In a strategic partnership, PwC will include advanced Shorthills AI’s generative AI features into its suite of tools. The aim is to leverage data and AI, streamline operations, and drive return on investment.

Under this partnership, PwC will deploy Shorthills AI’s GenAI solutions, including GenAI-powered search, automatic classification of entries, summarization of large documents, and natural language-based Q&A.

Shorthills AI has a proven methodology of training and fine-tuning modern LLMs with custom data. Since its inception in 2018, the founders Pawan Prabhat and Paramdeep Singh aim to leverage AI to solve complex business challenges. “Collaborating with PwC allows us to showcase the transformative power of our GenAI solutions,” said Paramdeep Singh, co-Founder, Shorthills AI.

PwC has consistently invested in emerging AI technologies to stay ahead in the competitive market. This partnership with Shorthills AI is a testament to PwC’s dedication to adopting the latest advancements in AI to enhance its service delivery. 

By employing their expertise, PwC will be able to offer more personalised services to its clients making it a leader in the consulting  space. “We look forward to partnering with Shorthills AI to bring innovative GenAI technologies to our clients. This partnership will enable us to deliver even greater value to our clients through enhanced efficiency and personalised solutions,” said Siddharth Mehta, Partner, PwC India. 

PwC’s legacy in integrating AI into its solutions help them revolutionise the consulting world. Previously, PwC formed alliances with major AI technology vendors, including foundation model providers AWS, Anthropic, Google, Meta, Microsoft, and OpenAI. Earlier last month, PwC India inaugurated its GenAI Experience Lab in Gurgaon, in a bid to explore Generative AI’s transformative capabilities. They also teamed up with Microsoft India to bolster its incident response and recovery capabilities.to tackle cyber threats within organisations.

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In a strategic announcement, Google Cloud collaborates with ParallelDots, one of the leaders in Retail Image Recognition solution, to deliver advanced, real-time AI solutions to global Consumer Packaged Goods (CPG) manufacturers and retailers. The goal is to employ the strengths of both the companies and unlock enhanced data accuracy and simplified AI training for CPGs. As a consequence of this, the in-store execution, customer satisfaction, and sales would increase. 

AI in Retail

Google Cloud ensures end-users with reliable performance and security due to its secure infrastructure. This integration will allow customers to deploy ParallelDots’ solutions quickly and easily. Customers will be able to  integrate ParallelDots’ shelf data within the Google Cloud platforms, eliminating the costly and complex task of manual integration.

This partnership helps combat losses related to the retail industry which is currently plummeting with a loss of 25% of sales annually due to poor in-store execution. Due to the limitations of manual store audits, there is also a dearth of  real-time data, efficient audits, and timely reporting. In such a context, advanced AI & IR solutions are gaining significance as they address problems like missing SKUs and price labels or incorrect product placement. 

Commenting on the partnership, Bikram Singh Bedi, Vice President and Country MD, Google Cloud India said, “Our aim is to offer our customers a secured infrastructure and capabilities to seamlessly perform complex tasks. With this collaboration we aim to empower ParallelDots to deliver unparalleled solutions to their customers. Our advanced cloud capabilities combined with their innovative technologies will simplify tasks, efficiently run complex problems and enhance cost efficiency.”

Ankit Singh, Co-founder & CTO, from ParallelDots is confident with this partnership, “This milestone marks a significant advance in delivering a world-class Retail Image Recognition solution to the global CPGs and retailers. Our partnership with Google Cloud enhances the reliability, security, and speed of our solutions, dramatically reducing deployment time, scaling our Image Recognition solution ShelfWatch, and boosting platform reliability and cost-effectiveness. This is a pivotal moment in our mission to create the world’s foremost retail shelf insights platform,” he said. Google Cloud’s strong presence in the retail space strengthens this collaboration in the marketplace. This joint solution stands as it allows end-customers to adopt ParallelDots’ technology, especially due to its platform security and robust infrastructure.

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Jaspreet Bindra, a tech evangelist and author, launched his AI literacy initiative “AI&Beyond” with the goal to make AI more accessible and understandable. It is in collaboration with Anuj Magazine, an AI & cybersecurity expert, it is spread across industries. 

“At AI&Beyond, we believe that AI is no longer the future – it is very much the present. Our goal is to ensure that AI literacy becomes as fundamental as reading and arithmetic, especially in large organisations where AI’s impact will be profound. Through AI&Beyond, we aim to bridge the gap between AI’s capabilities and its practical application across various sectors,” said Jaspreet Bindra. 

