It's been an unusual year in tech: Apart from maybe Snapchat's elusive Spectacles, there hasn't been a truly game-changing, mass-market piece of technology that's totally dominated the conversation.

Instead, the biggest companies in tech, especially Facebook, Microsoft, Google, and Amazon, spent their 2016 hyping up something a little more abstract: their ongoing quests to build a so-called "general artificial intelligence," and all the extra smarts they're adding to the product while they're at it. 

Just in the last few weeks, Google released a bunch of code from its famed DeepMind as free open source. Facebook issued a series of videos explaining, in simple terms, exactly how artificial intelligence works. Microsoft released a huge set of data to help train virtual assistants like Siri and Cortana. And Amazon announced Amazon Lex, a service for developers to build Alexa-like intelligence to their apps.

If you ask the CEOs of these companies, it's all about building new features and functions to their apps — from facial recognition in Google Photos and instantly translating comments on Facebook, to more subtle things, like using AI to intelligently route internet traffic and make it possible to virtually "teleport" with Microsoft HoloLens.

Still, it's a weird thing for these companies to put so much public focus on. No matter how hyped the tech community gets for artificial intelligence, it's still very much abstract to a broad audience. You can't touch it, feel it, wear it, or, really, buy it. Unless you follow the ebb and flow of tech closely, it all feels like very "inside baseball" kind of stuff.

And yet, beyond the imminent promise of truly intelligent robots, there are some very practical reasons why Microsoft, Amazon, Google, Facebook, and everybody else is talking about artificial intelligence right here, right now, in just the last several months.

Reason #1: Recruiting

If you ask the biggest tech companies in the world, the rise of artificial intelligence is just as big a deal, if not more so, than the transition from desktop PCs to mobile computing.

Google CEO Sundar Pichai, a very vocal fan of AI, has said that he envisions a world in which every user gets their own personal Google. Facebook and Microsoft, too, have made a big point of showing how artificial intelligence is improving their own apps, enabling stuff (like helping you design better PowerPoints) that would never have been otherwise possible.

It all points to a brighter future, where all of our photos, our documents, our messages, and, in general, our lives, are more organized, more intelligent, and overall better, even as we increase our reliance on software for everything from productivity to shopping to getting around.

Cool. There's just one problem: "You can't hire enough people who are machine learning experts in the world,"Joaquin Quiñonero Candela, the head of Facebook's Applied Machine Learning (AML) research division, recently put it to Business Insider.

Silicon Valley firms are already insanely competitive for programmers; top-tier AI experts are an even hotter commodity, making them ripe targets for poaching from one tech giant to another. 

All of this hype, and all of this spotlight on artificial intelligence, then, is less about pushing the concept to consumers, and more about getting developers excited. That's not just developers working today; it's college students and self-taught individuals.

It's in Facebook's best interests, and Google's, and everyone else's best interest to get these folks interested in AI — especially since, thanks in no small part to pop culture, AI has a reputation as something that's practically magic, or at least obscenely difficult to learn.

In reality, AI is just applied math and statistics. And you can learn math. But if the next generation of coders is intimidated by the notion of AI, that's a problem for Google, Facebook and all the other tech giants planning their future on AI.

Put even more simply: If you want a job in tech in a few years, spend some time learning programming and statistics and invest some time in playing with the AI code that Microsoft, Facebook, Google, et al, have freely released.

Reason #2: The cloud wars

This one applies especially to Amazon, Microsoft, and Google, usually considered the first, second, and third place players in the cloud computing market, respectively.

In cloud computing, startups and Fortune 500 companies alike pay as they go for access to functionally unlimited supercomputing power. You don't need much more than a credit card to get started.

Amazon Web Services is leading the pack among public cloud providers, and by all indications, it's not even close. There's even a chance it'll become Amazon's biggest revenue driver within only a few years. But Microsoft and Google are chasing the opportunity with everything they've got, throwing money and resources into building out their Microsoft Azure and Google Cloud Platform products and hiring execs to lead them.

To win at cloud computing, though, you need the love and support of developers. With so many options, and the barrier to entry so low, these three tech titans are tripping over themselves to be the option that software developers actually want to use.

That means adding the features and capabilities they want. It also means adding the things they will want.

And while artificial intelligence is still very young today, lots of companies are going to want it tomorrow. First, startups, then within a few years, those Fortune 500 companies, too. For Amazon, Google, and Microsoft, hammering on their AI expertise today will pay dividends tomorrow.

So while this year's MacBook Pro launch may have been a bit of a bore, know that these companies aren't exactly asleep at the wheel. It's just that they're laying the groundwork for bigger things later.

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Sam Altman is putting a lot of faith in the idea that giving people free money will make them happier and healthier, but that doesn't mean he's betting it all.

Altman is the president of Y Combinator, Silicon Valley's largest startup accelerator. He and a group of YC researchers plan to launch an experiment in Oakland, California, next year in which roughly 100 families will get $2,000 a month.

The goal is to capture data that reveals whether free money on a regular basis (known as a basic income) really does help people escape poverty and live healthier lives.

Data from underdeveloped countries, such as Kenya and Honduras, already suggests people do see those benefits.

But the US is uncharted territory for basic income. Altman, one of Silicon Valley's largest champions for basic income, acknowledges that even he has some doubts.

"I believe that when automation comes, we will have enough extra money that the math will work," he told Business Insider. In other words, when robots and artificial intelligence replace huge swaths of the American labor force, basic income could sustain the displaced workers.

"What's unclear to me," Altman continued, "is will people be net-happier or are we just so dependent on our jobs for meaning and fulfillment?"

Altman envisions a scenario in which the majority of Americans, fresh off their initial basic-income payments, become perfectly content to sit at home in their virtual-reality headsets. He questions whether that future is necessarily better than the stimulation and (modest) physical activity found in commuting to work and chatting with coworkers.

"People do form bonds with their community and their society through work," he says. "And I think it does contribute to our national cohesion."

Staunch basic-income advocates don't necessarily disagree with Altman, but they also don't share his pessimism that basic income will produce a society of couch potatoes. Many say the system would merely free people up to do different kinds of work — work they find more satisfying than the kind that pays the bills.

In other words, accountants could paint more. Lawyers could practice the drums more. Everyone with a hobby could make that their sole focus. And yes, people who like VR could use VR to their heart's desire.

