Supposed replicas of Apple’s next-generation iPhone lineup are beginning to circulate in Asia ahead of an expected release this fall, offering an opportunity to compare the mockups’ physical dimensions with existing models.
In a report on Thursday, Japanese Apple blog Mac Otakaracompares and contrasts a set of 3D printed “iPhone XI” mockups obtained from an Alibaba marketplace source with Apple’s existing iPhone XR and XS models. Specifically, the samples depict 6.1- and 6.5-inch OLED-toting handsets rumored to arrive in September as refreshes to the iPhone XR and XS Max.
The dummies were created using supposedly leaked CAD files, though the origin of the data remains undisclosed. Earlier today, graphical mockups of a 5.8-inch OLED model, thought to replace the iPhone XS, hit the web and were based on “final CAD renders of the device.”
Whether the two CAD leaks are related is unknown.
Mac Otakara notes the 6.1-inch version measures in at 143.9mm tall, 71.3mm wide and 7.9 mm thick, which is approximately 0.3mm taller, 0.4mm wider and 0.2mm thicker than the 5.8-inch iPhone XS. That extra space could allow for the inclusion of a larger 6.1-inch display, as claimed in the report, but Apple would likely need to slim down bezel size to make the screen fit. How the supposed change impacts screen ratio is unclear.
Compared to the current iPhone XR, which boasts a 6.1-inch LCD, the 6.1-inch mockup is 6.1mm shorter, 4.3mm more narrow and 0.4mm slimmer.
As for the 6.5-inch version, the mockup comes in at 157.6mm tall, 77.6mm wide and 8.1mm thick, roughly 0.1mm taller, 0.2mm wider and 0.4mm thinner than the 6.5-inch iPhone XS Max.
Both mockups incorporate a large square camera “bump” that features three lenses in a triangular layout alongside a single TrueTone flash module.
The publication conducted a similar comparison of mockups from Alibaba last year, a test that yielded largely accurate results and foreshadowed what would become iPhone XR, XS and XS Max.
Apple is expected to refresh its iPhone lineup later this year with so-called “iPhone XI” models. According to analyst Ming-Chi Kuo, the smartphones will include camera improvements like a super-wide rear-facing lenser and an improved 12 megapixel front-facing camera. As for displays, Kuo believes Apple to carry over OLED technology for the 5.8- and 6.5-inch versions, while others, namely Mac Otakara, predict a move to OLED for the 6.1-inch model.
Preparing Your Enterprise for the Worst With Disaster Recovery, Monitoring
With the rise of both man-made and natural disasters (including fires and earthquakes), the disaster recovery (DR) market has growing importance in protecting an enterprise and its user community, according to RackWare co-founder and CEO Sash Sunkara.
“It is a really critical requirement [for enterprises] and when people think about DR there are certain things that they should really think about when putting a plan together,” Sunkara said. This includes implementing the correct infrastructure monitoring and disaster recovery toolset so that an enterprise’s network, applications, and end-users don’t experience any downtime.
Avengers: Endgame Isn't The Final Movie In Marvel's Phase 3, Apparently
In a weird bit of news, it turns out that Avengers: Endgame won't be the last movie in Phase 3 of the Marvel Cinematic Universe. According to Marvel Studios President Kevin Feige, it will be Spider-Man: Far From Home, which we all thought was the start of Phase 4.
During an Endgame event in Shanghai last week, BiliBili asked about the upcoming Spider-Man movie, and although the excessive crowd noise in the background blocks out much of the question being asked, signs point to Far From Home closing out Phase 3. "It's the end of the third phase," explained Feige. "You're the first person I told that to."
This is a bit of a head-scratcher, as most of us assumed that the Infinity Saga would close up with Endgame, bringing a natural conclusion to the arc. However, it's not entirely out of the ordinary, when you think about it. Sure, Phase 1 wrapped up in 2012 with the first Avengers film. However, Phase 2 closed not with 2015's Avengers: Age of Ultron but with Ant-Man. So for fans, like myself, thinking these Phases always end with an Avengers movie, we're wrong. It just feels so weird to end this chapter at Marvel with the Spider-Man sequel.
