Gen AI for Business Weekly Newsletter # 30

Welcome to the 30th edition of Gen AI for Business, where I bring you the latest insights, tools, and strategies on how generative AI is transforming the business landscape. From groundbreaking technologies to thought-provoking news, this newsletter is your trusted guide to navigating the rapidly evolving world of AI.

As the dust settles on the U.S. election, all eyes turn to the incoming administration and its approach to AI regulation and innovation. Meanwhile, AI adoption in defense continues to gain momentum, with companies racing to deliver solutions that enhance national security. Generative AI’s explosive growth around the globe remains a focal point, but challenges surrounding governance, accuracy, and ethics highlight the ongoing balance between innovation and responsibility.

What stood out to me this week:

• AI in Defense: The rising traction of generative AI in national security applications.
• AI Framework Tracker: Breaking down the latest regulatory developments.
• AMD’s Bold Moves: How AMD is advancing large language models in the AI race.

For those new to this newsletter, I’m Eugina Jordan—CMO in tech, holder of 12 patents spanning AI, Open RAN, and 5G, and a creator of a new market category in telecom (Open RAN). As a frequent speaker on AI and contributor to industry articles, I am passionate about exploring how generative AI drives growth, efficiency, and competitive advantage for businesses navigating an ever-changing landscape.

If you find value in this newsletter, I’d be grateful if you could leave a like, share a comment, or pass it along to your network. Together, we can unlock the transformative power of generative AI, because knowledge is power.

Thank you, Eugina

Models

The Llama-3-Nanda-10B-Chat model, optimized for Hindi NLP tasks, leverages a 10-billion-parameter architecture developed by Mohamed bin Zayed University of AI, Inception UAE, and Cerebras Systems, addressing critical gaps in Hindi-language AI. Meanwhile, a leak of OpenAI’s upcoming o1 model revealed advanced reasoning and multimedia analysis capabilities, positioning it as a transformative evolution from previous models like GPT-4. Lastly, AMD OLMo, AMD’s first open-source 1-billion-parameter model, showcases the power of AMD GPUs for scalable AI tasks, offering open access to training details to empower innovation across industries.

• Llama-3-Nanda-10B-Chat: A 10B-Parameter Open Generative Large Language Model for Hindi with Cutting-Edge NLP Capabilities and Optimized Tokenization - MarkTechPost The Llama-3-Nanda-10B-Chat model is a new AI model specifically designed for Hindi natural language processing (NLP), addressing the limitations in current Hindi language models. It is based on the Llama model architecture, specifically built from the Llama-3-8B model. It uses Llama’s foundational structure but extends it to focus on Hindi language processing. Developed by the Mohamed bin Zayed University of Artificial Intelligence, Inception UAE, and Cerebras Systems, Nanda is a 10-billion-parameter model optimized for Hindi and tailored to handle both Hindi and English with a bilingual data mix (65 billion Hindi tokens and 21.5 million English tokens). The model's architecture, with 40 transformer blocks, is designed to enhance language adaptation, outperforming general-purpose multilingual models in Hindi-specific tasks. This configuration is enabled by Condor Galaxy 2 AI supercomputer infrastructure, which supports efficient, large-scale data processing. In benchmark tests, Nanda scored an impressive 47.88 on Hindi zero-shot tasks and 59.45 on English tasks, showcasing its versatility without compromising performance. Additionally, Nanda incorporates a balanced tokenizer to minimize tokenization costs and a safety-focused dataset to handle sensitive content, making it a robust solution for high-quality, culturally-sensitive Hindi applications. This model exemplifies advancements in bilingual NLP, filling a critical gap in AI for Hindi-speaking audiences.

My take: Unlike general multilingual models, which often dilute their effectiveness across many languages, Nanda is optimized with a Hindi-centric approach, enabling higher accuracy, better tokenization, and more efficient processing for Hindi text. This focus not only improves performance in Hindi tasks but also fills a crucial gap in NLP for Hindi, which has often been underserved in AI research. The model’s inclusion of English support as a secondary function ensures bilingual flexibility, but its core strength is in its dedicated Hindi capabilities, which stand out in a landscape primarily dominated by English-centric AI models.

• OpenAI’s o1 model leaked on Friday and it is wild — here’s what happened | Tom's Guide ? A recent leak has revealed details about OpenAI's upcoming o1 model, showcasing advanced reasoning capabilities that extend beyond previous AI iterations. The o1 model differs significantly from the GPT-style models, including GPT-4, due to its ability to reason through complex problems over time. During a brief two-hour period on Friday, users accessed the model by modifying a URL parameter, witnessing its enhanced performance, particularly in analyzing images and multimedia inputs. For instance, the model demonstrated the ability to solve intricate image-based puzzles and provided detailed descriptions of visual elements, highlighting potential applications in image processing and other fields requiring deep analytical insights. Although OpenAI quickly restricted access, the leak confirmed that o1 will represent a major step forward in AI’s problem-solving and multimedia comprehension abilities.

• Introducing the First AMD 1B Language Models: AMD OLMo AMD has introduced its first open-source 1 billion parameter language model, AMD OLMo, which aims to support diverse AI applications through accessible, community-driven innovation. The AMD OLMo series includes three versions, each pre-trained with 1.3 trillion tokens on AMD’s Instinct™ MI250 GPUs, demonstrating the GPUs' capabilities in high-scale AI tasks. The models were developed in three stages: pre-training on a large corpus, two-phase supervised fine-tuning on high-quality datasets, and alignment through Direct Preference Optimization (DPO) for human value consistency. Designed to enhance general reasoning, instruction-following, and chat performance, AMD OLMo outperforms similar-sized open-source models on various benchmarks. With AMD Ryzen™ AI Software, users can also run these models on Ryzen AI PCs, enabling secure, energy-efficient local deployments. By open-sourcing the training details and model weights, AMD aims to empower developers and researchers to further customize and build upon these models, fostering innovation across industries.

News 

Meta has officially opened its Llama AI models to U.S. defense organizations, marking a significant shift in policy to support national security efforts while raising questions about the ethical complexities of open-source AI in sensitive areas. Orca Computing unveiled its PT-2 photonic quantum computer, leveraging Nvidia's CUDA-Q platform to integrate quantum and classical computing for applications like vaccine design and biological imaging. Amazon's updated Alexa, intended to integrate advanced conversational AI, has been delayed to 2025 due to struggles with basic functionality, highlighting challenges in combining command-based and generative AI capabilities. Disney launched the Office of Technology Enablement to oversee AI and extended reality technologies across its entertainment segments, aiming to enhance creative projects and consumer experiences. Cohere introduced Embed 3, a multimodal search tool designed to improve productivity and customer experience by enabling efficient data retrieval across multilingual and noisy environments. OpenAI executives participated in an AMA to discuss the launch of SearchGPT, emphasizing its potential to transform complex research tasks. Finally, OpenAI is in talks to restructure into a for-profit entity, sparking criticism from Elon Musk, who questions its alignment with its original mission of advancing AI for public benefit.