How the future shakes out could come down to how Americans learn about such a system: As psychologist Daniel Kahneman once noted, "People don't choose between things, they choose between descriptions of things."

If basic income is presented as a radical style of welfare, it's likely to upset people who oppose welfare in its existing form. People who see value in work for its own sake could bristle at the idea of severing income from a work requirement.

On the other hand, if it's presented as a means to freedom from work you hate, people might see basic income as inherently more American than the existing labor structure. Citizens could finally do the work that matters most to them rather than the work that pays the best.

The future hasn't arrived yet, so it's impossible to know what kind of effects an overhaul of that scale — free money for all, no strings attached — would end up having. Until the data offers that clarity, Altman says, he'll continue experimenting with tiny versions of the future to see which look the most appealing.

SEE ALSO: Facebook's cofounder just launched a $10 million initiative with other big names to figure out whether basic income works

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Facebook CEO Mark Zuckerberg's personal project for 2016 has been to build an artificially intelligent, voice-controlled assistant for his home.

Now that he's finished the first version of his own "Iron Man"-inspired Jarvis, he's considering giving away the code he created.

In a lengthy Facebook post on Monday, Zuckerberg described how he went about creating his digital butler for his home in San Francisco. He spent a total of 100 hours building the assistant, which can control his lights and music and even make toast with a retrofitted toaster from the 1950s.

"In some ways, this challenge was easier than I expected," Zuckerberg wrote. "In fact, my running challenge (I also set out to run 365 miles in 2016) took more total time. But one aspect that was much more complicated than I expected was simply connecting and communicating with all of the different systems in my home."

Here are the main things Zuckerberg's personal assistant can do:

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Control his main appliances, including his lights and toaster

"It's possible to control some of these using internet-connected power switches that let you turn the power on and off remotely," he wrote. "But often that isn't enough. For example, one thing I learned is it's hard to find a toaster that will let you push the bread down while it's powered off so you can automatically start toasting when the power goes on. I ended up finding an old toaster from the 1950s and rigging it up with a connected switch. "

Play music based on his or his wife's preferences, depending on who asks

Since Zuckerberg trained his assistant to recognize both his voice and the voice of Priscilla Chan, his wife, it will play different music tailored to whoever asks. If the mood is off, they can say general statements like "That's not light — play something light," and the assistant will correct itself.

"In general, I've found we use these more open-ended requests more frequently than more specific asks," Zuckerberg wrote. "No commercial products I know of do this today, and this seems like a big opportunity."

Scan the faces of his visitors and let them in through the front door

Zuckerberg used Facebook's facial-recognition technology to scan the faces of his visitors from cameras positioned at his front door.

"I built a simple server that continuously watches the cameras and runs a two-step process," he wrote. "First, it runs face detection to see if any person has come into view, and second, if it finds a face, then it runs face recognition to identify who the person is. Once it identifies the person, it checks a list to confirm I'm expecting that person, and if I am then it will let them in and tell me they're here."

The artificially intelligent virtual assistant that Mark Zuckerberg built for his home is voiced by none other than Morgan Freeman.

Zuckerberg posted a humorous video to his Facebook page on Tuesday that shows his personal assistant in action, from waking him up in the morning to teaching his daughter how to speak Mandarin.

The video comes just a day after Zuckerberg published a lengthy post detailing how he built his own AI assistant from scratch. He named it "Jarvis" as a nod to Iron Man, which led the actor Robert Downey Jr. (who plays Iron Man) to offer to be its voice.

It looks like Zuckerberg went with Morgan Freeman's voice instead, which is also pretty fitting.

You can watch Zuckerberg's full video below:

And here's a second video from the perspective of Zuckerberg's wife, Priscilla Chan:

SEE ALSO: Mark Zuckerberg is considering giving away the personal virtual assistant it took him 100 hours to build - here's how it works

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Japan has a sex problem. The country's birthrate is shrinking year after year, to the point where deaths are outpacing births.

Simply put, Japan's population is decreasing.

But let's be clear: Population change is a complicated subject affected by many factors.

Western media often correlates the decline in Japan's population size with recent studies of Japanese sexual habits and marriage. A 2016 study by the National Institute of Population and Social Security Research in Japan, for instance, found that "almost 70 percent of unmarried men and 60 percent of unmarried women are not in a relationship."

But just because people aren't in relationships doesn't mean they don't want companionship, of course. And that's where something like Gatebox comes in.

Yes, that is an artificially intelligent character who lives in a glass tube in your home. Her name is Azuma Hikari, and she's the star of Gatebox — a $2,500 Amazon Echo-esque device that acts as a home assistant and companion.

Here's what we know:

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A Japanese company named Vinclu created the Gatebox.

It's about the size of an 8-inch by 11-inch piece of paper, according to Vinclu. And there's a good reason for that: The device is intended to be "big enough for you to be able to put right beside you." You'll understand why you'd want a Gatebox so close soon enough.

The Gatebox is similar to Amazon's Echo — it's a voice-powered home assistant.

The Gatebox has a microphone and a camera because you operate it using your voice.

For now, it will respond only to Japanese; the company making Gatebox says it's exploring other language options. Considering that preorder units are available for both Japan and the US, we'd guess that an English-language option is in the works.

Gatebox does a lot of the same stuff that Echo does — it can automate your home in various ways, including turning on lights and waking you up in the morning.

Starbucks has led the way for not just fast-casual restaurants, but all of retail when it comes to using customer-facing technology in its stores. The company was the first major chain to integrate digital payment into its app, making it a common sight to see people pay by holding up their phones to a scanner. That happened well before payment via phone become a relatively common thing, and it forced other chains to follow.

Starbucks also led the way with Mobile Order & Pay. That technology allows people to skip the line, creating a better experience for regular customers while offering shorter lines for casual visitors. That system, which has since been copied by rivals Dunkin' Brands, and Panera Bread (along with countless other food chains) lets more people working in the limited space behind the counter focus on food preparation rather than order-taking. Doing that allows for not just a better, faster customer experience, but it lets the store serve more people, driving sales numbers up.

In some ways, Starbucks has been a technology company for the past few years as much as it has been a coffee purveyor. Now, in a presentation at its Dec. 7 Investor Day, the chain laid out its next steps for remaining ahead of the curve when it comes to integrating technology into its retail experience.

What happens next for Starbucks?

