You might remember the fist versions of assistants like siri, but you have to admit that in the last 10 years they had a big leap of development. Now not only phones have the abilities to support you daily through talking, but also speakers that can do things like playing music, teaching, shopping and other similar things. Since I needed a new music Speaker and i am a fan of simplicity and easiness I bought the 40$ Amazon Echo Dot 4 with my coupons that were left from Christmas. The day the package shipped I was nervous and happy at the same time. Since this was my first Smart Speaker, i didn't know what to expect but i was purely happy to finally receive a new gadget that could change my life for good.

If you looked up the term "artificial intelligence" on Google and found your way to this article, you've used (and hopefully benefitted from) AI. If you've ever taken an Uber or had your phone auto-correct a misspelled word, you've used AI. Although it may not always be immediately obvious, artificial intelligence impacts nearly all aspects of our lives in a nearly uncountable number of ways. In this article, we'll take a look at eight examples of how artificial intelligence saves us time, money, and energy in our everyday life. Before we can identify how artificial intelligence impacts our lives, it's helpful to know exactly what it is (and what it is not).

During a recent visit to my brother's house, my sister-in-law pointed out their new Amazon Echo Dot. "It's so cute," she said, before showing me her primary use case: "Alexa, tell me a joke!" The sound of Jimmy Fallon's voice suddenly filled the room with a corny joke that made my sister-in-law laugh as she went about her day. Later that afternoon, I was in the house alone. In the time-honored tradition of sibling pranks, I decided to ask Alexa a few precisely worded and detailed queries, asking it multiple times and in multiple ways.

Machine learning is an emerging common concept. It is along with terms like artificial intelligence and deep learning, finds its way into science and technology news. Machine Learning is the science and technique of getting computers to learn automatically. It's a form of artificial intelligence (AI) that allows computers to improve their learning as they encounter more data and act like humans. With the help of machine learning, computers can learn to make decisions and predictions without being directly programmed to do so.

Natural language processing (NLP) is with no doubt -- in my opinion -- the most famous field of data science. Over the past decade, it has gained a lot of traction "buzz" in both industry and academia. But, the truth is, NLP is not a new field at all. The human desire for computers to comprehend and understand our language has been there since the creation of computers. Yes, those old computers that could barely run multiple programs at the same time, nevertheless comprehend the complexity of natural languages! Natural language processing -- if you're new to the field -- is basically any human language, such as English, Arabic, Spanish, etc.

AI refers to a computer system that is trained to perform tasks with human-like abilities, especially cognitive abilities. The training process includes repetitive exposure to the same elements until the computer can recognize and recall those elements in future. As the computer learns, it also acquires the ability to self-correct and reason. The AI market is still growing. Predictions show that by 2026, demand for AI will reach over $300 billion.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.

One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common sense. In this paper, we propose a zero-shot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal). Our reasoner uses a state-of-the-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning. We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space. Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge. Therefore, we propose to conversationally elicit the missing knowledge from human users with our novel dynamic question generation strategy, which generates and presents contextualized queries to human users. We evaluate the model with a user study with human users that achieves a 35% higher success rate compared to SOTA.

Artificial Intelligence is no new concept. The phrase was first coined by John McCarthy in 1956[1], when he invited a group of researchers to discuss the notion of'thinking machines' during a conference at Dartmouth College. Since then, it has been a point of fascination for scientists, academics, software developers, and moviemakers alike. Fast-forward to today where you'll find lots of examples hiding in plain sight. From digital assistants like Amazon's Alexa or Apple's Siri, who use AI to learn from user interactions, to automated email responses and search engines predicting what you're looking for.

Artificial intelligence (AI) has virtually unlimited applications that are part of our everyday life. It offers countless solutions across all industries. Artificial intelligence is a major market player in the business world. AI plays a key role in data analysis, marketing, finance, business, advertising, medicine, technology, science and engineering where machines are learning from stimuli and reacting in ways more human than ever before. Artificial intelligence has several advantages and disadvantages, so it's important to know how to use it to maximize its potential within your organization.