Anticipation is growing for the July release of Tesla's'affordable' $35,000 Model 3. Now, the latest shots of Telsa testing the car may have revealed one of its final secrets - what the interior will look like. The shots, taken near Tesla's headquarters in Palo Alto, California, reveal the car has a single screen and no traditional instruments. Musk had previously addressed the issue of no traditional dashboard display, tell users who asked for one'You won't care' before confirming the car won't use a heads up display either. A new comparison of models on Tesla's own site confirms the spec, saying the Model 3 will only have a single 15inch display, while the more expensive Model S has a separate'driver display'. According to Teslerati, 'The latest photos gives us a clearer look at the landscape-mounted touchscreen, which resembles an off-the-shelf computer monitor that's been bolted onto the dashboard.'

Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.

Researchers have unveiled a new way to test self driving cars and says it could allow them to perform he equivalent of 100 million miles of driving in just 1,000. The researchers at the University of Michigan say their find would allow manufacturers to bypass the billions of miles they would need to log for consumers to consider them road-ready. The process, which was developed using data from more than 25 million miles of real-world driving, can cut the time required to evaluate robotic vehicles' handling of potentially dangerous situations by 300 to 100,000 times, saving 99.9 percent of testing time and costs, the researchers say. Researchers at the University of Michigan say their find would allow manufacturers to bypass the billions of miles they would need to log for consumers to consider them road-ready. The new accelerated evaluation process breaks down difficult real-world driving situations into components that can be tested or simulated repeatedly, exposing automated vehicles to a condensed set of the most challenging driving situations. In this way, just 1,000 miles of testing can yield the equivalent of 300,000 to 100 million miles of real-world driving.While 100 million miles may sound like overkill, it's not nearly enough for researchers to get enough data to certify the safety of a driverless vehicle.

These results show that when we train on less data, or test on data which is significantly different from the training set, then our epistemic uncertainty increases drastically. However, our aleatoric uncertainty remains relatively constant, which it should because it is tested on the same problem with the same sensor. Next I'm going to discuss an interesting application of these ideas for multi-task learning. Multi-task learning aims to improve learning efficiency and prediction accuracy by learning multiple objectives from a shared representation. It is prevalent in many areas of machine learning, from NLP to speech recognition to computer vision.

It seems that, anymore, the perception of machine learning is often reduced to passing a series of arguments to a growing number of libraries and APIs, hoping for magic, and awaiting the results. Maybe you have a very good idea of what's going on under the hood in these libraries -- from data preparation to model building to results interpretation and visualization and beyond -- but you are still relying on these various tools to get the job done. Using well-tested and proven implementations of tools for performing regular tasks makes sense for a whole host of reasons. Reinventing wheels which don't roll efficiently is not best practice... it's limiting, and it takes an unnecessarily long time. Whether you are using open source or proprietary tools to get your work done, these implementations have been honed by teams of individuals ensuring that you get your hands on the best quality instruments with which to accomplish your goals.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).

Machine learning is key technology behind use of artificial intelligence applications. We know that AI applications are growing tremendously and businesses are focusing on efficient use of such applications which is becoming mandate for every organization. We are hereby highlighting some viewpoints, facts, figures as findings on AI and machine learning in form of infographic.

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.

This paper deals with finding an $n$-dimensional solution $x$ to a system of quadratic equations of the form $y_i=|\langle{a}_i,x\rangle|^2$ for $1\le i \le m$, which is also known as phase retrieval and is NP-hard in general. We put forth a novel procedure for minimizing the amplitude-based least-squares empirical loss, that starts with a weighted maximal correlation initialization obtainable with a few power or Lanczos iterations, followed by successive refinements based upon a sequence of iteratively reweighted (generalized) gradient iterations. The two (both the initialization and gradient flow) stages distinguish themselves from prior contributions by the inclusion of a fresh (re)weighting regularization technique. The overall algorithm is conceptually simple, numerically scalable, and easy-to-implement. For certain random measurement models, the novel procedure is shown capable of finding the true solution $x$ in time proportional to reading the data $\{(a_i;y_i)\}_{1\le i \le m}$. This holds with high probability and without extra assumption on the signal $x$ to be recovered, provided that the number $m$ of equations is some constant $c>0$ times the number $n$ of unknowns in the signal vector, namely, $m>cn$. Empirically, the upshots of this contribution are: i) (almost) $100\%$ perfect signal recovery in the high-dimensional (say e.g., $n\ge 2,000$) regime given only an information-theoretic limit number of noiseless equations, namely, $m=2n-1$ in the real-valued Gaussian case; and, ii) (nearly) optimal statistical accuracy in the presence of additive noise of bounded support. Finally, substantial numerical tests using both synthetic data and real images corroborate markedly improved signal recovery performance and computational efficiency of our novel procedure relative to state-of-the-art approaches.

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.