<\/p>\n
Aggarwal, thus, joins the growing ranks of Indian companies that are building large language models (LLMs) trained on Indian languages. The companies include Bhashini\u2013a unit of the national language translation mission by the ministry of electronics and information technology (Meity); Tech Mahindra<\/a>\u2019s Indus project; AI4Bharat at IIT-Madras; Project Vaani\u2013part of the Bhasha AI project of ARTPARK and the Indian Institute of Science\u2019s pan-India language initiatives; Sarvam AI\u2019s OpenHathi series; and CoRover.ai\u2019s BharatGPT.<\/p>\n Generative AI, or GenAI, refers to the ability of LLM-powered chatbots such as ChatGPT<\/a> to create new content, including audio, code, images, text, simulations, and videos (hence the term, multimodal). GenAI systems fall under the broad category of machine learning, but unlike traditional ML that can analyse data patterns to make predictions, these systems create entirely new content with the help of \u2018prompts\u2019.<\/p>\n That said, can Ola\u2019s Aggarwal do a Google Gemini or OpenAI\u2019s GPT-4? And why is the parent of electric cars and scooters, Ola Electric, and ride-sharing startup, Ola Cabs, dabbling with foundational models, data centres, and silicon chips that require a lot of investment?<\/p>\n Aggarwal\u2019s Krutrim announcement comes at a time when the government is set to unveil its AI policy under the India AI programme on 10 January, which will include a policy framework for public-private partnership models on development of AI databases in Indic languages, as well as indigenous compute capacities,\u00a0according to Union minister of state for IT, Rajeev Chandrasekhar<\/a>. But the release of Krutrim\u2019s base foundation model also comes at a time when Ola Electric is gearing up to\u00a0file for an IPO<\/a>.<\/p>\n Backed by SoftBank, Ola Electric is targeting a valuation of $7-8 billion by early 2024. While that figure\u2019s much higher than the company\u2019s current estimated worth of\u00a0about $3.6 billion<\/a>, it\u2019s closer to Ola Electric\u2019s estimated valuation of $7.3 billion as at the end of 2021.\u00a0<\/p>\n Ola Electric plans to use the funds raised from the IPO for expanding its electric vehicle business and establishing a dedicated lithium-ion cell manufacturing unit.<\/p>\n Aggarwal has clarified that Krutrim is a \u201cseparate business altogether”, and will not \u201cbe integrated at a transactional level”.\u00a0<\/p>\n \u201cThere are some entities that I own 100%\u2014this is under my company, and not part of Ola or Ola Electric\u2019s corporate structure,”\u00a0he said<\/a>. Aggarwal did say Krutrim had \u201csome investments into (Ola Electric)”, but did not disclose any details.\u00a0<\/p>\n Further,\u00a0in a presentation<\/a>, Aggarwal said that all Ola group companies were \u201calready using Krutrim for a lot of their internal workloads, be it customer support, voice and chat, customer sales calls, and for other processes\u2026”\u00a0<\/p>\n This clearly implies that Krutrim\u2019s products and services will be cross-sold to enhance the offerings of the group companies.<\/p>\n The use of generative AI in the auto sector is not new. Mercedes-Benz, for instance, recently used ChatGPT to power voice assistants in a beta program available to more than 900,000 vehicles.<\/p>\n Also consider the example of a Formula E electric race car, the GENBETA,\u00a0 an enhanced GEN3 race car. The GEN3 is the fastest, lightest, electric race car with a top speed of more than 322 kmph, and is used by the 11 teams and 22 drivers in the ABB FIA Formula E World Championship.<\/p>\n Google Cloud provided generative AI to analyse the drivers\u2019 runs. Additionally, experts from McKinsey & Co.\u2019s AI arm, called QuantumBlack, built data and analytics components to create the driver interface that analysed and queried data in real-time using\u00a0generative AI<\/a>.<\/p>\n According to Nvidia, generative AI is also enabling new breakthroughs in autonomous vehicle development in research areas including the use of neural radiance field technology to turn recorded sensor data into fully interactive 3D simulations. These digital twin environments, as well as synthetic data generation, can be used to develop, test and validate autonomous vehicles at incredible scale.