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Why Data Is the Unsung Hero of AI Strategy
- AI adoption in enterprises is transitioning from pilots/experiments to strategic enterprise-scale integration.
- Data is a critical component for contextual, intelligent, and enterprise-specific AI models.
- AI's economic impact is projected to reach $20 trillion by 2030, largely influenced by investments in data and infrastructure.
- Biased, outdated, or poor-quality data can lead to ineffective AI outcomes, making data the foundation of AI strategy.
- The success of scaling AI depends on building trust in data and leveraging it strategically.
- Key considerations for preparing data for AI strategy include reusing existing data assets, metadata/data lineage, governance/compliance, master data, and data value.
- Metadata and data lineage are crucial for AI governance and scalability.
- Master data should serve as the foundation for AI strategies to ensure completeness and accuracy.
- Data should be viewed in terms of its value contribution to AI and the business, rather than just a cost center.
- Emphasizing the enduring importance of data over AI models, it's essential to prioritize data readiness when crafting an AI strategy.
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AI Data Centers: Addressing Growing Power Demands
- The AI revolution is leading to a significant increase in data centers, with predictions of a 33% rise by the end of the decade, resulting in higher electricity usage.
- States may see data centers consuming up to 36% of their electricity, putting pressure on already strained electricity grids.
- AI-driven power demands can potentially lead to an energy crisis, but adopting on-site thermal energy storage systems offers a solution.
- Thermal energy systems allow for load-shifting in data centers, reducing strain on the grid and lowering the risk of blackouts or brownouts.
- Efficient energy storage systems are crucial as the Global AI Race, like the $500 billion Stargate Project, is expected to require large amounts of electricity.
- Data center power demands are projected to challenge US energy supply, potentially leading to power shortages and increased prices.
- Thermal energy storage systems benefit all stakeholders by lowering grid strain, reducing the need for infrastructure upgrades, and stabilizing power demand.
- By incorporating thermal energy storage, data centers can save costs through differential pricing and time-of-use tariffs, especially in states reliant on solar energy.
- Thermal energy storage is seen as a safer and more sustainable option for data centers compared to lithium-ion batteries, which pose safety risks and resource scarcity issues.
- Adopting thermal energy solutions can help the data center industry reduce expenses, prevent blackouts, and manage the increasing power demands brought on by AI.
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Aaron Kesler, Sr. Product Manager, AI/ML at SnapLogic – Interview Series
- Aaron Kesler, Sr. Product Manager, AI/ML at SnapLogic, has a decade of experience focusing on AI-driven products, design thinking, and product discovery.
- SnapLogic is an AI-powered integration platform aiding in quick application, data, and API connections for digital transformation.
- Kesler's entrepreneurial journey with STAK and Carvertise shaped his product mindset early on.
- He emphasizes coaching aspiring PMs to argue from the customer's perspective for success.
- In an AI-augmented future, employees will work alongside multiple AI agents for more efficient workflows.
- To bridge the AI literacy gap, Kesler suggests starting small, hands-on learning, and promoting curiosity in mundane tasks.
- AI upskilling beyond generic training modules involves hands-on experience and allowing employees to experiment.
- Companies adopting AI tools without proper upskilling risk governance violations and underutilization of AI capabilities.
- At SnapLogic, AI is integral to product strategy for creating efficient and user-friendly solutions.
- AgentCreator at SnapLogic enables businesses to build AI agents without coding, leading to impactful outcomes like reduced IT backlog.
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Aaron Kesler, Director of AI Product Management at SnapLogic – Interview Series
- Aaron Kesler, Director of AI Product Management at SnapLogic, focuses on developing new AI-driven products and processes while mentoring aspiring PMs.
- SnapLogic is an AI-powered integration platform facilitating quick and efficient connections across applications, data, and APIs for enterprises.
- Kesler's entrepreneurial journey at STAK and Carvertise shaped his product mindset towards customer-centric problem-solving and business value.
- His advice for aspiring PMs revolves around arguing from the customer's perspective to gain insights and drive clarity in decision-making.
