After raising $25 million in funding, Beijing-based ZeroZero Robotics came out of stealth mode and launched their Hover Camera just days before the GMIC Beijing 2016 trade show, the ‘CES of China.’
Co-founder MQ Wang, a Stanford PhD alum focused on machine learning and natural language processing, was inspired to create the autonomous Hover Camera after watching a documentary about a man who walked 1600 miles solo across Australia.
Using a machine learning model trained on an NVIDIA Tesla K40 GPU, the camera automatically tracks your face to take photos and capture videos wherever you go.
“We wanted the user experience to be very natural — easy to use,” MQ told Mashable. “We wanted to make sure the learning curve for users is minimum. So there is no remote control or anything. There’s no calibration process.”
The final version of the product will be released this summer.
Minecraft on HoloLens may be cool, but with Minecraft on Oculus Rift, you feel more like you stepped into the world.
Minecraft is a delightful and hugely successful game, but no one would say its success hinges upon realism. It’s blocky graphics, full of sharp right angles and huge “pixels” are far from realistic, but it gives the game a signature visual style and plenty of charm. However, it turns out that Minecraft‘s massive open-world nature makes it a great game for virtual reality. Microsoft already showed the game running in HoloLens, and now the company is announcing that it’ll work with Oculus Rift, as well. I got a chance to see how the game works with the Rift at Microsoft’s spring showcase last week — and despite the game’s blocky style, it could be one of the best overall VR experiences out there.
For starters, it’s worth noting that this isn’t a new version of Minecraft; it has just been updated to work with the Oculus Rift. You can play in survival mode as well as join one of the many multiplayer servers out there. Once you start playing, you’re presented with two different view modes. The first puts you in a virtual castle with the game running on what amounts to a TV screen in front of you. It’s pretty meta and rather funny to be playing a game inside of a virtual reality game, but it’s not a bad way to view things if you need a break from the full VR experience.
When you jump in to that full experience, the game shifts and you’re completely immersed by what your character sees. Because of the massive scope of Minecraft‘s vast 3D landscapes, it really does feel like you’ve been transported away from reality, despite the humongous pixels and lack of fine detail. It’s one of the best and more immersive VR experiences I’ve had thus far. In fact, that lack of fine detail actually helps Minecraft be so successful — the game doesn’t try to mimic reality. Instead, it felt more like I stepped into a cartoon.
The demo experience Microsoft was showing off goes through a few of the games signature moments — I did some mining, fought some creeps, lit up some caves with torches, pressed a bunch of buttons to interact with the environment and eventually rode a mine cart way up the side of a huge building. That was probably the best part of the demo, as there was a real sense of speed and height as I rocketed skyward. A later mine cart ride let me look around in 360 degrees at the vast landscape from way on high as it headed towards a new area, and there was all sorts of activity and eye candy to take in on the trip.
As with most things VR, it’s hard to do the experience justice in words, but I’ll just say that the experience really highlighted the vastness of the world and did a great job of immersing me in Minecraft. It’s a less radically different version of the game than the HoloLens experience, mostly because the Oculus version doesn’t have gesture and voice commands, but it still seems like a great place to go exploring. Unfortunately, there’s no word on exactly when Minecraft will be publicly available in VR, but hopefully it won’t come terribly long after the Rift’s release later this month — “killer app” is a played-out term, but Minecraft has the potential to be one for the nascent VR scene.
So a computer program has learned how to play classic Atari games. Big deal. I mean, they’re just big blocks of pixels pushing smaller blocks of pixels around a screen, right?
Yet somehow, the UK artificial intelligence specialist Deep Mind, bought last year by Google for £240m, is extremely excited about the fact that it has developed an AI agent capable of learning how to play Space Invaders. I learned how to play Space Invaders in a cafe in Blackpool when I was six. But Google hasn’t acquired me. What’s going on?
