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Just 19 Percent of Americans Trust Self-Driving Cars With Kids

A new survey by AAA shows that most Americans distrust self-driving cars. In the past two years, public trust in the emerging technology has gone down.

Poor Turnout

While tech companies like Waymo, Uber, and Tesla race to be the first to build a fully-autonomous vehicle, the public is left eating their dust.

About 71 percent of Americans say that they don’t trust self-driving cars, according to a new American Automobile Association (AAA) survey. That’s roughly the same percentage as last year’s survey, but it’s eight points higher than in 2017, according to Bloomberg and just 19 percent say they’d put their children or family members into an autonomous vehicle.

Overall, the data is a striking sign of public fatigue with self-driving cars.

Track Record

Autonomous vehicles, unlike some other emerging technologies, have suffered very public setbacks, including when an Uber vehicle struck and killed a pedestrian a year ago.

“It’s possible that the sustained level of fear is rooted in a heightened focus, whether good or bad, on incidents involving these types of vehicles,” said AAA director of automotive engineering Greg Brannon in a statement obtained by Bloomberg. “Also it could simply be due to a fear of the unknown.”

Uphill Battle

The AAA survey found that Americans are more accepting of autonomous vehicle tech in limited-use cases. For example, 53 percent of survey respondents were okay with self-driving trams or shuttles being used in areas like theme parks, while 44 percent accepted the idea of autonomous food-delivery bots.

Self-driving car companies are currently engaging in public relations efforts to earn people’s trust, Bloomberg reports. But if these AAA numbers are any indication, there’s a long way to go.

READ MORE: Americans Still Fear Self-Driving Cars [Bloomberg]

More on autonomous vehicles: Exclusive: A Waymo One Rider’s Experiences Highlight Autonomous Rideshare’s Shortcomings

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Elon Musk: $47,000 Model Y SUV “Will Ride Like a Sports Car”

A Familiar Car

First, it was supposed to feature Model-X-style “falcon wing” doors, and then it didn’t. It was supposed to be built in the Shanghai factory, but that didn’t work out either.

Tesla finally unveiled its fifth production car, the Model Y, at its design studio outside of Los Angeles Thursday evening.

“It has the functionality of an SUV, but it will ride like a sports car,” Tesla CEO Elon Musk said during the event. “So this thing will be really tight on corners.”

Bigger than the 3, Smaller Than the X

Yes, acceleration is still zippy: zero to 60 in 3.5 seconds.

But the vehicle is less than revolutionary. It’s arguably the company’s second crossover sports utility vehicle, after the Model X, and it borrows heavily from the company’s successful Model 3. In fact, 75 percent of its parts are the same, according to CEO Elon Musk.

The back of the Y is slightly elevated in the back for a roomier cargo space. A long-range model will feature seven seats — just like the Model X, despite being slightly smaller. Range: still 300 miles with the Long Range battery pack, thanks to its aerodynamic shape.

It will also be “feature complete” according to Musk, referring to the fact that the Model Y will one day be capable of “full self-driving” that he says “will be able to do basically anything just with software upgrades.”

10 Percent Cheaper

As expected, the Model Y is ten percent bigger and costs roughly ten percent more than the Model 3: the first Model Y — the Long Range model — will be released in the fall of 2020 and will sell for $47,000. A dual-motor all-wheel drive version and a performance version will sell for $51,000 and $60,000, respectively.

If you want to save a buck and get the ten-percent-cheaper-than-the-Model-3 version, you’ll have to wait: a Standard Range (230 miles) model will go on sale in 2021 for just $39,000.

Overall, the Model Y seems like a compromise: it’s not a radical shift, but it seems carefully designed to land with a certain type of consumer — and, if Musk is to be believed, without sacrificing Tesla’s carefully-cultivated “cool factor.”

Investors seemed slightly underwhelmed, too — the company’s stock reportedly slid up to five percent after the announcement.