"We look at digital not as a marketing channel, but as a foundational part of the customer experience at Starbucks," said Chief Strategy Officer (CSO) Matt Ryan. "That's why we have a different track record with digital from so many other companies who just treat it as marketing."

The next step in evolving that platform will be the company's using what it has learned about its customers to better serve them. Imagine walking up to a Starbucks counter and the artificial intelligence (AI) in its app knows not only your history, but the past preferences of people with your ordering habits. That will allow the company to deliver personalized suggestions, not only based on what you have ordered, but what you might want to try.

This follows on what Starbucks has already been doing on a gross level. Currently the company can use real-time data to make special offers to customers via email. Say, for example, that you traditionally stop in to a Starbucks once or twice a week in the afternoon for a latte. The chain can take that info and offer you incentives -- discounts or rewards program bonuses -- to get you to make that visit more often or to entice you to add a food item to your latte.

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If you want to make predictions for the future, you need to find the trajectory of events in the past.

So to work out what shape digital technology will likely take next year, we should look back to the major developments of 2016.

And the past year’s developments point to a 2017 shaped by the next phase of virtual and augmented reality, the emergence of an internet for artificial intelligence and the creation of personalised digital assistants that follow us across devices.

Virtual world

One technology in particular has dominated the news throughout the year and made the birthday wish-lists of children and adults alike: virtual reality. VR began to bloom commercially in 2016 – with HTC, Oculus (owned by Facebook)and PlayStation all releasing their latest headsets. But 2017 will almost certainly be a pivotal year for VR, given its rather precarious position on the “hype cycle”.

This is a research methodology for predicting the commercial dominance of an emerging technology as it matures and goes through periods of increasing hype, sudden disillusionment and eventual success. Presently VR is on the precipice of the “peak of inflated expectations”, where the hype exceeds the reality and quality comes second to novelty.

In the hype cycle model, the peak of excitement is followed by an inevitable fall (the “trough of disillusionment”), as consumers realise the gap between what they expect and what they actually get. Here is where opinion is divided on VR. For some this will be a gentle dip, while for others the drop will be a portent to collapse.

The big question splitting these opinions is whether the consumer reaction to the VR games and applications currently being released will be the wrath of disillusionment or the mercy of patience. The more convincing assertion is that mobile phone-based VR platforms (with their greater ease of use, lower cost and wider range of games and applications) will help stabilise VR throughout 2017.

Augmented success

But stability is not the same as success. VR also has the problem that its consumer appeal is primarily recreational, limited largely to games and 360-degree videos. So far it has had relatively little impact on social or functional applications such as providing an interface for social media.

The same cannot be said for its more versatile but currently less well-known cousin, augmented reality. AR – which involves overlaying images of the real world with additional graphics or information – has enjoyed much success of late as a gaming platform, particularly thanks to the release of Pokémon Go.

Yet AR functionality already goes beyond games, and it is an ideal delivery mechanism for limitless forms of digital information. Concepts include heads-up displays attached to cyclists’ helmets that provide them with a 360-degree field of view and also alert them to potential dangers by tracking overtaking vehicles. But also applications such as visual overlays that can virtually redecorate your entire home without a single lick of paint.

The real future of AR however is in it’s potential to give us a new and improved means of accessing content and services we already cannot do without. As Microsoft’s HoloLens and Google Glass have alluded to, 2017 could see us using AR to check our emails, posting on Facebook and discovering the best route to our meeting place across town, with all content delivered straight to our eyes. Not a single aversion of our gaze or break in our stride required.

Current investment in the sector is prioritising advances in relevant underlying technologies such as depth-sensing camera lenses and physical environment mapping systems.

This suggests that the industry is readying hardware to ensure these exciting ideas can materialise. It doesn’t mean that all ambitions of AR will be realised in 2017, but they are tantalising possibilities, depending on whether the underlying technology can make them a reality.

Internet of Robots

The other area where we are likely to see some fascinating research developments moving into commercial applications is artificial intelligence and machine learning. And the application most likely to dominate 2017 is the Internet of Things, the connection of millions of ordinary devices, from cameras to kettles, to the internet.

The concept of the Internet of Things champions our seeming desire for constant connection, with the physical objects we use everyday all linked together in a glorious (or terrifying) chain. 2017 could be the year we’ll all be telling our telling our barista coffee machine at home to prepare us a chocolate fudge Café Cubano from five miles away, using a bespoke interface in our car as we’re driving home.

Or perhaps not. But this ethos of interconnectivity is already reaching the realm of artificial intelligence with Cloud Robotics. These systems allow robots that have been optimised for different tasks to work on specific problems individually, but to pass solutions between each other.

The robots use the cloud to share the data, enabling it to be analysed by any other robot or intelligence system also connected to the same network. One robot teaches something to another, who in turn develops it and passes it forward in a collaborative effort that could massively increase the learning potential and connectivity of machines.

Personal digital assistants

All of these trends comes together for our final 2017 prediction: the rise of humanised digital technology in the form of intelligent personal assistants. These are essentially human-emulating data hubs. They use advances in artificial intelligence to capture and interpret our data, the Internet of Things to operate everything around us, and the advances in augmented reality to project themselves convincingly into our mobile world.

This will provide a single, naturalistic interface between us and our digitally connected universe. It is the next iterative step for the likes of Siri, Cortana and Alexa: an intelligent assistant able to travel with us wherever we go, across every device we use, to assist us in nearly every aspect of our lives.

Tom Garner, Research Fellow, School of Creative Technologies, University of Portsmouth

This article was originally published on The Conversation. Read the original article.

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Apple has stayed true to its promise and published its first academic paper on artificial intelligence.

The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open.

The new Apple paper — published December 22 and titled "Learning from simulated and unsupervised images through adversarial training"— gives an insight into some of the techniques that Apple is using to develop AI.

In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image.

The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Why? Because synthetic image data is already labelled and annotated while real-world images aren't.

However, using synthetic images has its problems. The Apple researchers write that they are "often not realistic enough, leading the network to learn details only present in synthetic images" and adding that they "fail to generalise well on real images."

In order to get around this issue, the researchers propose using a technique they call "Simulated+Unsupervised learning," which combines unlabelled real image data with annotated synthetic images.

The paper's lead author was Apple researcher Ashish Shrivastava. Other authors include Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russ Webb.

