Eric Bonabeau’s Post

“You don’t need a PhD in machine learning or mathematics to build with AI.” Amazon SVP and Head Scientist, Artificial General Intelligence, Rohit Prasad tells #BrainstormAI the bar to using AI has been reduced. “The good news is that the bar to build with AI has suddenly reduced," he told the audience. Learn more:https://trib.al/M6JY1Iw

“You don’t need a PhD in machine learning or mathematics to build with AI.” Amazon SVP and Head Scientist of Artificial General Intelligence Rohit Prasad told #BrainstormAI the bar to using AI has been reduced. Prasad also brushed off concerns that AI foundation models have “hit a wall” in terms of how much improvement new releases are demonstrating over past versions. Read more: https://lnkd.in/eiRsRd7P

I’m happy to share that I’ve obtained a new certification: AI For Everyone from DeepLearning.AI! Thanks to instructor Andrew Ng for simplifying the concepts of AI, ML, DeepLearning / Neural network etc. As a wise person once remarked, "The best knowledge leads to excellence." In the modern era, this is demonstrated by a mathematical algorithm that creates artificial intelligence (AI) by receiving Big Data as input. #AIforEveryone #DeepLearningAI

A simple introduction to the trajectory of AI to Deep Fakes. #ai #deepfakes #ml #ibm https://lnkd.in/gt8gttk5

What Makes an AI model Biased? Machine learning algorithms themselves are Not inherently biased. However, the selection and curation of data by the engineers can introduce bias into the model’s outcomes. When a deep neural network produces biased results, the true responsibility lies with us, the data scientists and engineers, not the model itself. This is similar to how many historical statistical surveys have yielded remarkably biased results. As engineers, we are human and inherently susceptible to various biases (e.g., selection bias, confirmation bias, experimenter’s bias). When building machine learning models, it is crucial to be aware of these potential biases. We must take necessary steps to avoid introducing them during data selection, processing, and feeding stages. By doing so, we can strive to create fairer and more accurate AI models. #MachineLearning #ResponsibleAI

Let's start from the basics... An AI course advised by an AI. Learning about a technology, recommended by the same technology. #AI #ArtificialIntelligence #NewSkills #FuturePositive #Growth #TrustTheInnovation #LearningJourney https://lnkd.in/daK_-Czk

🧠✨ Ever thought about what really powers a neural network? Spoiler: It’s not magic—it’s math, and it’s beautiful. "AI" and "deep learning" might sound like tech wizardry, conjuring images of futuristic machines. But behind the scenes, it’s all powered by elegant math—matrix multiplications, activation functions, backpropagation, and gradient descent. These are the building blocks that turn code into models capable of learning and making decisions. Understanding this math transforms neural networks from mysterious ‘black boxes’ into tools you can truly master. It equips you to optimize, troubleshoot, and appreciate the logic behind AI. Have you explored the math behind neural networks? How did it change your view of AI? #NeuralNetworks #MathIsBeautiful #MachineLearning #AI

Still sorting out the difference between #MachineLearning, #AI, and #Transformers? Check out our handy guide to the different types of AI and how they work: https://bit.ly/3ASLwgq Make sure you're following Our Industrial Life to learn more.

Still sorting out the difference between #MachineLearning, #AI, and #Transformers? Check out our handy guide to the different types of AI and how they work: https://bit.ly/3Tp4S38 Make sure you're following Our Industrial Life to learn more.

Just to remind myself, what is AI and how can I use it again? https://lnkd.in/d4kYQv_Z #AI #machine_learning