As I found myself unexpectedly snowed in this weekend, I decided to take on a weekend project for fun. While searching for something to catch my fancy, I ran across an old Slate article claiming that they found a foolproof strategy for finding Waldo in the classic "Where's Waldo?" book series. Now, I'm no Waldo-spotting expert, but even I could tell that the strategy they proposed there is far from perfect. That's when I decided what my weekend project would be: I was going to pull out every machine learning trick in my tool box to compute the optimal search strategy for finding Waldo. I was going to crush Slate's supposed foolproof strategy and carve a trail of defeated Waldo-searchers in my wake.

Semidefinite programs have recently been developed for the problem of community detection, which may be viewed as a special case of the stochastic blockmodel. Here, we develop a semidefinite program that can be tailored to other instances of the blockmodel, such as non-assortative networks and overlapping communities. We establish label recovery in sparse settings, with conditions that are analogous to recent results for community detection. In settings where the data is not generated by a blockmodel, we give an oracle inequality that bounds excess risk relative to the best blockmodel approximation. Simulations are presented for community detection, for overlapping communities, and for latent space models.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

We investigate if kernel regularization methods can achieve minimax convergence rates over a source condition regularity assumption for the target function. These questions have been considered in past literature, but only under specific assumptions about the decay, typically polynomial, of the spectrum of the the kernel mapping covariance operator. In the perspective of distribution-free results, we investigate this issue under much weaker assumption on the eigenvalue decay, allowing for more complex behavior that can reflect different structure of the data at different scales.

A robot has just set a new record for the fastest-solved Rubik's Cube, according to its makers. The Sub1 Reloaded robot took just 0.637 seconds to analyse the toy and make 21 moves, so that each of the cube's sides showed a single colour. That beats a previous record of 0.887 seconds, which was achieved by an earlier version of the same machine using a different processor. Infineon provided its chip to highlight advancements in self-driving car tech. But one expert has questioned the point of the stunt.

A new speed record has been set for solving the notoriously difficult Rubik's Cube. TC Newman (@PurpleTCNewman) has the details. A link has been sent to your friend's email address. A new speed record has been set for solving the notoriously difficult Rubik's Cube. TC Newman (@PurpleTCNewman) has the details.

The Rubik's cube was devised by Hungarian architect Erno Rubik more than 30 years ago, but he likely never envisioned his puzzle being cracked this quickly. A robot has this week solved a Rubik's cube in 0.637 seconds, at the Electronica Trade Fair in Munich, Germany. The machine, known as'Sub1 Reloaded' and developed by German tech company Infineon, was aided by one of the world's most powerful microcomputers. The machine, known as'Sub1 Reloaded' and developed by German tech company Infineon, was aided by one of the world's most powerful microcomputers The robot took a fraction of a second to analyse the cube and make 21 moves to solve the puzzle. Its time of 0.637 seconds beat the previous world record of 0.887 seconds, set by an earlier prototype of the same machine.

The shape of multicoloured three-dimensional puzzle Rubik's Cube is not a trademark, the European Court of Justice has ruled. It means the shape of the cube alone is not enough to protect it from being copied. UK company Seven Towns, which manages Rubik's Cube's intellectual property rights, registered its shape as a trademark in in the 1990s. But German firm Simba Toys challenged the trademark protection in 2006. The European Court of Justice (ECJ) agreed that the cube's ability to rotate should be protected by a patent and not a trademark.

It's the familiar multicoloured "cube" brain-teaser which has challenged puzzle solvers for more than 40 years and is still the world's bestselling toy of all time. But on Thursday – following a 10-year legal tussle – Rubik's Cube lost a key trademark battle after the European court of justice (ECJ) said its shape was not sufficient to grant it protection from "copycat" versions. The eponymous puzzle, invented in 1974 by Hungarian sculptor and architect Erno Rubik, is popular among young and old, with more than 350m cubes sold to date worldwide. UK company Seven Towers, which oversees Rubik's Cube intellectual property rights, registered the shape as a three-dimensional EU trademark with the European Union Intellectual Property Office (EUIPO) in April 1999. But the court ruled that the EU trademark representing the shape of the Rubik's Cube is invalid, triggering fears it will lead to a surge of cheap, mass-produced versions and a weakening of European intellectual property protection.

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the test-time usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highly-optimized attention-based seq2seq system and other baselines on three different sequence to sequence tasks: word ordering, parsing, and machine translation.