

A spokesperson from Runway shared that the initial rollout will support 5 and 10-second video generations, with significantly faster processing times. Specifically, a 5-second clip will take 45 seconds to generate, while a 10-second clip will take 90 seconds.  

Accelerating to Get Ahead 

Since the release of the Gen-3 Alpha model, internet users have been showcasing their unique creations in high-definition videos, demonstrating the versatility and range of Runway AI’s latest AI model.

As Runway makes a bold move, there is a significant shift in the generative AI video space. The company describes this update as “first in a series of models developed by Runway on a new infrastructure designed for large-scale multimodal training,” and a “step toward creating General World Models.”

Germanidis also revealed that Gen-3 Alpha will soon enhance all existing Runway modes and introduce new features with its advanced base model.

He also noted that since Gen-2’s 2023 release, Runway has found that video diffusion models still have significant performance potential and create powerful visual representations. 

While the startup states that Gen-3 Alpha was “trained on new infrastructure” and developed collaboratively by a team of researchers, engineers, and artists, it has not disclosed specific datasets, following the trend of other leading AI media generators that keep details about data sources and licensing confidential.

Interestingly, the company also notes that it has already been “collaborating and partnering with leading entertainment and media organisations to create custom versions of Gen-3,” which “allows for more stylistically controlled and consistent characters, and targets specific artistic and narrative requirements, among other features.”

AI comes to filmmaking

Additionally, Runway hosted its second annual AI Film Festival in Los Angeles. To illustrate the event’s growth since its inaugural year, Valenzuela noted that while 300 videos were submitted for consideration last year, this year they sent in 3,000.

Hundreds of filmmakers, tech enthusiasts, artists, venture capitalists, and notable figures, including Poker Face star Natasha Lyonne, gathered to watch the 10 finalists selected by the festival’s judges.

Now, the films look different, as does the industry with generative AI.

Meanwhile, amidst all this, it is evident that Runway is not giving up the fight to be a dominant player or leader in the rapidly advancing generative AI video creation space.

The post Is Runway’s Gen-3 Update the First or Last Frame? appeared first on AIM.

Paul Buchheit, the creator of Gmail recently expressed concerns about the potential dangers of AI development in countries like China. Buchheit warned that if China leads the AI race, the world could face a permanent lockdown, with even our thoughts under surveillance and censorship. 

Responding to Buchheit’s remarks, Rajeev Chandrasekhar, Former Union Minister of India, emphasised the importance of ensuring that democracies and their neighboring nations shape the future of technology. 

He stated, “Its extremely important – more than critical – that future of Tech is shaped by  democracies and their partner countries” 

There is no denying that despite pressing concerns about the ethical and privacy implications of AI, China remains a central player on the global stage. 

China’s Approach to AI Development

Source: Reddit

In October 2023, Chinese tech giant Baidu unveiled Ernie 4.0, the latest version of its generative AI model, claiming capabilities comparable to OpenAI’s GPT-4. 

This significant advancement highlights China’s rapid progress in AI, driven by substantial government support and strategic initiatives like the ‘New Generation Artificial Development Plan’. 

Launched in 2017, this plan aims to position China as a global AI leader by 2023, emphasising research funding, talent recruitment, and infrastructure development. These efforts underscore China’s commitment to dominating the AI sector, often pushing the boundaries of ethical and privacy considerations. 

China’s AI development is also fueled by leading companies including Tencent, Alibaba, Baidu, and SenseTime, which attract top group talent and drive innovation. 

Meanwhile, China’s strides in hardware and robotics, with companies like Dreame Technology and Fourier Intelligence at the forefront, reflect a comprehensive approach to AI applications. 

Additionally, China’s advancements in facial recognition technology, widely deployed in public spaces, illustrate the country’s capability to implement AI solutions on a large scale. These often move rapidly as ethical concerns are not always given paramount importance. 

The post ‘It’s Extremely Important that Future of Tech is Shaped by Democracies and their Partner Countries,’ says Rajeev Chandrasekhar appeared first on AIM.

Mufeed VH, the creator of the AI software engineer Devika, has launched its AI tool and startup, Asterisk, along with Vivek R and Asjid Kalam.

