We take inspiration from the non-negative matrix factorization (NMF) problem. In NMF, one large m n In 2006 Netflix released a data set -- roughly 100 million matrix M with non-negative values is factored as a product ratings of 17770 titles, given by 480189 viewers -- of two smaller non-negative matrices R and C of size and posed a challenge: Use this training data to predict m l and l n, respectively (where l m,n). Imagining the ratings in a separate, hidden set of ratings involving the set of ratings as the M matrix, with each row the same movies and viewers. The first to do so with a corresponding to a viewer and each column corresponding root-mean-square prediction error (RMSE) at least 10% to a movie, one can think of each row of R as an lower than that of Netflix's own system would receive a attribute vector for the corresponding viewer.

Query reformulation aims to alter potentially noisy or ambiguous text sequences into coherent ones closer to natural language questions. In this process, it is also crucial to maintain and even enhance performance in a downstream environments like question answering when rephrased queries are given as input. We explore methods to generate these query reformulations by training reformulators using text-to-text transformers and apply policy-based reinforcement learning algorithms to further encourage reward learning. Query fluency is numerically evaluated by the same class of model fine-tuned on a human-evaluated well-formedness dataset. The reformulator leverages linguistic knowledge obtained from transfer learning and generates more well-formed reformulations than a translation-based model in qualitative and quantitative analysis. During reinforcement learning, it better retains fluency while optimizing the RL objective to acquire question answering rewards and can generalize to out-of-sample textual data in qualitative evaluations. Our RL framework is demonstrated to be flexible, allowing reward signals to be sourced from different downstream environments such as intent classification.

Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).

Don't fly near airports or airfields. Remember to stay below 400ft (120m).

Flocks of robotic birds are taking to the skies of China equipped with high-tech surveillance technology, according to a report. The so-called "spy bird" programme, first reported by the South China Morning Post, is already in operation in at least five provinces and provides another tendril in the country's already advanced surveillance network. The dove-like drones are being developed by researchers at Northwestern Polytechnical University in the Shaanxi province, who have previously worked on stealth fighter jets used by China's airforce. One of the researchers involved said the roll out of the technology was still in its early stages. "The scale is still small," said Yang Wenqing, an associate professor at the university's School of Aeronautics who worked on the programme.

The results are in for Fortnite's second Showdown event, this time for Blitz with a few rules changes Epic made in the wake of the first Solo Showdown. This time around you only had 25 games to prove your worth, and in addition to placing high, kills also gave you points as well, so aggressive play and finishing well is rewarded, like many players were requesting. Also what was different this time is that Epic separated the contest by region, crowning 5 winners and 500 prize winners, rather than 1 and 100. They reduced the individual list prize pool as a result, but it was essentially doubled overall, with far more winners earning V-bucks. So, did you win? Probably not, given that tens of millions of people play this game and only 500 are walking away with anything.

The X Prize Foundation and IBM have officially launched a global artificial intelligence competition that provides participants with the chance to compete for 5 million. The contest, dubbed "IBM Watson A.I. XPRIZE: A Cognitive Computing Competition," was announced this morning at TED2016. The competition invites teams from around the world to harness the power of artificial intelligence (AI) in order to brainstorm a solution to some of the planet's most pressing problems. "X Prize believes that artificial intelligence is the best tool in our toolkit to address the world's grand challenges and biggest problems," said Stephanie Wander, X Prize development associate and lead prize designer, to IFLScience. Which grand problem will the developers have to tackle?

Between the 25 heroes at launch, the five factions, three competitive modes, and nine story episodes, players that pick up Battleborn should have plenty to accomplish. While teaming up with up to five players, Battleborn heroes work to earn loot, level up through three types of progression, and save the last star from slipping into the void. It is no secret that Battleborn players will be able to take up arms against each other in the game's competitive modes; Incursion, Meltdown, and Capture. However, Battleborn also offers co-operative players a chance to do their part. Nine story missions, and another five post-launch, are infinitely repeatable for loot, experience, and accolades.