I am gonna give a talk on introduction to deep learning. I wish to try to answer some elementary / basic questions that a novice in the field might have. I also would like to point out some caveats in DL. So, what questions and topics should I focus on? I shall put your references in my talk too.

There's no human instinct more basic than speech, and yet, for many people, talking can be taxing. One in 14 working-age Americans suffer from voice disorders that are often associated with abnormal vocal behaviors -- some of which can cause damage to vocal cord tissue and lead to the formation of nodules or polyps that interfere with normal speech production. Unfortunately, many behaviorally-based voice disorders are not well understood. In particular, patients with muscle tension dysphonia (MTD) often experience deteriorating voice quality and vocal fatigue ("tired voice") in the absence of any clear vocal cord damage or other medical problems, which makes the condition both hard to diagnose and hard to treat. But a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) believes that better understanding of conditions like MTD is possible through machine learning.

Many of the inventors who fueled the digital revolution have become household names. Innovators such as Steve Jobs, Bill Gates, and Mark Zuckerberg all contributed mightily to the technologies that have transformed our daily lives and society. If you're not an engineer, however, you have probably never heard of the brilliant inventor Rudolf Kálmán, a Budapest-born engineer and mathematician who died on July 2 in Gainesville, Florida, at age 86. His fundamental contribution, an algorithm called the Kalman filter, made possible many essential technological achievements of the last 50 years. These include aerospace systems such as the computers that landed Apollo astronauts on the moon, robotic vehicles that explore our world from the deep sea to the outer planets, and nearly any endeavor that needs to estimate the state of the world from noisy data. Someone once described the entire GPS system--an Earth-girdling constellation of satellites, ground stations, and computers as "one enormous Kalman filter."

I am just a humble technologist and receive average of three emails per day ( I know this is a very small number of emails when you compare to people who have oodles of followers). These are the emails asking me suggestions about "how to become a data scientist". I did a napkin calculation and it added up to 1000 emails per year. I am so happy to get these emails and answer them one by one. By nature and nurture I love teaching, helping and mentoring.

There's been a lot of talk in the office about "bots", while definitely a buzz word at the moment is the hype justified? What are bots and what do they do? Bots are primarily services you can interact with, in a pseudo-human way, via a chat interface; be it text based, speech or visual communication. Essentially they let you do everyday tasks without having to deal with the tedium of human interaction. Although being thrust into the limelight by companies like Facebook, Microsoft and Slack the chatbot has a much longer history than you might expect.

From Elon Musk to Stephen Hawking, individuals around the globe are calling for caution and warning about the potentially dangerous pitfalls of artificially intelligent systems. It seems that each day brings forth a new autonomous robot or a new use for an intelligent system. Add to that all the movies and books in which AI is the evil "bad guy," and it's easy to feel like undercurrents of fear are slowly permeating our society. But how much of our fear is a joke, and how much of our fear is justified? We have previously covered the sensationalism and hype that often surrounds conversations about AI, but here, the Future of Life brings experts together to take a deeper dive into these issues.

When Manoj Saxena surveys what's next in the world of computing, he sees a huge change coming that will transform the way we live our lives. The former general manager of IBM's Watson project, which scours millions of pages of data to answer questions posed in natural language, says machine learning and artificial intelligence represent the fourth big inflection point in history -- after the alphabet, the printing press and the Internet. "This shift is going to make the Internet look small in terms of impact," he says. The 2 billion already invested in AI is just one-tenth of what's to come. "In about five years, saying, 'My software does AI,' will be like saying, 'My software does the Internet,'" he said, in a recent interview with Texas CEO Magazine.

Researchers at Google's DeepMind artificial intelligence division claim to have come up with a way of producing much more natural-sounding synthesized speech, compared with the techniques that are currently in use. Existing text-to-speech (TTS) systems tend to use a system called concatenative TTS, where the audio is generated by recombining fragments of recorded speech. There's also a technique called parametric TTS that generates speech by passing information through a vocoder, but that sounds even less natural. So DeepMind has come up with a new technique called WaveNet that learns from the audio it's fed, and produces raw audio sample-by-sample. To give an idea of how detailed that is, we're talking at least 16,000 samples per second.

Google's DeepMind unit, which is working to develop super-intelligent computers, has created a system for machine-generated speech that it says outperforms existing technology by 50 percent. U.K.-based DeepMind, which Google acquired for about 400 million pounds ( 533 million) in 2014, developed an artificial intelligence called WaveNet that can mimic human speech by learning how to form the individual sound waves a human voice creates, it said in a blog post Friday. In blind tests for U.S. English and Mandarin Chinese, human listeners found WaveNet-generated speech sounded more natural than that created with any of Google's existing text-to-speech programs, which are based on different technologies. WaveNet still underperformed recordings of actual human speech. Many computer-generated speech programs work by using a large data set of short recordings of a single human speaker and then combining these speech fragments to form new words.

Inspired by this article about sentiment analysis and this guide to webscraping, I have decided to get my hands dirty by scraping and analyzing a sample of reviews on the website Goodreads. The goal of this project is to demonstrate a complete example, going from data collection to machine learning analysis, and to illustrate a few of the dead ends and mistakes I encountered on my journey. We'll be looking at the reviews for five popular romance books. I have voluntarily chosen books in the same genre in order to make comments text more homogeneous a priori; these five books are popular enough that I can easily pull a few thousands reviews for each, yielding a significant corpus with minimum effort. If you don't like romance books, feel free to replicate the analysis with your genre of choice!