• Meta opens Llama AI to defense orgs | LinkedIn After news that Chinese defence used Llama in their AI efforts, Meta officially announced that it opened up their model to defense org. What does this mean?  This highlights the complexities of open-source AI in national security. By making Llama available to the U.S. government, Meta aims to ensure that American defense efforts stay competitive as China continues investing heavily in AI for military purposes. This situation underscores both the benefits and risks of open-source AI. While openness fosters innovation and collaboration, it also means that entities with different objectives can access and repurpose the technology. Meta’s move brings to light the need for policies and ethical standards that can guide the use of open-source AI in sensitive areas like defense, balancing innovation with security.

• Here is a good analysis of the news: Meta Permits Its A.I. Models to Be Used for U.S. Military Purposes - The New York Times This policy change permits federal agencies and contractors focused on national security to deploy Llama models, a move framed by Meta as supporting "responsible and ethical" tech innovations. Meta's open-source Llama models, which enable developers and governments to freely utilize and modify the technology, are now accessible to major defense contractors like Lockheed Martin, Booz Allen, and tech firms like Palantir. The decision marks a significant change in Meta's acceptable use policy, which previously banned using its AI for military or warfare purposes. Nick Clegg, Meta's president of global affairs, emphasized that this partnership aims to strengthen America's technological position while aligning with democratic values. Additionally, Meta intends to share this AI technology with members of the Five Eyes alliance, which includes Canada, Britain, Australia, and New Zealand, alongside the U.S. Meta has defended its open-source approach to AI despite concerns that releasing such powerful technology to the public could lead to misuse. The company believes that broad access will foster improvements and enhance safety by enabling scrutiny from a wide audience. This new direction comes amid a broader debate in Silicon Valley, where other tech giants like OpenAI and Google have kept their AI models proprietary, citing potential risks.

• Meta’s Blog announcing the move highlights the key partnerships. Open Source AI Can Help America Lead in AI and Strengthen Global Security | Meta The initiative, led by partnerships with industry leaders like Accenture, Oracle, IBM, and AWS, showcases how open source AI models can streamline operations, from aircraft maintenance to mission planning. By using these models, Meta aims to help the U.S. set global AI standards, ensuring American values of transparency and accountability shape AI’s future. This collaboration not only advances U.S. leadership in AI but also strengthens defense and public sector capabilities, offering benefits such as cost efficiency, enhanced innovation, and improved public service delivery.: 

• Quantum Computer Launched for Generative AI   Orca Computing has unveiled its PT-2 photonic quantum computer, aimed at integrating quantum computing with generative AI for applications in pharmaceutical development, vaccine design, and biological imaging. Using Nvidia's CUDA-Q platform, PT-2 enables hybrid processing with QPUs, GPUs, and CPUs, enhancing machine learning workflows with quantum capabilities. This technology promises industrial-scale AI advancements by addressing issues like power use, model cost, and quality, according to Bob Sorensen from Hyperion Research. Orca plans to deploy PT-2 at the National Quantum Computing Centre, providing a testbed for quantum-classical neural networks. Researchers are already leveraging PT-2 for vaccine peptide design, and other sectors such as manufacturing and energy are exploring its capabilities for molecular generation and imaging applications.

Quantum computing is ideal for applications like vaccine design and biological imaging because it can handle incredibly complex calculations at speeds traditional computers can’t match. Unlike regular computers that process one piece of data at a time, quantum computers use “qubits,” which can process multiple possibilities simultaneously. This makes them especially powerful for tasks like simulating molecules or analyzing massive amounts of biological data, where countless variables and interactions are involved.

• Amazon’s new Alexa has reportedly slipped to 2025 Amazon’s planned update to Alexa, which was expected to bring ChatGPT-like conversational abilities and smarter AI-powered interactions, has been delayed to 2025. Sources indicate the new version has struggled with basic commands like controlling smart home devices, leaving testers unimpressed with its performance. The integration of large language models, while enhancing Alexa’s ability to handle complex queries, has caused issues with simple tasks, revealing the challenges of combining conversational AI with command-based functions. Amazon’s CEO, Andy Jassy, has yet to outline a clear future for an LLM-powered Alexa, as the company refocuses on better hardware and functionality under new leadership in its devices division. 

• Exclusive: Walt Disney forms business unit to coordinate use of AI, augmented reality | Reuters Disney has launched the Office of Technology Enablement, a new business unit aimed at managing the company's engagement with cutting-edge technologies, particularly artificial intelligence (AI) and extended reality (XR). Jamie Voris, previously the CTO of Disney's film studio and the leader behind Disney’s app for Apple Vision Pro, will head the new group, with Eddie Drake stepping in as the studio’s CTO. Disney aims to leverage these technologies across its entertainment segments—including film, television, and theme parks—to enhance consumer experiences and fuel creative projects. Alan Bergman, Disney Entertainment Co-Chairman, emphasized that as AI and XR rapidly evolve, this group will be crucial for seizing new opportunities and carefully addressing associated risks. The initiative demonstrates Disney's commitment to integrating advanced technology while navigating its potential impacts on its diverse business portfolio.  

• Cohere “enters the chat” with their recent announcement: Introducing Multimodal Embed 3: Powering AI Search Embed 3 enables enterprises to perform efficient multimodal searches, allowing employees to locate insights from graphs, e-commerce catalogs, and design files quickly and accurately. This tool not only accelerates productivity but also improves customer experiences, particularly for industries with vast data assets. Its performance in multilingual and noisy data environments makes it a versatile asset for global companies. Embed 3 is available on Cohere’s platform, Microsoft Azure AI Studio, and Amazon SageMaker, with options for secure, private deployment.

• r/ChatGPT on Reddit: AMA with OpenAI’s Sam Altman, Kevin Weil, Srinivas Narayanan, and Mark Chen On November 7, 2024, OpenAI's CEO Sam Altman, Chief Product Officer Kevin Weil, and Vice President of Engineering Srinivas Narayanan participated in an "Ask Me Anything" (AMA) session on Reddit's r/ChatGPT community. They addressed a wide range of topics, including the recent launch of SearchGPT, OpenAI's AI-powered search engine integrated into ChatGPT. Altman highlighted SearchGPT's efficiency and its potential to enhance complex research tasks. Read through it on the link.  

