Is it just me, or has this Oscar season lasted five years? Maybe it's because the Barbenheimer phenomenon has been with us since the summer. Or maybe we've just heard one too many times that Bradley Cooper spent half a decade learning how to conduct for "Maestro." Whether you are a casual observer or an Oscar completist who is now cramming in a few more animated shorts, it all ends Sunday, with the ninety-sixth Academy Awards. Much is already known, or widely assumed: we're likely to hear the word "Oppenheimer" several times, Da'Vine Joy Randolph ("The Holdovers") is a shoo-in for Best Supporting Actress, and Jimmy Kimmel will be back to poke fun at Hollywood and move things along.

Researchers have developed a tiny camera that takes amazingly clear photos. At the size of a coarse grain of salt, you may never find it again. Smaller cameras could mean lighter smartphones and new James Bond–style gadgets. Cameras on this scale could swim through the body, hitch a ride on an insect, scope out your brain or monitor hostile environments. And those are just a few of the possibilities.

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.

In 1969, as revolutionary fires burned, the Academy gave its Best Picture award to "Oliver!" Hollywood, still ruled by the crumbling studio system, was almost willfully blind to the nineteen-sixties; even breakthrough films such as "2001: A Space Odyssey" and "Rosemary's Baby" were left off the Best Picture list, which included representatives of such superannuated genres as the big-budget musical ("Funny Girl") and the medieval costume drama ("The Lion in Winter"). Under the newly devised rating system, "Oliver!" became the first G-rated film to win Best Picture, and it remains the last. By the next year, movies like "Midnight Cowboy" and "Easy Rider" finally injected the ceremony with a dose of sixties counterculture--but the decade was over. Two of this year's eight Best Picture nominees are set largely in 1969, and they show what Hollywood wouldn't bring itself to see back then. "The Trial of the Chicago 7" dramatizes the politicized court proceedings against activists who, the year before, protested the Democratic National Convention in Chicago.

Each week, I receive tons of questions from my listeners about tech concerns, new products and all things digital. Sometimes, choosing the most interesting questions to highlight is the best part of my job. This week, I received questions about old computers, fake videos, Jeff Bezos' phone and more. Do you have a question you'd like to ask me? Tap or click here to email me directly.

Following the success of predicting 6 out of 6 for the Oscars last year, we have the bar set high for using Machine Learning to predict the 2019 Oscars winners. This year, however, the results are not as obvious. For some of the top categories, our projected results show ties for who gets to take home the coveted gold statuette. Nevertheless, we are excited to share our predictions and see how the Academy Awards pan out this Sunday! Once again, we apply the standard Machine Learning workflow of collecting and preparing a dataset, building and evaluating models, to ultimately make predictions.

Ahead of Google's Pixel 3 event tomorrow, the larger variant of its phone suffered multiple leaks before we got hands-on time with a retail boxed version over the weekend. Just to (hopefully) put a cap on things before the show starts, Evan Blass has posted pictures showing clear looks of the Pixel 3 and Pixel 3 XL in white, black and a new "Pink Sand" color, as well as a clearer-than-ever look at the Pixel Slate with its keyboard attached. We checked out some software features of the Android Pie-equipped Pixel 3 XL on Saturday, but today 9to5Google has detailed some AI-powered software features we should expect as well. A "Top Shot" feature shoots in burst mode then selects the best picture based on machine learning, while "Photobooth" brings Google Clips like selfie shooting. Simply place the phone somewhere convenient, then pose and it will try to figure out when it has the best picture based on cues like "when everyone is looking at the camera and smiling."

The biggest night in Hollywood is around the corner, and with it comes the possibility for a fresh batch of records to be broken and history to be made. Below, a few of the biggest Oscar milestones that may come to fruition on Sunday night. Outside of the technical categories, sci-fi movies have always had a tough go at the Academy Awards. With rare exception, they're usually overlooked for Best Picture--but depending upon who you ask, Guillermo del Toro's Creature From the Black Lagoon–inspired love story between a woman and a fish-man is the current front-runner (or close to it). Though their respective films, 12 Years a Slave and Moonlight, were crowned Best Picture, Steve McQueen and Barry Jenkins didn't manage to take home the coveted directing award.

A US company claims to already know the nominations and winner of Best Picture for next months annual Academy Awards – aka the Oscars, by using artificial intelligence. The Massachusetts based start-up, Luminoso, unveiled its list (see below) almost two weeks before voting for the list of nominees officially closes (January 24) – and more than a month before the awards takes place at the Dolby Theatre in Hollywood (February 26). The firm generated the results by first pulling together over 84,000 reviews written by movie goers (not critics) which have been published on the IMDB website over the past four years (2013-2016) . It then used its Natural Language Processing software, 'Luminoso Analytics', to analyze the text and identify correlations between topics discussed in the reviews and the eventual Oscar nominees and winners. It found that certain terms, including "narrative," "cinematography," "plot," "visuals," "stunning," "experience," and "masterpiece," were more prevalent in reviews of moves that later went on to be nominated and/or win the Oscars.

If you're placing any Golden Globes bets, then maybe you might want to consider what the latest A.I. predictions are saying. In both Golden Globes Best Picture categories -- comedy and drama -- Unanimous A.I. and applied their unique systems to forecast possible winners. And it looks like this year, you can place a lot of faith in a certain sea monster cleaning up. The Shape of Water is looking good in the categories of Best Actress, but also Best Picture, too. On Friday, in order to forecast possible outcomes in a variety of categories at the Golden Globes, Unanimous A.I. used what's known as "swarm intelligence."