Apple's software can already identify people's faces in photos but the company and its rivals now stand to benefit by programming machines to learn how to identify places, animals, brands, and other things.

Apple's paper comes after Facebook's head of AI Yann LeCun said that Apple's reluctance to let researchers publish their work could be hindering its hiring efforts in the highly competitive field where Google, Amazon, and DeepMind are also looking to recruit the best talent.

Describing how Facebook gets the most talented software engineers in the world to come and work on Facebook's AI efforts, LeCun said: "Offering researchers the possibility of doing open research, which is publishing their work.

"In fact, at FAIR [Facebook Artificial Intelligence Research], it’s not just a possibility, it’s a requirement," he said in London. "So, [when] you’re a researcher, you assume that you’re going to publish your work. It’s very important for a scientist because the currency of the career as a scientist is the intellectual impact. So you can’t tell people 'come work for us but you can’t tell people what you’re doing' because you basically ruin their career. That’s a big element."

Jack Clark, who writes the Import AI newsletter, wrote in his latest email: "Apple’s participation in the AI community will help it hire more AI researchers, while benefiting the broader AI community."

It's likely that majority of Apple's AI research takes place at its headquarters in Cupertino but the iPhone maker also has a number of satellite AI outposts around the world, including a secret Siri lab in Cambridge, UK.

Apple did not immediately respond to Business Insider's request for comment.

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I activated my Amazon Echo for the first time in December 2015. It's since become one of my favorite tech gadgets ever.

Amazon's family of Echo speakers were some of the hottest gifts this holiday season — the smaller $50 Echo Dot has been the top-selling item on Amazon.com since November 1— so right now, many people are activating their Echo units for the first time.

Here's what you need to know: These speakers, which can respond to either "Alexa" or "Amazon," are extremely quick to respond and understand your commands way better than any other device I've used.

Thanks to its excellent audio system, with seven microphones for listening and a 360º omni-directional audio grille for speaking, Amazon Echo works exceedingly well wherever I am in my home. I can hear it — and it can hear me — almost perfectly.

Amazon Echo has completely transformed the way I live in my apartment. There's just so much you can do with Echo. Take a look.

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"Alexa, what time is it?"

Honestly, the best use cases for Amazon Echo are the simplest ones. With the Echo, I don't need to bother searching for my phone just to get the time — you can ask for the time from anywhere in your house and get the answer immediately. It's a small thing, but it totally makes a difference when you're rushing in the morning.

"Alexa, how's the weather outside today?"

Again, it's a simple task, but it's way quicker and better than pulling out your phone and opening your favorite weather app. Amazon Echo will not only tell you the current temperature, but also the expected high and low temperatures throughout the day, and other conditions such as clouds and rain.

"Alexa, set a timer for 10 minutes."

Amazon Echo is the perfect cooking or baking companion because it's totally hands-free. When the timer's up, a radar-like ping will sound until you say "Alexa, stop."

Facebook. Google. Apple. Baidu. Not a day goes by when we don’t hear of something related to artificial intelligence in the news.

But AI (sometimes confused with machine learning, which is simply a technique within AI) wouldn’t be where it is today if it weren’t for one seminal event in 2016: AlphaGo beating Lee Sedol.

It’s the tech equivalent of Leicester City winning the Premier League, or the Golden State Warriors winning 73 games in the NBA. It’s the unimaginable happening, right in front of our own eyes.

In March last year, an AI program that trained itself to play the ancient game of Go beat the 18-time world champion. The reason it was such a feat for AI, was because Go is about feel, strategic judgment and winning multiple battles across the board – and a computer cannot simply memorise all possible combinations of board pieces, assess the situation, construct and execute a strategy to win, like chess.

So the programmers of AlphaGo, from Google DeepMind, set up the basic heuristics of the game, allowed AlphaGo to analyse previous games and then split its brain so it could play itself millions of times.

Imagine the 1983 film War Games in which a computer was taught to learn by playing tic-tac-toe against itself. This time, however, the game is Go.

The results were stunning. AlphaGo beat Lee Sedol convincingly, 4 games to 1. So much for human intuition. And to add insult to injury, the Korea Baduk Association awarded AlphaGo the highest Go grandmaster rank.

This event was important because it made clear that AI was here to stay. Of course, AI is also receiving a big push from Facebook and Google as they invest billions of dollars through OpenAI and DeepMind to democratise the technology – not to mention countless acquisitions of AI talent. But the corporate activity merely proves that big-business has plans for AI, whereas AlphaGo beating Lee Sedol signals the possibility that AI could indeed be more capable than humans.

And that is a conceit that an AI investor like me is comfortable with. There are certainly some things that machines are better at than humans. In fact, there are many things that machines should be better at.

Self-driving cars is one example. Imagine a world where traffic accidents are a relic of the past and very few lives are lost. Imagine a future where your cancer prognosis is not misdiagnosed by multiple doctors and visitations, but instead you are told in one go what cancer you have, how long you have to live and what stage it is at. Imagine a future where your frustrated customer service hotline calls are replaced by an AI that recognises everything you say and responds in the best way. In many ways, the human variability that makes us all frustrated will be replaced with machine predictability.

Many people are aghast when I mention the predictability versus variability argument. “Human fallibility is great!” they say. “It’s what makes us alive!” Bollocks. Human fallibility and variability are great for art, where it is a necessary requirement to produce even greater art. It’s great for startups, when we are looking for the next big innovation and we don’t know where to look. But it’s absolutely terrible for things like cancer detection, driving, aviation and a whole host of things that don’t need human intervention or decision-making.

I am biased, of course, but I’ll make two predictions about AI for 2017 and beyond. One, AI will be the biggest thing since sliced bread.

You can consider corporate activity like Facebook, Google, Snapchat, Baidu as a signal of trust. You can also trust the direction of car companies like GM, Mercedes-Benz, Uber, and Tesla towards autonomous cars. And you can trust academics, the White House and other longitudinal thinkers when they say past academic research, computational power and large accessible data sets will together create a perfect storm for AI impacting our lives. All signals show AI will be big.

Another prediction: Asia will be big in AI. If you look at the data trends, there is a tonne of AI research done in China, Japan, South Korea and Singapore. In fact there are more cited publications in China than in the United States. The main problem, however, is the cultural barriers to sharing this knowledge between Asia and the rest of the world, many of which stem from language differences.

