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Key Concepts : Pretrained Model: A model trained on a large dataset (like ImageNet for images, or BERT for language). Fine-tuning: The pretrained model is adapted to a new task by continuing training on the new, typically smaller, dataset. Feature Extraction: Using the pretrained model’s learned representations (features) as input to a new model without retraining the whole network. Common Use Cases of Transfer Learning : Computer Vision: Using models like ResNet or VGG pretrained on ImageNet to classify medical images or detect defects. Natural Language Processing: Using BERT Will Transfer Learning Make Future AI Models Faster to Build? As artificial intelligence advances, a natural question arises: Will each new generation of AI models take less time to build, thanks to transfer learning? At first glance, it seems obvious, if we can reuse what we've already learned, shouldn't that make things faster? The truth is a bit more nuanced. While transfer learning is a game-changer, ...

Generative AI is advancing at an unprecedented rate, with new models, techniques, and applications constantly emerging. Keeping up with these developments is essential for staying competitive and fully utilizing AI driven innovations. Ongoing training and continuous learning enable professionals to grasp the latest breakthroughs, enhance AI applications, and tackle ethical and technical challenges effectively. As AI models grow more powerful and complex, staying informed about current research, industry trends, and best practices ensures responsible and efficient AI usage. Whether through online courses, research papers, or hands-on experimentation, staying updated is key in this rapidly evolving field. This video provides a helpful overview of the many Generative AI courses currently available. The Basics Generative AI Fundamentals Specialization (IBM, Coursera) Introduction to Generative AI (University of Colorado, Boulder, Coursera) Generative AI Primer (Vanderbilt University, Cours...

As the technological landscape evolves, organizations face the dual challenge of harnessing innovation while navigating its risks. Gartner’s 2025 Strategic Technology Trends provide a roadmap to achieve this balance, grouping key advancements into three themes: AI imperatives and risks, new frontiers of computing, and the synergy between humans and machines. These trends highlight how responsible innovation can drive progress across industries. AI Imperatives and Risks: Balancing Innovation and Protection Artificial intelligence continues to revolutionize how businesses operate, but it also necessitates safeguards to ensure ethical and secure adoption. Agentic AI , one of this year’s trends, represents autonomous systems capable of setting and achieving goals based on user input. This technology holds immense potential to augment human efforts by acting as a virtual workforce, taking on repetitive or complex tasks. However, its transformative power requires careful oversight to ensure ...

The era of artificial intelligence (AI) is in full swing. As generative AI continues to advance, tech giants like Intel, AMD, and Qualcomm are focusing not only on the software side but also on the hardware that powers these innovations. A key player in this revolution is the NPU—Neural Processing Unit—a specialized processor designed to accelerate AI tasks and enhance the performance of machine learning applications. Although NPUs are not entirely new (Apple has included them in its chips for years), they’re now being hailed as the next big thing in computing. As AI-driven technologies become more widespread, NPUs are set to play a crucial role in shaping the future of computing. Let’s explore what an NPU is and why it’s generating so much buzz. What is an NPU?  At its core, an NPU is a processor specifically designed to handle the unique demands of AI workloads, particularly neural networks. Unlike traditional CPUs (Central Processing Units) or GPUs (Graphics Processing Units), ...

Artificial intelligence (AI) is one of the most transformative technologies of our time, with the potential to impact various aspects of human life, such as health, education, economy, security, and environment. As AI becomes more advanced and ubiquitous, it also raises important questions about its ethical, social, and legal implications. Therefore, it is crucial for countries to develop and implement effective strategies and policies to foster the development and adoption of AI, while ensuring its responsible and beneficial use for society. In this blog post, we will explore which countries are leading in AI and how are they preparing for the future. We will use the Global AI Index 2021 as a reference, which ranks 62 countries based on their AI readiness, research, development, deployment, and governance. According to the index, the top five countries in AI are: - The United States: The US is the global leader in AI, with the highest scores in research, development, and deploymen...

Artificial intelligence is advancing rapidly and transforming various domains of human activity. From self-driving cars to smart assistants, AI is becoming more capable and ubiquitous. However, not all jobs are equally susceptible to automation by AI. Some jobs require skills and abilities that are hard to replicate or replace by machines, such as creativity, empathy, intuition, and social intelligence. In this blog post, we will explore some of the jobs that would be most challenging for an AI to replace in the future. One of the jobs that would be difficult for an AI to replace is that of an artist. Artists create original and expressive works of art that reflect their personal vision, emotions, and experiences . They use various media and techniques to convey their ideas and feelings, such as painting, sculpture, music, literature, and film. Artists also need to understand the context and audience of their work, and adapt their style and message accordingly. While AI can generate ...

The field of artificial intelligence (AI) is advancing rapidly, with new models and applications emerging every year. Some of the most exciting developments are happening in the area of generative AI, which can produce novel and realistic content such as text, images, videos, and audio from a given input. One of the key drivers of generative AI is the use of large-scale neural networks that can learn from massive amounts of data. These models, such as GPT-4, DALL-E, and Gato, can perform multiple tasks across different modalities, such as natural language processing, computer vision, and reinforcement learning. GPT-4 is the latest version of OpenAI's language model that can generate coherent and fluent text on almost any topic. It is expected to be released in early 2023 and to surpass its predecessor, GPT-3, in terms of performance and capabilities. GPT-4 may also be able to handle more than just language, by combining text with image or video recognition and generation. DAL...

" Every industry will be revolutionized , reborn. What will you create?" The first few minutes of his speech was in Mandarin and Taiwanese, the rest of the speech was delivered in English.  Jensen Huang  Jensen Huang is a Taiwanese American business magnate, electrical engineer, and co-founder of Nvidia Corporation. He was born on February 17, 1963, in Tainan, Taiwan. After college, he worked as a director at LSI Logic and a microprocessor designer at Advanced Micro Devices. Huang co-founded Nvidia in 1993 and has served as president and CEO ever since. In addition to his successful career in the technology industry, Huang is an active philanthropist who donated $50 million to Oregon State University in 2022. Over the years, he has received numerous awards for his contributions to the field of technology. Are you curious about all the hype about AI? Want to write reports and emails more effectively?  Try Jasper writing assistant today for free . #AI #ArtificialIntellige...

The Age of AI is Here, Don't Get Left Behind Artificial intelligence has been a rapidly evolving field over the last few decades, with advancements being made at an unprecedented pace. The rise of machine learning and deep learning algorithms has enabled machines to learn from large datasets and make predictions based on patterns and insights. This has led to the development of intelligent systems that can perform tasks previously thought impossible for machines, such as natural language processing and image recognition. As AI technology continues to evolve, it has become an increasingly important tool in various industries, including healthcare, finance, and marketing, among others. The rapid development of AI has also raised concerns about its ethical implications and the need for responsible use and regulation. Today, we will cover the following: AI Technology and Its Use in Various Industries Importance of Staying Updated and Incorporating AI Into Businesses Understanding AI Te...