Mike Abbott, Partner of Kleiner Perkins Caufield & Byers, sits with Dr. Fei Fei Li, Associate Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Dr Li's main research areas are in machine learning, deep learning, computer vision and cognitive and computational neuroscience. She has published more than 150 scientific articles in top-tier journals and conferences and invented ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI.
Last year, Microsoft released an artificially intelligent Twitter chatbot named "Tay" aimed at engaging Millennials online. The idea was that Tay would spend some time interacting with users, absorb relevant topics and opinions, and then produce its own content. In less than 24 hours, Tay went from tweeting "humans are super cool" to racist, neo-Nazi one-liners, such as: "I f-- hate n--, I wish we could put them all in a concentration camp with kikes and be done with the lot." Needless to say, Microsoft shut down Tay and issued an apology.
But with the infiltration of the deep mind knowledge base, an artificial intelligence able to write its own code and learn, not unlike biological creatures, has risen online. Do we live in a unique generation who will see the deep web, deep mind, deep patient, deep dream and deep neural networks converges with the biblical abyss?
Over the last few weeks we've been working on applying Deep Learning algorithms for a new VentureRadar feature we're adding in the coming weeks. This piqued my interest in finding out more about the startups leading the way in developing and applying Deep Learning, so I decided to pick out the eighteen highest ranked companies in this emerging field from the VentureRadar database, and take a closer look at them.
In order to measure the trust and popularity of a tweet, I use the following features from tweets: retweets count, followers count, friends acount. Then I rank those tweets based on TP score. Thus, the tweets we crawled has been filtered by query words and posting time. All we need is to consider about the retweets countsm, followers counts, friends counts.
Recently, I decided to learn how Regional sentiment analysis can help people to make specific decisions or policy strategies for different regions. Notably, Tweets scraped from Twitter can provide tremendous real-time data for our analysis. In my approach, I develop a Twitter Sentiment Classifier, which will classify a scraped tweet into three main polarities: Positive, Negative and Neutral. To evaluate the performance, a data set with 8692 labeled scraped tweets is used in this experiment for training dataset, and 2336 scraped data for testing data set.