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) …
What are the biggest machine learning trends of 2019 so far, and where are we heading? The most notable trend right now in machine learning is the rapid growth in machine learning developer tooling and how that changes the process of building, deploying and managing machine learning. On one end of the spectrum, we have the growth of AutoML like tools which provides powerful machine learning models as a plug and play solution without the need for deep machine learning expertise. This would rapidly bring the power of machine learning to more and more industries. On the other end of the spectrum, there are numerous tools and products that standardize and provide powerful abstractions for different aspects of machine learning development that lets the data scientists to focus exclusively on their core competencies.
What are the most significant AI advances we'll see over the next few decades? The AI revolution - and yes, I do think it's safe to call it a revolution - is only in its infancy. We've seen some exciting applications of artificial intelligence in the last few years in areas like natural language processing, image recognition, and process automation. In fact, these advances have helped our own company, ThoughtSpot, bring artificial intelligence to the domain of analytics. While these advances have been exciting for those of us in the industry, there remains a disconnect between our enthusiasm and the impact society at large is experiencing as a result of AI.
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).
How can startups compete in Deep Learning when the tech giants (Google, Amazon, Baidu, Microsoft, Apple) have so much more data? There is this narrative out there that it is "all about the data," "whoever has the most data wins…" I may have subscribed to that theory in the past but I now think it is largely wrong, and may have been perpetuated by the big companies to tout their advantage. Deep Learning tools and frameworks are so nascent, and the skill set so rare, that meaningfully better algorithms are possible, and make a huge difference. For example, I believe Blue Hexagon has those. When there are literally 100's of thousands of new malware variants PER DAY, it just makes sense that IF you could get neural networks to analyze the traffic, at line speed, it would be order of magnitude better than the current signature and sandbox based approaches.
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.