• OpenAI in talks with regulators to become a for-profit company: Report OpenAI, the $157 billion AI giant, is reportedly in talks with regulators in California and Delaware to restructure into a for-profit company while retaining its nonprofit arm. This shift aims to attract investors but faces legal and valuation hurdles, particularly regarding its intellectual property. Critics, including Elon Musk, have questioned the move’s alignment with OpenAI's original mission of developing AI for public benefit. Founded in 2015, OpenAI’s profitability remains uncertain, with projections of a $5 billion loss in 2024 despite expected revenues of $100 billion by 2029. Elon Musk’s concern stems from his deep ties to OpenAI’s origins and his broader views on AI safety. As a co-founder and early investor, Musk played a significant role in shaping OpenAI’s mission as a nonprofit focused on developing AI for the benefit of humanity. He has publicly criticized the company’s transition to a for-profit structure, arguing it deviates from its original ethos of transparency and public good. Musk is particularly wary of OpenAI’s close ties with Microsoft and its move toward closed-source AI, which he sees as a step toward monopolized development and less oversight. Additionally, Musk has long warned about the existential risks of advanced AI and fears that a focus on commercialization over safety could lead to uncontrolled developments with significant societal implications. His criticism reflects a blend of philosophical, ethical, and practical concerns about the company’s trajectory.

Regulatory 

The EU AI Act, set to be enforced in 2026, establishes a pioneering regulatory framework assigning AI applications to risk categories, requiring high-risk AI tools to meet stringent requirements while fostering innovation with minimal restrictions for others. The COMPL-AI framework complements this by providing technical benchmarks for compliance, highlighting gaps in robustness, fairness, and interpretability across major LLMs. Meanwhile, the EU Council emphasizes scaling AI investments, fostering collaboration, and balancing growth with environmental considerations to strengthen Europe's AI ecosystem. In the U.S., a potential Trump administration shift could dismantle Biden's AI Executive Order, raising concerns about innovation versus safety. Similarly, China’s proposed AI labeling regulations demand explicit and implicit content labels for traceability, offering more detailed guidelines than the EU and California laws set to take effect in 2026. Together, these regulatory movements reflect global efforts to balance innovation, transparency, and ethical AI development, with each region adopting distinct approaches to enforcement and governance. And we also provide a link where you can track all regulatory developments in one place.

• EU AI Act Learn more about he EU AI Act, the world’s first comprehensive AI regulation, which assigns AI applications to risk categories: banned (unacceptable risk), high-risk, and largely unregulated applications. High-risk AI, such as CV screening tools, must meet strict legal requirements, while others face minimal restrictions. This regulation is expected to set global standards, much like GDPR did for data privacy, influencing how AI shapes industries worldwide. Tools like the Compliance Checker help organizations determine their legal obligations or enhance their trustworthiness. The Act becomes enforceable in 2026, with preparatory tasks outlined for the European Commission and EU Member States through 2024 and 2025. 

• COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act The paper "COMPL-AI Framework" provides a comprehensive technical framework for evaluating compliance of large language models (LLMs) with the EU's Artificial Intelligence (AI) Act. COMPL-AI interprets the AI Act's broad regulatory requirements into measurable technical criteria and establishes a benchmarking suite to evaluate LLMs against these standards. The framework focuses on six ethical principles from the Act—robustness, privacy, transparency, fairness, safety, and environmental well-being—each addressing different risks associated with AI, such as discrimination, privacy, and security concerns. The study assesses 12 prominent LLMs, revealing deficiencies in areas like robustness, fairness, and interpretability, which are often overlooked in current LLM benchmarks that prioritize capabilities over regulatory alignment. COMPL-AI’s findings advocate for balanced LLM development aligned with regulatory guidelines, aiming to bridge the gap between legal requirements and practical compliance tools for safer and fairer AI systems. 

• And more from Europe: Artificial intelligence (AI): Council approves conclusions to strengthen EU’s ambitions - Consilium The Council of the European Union recently approved conclusions based on the European Court of Auditors' (ECA) report to boost the EU's artificial intelligence (AI) ambitions. These conclusions emphasize the need for the EU to scale up AI investments, expand access to digital infrastructure, and foster a robust ecosystem of talent and trust. The Council supports the ECA’s call for EU leadership in global AI development and deployment, stressing the importance of balancing growth with environmental considerations, such as enhancing energy efficiency and securing a stable hardware supply chain. The Council also highlighted the necessity of collaboration with member states and international organizations to maximize investment impact, ensuring a synergistic approach at both EU and national levels. This collective strategy is vital for the EU to establish itself as a global AI governance leader. Additionally, the Council agrees on the importance of setting measurable performance indicators to monitor the success of AI initiatives, although it cautions against placing excessive administrative burdens on stakeholders. The conclusions stem from ECA’s May 2024 report, which evaluated the EU's AI framework, noting that while funding has increased, the EU still requires a stronger governance structure and an effective performance monitoring system.

My take: The EU's AI policy goals, as highlighted by the Council of the European Union's conclusions, align closely with the aims set out in the AI Act, which is designed to establish a comprehensive regulatory framework for AI across Europe. The Council's recent conclusions reinforce these principles by encouraging enhanced investment, collaboration, and performance monitoring. This is essential for creating an AI ecosystem where responsible innovation flourishes. By prioritizing environmental impact, reliable infrastructure, and measurable success indicators, the Council's recommendations contribute to the AI Act’s objective of setting clear standards for AI deployment and governance in Europe. This alignment ensures that EU investments in AI, along with efforts to build a more collaborative and regulated environment, complement the AI Act’s regulatory framework. Together, they address both the technical and ethical aspects of AI development, aiming to position the EU as a leader in global AI governance and as a competitive actor on the world stage.

Compiled by Eugina Jordan, use with proper credit only.

• Aaaand now regulatory speculatory ;) What Trump's victory could mean for AI regulation | TechCrunch With Donald Trump poised to assume the presidency and Republicans likely controlling both the Senate and possibly the House, a substantial shift in AI policy is anticipated. Trump has expressed intentions to dismantle Biden’s AI Executive Order (EO), a policy framework emphasizing transparency and safety in AI development. Biden’s AI EO, enacted in October 2023, encourages voluntary guidelines for AI’s application in healthcare, IP protection, and security measures. Key elements include requiring companies to report model vulnerabilities and directing the National Institute of Standards and Technology (NIST) to provide guidance on model safety and bias correction. Critics, including prominent Republicans, argue these provisions infringe on innovation and have labeled them executive overreach. 

