Machine Learning (ML) is NOT the same as Artificial Intelligence. That said, the connection between the two is undoubtedly undeniable. Machine learning is part of AI in which the algorithms allow the system to locate patterns and learn the trends in the data and try to make decisions without human intervention. ML technology is evolving so rapidly that every generation is entirely different from the last. The first types of ML were just programmed to perform certain tasks in case of a specific event.

IoT and artificial intelligence are two of the major disruptive technologies that have penetrated into the health and fitness industry in recent years. People are now more concerned with their health and fitness to keep their bodies fit from many potential diseases such as diabetes, thyroid, and many more. To keep up with the huge demand, the fitness industry has started transforming traditional gyms into smart gyms by implementing AI models in gyms. People are getting attracted to smart gyms to become more fit and healthy to raise the standard of living. The fitness industry is booming in the 21st century with the help of these user-friendly disruptive technologies.

When you sit back and think about the changes that have been made in technology over the past few years, it is quite amazing. It was not that long ago that home phones were more common than mobile phones. However, now almost everyone has a mobile phone and many people do not have a landline. The same can be said for newer technologies such as Google Nest Audio or Amazon Alexa. Households that own a smart speaker state that using it has become an essential part of their day.

Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., "It doesn't look good for a date"), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., "I prefer more romantic") in order to retrieve reviews pertaining to potentially better recommendations (e.g., "Perfect for a romantic dinner"). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.

Deb Norton spends her days helping teachers in Wisconsin's Oshkosh Area school district get more comfortable with technology tools they're using to engage students. A few years ago, she started seeing increasing mentions of artificial intelligence. Around then, the International Society for Technology in Education asked her to lead a course on the uses of artificial intelligence in the K-12 classroom. She was initially intrigued when she saw students light up at the mention of artificial intelligence. It soon became clear to her that they were already experiencing AI in their daily lives, with tools like Instagram filters or chatbots on websites.

Smartphones equipped with artificial intelligence apps take note of our usage data and behavior patterns to make suggestions and automate mundane tasks. Imagine you left your smartphone at home for a day. Can you even think of going through a day without your smartphone? An ever-increasing number of people would say "No." That's how dependent we've become on smartphone technology to deliver the latest in news, entertainment, education, communication, image enhancement, and more.

Ted Lasso is quite the eligible bachelor, but he isn't on Tinder, Bumble or Hinge. He and other characters on Apple TV's latest streaming sensation are on Bantr, a fictional dating app centered on playful conversation rather than traditional selfies and swipes. And while you can't buy a piece of Bantr, you can still bet on love the Lasso Way by exploring the growing list of fringe dating apps. The sector is unique in that consumers use several apps at a time. Since January 2019, App Annie data show U.S. users have spent a collective average of 7.4 million hours on dating apps a month on Android phones alone.

The US Federal Trade Commission (FTC) warns of extortion scammers targeting the LGBTQ community via online dating apps such as Grindr and Feeld. As the FTC revealed, the fraudsters would pose as potential romantic partners on LGBTQ dating apps, sending explicit photos and asking their targets to reciprocate. If they fall for the scammers' tricks, the victims will be blackmailed to pay a ransom, usually in gift cards, under the threat of leaking the shared sexual imagery with their family, friends, or employers. "To make their threats more credible, these scammers will tell you the names of exactly who they plan to contact if you don't pay up. This is information scammers can find online by using your phone number or your social media profile," the FTC said.

Data is the lifeblood of business – it drives innovation and enhances competitiveness. However, its importance was brought to the fore by the pandemic as lockdowns and social distancing drove digital transformation like never before. Forward-thinking businesses have started to grasp the importance of their data; they understand the consequences of not fully mobilizing it, but many are sat at the start of their journey. Even the best organizations are failing to extract the maximum benefits from their data while keeping it safe. This is where artificial intelligence (AI) comes into play – it can benefit enterprises with their data in three fundamental ways.

AI has become intertwined with every aspect of our lives. Each one of us is currently using this technology in one form or the other. From personal digital assistants like Siri, google assistant, Alexa, to self-driving cars, it's being used very widely. The use is increasing on a daily basis in fast growing sectors such as healthcare, finance, e-commerce, and manufacturing. Also, businesses like Swiggy and Zomato, which have invested heavily in AI over the past couple of years, have witnessed the power of technology to both sustain and increase growth.