Avengers: Endgame comes to theaters on April 26, and you can check out GameSpot's review of the movie--we liked it. As for the end of Phase 3, that won't happen until the recently moved up Spider-Man: Far From Home on July 2.
Posted by: xSicKxBot - 04-25-2019, 03:17 PM - Forum: Windows
- No Replies
Machine teaching: How people’s expertise makes AI even more powerful
Most people wouldn’t think to teach five-year-olds how to hit a baseball by handing them a bat and ball, telling them to toss the objects into the air in a zillion different combinations and hoping they figure out how the two things connect.
And yet, this is in some ways how we approach machine learning today — by showing machines a lot of data and expecting them to learn associations or find patterns on their own.
For many of the most common applications of AI technologies today, such as simple text or image recognition, this works extremely well.
But as the desire to use AI for more scenarios has grown, Microsoft scientists and product developers have pioneered a complementary approach called machine teaching. This relies on people’s expertise to break a problem into easier tasks and give machine learning models important clues about how to find a solution faster. It’s like teaching a child to hit a home run by first putting the ball on the tee, then tossing an underhand pitch and eventually moving on to fastballs.
“This feels very natural and intuitive when we talk about this in human terms but when we switch to machine learning, everybody’s mindset, whether they realize it or not, is ‘let’s just throw fastballs at the system,’” said Mark Hammond, Microsoft general manager for Business AI. “Machine teaching is a set of tools that helps you stop doing that.”
Machine teaching seeks to gain knowledge from people rather than extracting knowledge from data alone. A person who understands the task at hand — whether how to decide which department in a company should receive an incoming email or how to automatically position wind turbines to generate more energy — would first decompose that problem into smaller parts. Then they would provide a limited number of examples, or the equivalent of lesson plans, to help the machine learning algorithms solve it.
In supervised learning scenarios, machine teaching is particularly useful when little or no labeled training data exists for the machine learning algorithms because an industry or company’s needs are so specific.
YouTube Video
In difficult and ambiguous reinforcement learning scenarios — where algorithms have trouble figuring out which of millions of possible actions it should take to master tasks in the physical world — machine teaching can dramatically shortcut the time it takes an intelligent agent to find the solution.
It’s also part of larger goal to enable a broader swath of people to use AI in more sophisticated ways. Machine teaching allows developers or subject matter experts with little AI expertise, such as lawyers, accountants, engineers, nurses or forklift operators, to impart important abstract concepts to an intelligent system, which then performs the machine learning mechanics in the background.
Microsoft researchers began exploring machine teaching principles nearly a decade ago, and those concepts are now working their way into products that help companies build everything from intelligent customer service bots to autonomous systems.
“Even the smartest AI will struggle by itself to learn how to do some of the deeply complex tasks that are common in the real world. So you need an approach like this, with people guiding AI systems to learn the things that we already know,” said Gurdeep Pall, Microsoft corporate vice president for Business AI. “Taking this turnkey AI and having non-experts use it to do much more complex tasks is really the sweet spot for machine teaching.”
Today, if we are trying to teach a machine learning algorithm to learn what a table is, we could easily find a dataset with pictures of tables, chairs and lamps that have been meticulously labeled. After exposing the algorithm to countless labeled examples, it learns to recognize a table’s characteristics.
But if you had to teach a person how to recognize a table, you’d probably start by explaining that it has four legs and a flat top. If you saw the person also putting chairs in that category, you’d further explain that a chair has a back and a table doesn’t. These abstractions and feedback loops are key to how people learn, and they can also augment traditional approaches to machine learning.
“If you can teach something to another person, you should be able to teach it to a machine using language that is very close to how humans learn,” said Patrice Simard, Microsoft distinguished engineer who pioneered the company’s machine teaching work for Microsoft Research. This month, his team moves to the Experiences and Devices group to continue this work and further integrate machine teaching with conversational AI offerings.
Microsoft researchers Patrice Simard, Alicia Edelman Pelton and Riham Mansour (left to right) are working to infuse machine teaching into Microsoft products. Photo by Dan DeLong for Microsoft.