- Kesler envisions an AI-augmented future where employees work alongside multiple AI agents handling repetitive tasks, enabling focus on higher-level thinking.
- For bridging the AI literacy gap, Kesler suggests starting small, having a clear plan, hands-on experience, and iterating based on feedback.
- Effective AI upskilling strategies involve hands-on experience and empowering employees to explore AI tools within their daily work.
- The potential risks of adopting AI tools without proper upskilling include governance violations, data security risks, and lack of understanding of AI capabilities.
- At SnapLogic, AI plays a central role in product innovation, focusing on delivering efficient, easy-to-use solutions through AI-powered products.
- Tools like AgentCreator and SnapGPT by SnapLogic help businesses build AI agents without coding, democratizing AI access for non-technical users.
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Private AI: The Next Frontier of Enterprise Intelligence
- Artificial intelligence adoption is rapidly increasing, with an expected surge in global AI users by 20%, reaching 378 million by the end of the year.
- Enterprises are shifting focus from advanced models to the importance of data as the key differentiator in the AI race.
- The emergence of Private AI emphasizes running AI workloads securely without moving sensitive data, offering a strategic response to data privacy challenges.
- Private AI complements trends like federated learning and edge intelligence, providing a secure foundation for scalable AI systems.
- Private AI operates by bringing compute directly to where data resides, enabling AI workloads to run securely on-site or in local environments.
- Benefits of Private AI include eliminating data movement risk, enabling real-time insights, strengthening compliance, supporting zero-trust security models, and accelerating AI adoption.
- Private AI is already being used in healthcare, financial services, retail, and global enterprises for initiatives like AI-powered diagnostics, fraud detection, personalized recommendations, and cross-border data processing.
- Private AI matters now due to the increasing importance of trust, transparency, and control in AI deployment, aligning technical capability with ethical responsibility.
- By adopting Private AI, organizations can innovate securely, respect data sovereignty and privacy, and lead in a world where trust is a critical factor in AI-driven decision-making.
- Private AI is not just a solution but a mindset shift prioritizing trust, integrity, and security at each stage of the AI lifecycle.
- Embracing Private AI allows organizations to unlock the full potential of their data, accelerate innovation, and confidently navigate the complexities of an AI-driven future.
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Nvidia
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Wandercraft Begins Clinical Trials for Physical AI-Powered Personal Exoskeleton
- Nicolas Simon, co-founder of Wandercraft, aims to advance the field of robotics with AI-powered exoskeletons to help individuals with mobility challenges, including those with Charcot-Marie-Tooth disease.
- Wandercraft's Personal Exoskeleton, currently in clinical trials, integrates AI mechanisms for stability and movement, enabling users to stand, walk, and control the device with a joystick.
- The company's FDA-cleared Atalante X exoskeleton has already helped patients in over 100 clinical settings worldwide, and the latest Personal Exoskeleton is designed for indoor and outdoor use, adapting to users' movements in real time.
- Wandercraft is utilizing NVIDIA technologies for simulation, reinforcement learning, and AI-driven robotics solutions to enhance the functionality of exoskeletons and improve users' mobility in daily life.
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The Robot Report
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Your guide to Day 2 of the 2025 Robotics Summit & Expo
- Day 2 of the 2025 Robotics Summit & Expo features key events including the Women in Robotics Breakfast, keynotes by industry experts, and breakout sessions covering diverse topics in robotics and AI.
- The day kicks off at 8:00 a.m. ET with the Women in Robotics Breakfast featuring Laura Major and Joyce Sidopoulos discussing robotics advancements.
- Keynotes include insights on Amazon's robots, physical intelligence, and advanced bionics for humans and robots.
- The expo show floor opens at 10:00 a.m., with the MassRobotics Career Fair starting at 3:30 p.m.
- Breakout sessions cover AI-powered robotics, systems programming, surgical robots, AI in robot logs, warehouse order fulfillment, and more, starting at 11:30 a.m.
- Additional sessions include discussions on robotics hardware and software design, generative AI, healthcare robotics startups, humanoid robots, and more.
