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Azure Machine Learning vs IBM Watson: Software comparison

#artificialintelligence

With the ability to revolutionize everything from self-driving cars to robotic surgeons, artificial intelligence is on the cutting edge of tech innovation. Two of the most widely recognized AI services are Microsoft's Azure Machine Learning and IBM's Watson. Both boast impressive functionality, but which one should you choose for your business? Azure Machine Learning is a cloud-based service that allows data scientists or developers to train, build and deploy ML models. It has a rich set of tools that makes it easy to create predictive analytics solutions. This service can be used to build predictive models using a variety of ML algorithms, including regression, classification and clustering.


Data alternatives for pretraining computer vision models

#artificialintelligence

Not only did a classifier pre-trained on Task2Sim's fake images perform as well as a model trained on real ImageNet photos, it also outperformed a rival trained on images generated with random simulation parameters. Task2Sim even transferred its know-how to entirely new tasks, creating images to teach a classifier how to identify cactuses and hand-drawn numbers. "The more tasks you use during training, the more generalizable the model will be," Feris said. A related tool, SimVQA,2 also appearing at CVPR, generates synthetic text and images for training robot agents to reason about the visual world. In a typical visual-reasoning task, an agent might be asked to count the number of chairs at a table or identify the color of a bouquet of flowers.


Is 'fake data' the real deal when training algorithms?

The Guardian

You're at the wheel of your car but you're exhausted. Your shoulders start to sag, your neck begins to droop, your eyelids slide down. As your head pitches forward, you swerve off the road and speed through a field, crashing into a tree. But what if your car's monitoring system recognised the tell-tale signs of drowsiness and prompted you to pull off the road and park instead? The European Commission has legislated that from this year, new vehicles be fitted with systems to catch distracted and sleepy drivers to help avert accidents.


Synthetic Data Is About To Transform Artificial Intelligence

#artificialintelligence

These people do not exist. These faces were artificially generated using a form of deep learning ... [ ] known as generative adversarial networks (GANs). Synthetic data like this is becoming increasingly indistinguishable from real-world data. Imagine if it were possible to produce infinite amounts of the world's most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result? This is a reality today. It is called synthetic data. Synthetic data is not a new idea, but it is now approaching a critical inflection point in terms of real-world impact.


Training Machine Learning Models Using TensorFlow or PyTorch

#artificialintelligence

AI and machine learning are very hot topics these days. These are only some of the applications that cannot exist without machine learning. But how can machines learn? I will show you how the magic works in this article, but I won't talk about neural networks! I will show you what is in the deepest deep of machine learning. One of the best presentations about machine learning is Fei Fei Li's TED talk.


Lessons From Deploying Deep Learning To Production

#artificialintelligence

When I started my first job out of college, I thought I knew a fair amount about machine learning. I had done two internships at Pinterest and Khan Academy building machine learning systems. I spent my last year at Berkeley doing research in deep learning for computer vision and working on Caffe, one of the first popular deep learning libraries. After I graduated, I joined a small startup called Cruise that was building self-driving cars. Now I'm at Aquarium, where I get to help a multitude of companies deploying deep learning models to solve important problems for society.


What is AI Visual Inspection for Defect Detection?

#artificialintelligence

Artificial intelligence is a crucial differentiator for businesses, with numerous applications in almost every domain. From self-driving cars to Siri and Alexa, AI is the critical enabler for next-generation services transforming the way we live. AI can enable systems to make intelligent decisions based on past data, from deciding which products customers might like best to identifying potential medical problems before they escalate into emergencies. Among this wide range of AI applications around the globe, automated visual inspection is highly appreciated. Visual inspection is one of the most commonly used approaches in the production process.


Seven Key Dimensions to Help You Understand Artificial Intelligence Environments - KDnuggets

#artificialintelligence

Every artificial intelligence(AI) problem is a new universe of complexities and unique challenges. Very often, the most challenging aspects of solving an AI problem is not about finding a solution but understanding the problem itself. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment.


How Machines Can Learn Using TensorFlow or PyTorch

#artificialintelligence

AI and machine learning are very hot topics these days. These are only some of the applications that cannot exist without machine learning. But how can machines learn? I will show you how the magic works in this article, but I won't talk about neural networks! I will show you what is in the deepest deep of machine learning. One of the best presentations about machine learning is Fei Fei Li's TED talk.


Neuralink and Tesla have an AI problem that Elon's money can't solve

#artificialintelligence

Elon Musk's problems are bigger and more important than yours. While most of us are consumed with our day-to-day activities, Musk has been anointed by a higher power to save us all from ourselves. He's here to ensure we eliminate car accidents, make traffic a thing of the past, solve autism (his words, not mine), connect human brains to machines, fill the night sky with satellites so everyone can have internet access, and colonize Mars. He doesn't exactly know how we're going to accomplish all those things, but he has more than enough money to turn any and every single good idea he's ever had into a functioning industry. Who cares if Tesla's 10, 20, or 100 years away from actually solving the driverless car problem?