Unite.AI
MARCH 9, 2025
However, AI is overcoming these limitations not by making smaller transistors but by changing how computation works. Instead of relying on shrinking transistors, AI employs parallel processing, machine learning , and specialized hardware to enhance performance. These efforts are critical in guiding AI development responsibly.
Unite.AI
NOVEMBER 28, 2024
GPUs, originally developed for rendering graphics, became essential for accelerating data processing and advancing deep learning. This period saw AI expand into applications like image recognition and natural language processing, transforming it into a practical tool capable of mimicking human intelligence.
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Unite.AI
DECEMBER 19, 2024
Much of what the tech world has achieved in artificial intelligence (AI) today is thanks to recent advances in deep learning, which allows machines to learn automatically during training. I dont believe we are going to be close to giving them human-level sensations, nor that its actually needed.
Unite.AI
JANUARY 24, 2025
Claudionor Coelho is the Chief AI Officer at Zscaler, responsible for leading his team to find new ways to protect data, devices, and users through state-of-the-art applied Machine Learning (ML), Deep Learning and Generative AI techniques. He also held ML and deep learning roles at Google.
Unite.AI
DECEMBER 17, 2024
AI systems, especially deep learning models, can be difficult to interpret. To ensure accountability while adopting AI, banks need careful planning, thorough testing, specialized compliance frameworks and human oversight.
Marktechpost
MARCH 30, 2024
In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. Below are the top ten Python libraries that stand out in AI development.
Machine Learning (Theory)
MARCH 5, 2025
As an example, the speech recognition community spent decades focusing on Hidden Markov Models at the expense of other architectures, before eventually being disrupted by advancements in deep learning. Support Vector Machines were disrupted by deep learning, and convolutional neural networks were displaced by transformers.
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