Inspired by CLR James’ book Beyond A Boundary, AI&Beyond aims to simplify and distill the concept of AI beyond the technical aspects of it. 

One of the initiative’s flagship programme, The Generative AI Bootcamp,  is designed to equip participants with the necessary and most important AI skills. Additionally, the platform’s Ethics Bootcamp is crucial in today’s AI-driven world, helping organisations not only adopt AI, but do so with a strong ethical foundation. 

The underlying idea is to make the learning experiential and immersive – that form the foundation of future AI businesses. These resources – including workshops, briefings, webinars, and consulting services – would empower organisations to become more agile, innovative and competitive in the digital age.

Jaspreet Bindra on GenAI

Bindra has been a staunch advocate of Make in India, and to that end, proposed that India should consider building generative AI as a digital public good – known as JanAI or GenAI for the people. “If you look at ChatGPT or Bard, they are all trained on the internet, where almost 80 to 90% of the data is English, and West-oriented. It doesn’t have vernacular data nor an Indian context,” said Bindra, while emphasising on why in the current LLM market, he wanted to bring an Indian context to the training model. 

Also, at MLDS 2024 – India’s biggest generative AI summit – hosted by AIM, Jasprit Bindra spoke about the history of AI and the concept of singularity in today’s generative trends. 

The post Tech Veteran Jaspreet Bindra launches ‘AI&Beyond’ to Democratise AI Literacy  appeared first on AIM.

HARMAN, a Connecticut-based Samsung subsidiary, specialising in audio electronics, launched HARMAN ForecastGPT, a predictive analytics platform that enables organisations to become more efficient through future forecasting and optimum resource allocation. It is aimed at understanding complex data patterns, and some of its chief features are advanced AI capabilities, real-time adaptability and the ability to seamlessly integrate with various platforms. It is compatible with any data format and source including CSV, Excel, SQL, and API.

HARMAN ForecastGPT’s Versatility 

It can benefit businesses in a plethora of ways including sales forecasting, supply chain forecasting, financial planning, and marketing even. 

“Embracing AI is imperative for business success and at HARMAN DTS, we are pioneering the application of AI to deliver tangible, bottom-line results. By understanding the unique challenges and aspirations of each client, we’re crafting AI solutions that go beyond generic predictions. The ForecastGPT platform is a testament to our commitment to equipping businesses with the tools to move past challenging roadblocks and fully capitalise on the potential of AI,” said Nick Parrotta, President – Digital Transformation Solutions & Chief Digital and Information officer at HARMAN.  

HARMAN & its Growing Capabilities 

HARMAN has a wide portfolio including audio and video systems, car audio, connected car solutions, professional audio and lighting equipment, and more. Additionally, it is also the parent company to brands like JBL, Harman Kardon, AKG, Mark Levinson, and Infinity Systems. 

“HARMAN’s data science team has made significant contributions by incorporating machine learning and deep learning models into a range of applications, such as predictive analytics, computer vision, NLP, and graph analytics,” said Dr Jai Ganesh, chief product officer of HARMAN, in an exclusive interview with AIM previously. 

The data science team of HARMAN employs both open source as well as commercial tools such as Python, TensorFlow, PyTorch, AWS, Azure, or Google Cloud Platform, Java, C++, Git, Jenkins, Docker, Kubernetes, R, Jupyter, SAS, MongoDB, Spark, Kafka, MySQL, RStudio, KNIME, RapidMiner, H2O etc.

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Organisations often struggle with data silos and inefficiencies when implementing generative AI solutions. This affects over 70% of enterprises today, but global software company Pegasystems, aka Pega, seems to have cracked the code by using its patented ‘situational layer cake’ architecture. 

This approach democratises the use of generative AI across its platform, allowing clients to seamlessly integrate AI into their processes. They can choose from any LLM service provider, including OpenAI, Google’s Vertex AI, and Azure OpenAI Services, thereby ensuring consistent and efficient AI deployment across all business units.

“Our GenAI implementation at the rule type levels allows us to democratise the use of LLMs across the platform for any use case and by mere configuration, our clients can use any LLM service provider of their choice,” said Deepak Visweswaraiah, vice president, platform engineering and side managing director at Pegasystems, in an interaction with AIM. 

Pega vs the World 

Recently, Salesforce announced the launch of two new generative AI agents, Einstein SDR Agent and Einstein Sales Coach Agent, which autonomously engage leads and provide personalised coaching. This move aligns with Salesforce’s strategy to integrate AI into its Einstein 1 Agentforce Platform, enabling companies like Accenture to scale deal management and focus on complex sales.

