Everyone is on social media. According to Pew Research, in 2015 an astounding 65 percent of all Americans were using social media. That's more than 200 million people in the USA alone, and is approximately the same amount as the number of people in America who own pets. Worldwide, the total on social media already exceeds 2 billion people. That's twice as many people worldwide as the number who own a car.

Musk has been vocal in his warnings about the potential downside of the rise of the robots. He has invested millions in OpenAI, a project to ensure that artificial intelligence benefits mankind, rather than destroys it, and last week he said that it was only a matter of time before AI was used to take down the internet. "There is a pretty good chance we end up with a universal basic income, or something like that, due to automation," Musk told CNBC. "I am not sure what else one would do. I think that is what would happen."

The next big thing in AI may not be getting a machine to perform a task--it might be requiring the machine to communicate why it took that action. For instance, if a robot decides to take a certain route across a warehouse, or a driverless car turns left instead of right, how do we know why it made that decision? According to Manuela Veloso, professor of computer science at Carnegie Mellon University, explainable AI is essential to building trust in our systems. Veloso, who works with co-bots (collaborative robots), programs the machines to verbalize their decision process. "We need to be able to question why programs are doing what they do," Veloso said.

The Google Cloud, Google's eponymous artificial intelligence platform, is quite the capable little set of services. Its algorithms can handle everything from language translation to the identification of objects and landmarks. On Tuesday, Google Cloud chief Diane Greene announced the formation of a new team, the Google Cloud Machine Learning group, that will manage the Mountain View, California-based company's cloud intelligence efforts going forward. The group will be helmed by Jia Li, former head of research at Snapchat and pioneer behind the feature that lets you attach emojis to real-world objects, and Fe-Fei Li, former director of AI at Stanford. They will oversee a slew of upgrades to Google's cloud services in the coming months, much of which will involve Google Cloud's hardware infrastructure.

Technology is getting more and more dynamic bringing radical changes into the world we live in. Things that we deemed impossible a few years ago are happening now, such as genomics and artificial intelligence. Because of this, any old assumptions we know are becoming unapplicable. Technology is changing these rules and assumptions with something new. Here are the six new tech rules that will shape the future.

Lip-reading is notoriously difficult, depending as much on context and knowledge of language as it does on visual clues. But researchers are showing that machine learning can be used to discern speech from silent video clips more effectively than professional lip-readers can. In one project, a team from the University of Oxford's Department of Computer Science has developed a new artificial-intelligence system called LipNet. As Quartz reported, its system was built on a data set known as GRID, which is made up of well-lit, face-forward clips of people reading three-second sentences. Each sentence is based on a string of words that follow the same pattern.

Here is a non-exaustive, work in progress set of resources for data science, machine learning, artificial intelligence, data and text analytics, and data visualization. I've also included links for web and API development, programming languages, DevOps tools, cloud computing, and more. Note that resources are listed in no particular order of preference or relevance.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Using a system built in house, Ocado is in the process of eliminating manual review and sorting of the more than 2,000 daily customer service emails it receives. British web grocer Ocado Group Plc. receives about 2,000 customer service emails a day on average. That number can easily double or triple during the holiday season or when other issues come up such as bad weather that may delay a customer's order, says Dan Nelson, head of data for Ocado, No. 23 in the Internet Retailer 2016 Europe 500. Nelson says the problem until recently is that retailer, which sells solely online and was launched in 2000, used to task its customer service staff with going through and sorting each email that came in. "A lot of customer service reps' time was spent filtering emails," Nelson says.

OpenSim is a biomechanical physics environment for musculoskeletal simulations. Biomechanical community designed a range of musculoskeletal models compatible with this environment. These models can be, for example, fit to clinical data to understand underlying causes of injuries using inverse kinematics and inverse dynamics. For many of these models there are controllers designed for forward simulations of movement, however they are often finely tuned for the model and data. Advancements in reinforcement learning may allow building more robust controllers which can in turn provide another tool for validating the models.