- Robotics Engineering Theater sessions focus on design, Copilot Robotics, and the MassRobotics Form & Function Challenge Finale.
- The day offers a comprehensive look at cutting-edge robotics technologies, industry trends, and innovative breakthroughs.
- Attendees can expect to gain valuable insights, network with industry experts, and explore the latest advancements in robotics.
- The 2025 Robotics Summit & Expo aims to showcase the future of robotics and inspire collaboration and innovation within the industry.
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Livescience
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Swarm of 30 robots can 'flow like water' and harden up to support the weight of a person
- Researchers have developed a swarm of cylindrical robots that can behave like a material and support the weight of a person.
- These robots, equipped with magnets and gears, can perform structure-forming, healing, and support significant weight.
- Inspired by embryonic cells, the robots can self-organize into various shapes and exhibit behaviors similar to cells within a honeycomb.
- The team's primary goal was to create a material that can switch between rigid and soft states, with the ability to change shape and strength as needed.
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CNTXT AI Launches Munsit: The Most Accurate Arabic Speech Recognition System Ever Built
- CNTXT AI has introduced Munsit, an extremely precise Arabic speech recognition model, surpassing global competitors like OpenAI and Meta.
- Munsit, developed in the UAE, represents a significant advancement in sovereign AI for the Arabic language.
- The model was built using weakly supervised learning to combat the lack of labeled Arabic speech data.
- CNTXT AI processed over 30,000 hours of unlabeled Arabic audio to create a high-quality training dataset.
- The Conformer architecture lies at the core of Munsit, utilizing convolutional layers and transformers for efficient processing of spoken language nuances.
- Munsit outperformed other leading ASR models on various Arabic datasets, showcasing its superior accuracy.
- It achieved remarkable results across benchmarks, demonstrating higher accuracy than systems from OpenAI, Meta, Microsoft, and ElevenLabs.
- Munsit's impact extends beyond transcription, influencing Arabic voice technologies like text-to-speech and real-time translation.
- This launch marks a milestone for Arabic AI, emphasizing the importance of region-specific models for linguistic and cultural relevance.
- CNTXT AI aims to pave the way for indigenous AI development, highlighting the potential for Arabic-language technologies on a global scale.
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Balancing productivity and privacy: Safeguarding data in the age of AI-driven tools
- Artificial intelligence tools are reshaping the way we work, boosting productivity by up to 40% and providing various automation solutions.
- While AI offers significant benefits, data privacy concerns arise as users seek more control over how organizations collect and utilize their data.
- The shift in data handling towards using data for product development raises questions about privacy, data ownership, and long-term impacts.
- Challenges arise in controlling data once shared, as seen in cases like 23andMe where user DNA data faces potential sale.
- Extracting data from AI models becomes difficult post-training, leading to ethical concerns regarding user consent and compliance with data protection laws.
- Best practices for safeguarding data privacy include choosing companies that don't train on user data and understanding privacy rights and regulations like GDPR.
- Reading terms of service agreements and advocating for greater regulation in the AI space are also crucial steps in ensuring data privacy.
- The integration of AI productivity tools necessitates robust data privacy measures to balance productivity gains with protecting user data.
- Ensuring transparency, staying informed, and pushing for ethical practices are essential for maintaining data privacy in an AI-driven environment.
- Stronger protections, clearer regulations, and ethical standards are necessary to harness the benefits of AI without compromising privacy.
- By addressing privacy concerns and advocating for responsible AI practices, a safe and secure environment can be created for both businesses and users.
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Leveraging Generative AI for Document Automation: Beyond Legal and Finance
- Generative AI is expanding beyond legal and finance to benefit various sectors like customer support, technical writing, academic research, healthcare, manufacturing, and more by automating documentation creation with industry-specific jargon and complex layouts.
- AI assists technical writers in creating code-laden API docs and troubleshooting guides, helps customer support teams in producing tailored support documentation, and enables academic researchers to draft grant proposals and literature reviews accurately.
- Generative AI combined with document automation facilitates the creation of specialized documents in healthcare, manufacturing, and energy sectors, streamlining editing, reducing manual work, and ensuring accuracy and consistency in technical documentation.
