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 risks and benefits of artificial intelligence? It's a complicated topic, but I'll try to unpack a few key points here. Let's start with a quick definition: AI is the simulation of human intelligence by machines. Example of AI systems used regularly in developed countries include Amazon's Alexa, smart replies in Gmail, Chatbots, predictive searches in Google, and recommendations. At a baseline level, AI helps improve our everyday lives by solving pain points, streamlining processes, and advancing human knowledge.
What are the differences between econometrics, statistics, and machine learning? I discovered this myself a couple years ago, through an analysis of the economics literature that required the research team to classify articles into economics fields (like labor and macro) and research styles (like theory and econometrics). The project was motivated by frustration with complaints lodged against academic economics in the wake of the Great Recession (perhaps you've seen the movie version: Inside Job). I thought: "What's with all the whining? "Economics has never been better!"
What will data science jobs look like in the future? I'll get into my response, but will caveat it by saying that, given this is a projection into the future, I am likely to be wrong. Hopefully the response will be somewhat thought-provoking, though. As a direct outcome of #1 and #3, much of the work that data scientists are doing today will ultimately be transferred to less highly trained workers who have sufficient coding and statistics exposure to effectively use robust packages and technologies and build machine learning models. We are not at that point yet because many academic programs have not fully caught up to driving their students to develop appropriate technical skills, but I can see this being the norm within the next 5 years - there will simply be a higher volume of graduates coming out of undergraduate and master's programs capable of applying machine learning without being experts in the field.
Actually, whatever your job is, Laurent Alexandre once said "If you AI AI, you will soon be unemployed". So if you are NOT complementary to an AI ... So far, these are the best public datasets to feed your ML, DL, AI models. Google and NASA's Quantum Artificial Intelligence Lab appears to be Google and NASA's answer to your question. Software engineer by day and a geek by night, I've made a not-few projects using various programming languages mainly Java & JS). Just start and learn things as long as you build your apps.
How does AI contribute to digital forensics? Algorithms already play a significant role in helping digital forensics investigators analyze the vast amount of data that is created by mobile devices and stored on the cloud. Like many industries, demand outstrips supply when it comes to qualified, trained professionals who can sift through the backlog of digital forensics data relevant to modern criminal cases. Artificial Intelligence (AI) can help automate some processes and more quickly flag content or insights that would otherwise take investigators longer to uncover. It's a bit like the show Mindhunter on Netflix, which tells the story of the first psychological profiling that helped coin the term "serial killer" and establish a new methodology for tracking criminals and identifying behavioral patterns early on.
What roles will human workers play in the AI economy of the future? The question is no longer if AI can coexist with humans, it's now a question of how (and what) they can achieve together. As we assess the economy of the future, it's important to understand the value people bring to AI – and that's data. So first of all, creating royalties and residuals based on the value and information that people are adding to the AI – paying them for their data, information, knowledge, and expertise – is the fundamental shift needed to account for how people will work alongside AI in the economy of the future. Otherwise, the economy will be split between those who are working with AI, and those who are not, and it's imperative our economic models avoid that.
How should modern enterprises go about implementing artificial intelligence? Enterprises in every industry would want to adopt AI. I've yet to speak with an executive who hasn't considered how AI could impact their team or company. They know it can deliver unparalleled operational efficiency, enable new business models, delight customers, and ultimately drive their bottom line. Despite this, only 30% of enterprises report piloting an AI project.
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).