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 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.
This is the capacity is there for it to alternate the style our organization prevail. All it hold on to begin is a straight forward face to consist of latest opportunities anywhere and each time available. This is pushing the limits of the computer (Machine) authorize ability. This flows torrent generation enables systems to works with a diploma of autonomy, the ensuing useless execution of iterative obligations. Even it campaign down the time taken to perform work.
The likes of Uber and Ola are already using machine learning to connect riders with ride providers, and they are providing excellent services like minimal waiting time, predictable fares, predictable time to reach the destination, driver verification etc. Many of these thing were not possible in traditional businesses at the price point available now.
It is actually a very long answer. Artificial Intelligence field has a very wide scope in computation and automation world. AI research has been happening since 1950 with limited success. Now, with the significant advancements of memory, storage capacity and computational power available, we have started seeing practical, real-life examples of AI. AI is applicable in almost every type of industry vertical one comes across.
What jobs will AI probably not destroy? The jobs that are most susceptible to automation in the near term are those that are fundamentally routine or predictable in nature. If you have a boring job--where you come to work and do the same kinds of things again and again, you should probably worry. The tasks within jobs like this are likely to be encapsulated in the data that is collected by organizations. So it may only be a matter of time before a powerful machine learning algorithm comes along that can automate much of this work.
Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares the performance of deep neural networks and gradient tree boosting, and explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using three heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and academic English Stack Exchange. Ultimately, our study exposes the alternative hypothesis that the meaning of a "duplicate" is not inherently general-purpose, but rather is dependent on the domain of learning, hence reducing the chance of transfer learning through adapting to the domain.
What will be the next thing to revolutionize data science in 2019? Reinforcement learning will be the next big thing in data science in 2019. While RL has been around for a long time in academia, it has hardly seen any industry adoption at all. Why? Partly because there have been plenty of low-hanging fruits to pick in predictive analytics, but mostly because of the barriers in implementation, knowledge and available tools. The potential value in using RL in proactive analytics and AI is enormous, but it also demands a greater skillset to master.
In what ways is artificial intelligence leveraged to harm people? Currently most AI is used for purposes that are either beneficial to people, or not actively harmful. But any powerful tool can be abused, and we should expect increasing weaponization of AI. Social manipulation: Companies and organizations use big data to spread propaganda, influence elections and foment social discord via targeted marketing. Political actors like Cambridge Analytica and the Internet Research Agency have been prominent in this.
Why call it machine learning when it has nothing to do with thinking machines? Machine learning may not have much to do with thinking machines today, but that wasn't always the case. It's important to realize that when the terms "Machine Learning" and "Artificial Intelligence" were coined in the late 50s, the goal was very much to create thinking machines. They not only thought what we now call "Artificial General Intelligence" was possible, but that it was bound to happen within a matter of decades if not years. And furthermore, they thought that the various things they were exploring -- like logical programming and machine learning -- would be enough to get them there.