The thing is, classic Atari games such as Space Invaders, Pong and Breakout were, despite their visual limitations, much more complex than our modern minds give them credit for. Indeed, many of these games set in place the fundamental mechanics that modern titles are still utilising – and they are deceptively deep, even for a Deep Mind AI gamer.
It turns out the Deep Mind program fared very well against titles that allowed it to learn systems through simple trial and error such as Video Pinball and Breakout. However, it failed spectacularly at games that required the participants to build a mental map of the level and develop long-term strategies, or that required the mastery of different skillsets, such as the more complex shooters Zaxxon and Gravitar.
However, learning to play and master any of these games is a significant feat, despite their chunky aesthetics. Here are four classic Atari titles and why they’re not quite as basic as they appear.
Designed by electrical engineer Al Alcorn and launched as an arcade machine in 1972, Atari’s legendary bat and ball simulation, is widely considered the most basic interactive electronic game possible. Indeed, Atari founder Nolan Bushnell once said to Alcorn: “I want to make a game that any drunk in any bar can play.”
But Pong is actually a very important early example of game physics – the ball reacting to surfaces in different ways. When it hits a wall, it simply rebounds at a mirror-opposite angle.
However, each of the bats is actually divided into eight sections, each providing a slighting different angle of return. Therefore players are able to develop strategies, planning the return gradient so that the opponent is unable to quickly guess where the ball will return to, leaving them stranded in the wrong part of the screen. Consequently, players – or indeed computer programs – with a knowledge of projectile displacement physics have a distinct advantage.
Created as a single-player version of Pong, Atari’s wall-breaking classic was designed by Bushnell, with help from Steve Wozniak and Steve Jobs who would, of course, later go on to found Apple. Players simply use a bat at the bottom of the screen to knock a ball into rows of coloured bricks, smashing anything it hits. Once again, the bat has different impact zones that affect the angle of the bounce.
Extra depth comes from the fact that the ball speeds up when the player reaches the upper layers of the walls, and then the bat shrinks to half its size when the ball finally hits the top wall. This is an early example of gameplay balance, with a system that increases the level of challenge as the player begins to excel. The fiendishly compulsive “tidying up” format of the game would later become a staple of the puzzle genre (see Tetris).
Originally devised by Tomohiro Nishikado, a designer at Japanese arcade company Taito, Space Invaders is – alongside Pac-Man – one of the best-known video games of all time. Players shoot at waves of aliens as they descend to Earth, occasionally blasting a UFO as it whizzes across the top of the display.
Seemingly simple, the game was one of the first to introduce a smooth difficulty curve, the invaders speeding up as they get closer. Incredibly, this was actually a bug: the archaic processor was able to handle the movement more efficiently when there were fewer objects on screen. But Nishikado kept it.
Alongside the cowboy shoot-’em-up Gun Fight, Space Invaders (which was later ported to the Atari 2600 console) also popularised the “cover” mechanic, providing a row of destructible barriers for the player to hide behind. It is a feature that would later be brought to more complex action adventures such as Grand Theft Auto and Gears of War.
Indeed, Hideo Kojima, the creator of the multimillion-selling Metal Gear Solid series credited Space Invaders with inventing the “stealth” game genre, as players could sneak from barrier to barrier evading the attention – and bullets – of the extraterrestrial enemies.
Created using monochrome “vector” graphics by Atari coders Ed Logg and Lyle Rains, Asteroids has players controlling a tiny ship as it blasts passing space rocks. Once again, however, the basic set-up hides a complex challenge. The ship itself is piloted using thrust and inertia, an intricate physics system, that adds a significant skill factor to the multi-directional movement.
The game also exhibits a clever difficulty curve: when larger asteroids are blasted they split up into faster moving rocks, considerably increasing the challenge. And it experiments with contrasting AI enemies. Two different flying saucers can appear on screen – a large one that fires inaccurately, and a smaller one that is much more deadly.
