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) …
M3 is a deep learning system that infers demographic attributes directly from social media profiles--no further data is needed. This web demo showcases M3 on Twitter profiles, but M3 works on any similar profile data, in 32 languages. To learn more, please see our open-source Python library m3inference or read our Web Conference (WWW) 2019 paper for details. The paper also includes fully interpretable multilevel regression methods that estimate inclusion probabilities using the inferred demographic attributes to correct for sampling biases on social media platforms. This web demo was created by Scott Hale and Graham McNeill.
This is the second post in the series Deep Learning for Life Sciences. In the previous one, I showed how to use Deep Learning on Ancient DNA. Today it is time to talk about how Deep Learning can help Cell Biology to capture diversity and complexity of cell populations. Single Cell RNA sequencing (scRNAseq) revolutionized Life Sciences a few years ago by bringing an unprecedented resolution to study heterogeneity in cell populations. The impact was so dramatic that Science magazine announced scRNAseq technology as the Breakthrough of the Year 2018.
Google AI yesterday released its latest research result in speech-to-speech translation, the futuristic-sounding "Translatotron." Billed as the world's first end-to-end speech-to-speech translation model, Translatotron promises the potential for real-time cross-linguistic conversations with low latency and high accuracy. Humans have always dreamed of a voice-based device that could enable them to simply leap over language barriers. While advances in deep learning have contributed to highly improved accuracy in speech recognition and machine translation, smooth conversations between different language speakers remained hampered by unnatural pauses during machine processing. Google's wireless headphone Pixel Bud released in 2017 boasted real-time speech translation, but users found the practical experience less then satisfying.
As I am taking a course on Udemy on Deep Learning, I decided to put my knowledge to use and try to predict whether a visitor would make a purchase (generate revenue) or not. The dataset has been taken from UCI Machine Learning Repository. The first step is to import necessary libraries. Apart from the regular data science libraries including numpy, pandas and matplotlib, I import machine learning library sklearn and deep learning library keras. I will use keras to develop my Artificial Neural Network with tensorflow as the backend.
While artificial intelligence and machine learning are increasingly powering a digital health boom, AI is still very much in its infancy when it comes to mental and behavioral well-being. This isn't really a surprise, as the ability to understanding human thoughts and feelings --not'merely' crunching blood test data or medical scans for signs of disease-- is much harder than telling if a person's kidney is about to fail. Boosting one's mood or providing personalized treatments for psychiatric disorders, and particularly depression, is harder still. Depression is the world's leading health burden according to the World Health Organization. Patients often face tedious trial and error processes to navigate the ocean of antidepressants.
The scikit-learnthe library has functions that enable us to build these pipelines by concatenating various modules together. We just need to specify the modules along with the corresponding parameters. It will then build a pipeline using these modules that processes the data and trains the system. The pipeline can include modules that perform various functions like feature selection, preprocessing, random forests, clustering, and so on. In this section, we will see how to build a pipeline to select the top K features from an input data point and then classify them using an Extremely Random Forest classifier.
In Machine learning, classification problems with high-dimensional data are really challenging. Sometimes, very simple problems become extremely complex due this'curse of dimensionality' problem. In this article, we will see how accuracy and performance vary across different classifiers. We will also see how, when we don't have the freedom to choose a classifier independently, we can do feature engineering to make a poor classifier perform well. For this article, we will use the "EEG Brainwave Dataset" from Kaggle.
AI is the future, or so you're hearing. Every day, news of another organization leveraging AI to produce business outcomes that outstrip competition hit your inbox, but your company either hasn't started at all or is mired in the discussion. AI, machine learning, and deep learning are sometimes used interchangeably, but they aren't the same. If your business is going to leverage advances in technology, you need to know the difference and when to choose machine learning over deep learning and vice versa. Short story: deep learning is a subset of machine learning and both fall under the umbrella of AI.
We have all witnessed the rapid development of reinforcement learning methods in the last couple of years. Most notably the biggest attention has been given to off-policy methods and the reason is quite obvious, they scale really well in comparison to other methods. Off-policy algorithms can (in principle) learn from data without interacting with the environment. This is a nice property, this means that we can collect our data by any means that we see fit and infer the optimal policy completely offline, in other words, we use a different behavioral policy that the one we are optimizing. Unfortunately, this doesn't work out of the box like most people think, as I will describe in this article.
This concludes our high level explanation of neural style transfer. We use a trained convolutional neural network (CNN) model such as VGG19 to acquire the content and style loss functions. Recall that content are high level features that describe objects and their arrangement in the image. An image classification model needs to be well-trained on content in order to accurately label an image as "dog" or "car". A convolutional neural network (CNN) is designed to filter out the high level features of an image.