Elon Musk's new master plan to produce a fleet of fully self-driving vehicles and autonomous technology flies in the face of the techno-luminary's self-described fear of a robot takeover. Musk's grand plan to merge solar panel producer SolarCity and Tesla includes producing a giant, self-controlled machine that builds the machines that the electric vehicle maker produces -- in effect, the plan is to turn Tesla's factories into a product themselves. The so-called master plan, which was announced Wednesday in a blog post on the Tesla website, involves creating fully autonomous technology, which will allow customers to let their computer overlords take control of the steering wheel. Customers will also be able to use their Tesla fully-self driving vehicles as a type of Uber rider sharing, money making product -- a person's Tesla can generate income while they're at work or on vacation simply by tapping a Tesla app; this, according to Musk, could help dramatically lower the full cost of a Tesla. Musk's plan to produce what amounts to a self-perpetuating technology appears to run counter to his campaign against artificial intelligence.

Much virtual ink gets spilled each week enumerating the many horrors that could be ours in world filled with artificial intelligence, but top researchers in the field are already thinking ahead and making plans to ensure none of that happens. In particular, the importance of making A.I. "human-aware" has come to be viewed as a top imperative for the field, earning it special status as an official theme of the International Joint Conference on Artificial Intelligence taking place this week in New York. "It's crucial that we design smart systems to work well with people," said Harvard professor Barbara Grosz during a panel discussion at the conference on Tuesday. A.I. should be a complement for human intelligence, not a replacement, Grosz said. As such, an ability to understand with whom it's interacting and respond accordingly -- such as by explaining the decisions it makes -- is essential.

Google acquired DeepMind in 2014 to apply machine learning and artificial intelligence in applications that could change human lives. DeepMind just announced a medical research project with an NHS Trust to combat sight loss specifically related to Diabetes and Age-related Macular Degeneration (AMD). Diabetes is on the rise. It's estimated that 1 in 11 of the world's adult population are affected. It's also the leading cause of blindness in the working age population – if you're diabetic you are 25 times more likely to suffer some kind of sight loss.

Now there is an A.I. that can do your job. Customers can direct exactly how their new website should look. This A.I. is still in a limited beta but it is coming. It's called The Grid and it came out of nowhere. It makes you feel like you are interacting with a human counterpart.

No need to panic, but automation and artificial intelligence may already be playing a role in reducing IT hiring in India. According to Nasscom, an Indian IT industry association, fresh hiring in the current financial year is expected to decline compared to last year as IT companies face stiff margins, and move more jobs to automation. Campus hiring may fall for the first time since 2009. "Hiring activity in the year before last was 2.20 lakh (new jobs were created in IT sector). Last year there were about two lakh additions. This financial year, we are expecting it to be on the lower side of that," Nasscom President R Chandrashekhar said.

The unveiling of the Facebook chatbot platform has created a veritable maelstrom of activity in Silicon Valley. Every day, a new article either bemoaning the digitization of communication or celebrating the simplicity of automation, weighs in on this phenomenon. Is 2016 the year of the chatbot? Will chatbots prove to be the Google of A.I.? Will they replace our current models of customer service? Despite WIRED declaring that "Facebook believes messenger will anchor a post-app internet," the much more modest (and realistic) assessment of bots is that they will exist harmoniously alongside apps.

ARTIFICIAL INTELLIGENCE: This Is the Robot Maid Elon Musk Is Funding Will Knight MIT Technology Review "Abbeel's robots learn tasks from scratch, using a neural network that receives sensor input and controls physical movement. The network adjusts its parameters automatically as it inches closer to its goal. A robot might try thousands of grips, for instance, in the process of learning how to hold a certain object." INTERNET & SOCIETY: In the Future You Will Own Nothing and Have Access to Everything Kevin Kelly Boing Boing "The underlying mathematics and physics remain. As we increase dematerialization, decentralization, simultaneity, platforms, and the cloud--as we increase all those at once, access will continue to displace ownership. For most things in daily life, accessing will trump owning...The digital native is free to race ahead and explore the unknown. Accessing rather than owning keeps me agile and fresh, ready for whatever is next."

Abstract--Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of locomotive and local activity. We show an application to visualization of trajectory datasets, and discuss the use of the summary as an index allowing efficient queries which are otherwise impossible or computationally expensive, over very large datasets.

This paper introduces an extended version of the Linear Temporal Logic (LTL) graphical interface. It is a sketch based interface built on the Android platform which makes the LTL control interface more straightforward and friendly to nonexpert users. By predefining a set of areas of interest, this interface can quickly and efficiently create plans that satisfy extended plan goals in LTL. The interface can also allow users to customize the paths for this plan by sketching a set of reference trajectories. Given the custom paths by the user, the LTL specification and the environment, the interface generates a plan balancing the customized paths and the LTL specifications. We also show experimental results with the implemented interface.

Building on a raft of machine learning-related announcements it made earlier in the year, Google has just launched two new machine learning APIs into beta. The most exciting of the two looks to be the new Google Cloud Natural Language API, which is aimed at helping developers build applications that understand human language. The API works by letting users reveal the structure and meaning of a text, and is available in English, Spanish and Japanese for now, with the promise of support for additional languages to come. In a second blog post focusing on the Cloud Natural Language API, Google demonstrates how it can be used to analyze a report in the New York Times. Per Google's example, you can perform sentiment analysis on various blocks of text using the API, run the results in a BigQuery table, and then use Google Data Studio to visualize them: In a second example, Google showed how digital marketers can use the sentiment analysis capabilities in the Cloud Natural Language API to monitor customer calls to service centers and online reviews.