READ MORE:  Tesla unveils Model Y electric SUV with 300 miles range and 7-seats [Electrek]

More on the Model Y: Elon Musk: Tesla Will Unveil Model Y Next Week

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Elon Musk: $47,000 Model Y SUV “Will Ride Like a Sports Car”

Samsung Is Working on Phone With “Invisible” Camera Behind Screen

A Samsung exec has shared new details on the company's efforts to create a full-screen phone, one with the camera embedded beneath the display.

Punch It

Just last month, South Korean tech giant Samsung unveiled the Galaxy S10, a phone with just a single hole punched in the screen to accommodate its front-facing camera.

On Thursday, a Samsung exec shared new details on the company’s intentions to create a “perfect full-screen” phone, with an “invisible” camera behind the screen to eliminate the need for any visible holes or sensors — confirming that one of the biggest players in tech sees edge-to-edge screens as the future of mobile devices.

Hidden Tech

During a press briefing covered by Yonhap News Agency, Samsung’s Mobile Communication R&D Group Display Vice President Yang Byung-duk said the company’s goal is to create a phone with a screen that covers the entire front of the device — but consumers shouldn’t expect it in the immediate future.

“Though it wouldn’t be possible to make (a full-screen smartphone) in the next 1-2 years,” Yang said, “the technology can move forward to the point where the camera hole will be invisible, while not affecting the camera’s function in any way.”

Quest for Perfection

This isn’t Samsung’s first mention of an uninterrupted full-screen phone — as pointed out by The Verge, the company discussed its ambitions to put the front-facing camera under a future device’s screen during a presentation in October.

That presentation included a few additional details on how the camera in a full-screen phone would work.

Essentially, the entire screen would serve as a display whenever the front-facing camera wasn’t in use. When in use, however, the screen would become transparent, allowing the camera to see through so you could snap the perfect selfie — and based on Yang’s comments, that new innovation could be just a few years away.

READ MORE: Samsung Seeks Shift to Full Screen in New Smartphones [Yonhap News Agency]

More on Samsung: Samsung Just Revealed a $1,980 Folding Smartphone

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Samsung Is Working on Phone With “Invisible” Camera Behind Screen

Special Announcement: Futurism Media and Singularity University

Futurism acquired by Singularity University

So, Readers –

As always, we’ve got some news about the future. Except this time, it’s about us.

We’re about to enter the next chapter of Futurism, one that will usher in a new era for this site. It’ll come with new ways we’ll be able to deliver on everything you’ve grown to read, watch, subscribe to, and love about what we do here. And also, more in volume of what we do, with larger ambitions, and ultimately, a higher level of quality with which we’re able to bring those ambitions to fruition.

As of today, Futurism Media is proud to announce that we’re joining operations with Singularity University. In other words: They bought us, they own us, and quite frankly, we’re excited about the deal.

It’s an excitement and an occasion we share in with you, our community of readers — aspiring and working technologists, scientists, engineers, academics, and fans, who carried us to where we are, who helped make this independent media company what it is today. We’ve always been humbled by your support, and we’ve worked to reciprocate it by publishing one of the most crucial independent technology and science digital digests, every day, full stop.

What this changes for you? Nothing. Really. Except: More of what you’ve come to count on Futurism.com to deliver every time you’ve read our stories, opened our emails, swiped up on our ‘Gram, watched our videos, dropped in on our events, clicked through a Byte, and so on. This partnership represents the sum total of the work you’ve engaged with, and the start of a new chapter in which we’ll be able to deliver on more of the above.

That means increased coverage of the emergent, cutting-edge innovation and scientific developments changing the world, and the key characters and narratives shaping them (or being shaped by them). It means an expanded, in-depth feature publishing program, arriving this Spring (it’s rad, and it’s gonna blow your socks off). It means more breaking news reporting and analysis. It means original media products you haven’t seen from us before — new verticals, microsites, other ways for you to get in the mix with our coverage. And yes, by working in concert with Singularity University, we’re going to have a pretty decent competitive advantage: Direct access to the characters and personas shaping our future, the people, ideas, and innovations right at the frontier of exponential growth technologies. Our branded content team, Futurism Creative, will also continue to produce guideline-abiding, cutting-edge, thoughtful and engaging content for our partners, and for the partners of SU, too. And finally, our Futurism Studios division will continue to push the envelope of feature-length narrative storytelling of the science fiction (and science fact) stories of that future.

