If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In order to create effective machine learning and deep learning models, you need copious amounts of data, a way to clean the data and perform feature engineering on it, and a way to train models on your data in a reasonable amount of time. Then you need a way to deploy your models, monitor them for drift over time, and retrain them as needed. You can do all of that on-premises if you have invested in compute resources and accelerators such as GPUs, but you may find that if your resources are adequate, they are also idle much of the time. On the other hand, it can sometimes be more cost-effective to run the entire pipeline in the cloud, using large amounts of compute resources and accelerators as needed, and then releasing them. The major cloud providers -- and a number of minor clouds too -- have put significant effort into building out their machine learning platforms to support the complete machine learning lifecycle, from planning a project to maintaining a model in production.
AI has made several breakthroughs when it comes to implementation into games. The functionalities of AI in video games include various domains such as real-time facial emotion recognition, automated difficulty adaptation, sentiment analysis, non-verbal bodily motion, lip-synchronised speech and more. This technique has been used in games to enhance graphical realism, to generate levels, sceneries and storylines, to establish player profiles, balance complexity or to add intelligent behaviours to non-playing characters. In this article, we list down the seven popular AI projects that work on gesture gaming. About: This project will help you understand how to use TensorFlow object detection API with the computer's webcam to play a snake game by using hand gestures.
The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.
This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.
Here you can see the all 16 layers of the VGG16 model which got some description in the bottom. "Total params" are the total number parameter that a model can have overall. "Trainable params" is the number of the parameters that you can train, basically this model is empty with weights which means you have only got the architecture of vgg16. Lastly "Non-trainable params" as name says, these are the parameters which are freezed and not updatable during the training. Note that here you don't see the last layer because we set that to "false".
All said, I'm extremely excited to see which new technologies are built on GPT-3 and how OpenAI continues to improve on its model. Increased attention and funding in NLP and GPT-3 might be enough to ward off fears from many critics that an AI winter might be coming (myself included). Despite the shortfalls of the model, I am hoping that everyone can be optimistic about a future where humans and machines will communicate with each other in a unified language and the ability to create tools using technology will be accessible to billions of more people.
With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language modeling, machine translation, etc. However, this performance of deep learning models in NLP pales in comparison to the performance of deep learning in Computer Vision. One of the main reasons for this slow progress could be the lack of large labeled text datasets. Most of the labeled text datasets are not big enough to train deep neural networks because these networks have a huge number of parameters and training such networks on small datasets will cause overfitting. Another quite important reason for NLP lagging behind computer vision was the lack of transfer learning in NLP.
Google Brain has released the pre-trained models and fine-tuning code for Big Transfer (BiT), a deep-learning computer vision model. The models are pre-trained on publicly-available generic image datasets and can meet or exceed state-of-the-art performance on several vision benchmarks after fine-tuning on just a few samples. Paper co-authors Lucas Beyer and Alexander Kolesnikov gave an overview of their work in a recent blog post. To help advance the performance of deep-learning vision models, the team investigated large-scale pre-training and the effects of model size, dataset size, training duration, normalization strategy, and hyperparameter choice. As a result of this work, the team developed a "recipe" of components and training heuristics that achieves strong performance on a variety of benchmarks, including an "unprecedented top-5 accuracy of 80.0%" on the ObjectNet dataset. Deep-learning models have made great strides in computer vision, particularly in recognizing objects in images.