Salesforce integrates AI across all key offerings through its unified Einstein 1 Platform, which enhances data privacy, security, and operational efficiency via the Einstein Trust Layer. 

“We have generative AI capabilities in sales cloud, service cloud, marketing cloud, commerce cloud, as well as our data cloud product, making it a comprehensive solution for enterprise needs,” said Sridhar H, senior director of solution engineering at Salesforce.

SAP’s generative AI strategy, on the other hand, centres around integrating AI into core business processes through strategic partnerships, ethical AI principles, and enhancing its Business Technology Platform (BTP) to drive relevance, reliability, and responsible AI use across industries.

“We are adding a generative AI layer to our Business Technology Platform to address data protection concerns and enhance data security,” stated Sindhu Gangadharan, senior VP and MD of SAP Labs, underscoring the company’s focus on integrating AI with a strong emphasis on security and business process improvement.

Oracle, on the other hand, focuses on leveraging its second-generation cloud infrastructure, Oracle Cloud Infrastructure (OCI). It is designed with a unique, non-blocking network architecture to support AI workloads with enhanced data privacy while extending its data capabilities across multiple cloud providers.

“We’re helping customers do training inference and RAG in isolation and privacy so that you can now bring corporate sensitive, private data…without impacting any privacy issue,” said Christopher G Chelliah, senior vice president, technology & customer strategy, JAPAC at Oracle.

Meanwhile, IBM has watsonx.ai, an AI and data platform designed to help companies integrate, train, and deploy AI models across various business applications.

IBM’s generative AI strategy with watsonx.ai differentiates itself by offering extensive model flexibility, including IBM-developed (Granite), open-source (Llama 3 and alike), and third-party models, along with robust client protection and hybrid multi-cloud deployment options. At the same time, Pega focuses on deeply integrating AI within its platform to streamline business processes and eliminate data silos through its unique situational layer cake architecture.

Pega told AIM that it distinguishes itself from its competitors by avoiding the limitations of the traditional technological approaches, which often lead to redundant implementations and data silos. “In contrast, competitors might also focus more on channel-specific designs or product-centric implementations, which can lead to inefficiencies and fragmented data views across systems,” said Visweswaraiah. 

Situational Layer Cake Architecture 

Pega told AIM that its approach to integrating GenAI processes into business operations is distinct due to its focus on augmenting business logic and decision engines rather than generating code for development. 

It employs the situational layer cake architecture, which as a part of Pega’s exclusive centre-out architecture, helps to adapt microjourneys for different customer types, lines of business, geographies, and more. 

“Our patented situational layer cake architecture works in layers making specialising a cinch, differentiating doable, and applying robust applications to any situation at any time, at any scale,” said  Visweswaraiah.

He added that enterprises can start with small, quick projects that can grow and expand over time, ensuring they are adaptable and ready for future challenges.

In addition to this, the team said it has the ‘Pega Infinity’ platform, which can mirror any organisation’s business by capturing the critical business dimensions within its patented situational layer cake. 

“Everything we build in the Pega platform, processes, rules, data models, and UI is organised into layers within the situational layer cake. This means that you can roll out new products, regions, or channels without copying or rewriting your application,” shared  Visweswaraiah. 

He further said that the situational layer cake lets you declare what is different and only what is different into layers that match each dimension of your business. 

Simply put, when a user executes the application, the Pega platform slices through the situational layer cake and automatically assembles an experience that is tailored exactly to that user’s context. 

Visweswaraiah believes that this architecture has given them a great opportunity to integrate GenAI into the platform at the right layers so it is available across the platform. 

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Last week, in a one of a kind effort, Open AI signed an MOU with the US Artificial Intelligence Safety Institute (US AISI), part of the larger US Department of Commerce’s National Institute of Standards and Technology. In this juncture of AI’s revolution,  this collaboration is aimed at furthering OpenAI’s commitment for safety, transparency and human centric innovation – by building a framework that the world can contribute to. This would enable the US AI Safety Institute to get early access to test and evaluate future models prior to its public release. Anthropic has also agreed to sign this partnership. 

Sam Altman, CEO of OpenAI, took to X (formerly Twitter), to underscore the significance of this partnership. “We are happy to have reached an agreement with the US AI Safety Institute for pre-rlease testing of our future models,” said Altman, saying that this is important, and suggested for this to happen at a national level. “US needs to continue to lead!”

But Why US AI Safety Institute? 