Millions of potential AI users
Simard first started thinking about a new paradigm for building AI systems when he noticed that nearly all the papers at machine learning conferences focused on improving the performance of algorithms on carefully curated benchmarks. But in the real world, he realized, teaching is an equally or arguably more important component to learning, especially for simple tasks where limited data is available.
If you wanted to teach an AI system how to pick the best car but only had a few examples that were labeled “good” and “bad,” it might infer from that limited information that a defining characteristic of a good car is that the fourth number of its license plate is a “2.” But pointing the AI system to the same characteristics that you would tell your teenager to consider — gas mileage, safety ratings, crash test results, price — enables the algorithms to recognize good and bad cars correctly, despite the limited availability of labeled examples.
In supervised learning scenarios, machine teaching improves models by identifying these high-level meaningful features. As in programming, the art of machine teaching also involves the decomposition of tasks into simpler tasks. If the necessary features do not exist, they can be created using sub-models that use lower level features and are simple enough to be learned from a few examples. If the system consistently makes the same mistake, errors can be eliminated by adding features or examples.
One of the first Microsoft products to employ machine teaching concepts is Language Understanding, a tool in Azure Cognitive Services that identifies intent and key concepts from short text. It’s been used by companies ranging from UPS and Progressive Insurance to Telefonica to develop intelligent customer service bots.
“To know whether a customer has a question about billing or a service plan, you don’t have to give us every example of the question. You can provide four or five, along with the features and the keywords that are important in that domain, and Language Understanding takes care of the machinery in the background,” said Riham Mansour, principal software engineering manager responsible for Language Understanding.
Microsoft researchers are exploring how to apply machine teaching concepts to more complicated problems, like classifying longer documents, email and even images. They’re also working to make the teaching process more intuitive, such as suggesting to users which features might be important to solving the task.
Imagine a company wants to use AI to scan through all its documents and emails from the last year to find out how many quotes were sent out and how many of those resulted in a sale, said Alicia Edelman Pelton, principal program manager for the Microsoft Machine Teaching Group.
As a first step, the system has to know how to identify a quote from a contract or an invoice. Oftentimes, no labeled training data exists for that kind of task, particularly if each salesperson in the company handles it a little differently.
If the system was using traditional machine learning techniques, the company would need to outsource that process, sending thousands of sample documents and detailed instructions so an army of people can attempt to label them correctly — a process that can take months of back and forth to eliminate error and find all the relevant examples. They’ll also need a machine learning expert, who will be in high demand, to build the machine learning model. And if new salespeople start using different formats that the system wasn’t trained on, the model gets confused and stops working well.
By contrast, Pelton said, Microsoft’s machine teaching approach would use a person inside the company to identify the defining features and structures commonly found in a quote: something sent from a salesperson, an external customer’s name, words like “quotation” or “delivery date,” “product,” “quantity,” or “payment terms.”
It would translate that person’s expertise into language that a machine can understand and use a machine learning algorithm that’s been preselected to perform that task. That can help customers build customized AI solutions in a fraction of the time using the expertise that already exists within their organization, Pelton said.
Pelton noted that there are countless people in the world “who understand their businesses and can describe the important concepts — a lawyer who says, ‘oh, I know what a contract looks like and I know what a summons looks like and I can give you the clues to tell the difference.’”
Microsoft Corporate Vice President for Business AI Gurdeep Pall talks at a recent conference about autonomous systems solutions that employ machine teaching. Photo by Dan DeLong for Microsoft.
Making hard problems truly solvable
More than a decade ago, Hammond was working as a systems programmer in a Yale neuroscience lab and noticed how scientists used a step-by-step approach to train animals to perform tasks for their studies. He had a similar epiphany about borrowing those lessons to teach machines.
That ultimately led him to found Bonsai, which was acquired by Microsoft last year. It combines machine teaching with deep reinforcement learning and simulation to help companies develop “brains” that run autonomous systems in applications ranging from robotics and manufacturing to energy and building management. The platform uses a programming language called Inkling to help developers and even subject matter experts decompose problems and write AI programs.
