Walmart and Amazon Turn to GenAI, Redefining the Search Experience

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TheGen.AI News

Walmart and Amazon Turn to GenAI, Redefining the Search Experience

As Walmart and Amazon vie to deliver the best online shopping experiences, both companies are ramping up their use of generative AI (GenAI) to enhance their search capabilities.

On Wednesday (Oct. 9), Walmart revealed its adoption of AI and augmented reality, among other digital innovations, to offer more immersive and customized shopping experiences. Suresh Kumar, Walmart's global chief technology and development officer, stated that "a traditional search bar is no longer the quickest way to make a purchase—we need to leverage technology to cater to customers' individual preferences and requirements."

The company shared that it has developed large language models specifically for retail applications and improved its AI-powered customer service chatbot for more personalized interactions. Additionally, Walmart announced plans to launch a "personalized homepage for each shopper" in the U.S. by 2026, driven by GenAI.

Meanwhile, Amazon also announced on Wednesday, through a blog post, that it is expanding its use of GenAI by introducing AI Shopping Guides on its U.S. apps and mobile website. Daniel Lloyd, Amazon's vice president of personalization, explained that these guides aim to streamline product research by consolidating key details and recommendations into one place, making it easier for customers to quickly find the right product. Using large language models, the guides identify important product attributes and customer insights to provide tailored recommendations across more than 100 product categories.

On the same day, Matt Woods, a long-time Amazon executive who recently served as vice president of AI, announced his departure from the company in a LinkedIn post after 15 years.

While consumers are still wary of AI’s potential misuse, with 53% expressing concerns in a PYMNTS Intelligence study, many appreciate the personalized experiences that GenAI offers. Data from the March edition of the Payments Orchestration Tracker® showed that 85% of consumers feel their favorite brands provide individual attention.

Businesses also view GenAI as essential for their future. The “How Simple, Routine GenAI Use Can Remake Enterprise Marketing” report from PYMNTS Intelligence surveyed 60 chief marketing officers from U.S. firms with revenues exceeding $1 billion. The findings revealed that eight out of ten consider GenAI to be crucial for enhancing the customer experience.

As Walmart and Amazon accelerate their use of generative AI, both companies are positioning themselves to shape the future of eCommerce. Although consumers remain cautious about the technology, their desire for more personalized shopping experiences continues to fuel demand, with AI-powered search and tailored recommendations becoming integral to the online retail landscape.

Generative AI Adoption: Organizations Under External and Internal Pressure

According to Deloitte's recent "State of AI- Insights from India" report, generative AI (Gen AI) has the potential to revolutionize organizations, yet most firms are under both external and internal pressure to adopt this technology. Over 95% of respondents acknowledged this pressure, with the exception of the Energy, Resources & Industrials sector, which faces less external demand due to minimal AI requirements.

Indian firms are particularly price-sensitive when adopting Gen AI models, with nearly half of respondents identifying cost as their top concern, followed by performance and flexibility. The survey revealed that 75% of participants believe Gen AI will transform their organizations within the next three years, and 70% expect significant changes in 1-3 years. Additionally, 48% foresee similar transformations across their industries, underscoring the growing urgency to adopt AI across various sectors.

While there have been advancements in data management, strategy, and tech infrastructure, with over 40% of respondents feeling well-prepared in these areas, significant gaps remain in risk management, governance, and talent, where only 25% feel adequately equipped.

Gen AI has already delivered benefits such as increased productivity and efficiency, cited by 42% of respondents. This aligns with earlier expectations, where 61% had anticipated these gains. S Anjani Kumar, a partner at Deloitte India, emphasized that Gen AI can reshape the future of work by driving innovation and efficiency but highlighted the need for CEOs and senior leaders to address challenges like implementation, investment, risk, and talent management.

The report also highlights that, despite growing investment in Gen AI—particularly in data management, cloud technology, and AI/ML capabilities—more than 50% of organizations allocate less than 20% of their AI budgets to Gen AI. Key obstacles to adoption include concerns around sensitive data usage, cited by 68%, as well as data privacy and security, identified by 65% of respondents. Companies with less AI experience struggle with acquiring the necessary talent, while those with more experience face difficulties in implementation.

