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) …
The advent of automated machine learning platforms has expanded the access and availability of algorithmic interpretation over the past several years. But how do the different machine learning platforms stack up from a performance perspective? That's the question that researchers from Arizona State University sought to answer. As the market for machine learning platforms expands, users are naturally inclined to seek sources of information to rank and rate the various options that are available to them. Which systems are the easiest to use?
Machine learning in essence, is the research and application of algorithms that help us better understand data. By leveraging statistical learning techniques from the realm of machine learning, practitioners are able to draw meaningful inferences from and turn data into actionable intelligence. Furthermore, the availability of several open source machine learning tools, platforms and libraries today enables absolutely anyone to break into this field, utilizing a plethora of powerful algorithms to discover exploitable patterns in data and predict future outcomes. This development in particular has given rise to a new wave of DIY retail traders, creating sophisticated trading strategies that compete (and in some cases, outperform others) in a space previously dominated by just institutional participants. In this introductory blog post, we will discuss supportive reasoning for, and different categories of machine learning.
Machine learning generates a lot of buzz because it's applicable across such a wide variety of use cases. That's because machine learning is actually a set of many different methods that are each uniquely suited to answering diverse questions about a business. To better understand machine learning algorithms, it's helpful to separate them into groups based on how they work.
Data classification is the central data-mining technique used for sorting data, understanding of data and for performing outcome predictions. In this small blog we will use a library Smilecthat includes many methods for supervising and non-supervising data classification methods. We will make a small Python-like code using Jython top build a complex Multilayer Perceptron Neural Network for data classification. It will have large number of inputs, several outputs, and can be easily extended for cases with many hidden layers. We will write a few lines of Jython code (most of our codding will deal with how to prepare an interface for reading data, rather than with Neural Network programming).
For most purposes, whether teaching data science or dealing with a lot of real-life scenarios, this would be ok. These kinds of scenarios include the typical examples of classifying a given e-mail as Spam/Legitimate, classifying an image of a skin mole as being a melanoma/normal, or the music genre of some song playing on the radio. But what if, for example, we want to be more granular, and are trying to predict the artist playing the music? There are simply too many of them to consider at once – but at the same time, we know that there are some shared characteristics among them and that we can group them together based on those characteristics and exploit their relationships. Due to the very nature of the diagnosis process, the system is actually hierarchically organized, so that we can start by the top level and consider only around 20 major categories – and go from there, following a narrowing path of prediction.
Accurate diagnosis is essential for appropriate disease treatment. A core technique used to diagnose brain cancer today is the microscope-based analysis of tumour samples on glass slides, termed histology. However, this requires the appraisal of subtle cellular alterations, which in some cases may lead to different classifications for a given sample by different individuals. Nowadays, technological developments enable vast amounts of molecular data to be obtained and assessed for a tumour without the need for such subjective diagnostics. Machine-based-learning approaches are being developed to aid the diagnosis of clinical samples, and in a paper in Nature, Capper et al.1 report such a method for classifying brain tumours on the basis of molecular patterns.
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. In contrast to the existing text moderation service that flags profanity terms, the text classification feature helps detect potentially undesired content that may be deemed as inappropriate depending on context. In addition, to convey the likelihood of each category it may recommend a human review of the content. The text classification feature is in preview and supports the English language.
Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow color hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.
Historically, building a system that can answer natural language questions about any image has been considered a very ambitious goal. So, how many players are in the image? Well, we can count them and see that there are eleven players, since we are smart enough not to count the referee, right? Although as humans we can normally perform this task without major inconveniences, the development of a system with these capabilities has always seemed closer to science fiction than to the current possibilities of Artificial Intelligence (AI). However, with the advent of Deep Learning (DL), we have witnessed enormous research progress in Visual Question Answering (VQA), in such a way that systems capable of answering these questions are emerging with promising results. In this article I will briefly go through some of the current datasets, approaches and evaluation metrics in VQA, and on how this challenging task can be applied to real life use cases.
In the real world, many online shopping websites or service provider have single email-id where customers can send their query, concern etc. At the back-end service provider receive million of emails every week, how they can identify which email is belonged of a particular department? This paper presents an artificial neural network (ANN) model that is used to solve this problem and experiments are carried out on user personal Gmail emails datasets. This problem can be generalised as typical Text Classification or Categorization . Electronic mail or e-mail is a method of electronic communication between two or more users using the Internet.