"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.
There are 3 kinds of machine learning approaches- Supervised, Unsupervised, and Reinforcement Learning techniques. Supervised learning as we know is where data and labels are present. Unsupervised Learning is where only data and no labels are present. Reinforcement learning is where the agents learn from the actions taken to generate rewards. Imagine a situation where for training there is less number of labelled data and more unlabelled data.
The use of aggregated data by technology service providers is quite common in today's landscape, and something that even traditionally cautious customers have become amenable to in the right circumstances and subject to proper limitations. As widespread adoption of artificial intelligence (AI) technology continues, providers and customers of AI solutions should carefully consider the proper scope of aggregated data use in the design and implementation of the AI solutions. We previously discussed recommendations for customers considering rights to use aggregated data in service relationships. In addition to those generally applicable considerations, the nature of AI technology presents some unique challenges relating to aggregated data usage. While service providers of traditional services and SaaS or other technology solutions often try to present aggregated data usage as a necessary and inherent component of their offerings, the reality is that the benefits provided on account of aggregated data are often relatively distinct from their core offerings.
In the previous post, we gave a walk-through example of "Character-Based Text Generation". In this post, we will provide an example of "Word Based Text Generation" where in essence we try to predict the next word instead of the next character. The main difference between those two models is that in the "Character-Based" we are dealing with a Classification of around 30–60 classes i.e as many as the number of unique characters (depending on if we convert it to lower case or not), wherein "Word Based" we are dealing with a Classification of around 10K classes, which is the usual number of unique tokens in any big document. Again, we will run it on colab and as a training dataset, we will take the "Alice's Adventures in Wonderland". We will apply an LSTM model.
The ability to build artificial intelligence (AI) or machine-learning (ML) models is moving quickly away from the data scientist's domain and toward the citizen developer. Creating results from AI is getting easier, thanks to open-source tools that can convert AI/ML data streams into clear information that drives visualizations. It's essential to visualize AI and ML data in a way that helps you draw insights and find trends and patterns. The quality and quantity of the data available to you are critical factors. A visual representation should have some basic features.
Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Experience in Python is helpful but not necessary.
We are excited to announce PyCaret 2.2 -- update for the month of Oct 2020. PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only. This makes experiments exponentially fast and efficient. Installing PyCaret is very easy and takes only a few minutes.
At the very beginning of this millennium, when my hair was a lot darker, I wrote a little article with a pretty long name, entitled "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems". It was a straightforward article, which I decided to write, driven by a very practical need for a method to deal with data types that were hard to plug into Machine Learning (ML) models. At the time, I was working on ML models to detect fraudulent e-commerce transactions. Therefore I was dealing with very "sparse" categorical variables, like ZIP codes, IP addresses, or SKUs. I could not find an easy way to "preprocess" such variables, except for the traditional one-hot encoding, which didn't scale well to situations where one deals with hundreds or even thousands of unique values. A decision tree method popular at the time, the C5.0 algorithm by R. Quinlan, provided the capability to group together individual sets of values as part of the tree generation process.
Reinforcement learning has its limitations, though. Agrawal notes that it often takes a huge amount of training to learn a task, and the process can be difficult if the feedback required isn't immediately available. That's where curiosity could help. The researchers tried the approach, in combination with reinforcement learning, within two simple video games: Mario Bros., a classic platform game, and VizDoom, a basic 3-D shooter title. In both games, the use of artificial curiosity made the learning process more efficient.