Infosys Enhances Microsoft Collaboration to Propel Cloud and AI Solutions Forward

Infosys, a leading software services company, has announced an expanded partnership with Microsoft to accelerate the global adoption of Generative AI and Microsoft Azure. According to a press release, the collaboration aims to help joint customers maximize their technology investments and achieve transformative outcomes. Infosys, an early adopter of GitHub Copilot, has seen significant improvements in code modernization and completion efficiency, with over 18,000 developers generating more than 7 million lines of code, positioning Infosys as a top GitHub Copilot "customer zero."

Infosys has also established an industry-first GitHub Centre of Excellence (CoE), positioning the company to drive enterprise AI innovation globally. Additionally, Infosys has been selected as a strategic partner to assist Microsoft’s enterprise customers with Cloud and AI workloads. Infosys will integrate Microsoft’s Gen AI solutions into its Solution IP portfolio, offering enhanced cost-efficiency, scalability, and agility.

This collaboration will strengthen Infosys’ offerings, including Infosys Topaz, Infosys Cobalt, and the AI-powered Infosys Aster marketing suite, to enhance customer experiences and drive enterprise AI adoption. The partnership will focus on several key areas:

• Financial Services: Combining Infosys' Finacle expertise and Microsoft’s tools to help financial institutions innovate and transform efficiently.

• Healthcare: Leveraging Infosys Helix, an AI/ML-driven healthcare platform on Azure, to optimize patient outcomes and streamline operations.

• Supply Chain: Enhancing agility and process optimization using TradeEdge and Azure OpenAI services.

• Telecommunications: Improving connectivity and customer experiences with Microsoft’s generative AI and Infosys Live Operations platforms.

• Energy Management: Supporting the NetZero journey with Infosys’ Energy Management Solution and Microsoft’s sustainability initiatives.

• Customer Service: Integrating Infosys Cortex, an AI-powered engagement platform, with Microsoft GenAI and Copilot for tailored customer service solutions.

Many of these solutions will be available on Azure Marketplace, enabling customers to leverage their Microsoft Azure Consumption Commitment (MACC) for mutual benefit.

Both companies are committed to promoting responsible AI practices. Infosys plays a key role in Microsoft’s Responsible AI Partner Initiative and contributes to ethical AI guidelines through its Responsible AI (RAI) Office. The collaboration also prioritizes workforce development, equipping employees with essential AI expertise.

Anand Swaminathan, EVP and Global Industry Leader at Infosys, stated that this partnership addresses business challenges by delivering value to clients with a customer-centric approach, ensuring scalability, agility, and cost-efficiency in sectors such as finance, healthcare, supply chain, and telecommunications. Nicole Dezen, Microsoft’s Chief Partner Officer, added that the expanded partnership will transform industries, enhance business operations, improve employee experiences, and create new value for customers.

TheOpensource.AI News

Open-Source AI Definition Launches First Release Candidate, Balances Data Transparency

Bringing open-source and artificial intelligence (AI) together is no simple task, as the Open Source Initiative (OSI) can attest. For the past two years, the OSI, the organization responsible for overseeing the open-source definition, has been working on crafting an open-source AI definition. Progress is being made, with the first release candidate, RC1, now available.

The new definition aims to address the often heated debates around open-source AI. It outlines four essential freedoms an AI system must offer to qualify as open source: the ability to use the system for any purpose without permission, to examine its workings, to modify it as needed, and to share it, whether altered or not.

However, the OSI has taken a pragmatic approach regarding training data. Acknowledging the difficulties in sharing entire datasets, the current definition requires "sufficiently detailed information" about the training data rather than demanding the full dataset. This strikes a balance between transparency and practical, legal limitations.

This compromise has sparked some disagreement. Critics argue that without full access to all data, large language models (LLMs) based on such data can't truly be considered open source. The OSI recognizes this perspective, noting that some believe unrestricted access to all training data is necessary for reproducibility, transparency, and security in AI systems. They warn that limiting data sharing could confine open-source AI to a niche of models trained solely on open data.

The OSI agrees that sharing all training data is ideal but highlights the complexities of doing so. There are four data categories—open, public, obtainable, and unshareable—each with distinct legal considerations. Data can only be shared to the extent that the law allows, especially with respect to copyright and privacy concerns.