Will this change our journalism? Not in the slightest. We’ll still be operating as an independent, objective news outlet, without interference from our partners, who will continue to hold us to the same ethics and accountability standards we’ve held ourselves to these last few years. There might be more appearances from the folks at SU in our work (not that SU’s proliferate network of notable alumni or board members haven’t previously made appearances around these parts prior to this), but by no means will SU be shoehorning themselves into what we do here.

Yet: Where the opportunity exists, we’ll absolutely seize on the chance to co-create and catalyze action together to shape the technology and science stories on the horizon, to say nothing of that future itself. We’ll continue to make quality the primary concern — and they’re here to support that mandate, and augment this team with additional resources to accomplish it. If even the appearance of a conflict presents itself, as always, we’ll default to disclosure. But it’d be absurd of us not to take advantage of the immense base of knowledge our new partners in Mountain View have on offer (an apt comparison here would be, say, Harvard Business Review to H.B.S. or M.I.T. and our contemporaries at the MIT Technology Review).

We’ve been circling this partnership for a while; they, fans of ours, and us, fans of theirs. The original mandate of Futurism as written by our C.E.O. Alex Klokus was to increase the rate of human adaptability towards the future through delivering on the news of where that future is headed. Singularity University concerns itself with educating the world on the exponential growth technologies changing our lives. It’s a perfect merging of interests. Where exponential growth technologies are concerned: One only need look as far as the way online advertising and social platforms changed the economics of media to see this. To find a home with a growing institution that will prove increasingly vital to the growing global community they’ve already established in spades is the best possible outcome. And no, we didn’t get crazy-rich or anything. But we did galvanize the future (and all its possibilities) for everyone at this company, and our ability to keep serving you, our readers.

We’re immensely proud of the scrappy, tight team here; and especially you, our community of readers and partners we’ve grown with these last few years. We’re proud of the product we’ve created, especially last year, when we steered away from reliance on social media platforms for an audience, and reconfigured an editorial strategy around the priority of driving you directly to Futurism.com daily, by prioritizing quality, topicality, reliability, and on-site presentation (shocker: it worked). Now, we proud to be able to do more, better, of what we’ve always done here:

Tell the stories of tomorrow, today. On behalf of the entire Brooklyn-based Futurism team, thanks for being along for the ride so far, and on behalf of the new Futurism x Singularity University family, here’s to more of where that came from.

The future, as ever, is looking bright. We can’t wait to tell you about it.

– Foster Kamer
Director of Content

James Del
Publisher

Sarah Marquart
Director of Strategic Operations

Geoff Clark
President of Futurism Studios

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Presidential Hopeful Beto O’Rourke Belonged to Infamous Hacker Group

2020 Presidential hopeful Beto O'Rourke was reportedly part of the hacktivist group known as the Cult of the Dead Cow during his teenage years.

Political Hack

Presidential candidate Beto O’Rourke just admitted to spending his teenage years as part of the Cult of the Dead Cow (CDC), a group of hackers that first coined the term “hacktivism.”

O’Rourke, who failed to unseat Senator Ted Cruz in the 2018 midterm election and recently decided to run for president instead of challenging Senator John Cornyn in 2020, told Reuters that he credits the hacker group for helping develop his worldview — an intriguing admission for an unusual candidate who skateboards and used to play in a punk band.

Hacker-Lite

According to Reuters, there’s no evidence that O’Rourke actually engaged in any sort of serious hacking, though he did cop to stealing the long-distance phone service necessary for reaching the online message boards of the day.

Rather, O’Rourke seemed to spend his time in the Cult of the Dead Cow writing and sharing fiction with the community, such as a short story he wrote at age 15 about running over children in a car, Reuters reports.

