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) …
While the economy has shaken worldwide due to COVID-19 pandemic, the demand for customer support is at an all-time high. B2B companies are somewhat better off, but only for now. Today's consumer sentiment will become tomorrow's enterprise sentiment. Sales strategies have to be reimagined for a world where travel and meetings are off the table. Customer support is hard to be redesigned to be not dependent on people coming to call-centres.
In my previous article, I've been analyzing text reviews of women clothing online purchases in order to extrapolate the sentiment of customers. The idea was to investigate whether the sentiment was consistent with the purchase recommendation. In this article, I'm going to keep analyzing the text review, but now focusing on the rating column, which indicates a score from 1 (worst) to 5 (best) attributed to the item. Let's refresh which is the dataset we are talking about (you can find it on Kaggle): For this purpose, we are going to build a Neural Network using Keras (with Tensorflow embedded). Before starting, we need to do some further preprocessing of our dataset.
Natural Language Processing (NPL) is a field of Artificial Intelligence whose purpose is finding computational methods to interpret human language as it is spoken or written. The idea of NLP goes beyond a mere classification task that could be carried on by ML algorithms or Deep Learning NNs. Indeed, NLP is about interpretation: you want to train your model not only to detect frequent words, but also to count them or to eliminate some noisy punctuations; you want it to tell you whether the mood of the conversation is positive or negative, whether the content of an e-mail is mere publicity or something important, whether the reviews about thriller books in last years have been good or bad. The good news is that, for NLP, we are provided with interesting libraries, available in Python, that offer a pre-trained model able to inquire about written text. Among those, I'm gonna be using Spacy and NLTK.
Researchers using artificial intelligence to grade decades of conservation efforts have determined we're getting better at reintroducing once-endangered species to the wild. In their study published Thursday in the journal Patterns, the researchers analyzed the abstracts of more than 4,000 studies of species reintroduction across four decades and found that we're generally improving in our conservation efforts. The authors hope that machine learning could be used in this field, as well as others, to discover the best techniques and solutions from the ever-growing plethora of scientific research. "We wanted to learn some lessons from the vast body of conservation biology literature on reintroduction programs that we could use here in California as we try to put sea otters back into places they haven't roamed for decades," said senior author Kyle Van Houtan, chief scientist at Monterey Bay Aquarium in California. "But what sat in front of us was millions of words and thousands of manuscripts. We wondered how we could extract data from them that we could actually analyze, and so we turned to natural language processing."
Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r .7,
A recent article in the Financial Times argued -- fairly -- that despite the billions of dollars poured into "AI" companies, investors have, on the whole, not seen returns consistent with the hype. There are exceptions of course, but, by and large, the promise(s) appear to have not been met, as of yet. The argument was not simply a lamentation, however, with the author suggesting that the next wave of focused AI solutions might indeed generate better results and returns. Such a sentiment is not uncommon in technology. In order to garner investment, entrepreneurs employ hyperbolic language to excite potential investors and the business press follows this lead in order to ensure that they don't miss out on the appearance of prescience.
As you may have guessed, current laws that govern simple systems (i.e. Newtonian physics) allow for the observation of independent variables. That is, if you were to observe a variable in this so-called simple system, information isn't lost from other variables in the system of observation. However, this independence becomes difficult to observe accurately as we increase the number of variables, and where we find existing relationships between variables. These variables, that usually come in clusters or systems, which are inter-dependent are the units which make up a complex system.
We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. In PN, emotion carriers are the speech or text segments that best explain the emotional state of the user. Such segments may include entities, verb or noun phrases. Advanced automatic understanding of PNs requires not only the prediction of the user emotional state but also to identify which events (e.g. "the loss of relative" or "the visit of grandpa") or people ( e.g. "the old group of high school mates") carry the emotion manifested during the personal recollection. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives. Compared to other text genres such as news and microblogs, spoken PNs are particularly challenging because a narrative is usually unstructured, involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. In this work, we experiment with annotating emotion carriers from speech transcriptions in the Ulm State-of-Mind in Speech (USoMS) corpus, a dataset of German PNs. We believe this resource could be used for experiments in the automatic extraction of emotion carriers from PN, a task that could provide further advancements in narrative understanding.
There are a variety of tools that can help researchers analyze large volumes of written material. In this post, I'll examine two of these tools: part-of-speech tagging and tone analysis. I'll also show how to use these methods to find patterns in a large set of Facebook posts created by members of Congress. Part-of-speech (POS) tagging is a process that labels each word in a sentence with an algorithm's best guess for the word's part of speech (for example, noun, adjective or verb). This is based on both the definition of each word and the context in which it appears.