• China’s proposed AI Labelling Regulations: Key points | Global law firm | Norton Rose Fulbright China’s Cyberspace Administration recently proposed draft regulations for AI-generated content labeling, aiming to improve traceability, transparency, and address risks associated with deepfakes. The Draft AI Labelling Measures and Draft Labelling Method Standard apply to internet information service providers targeting the Chinese public and define explicit and implicit labeling requirements. Explicit labels are visible to users, while implicit labels are embedded in metadata. The regulations also outline additional obligations, such as incorporating labeling terms in user agreements and verifying label functionality in app stores. App stores and users uploading AI-generated content are also required to label content. These measures align with China’s broader objectives for AI governance and protection against misinformation, and follow similar labeling efforts globally. The EU’s AI Act, effective August 2026, and California’s SB 942, effective January 2026, mandate similar transparency for AI-generated content with both visible and metadata-based labeling requirements. However, while the EU and California laws lack specific technical standards for compliance, China’s proposal offers detailed guidelines, potentially influencing global AI labeling standards.

My take: China’s AI labeling regulations align with European and U.S. frameworks in their shared goal of improving transparency and traceability for AI-generated content to mitigate misinformation risks. However, they differ in scope, standards, and enforcement approaches. China’s Draft AI Labelling Measures apply broadly to domestic and international providers targeting Chinese users, with specific requirements for visible labels (explicit) and metadata-embedded tags (implicit). This approach is more prescriptive, ensuring traceability even if visible labels are removed. In contrast, the EU AI Act, effective in 2026, mandates labeling for high-risk AI content like deepfakes but is less technically stringent, allowing for industry-defined methods such as watermarking without requiring specific tools. Similarly, California’s SB 942 law, also effective in 2026, applies to major AI providers, requiring visible and metadata labels and mandating an AI detection tool with API support for user accessibility. In terms of enforcement, China’s Cyberspace Administration (CAC) holds strong oversight with the authority to penalize non-compliance, reflecting a stringent regulatory stance. Meanwhile, the EU and California emphasize consumer rights and allow flexibility, with the EU fostering voluntary compliance and California focusing on user control and consumer protections. This difference reflects China’s centralized, robust framework, while the U.S. and EU prefer industry-driven, adaptable standards that balance innovation with regulatory oversight. While all three regions share a commitment to transparency in AI-generated content, China’s approach is more stringent and prescriptive regarding labeling requirements and enforcement scope. The EU and California adopt a more flexible, industry-driven approach, focusing on high-risk AI use cases and allowing companies greater autonomy in implementation. These regulatory differences reflect varying attitudes toward balancing innovation and control, with China setting a robust, centralized framework, while the U.S. and EU emphasize consumer choice and adaptability.

• AI Framework Tracker: AI Framework Tracker  

Regional Updates

A report on generative AI adoption in GCC countries reveals its potential to add $21-35 billion annually to the regional economy, with applications in energy, finance, and infrastructure, though only a few organizations have successfully scaled the technology. Meanwhile, Google announced plans for an AI hub in Saudi Arabia to develop Arabic AI models and region-specific applications, sparking criticism given its climate pledges and the likelihood of AI being used to optimize oil and gas operations.

• The state of gen AI in the Middle East’s GCC countries: A 2024 report card A recent report on generative AI (gen AI) adoption in Gulf Cooperation Council (GCC) countries shows that while many organizations are investing in gen AI, only a few are realizing significant value. GCC countries have shown enthusiasm for gen AI’s potential, with applications in customer service, content creation, and coding. McKinsey research suggests that gen AI could add between $21 billion and $35 billion to the GCC economy annually, particularly benefiting sectors like energy, infrastructure, and finance. Despite high adoption rates, only a small group of "value realizers"—those that earn over 5% of revenue from gen AI—are effectively scaling the technology. These leaders have developed strategic roadmaps, integrated gen AI into multiple functions, and established strong performance tracking, external partnerships, and data management practices. The report highlights that realizing gen AI’s full value requires organizations to advance capabilities across five key areas: technology, data, talent, operating models, and risk management. GCC organizations will need to overcome challenges in data centralization, talent acquisition, and cybersecurity to scale gen AI effectively. 

• Google is opening an AI hub in oil-rich Saudi Arabia | TechCrunch Google is establishing a new AI hub in Saudi Arabia, focusing on developing Arabic language AI models and "Saudi-specific AI applications." This venture, announced by the Saudi Public Investment Fund and Google, aligns with the tech giant's growing global AI footprint, though it has raised questions about Google's climate commitments. Despite pledging in 2020 to halt algorithm development for oil and gas production, and aiming to halve emissions by 2030, Google’s expansion into the fossil fuel-rich kingdom has drawn attention. Saudi Arabia’s oil giant Aramco has already demonstrated AI’s impact on production, citing a 15% increase in output at one field due to AI implementations. While details on the AI hub’s specific applications remain undisclosed, given the role of oil in Saudi Arabia’s economy, it’s likely that some AI efforts could be directed toward optimizing oil and gas operations.  

Partnerships

Universal Music Group has partnered with Klay Vision, an AI-driven music company, to develop ethical AI models that respect copyright and artist rights while advancing generative AI in the music industry. Meanwhile, Anthropic has joined forces with Palantir and AWS to integrate its Claude AI models into classified defense environments, enhancing intelligence analysis and decision-making for U.S. defense agencies. These collaborations highlight the increasing role of AI in both creative and defense sectors, with a focus on ethical use and innovation.

• Universal Music Strikes Strategic Deal With “Ethical AI Music Company” Klay Vision Universal Music Group (UMG), under CEO Lucian Grainge, has partnered with Klay Vision, an AI-driven music company in Los Angeles, to develop ethical AI models for the music industry. This collaboration aims to create technology that respects copyright and artist rights while advancing generative AI's role in music. Klay Vision, described as an “ethical AI music company,” seeks to integrate AI in ways that support human creativity and respect artists' rights. The partnership aligns with UMG's ongoing commitment to harness new technologies for innovation in music while protecting the integrity of human artistry.  

