Thomas Schmidt’s Post

Rumors state that GPT costs OpenAI $700,000 a day to run. Who can afford or even justify such a bill for domain-specific use cases? Companies wishing to adopt AI are also questioning the initial investment costs. So has the AI bubble burst already? Not quite yet. I would say it’s time to shift the focus to SLMs. SLMs are language models with less than 7 billion parameters. Compare this with GPT and Claude’s 135B+, and you get an idea of scale. But what about performance? I hear you! Obviously, an SLM cannot do everything an LLM can. It may not be able to write love poems, translate languages, and explain theoretical physics with the same panache. But do you really need all this capability for a single use case? In my experience, customers want language models that solve problems efficiently, and business leaders want language models that run cost-efficiently. SLMs fit the bill. My team's experiments showed that fine-tuning SLMs takes significantly lower training data points. You can get the job done in minutes/hours instead of the days it takes for larger models. Once we fine-tuned for specialized use cases, the SLMs also demonstrated capabilities that were remarkably close to those of LLMs in terms of natural language understanding. They performed acceptably well for everyday tasks like summarization, categorizing, and customer support chatbots. No wonder early adoption of SLMs in the enterprise is on the rise. What are your thoughts on the topic? #GenerativeAI #AI #AIInnovation #LLM #SLM #EnterpriseAI #TechLeadership

Recent academic writings emphasize that ethical frameworks should define AI adoption. 1. AI systems inherit and amplify biases found in their training data. You want to create fair AI systems that don't reinforce existing inequalities. 2. Users want to understand the reasoning behind AI decisions. Transparency is vital for building trust and ensuring accountability. 3. Rigorous testing and security measures are necessary to prevent AI from producing harmful outputs or being exploited by malicious actors.  4. Ensuring safety and reliability is especially important for AI applications in critical environments. AI systems in health care, finance, and public policy impact social norms and individual lives. As AI gains momentum, the phrase “ethical AI” is becoming a hot topic for various industries. Ethical AI bridges the access gap, making AI technologies accessible and beneficial to diverse global populations. However, what practical steps can you take beyond empty buzzwords to include ethical considerations in AI development? ▶️ Regular audits by independent bodies can assess AI systems for fairness, accuracy, and security vulnerabilities. ▶️ Diverse development teams in terms of gender, race, ethnicity, and background brings varied perspectives. They can reduce bias and ensure inclusivity in AI products. ▶️ Engaging stakeholders to understand their needs and ethical concerns during the design process ensures that AI solutions align with user values and contexts. The field of AI ethics is constantly evolving. I believe ongoing education and training for AI developers and stakeholders can keep teams updated and compliant. We can move beyond seeing Ethical AI as merely a regulatory necessity or marketing tactic. #EthicalAI #AIResponsibility #AITransparency

Is AI an ally or a threat to the environment? Despite the power-hungry LLMs in commercialized AI, I believe that AI is a powerful ally in our fight to protect and preserve our planet. Here's why. One of the most exciting applications of AI is its ability to calculate the carbon footprint of projects. Imagine building a new residential neighborhood with AI—you could optimize every step of the construction process for sustainability. Energy generation and consumption has the biggest impact on environment. AI can help companies identify the best strategies for energy use. The windnode project that used AI to design smart energy systems in North-East Germany is just one such example. AI could optimize energy installations and help businesses make informed, sustainable decisions all around globe! I think we are at a very exciting stage of AI development. There is great opportunity to use AI for sustainable development in ways we cannot imagine. Do you think AI applications can counterbalance the impact of AI models on the environment? #GenerativeAI #AI #AIInnovation #DigitalTransformation #EnterpriseAI #TechLeadership