Asterisk, a Y Combinator S24-backed AI agent, is revolutionising cybersecurity by automatically detecting and patching security vulnerabilities in codebases. Unlike traditional static security tools, which produce nearly 95% false positives and miss critical business logic errors, Asterisk offers a groundbreaking solution.

The AI agent mimics the analysis process of human security experts, identifying vulnerabilities such as unauthorised access, privilege escalation, and cost-inflating bugs. Asterisk operates autonomously, testing vulnerabilities in a sandbox environment and producing reports without any user intervention, ensuring zero false positives.

Asterisk confirms vulnerabilities by launching a sandbox environment, running the scanned software, and actively attempting to exploit the identified bugs. When Asterisk flags a vulnerability, it’s a confirmed threat.

The team has played a key role in securing major companies, including Google, Mastercard, Okta, NVIDIA, and Microsoft.

Asterisk possesses also a deep understanding of a company’s codebase, enabling it to simulate attacks like a malicious hacker would. This allows it to devise attack scenarios, similar to what was seen in the recent CrowdStrike incident.

Mufeed was earlier the founder of Lyminal and Stition.AI, which is now known as Asterisk, where the team was researching on security within AI models. He was also the gold medalist at the IndiaSkills 2021 Nationals in cybersecurity when he was just 19 years old. 

Kalam is Silver medalist at IndiaSkills and former Security Research Engineer at Emirates National Bank (UAE). Vivek is former Distributed Systems/Platforms Engineer at Chorus One, one of the largest Proof-of-Stake (POS) validators.

The post Devika Creator Launches Asterisk, YC-backed AI Agent Startup appeared first on AIM.

Convin, an AI-powered conversation intelligence platform for call centre setups, recently launched its advanced Large Language Model (LLM), with 7 billion parameters. This model is specifically designed to improve the business output and resolve the unique challenges of customer-facing teams such as sales, support, and collections. 

Convin’s new LLM addresses these gaps and significantly outperforms leading models like GPT-3.5 by 40% and GPT-4 Turbo by 20% in accuracy, according to the company.

Trained on over 200 billion tokens and supporting 35+ Indian and South Asian languages, including codemixed variations, the model ensures precise transcriptions, zero to low hallucinations, and contextual understanding. It reassures businesses of a critical advantage in delivering high-quality, culturally sensitive customer interactions.

“Traditional language models often fail to deliver accurate results, but purpose-built models such as Convin LLM produce better results and are more accurate. By addressing major challenges such as agent inefficiencies, call centres can improve handling time, response time, and inconsistent customer experience. This streamlines processes and enhances customer satisfaction by providing precise, data-driven insights and predictive analytics. As a result, call centres realize a substantial cost reduction and new revenue generation,” Atul Shree, CTO of Convin, said.

The process begins by identifying specific objectives related to inefficiencies in the contact centre setup and selecting relevant data sources. Data is then collected and preprocessed to ensure high quality, including filtering, deduplication, and tokenization.

Pre-training on this cleaned dataset helps the model understand linguistic patterns and adapt to different languages. Finally, the model undergoes fine-tuning with task-specific labelled data, refining its parameters to predict labels accurately and deliver optimal performance.

With the enhanced capabilities and efficiencies introduced by this model, Convin anticipates a 200%  increase in customer acquisition and a 3X boost in overall revenue for 2024-25.

The post Convin Launches a 7 Bn Parameter LLM Tailored for Indian Contact Centres appeared first on AIM.

K. T. Rama Rao, Bharat Rashtra Samithi (BRS) Working President, recently highlighted a decline in IT employment in Telangana. He shared data showing that the number of jobs in the sector dropped from 1,27,594 in 2022-23 to just 40,285 in 2023-24. 

Well, the picture is not much different in the entire country. As per the Nasscom data, the IT sector will create only 60,000 new jobs in FY24 compared to 2,70,000 jobs that were created by the sector in the previous fiscal year.

And with Indian IT companies like Wipro, TCS, and Infosys delaying the onboarding of 10,000 freshers, and refusing to provide a joining date, it is evident that the sector continues to witness a trend of decline.

Is There Anyone Hiring?

In FY24, Infosys hired 11,900 freshers, marking a significant 76% decrease from the 50,000 hired in FY23. Similarly, TCS recruited 40,000 freshers in FY24, experiencing a slight reduction from the 44,000 hired the previous year. 

