SAN FRANCISCO -- You already chat with friends on Facebook Messenger. Now Messenger wants you to chat with businesses and get updates from them, too. That's the future Facebook pitched Tuesday at f8, its annual conference for software developers in San Francisco. "We think you should message a business just the way you would message a friend," Mark Zuckerberg said on stage at f8. He laughed: "To order flowers on 1-800-Flowers,you never have to call 1-800-Flowers again."

In 1950, artificial intelligence pioneer Alan Turing famously proposed what came to be known as the Turing Test: the proposition that a machine had achieved intelligence if it could carry on a conversation that was indistinguishable from a human one. In 2016, Turing's ghost has come to haunt Silicon Valley in a big way. Companies are racing to build technology that can talk with you. Last month, Microsoft launched the Microsoft Bot Framework, a set of software tools that let companies create their own conversational bots. Customers of Domino's, for instance, can order products by chatting back and forth with a robot, as if they were sending a text message.

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.

Machine Learning (ML) and related computational disciplines have received a lot of attention in recent years. ML enables companies to leverage the vast amount of data they are routinely collecting to personalize their offerings and to automate evidence-driven decision making. Amazon has been a pioneer in the application of ML in eCommerce, with innovations such as product recommendation engines, fraud detection, and large-scale optimization of fulfillment center operations. As Amazon continues to rapidly grow and diversify, there are numerous new opportunities for delivering steep improvements from the application of ML techniques to problems in user profiling, forecasting, natural language processing, computer vision, or robotics. I am responsible for senior hiring across Amazon globally for machine learning and applied sciences.

Amazon recently announced Amazon Machine Learning, a cloud machine learning solution for Amazon Web Services. Able to pull data effortlessly from RDS, S3 and Redshift, the product could pose a significant threat to Microsoft Azure ML and IBM Watson Analytics. Upon selecting a model, the service asks whether the user would like to holdout data for validation from the training set or to provide holdout data from a different source. Once these selections are made, Amazon ML trains the model on the given dataset. Using the sample dataset of dummy bank customers (5MB in size), training takes roughly 10 minutes. When evaluating the evaluation metric for a binary classification task, Amazon ML reports the area under the ROC curve (AUC).

The universal scope of Internet advertising makes it an ideal way to attract customers and lure young participants into human trafficking activities including sex and prostitution. But behind those ads are layers of online data that can help investigators track down the criminals who organize and profit from such exploitation. A fledgling Pittsburgh startup, Marinus Analytics, has a software product, Traffic Jam, that mines the so-called "deep Web" for information and clues about trafficking operations. It is being used by law enforcement officials, including the FBI, to identify offenders and rescue victims. Emily Kennedy, the founder of Marinus founder and its chief executive, began developing the product while she was a student at Carnegie Mellon University.

For nearly 60 episodes, one question has persisted through What's Tech: what is happening in the ending of Steven Spielberg's 2001 sci-fi film A.I. Artificial Intelligence? To settle the question once and for all, I invited my friend and artificial intelligence expert, Sam Byford, to appear on the show. Sadly, as you will hear me learn, Sam Byford is in expert in actual artificial intelligence, not the film Artificial Intelligence. Truth is, he's never even seen the film. This is still one of my favorite episodes of What's Tech, as Byford shares what it was like to attend the recent match-up of human Go champion Lee Se-dol and artificial intelligence AlphaGo.

It's a tantalizing thought: Log onto Facebook, order a pizza from a chat bot, ask another to look for vacation packages while you ask a third to make a restaurant reservation. Facebook's annual F8 developer's conference kicks off today -- and analysts expect the social network could announce a plan for integrating chat bots onto the site. Chat bots are virtual agents able to mimic human conversation using artificial intelligence. "It's this notion of conversations as a service," Patrick Moorhead, an analysts at Moor Insights & Strategy, told ABC News. "The whole element of bots are we can use them exactly like we chat today -- over SMS [text messages] or Messenger. Imagine connecting with a company where it automatically is either a real human or uses AI to know exactly what you want."

Bots can fabricate the sensation of artificial intelligence -- but do not need to be true deep learning. Most bots rely on fabricated answers. Pre-strung answers to predicted questions. Let's call these artificial artificial intelligence. They help you with things that you do not want to be doing -- but do not make decisions for you.

A few months ago, I stumbled upon the API treasure of Microsoft. It was a jaw dropping moment when I saw more than a dozen Artificial Intelligence based APIs. This treasure has a sci-fi name and is known as Project Oxford which was announced in Build 2015 developer conference in San Francisco. As stated on Microsoft's blog - 'Project Oxford helps developer to build more intelligent apps'. This project was developed to provide the developer some complex machine learning that is otherwise not possible for a single developer to build due to time and resource constraints. These APIs will add more fun and the wow factor to an app that wasn't easy before.