Elizabeth Kelly, director of the US AI Safety Institute, has been a strong proponent of safety in AI innovation and has brokered many such strategic partnerships in the past. “With these agreements in place, we look forward to beginning our technical collaborations with Anthropic and OpenAI to advance the science of AI safety,” she said in a statement. 

The US AI Safety Institute was born in 2023, under the Biden-Harris administration, to help develop testing guidelines for safe AI innovation in the US. 

“Safety promotes trust, which promotes adoption, which drives innovation, and that’s what we are trying to promote at the US AI Safety Institute.” she said in another interview, highlighting the role of this institute in the coming future. 

Through initiatives like this, the US could lead the way for more voluntary AI safety adoption. Anthropic, OpenAI rival, has previously collaborated with government bodies – like the UK’s Artificial Intelligence Safety Institute (AISI) – to conduct pre-deployment testing of their models. It would be interesting to see if OpenAI also partners with them too.  

Why it matters? 

The need for the US AI Safety Institute (US AISI) arises from concerns about the impact of poorly managed AI systems on democracy, as highlighted by Dario Amodei, CEO of Anthropic. 

Amodei said that AI must be aligned with human values and ethics to support democratic institutions effectively. The collaboration between Anthropic, OpenAI, and the US AISI is a response to the growing power of AI, which, if left unchecked, could exceed that of national governments and economies. This partnership aims to establish safety standards, conduct pre-deployment testing, and regulate AI to prevent misuse, particularly in politically sensitive areas such as elections.

“I think it’s just really important that we provide these services well. It makes democracy as a whole more effective, and if we provide them poorly, it undermines the notion of democracy,” said Amodei. 

US vs China vs India 

The US’s push for AI safety leadership with OpenAI and Anthropic aims to counter China’s rapid AI advancements and maintain global dominance in ethical AI governance. 

At the same time, there are concerns around China winning the AI race due to its efficient state control, outpacing US efforts hindered by political gridlock. “China is probably going to win the AI game, as their state control is much more efficient than corrupt US politicians,” said Pratik Desai, saying that he wants US and freedom to win. “I just don’t trust the current bunch of politicians.” 

China’s dominance in several AI and technological is quite evident with them leading in “visual AI and LLM field as they have the best state-operated surveillance system,” added Desai. 

On the bright side, standardised scrutiny could promote a more democratic approach to developing models, which is perhaps lacking in economies like China. More countries are slowly realising the importance of AI institutes; and the need for investing in AI safety as much as they do on AI development. 

It’s high time India also considered establishing an AI Safety Institute, akin to those in the UK and US, to responsibly manage the rapid growth of AI technologies. “We need an AI Safety Institute here and now” said former director-general of CSIR, Raghunath Mashelkar, in order to maximise the benefits and minimise the risks associated with AI in the world’s most populous nation.

Former Union Minister of India, Rajeev Chandrasekhar, also underscored the critical need for democracies and their allied nations to shape the future of technology, particularly in light of concerns raised by Paul Buchheit, creator of Gmail, about the potential dangers of AI development led by China, which could lead to global surveillance and censorship. 
“It’s extremely important — more than critical — that the future of tech is shaped by democracies and their partner countries,” said Chandrasekhar.

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OpenAI, ChatGPT maker, is expected to raise funding from Apple and NVIDIA at a valuation of $100 billion. Per reports, Thrive Capital, an NYC based VC firm, will lead the round. OpenAI is currently valued at $86 billion. 

Earlier this year, Microsoft, OpenAI’s oldest, and most significant strategic partner, valued them at $30 billion. While they are still in the talks to participate in the upcoming fundraise, the details are unclear. 

Apple and OpenAI could see this fundraise as a new way to strengthen their existing relationship that saw a new height with the “Apple Intelligence” initiative where OpenAI’s AI features were made a core part of apple’s products. This acquisition is also telling about OpenAI’s value within the apple ecosystem, and how Tim Cook, CEO of Apple, wants an inside on OpenAI’s future innovations.  

NVIDIA is optimistic in OpenAI commitment to “Humanity, Computing and AI.”  In line with this, earlier this year, Jensen Huang, NVIDIA’s CEO, personally hand-delivered the first NVIDIA DGX H200 to OpenAI. 

What does Open AI’s future hold?

OpenAI’s growth since the launch of 2022 has been record breaking. It has sparked a reckoning in the AI community, especially among investors and VC firms, who are constantly in the race of staying  on top of cutting edge AI driven technologies and capturing market share. 