• And a new partnership to address again, defense needs. Anthropic teams up with Palantir and AWS to sell AI to defense customers | TechCrunch   Anthropic has teamed up with Palantir and AWS to provide its Claude AI models to U.S. intelligence and defense agencies. The collaboration integrates Claude within Palantir’s IL6 environment, which supports data critical to national security at the "secret" level, using AWS hosting. This partnership aims to enhance intelligence analysis, streamline resource-intensive tasks, and improve decision-making efficiency in classified settings. Anthropic’s terms of service emphasize responsible AI use, banning applications like disinformation campaigns or domestic surveillance. This partnership follows Anthropic’s expansion to AWS GovCloud earlier this year and aligns with a broader trend of AI adoption in government operations, as AI-related government contracts surged by 1,200% in 2024. Anthropic continues to position itself as a safety-conscious alternative to competitors like OpenAI (while closely following them in their steps) while seeking new funding at a valuation of up to $40 billion.

Cost 

Taiwan Semiconductor (TSMC) has raised its revenue forecast, expecting AI chips to contribute a mid-teens percentage of revenue this year, driven by surging demand for GPUs and AI accelerators, solidifying its leadership in the AI-driven semiconductor market. Nvidia’s replacement of Intel on the Dow Jones Industrial Average highlights the industry’s pivot toward AI-focused innovation, with Nvidia and TSMC leading the charge while Intel faces challenges in adapting to AI growth. Meanwhile, Anthropic has introduced Claude 3.5 Haiku, an advanced model priced significantly higher than its predecessor due to enhanced performance in tasks like coding and content moderation, signaling a strategic shift in AI model pricing to match rising capabilities.

• You'll Never Believe What Taiwan Semiconductor's CEO Just Said About Artificial Intelligence (AI) Chip Demand | Nasdaq Taiwan Semiconductor (TSMC), the world’s largest chip foundry, reported significant growth in AI chip demand, surpassing previous projections. Originally expecting AI chips to reach a low-teens percentage of revenue by 2028, TSMC’s CEO now forecasts they will contribute a mid-teens percentage this year, driven by high demand for GPUs, AI accelerators, and CPUs. This surge has led TSMC to raise its revenue forecast, anticipating a 30% year-over-year growth. Following the earnings report, TSMC’s stock initially rose by 10% but has since stabilized, offering investors an opportunity to buy. While trading at a premium, TSMC’s position in AI chip manufacturing suggests its valuation may remain high, making it a strong choice for long-term investors looking to capitalize on AI-driven growth.

My take: Taiwan Semiconductor’s (TSMC) success with CHIPS Act funding, bolstered by a 30% revenue growth forecast and mid-teens revenue contribution from AI chips, contrasts starkly with Intel’s struggles. TSMC leveraged billions from the CHIPS Act to ramp up production of AI chips, benefiting from robust data partnerships with Nvidia and Apple. This data advantage allows TSMC to refine its manufacturing processes and quickly meet the surging demand in AI, which they forecasted to grow 50% annually. Intel, however, is hindered by legacy infrastructure and a lack of similar data-driven agility, slowing its transition into advanced fabrication for AI chips despite similar funding. The high costs of catching up in chip technology and the absence of high-volume AI data partnerships have delayed Intel’s progress, leaving it struggling to compete in the AI-driven semiconductor market.

• Nvidia replaces Intel on the Dow index in AI-driven shift for semiconductor industry - The Globe and Mail Nvidia’s recent replacement of Intel in the Dow Jones Industrial Average marks a significant shift in the semiconductor industry, highlighting Nvidia’s dominance in AI-driven chip technology. Nvidia’s stock is up over 173% this year, driven by demand for AI-focused GPUs, while Intel has struggled, with shares down over 50% amid declining revenue. Intel’s third-quarter revenue fell to $13.3 billion, a 6% decrease from the prior year, as it works to cut costs and refocus its portfolio. Unlike Intel, which manages its own chip production, Nvidia relies on Taiwan Semiconductor Manufacturing (TSMC), a top industry partner also benefiting from the AI boom. This Dow reshuffle reflects the market's move toward companies capitalizing on AI’s exponential growth, with Nvidia and TSMC leading the charge, leaving Intel facing the challenge of catching up in the competitive landscape. 

•  Pricing \ Anthropic   Anthropic has introduced Claude 3.5 Haiku, an advanced AI model that surpasses its predecessor, Claude 3 Haiku, and flagship Claude 3 Opus on many benchmarks, although it lacks image analysis capabilities for now. Claude 3.5 Haiku’s strengths include coding suggestions, data extraction, labeling, and content moderation, accessible through Anthropic’s API and platforms like AWS Bedrock. Notably, Anthropic raised the model’s price to $1 per million input tokens and $5 per million output tokens, quadrupling the cost of Claude 3 Haiku, which remains available for users needing image processing and lower costs. This price increase, attributed to Claude 3.5 Haiku’s enhanced performance, marks a rare move in AI pricing and signals Anthropic’s evolving strategy as its models gain capabilities and complexity. 

Investments

Perplexity AI is seeking a $9 billion valuation in a new funding round, doubling its valuation since June, amid rising interest in generative AI despite facing plagiarism allegations. Meanwhile, Big Tech firms like Meta and Microsoft struggle with high AI development costs and slow revenue returns, while infrastructure providers like Nvidia and Amazon benefit immediately from the demand for AI chips and cloud services. Amazon is also considering a multibillion-dollar investment in Anthropic, contingent on using AWS silicon for AI training, highlighting the startup’s rapid financial growth, with a projected $2.7 billion burn rate in 2024 compared to OpenAI’s more gradual funding over the years.

• Perplexity AI seeks valuation of about $9 billion in new funding round Perplexity AI, an AI search engine startup, is in talks to raise $500 million in its latest funding round, aiming to more than double its valuation from $3 billion in June to about $9 billion. This marks the company’s fourth funding round this year, fueled by the surge in generative AI interest. Perplexity, which seeks to challenge Google’s dominance in search, has faced plagiarism allegations from media outlets like the New York Times, though the company has denied the claims.

• Big Tech Is Facing Down a Major Problem With Its AI Plans Big Tech companies like Microsoft and Meta are grappling with high costs and investor skepticism as they pour billions into AI infrastructure. Despite the massive capital expenditures, the companies have yet to see significant revenue returns from generative AI. Issues like chip shortages and ongoing tech costs are compounding the financial burden, with Meta forecasting a further rise in AI-related expenses. Meta’s CEO, Mark Zuckerberg, defended the spending as necessary for long-term AI opportunities, though investors remain concerned about rising costs and potential impacts on Meta’s core ad revenue. Meanwhile, companies like Amazon and Nvidia, which provide the cloud and chip resources for AI development, are currently benefiting from the surge in Big Tech’s AI investments. 