Deep reinforcement learning, a branch of AI in which algorithms learn by trial and error based on a system of rewards, has successfully outperformed people in video games. But those models have struggled to master more complicated real-world industrial tasks, Hammond said.
Adding a machine teaching layer — or infusing an organization’s unique subject matter expertise directly into a deep reinforcement learning model — can dramatically reduce the time it takes to find solutions to these deeply complex real-world problems, Hammond said.
For instance, imagine a manufacturing company wants to train an AI agent to autonomously calibrate a critical piece of equipment that can be thrown out of whack as temperature or humidity fluctuates or after it’s been in use for some time. A person would use the Inkling language to create a “lesson plan” that outlines relevant information to perform the task and to monitor whether the system is performing well.
Armed with that information from its machine teaching component, the Bonsai system would select the best reinforcement learning model and create an AI “brain” to reduce expensive downtime by autonomously calibrating the equipment. It would test different actions in a simulated environment and be rewarded or penalized depending on how quickly and precisely it performs the calibration.
Telling that AI brain what’s important to focus on at the outset can short circuit a lot of fruitless and time-consuming exploration as it tries to learn in simulation what does and doesn’t work, Hammond said.
“The reason machine teaching proves critical is because if you just use reinforcement learning naively and don’t give it any information on how to solve the problem, it’s going to explore randomly and will maybe hopefully — but frequently not ever — hit on a solution that works,” Hammond said. “It makes problems truly solvable whereas without machine teaching they aren’t.”
Related machine teaching links:
Jennifer Langston writes about Microsoft research and innovation. Follow her on Twitter.
The search for immortality is just one reason that brings you to Yingzhou Island, located deep in the Dragon?s Triangle. Playing as ex-special forces soldier Tyre, you?re also here to discover the truth behind your mother?s mysterious death. Finding out how that event and her enigmatic past are tied to the secrets that lie at the heart of the island will see you battle through not only a mercenary army sent in to excavate it, but the horrors they?ve unwittingly set free.
[*Reviews go up at Thursday, April 25 at 5:01 am Pacific*] Set in the beautiful, volcanic scarred high-desert of the Pacific Northwest, Days Gone is an open-world action-adventure game in which you assume the role of Deacon St. John, a Drifter and bounty hunter who would rather risk the dangers of the broken road than live in one of the ?safe? wilderness encampments. The game takes place two years following a global pandemic which has wiped out just about everyone, but transformed millions of others into what survivors call Freakers ? mindless, feral creatures, more animal than human but very much alive and rapidly evolving. Made up of hundreds of individual Freakers, Hordes eat, move and attack together, seemingly as one. Some Hordes roam the highways at night, while others, like the one in the demo, have found a food source that keeps it in a single location. Skills learned in his prior life as an outlaw biker have given Deacon a slight edge in the seemingly never-ending fight to stay alive. But will it be enough? [Bend Studios]
Grimshade is a party-based role-playing game inspired by JRPG of the 90s, featuring a tactical turn-based combat system and a grim story of war and personal choices.
Posted by: xSicKxBot - 04-25-2019, 08:58 AM - Forum: Lounge
- No Replies
James Bond 25 To Be Revealed Today
The next James Bond film, known at present only as Bond 25, is to be revealed very soon. The official 007 Twitter account confirmed the movie will be revealed in a livestream starting on April 25 at 5:10 AM PDT / 10:10 AM EDT / 1:10 PM BST / 10:10 PM AET.
Little else is known about Bond 25 at present, and the aforementioned tweet didn't give much else away. The reveal is taking place "from an iconic 007 location," it said, and fans will have the opportunity to ask the cast questions.
The Bond actor himself, Daniel Craig, appeared reluctant to return for Bond 25. After wrapping Spectre, the previous film in the series, he said he'd rather slash his wrists than play the character again. He also said if he were to play Bond again, it would "only be for the money." A year later, a report claimed that Craig was offered $150 million to star in the next two 007 movies, before the actor confirmed his return in August 2017.