Wipro’s hiring also dropped by up to 50%, with only 10,000–12,000 freshers onboarded in FY24, compared to 20,000 in FY23. 

On the other hand, Cognizant and Capgemini were more aggressive in their FY23 hiring, bringing on 60,000 and 61,182 freshers, respectively, though data for FY24 isn’t provided for these two companies.

The trend of this decline in hiring is accompanied by an increase in layoffs. India’s IT sector laid off around 20,000 techies during the calendar year 2023, described as a “silent layoff,” according to data shared by All India IT & ITeS Employees’ Union (AIITEU).

Sources indicated that Infosys laid off nearly 200-500 employees in 2024, though company CEO Salil Parekh said that there were no plans for downsizing or job cuts. Further, the January 2024 report mentioned that Wipro is firing a mid-level workforce to enhance profit margins. 

Is GenAI the Reason?

Funnily enough, at the same time, many of the graduates do not want to join the Indian IT firms. The reluctance of recent graduates to pursue careers in Indian IT can be attributed to the prolonged stagnation of entry-level salaries, which have remained at INR 3.5-4 LPA for over a decade. High-paying product companies with compensation packages ranging from Rs 10-20 LPA have become more attractive.

Meanwhile, Indian IT is upskilling its existing employees with genAI, instead of hiring new ones. This is also one of the reasons why the workforce is declining and the benches are full. 

As of Feb 2024, the top 10 Indian IT services companies including TCS collectively had nearly 450 genAI projects and proofs of concept (PoCs) in development, reflecting a current deal pipeline valued between $150 million and $250 million, as per the data by market intelligence firm UnearthInsight. 

Sonata Software, a mid-tier IT company, reported that around 80% of its project pipeline is focused on genAI, according to chief information officer Samir Dhir. The company has a $50 million AI deal pipeline. 

Out of 6,532 employees only about 2,000 are currently trained in genAI, with the company aiming for 50% of its staff to be trained in this field by 2025. 

Therefore, freshers need to be already trained with genAI if they want to compete with the ones being trained at the IT firms. But, there is a significant gap between the curricula and industry expectations when it comes to employees being generative AI ready. 

Despite this, 70% of academic institutions believe their graduates are job-ready. In reality, only 16% of companies share the same view. 

In June, TCS struggled to hire for 80,000 job openings citing skill gap as the reason. The same is true for Wipro. The IT giant recently announced that it is now rolling out 30,000 offer letters for freshers from 2022 with a compensation of INR 3.5 LPA, and other CGPA requirements. 

The truth is that it is extremely difficult to find good, or even decent, software engineers with coding skills for such small compensation. At the same time, there is a supply of mediocre graduates who are not ready for taking up jobs. 

The post Why Top IT Companies in India are not Hiring? appeared first on AIM.

The allure of innovative and specialised engineering programs, such as a PGP in AI, or an 11-month course in ML, offered by new-age universities in India, is undeniable. These courses have been designed in a way that would boost the current youth of India to be prepared for the jobs of the future. 

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However, are there any jobs in India that are looking for such roles, specifically outside the startup sector?

Prof. V Ramgopal Rao, group Vice-Chancellor of BITS Pilani Campuses and former director of IIT Delhi, voiced a growing concern. He observed that while these innovative programs may seem exciting, they could inadvertently limit students’ prospects. “Students graduating with fancy discipline titles will neither be eligible for master’s programs in India nor for any government jobs,” Rao warned. 

While the crux of the problem lies in the rigid eligibility criteria of government jobs and higher education admissions, which are slow to adapt to the rapidly evolving educational landscape, the problem with the allure of these courses is also undeniable. 

The rigidity could leave students in a precarious position, with limited options other than joining private industries or seeking opportunities abroad. At the same time, “Students and parents need to be mindful of what they are getting into,” concluded Rao, while also adding that there is nothing wrong with these programmes as they are approved by the government in the first place.

Voices from Academia

These concerns are echoed by many in the field. Mahadevan Chandramouleeswaran, an investor at Akshamala Tech Services, shared a personal account. His daughter, a top scorer in a five-year integrated M Tech software engineering course, found her application for an assistant professor position rejected because her degree did not align with the traditional 4+2-year degree format. 