My take: The disparity between thriving and struggling companies in AI largely stems from their roles in the AI ecosystem. Companies like Nvidia and Amazon, which provide essential infrastructure such as AI chips and cloud services, are positioned to see immediate revenue from AI investments due to direct demand for their products. In contrast, companies like Meta and Microsoft are focused on developing AI applications, which involves high costs and uncertain returns, as these products require substantial research and tuning to meet user needs and resolve technical challenges like “hallucinations” and copyright compliance. This revenue gap also reflects differences in market expectations: investors are more patient with companies like Amazon and Nvidia, whose tangible product sales align closely with AI’s current demand. Meanwhile, companies building AI applications face pressure to show financial returns in the near term, even as they navigate rapid technological changes and evolving user expectations. Thus, while infrastructure providers are reaping early benefits, application-focused companies are investing heavily without guaranteed or immediate financial gains.

•  Amazon may up its investment in Anthropic — on one condition | TechCrunch Amazon is considering a multibillion-dollar investment in OpenAI rival Anthropic, following a $4 billion deal made last year. The proposed funding hinges on Anthropic utilizing Amazon's custom silicon on AWS to train its AI, despite the company’s preference for Nvidia chips. Anthropic is reportedly burning through $2.7 billion in 2024 as it scales AI products and seeks new funding at a $40 billion valuation. To date, Anthropic has raised $9.7 billion, nearly half of OpenAI's $21.9 billion.

Let’s do some math, though as it ain’t mathing. Anthropic, founded in 2021, has rapidly scaled its operations, leading to significant expenditures. In 2024 alone, the company is projected to spend over $2.7 billion to develop and expand its AI products. This rapid financial outlay is notable, especially when compared to OpenAI, established in 2015, which has raised approximately $21.9 billion to date. Anthropic's swift growth trajectory underscores the intense competition and substantial investment required in the AI sector.

 So, This burn rate is much faster than OpenAI, which, though older (founded in 2015), has raised $21.9 billion over a longer timeline.

Research 

An IBM study reveals that nearly 50% of CEOs are concerned about AI accuracy and bias, with only 21% of organizations reporting mature AI governance practices, emphasizing the need for cultural integration, workforce AI literacy, and robust risk management frameworks. Meanwhile, ChatGPT traffic surged 115.9% year-over-year to 3.7 billion monthly visits, surpassing Microsoft’s Bing, as generative AI tools like Google’s NotebookLM, Microsoft CoPilot, and Claude continue to gain traction across industries, despite challenges in optimizing AI visibility for users.

• Accuracy, Bias in AI Concerns Most CEOs: IBM Study   An IBM Institute for Business Value survey, covering 5,000 executives across 24 countries, reveals that nearly 50% of CEOs are concerned about AI accuracy and bias, and only 21% consider their AI governance practices to be mature. Key findings show that 60% of organizations have appointed AI champions, 78% maintain extensive documentation for explainability, 74% conduct ethical assessments, and 70% perform risk-focused user testing. Additionally, 80% of C-suite executives have created specialized risk functions for AI oversight. IBM’s Phaedra Boinodiris emphasizes that strong AI governance requires cultural integration, increased workforce AI literacy, and alignment with organizational values. Mature organizations prioritize governance from the design stage, with IBM advocating flexible frameworks to support risk management and market adaptation.

• https://www.cmswire.com/digital-experience/generative-ai-boom-chatgpts-traffic-surges-by-1159-year-over-year/ ChatGPT has seen a 115.9% year-over-year traffic increase, reaching 3.7 billion monthly visits globally, according to Similarweb. October alone recorded a 17.2% growth in traffic. Other generative AI tools like Google’s NotebookLM, Microsoft CoPilot, and Claude also experienced significant growth, showcasing the rising adoption of generative AI across industries. ChatGPT’s growth trajectory, surpassing Microsoft’s Bing in traffic, highlights its increasing role in customer engagement and marketing strategies, though challenges in AI Visibility Optimization (AIVO) remain.

Concerns

AI-driven chatbots like Google’s and Bing’s face trust issues as researchers highlight their susceptibility to "generative engine optimization" (GEO), which manipulates responses for specific agendas, raising concerns about biased or incomplete answers. In finance, generative AI’s primary benefit lies in time savings from automating manual tasks, though executives note challenges in measuring ROI and remain uncertain about its long-term impact. Meanwhile, MIT researchers reveal that LLMs like GPT-4 lack a coherent understanding of the world, leading to failures in adaptive tasks despite their impressive outputs. On the legal front, OpenAI’s court victory in a copyright lawsuit over AI training data sets a precedent for "fair use," potentially shaping future legal challenges around AI and intellectual property.

• The chatbot optimisation game: can we trust AI web searches? | Artificial intelligence (AI) | The Guardian As AI-driven chatbots like Google’s and Bing’s increasingly offer single-sentence summaries for search queries, concerns about their trustworthiness are rising. Research from the University of California, Berkeley, shows these models often prioritize keyword-heavy content, which can be manipulated through "generative engine optimization" (GEO) to influence chatbot responses. Like SEO for search engines, GEO encourages techniques that may distort AI responses for specific agendas, leaving users with potentially biased or incomplete answers. Additionally, underhand methods like “strategic text sequences” can control AI outputs, casting doubt on the objectivity of AI-generated answers, which could shift online visibility and traffic toward those who know how to game the system.

• Main GenAI benefit so far is time saved, finance execs say | CFO Dive   Finance executives on a Controllers Council panel highlighted that the main benefit of generative AI in their field is time saved by automating manual tasks, such as invoice processing and data entry, allowing teams to focus on strategic work. However, measuring AI’s ROI in precise dollar terms remains challenging. Teddy Collins of SeatGeek noted that while AI’s productivity gains are valuable, exact financial impacts are elusive. Similarly, Erik Zhou of Brex compared AI’s benefits to cloud computing’s impact, stating that time savings and enhanced tool capabilities justify AI investments without precise calculations. Julian Cifliku of Kroger emphasized that automation allows his team to provide more strategic insights. All panelists agreed that with AI evolving so rapidly, predicting its future impact is uncertain.