But there is another barrier. In the West there is a tenancy, even incentives, to commercialise research, but the same dynamics are not present in Asia.

Nevertheless, the underlying core principle can not be denied – AI’s potential in Asia is rising and Hong Kong would do well to find a way to ride this wave.

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Alphabet's DeepMind is paying to put future artificial intelligence (AI) gurus through Oxford University as the battle for AI talent increases between the tech giants.

The London-based AI lab — acquired by Google in 2014 for £400 million— has been quietly funding PhDs since at least last October, a LinkedIn search suggests.

It is one of several organisations offering to sponsor 15 students through Oxford PhDs (or DPhils as Oxford refers to them) from October 2017 onwards, according to a page on the university's website that was posted last September.

"The studentships are for three years and are open to students of any nationality," reads the Oxford University page. "Each studentship will cover university and college fees with a stipend of at least £14,296 per year." Oxford goes on to say that the "topics for the studentships are open, but should relate to the interests of one of the Department’s research areas."

Research areas include AI, machine learning, security, computational biology, and multi-agent systems, among others.

All of the tech giants are now looking at innovative ways to find people who can help them make AI breakthroughs that they can embed into their products and services, potentially making their personal assistants more useful and helping their platforms to understand who is in photos and what the user might want next.

DeepMind, which employs around 400 people inside a new Google office in London, has hired more than a dozen AI and machine learning scientists from Oxbridge since it was acquired by Google, as well as others from the likes of MIT, Stanford, and Harvard.

But hiring these people isn't easy. Companies like Facebook, Apple, and Amazon are all offering big salaries (often in excess of £100,000 a year) in a bid to get the best people in the industry.

Offering a scholarship to students who are about to learn under some of the brightest thinkers in the world is likely to help DeepMind in its hiring efforts.

LinkedIn shows that a number of PhD students are already studying at Oxford on DeepMind scholarships.

Yannis Assael, for example, who has studied at Imperial College London and Oxford previously, writes on his LinkedIn page that he was awarded a scholarship from Google Deepmind for a DPhil in Machine Learning at Oxford in October 2015. Xiaoxuan Lu, who holds a degree from Nanjing University of Aeronautics and Astronautics, is on the same scholarship, according to LinkedIn.

DeepMind did not immediately respond to Business Insider's request for comment.

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When it comes to security in their financial transactions, American consumers want it all. In Total System Services, Inc.'s 2016 U.S. Consumer Payments Study, a whopping 74% of all respondents said they would choose the credit card with the best security features over the credit card with the best rewards. Payment networks like Mastercard Inc. have done their best to respond.

In the past couple of years, Mastercard has experimented with a number of cutting-edge security initiatives. These innovations have included everything from an app that allows cardholders to take a selfie for payment authentication to a wearable band that uses the account holder's unique heartbeat as authentication.

Late last month, Mastercard introduced its latest pioneering security platform, Decision Intelligence. According to the press release, this program "uses artificial intelligence technology to help financial institutions increase the accuracy of real-time approvals of genuine transactions and reduce false declines." The service leverages machine learning so that each transaction is assigned a score, which is then used to help judge future payments.

False declines are a big problem

This service is not only being launched to help root out fraud, but to help prevent another huge problem haunting the payments industry: purchases declined for fraud when customers are in fact making legitimate purchases. False declines hurt everyone from consumers to merchants to card-issuing banks.

For consumers, a card deactivation can follow a false decline, meaning cardholders must deal with the extra hassle of ordering a new card and waiting for the replacement to arrive before using it again on top of the annoyance of having to wait to complete a purchase.

Retailers lose an estimated $118 billion per year on false declines. Adding insult to injury, over a third of shoppers will not return to a merchant after being falsely declined at a physical location or website.

For card-issuing financial institutions, the problem of false declines might be the most serious of all. According to a 2015 study by the research consulting firm Javelin, about 15% of all cardholders have experienced a false decline for a legitimate purchase. Of these cardholders, nearly 40% abandoned their card after the false decline.

Criticism of the service

Just days after the press release announcing the launch, an article appeared in Computerworld criticizing the service. The piece centered on two criticisms: 1) Mastercard is only one of several card issuers and so has access to only a minority of credit card purchases to learn from; and 2) Even if Mastercard's service worked perfectly, merchants would still have to utilize additional antifraud measures for purchases made via other payment methods.

While it is way too early to judge the effectiveness of Mastercard's Decision Intelligence service, these two criticisms almost completely miss the point of the program. First, Mastercard processed nearly 14.5 billion transactions in its most recently reported quarter. That should provide more than sufficient data to properly educate its machine-learning program.

Second, while it is true merchants must use other measures to cut down on fraud and false declines for purchases made by other payment methods, Mastercard's immense volume should guarantee a significant reduction of fraudulent purchases and false declines over time.

Why this matters to Mastercard investors

Critics are overlooking who this platform was designed to serve. It isn't primarily intended for consumers or merchants (though it does offer benefits to both those groups), but for Mastercard's card-issuing banks.

A little over a year ago, Mastercard lost USAA's credit and debit card portfolio to Visa. USAA had been Mastercard's largest debit card issuer, and the two companies had enjoyed a 30-year relationship. And while investors are not privy to all the details of the split, they do know that as a result of this loss, Mastercard projects a drag on its earnings and revenue for at least a few more quarters.

While the USAA break was a rare one for Mastercard, it shows how devastating the loss of a major card issuer can be for a payment-network company. This is why Mastercard has spent the past several years developing in-house platforms and acquiring companies designed to make it much more difficult for financial institutions to leave the Mastercard ecosystem.

These services include fraud-protection services like Decision Intelligence, and other services such as consulting and research, loyalty and reward program management, and data analytics. Importantly, the services are a growing revenue source for Mastercard; collectively, they grew revenue by 23% year over year, in Mastercard's most recently reported quarter, to a total of $620 million.

But even more important to Mastercard than these growing sources of revenue is the value they add to the company's relationship with its card issuers. Its services are often prized by financial institutions, as many of them fall far outside their core competencies. When card-issuing banks and credit unions begin to rely on Mastercard to provide essential services, it cultivates an incredibly sticky relationship. This, of course, makes it harder for a competitor to poach that customer and supplant Mastercard...as Visa did with USAA.

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A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders.

