Rajesh Kandaswamy

The future of AI isn't a sudden leap, but a steady climb. INSEAD experts predict a fusion of rule-based and neural systems, promising enhanced accuracy and broader knowledge. Yet, the holy grail of human-like AI remains elusive. What's your take on AI's evolutionary path? #ArtificialIntelligence #FutureOfTech #AIEthics #InnovationTrends #TechLeadership

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😕 ⁉ One of the biggest problems with technology can often be, well, technologists. At least when it comes to explaining what the tech actually DOES in a way that the rest of us can understand. Their awe for - and deep knowledge of - the latest digital development can blind them into assuming we all know our Rs from our LBEs (Reader, I don't...). So it's always a relief to chat to experts such as Dr. Antonio J. Jara. In this episode of the Huawei #TransformTalks series, he effortlessly translates AI and the Internet of Things into everyday human impact: from boosting data privacy to easing traffic jams. Suffice to say it's going to change our lives for the better, but watch the video for a simple explanation of how... PS - Loved his lack of concern for whether or not the public understand how IoT works: "If you open the tap and water is there, clean and healthy, you don't care about water supply. Like IoT, the important thing is it works". It may not be water into wine, but as a tech amateur I'm happy to raise a 🍷 to that...

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Can AI Scaling Continue Through 2030? As AI advances at an unprecedented pace, one pressing question remains: can this growth trajectory be sustained through 2030? Recent analysis from Epoch AI outlines four potential constraint: 1. Power Availability. Mega data centers (1-5 GW) and distributed energy systems leveraging global grids make power scalability feasible. Projections suggest we could support 2-45 GW of AI infrastructure by 2030. 2. Chip Manufacturing Capacity. Despite challenges in packaging and high-bandwidth memory, manufacturers are scaling production. By 2030, capacity for up to 100M H100-equivalent GPUs is within reach. 3. Data Scarcity. AI depends on vast datasets. While human-generated data has limits, web data offers a 30x expansion. Multimodal learning (combining text, images, audio, and video) and synthetic data could further bridge the gap, though with added compute costs. 4. Latency Wall As models grow larger, processing times increase. However, this constraint is unlikely to become a critical issue before 2030. 🤔The Big Decision: To Scale or Not to Scale? ✔️ Scale: Scaling has consistently improved AI capabilities. With the potential to automate industries and generate trillions in economic value, the investment case remains strong. ❌ Not Scale: The cost of scaling—potentially hundreds of billions—is steep, and the ROI isn’t guaranteed. The Verdict: Whether AI continues scaling will depend on one key factor: ROI. If AI delivers breakthroughs and significant economic value, it’s likely that labs, corporations, and governments will continue funding the next phase of innovation. #ArtificialIntelligence, #Ai, #Aitech, #Startups https://lnkd.in/eCdNhtDt

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As a board director, I've been closely following the breakneck pace of AI innovation - and the implications are profound. NVIDIA's recent announcements and Jensen Huang’s piece on CNBC underscore the urgent need for boards to exercise foresight and insight when it comes to AI governance. Key takeaways: 🔥 NVIDIA Blackwell GPUs hit the market in Q4 2024 - the latest salvo in the GPU arms race fueling AI. 📈 Cloud giants continue investing heavily in AI compute capacity. Server shipments with specialized AI chips grow unabated. 🧠 NVIDIA is more than just a GPU company - it builds entire AI systems, giving customers flexibility. This allows NVIDIA to offer customers the flexibility to purchase just the parts they need, whether that's cloud hyperscalers, system vendors, or specialized GPU-as-a-service providers. ⚠️ Despite NVIDIA's dominance, the competitive landscape is fierce. Boards must scrutinize management's high-risk technical bets and capital allocation. For example, Small language models are more compact and efficient than their large model counterparts. As such, SLMs require less memory and computational power, making them ideal for resource-constrained environments such as edge devices and mobile apps, or even for scenarios where AI inferencing—when a model generates a response to a user’s query—must be done offline without a data network. As board directors, we have a vital role in providing oversight with foresight and insight. Understanding the rapid evolution of AI technology and its implications for businesses is crucial for making informed decisions that drive long-term success. I welcome further discussion on how boards can elevate their approach to AI governance. As stewards of our organizations, boards have a vital role in providing AI oversight with foresight. I welcome discussing how boards can elevate their approach to this critical issue. Thoughts? 💭 #CEO #KSgems #BoardOversight #CIO #CTO #CISO #BoardGovernance #AI Nitin Gaur

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Generative AI: revolutionary tech or overhyped buzzword? As companies rush to adopt AI, the line between transformative potential and inflated expectations blurs. The key lies in critical assessment and strategic implementation. What's your take on AI's role in business transformation? #GenerativeAI #BusinessStrategy #TechInnovation #AIAdoption #CriticalThinking

