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) …
In this blog we are going to see how Expectation-maximization algorithm works very closely. This blog is in strict continuation of the previous blog. Previously we saw how probabilistic clustering ended up into chicken-egg problem. That is, if we have distribution of latent variable, we can compute the parameters of the clusters and vice-versa. To understand how the entire approach works we need to learn few mathematical tools, namely: Jensen's inequality and KL divergence.
I was talking with a senior banker, who told me that he was in charge of the Artificial Intelligence program in the bank. I was impressed as he is part of the executive leadership team of the bank and not the CIO. We have invested widely across the board in AI, he told me, and achieved great results. Sure, I hear that a lot. How many people have you laid off as a result?
For most companies, the prevailing Conversational AI strategy has been to focus on relatively narrow use cases addressing customer experience and internal efficiency through voice-based virtual agents, chatbots and digital assistants. This thinking can certainly yield positive results, such as the development of best practices and plans to further operationalize, scale and accelerate these solutions across the organization. Businesses are realizing even greater results when conversational AI is deployed, not as a discrete, stand-alone system but instead applied with other powerful technology approaches, such as RPA, IoT and the emerging area of evolutionary computation. At present, most companies that have deployed Conversational AI solutions are satisfied with their results. Leaders across industries are seeking the same goals, whether they're focusing on web,contact center, mobile apps, smart speakers, messaging or, increasingly, all of the above.
Hello Guys, This blog contains all you need to know about regularization. This blog is all about mathematical intuition behind regularization and its Implementation in python.This blog is intended specially for newbies who are finding regularization difficult to digest. For any machine learning enthusiast, understanding the mathematical intuition and background working is more important then just implementing the model. I am new to world of blogging so If anyone encounters any problem whether conceptual or language-related please comment below. Back in the days, when I came across regularization it became difficult for me to to get mathematical intuition behind it.
Technology is having an impact on every aspect of our lives, and no less in our job search. Tech is not just changing the way I, as a recruiter, find the best candidates for a role but also how the candidate searches for their next opportunity. We often see the positives of technology and how it has made our lives that bit easier, the tech used in job searching is often seen as a positive, but can it also have a negative impact on the way we now search for jobs? Everything is done digitally now, from searching for a role to applying for it, some companies are even taking it as far as pre-screening through the use of a chatbot. While this can save time and be more convenient it does make me question the personal side of recruitment.
AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that's not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking. Let's go by the stats to see how AI and ML will fare in the global market or is there a scope for AI and ML in the near future. As per stats by The Motley Fool, The AI market will grow to a $5.05 billion dollar industry by 2020. Such predominant stats have reassured the assurance in the cascading power of these intimidating technologies.
In this SAS How To Tutorial, Ari Zitin explores some machine learning fundamentals by digging into details on decision tree and neural network models. Ari walks through several examples of machine learning tasks and gives a focused explanation using HMEQ banking data, that's linked to below. You'll note that Ari uses SAS Model Studio to explain the different machine learning algorithms. Content Outline 00:16 – Introduction to Machine Learning and Examples Covers in this Video 01:30 – Example Introduction – Loan Default Prediction Using Historical Banking Data 03:07 – Machine Learning Decision Tree Example 10:56 – Machine Learning Neural Network Example 16:32 – Comparison of the Results for the Two Different Machine Learning Models Related Content Download the HMEQ data set that Ari uses – http://support.sas.com/documentation/... Video: Getting started in SAS Model Studio – https://video.sas.com/detail/videos/m... Video: Variable selection node in SAS Model Studio – https://video.sas.com/detail/videos/m... Learn more about SAS Software Which machine learning algorithm should I use? A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident.
When people think of tech in media, their first thoughts are likely around digital platforms that bring more content to our fingertips. Now, leading innovations -– namely, AI and blockchain -- are enabling media companies to take this a step further by delivering even more compelling content to broader audiences. AI is being used to watch and understand live video in real-time to provide insights that enhance the viewer experience. Technologies such as computer vision and image recognition can tag video based on different characteristics. There is no better example of using AI to create dynamic experiences than in sports, where AI can identify exciting plays by evaluating players' actions or an audience's reaction.
Financial marketers have to crank out a lot of content -- blogs, ad copy, social media posts, web content, and more. Everyone grinding out content would like to have more help. Would you hire the writer who put together the following paragraph on Search Engine Optimization? "Search engine marketers find and rank on websites by making sure they have content relevant to other visitors. In order to have useful content, try clicking on links in a newspaper, magazine, book or website. Do that, and now you will have found good links in other sites that you would now like to link to in a future article. To promote it more, list it in your blog, which will then promote it much more on the web."