Fukoku Mutual Life Insurance believes it will increase productivity by 30% and see a return on its investment in less than two years. The firm said it would save about 140m yen (£1m) a year after the 200m yen (£1.4m) AI system is installed this month. Maintaining it will cost about 15m yen (£100k) a year.

The move is unlikely to be welcomed, however, by 34 employees who will be made redundant by the end of March.

The system is based on IBM’s Watson Explorer, which, according to the tech firm, possesses "cognitive technology that can think like a human", enabling it to "analyse and interpret all of your data, including unstructured text, images, audio and video".

The technology will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, according to the Mainichi Shimbun.

While the use of AI will drastically reduce the time needed to calculate Fukoku Mutual’s payouts – which reportedly totalled 132,000 during the current financial year – the sums will not be paid until they have been approved by a member of staff, the newspaper said.

Japan’s shrinking, ageing population, coupled with its prowess in robot technology, makes it a prime testing ground for AI.

According to a 2015 report by the Nomura Research Institute, nearly half of all jobs in Japan could be performed by robots by 2035.

Dai-Ichi Life Insurance has already introduced a Watson-based system to assess payments - although it has not cut staff numbers - and Japan Post Insurance is interested in introducing a similar setup, the Mainichi said.

AI could soon be playing a role in the country’s politics. Next month, the economy, trade and industry ministry will introduce AI on a trial basis to help civil servants draft answers for ministers during cabinet meetings and parliamentary sessions.

The ministry hopes AI will help reduce the punishingly long hours bureaucrats spend preparing written answers for ministers.

If the experiment is a success, it could be adopted by other government agencies, according the Jiji news agency.

If, for example a question is asked about energy-saving policies, the AI system will provide civil servants with the relevant data and a list of pertinent debating points based on past answers to similar questions.

The march of Japan’s AI robots hasn’t been entirely glitch-free, however. At the end of last year a team of researchers abandoned an attempt to develop a robot intelligent enough to pass the entrance exam for the prestigious Tokyo University.

"AI is not good at answering the type of questions that require an ability to grasp meanings across a broad spectrum," Noriko Arai, a professor at the National Institute of Informatics, told Kyodo news agency.

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A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders.

Fukoku Mutual Life Insurance believes it will increase productivity by 30% and see a return on its investment in less than two years.

The firm said it would save about 140m yen (£1m) a year after the 200m yen (£1.4m) AI system is installed this month. Maintaining it will cost about 15m yen (£100k) a year.

The move is unlikely to be welcomed, however, by 34 employees who will be made redundant by the end of March.

The system is based on IBM’s Watson Explorer, which, according to the tech firm, possesses “cognitive technology that can think like a human”, enabling it to “analyse and interpret all of your data, including unstructured text, images, audio and video”.

The technology will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, according to the Mainichi Shimbun.

While the use of AI will drastically reduce the time needed to calculate Fukoku Mutual’s payouts – which reportedly totalled 132,000 during the current financial year – the sums will not be paid until they have been approved by a member of staff, the newspaper said.

Japan’s shrinking, ageing population, coupled with its prowess in robot technology, makes it a prime testing ground for AI.

According to a 2015 report by the Nomura Research Institute, nearly half of all jobs in Japan could be performed by robots by 2035.

Dai-Ichi Life Insurance has already introduced a Watson-based system to assess payments - although it has not cut staff numbers - and Japan Post Insurance is interested in introducing a similar setup, the Mainichi said.

AI could soon be playing a role in the country’s politics. Next month, the economy, trade and industry ministry will introduce AI on a trial basis to help civil servants draft answers for ministers during cabinet meetings and parliamentary sessions.

The ministry hopes AI will help reduce the punishingly long hours bureaucrats spend preparing written answers for ministers.

If the experiment is a success, it could be adopted by other government agencies, according the Jiji news agency. 

If, for example a question is asked about energy-saving policies, the AI system will provide civil servants with the relevant data and a list of pertinent debating points based on past answers to similar questions.

The march of Japan’s AI robots hasn’t been entirely glitch-free, however. At the end of last year a team of researchers abandoned an attempt to develop a robot intelligent enough to pass the entrance exam for the prestigious Tokyo University.

“AI is not good at answering the type of questions that require an ability to grasp meanings across a broad spectrum,” Noriko Arai, a professor at the National Institute of Informatics, told Kyodo news agency.

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A bunch of top poker pros are getting ready to take on Libratus, the latest and greatest poker bot, in a 20-day Heads-Up No-Limit Texas Hold’em challenge. Since the bot's predecessor, Claudico, was almost good enough to beat top players, and Libratus is supposed to be a lot better, the humans could be in trouble. On the other hand, humans are getting better, too, says competitor Jason Les.

"From the human side, poker has gotten much tougher in the last 20 months," Les told Carnegie Mellon University, which developed the bot.

Les says that humans are getting better at poker thanks to computer-assisted analysis tools like PioSOLVER and PokerSnowie. In particular, players are starting to bet more aggressively to obtain small advantages.

As for how this plays out in Hold'em, a game where players can bet after being dealt two cards ("the pocket") and again after seeing three shared cards ("the flop") and a fourth shared card ("the turn") and a fifth shared card ("the river"), Les gave a couple of examples:

—Higher three-bets (i.e., responding to a raise in the initial round of betting with a new and particularly large raise). Les, in an email, writes that three-bets these days are often 50% larger than they used to be. Players are "trying to make the pot very large with their good hands and some bluffs … therefore, charging people a very big price to see the flop or attempt to four-bet back at them."

—More frequent continuation betting (i.e., when a player who raised in the initial round of betting raises again in the second round). Les writes: "I think players are liking this strategy because it doesn’t let their opponent see the turn for free … they have to pay a little if they want to further realize their equity."

Both of those tactics may seem obvious, but it takes computer analysis to recognize how significant small advantages like that can be.

Poker is well behind chess in this way: the king’s game was, after all, conquered by AI back in the 90s, and it has turned into a game of tiny advantages thanks to decades of computer analysis.  Poker, which requires information based on incomplete information as well as tactics like bluffing, is a lot harder. But AI is catching up.

"AI’s influence over poker has been much more of a recent effect on the game. Quite frankly, the tools just weren’t there 10 years ago," Les writes.

AI tools are not only helpful for pros trying to find small advantages, but also for amateurs looking to pick up the game.