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MIT Technology Review - The great acceleration: CIO perspectives on generative AI Source: https://lnkd.in/gnCnUi76 #great #acceleration #cio #perspective #genai #adoption #tools #ai Highlights: Generative AI and LLMs are democratizing access to artificial intelligence, finally sparking the beginnings of truly enterprise-wide AI. Powered by the potential of newly emerging use cases, AI is finally moving from pilot projects and “islands of excellence” to a generalized capability integrated into the fabric of organizational workflows. Technology teams no longer have to “sell” AI to business units; there is now significant “demand pull” from the enterprise. A trove of unstructured and buried data is now legible, unlocking business value. Previous AI initiatives had to focus on use cases where structured data was ready and abundant; the complexity of collecting, annotating, and synthesizing heterogeneous datasets made wider AI initiatives unviable. By contrast, generative AI’s new ability to surface and utilize once-hidden data will power extraordinary new advances across the organization. The generative AI era requires a data infrastructure that is flexible, scalable, and efficient. To power these new initiatives, chief information officers and technical leads are embracing next-generation data infrastructures. More advanced approaches, such as data lakehouses, can democratize access to data and analytics, enhance security, and combine low-cost storage with high-performance querying Some organizations seek to leverage open-source technology to build their own LLMs, capitalizing on and protecting their own data and IP. CIOs are already cognizant of the limitations and risks of third-party services, including the release of sensitive intelligence and reliance on platforms they do not control or have visibility into. They also see opportunities around developing customized LLMs and realizing value from smaller models. Automation anxiety should not be ignored, but dystopian forecasts are overblown. Generative AI tools can already complete complex and varied workloads, but CIOs and academics interviewed for this report do not expect large-scale automation threats. Instead, they believe the broader workforce will be liberated from time-consuming work to focus on higher value areas of insight, strategy, and business value. Unified and consistent governance are the rails on which AI can speed forward. Generative AI brings commercial and societal risks, including protecting commercially sensitive IP, copyright infringement, unreliable or unexplainable results, and toxic content. To innovate quickly without breaking things or getting ahead of regulatory changes, diligent CIOs must address the unique governance challenges of generative AI, investing in technology, processes, and institutional structures.

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Gartner Global Chief of Research Chris Howard explains the new concept of neuro-symbolic AI and how it can lead to groundbreaking innovation. What is neuro-symbolic AI? It's a fusion of neural and symbolic techniques that heighten's AI accuracy and fluency. Get AI ready with 4 key initiatives: https://gtnr.it/3xfyZC3 #ArtificialIntelligence #AI

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AI agents are transforming how we think about digital tools by acting as ‘digital twins’ for employees, capable of executing tasks with human-like context and efficiency. This article from The New Stack provides an excellent breakdown of AI agent anatomy for developers, from defining a ‘persona’ to integrating memory for more personalized interactions. https://lnkd.in/g_JXR2Zp #AI #DeveloperTools #DigitalTwins

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We must increase the pipeline, consistency, and handoff of capital flows across the evidence-building tiers to advance a more robust research and development #edtech infrastructure. Read the latest resource from GETN-US: https://lnkd.in/dWn4pHdy Grateful to have the opportunity to contribute to this piece & learn from collaborators: Stephanie (Short) Loeck, Erin Mote from InnovateEDU, Katie Boody Adorno from Leanlab Education, Malvika Bhagwat from Owl Ventures, Amber Oliver from the Robin Hood Learning & Technology Fund. This latest resource follows the Fall 2023 Tenets & Principles of EdTech Trialing Networks & Environments resource ( https://lnkd.in/diKef5ww) outlined a new shared language to drive a modernized and equity-centered approach to conducting EdTech R&D in learning environments. The report includes a summary of the barriers to and benefits of a more robust innovation infrastructure and the guiding ideas – four tenets and ten principles – necessary to yield unprecedented breakthroughs in the field. Read this resource here: https://lnkd.in/dWn4pHdy GETN-US is a collaborative group of EdTech researchers, founders, nonprofits, and capital providers—venture capitalists, philanthropists, and federal leaders—from the United States committed to working together to accelerate the success and scale of effective, usable products that enhance students’ learning. The Global Edtech Trialing Network is a collaborative effort supported by the Jacobs Foundation to advance best practices in the field of edtech co-development and evaluation through “testbeds” or authentic school environments in which to trial emerging education technologies.