The academic community is increasingly aware of the challenges posed by these new programs. Dr. Shashi Bhushan Arya, a professor and national expert in aluminium recycling, noted a decline in enrollment rates in core branches of engineering. He suggested that universities may be using fancy degree titles as a marketing strategy to fill seats, particularly in expensive management quotas. 

This trend raises questions about the long-term viability and recognition of these programs, while the number of STEM graduates in India are increasing rapidly.

However, not all voices are critical. Prof Ravikumar Bhaskaran, life fellow of IIT Kharagpur, pointed out that IIT Kharagpur has a history of introducing new B.Tech programs in emerging fields, often ahead of other institutions. While these programs initially faced scepticism regarding job prospects and postgraduate opportunities, they eventually gained acceptance and recognition. 

Bhaskaran’s perspective offers a glimmer of hope, suggesting that new programs, given time and support, can find their place in the academic and professional landscape.

The root of the problem, as many experts suggest, lies in the inflexibility of current policies. Dharmendra Saraswat, a professor of agricultural and biological engineering, urges policymakers to bring about the necessary changes to match the evolving landscape of engineering education in India. 

A Call for Policy Reform?

Similar cases have also been reported by others. The mismatch between new-age degrees and existing eligibility criteria isn’t just an isolated issue; it’s a systemic one. As Prof Rao highlighted, this problem isn’t limited to undergraduate programs. 

He recently chaired a committee for scientist recruitment at a CSIR lab, where candidates from top institutions like IIT Bombay and IIT Delhi were disqualified because their degrees didn’t match the advertised requirement of “Electronics engineers.” Such cases underline the urgent need for coordination across government bodies and educational institutions to ensure that degree titles and job requirements align.

The rigidity of the system is further exemplified by Reuben Mathew, an aerospace engineer, who recounted his experience of being rejected for an engineering position at DRDO/NAL due to a mismatch between his degree title and the traditional degree names listed in the job advertisement. 

Mathew’s frustration is a shared sentiment among many graduates of specialised programs, who find themselves excluded from opportunities despite possessing the necessary skills and experience.

Without these changes, graduates from these interdisciplinary programs may find themselves at a disadvantage. Dr. Chennakesava Kadapa, a lecturer in mechanical engineering, sees an opportunity in this challenge. He argues that IITs and other premier institutions should update the GATE exam and introduce new master’s programs in fields like AI, robotics, and sustainable energy. 

This proactive approach could ensure that new-age programs are recognised and valued, both in academia and the job market.

The rise of specialised engineering programs is a response to the evolving demands of technology and industry. However, without corresponding changes in the eligibility criteria for government jobs and postgraduate programs, these degrees may leave students stranded.

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Most AI approaches rely heavily on the program developed by humans. They can only do what they were programmed to do. They can only learn what they are taught. When faced with new environments, these systems get stuck. But this is slowly changing with the rise of AI agents. But is that enough?

In a recent interview, British neuroscientist Karl Friston underscored a transformative potential in current AI agents: the integration of curiosity. 

“Their inability to independently select training data limits their capacity for genuine intelligence. While they excel at data processing and prediction, they lack the curiosity and independent thought essential for true scientific advancement and problem-solving,” he said.

Why is curious AI even necessary?  To answer this, look at these four research papers to understand the need for curiosity. 

A “linear vs. loopy maps” study found that while LLMs do well on simple, linear tasks, they have trouble on complex ones that involve cycles or dead ends. 

This restriction was investigated in further detail in the “TravelPlanner” study, which showed that LLMs frequently perform poorly in complicated decision-making scenarios that call for taking into account a variety of limitations and possible outcomes.

An additional significant vulnerability is to an LLM’s capacity to efficiently retrieve and apply the knowledge it has received training on. Studies on “LLM lookup capabilities” have revealed that these models’ performance in locating pertinent data might vary, which can result in errors and imprecise results.

How Can Being Curious Solve the AI Problem

Nick Clegg, the president of global affairs at Meta, has called AI models stupid, similar to how Yann LeCun calls them ‘dumb’. “They can process information and identify patterns incredibly fast, but they don’t truly understand the world in the same way humans do. They’re essentially sophisticated pattern-matching machines,” he said.