AI Startup Dilemma: Large open-source AI models are great, but can we democratize AI model training?    Your one-time AI model training runs cost millions. The cost of model inference in responding to user prompts also scales with your user base.    Largest AI models take months to train on hundreds of GPUs. Suddenly, Sam Altman’s demands of $7 Trillion funding for a universal AGI company seem to make sense!    And VCs are already at it. AI startups now dominate VC investments in 2024 – up 47% in Q2!    So, is the AI boom pushing us back into the VC-led innovation era? Or can we democratize AI model #training and #inference at a lower cost?     Well, you can’t build your own data centers and cloud TCO can get prohibitively expensive. You need a single on-prem turnkey solution that is ready for deployment: Scalable AI #GPU clusters, a fully integrated data lake #storage platform and #HPC grade network fabric.    Let’s look at how the AI NVIDIA #DGX SuperPOD fills this gap for AI startups:  Multi-gpu #H100 and #A100 AI infrastructure for scalable AI workloads.  A full AI software stack across all layers: from infrastructure and orchestration to application workflows and administration.  High speed networking: NVLink and Quantum-2 architecture enables 400Gb/s RDMA!  I think we can finally democratize AI model training with NVIDIA SuperPOD!    #NVIDIA #SuperPOD #cloud #VC #startups #AGI #GPU

It's never an easy decision: What hardware should I choose to run my algorithms? The right AI infrastructure is crucial, whether you're coding on a laptop or deploying scalable models in the cloud. Here's how to navigate your options:    1. Explore Hardware Options: Understand the distinct capabilities of CPUs, GPUs, TPUs, and FPGAs, and how they cater to different computational needs in AI. 2. Location Matters: Compare on-premise versus cloud solutions to determine what best suits your project's security, scalability, and collaboration needs. 3. Cost Versus Performance: Balance the scales between initial investments and long-term benefits to find a cost-effective solution without compromising performance. 4. Software Ecosystem Compatibility: Choose hardware that supports a rich software ecosystem, ensuring ease of implementation and future-proofing your infrastructure. 5. Availability and Scalability: Select readily available hardware that can scale with your project's growth to avoid future bottlenecks.    At AMBER AI & Data Science Solutions GmbH, we make use of NVIDIAs DGX systems and NVIDIA Omniverse to enhance AI deployments ensuring that our infrastructure is both powerful and flexible enough to handle tasks.    #AIInfrastructure #MachineLearning #CloudComputing #NVIDIA #TechInnovation #AMBERAI 

AMBER AI & Data Science Solutions GmbH Achieves DIN EN ISO 9001:2015 Certification: We are pleased to announce that AMBER AI & Data Science Solutions GmbH has been awarded the DIN EN ISO 9001:2015 certification! The audit process was thorough, examining all aspects of our operations. The certification confirms that our processes meet the rigorous requirements set by the DIN EN ISO 9001:2015 standard, focusing on Quality Management Systems, Process Approach, Continuous Improvement, Customer Orientation and Evidence-Based Decisions.   For our clients, this certification is a testament to our dedication to delivering exceptional solutions and services. We will continue to strive for excellence and uphold the standards that our customers have come to expect from us.   #AMBER #ISO9001 #QualityManagement #ICT #NVIDIA