<\/p>\n Aggarwal\u2019s AI ambitions, however, appear to go far beyond just the auto sector, given that the Ola group\u2019s businesses extend beyond mobility to financial services offerings including payment systems, insurance agents and cloud kitchens.<\/p>\n Krutrim\u2019s AI model, according to the company, has been trained on more than 2 trillion tokens (loosely, numerical representation of pieces of words and sub-words that an LLM can understand. For instance, banana is a word, while homework can be split into two words\u2013home and work). While Aggarwal compared Krutrim to GPT4, the latter has been trained on more than 13 trillion tokens.\u00a0<\/p>\n That said, the strength of Krutrim may lie in its understanding of 20 Indian languages and generating content in 10 Indian languages, including Marathi, Hindi, Telugu, Kannada, and Odiya. Aggarwal said Krutrim has been \u201ctrained on 20 times more Indic tokens than any other model, ensuring a deep understanding of Indian culture, values, and aspirations”.<\/p>\n While there\u2019s a waitlist if you register for the base LLM model at\u00a0OlaKrutrim<\/a>, Aggarwal plans to make the \u201cwhole platform” available for developers to build application programming interfaces, or APIs, for enterprise applications, in February. Ola also plans to launch Krutrim Pro in the next quarter.<\/p>\n That said, building a foundational model from scratch is an expensive affair. OpenAI\u2019s GPT was in the works for more than six years and cost upwards of $100 million and used an estimated 30,000 graphics processing units (GPUs). Aggarwal has not disclosed any details of his investments, or the costs, in Krutrim so far.<\/p>\n In FY22, Ola derived about 61% of its revenue, or \u20b9<\/span>1,208.6 crore, from its ride-hailing business in India, while posting a loss of \u20b9<\/span>101 crore. Financial services comprised a small part of the revenue. The group posted a\u00a0consolidated operating revenue<\/a> of \u20b9<\/span>1,970.4 crore in FY22, rising from \u20b9<\/span>983.2 crore in the year before. Ola\u2019s net losses, though, widened in FY22 to \u20b9<\/span>1,522.33 crore from \u20b9<\/span>1,116.6 crore in the previous year.<\/p>\n That said, since Krutrim is a separate business, Aggarwal may be bootstrapping the venture, given that he has a\u00a0personal net worth<\/a> of a little over $1.4 billion. One, however, will have to wait till Aggarwal discloses more details about his investment plans in designing silicon chips and building the LLM ecosystem.<\/p>\n The fact remains that even though India is home to more than 400 languages, making it one of the most linguistically diverse countries in the world, most foundation models and LLMs are trained primarily using internet data, which is predominantly English. As per Statista, English was the most popular language for web content, representing nearly 59% of websites as of January this year. Russian ranked second with 5.3% of web content, followed by Spanish with 4.3%.<\/p>\n While one can only but laud the contribution of India\u2019s Centre for Development of Advanced Computing (C-DAC) in developing the country\u2019s multilingual ecosystem\u00a0over the past three decades<\/a>, the fact remains that AI models need to be trained using regional languages to bridge the digital divide in countries like India, which is why efforts such as Krutrim make a lot of sense.<\/p>\n Krutrim, on its part, says it will tap Bhashini, whose technology comprises automatic speech recognition, optical character recognition, natural language understanding, machine translation, and text-to-speech. The Bhashini platform, for instance, uses optical character recognition (OCR) to extract text from data of printed materials such as brochures to train AI models in 14 languages.\u00a0<\/p>\n But getting local datasets is a challenge, according to the CEO of Bhashini, Amitabh Nag, who\u00a0pointed out<\/a> that many of the 22 official Indian languages do not have digital data, which makes it challenging to build and train an AI model. Bhashini has so far spent $6-7 million to collect data from different sources and employed more than 200 people to collect data (text as well as speech) and feed it into the system, following which the data is curated, annotated, and labelled.<\/p>\nWhat\u2019s Krutrim got to do with Ola?<\/h2>\n
How\u2019s GenAI used in vehicles?<\/h2>\n
What\u2019s the plan with Krutrium?<\/h2>\n
Can Ola afford a Krutrim?<\/h2>\n
How can GenAI work with regional languages?<\/h2>\n
What other Indic LLMs are in the works?<\/h2>\n