On 29th August, OpenAI reported that ChatGPT has surpassed more than 200 million active users.  Fortune 500 companies are heavily dependent on OpenAI, with around 92% of them using the latter’s products, per an OpenAI spokesperson. According to her, the API usage has also doubled since the launch of GPT-4o mini. OpenAI’s recent collaboration with AI Safety Institute  also highlights their commitment to advance safe and trustworthy AI innovation for all.

The post Apple, NVIDIA in talks to Raise Funding for OpenAI appeared first on AIM.

In a collaborated effort with Waymo & University of Southern California, Meta FAIR released its research on the importance of multi-modal generative models. Transfusion aims to unite and simplify the gap between discrete sequence modeling and continuous media generation. 

The Transfusion Model 

The model is trained equally on text and image. Per Meta, Transfusion is more advanced than quantising images and training a language model over discrete image tokens. The model’s performance can be enhanced through “modality-specific” encoding and decoding layers. The model predicts the next word in a sequence. Trained on improving predictions, it reduces the difference between guessing and actual words. It is imperative to note that with 7 billion parameters and 2 trillion multi modal tokens, Transfusion is at par with other larger models that create image and text – and outperforms models like DALL-E 2 and SDXL. It works better than Chameleon as it takes lesser computing power and generates better results. 

One limitation is perhaps that diffusion models do not perform at par with traditional language models. A lot of research is yet to be done in this area to improve overall performance.  

Transformer’s Uniqueness & the Future of Innovation in AI Research 

What differentiates Transformer from the rest is its unified architecture that runs end to end to generate text and images. Existing models like Flamingo, LLaVA, GILL, and DreamLLM combine separate architectures for different types of data, which are trained separately. 

The goal of this Transfusion is to synergise two modalities in a single joint model – with each of them fulfilling their objective. The incentives are that these are versatile, resource efficient, and cost effective for handling different types of data without any additional costs. 

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Cursor, an AI-powered product built by Anysphere, is transforming the developer community, helping them turn ideas into reality across sectors. From empowering an eight-year-old to build a chatbot within an hour to building a financial dashboard using only voice, Cursor AI is currently reeling in its force multiplier era. 

At its core, what pushes Cursor AI ahead of the curve are its seamless features like integration to workflow, customisations, and GPT-4 assistance, that democratise coding. Some of these features are available in its free version, while the paid plan is more suitable for larger teams or those with specific, and more advanced requirements. 

Meanwhile, developers and tech enthusiasts have taken over the internet to share their insights on leveraging Cursor AI. It is interesting to note their custom prompts and workflows that have led to building apps and features in record time. 

Cursor 101

Corbin Brown, a creator and an investor with 49.9K YouTube subscribers, has made a Cursor 101 explainer video for beginners. The video highlights features like codebase integration, AI driven edits, and privacy first approach making the tool extremely secure and easy to use for developers. 

Web Apps using Cursor

Abdul Majed Raja, who specialises in AI, ML and open source, has built a community of 79.7K subscribers on YouTube. He has been covering several aspects of Cursor AI through his lucid tutorials around building a Chrome extension, creating a web app, and a JS game – all from scratch. 

Building Financial Dashboard using Cursor

Mckay Wrigley, the founder of Takeoff, made a tutorial video on building a financial dashboard in 5 minutes. Additionally, he is also offering a paid course with a 20% launch discount for those interested in a comprehensive understanding of the tool and its potential. 

UI/UX Using Cursor

Meng To, a Singapore-based designer, built his dream video editor using prompts within three weeks – something which usually takes way longer. While his post is taking X by storm, he is on a mission to teach design to coders and coding to designers. 

Real-time Problem Solving Using Cursor

Ray Fernando, a former engineer with Apple, built an entire fully functional app in real time on livestream using Cursor AI. His method of teaching is gaining traction online, where he talks about the basics of coding, integrating OpenAI’s GPT-4, and follows up with real-time problem-solving. 

Cursor for Beginners 

Steven, a self-taught developer with eight years of experience, aims to democratise his knowledge about web development and coding through easy-to-learn videos. He delves into the features, user interface, and ways in which it speeds up your workflow. 

Enhance Productivity using Cursor

Zachary Lee, an NYC-based tech generalist, focuses on deconstructing LLMs and AI products in his videos. In this short tutorial, he talks about Cursor AI’s four core features, like inline editing, code generation, and cursor chat that can be leveraged to maximise productivity. 