By integrating AI technologies, farmers are now able to enhance livestock care in several transformative ways. Here are some key areas where AI is making a significant impact, facilitated by AMBER AI & Data Science Solutions GmbH's advanced computing solutions: Early Disease Detection and Diagnosis: AI-driven systems, powered by AMBER’s NVIDIA DGX and NVIDIA RTX systems, use a combination of sensors, cameras, and predictive analytics to continuously monitor the health of each animal. These systems can detect subtle changes in behavior, movement, or physical appearance that may indicate illness long before visible symptoms emerge. For example, AI can analyze cough sounds in pigs to detect respiratory issues early or use thermal imaging to spot the onset of infections or inflammations. Early detection allows for timely intervention, reducing the spread of disease and improving recovery rates. Reproductive Management: AMBER’s AI tools, leveraging NVIDIA CUDA-X microservices, analyze data to predict the best time for breeding, ensuring higher success rates in reproduction. This includes more accurately monitoring signs of heat in animals and predicting the best timing for insemination. Improved reproductive management leads to better genetic traits over generations, healthier livestock, and more efficient herding. Behavioral Analysis: AI-powered video analytics, supported by AMBER’s networking solutions like NVIDIA InfiniBand and NVIDIA Ethernet, help in monitoring livestock behavior to ensure their welfare. Changes in behavior patterns can indicate stress, discomfort, or illness. By keeping an eye on these patterns, farmers can adjust housing conditions, manage group dynamics, and take other measures to ensure animal comfort and reduce stress, thereby enhancing overall well-being and productivity. Welfare Monitoring: Continuous welfare assessment using AI helps in maintaining or improving living conditions for livestock. This includes automated systems that adjust lighting, temperature, and humidity based on the preferences and needs of the animals, powered by AMBER's NVIDIA solutions. AI can also monitor sound levels to reduce noise stress or even control the mix of animals in pens to optimize social interactions and reduce aggression. Automated Systems for Treatment and Care: AI is also used in robotic systems for delivering treatments like vaccinations or medications with precision and minimal stress to the animals. These systems, which can operate outside of regular human working hours, ensure timely care without the need for human presence, which is particularly useful in large-scale operations. By integrating AMBER’s AI and computing technologies into livestock farming, producers not only improve the health and welfare of their animals but also enhance operational efficiency and productivity. #AMBERAI #LivestockFarming #NVIDIA #DGX #AI #NVIDIARTX

What an unforgettable experience at the WeAreDevelopers World Congress 2024 in Berlin! It was a great pleasure to deliver my talk on "The Secrets to Effectively Scaling AI Factories" and dive into the exciting journey of AI development and the critical role of AI factories in today's technology landscape. I was especially delighted by the great interest in my talk and the engaging conversations that followed. It was wonderful to see so many of you interested in #AIinfrastructure development, sustainable AI production, and the latest #GPU technologies. A big thank you to everyone who attended my talk and participated in the discussions. Special thanks also go to our partners at NVIDIA for the fantastic collaboration.  And because so many of you asked for the presentation slides: Just comment below or send me a private message, and I'll be happy to share them with you. For more details, please check out our last blog post: https://lnkd.in/eb6hgPjQ #WeAreDevelopers #AI #DeepLearning #aifactory

Understanding and predicting global climate and weather with high precision is becoming increasingly crucial as the world grapples with more frequent and severe weather events. From predicting droughts to preparing for hurricanes, the stakes are high, and the need for advancements in climate technology has never been more pressing.    NVIDIA's recent announcement of the Earth-2 climate digital twin platform marks a significant step in this direction. This platform aims to transform how we simulate and visualize climate and weather patterns, utilizing high-resolution, AI-powered models to deliver faster and more accurate predictions than ever before. Earth-2 leverages NVIDIA's cutting-edge AI and computing capabilities, including the CorrDiff generative AI model, which uses diffusion techniques to generate visuals and data at scales previously unimaginable in the climate tech field. This model promises to deliver predictions that are not only quicker but also much finer in detail, with the potential to operate up to 12.5 times higher resolution and 1,000 times faster than current numerical models.  Key adopters like The Weather Company and the Central Weather Administration of Taiwan are already integrating these capabilities to enhance their forecasting services. These early implementations suggest a significant shift towards more dynamic and responsive weather prediction systems.    However, while the potential benefits of Earth-2 are vast, the platform also raises questions about accessibility and the integration of such advanced technologies into existing meteorological infrastructures. Will all regions and countries be able to harness these capabilities equally, or will there be a digital divide in weather prediction technologies?  As we continue to face climate challenges, the development and deployment of technologies like NVIDIA's Earth-2 could be pivotal. But it is also essential to consider how these technologies will be implemented globally and what measures need to be in place to ensure they benefit not just some but all.    What are your thoughts on the potential impact of such technologies? Can they truly redefine our approach to managing climate and weather risks? #AMBERAI #TechnologyAdoption #DigitalTwin #NVIDIA