The release candidate also covers other essential AI components, requiring that the full source code used for training and running the system be made available under OSI-approved licenses. Additionally, model parameters and weights must be shared openly.

Stefano Maffulli, OSI’s executive director, stressed that this definition is crucial to combat "open washing," where companies claim to be open-source without meeting the true criteria. He emphasized that for a company to call itself open source, it must uphold the principles defined by the OSI, avoiding any misleading claims.

In an interview at the Open Source Summit Europe, Maffulli noted that it's not only open-source purists who have concerns but also corporations, which see their training processes and data handling methods as trade secrets. These businesses are hesitant to share such information, echoing arguments heard in the 1990s when Microsoft resisted open-sourcing its code.

The RC1 introduces two key new elements. First, open-source AI code must be sufficient for downstream users to understand the machine learning training process. Since innovation often occurs during training, companies are reluctant to release this information, which is necessary for forking AI systems. Second, the text allows creators to impose copyleft terms on AI code, data, and parameters, either individually or as a combined package, although no legal framework for this currently exists.

The OSI doesn’t expect to add any new features to the definition, but they acknowledge potential flaws that may require significant revisions. The main effort going forward will focus on refining documentation.

Moreover, the OSI recognized a gap in their earlier drafts: they hadn't made it clear that “if you can share the data, you must.” Assuming all goes well, the final version of the Open Source AI Definition is expected to be unveiled at the All Things Open conference on October 28, 2024. The finish line is in sight.

PwC India Partners with Meta to Scale Open-Source AI Solutions

PwC India has announced a partnership with Meta to scale and extend its open-source AI solutions for businesses and citizen services using Meta's Llama models. According to a press release, the collaboration aims to promote the adoption of the Llama open-source platform while enabling PwC to develop innovative solutions powered by Generative AI (GenAI) on a global level.

Sanjeev Krishan, Chairperson of PwC India, emphasized the potential of Llama models to democratize AI technology across industries and drive innovation, helping solve real-world problems. He highlighted the collaboration’s goal to create a digitally empowered future for clients and communities by delivering value and facilitating meaningful change at scale.

The partnership between PwC India and Meta is designed to democratize GenAI by making it more accessible to businesses. The two companies will work together to build and deploy enterprise-level and citizen-service GenAI solutions.

By combining Meta’s technical expertise and Llama's powerful open-source platform with PwC India's industry knowledge, the collaboration aims to create an ecosystem of GenAI solutions.

Sandhya Devanathan, Vice President and Head of Meta India, noted that GenAI solutions, including Llama, are set to revolutionize businesses in India and accelerate the country’s digital transformation. She added that Llama’s capabilities in natural language understanding and generation can improve efficiencies, enhance customer experiences, and support data-driven decision-making, playing a key role in India’s journey toward becoming a leading digital economy.

TheClosedsource.AI News

OpenAI Chairman Bret Taylor's Startup Sierra Seeks $4B Valuation in New Funding Round

Bret Taylor, Chairman of OpenAI, is reportedly in talks to secure new funding for his company, Sierra, which is now valued at over $4 billion. Sierra, co-founded last year by Taylor and former Google executive Clay Bavor, is aiming to raise several hundred million dollars in its latest funding round. According to a Bloomberg source, the deal is still in progress and not yet finalized. If successful, this funding round could triple Sierra’s valuation from a previous deal in January that had already established it as a unicorn.

Sierra is focused on developing AI solutions for corporate customer service, specifically working on an AI “agent” to automate tasks like voice calls. The company’s technology leverages four to five AI models to handle tasks such as generating responses and preventing hallucinations. When asked about potential conflicts of interest between his roles at Sierra and OpenAI, Taylor stated he does not view Sierra as a competitor to OpenAI.

The current funding round is led by Greenoaks Capital and adds to the $110 million Sierra has already raised. Securing a deal valued at over $4 billion would further elevate Taylor’s standing in the AI sector since joining OpenAI’s board. Sierra’s funding efforts come on the heels of OpenAI raising $6.6 billion, which pushed its valuation to $157 billion, with major backing from investors like Thrive Capital and Microsoft. The potential Sierra deal underscores the ongoing investor excitement around AI, despite concerns about uncertain use cases and rising valuations.

