With Christmas right around the corner, Santa is loading up his sleigh and getting ready to make his gift-giving trek around the world. Waiting for Santa's arrival is easier said than done (especially for kids). However, you and your family can enjoy holiday fun together by using smart assistants like Amazon Alexa and Google Assistant to track Santa's location. A smart display, like the Google Nest Hub (second-gen) or the Amazon Echo Show 8 (second-gen), provides the most engaging Santa tracking experience with fun on-screen holiday visuals for both kids and adults. You can also keep tabs on Santa's arrival using a smart speaker like Google's Nest Audio or Amazon's Echo Dot, or by using the Amazon Alexa or Google Assistant app.

Artificial intelligence software is software that uses AI technology and special algorithms to automate different tasks. Companies save time and effort by automating their business processes and enabling their employees to work more productively. A variety of AI software tools are used to make the business process much simpler and more effective. For example, software that recommends products to users in an e-commerce shop, chatbots, software that automates content marketing creation, tools that predict sales, etc. AI is widely used in product recommendation systems. These are systems that suggest products or information to users based on special data analysis methods.

Siri, Apple's iconic AI virtual assistant, appeared to not be satisfied with Sen. Ted Cruz's attacks on Big Tech while he sat down with Fox News for an interview. Cruz, R-Texas, who was among several Republican lawmakers who spoke at Turning Point USA's AmericaFest in Phoenix this week, declared Big Tech "the single greatest threat to free speech" and "free and fair elections in America." "Big Tech, they are hard left. They're to the left of the Democratic Party. And they're trying to drive the Democratic Party left," Cruz told Fox News Digital on Monday.

Before learning about conversational AI terminology let us see what is AI stands for? AI means artificial intelligence is the ability of a computer to perform all the tasks which require human intelligence for execution. Conversational AI is the latest software that helps in chatting humans and computers and makes them work together designed in such a way that is more accurate in prediction for complex and predefined processes. Conversational AI gets connected with humans in two ways: human-to-machine interaction and human-to-human interactions. Conversation AI is the quickest and reliable software acting using AI assistant tools used for retrieving the previous data or chats stored and implemented for further required actions increasing the efficiency of the processes compared to human actions.

There are many sources which give answers to the question, "What is AI?". By the 1950's, there were many scientists, mathematicians and philosophers that were looking into the concept of Artificial Intelligence. One such person was Alan Turing, who to this day is considered by many to be the Father of AI. He formed the idea and mathematical and logical reasoning behind the concept of machine intelligence where machines and computers would be able to replicate the behavior of humans and their intelligence. Fast forward 70 years into the future and we are now in a world where computers are able to converse with humans, albeit with limitations, but this is the progress we see as our world progresses to a more sophisticated AI.

Artificial Intelligence shapes a lot of things we do in our day-to-day lives. The Netflix show you're binge-watching while on quarantine, the compulsive purchases you make on Amazon, and even the things you search on the internet come to us courtesy of AI. Investments in AI and its key subset – machine learning, are increasing more than ever. The total global investments by private businesses on AI accumulated to a total of $70 Billion in 2020. A survey by McKinsey reported that 82% of enterprises using AI and machine learning across their organizational activities have received a significant return on investment.

It is no surprise to a millennial that today, Alexa, Siri, or Google answers most of their tantalising questions the very next second after their question is presented to the machine/device. While to people of older generations, it is definitely a hard fact to get used to. Someone once said "Artificial Intelligence is no match for natural stupidity". While AI may seem like a very perfect alternative that won't make any errors in decisions making, the'what-if' aspect of it is dead since we only find that in human errors (not implying that human errors are needed). Machines run by computed decision making systems would never stop to think, or analyse.

As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and healthcare, full automation is often not desirable due to safety, ethical, and legal concerns, yet fully manual approaches can be inaccurate and time consuming. As a result, there is growing interest in the research community to augment human decision making with AI assistance. Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions. To invite and help structure research efforts towards a science of understanding and improving human-AI decision making, we survey recent literature of empirical human-subject studies on this topic. We summarize the study design choices made in over 100 papers in three important aspects: (1) decision tasks, (2) AI models and AI assistance elements, and (3) evaluation metrics. For each aspect, we summarize current trends, discuss gaps in current practices of the field, and make a list of recommendations for future research. Our survey highlights the need to develop common frameworks to account for the design and research spaces of human-AI decision making, so that researchers can make rigorous choices in study design, and the research community can build on each other's work and produce generalizable scientific knowledge. We also hope this survey will serve as a bridge for HCI and AI communities to work together to mutually shape the empirical science and computational technologies for human-AI decision making.

In the implicit feedback recommendation, incorporating short-term preference into recommender systems has attracted increasing attention in recent years. However, unexpected behaviors in historical interactions like clicking some items by accident don't well reflect users' inherent preferences. Existing studies fail to model the effects of unexpected behaviors, thus achieve inferior recommendation performance. In this paper, we propose a Multi-Preferences Model (MPM) to eliminate the effects of unexpected behaviors. MPM first extracts the users' instant preferences from their recent historical interactions by a fine-grained preference module. Then an unexpected-behaviors detector is trained to judge whether these instant preferences are biased by unexpected behaviors. We also integrate user's general preference in MPM. Finally, an output module is performed to eliminate the effects of unexpected behaviors and integrates all the information to make a final recommendation. We conduct extensive experiments on two datasets of a movie and an e-retailing, demonstrating significant improvements in our model over the state-of-the-art methods. The experimental results show that MPM gets a massive improvement in HR@10 and NDCG@10, which relatively increased by 3.643% and 4.107% compare with AttRec model on average. We publish our code at https://github.com/chenjie04/MPM/.

It was in 1956 that the term Artificial Intelligence (AI) was coined by John McCarthy, Marvin Minsky and Claude Shannon at the Dartmouth Conference, a conference where predictions so optimistic were made that they never came true. Today, having vastly surpassed those forecasts, AI is an indispensable part of our world. The applications of Artificial Intelligence are increasingly numerous and surprising, ranging from sophisticated recommendation algorithms for online shopping to improved diagnosis and treatment of diseases. Innovation in this field is advancing by leaps and bounds, making it possible for us to live in an increasingly automated society. A clear example of the use of AI in the home is virtual assistants, which have become a well-known success story: Aura (Telefónica), Alexa (Amazon) Cortana (Microsoft) and Siri (Apple) are spectacular technological developments.