- AI models have advanced to handle technical language nuances, improving data extraction, layout awareness, and data standardization, reducing human error and enhancing the efficiency of creating and editing documents at scale.
- Generative AI is already being leveraged for software documentation by CortexClick, literature survey by Elsevier’s ScienceDirect AI, clinical documentation by Sporo Health's AI Scribe, automation engineering by Siemens' Industrial Copilot, and project documentation by C3IT's Copilot PM Assist.
- To implement Generative AI document automation effectively, map out workflows, train AI models with relevant data, ensure human oversight to audit outputs, detect biases, and catch hallucinations before publishing, and anticipate future advancements in intelligent document processing for greater efficiency and precision.
- The evolution of Generative AI in documentation automation promises significant gains in efficiency, accuracy, and consistency across various sectors, with the potential for sophisticated document agents that monitor changes, conduct version control, and auto-deploy updates, revolutionizing the document generation landscape.
- Generative AI presents vast opportunities for organizations to streamline document creation processes, enhance quality, and increase productivity, paving the way for end-to-end automation with human oversight ensuring the safety and reliability of AI-generated outputs.
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Samer Saab, Founder and CEO of Explorance – Interview Series
- Samer Saab is the founder and CEO of Explorance with over 22 years of experience in the technology industry, having worked at companies like Bombardier, Nortel, and Nakisa.
- Explorance offers feedback analytics solutions like Blue, MLY, Metrics That Matter, and Forms, aiding in data-driven decision-making for various industries.
- The company leverages AI to interpret sentiment, streamline workflows, and provide customized reporting for improved engagement and growth.
- Samer Saab founded Explorance in 2003 to address the importance of honest, caring, and actionable feedback in empowering individuals and organizations.
- Bootstrapped from the beginning, Explorance faced early challenges like financial constraints, operational efficiency, and decision-making under pressure.
- Explorance's values of trust, purpose, and shared sacrifice have guided its growth and evolution, focusing on creating a culture of purpose, growth, and impact.
- Samer Saab's experience at companies like Nortel influenced his approach as a founder, emphasizing trust, empowerment, and human-centric leadership.
- Explorance's AI-powered feedback intelligence engine, MLY, analyzes qualitative feedback to provide insights for informed decision-making at scale.
- Building MLY posed technical challenges in data sourcing, model accuracy, inclusivity, and ensuring psychological and physical safety in feedback analysis.
- Explorance prioritizes Responsible AI development by upholding data privacy, accuracy, inclusivity, and ethical usage, aligning with its human-centric values.
- Samer Saab's passion for learning influences Explorance's products, aiming to create listening cultures that drive progress and transformation in education.
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Leading Teams Through the Transition to Agentic AI
- Agentic AI is a transformative form of artificial intelligence that actively makes decisions and initiates tasks with minimal human intervention, necessitating a cultural and strategic shift within organizations.
- Successful adoption of Agentic AI requires clear communication, organizational alignment, and an engaging rollout strategy involving everyone from leadership to employees.
- Leaders should start by laying out a compelling vision for Agentic AI, ensuring that all members of the organization understand the purpose and benefits of the transition.
- Open conversations and transparent communication are crucial to address concerns and foster trust among employees during the transition to Agentic AI.
- Training plays a vital role in helping employees adapt to Agentic AI, requiring department-specific, hands-on sessions and creating a supportive environment for experimentation.
- Making the transition enjoyable and fun through internal contests, interactive newsletters, and collaborative projects can boost engagement and accelerate adoption of Agentic AI.
- Continuous dialogue and feedback loops are essential post-rollout to address evolving questions, doubts, and opportunities related to Agentic AI implementation.
- Agentic AI signifies a paradigm shift in how work is done, decisions are made, and value is created, presenting organizations with a significant opportunity for innovation and empowerment.
- By equipping and inspiring people while aligning them behind a shared vision, organizations can leverage Agentic AI to drive greater innovation and cultivate a culture of empowerment and achievement.
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