OpenAI’s GPT-4 Achieves Facial Recognition Accuracy Comparable to Specialized Models

A recent study reveals that GPT-4 can accurately recognize faces, determine gender, and estimate age in photos, achieving performance levels comparable to specialized algorithms, despite not being explicitly trained for these tasks.

The research, conducted by the Norwegian University of Science and Technology in collaboration with the Mizani and Idiap Research Institute, tested GPT-4’s biometric abilities. The results showed that its performance was on par with facial recognition algorithms like MobileFaceNet. Notably, GPT-4 achieved 100% accuracy in gender recognition on a dataset of 5,400 images, outperforming DeepFace, a model specifically designed for this task, which scored 99%. In age estimation tests using the UTKFace dataset, GPT-4 correctly identified the age range 74.25% of the time, though it tended to estimate broader age ranges for individuals over 60.

The study also uncovered a potential safety risk. Researchers found they could bypass GPT-4’s safeguards against analyzing sensitive biometric information by simply stating in the prompt that an image was AI-generated. This workaround allowed the model to analyze real photos, pointing to a need for more safety research on large language models, especially given their strong performance in biometric tasks.

The researchers cautioned against relying solely on GPT-4 for recognition tasks, as it can sometimes generate convincing but inaccurate descriptions. While it’s known that large language models possess biometric capabilities, OpenAI has previously highlighted these concerns. For instance, in the "Be My Eyes" app, person recognition was disabled early on.

What’s new in this study is that OpenAI’s safety protocols for GPT-4 were bypassed with a simple trick, and that GPT-4 demonstrates a high level of accuracy in biometric tasks.

AI Startup Writer Targets $1.9B Valuation, Unveils New Model to Rival OpenAI

San Francisco-based AI startup Writer launched a large AI model on Wednesday, aimed at competing with enterprise solutions from companies like OpenAI and Anthropic. However, unlike some of its competitors, Writer has managed to reduce costs in training its AI. The company revealed to CNBC that it spent around $700,000 to train its new model, covering the costs of data and GPUs, a significantly lower amount compared to the millions spent by rival startups. This cost-effective approach has drawn the attention of investors.

According to a source familiar with the matter, Writer is in the process of raising up to $200 million at a valuation of $1.9 billion, which is almost four times the valuation it achieved in September 2023, when it raised $100 million at a valuation exceeding $500 million.

The company’s ability to cut costs is attributed to its use of synthetic data—AI-generated data designed to replicate real-world information while maintaining privacy. Synthetic data is becoming a more popular method for AI training. A study revised in June by AI researchers predicted that with current development trends, tech companies could deplete publicly available training data between 2026 and 2032, noting that "human-generated public text data cannot sustain scaling beyond this decade."

Other major tech companies, including Amazon, Meta, and Microsoft-backed OpenAI, have also utilized synthetic data for training purposes, with Amazon using it for Alexa, and Meta applying it to fine-tune its Llama models. However, some experts caution that synthetic data needs to be used carefully, as it could reduce model performance and amplify existing biases.

Waseem Alshikh, Writer’s co-founder and CTO, clarified to CNBC that the company's synthetic data pipeline has been in development for years. He emphasized that their approach doesn't involve training models on fake or hallucinated data. Instead, they take real, factual data and convert it into synthetic data that is structured more effectively for training.

Writer’s generative AI technology enables corporate clients to leverage its large language models (LLMs) for generating human-like text for various applications, including LinkedIn posts, job descriptions, and mission statements. It also provides tools for analyzing and summarizing data, as well as building custom AI applications for market analysis and more. The company currently serves over 250 enterprise customers, including major names like Accenture, Uber, Salesforce, L’Oréal, and Vanguard, across industries such as support, IT, operations, sales, and marketing.

The generative AI market is projected to surpass $1 trillion in revenue within the next decade. In 2024 alone, investors have poured $26.8 billion into 498 generative AI deals, following a total of $25.9 billion raised in 2023, marking a 200% increase from 2022, according to PitchBook data.

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