“We weren’t deliberately looking for hacking chops,” CDC founder Kevin Wheeler told Reuters, describing the group’s attitude during the period of time O’Rourke was most active. “It was very much about personality and writing, really. For a long time, the ‘test,’ or evaluation, was to write [text files]. Everyone was expected to write things. If we were stoked to have more hacker-oriented people, it was because we’d be excited to have a broader range in our t-files.”

Formative Years

“There’s just this profound value in being able to be apart from the system and look at it critically and have fun while you’re doing it,” O’Rourke said. “I think of the Cult of the Dead Cow as a great example of that.”

The presidential hopeful, who espouses a mix of traditional liberal and libertarian views, describes the group as a sort of network for outcasts from society.

“When Dad bought an Apple IIe and a 300-baud modem and I started to get on boards, it was the Facebook of its day,” he said. “You just wanted to be part of a community.”

READ MORE: Beto O’Rourke’s secret membership in America’s oldest hacking group [Reuters]

More on hacktivism: It’s Now Scary to Be A White Hat Hacker Thanks to the US Government

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Presidential Hopeful Beto O’Rourke Belonged to Infamous Hacker Group

Elon Musk: 2019 Will Be “the Year of the Solar Roof”

During the unveiling of Tesla's highly anticipated Model Y, CEO Elon Musk announced that the company would focus on its Solar Roof and Powerwall in 2019.

Looking Up

During the unveiling of Tesla’s highly anticipated Model Y Thursday night, CEO Elon Musk shared his vision for his company’s immediate future — and it had little to do with cars.

“This is definitely going to be the year of the Solar Roof and Powerwall,” he told the audience, according to Inverse — a sign that Tesla is shifting its focus from the road to the home, with the ultimate goal of creating a fully sustainable future.

Pretty Picture

In August 2017, Tesla gave the world its first glimpse of an installed Solar Roof, and it looked, well, a lot like any other roof. But that was the point — Tesla’s solar tiles didn’t have the jarring appearance of many home solar panels.

That aesthetically pleasing design — combined with the tiles’ affordability and “infinity warranty” — had solar energy expert Senthil Balasubramanian predicting Tesla would be a “game changer” for clean energy.

With the exception of the occasional massive battery project, though, we haven’t heard much about Tesla’s home energy products since then. The company spent much of 2017 and 2018 focused on getting through the Model 3’s “production hell” and dealing with the fallout from Musk’s latest public misstep.

Under One Roof

But now that Model 3 production is humming along, Tesla has the bandwidth to shift some of its engineering focus back to its Solar Roof and home batteries, according to Musk — and that should go a long way toward helping the company meet its ambitious goal of a more sustainable energy system.

“Solar plus battery plus electric vehicles, we have a fully sustainable future,” Musk told the audience Thursday. “That’s a future you can feel really excited and optimistic about. I think that really matters.”

READ MORE: Tesla Solar Roof: Elon Musk Declares 2019 Will Be the Year of the Roof [Inverse]

More on Tesla: Solar Expert Predicts Tesla Will Be a “Game-Changer” for Clean Energy

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Elon Musk: 2019 Will Be “the Year of the Solar Roof”

This Guy Spent a Whole Week In a VR Headset

Jak Wilmot, co-founder of Disrupt VR, an Atlanta-based VR content studio, spent 168 consecutive hours in a VR headset, locked up in his apartment.

The Dumbest Thing

Jak Wilmot, the co-founder of Atlanta-based VR content studioDisrupt VR, spent 168 consecutive hours in a VR headset — that’s a full week — pent up in his apartment.

“This is quite possibly the dumbest thing I’ve ever done, but welcome to a week in the future,” he said in a video about the experiment.

To make the experience even more futuristic, Wilmot livestreamed the entire week on Twitch late last month, later uploading a wrapup video on his entire week on YouTube.

The rules were simple: he could switch from a computer-based Oculus headset to a different, untethered headset for thirty seconds while his eyes were closed. His windows were blacked out, he said, so that his physical body didn’t have to rely on the daylight-dependent circadian rhythm.