Les made a big comeback against Claudico in the 2015 competition. He explains that he was ultimately able to figure out weaknesses he could exploit.

"Playing this type of a computer … the goal is to find any area it may be weak and take as much advantage of that weakness as possible. Unlike a human, you don’t have a concern with what your opponent is thinking of you or how ridiculous your strategy may look," Les writes.

He says the pros realized in particular that Claudico was limping (that is, calling rather than raising in the small blind position) on too many bad hands, inadvertently revealing when it was in a bad position.

Libratus, created at CMU by Tuomas Sandholm and PhD student Noam Brown, aims to be the first bot that can beat top human players at Head’s Up No-Limit Hold’em. Head’s up means a game with two players — it’s easier to solve than group poker games. No-limit means there’s no limit to bets: it’s harder to solve than limit hold’em, which a bot from the University of Alberta conquered in 2015.

Figuring out poker could help AI researchers take on real-world problems involving incomplete information, such as negotiations where each side is hiding information.

"In the real world, all of the relevant information is not typically laid out neatly like pieces on a chessboard," Brown writes in an email. "There will be important information that is missing or hidden, and the AI needs to be able to handle that."

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Morgan Stanley has released a big report on the "Big Debates" of 2017, which highlights the key questions they think will shape global markets over the next 12 months.

Will protectionism hinder US growth? Will the bank stock rally continue?

Business Insider breaks down nine debates that will dominate Wall Street in the new year. 

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1. Will protectionism hurt US growth?

There has been a major shift in American politics, on both sides of the aisle, away from free-trade and economic liberalism towards protectionism. 

While most analysts are in agreement that protectionist policies will negatively affect emerging markets such as Asia, there is not a consensus on its implications for the US. 

Morgan Stanley is not taking an optimistic stance:

"Under a more protectionist regime, there is potential for near-term upside in US growth, driven entirely by a narrowing of the trade balance. However, this would come at a cost of permanent output loss."

2. Will we see corporate tax reform?

It is very likely that there will be corporate tax reform in the US in 2017, according to Morgan Stanley, but what that reform will look like and the degree to which it will benefit the economy is still up for debate.

"The good news for investors and the economy is that Congress has the motive and the opportunity to execute tax reform in 2017 ... the bad news is that this stimulus may lack punch given the likelihood of resulting Fed hawkishness and potential corporate finance disruptions," the bank said.

3. Will border adjustments lead to an appreciation of the USD?

One of the cornerstones of Donald Trump's campaign was a promise to impose taxes on imports into the US, or border adjustments, in order to buoy America's ailing manufacturing sector.

Traditionally, Republicans have not been in favor of implementing such taxes, but as of late it looks like some key GOP legislators have had a change of heart. 

"Recent news suggests House Republicans and President-elect Trump are coming together around [border adjustments]," the bank said.

As such, Morgan Stanley believes border adjustments stand a serious chance in 2017. 

However, they don't subscribe to the popular opinion that border adjustments would have a profound affect on FX markets.

"We believe border adjustment would not result in a full exchange rate offset (i.e we don't expect USD appreciation of 25%) ... instead we think a 10-15% rise in USD is reasonable," the bank concluded.

Microsoft has purchased Maluuba, a Samsung-backed artificial intelligence startup based in Montreal, in its quest to build better, smarter artificial intelligence that can be used in the workplace, the company says in a blog post.

And as part of the deal, Maluuba advisor Yoshua Bengio — a University of Montreal professor who also happens to be a leading authority on artificial intelligence — will be signing on with Microsoft in an advisory role, working directly with Microsoft Research boss Harry Shum.

This acquisition reflects a big strategic imperative for Microsoft, as it banks that its combination of artificial intelligence expertise, plus its deep hooks into the business software that people use every day, will give it a big leg up as Amazon and Google make increasing inroads into the enterprise IT market. 

As for what Maluuba actually does: "Maluuba’s vision is to advance toward a more general artificial intelligence by creating literate machines that can think, reason and communicate like humans — a vision exactly in line with ours," Shum writes in that blog entry.

In a more material sense, Shum lays out a scenario where a Maluuba-powered bot could help you sift through piles of emails, documents, and directories to find the best tax experts in your company, delivering an instant answer, rather than just a dumb search bar that returns every single file that mentions "tax law."

That's helpful for Microsoft, as it builds out the capabilities of Windows 10's Cortana virtual assistant to fill more roles and answer more questions in the workplace, as a counterpoint to the more consumer-focused Amazon Alexa.

Maluuba had raised a relatively modest $11 million in venture capital funding since it was founded in 2011, including seed funding from Samsung Ventures.

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Financial institutions (FI’s) face many challenges, but one of the biggest of all is fighting fraud. With so much money and valuable data at stake, the financial system will always be a target for criminals and FI’s must shoulder a lot of the responsibility for security.

One of the most effective methods of keeping fraudsters at bay is staying up to date with the latest technologies and there are few areas of technological innovation more exciting than artificial intelligence (AI). It's true that we are still a long way off developing true AI, but the developments that have occurred in this field are already offering great potential for FI’s.

Fighting fraud while serving the customer

How do you ensure that your security safeguards and authentication processes are rigorous enough to combat fraud, while delivering the speed and reliability modern-day consumers expect? That's a big question for FI’s today, particularly on the payments front.

AI could come in useful here, as emphasized by MasterCard’s recent announcement that it was rolling out a new solution to improve the accuracy of real-time approvals and reduce false declines. This could help to address a major source of frustration for consumers: when a perfectly legitimate transaction is rejected.

The launch of Decision Intelligence marks the first deployment of AI on a global scale across the MasterCard network. The system goes further than traditional decision-scoring products, taking a broader approach to assessing and scoring each transaction and learning from previous activity.

Al Pascual, senior vice-president, research director and head of fraud and security at Javelin Strategy & Research, said: "We estimate that in the US alone, the value of false declines is more than 13 times the total amount lost to actual card fraud. Applying machine learning to decision-scoring is a new way of creating a positive consumer experience, while also minimizing fraud."

Machine learning is becoming an increasingly important part of many fraud detection and prevention solutions, which can deliver intelligent, dynamic protection against various threats across all channels, from the ATM to mobile.