The idea is to compound curiosity with intelligence. By combining human and AI curiosity, we can leverage their unique strengths to compound our creative potential. The intuitive nature of human curiosity can work in tandem with the computative power of AI curiosity to accelerate discoveries and drive innovation. 

Putting it in the words of Sonya Huang, partner at Sequoia: “As the models get bigger and bigger, they begin to deliver human-level, and then superhuman results.”

This proved true in the new algorithm developed by researchers at MIT’s Improbable AI Laboratory and CSAIL. The paper highlighted that the algorithm automatically piques interest when needed, but when not, it stifles it until the agent gathers enough information from its surroundings to decide what to do.

When evaluated on more than 60 video games, the method performed well on both hard and easy exploration tasks, whereas prior algorithms could only handle a hard or easy domain on their own. 

“Previously what took, for instance, a week to successfully solve the problem, with this new algorithm, we can get satisfactory results in a few hours,” co-author Zhang-Wei Hong said. This efficiency is crucial in real-world applications where time and resources are limited.

The study expands on past research by OpenAI that showed how AI agents driven by curiosity could succeed in challenging gaming environments such as Montezuma’s Revenge. 

Making AI Agents More Curious

Over the years, scientists have worked on algorithms for curiosity, but copying human inquisitiveness has been tricky. For example, most methods aren’t capable of assessing AI agents’ gaps in knowledge to predict what will be interesting before they see it. 

For example, TEXPLORE-VENIR is a reinforcement learning method that was created by Todd Hester and Peter Stone. It incentivises programs to find new knowledge and reduce uncertainty. It offers intrinsic rewards for comprehending novel concepts, such places or recipes, in contrast to traditional approaches that only concentrate on reaching predetermined goals. 

Another example is IBM’s Project Debater AI which aims to engage in competitive debates rather than genuinely exploring the nuances of the topics discussed. IBM claims that Debater AI is the first-ever AI system designed to meaningfully engage with humans in a debate.

This improves curiosity-driven exploration and learning efficiency.

Take chatbots, for example—it’s common to see chatbots that can answer frequently asked questions (FAQs). On the other hand, customer service quality can significantly improve if chatbots have a certain level of perceived emotional intelligence that can be achieved by injecting curiosity-driven behaviours.

In healthcare, more curious models could accelerate drug discovery by exploring vast chemical spaces with greater efficiency. In robotics, curious AI could enable robots to adapt to new environments and tasks more rapidly.

Definitely, making AI agents curious is something to look forward to. 

The post To Be an AI Agent is to be Curious appeared first on AIM.

CloudKeeper, a leading provider of comprehensive cloud cost optimisation services, has acquired WiseOps, an AI automation platform specialising in AWS cost and usage optimisation.

While the financial details of the deal were not disclosed, CloudKeeper made payments in equity and cash.

WiseOps previously secured an undisclosed pre-seed investment from CORE91.VC in December 2023. With a growing customer base of 50 clients, the startup has achieved over $100,000 in revenue till date.

Founded in 2023, the startup is known for AI-driven recommendations and automated optimisations, empowering teams to significantly reduce cloud spend without compromising on performance or workflow efficiency.

By integrating WiseOps’ intelligent tools into CloudKeeper’s robust ecosystem, clients can now access a truly end-to-end cloud optimisation solution that promises enhanced savings and workflow efficiency.

Sharing about the journey of the company, Praneet Chandra, Co-founder of WiseOps, stated, “Fifteen months ago, Ronak and I founded WiseOps in response to companies struggling with rising costs and cloud infrastructure challenges, leading to layoffs. Our journey began with our first customer, where we reduced their cloud bill by 50%.”

“WiseOps was the missing piece of the puzzle,” said Deepak Mittal, co-founder and CEO of CloudKeeper. “By joining forces with them, CloudKeeper has become a truly comprehensive cloud cost optimization solution. It will enable us to cater to a broader range of clients, address more complex use cases, and help businesses optimize and engineer their cloud environments more effectively.”

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The Mixture of Experts (MoE) and Mixture of Agents (MoA) are two methodologies designed to enhance the performance of large language models (LLMs) by leveraging multiple models.
MoE focuses on specialised segments within a single model, MoA utilises full-fledged LLMs in a collaborative, layered structure, offering enhanced performance and efficiency.

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