When you think of artificial intelligence (AI), do you imagine Will Smith battling humanoid robots? Well, think again…did you know that AI is already being applied in the Internet, helping you go about your daily life without drawing attention to itself? Artificial intelligence simulates traditionally human processes like learning, reasoning and self-correction. Unlike traditional programs, AI-based applications don't need to be continually fed data or manually coded to make changes to their functionality and output. AI can be (and already is) immensely useful to B2B professionals in all industries.
Over a year ago, following an original presentation at MLConf, I wrote a blog post entitled "10 Lessons Learned from building ML systems". At that point, I was leading the Algorithms Engineering team at Netflix and those lessons reflected lessons we had learned there over the last few years. When you do a post/presentation like that, you don't really know how it is going to be received. Some things might be obvious to many while others might be controversial and some will not agree. It turns out though that it was very well received and referenced elsewhere (e.g.
WhatsApp and Facebook Messenger are the most secure chat platforms, according to Amnesty International. But that decision has already met with scepticism from people in the technology community, some of whom have warned that it might not be safe to use the apps at all. Amnesty gave Facebook and WhatsApp a score of 73 out of 100 – its highest – to the two apps, which it didn't distinguish between. But it particularly picked out WhatsApp, which it said was "the only app where users are explicitly warned when end-to-end encryption is not applied to a particular chat". It did have some criticism for Facebook, which doesn't apply strong encryption by default and doesn't warn users that they're not using the most secure technology.
Users don't know what they want until you show them. If you build an perfect engine to show perfect recommend items, the success is all yours .This is the main motto for recommendation engine in E-commerce sites,social networks but how to build an perfect engine to recommend perfect recommend items for users. Here is an basic collaborative recommendation engine implementation in python. Have fun for this weekend.
With the big data growing bigger and bigger and social media penetrating every facet of the society, construing and monitoring data is one of the biggest challenges faced by the enterprises. Gone are those days when customers have to lodge a formal complaint to register the malfunctioning of any product/services provided by the business enterprise, rather, users these days take it to the social media forum to express their dissatisfaction and anguish towards any improper services/products. Inputs such as tweets, facebook comments could be of significant value to the enterprise to analyze their products/services/ performances, customer behavior and demands. Below is a small case study on Flipkart and Snapdeal performance when'World Book Day' was trending on Twitter. Below is the screenshot of'Flipkart' and'Snapdeal' on the occasion of'World Book Day'.