Released in 1979, Asteroids was a smash hit, selling more than 70,000 arcade machines. Players were so addicted to its merging of simple visuals with intense action that they began to work out and exploit its systemic features. A good Asteroids player knows that leaving a single slow moving rock on screen and using it as a barrier from which to blast saucers is the way to huge high scores. It’s a technique known as “lurking” and its one of the earliest example of players gaming the system for tactical advantage.
In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.
If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.
If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.
As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos.
- Statistical Learning and Regression (11:41)
- Curse of Dimensionality and Parametric Models (11:40)
- Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
- Classification Problems and K-Nearest Neighbors (15:37)
- Lab: Introduction to R (14:12)
- Simple Linear Regression and Confidence Intervals (13:01)
- Hypothesis Testing (8:24)
- Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
- Model Selection and Qualitative Predictors (14:51)
- Interactions and Nonlinearity (14:16)
- Lab: Linear Regression (22:10)
- Introduction to Classification (10:25)
- Logistic Regression and Maximum Likelihood (9:07)
- Multivariate Logistic Regression and Confounding (9:53)
- Case-Control Sampling and Multiclass Logistic Regression (7:28)
- Linear Discriminant Analysis and Bayes Theorem (7:12)
- Univariate Linear Discriminant Analysis (7:37)
- Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
- Quadratic Discriminant Analysis and Naive Bayes (10:07)
- Lab: Logistic Regression (10:14)
- Lab: Linear Discriminant Analysis (8:22)
- Lab: K-Nearest Neighbors (5:01)
- Estimating Prediction Error and Validation Set Approach (14:01)
- K-fold Cross-Validation (13:33)
- Cross-Validation: The Right and Wrong Ways (10:07)
- The Bootstrap (11:29)
- More on the Bootstrap (14:35)
- Lab: Cross-Validation (11:21)
- Lab: The Bootstrap (7:40)
- Linear Model Selection and Best Subset Selection (13:44)
- Forward Stepwise Selection (12:26)
- Backward Stepwise Selection (5:26)
- Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared (14:06)
- Estimating Test Error Using Cross-Validation (8:43)
- Shrinkage Methods and Ridge Regression (12:37)
- The Lasso (15:21)
- Tuning Parameter Selection for Ridge Regression and Lasso (5:27)
- Dimension Reduction (4:45)
- Principal Components Regression and Partial Least Squares (15:48)
- Lab: Best Subset Selection (10:36)
- Lab: Forward Stepwise Selection and Model Selection Using Validation Set (10:32)
- Lab: Model Selection Using Cross-Validation (5:32)
- Lab: Ridge Regression and Lasso (16:34)
- Polynomial Regression and Step Functions (14:59)
- Piecewise Polynomials and Splines (13:13)
- Smoothing Splines (10:10)
- Local Regression and Generalized Additive Models (10:45)
- Lab: Polynomials (21:11)
- Lab: Splines and Generalized Additive Models (12:15)
- Decision Trees (14:37)
- Pruning a Decision Tree (11:45)
- Classification Trees and Comparison with Linear Models (11:00)
- Bootstrap Aggregation (Bagging) and Random Forests (13:45)
- Boosting and Variable Importance (12:03)
- Lab: Decision Trees (10:13)
- Lab: Random Forests and Boosting (15:35)
- Maximal Margin Classifier (11:35)
- Support Vector Classifier (8:04)
- Kernels and Support Vector Machines (15:04)
- Example and Comparison with Logistic Regression (14:47)
- Lab: Support Vector Machine for Classification (10:13)
- Lab: Nonlinear Support Vector Machine (7:54)
- Unsupervised Learning and Principal Components Analysis (12:37)
- Exploring Principal Components Analysis and Proportion of Variance Explained (17:39)
- K-means Clustering (17:17)
- Hierarchical Clustering (14:45)
- Breast Cancer Example of Hierarchical Clustering (9:24)
- Lab: Principal Components Analysis (6:28)
- Lab: K-means Clustering (6:31)
- Lab: Hierarchical Clustering (6:33)
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