• Despite its impressive output, generative AI doesn’t have a coherent understanding of the world | MIT News MIT researchers have revealed that large language models (LLMs), like GPT-4, don’t truly understand the world or its rules, which can lead to unexpected failures. The study found that although LLMs can perform tasks such as providing accurate driving directions in New York City, they lack an internal map of the city and fail when conditions change, like when roads close. Researchers created two metrics—sequence distinction and sequence compression—to assess a model’s coherence. Tests showed that LLMs perform well without grasping underlying structures, as in board games like Othello, where they predicted moves without understanding the rules. This suggests that while LLMs show impressive capabilities, they don’t form a coherent model of the world, a limitation that could hinder their application in real-world tasks requiring adaptive understanding. 

• OpenAI defeats news outlets' copyright lawsuit over AI training, for now | Reuters OpenAI has successfully defended against a copyright infringement lawsuit filed by several news organizations, including The New York Times and The Washington Post. The lawsuit alleged that OpenAI unlawfully used their articles to train its AI models without proper authorization. However, the court ruled in favor of OpenAI, determining that the use of the articles constituted "fair use" under copyright law. This decision is significant for the AI industry, as it sets a precedent for the permissible use of copyrighted material in training AI systems. The ruling may influence future legal challenges concerning AI training data and copyright infringement. 

Case Studies 

AI is making strides across industries, from psychiatry to underwriting and beyond. A study on AI in psychiatry highlights its potential to complement clinical reasoning by analyzing complex data while emphasizing the irreplaceable role of human empathy and intuition. In insurance, AIG’s adoption of generative AI has boosted underwriting accuracy to over 90% and streamlined operations, aligning with a broader shift toward "agentic" AI embedded in systems like Microsoft Dynamics 365. General Mills has found business value in generative AI through its MillsChat tool, leveraging cloud and machine learning foundations to align AI-driven initiatives with measurable business goals. Meanwhile, OpenAI is expanding ChatGPT Enterprise within the U.S. federal government, providing secure AI solutions to agencies like the Treasury Department and NASA, as it works toward FedRAMP accreditation for enhanced deployment in national security applications.

Healthcare

• Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: Current insights and future directions - ScienceDirect   examines the evolving intersection of artificial intelligence (AI) and clinical reasoning in psychiatry, highlighting the unique cognitive processes that shape psychiatric care and their current relationship with AI's analytical capabilities. While statistical and AI-based prediction models can aid in handling complex data, they cannot replace the nuanced, empathic decision-making integral to effective psychiatry. AI, especially deep learning (DL), shows promise in processing high-dimensional data and may complement clinical reasoning by offering new insights into data patterns. However, AI lacks critical human traits, such as empathy and intuition, that remain essential for understanding complex patient narratives. The study underscores the importance of collaboration between clinicians and data scientists to ensure AI’s strengths are balanced by clinicians’ human-centered care expertise. As psychiatry advances with AI, a focus on explainability, transparency, and interdisciplinary synergy is key to integrating AI into clinical practice without losing the irreplaceable value of human expertise. 

Underwriting

• AIG leans on generative AI to speed underwriting | CIO Dive   American International Group (AIG) is accelerating its underwriting process with generative AI, as part of a broad digital transformation under CEO Peter Zaffino’s leadership. During AIG’s Q3 2024 earnings call, Zaffino highlighted that early AI pilots have improved data accuracy rates in underwriting from 75% to over 90% while significantly cutting processing time. AIG’s GenAI ecosystem integrates data from multiple sources to enhance data quality and streamline manual processes, contributing to AIG Next—a simplification initiative aimed at creating a more agile company post-divestiture of Corebridge Financial. AIG has invested heavily in technology, allocating $300 million to AI and digital workflows in the last two years and over $1 billion in foundational data tech over five years. Recently, CIO Roshan Navagamuwa joined AIG to lead its tech and cybersecurity strategy, and the company plans to centralize data engineering and AI operations at a new innovation hub in Atlanta by 2026. This hub will support GenAI-based solutions that bring underwriting, claims, and operations under one roof. AIG’s AI adoption strategy mirrors industry trends towards “agentic” AI—autonomous AI tools embedded in systems like Microsoft’s Dynamics 365 and expected to execute up to 15% of daily work decisions by 2028. However, AIG maintains that human oversight remains essential, keeping experienced underwriters central to the decision-making process. This approach balances technology advancements with human expertise, aligning with growing confidence in AI’s potential while addressing safety and accuracy challeng.

Food and Beverage

• How General Mills found business value in generative AI | CIO Dive General Mills has harnessed generative AI to create business value, leveraging its prior experience with machine learning (ML) to deploy MillsChat, an internal AI chatbot. Chief Digital and Technology Officer Jaime Montemayor shared that this digital transformation began years ago, with the company migrating data to the cloud and expanding ML capabilities across departments like marketing, sales, and supply chain. These foundations enabled the successful launch of MillsChat, a writing and summarization tool built with Google’s PaLM 2 model, initially rolled out to 900 users and now reaching 20,000 employees.The project’s success was driven by strong cross-functional collaboration, with a dedicated subgroup of senior leaders, including HR, legal, finance, and supply chain heads, working together to align AI initiatives with business goals. Finance, in particular, plays a critical role in tracking returns on investment, helping identify areas for acceleration or cutbacks. Montemayor emphasized the need to balance innovation with pragmatism, focusing on measurable capabilities that align with business priorities. By strategically building its talent foundation and fostering cross-departmental support, General Mills has not only implemented generative AI but also established a roadmap for future AI-driven initiatives.

Defense and Government

• Scale AI unveils ‘Defense Llama’ large language model for national security users | DefenseScoop   OpenAI is expanding its generative AI work within the U.S. federal government, with agencies like the Treasury Department and Air Force Research Laboratory adopting ChatGPT Enterprise. The tool is intended to reduce administrative workloads, improve efficiency, and support internal resources. Other agencies, including NASA and the National Gallery of Art, have also procured licenses. OpenAI’s goal is to make ChatGPT Enterprise more accessible to federal agencies, working towards FedRAMP Moderate accreditation for secure deployment. The Department of the Treasury tested ChatGPT on a small scale, and the Air Force Research Laboratory is using it for tasks like basic coding and access to resources. OpenAI has also partnered with the National Institute of Standards and Technology to focus on AI safety.

Women Leading in AI 

New Podcast:  Tune in for a discussion on “The Bio Design Revolution in AI ” with JoJo Platt, neuroscience leader and self appointed FOMO dealer. Listen on Spotify: https://open.spotify.com/episode/2kOzh2gJhTVNtqMJiOgAvi?si=TDuyLlxmTCm5J2M339g3pQ   It’s a really fun and engaging conversation at the intersection of AI and healthcare. 

