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) …
Data scientists are one of the most hirable specialists today, but it's not so easy to enter this profession without a "Projects" field in your resume. Furthermore, you need the experience to get the job, and you need the job to get the experience. Seems like a vicious circle, right? Also, the great advantage of data science projects is that each of them is a full-stack data science problem. Additionally, this means that you need to formulate the problem, design the solution, find the data, master the technology, build a machine learning model, evaluate the quality, and maybe wrap it into a simple UI.
How has machine learning and AI influenced game design? Games are more addictive today than ever, and we have machine learning to thank for a lot of that. When most people put "AI" and "games" together they tend to think "bots" or some other form of computer-controlled character (NPC). But the reality is that machine learning is virtually unused for such "AIs". As I've discussed in other answers , it's just too expensive and doesn't allow for the degree of control required by the design team.
Numeric representation of Text documents is challenging task in machine learning and there are different ways there to create the numerical features for texts such as vector representation using Bag of Words, Tf-IDF etc.I am not going in detail what are the advantages of one over the other or which is the best one to use in which case. There are lot of good reads available to explain this. It's a Model to create the word embeddings, where it takes input as a large corpus of text and produces a vector space typically of several hundred dimesions. The underlying assumption of Word2Vec is that two words sharing similar contexts also share a similar meaning and consequently a similar vector representation from the model. For instance: "Bank", "money" and "accounts" are often used in similar situations, with similar surrounding words like "dollar", "loan" or "credit", and according to Word2Vec they will therefore share a similar vector representation.
One of the impressive examples of AI in healthcare are virtual nurses, a solution which, according to Syneos Health Communications survey, is already an acceptable option for over 60% of surveyed patients. Even though patients still show some concerns regarding the possible lack of human oversight, the benefits of 24/7 access to medical support and non-stop monitoring of their health are solid arguments in favor of AI-enhanced care. The increasing use of virtual care can lead to a significant decrease in ER visits, which translates into reducing workload and on-the-spot personnel shortages. The Gartner Top Strategic Predictions for 2019 and Beyond report predicts that in 4 years, AI-enhanced virtual care of chronically ill patients can result in cutting down the number of ER visits by 20 million.
Would people who are strong in math be good in machine learning? Certainly having a strong background in mathematics will make it easier to understand machine learning at a conceptual level. When someone introduces you to the inference function in logistic regression, you'll say, "Hey, that's just linear algebra!" But surely deep learning must be something new? Not harder, just more (thank God for automatic differentiation).
A chatbot is a service, powered by rules and artificial intelligence, that interact with people via a chat interface and can perform relevant tasks. A chatbot can carry on a conversation long enough for a live agent, by having a chatbot, it can ask those mundane questions at the beginning of every support inquiry that helps identify the customer and the issue they're having. They bring 24/7 instant support, so always will be there and ready to provide instant support. Also, is capable of help to resolve any issue.
What were the most significant machine learning/AI advances in 2018? Let's look at all of this in some more detail. If 2017 was probably the cusp of AI fear mongering and hype (as I mentioned in last year's answer), 2018 seems to have been the year where we have started to all cool down a bit. While it is true that some figures have continued to push their message of AI fear, they have probably been too busy with other issues to make of this an important point of their agenda. At the same time, it seems like the press and others have come to peace with the idea that while self-driving cars and similar technologies are coming our way, they won't happen tomorrow.