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After the launch of the PCI Data Cooperatives Report (https://lnkd.in/g_CHSvdd) on September 16, the journey continues. Yesterday I spoke on Data Cooperatives and the Digital Commons at the Harvard Club of NYC, as part of the Leaders of Regeneration series. 🌍 As we face climate change and biodiversity loss, and ecosystem collapse, threatening 40% of lives and destabilizing economies, data cooperatives offer a powerful solution. By fostering collaboration and responsible data sharing, they can drive the innovation and collective action needed to protect ecosystems, enhance economies, and create a more sustainable future. #DataCooperatives #DigitalCommons #DigitalGovernance

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#artificialintelligence (AI) - No Finish Line I concur Extraordinary Women on Boards (EWOB), Microsoft’s inaugural “Responsible AI Transparency Report” is an important read for board directors, senior leaders, and curious minds eager to explore the future of AI. Here are some insights that caught my eye: 1. Empowered Employees: 99% of Microsoft employees completed the responsible AI module in the Standards of Business Conduct Training. What AI training and learning opportunities are being implemented within your organization? 2. Proactive Risk Management: By identifying risk through “red teaming models and applications,” organizations can simulate real-world attacks to uncover potential failures. It's all about staying one step ahead. 3. Advanced Filtering: Microsoft has ramped up its filtering systems with human annotators and linguists, online sampling to monitor regressions, jailbreak risk monitoring, and code of conduct attestation. 4. Trust Through Certification: The C2PA standard requires cryptographic methods to attach metadata with a trusted identity certificate, ensuring authenticity and trust in digital content. ChatGPT helped shed more light on C2PA! 5. Commitment to Process: Microsoft is dedicated to the NIST AI risk management framework, fostering partnerships, and honoring copyright commitments, showcasing a holistic approach to responsible AI. 6. Innovative Risk Tools: Tools like AI PyRIT are game-changers in identifying and managing AI risks effectively. There’s no finish line in this #ai race – only exciting new horizons, opportunities and risks, to explore! As a family member often reminds me, #gideeup. Thank you, EWOB learning thread for prompt. https://lnkd.in/eAGyKYGT

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from the WSJ: New York Set to Restrict Social-Media Algorithms for Teens This potential regulation on its surfaces targets social media, but it is actually regulating different aspects of an #AI system's lifecycle (inputs, processing, and outputs). Story by Jimmy Vielkind Questions to Ponder (1) does it violate the First Amendment? Is it constitutional? (2) should there be a private right of action to enforce such regulations? (3) Should here be a federal law that pre-empts states' laws or regulations? https://lnkd.in/gfunQ38K

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Congratulations to the Whistle Rewards team 🚀 🎉 and all the other incredible companies selected for the Google for Startups AI Academy: American Infrastructure! This is an important recognition of the work Whistle is doing in construction and the potential to harness the power of AI to solve some of our nation's most pressing challenges. #WhistleRewards #RobinAI #GoogleForStartups #aiAcademy #AmericanInfrastructure #AI #WhistleBuildsWithAI

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Please Vote TODAY - last chance https://lnkd.in/ew8_NT5v The Ethical Dilemma of AI: Exploring the Voices Leading the Charge at SXSW 2024   As AI continues to shape our world, the conversation around ethical AI has never been more urgent. At SXSW 2024, a pivotal panel titled "The Ethical Dilemma of AI: Is a Moral Code Achievable?" will bring together some of the most influential voices in the field to tackle this complex issue head-on. Esther Dyson, Founder of Wellville, Noelle Russell, Founder and CEO of the AI Leadership Institute, and Steven Rosenbaum, Executive Director of the Sustainable Media Center, will each bring their unique perspectives and expertise to the discussion.

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GenAI models are at the heart of the AI revolution today. As enterprises expand their initiatives, they recognize that the cost of developing, deploying and operating these models can be significant as use cases scale and expand. In a new SiliconANGLE & theCUBE article, Gartner expert Arun Chandrasekaran shares five best practices to optimize GenAI costs and how implementing these best practices allow organizations to maximize the return on their GenAI investment and unlock its full potential. #GartnerIT #GenAI #AI

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A much-needed consortium is essential not only to enhance AI but also to ensure its safe utilization. When properly implemented, this could represent a significant leap forward in regulating and safeguarding AI, maximizing its potential benefits for humankind. #safeai #regulatedai #ai

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The book "Generative Artificial Intelligence: What Everyone Needs to Know" by Jerry Kaplan explores Generative AI's potential to revolutionize fields like healthcare and creativity. It delves into the ethical considerations and challenges of this technology, emphasizing the need for responsible development. The author prompts readers to engage with the profound changes GAI promises and advocates for its ethical use. https://lnkd.in/eCrGGDwG #AI, #ArtificialIntelligence, #GAI, #GenAI, #GenerativeAI

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Businesses using #GenAI are noticing that off-the-shelf large language models (#LLMs) often don’t quite hit the mark for professional-grade results. As a result, there is a growing need for domain-specific genAI solutions. In this article for CIO Online, EXL's experts share how to create domain-specific LLMs that can help cut costs, boost accuracy, and speed things up, leading to a significant return on investment. Read the article to see how you can employ the power of domain-specific genAI solutions in your organization.

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