One of Australia's biggest banks appears to be getting onboard with AI and machine learning as it looks for new ways to tackle fraud and cybercrime. David Whiteing, chief information officer at the Commonwealth Bank of Australia, told the Australian Financial Review that tools like these can prove invaluable for analyzing large amounts of data.

"When you have a large data set and people can't see the signal for the noise, machine learning capability will sift out and provide you with the top five things that are worthy of further investigation," he said.

Are we getting a bit too excited about AI?

There's no doubt that AI is a topic of much debate and excitement in the financial services industry right now, but do we need to look beyond the hype and take a more pragmatic view of this concept and what it's capable of?

Firstly, it's important to remember that AI and machine learning technologies are still very much in their infancy, with many more years of progress and development required before they are able to realize their potential. For the time being, AI can only complement and improve our existing, human-led processes in the financial services industry, not replace them altogether.

It should also be acknowledged that there have been questions raised about the very idea of AI - whether it's a good thing for the human race or a cause for concern.

As far as financial services is concerned, machine learning and intelligent systems are already a part of the industry, encompassing everything from virtual customer assistants to complex functions that can find patterns in unstructured data. The question now is just how important these technologies will become, particularly in the ongoing fight against fraud.

Dena Hamilton is GM & Director of Enterprise Fraud & Security Solution Management at NCR Corporation.

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At the World Economic Forum in Davos, IBM CEO Ginni Rometty argued that AI and automation will render some professions obsolete, but, in the long run, create jobs, The Wall Street Journal reports.

She made these comments as part of a panel discussing the role AI will play in various spaces moving into the future.

As more jobs and tasks are automated, especially in the industrial and manufacturing sectors, AI will become more crucial to industrial operations, creating a larger need for programmers and developers, which is what Rometty seems to be suggesting.

Rometty appears to be arguing that AI and automation won't be the job killers many fear them to be. While many individuals in attendance at Davos are concerned that these factors could produce a long-term backlash, Rometty seems to disagree. She elaborated that it's the relationship between AI solutions, such as Watson, and human workers that will create new jobs in the long run, and that platforms like Watson can only succeed in adding to what people are able to do, rather than taking over for them entirely.

While Rometty sees reasons to be optimistic in the long term, the growing role of AI and automation is poised to disrupt much of the economy. Workflow automation leads companies to seek out IoT solutions; 52% of IoT users highlighted it as a driver that led their companies to the IoT, according to exclusive results from BI Intelligence’s 2016 Global IoT Executive Survey. According to that same survey, however, only 16% of those companies currently automate their IoT devices. This disruption to result from automation is just beginning, and will fundamentally reshape workplaces and operations across the globe.

The Internet of Things (IoT) is disrupting businesses, governments, and consumers and transforming how they interact with the world. Companies are going to spend almost $5 trillion on the IoT in the next five years — and the proliferation of connected devices and massive increase in data has started an analytical revolution.

To gain insight into this emerging trend, BI Intelligence conducted an exclusive Global IoT Executive Survey on the impact of the IoT on companies around the world. The study included over 500 respondents from a wide array of industries, including manufacturing, technology, and finance, with significant numbers of C-suite and director-level respondents. 

Peter Newman, research analyst for BI Intelligence, Business Insider's premium research service, has conducted an exclusive study with in-depth research into the field and created a detailed report on the IoT that describes the components that make up IoT ecosystem. We size the IoT market in terms of device installations and investment through 2021. And we examine the importance of IoT providers, the challenges they face, and what they do with the data they collect. Finally, we take a look at the opportunities, challenges, and barriers related to mass adoption of IoT devices among consumers, governments, and enterprises.

Here are some key takeaways from the report:

• We project that there will be a total of 22.5 billion IoT devices in 2021, up from 6.6 billion in 2016.
• We forecast there will be $4.8 trillion in aggregate IoT investment between 2016 and 2021.
• It highlights the opinions and experiences of IoT decision-makers on topics that include: drivers for adoption; major challenges and pain points; stages of adoption, deployment, and maturity of IoT implementations; investment in and utilization of devices, platforms, and services; the decision-making process; and forward- looking plans.

In full, the report:

• Provides a primer on the basics of the IoT ecosystem
• Offers forecasts for the IoT moving forward and highlights areas of interest in the coming years
• Looks at who is and is not adopting the IoT, and why
• Highlights drivers and challenges facing companies implementing IoT solutions

To get your copy of this invaluable guide to the IoT, choose one of these options:

1. Subscribe to an ALL-ACCESS Membership with BI Intelligence and gain immediate access to this report AND over 100 other expertly researched deep-dive reports, subscriptions to all of our daily newsletters, and much more. >> START A MEMBERSHIP
2. Purchase the report and download it immediately from our research store. >> BUY THE REPORT

The choice is yours. But however you decide to acquire this report, you’ve given yourself a powerful advantage in your understanding of the IoT.

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Samsung's Galaxy S8, as it's expected to be called, is said to be announced within the next couple months, and we have a bunch of good rumors from reliable sources to share.

Most recently, one of the most well known gadgets leakers posted an image of the supposed Galaxy S8, leaving very little to the imagination of what it could look like. 

Check out what we've seen and heard about Samsung's upcoming Galaxy S8:

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This is allegedly the Galaxy S8.

Reliable gadgets leaker Steve Hemmerstoffer posted the below on Twitter, claiming it's the Galaxy S8.

We have no way to confirm if this is, in fact, the real deal, but it does line up with rumors we've heard...

The home button will be a little different.

As the leaked photo above suggests, the Galaxy S8 won't have a physical home button.

The home button may be embedded underneath the front glass panel, according to ET News. If so, it could look and work similarly to the new home button on Xiaomi's new Mi 5s smartphones (pictured above).

That means it could react to touch rather than using a mechanical button you need to physically press down. 

There will be two models with different screen sizes.

There are a couple conflicting rumors surrounding the screen size of the upcoming Galaxy smartphones from Samsung, which I'll call the Galaxy S8 from now on.

One set of rumors claim the smaller model will have a 5.1-inch screen while the larger model will have a 5.5-inch screen, according to "industry sources" that spoke with the Korea Herald. However, the latest leaks suggest otherwise. GSM Arena allegedly obtained details about the upcoming Galaxy phones' dimensions form a case maker, and we could see the smaller Galaxy S8 model come with a large 5.7-inch screen. The larger Galaxy S8 "Plus," as it's being called, could have a huge 6.2-inch screen.