Featured AI Leader: 🏆Women And AI’s Featured Leader - Cassidy Reid 🏆She is the founder of Women In Automation and brought women together in NYC for an amazing event this weekend. Get the full insights on how Cassidy is leading in AI.  

Learning Center

Multi-agent AI systems are emerging as a significant advancement over single-agent LLMs, utilizing specialized agents to tackle complex tasks like document retrieval and workflow automation, bridging gaps in AI capabilities. IBM’s Watsonx tutorial highlights real-time safety guardrails for multimodal data with Meta's Llama Guard 3-11b-vision, ensuring ethical AI applications in areas like text and image safety. Optimizing Retrieval-Augmented Generation (RAG) through embedding tuning enhances accuracy and context alignment, particularly for applications like chatbots and customer service. Meta’s Llama Recipes provide tools for fine-tuning models efficiently using GPU configurations for tasks like summarization and question-answering. The 2024 LLM Leaderboard by Vellum ranks Claude 3.5 Sonnet and GPT-4o as top-performing models, while Gemini 1.5 Flash leads in input capacity, offering developers a guide to balancing performance and cost. Finally, GPT4ALL enables secure, customizable AI chatbot deployment by running LLMs locally, ensuring data privacy without reliance on internet-based interactions.

Learning

• Why multi-agent AI tackles complexities LLMs can't | VentureBeat Multi-agent AI systems are emerging as an important advancement over single-agent models like large language models (LLMs), which are limited by static data, restricted reasoning, and a lack of real-time updates. These agents, each with specific roles and memory, can independently retrieve information, perform reasoning, and take action to solve complex tasks. For instance, while a single agent can manage document retrieval in retrieval-augmented generation (RAG), multi-agent RAG leverages specialized agents for understanding, retrieving, and ranking documents, enhancing performance and reducing errors. In workflow applications like loan processing, multi-agent systems can streamline operations by breaking down tasks among specialized agents, each handling steps such as identity verification and financial checks. Frameworks like CrewAI and langGraph facilitate complex task management across agent networks, enabling efficient workflow automation while keeping humans in the loop for key validations. As AI evolves, multi-agent frameworks are positioned to address complex, multi-modal data needs, bridging gaps between current AI capabilities and the pursuit of artificial general intelligence (AGI).

• LLM guardrail tutorial with Llama Guard 3-11b-vision in watsonx | IBM The IBM tutorial on LLM guardrails provides an in-depth guide on using IBM’s Watsonx platform to apply safety and reliability guardrails with Meta's Llama Guard 3-11b-vision model. These guardrails are tailored for detecting "safe" and "unsafe" image and text combinations in real-time, addressing various safety concerns such as violent and nonviolent crimes, defamation, and privacy violations. This involves setting up a project on Watsonx, configuring API keys, encoding images, and implementing API calls for evaluating multimodal data. This setup aims to enhance the ethical application of LLMs, especially in computer vision.  

• Optimizing RAG with Embedding Tuning - KDnuggets The KDnuggets article on optimizing Retrieval-Augmented Generation (RAG) through embedding tuning offers an in-depth overview of how to make RAG systems more accurate and context-aware. RAG systems combine language models with external knowledge bases, which enables more specific and relevant responses for users, as the model can retrieve information outside its training data. This method is particularly useful for fields requiring high accuracy, such as business-specific chatbots or customer service applications. The article explains the structure of RAG, which comprises three main components: retrieval, augmentation, and generation. Retrieval focuses on pulling relevant data from a knowledge base, while augmentation aligns the information with the user’s context, and generation delivers a coherent, natural language response. Techniques for tuning embeddings—like adapting to specific domains, using contrastive learning, and adding signals from real data—help ensure that the retrieved information is relevant to the query. Embedding tuning plays a critical role in aligning the RAG system’s output with the user’s query by optimizing how semantic relationships are captured and retrieving the most contextually accurate information. The article also covers several evaluation metrics to ensure embedding quality, such as cosine similarity and human feedback, along with the challenges of implementing high-quality, domain-specific embeddings. Overall, RAG systems with optimized embeddings offer a cost-effective alternative to building custom language models, particularly for use cases requiring precision and domain expertise.

Tools and Resources

• https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/NotebookLlama This project is part of Meta's Llama Recipes, which provide scripts for fine-tuning Meta's Llama models using methods like Fully Sharded Data Parallel (FSDP) and Parameter-Efficient Fine-Tuning (PEFT). These recipes support both single and multi-node GPU configurations and cater to applications such as summarization and question-answering.

• LLM Leaderboard 2024 The 2024 LLM Leaderboard by Vellum provides a detailed comparison of leading large language models (LLMs), focusing on aspects such as performance, pricing, and context window sizes. Claude 3.5 Sonnet leads in performance with an impressive 82.10% average across benchmarks, particularly excelling in multilingual tasks (91.60%) and tool use (90.20%). Following closely, GPT-4o achieves an average of 80.53%, showing strengths in multilingual tasks (90.50%) and coding (90.20%). For context window size, Gemini 1.5 Flash offers the largest capacity at 1 million tokens, enabling handling of extensive inputs, while Claude models support a substantial 200K tokens for a balanced approach to input capacity. Regarding cost efficiency, Gemini Pro offers the lowest input cost at $0.125 per million tokens, ideal for cost-conscious, large-scale applications, with GPT-4o mini close behind at $0.15 per million tokens, balancing cost and performance. This leaderboard is a practical resource for developers and organizations, offering a clear overview of LLMs to assist in choosing the best options for specific requirements based on performance, input capacity, and cost. 

• The Easy Way To Run An AI Chatbot Locally On Your Laptop   To run a local LLM like GPT4ALL, start by downloading the model files from GPT4ALL’s official site or its GitHub repository, ensuring compatibility with your device. Next, install any required software—such as Python and its libraries on Windows, or similar tools on macOS and Linux—needed to support the model’s functionality. It’s often helpful to set up a virtual environment on your device to isolate the installation, which can be done using commands like python -m venv env_name and activating the environment according to your operating system. Within this environment, navigate to the downloaded GPT4ALL folder and follow the provided installation instructions, typically by running a command like pip install -r requirements.txt to install the necessary dependencies. Once everything is set up, launch the model locally by running a command like python main.py (based on GPT4ALL’s specific instructions). This approach allows you to interact directly with the LLM on your computer, ensuring that data stays secure without being sent over the internet. From there, you can begin experimenting and customizing the model in a private, controlled environment, providing a more secure AI experience.

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