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) …
At Google's I/O developer conference in 2018, Alphabet's Google CEO Sundar Pichai demonstrated the amazing Duplex feature of the Google Assistant, still in development. It was all over the internet in short order, but in case you didn't see it: In the demo, he asks his Google Assistant to schedule a haircut for him, and "behind the scenes" (though we get to see it in action in this demo) the Assistant spins off an agent that calls the salon in a voice that is amazingly human-sounding. Give it a listen in the video below. Later in the demo he has the Assistant (with a male voice this time) contact a restaurant to make reservations. There's a lot to discuss in these scenarios, but for this pattern we're focusing on its human-sounding-ness.
In 1848, the 25-year-old Phineas Gage was working on a railroad in Vermont, packing explosive powder into a hole with an iron tamper. Unexpectedly, the powder exploded, sending the tamper backwards through Gage's skull and brain. That he survived is a miracle, but astonishingly he even seemed capable of functioning effectively, maintaining normal memory, speech, and motor skills. Those that knew him, however, thought he was anything but the same, with friends remarking he was "no longer Gage." "…his equilibrium, or balance, so to speak, between his intellectual faculties and animal propensities seems to have been destroyed.
For me, studying Logistic regression first helped a lot when I started to learn Neural Networks. You can think of each neuron in the network as a Logistic Regression, it has the input, the weights, the bias you do a dot product to all of that, then apply some non linear function. Moreover, the final layer of a neural network is a simple linear model (most of the time). Let's look closer at the "output layer", you can see that this is a simple linear (or logistic) regression, we have the input (hidden layer 2), we have the weighs, we do a dot product and then add a non linear function (depends on the task). The first part (on the left) is trying to learn a good representation of the data that will help the second part (on the right) to perform a linear classification/regression.
By Rachel Thomas, Co-founder at fast.ai There is a powerful technique that is winning Kaggle competitions and is widely used at Google (according to Jeff Dean), Pinterest, and Instacart, yet that many people don't even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables. Despite what you may have heard, you can use deep learning for the type of data you might keep in a SQL database, a Pandas DataFrame, or an Excel spreadsheet (including time-series data). I will refer to this as tabular data, although it can also be known as relational data, structured data, or other terms (see my twitter poll and comments for more discussion). Tabular data is the most commonly used type of data in industry, but deep learning on tabular data receives far less attention than deep learning for computer vision and natural language processing.
Organizations are increasingly reliant on Machine Learning (ML) models to weigh in on decisions to hire, grant loans, sentence criminals, and release prisoners on parole. While it may seem that limiting the role of humans in such decisions would limit subjective biases, these ML models learn from data that are, in many cases, representative of existing societal biases. Researchers from Boston University and Microsoft have shown that software trained with text collected from Google News reproduced gender biases. When asked to complete the statement "Man is to computer programmer as woman is to [blank]," the trained software responded with "homemaker." Female representation is important in the fields of ML and AI to highlight, interrogate, and correct biases such as the ones implicit in the previous example.
JAXenter: What is the difference between image and text from a machine's point of view? Christoph Henkelmann: Almost all ML methods, especially neural networks, want tensors (multidimensional arrays of numbers) as input. In case of an image the transformation is obvious, we already have a three-dimensional array of pixels (width x height x color channel), i.e. except for smaller pre-processing the image is already "bite-sized". There is no obvious representation for text. Text and words exist at a higher level of meaning, for example, if you simply enter Unicode-encoded letters as numbers in the net, the jump from coding to semantics is too "high".
We will start with a very simple baseline. We will represent the headline by averaging the headline words in their Word2Vec representation. As previously mentioned, Word2Vec is a machine-learning method for representing words as vectors. The Word2Vec model is trained by predicting words close to the target word with a shallow neural network. You can read more about how the algorithm works here.
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
Note: This post was written together with the awesome Julian Eisenschlos and was originally published on the TensorFlow blog. Throughout this post we will show you how to classify text using Estimators in TensorFlow. Welcome to Part 4 of a blog series that introduces TensorFlow Datasets and Estimators. You don't need to read all of the previous material, but take a look if you want to refresh any of the following concepts. Part 1 focused on pre-made Estimators, Part 2 discussed feature columns, and Part 3 how to create custom Estimators.