Welcome to the first course in Term 2 as part of the series "MBA in Artificial Intelligence Digital Marketing". Artificial Intelligence (AI) seems to be a unique technology of making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining, and functioning when it is trying to solve problems. These kinds of problems are present in all fields, the most emerging ones, and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning, and problems along with the ability to manipulate things.

Version control tracks and manages changes in a collection of related entities. It records changes and modifications over time, so you can recall, revert, compare, reference, and restore anything you want. Version control is also known as source control or revision control. Each version is associated with a timestamp, and the ID of the person making the changes in documents, computer programs, files, etc. Version control prevents conflicts in concurrent work, and enables a platform for better decision-making and fostering compatibility. Version Control Systems (VCM) run as stand-alone software tools that implement a systematic approach to track, record, and manage changes made to a codebase. In this article, we're going to explore what version control means from different perspectives. This version control system consists of a local database on your computer that stores every file change as a patch (difference between files in a unique format).

NVIDIA has been steadily advancing its AI assistant technology in recent months, and now it's clear just how all the pieces fit together. The company has introduced Omniverse Avatar (for 3D assistant creation) and Riva (custom AI voice creation) platforms that, combined, lead to surprisingly realistic virtual personas with relatively little effort -- or, in one case, deliberately unrealistic. In one demo, used to highlight NVIDIA's AI-powered Maxine toolkit, the company created an Omniverse Avatar from a woman's photo and used Riva to train the voice based on that woman, convert text to speech and translate to different languages. The digital stand-in looks and sounds much like the real person (aside from a couple of stiff-sounding translations), and can even turn its head while maintaining natural-looking eye contact. As you might imagine, this could lead to more relatable virtual helpers at kiosks and websites.

Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage and act on them. These digital tools -- such as task management applications -- provide a unique opportunity to study and understand tasks and their connection to the real world, and through intelligent assistance, help people be more productive. By logging signals such as text, timestamp information, and social connectivity graphs, an increasingly rich and detailed picture of how tasks are created and organized, what makes them important, and who acts on them, can be progressively developed. Yet the context around actual task completion remains fuzzy, due to the basic disconnect between actions taken in the real world and telemetry recorded in the digital world. Thus, in this paper we compile and release a novel, real-life, large-scale dataset called MS-LaTTE that captures two core aspects of the context surrounding task completion: location and time. We describe our annotation framework and conduct a number of analyses on the data that were collected, demonstrating that it captures intuitive contextual properties for common tasks. Finally, we test the dataset on the two problems of predicting spatial and temporal task co-occurrence, concluding that predictors for co-location and co-time are both learnable, with a BERT fine-tuned model outperforming several other baselines. The MS-LaTTE dataset provides an opportunity to tackle many new modeling challenges in contextual task understanding and we hope that its release will spur future research in task intelligence more broadly.

Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs and preferences of individual users. In conversational recommendation approaches, these needs and preferences are acquired by the system in an interactive, multi-turn dialog. A common approach in the literature to drive such dialogs is to incrementally ask users about their preferences regarding desired and undesired item features or regarding individual items. A central research goal in this context is efficiency, evaluated with respect to the number of required interactions until a satisfying item is found. This is usually accomplished by making inferences about the best next question to ask to the user. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions is better than another one in a given application. With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation. This model, which is designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, in particular with respect to the computational complexity of devising optimal interaction strategies. Through such a theoretical analysis we show that finding an efficient conversational strategy is NP-hard, and in PSPACE in general, but for particular kinds of catalogs the upper bound lowers to POLYLOGSPACE. From a practical point of view, this result implies that catalog characteristics can strongly influence the efficiency of individual conversational strategies and should therefore be considered when designing new strategies. A preliminary empirical analysis on datasets derived from a real-world one aligns with our findings.

WhatsApp is the new buzzword for digital conversations as the post-pandemic world gets more digital-savvy and contactless shopping emerges as the new trend. According to a Statista report, WhatsApp is the most popular messaging platform with more than 2 billion active users outranking Facebook Messenger at 1.3 billion and WeChat at 1.2 billion users. Not only this, it has become the world's third-largest social networking platform after Facebook and YouTube. Given the popularity and demand of this new messaging channel, businesses too have started to turn their ships, the WhatsApp way. Also, recent innovations in artificial intelligence (AI) and machine learning (ML) make WhatsApp more profitable for businesses.

Every week, I help people like you on my national radio show with their technology or digital life issues. Sometimes, the answer is simple. I recommend a great way to get something done online, give a shopping recommendation, or share my tech wisdom. Other times, the issue is harder to pinpoint. Here's a common question I get: "A friend called and said they got a strange email from me that I don't remember sending.

Artificial intelligence (AI) is all around us. It is playing a vital role from Facebook News Feed to Google Search. Despite the technology's relative novelty, the general public as well as businesses have already witnessed numerous applications of AI and are, by now, convinced about AI's ability to replicate human thought and assist in performing cognitive and creative tasks. Artificial intelligence has quickly grown from being a distant hope to a casual part of the present reality. Computer programs capable of performing human-like cognitive and computational tasks without human intervention are rapidly growing in capability as well as ubiquity.

"I'm increasingly coming to terms with the fact that I may never get married," said Mustafa, a 34-year-old Black Muslim man who asked that we not use his real name. He has been on two dates with women he met on dating apps in the past year – and they left him feeling fatigued and doubtful that he would ever find a genuine connection with someone. He had turned to the apps, he said, because, there is no dating scene in his British-Somali community. But, he lamented, "it's really hard to find someone. This is not how Mustafa imagined his life would be in his mid-thirties. When he was younger, he pictured himself as a devoted husband and loving father to a couple of children by now. In this mental image of familial bliss, he was also living in a picturesque cottage in the English countryside complete with "a lake or something". Instead, he recently celebrated his 34th birthday single and living in a flat overlooking the Wembley Stadium arch in North West London. But, he added with a shrug, "I've started learning how to cycle." Discussing his hobbies and interests – cycling, reading, writing – he sounds more optimistic. He has directed his energy away from the fickle and unpredictable pursuit of love and towards those variables of his life he can control, like picking up new pastimes. 'All they see is a Black guy' Although the United Kingdom's Black Muslim community is culturally diverse, including people from a wide range of African and Caribbean backgrounds, it only comprises 10 percent of the UK's Muslim population. This can make dating or finding a marriage partner particularly difficult. A recent survey by Muzmatch, a Muslim-specific dating app that has been heralded for helping 20,000 Muslims meet and marry since its launch in 2015, revealed the challenges faced by Black Muslims dating in the UK. Muzmatch asked 471 of their members from different ethnic groups if they felt that race and ethnicity affected the matches they received and whether they had negative experiences as a result of this. In their answers, Black users pointed to a range of issues – including fetishisation, colourism and discrimination. Most of the Black women surveyed complained about being fetishised and branded "exotic". One West African woman described how dark-skinned women were considered unattractive and how she had been called the n-word by one user. A Sudanese man expressed concern that he was matched with women with similar interests to him who subsequently rejected him because their family wouldn't accept him. "It doesn't matter if you're on your deen and have a successful career.

"What projects can I do with machine learning?" We often get asked this question a lot from beginners getting started with machine learning. ProjectPro industry experts recommend that you explore some exciting, cool, fun, and easy machine learning project ideas across diverse business domains to get hands-on experience on the machine learning skills you've learned.