Ark: Survival Evolved Spin-Off PixARK Roars Onto Switch Next Month
Snail Games has announced that its upcoming open-world sandbox-survival game PixARK will launch on Nintendo Switch and all other major platforms on 31st May.
A new trailer for the announcement has been shared above, and you can learn more about the game, which is based on Ark: Survival Evolved, below:
To survive in this mysterious land, you must tame creatures both ferocious and cuddly, craft high tech and magical tools, and build your own base out of cubes. With a robust character creator, an infinite number of voxel based maps and procedurally generated quests, your PixARK adventure will be completely unique. Team up with friends to form a tribe, or play on your own. Spend your time building a towering fortress or go on a quest in a sprawling cavern. Fly on the back of a dragon and smite your enemies with a magic wand, or ride a mighty T-Rex and blast your foes with a rocket launcher. In the world of PixARK, how you play is up to you – as long as you survive!
If you’re feeling ready to explore a world of mystery, danger, ancient dinosaurs, mythical beasts, and cubes, you might want to check out this second trailer which was released earlier this year, too.
The game will be launching both physically and digitally, so make sure to keep an eye out for that lovely physical edition if you’re a collector.
Will you be picking this one up? Let us know in the comments.
Posted by: xSicKxBot - 04-25-2019, 02:15 AM - Forum: Lounge
- No Replies
Anthem Delays A Lot Of Anticipated Content
Anthem developer BioWare has announced some changes to the game's extra-content release schedule. Writing on Reddit, the developer announced seven planned releases have been delayed, including the major Cataclysm event.
"It's been 10 weeks since the early access release of Anthem. While we have been quiet publicly, we have been hard at work in the background and we wanted to provide an update on the state of the game," BioWare said. "We have learned a lot since the game went live. We have heard a lot of feedback from all of you, and we have been working diligently to improve as many things as we can in the short term."
BioWare has supported Anthem with bug fixes and additional changes (including the new 1.1.0 update that's out now) that help "point us in the right direction for the future." However, BioWare acknowledged that it still has "a long way to go before Anthem becomes the game we all want it to be."
Looking ahead, BioWare said it plans to prioritise fixing bugs and improving the overall stability of Anthem. "The reality is there are more things to fix and improve than we planned for. While this is the best thing to do for the game, it means some items from the calendar will be delayed," BioWare said.
The following Anthem features have been delayed:
Mastery System
Guilds
Legendary Missions – Phase II
Weekly Stronghold Challenge
Leaderboards
Some Freeplay Events
Cataclysm
For now, BioWare is keeping quiet on these delayed features, but new information should come in due time. "We want to make sure everything we add to the game has a purpose and fits with our long term goals. When we have information to share on the items above, we will do so," BioWare said.
The studio did, however, address some part of it. For example, the Cataclysm event is a "big focus," and BioWare said it sees it as an "important addition to the game."
"The Cataclysm will bring new challenges and rewards and pushes the story of Anthem forward. As our work continues, we will share more with you in May," it said.
BioWare also said it's aware of the concerns some have about end-game loot. "We agree that our loot and progression systems need to be improved and we are working towards this. When we have more information to share, we will," BioWare said.
Also in the post, BioWare admitted that it hasn't done the best job at communicating with fans. Going forward, BioWare said it plans to stay quiet for longer.
"A lesson we have learned is we have been talking about things too early. There are so many factors that can cause us to pivot on our plans--whether it’s bugs & stability issues, player feedback, or complications with a feature that require us to take more time to deliver it," it said. "Our goal is to tell you about new content and features once the work is closer to being done."
While that is true, BioWare is also hoping to communicate better with players earlier, too, through a test server on PC. BioWare will launch a Player Feedback Environment (PFE) where players can check out new content before it's released publicly to share feedback with BioWare.
In closing, BioWare said Anthem faces an long, uphill battle to get where it needs to be.
"We believe in Anthem. We believe the game will be great, but we recognize getting there will take a lot of hard work," the developer said. "We want to do that work and we want you all to join us on the journey to get there."
Anthem launched in February behind mixed reviews. Despite that, it had a strong first month of sales, in the United States at least.
