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) …
Artificial Intelligence (AI) is essentially a technique used to enable computers to'mimic' human behaviour. Some even see it as synonymous with an exact replica of the human thought process. However, when it is used in marketing we're not necessarily talking about actual artificial intelligence, but rather applications of the sort, including machine learning and natural language processing. AI presents myriad opportunities for businesses that use large sums of data. Applications such as machine learning, for example, enable businesses to uncover particular insights within their datasets and clearly see patterns in that data.
Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.
Back in 2006 when I was competing to secure my place on the U.S. Olympic figure skating team, I often found my body working faster than my brain could think. I would spin at nearly 300 rotations per minute, feeling each of my 650-plus muscles engage. I remember wondering how I could overcome the inefficiencies of being human: pain, fear and disorientation. Apparently, I was asking the same question as the ancient Greeks, who envisioned thinking machines capable of out-performing the human brain. While the pursuit of machines with human-like intelligence is an ancient one, it wasn't until recently that artificial intelligence was actually possible.
KARACHI: Pakistan has introduced the first-ever business robot journalist, Dante, who writes and publishes a comprehensive report on stocks traded at the Pakistan Stock Exchange (PSX) within a few seconds after the market's closure. "The Pakistan Stock Exchange is closed at 3:30pm (from Monday to Thursday and at 4:30pm on Friday) and it gets the daily report published before it is 3:31pm," said the award-winning tech startup, baseH Technologies, Founder and CEO Anisuddin Sheikh. TheRoboJournalist, also called Dante, had been writing the reports for the past few days, he said at a signing ceremony to get seed money from the Elahi Group of Companies for the project at the National Incubation Centre (NIC) at NED University on Saturday. Simultaneously, the artificial intelligence (AI)-based content writing software develops a video on share trading and gets it uploaded at YouTube, Sheikh said. The robot journalist is capable of doing sports and weather reporting as well.
Watch out, ghostwriters: Artificial intelligence (AI) is coming for you. In a paper accepted at the NeurIPS 2018 conference in Montreal ("Content preserving text generation with attribute controls"), data scientists from the University of Michigan and Google Brain describe a machine learning architecture that's capable of not only generating sentences from a given sample, but changing the mood, complexity, tense, or even voice of the original text while preserving its meaning. This might one day be used for paraphrasing, the team posits, or machine translation and conversational systems. And it could complement systems like those demonstrated by Microsoft Research in November, which leverage sophisticated natural language processing techniques to reason about relationships in weakly structured text. "In this work, we address the problem of modifying textual attributes of sentences," the researchers wrote.
Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the `persona' information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.
It's common knowledge that neural networks are really good at one narrow task, but they fail at handling multiple tasks. This is unlike the human brain which is able to use the same concepts at amazingly diverse tasks. For example, if you have never seen a fractal before and I show you one right now. After seeing the image of a fractal, you'll be able to handle multiple tasks related to it: How are you able to do all these tasks? Are there dedicated neural networks in your brain specializing in all these tasks?
It has come to Netflix's attention (perhaps a long time back) that its subscribers have been sharing their Netflix credentials with their loved ones. As harmless as this may seem on paper, Netflix reports the magnitude of revenue deficits it is experiencing because of this common practice. Netflix is the United States' most popular paid video streaming channel. Globally, 37% of the population that uses the internet is connected to Netflix in one way or the other. Netflix is a behemoth and is known to heavily invest in the production of its original series.
In 2019 we live in a world where Deepfake videos and even images of people can be created by AI that are totally manufactured. Meanwhile OpenAI's GPT-2 is being hyped for its ability to write convincingly and deceptively. In this new world AI can mimic human content in a variety of ways while potentially being used by bad actors and state-sponsored propaganda campaigns to influence public sentiment in a variety of ways. In February 2019, we are witnessing an explosion of deception online. In an era when even Facebook refuses to be called a Media company, what exactly are deepfakes?
Evolving needs and investors behaviors, combined with the advancement of technology, have created opportunities for traditional players and new disruptors to reshaping the wealth management industry. In recent years, companies have arrived on the scene offering wealth management solutions leveraging technologies such as artificial intelligence (AI), big data and cloud computing. This breed of fintech companies, referred to as wealthtechs, is on the rise around the world. Switzerland's Sentifi was founded in 2012 with the goal of improving the information people all over the world use to make investment decisions. Sentifi uses AI and machine learning to analyze alternative data, classify it, rank the sources and link the results to over 50,000 global companies, commodities and currencies.