Build AI Projects with Cursor

Kevin Kernegger, the founder of Macherjek GmbH with over a  decade of experience, teaches a nine-part course on skillpark.ai for $99. In a tailored manner, he helps individuals create their own AI projects. 

With the hype taking over the internet, software engineers claim it is tough to return to a time when this tool didn’t exist. Its adoption in the non-tech industry is also on the rise – especially its potential within the design community.

The post 8 Resources to Help You Learn Coding with Cursor appeared first on AIM.

On 27th August 2024, Google released a paper detailing the capabilities of their new model GameNGen. This research was authored by Dani Valevski (Researcher at Google Research), Yaniv Leviathan (Engineer at Google Research), Moab Arar (PhD candidate at Tel Aviv University), and Shlomi Fruchter (Engineer at Google DeepMind). 

GameNGen’s Capabilities 

Powered entirely by a neural model, GameNGen is one of the pioneer search engines that allows high-quality, real-time interactions with complex environments over long periods. “GameNGen can interactively simulate the classic game DOOM at over 20 frames per second on a single TPU. Next frame prediction achieves a PSNR of 29.4, comparable to lossy JPEG compression”, per Google. The training occurs primarily in two phases:  (1) an RL agent learns to play the game, with training sessions being recorded, and (2) a diffusion model is trained to predict the next frame using the sequence of previous frames and actions. 

This approach, while still in its nascent stage, is testament of a new paradigm where games are weights of a neural model, not lines of code, in an era where video games are programmed by humans. GameNGen reveals that a neural model can interactively run a complex game like DOOM on existing hardware. 

Architecture & How It Works

In order to collect data efficiently, an RL agent plays the game, and its actions and observations are recorded as training data. For more enhancement and consistency, the Stable Diffusion model is trained to predict the next game frame using previous frames and actions. Lastly, the model’s decoder is fine-tuned to improve image quality and reduce visual artefacts. 

AI in Gaming

Jack O Brien, ex Google, remarked on how this research is seminal and opens up new possibilities for the world of video generation and the use of AI.  Jensen Huang, Founder of NVIDIA, has been bullish on the inevitability of the world of gaming and the role played by AI in making it a reality. “We used AI to revolutionise computer graphics. Graphics enabled AI, and now AI is saving graphics”, Huang said. Last year, OpenAI’s acquisition of Global Illumination, a gaming and design startup and the creators of Biomes, highlights OpenAIs gaming focus for AI simulation and use. As AI research advances, companies will increasingly test their models in game worlds before real-world deployment.

The post Google Unveils GameNGen: Integrating Generative AI into Gaming  appeared first on AIM.

Rufus, a generative AI expert, aimed at enhancing customer experience by making shopping more easier and convenient, is now launched in India on 27th August 2024. With its initial rollout in the US earlier this year, Rufus is now open to only select customers for now. “Customers in the U.S. have already asked Rufus tens of millions of questions, and we’re excited to introduce it in India.” said Amazon in a blog post.

Rufus’ Generative AI Capabilities 

In a blog post, Amazon detailed how Rufus seamlessly transforms the customer’s shopping experience through a wide range of queries. The aim is to maximise AI to effectively meet the customers’ needs. 

Per Amazon, customers using Rufus on Amazon.in can enhance their shopping experience in several ways. They are offered tailored information, product comparisons, personalised recommendations, among other things.

Rufus & India’s E-commerce Space

Rufus is not new to this market. In 2023, Myntra collaborated with Microsoft to launch “My Stylist” a feature that recommends complete, shoppable looks tailored for fashion-forward customers. In the same year, they launched MyFashionGPT, a tool that enables users to search using natural language to give relevant suggestions. Even Flipkart leveraged LLMs, through AI-driven ‘Flippi’ to simplify shopping experience. As of last week, Seematti, a retail brand, launched India’s inaugural AI fashion ambassador, Isha Ravi, who as an Indian young woman with a passion for colour and travel, represents the brand in the Indian landscape. 

In the past 25 years, Amazon has built their suite of AI backed customer offerings. Personalised recommendations, like Alexa, have been successful for Amazon in the past. 

Amazon’s AI generated review highlights and product listings have enabled customers to shop better in the past. Rufus is at the heart of this AI expansion. Speaking on the potential of Generative AI  in the e-commerce space, Amazon recognises that while Generative AI is still in its early stages, they are committed to improving their models and make Rufus better over time. In the upcoming weeks, the beta version of Rufus will be extended to more customers. 

The post Rufus, Amazon’s AI Assistant, is Now Live in India appeared first on AIM.