His more mobile VR headset had a built in camera in the front, so that he was able to “see” his physical surroundings — but not directly with his own eyes.

“Everything is in the Headset”

Wilmot worked, ate and exercised inside virtual reality. Sleeping in the headset turned out to be “more comfortable” than Wilmot anticipated, though his eyes burned a bit.

“If one is feeling stressed, they can load into a natural environment for ten minutes and relax,” he said in the video. “If one is feeling energetic, they can dispel energy in a fitness game — these are like the new rules of the reality I’ve thrown myself in. Everything is in the headset.”

VR Connection

Wilmot believes that virtual reality is what you make it. If you want to be alone, you can spend time by yourself in a gaming session, slaying dragons in Skyrim VR. Or you can chose to join the cacophony of VRChat — a communal free-for-all multiplayer online platform that allows you to interact with avatars controlled by complete strangers.

“VR is stepping into the shoes of someone else, or stepping into a spaceship and talking to friends,” said Wilmot. “It’s very easy to find your tribe, to make friends, to communicate with others through a virtual landscape, where its no longer through digital window [like a monitor], but actually being there with them. To me that’s what VR is — connection.”

Escaping Virtual Reality

After seven days of living inside the headset, Wilmot took off the goggles and relearned what it’s like to live in the real world.

Experiment_01… ????????

Subject Status… ????? pic.twitter.com/HC0Jqb3aZq

— jak (@JakWilmot) February 27, 2019

Apart from slight dizziness and some disorientation, he came back to normal almost instantly.

One major advantage to not living inside a VR headset: “oh my gosh,” he said, “the graphics are so good.”

READ MORE: This Guy Is Spending A Full Week In VR, For Science [VR Scout]

More on virtual reality: Sex Researchers: For Many, Virtual Lovers Will Replace Humans

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This Guy Spent a Whole Week In a VR Headset

How Can We Build Cities to Accommodate 6.5 Billion People?

By 2050, 6.5 billion people will choose to live in cities. These individuals will require employment and access to better healthcare from an infrastructure that is already extremely vulnerable. The Global Maker Challenge asked makers and innovators to help put forward solutions for this issue, and they delivered.

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How Can We Build Cities to Accommodate 6.5 Billion People?

Here’s How Hackers Stole $15 Million From Mexican Banks

In April, bank hackers stole the equivalent of $20 million from Mexico's central bank thanks to a network rife with security flaws.

Ocean’s Once

In April 2018, hackers stole the equivalent of $15 million from Mexican banks — and now we know how they probably did it.

Penetration tester and security advisor Josu Loza was one of the experts called in to respond to the April heist, and on March 8 he presented his findings at the RSA Security conference in San Francisco.

Based on his analysis, Mexico’s central bank wasn’t doing nearly enough to protect its clients’ money — but other financial institutions could avoid the same fate if they’re willing to work together.

Easy Money

On Friday, Wired published a story detailing the information Loza shared with the audience at RSA’s conference. Based on his assessment, the success of the heist was due to a combination of expert bank hackers willing to spend months planning their crime and a banking network rife with security holes.

During the presentation, Loza made the case that the hackers might have accessed the Banco de México’s internal servers from the public internet, or perhaps launched phishing attacks on bank executives or employees to gain access.

Regardless of how they first got access, Loza said, the main problem was putting too many eggs in one security basket. Because many of the networks lacked adequate segmentation and access controls, he argued, a single breach could provide the bank hackers with extensive access.

That enabled them to lay the groundwork to eventually make numerous money transfers in smaller amounts, perhaps $5,000 or so, to accounts under their control. They’d then pay hundreds of “cash mules” each a small sum — Loza estimated that $260 might be enough — to withdraw the money for them.

Cyber Insecurity

The bank hackers are still at large, but the heist appears to have served as a wake-up call for the Banco de México.

“From last year to today the focus has been implementing controls. Control, control, control,” Lazo said during his presentation, according to Wired. “And I think the attacks aren’t happening today because of it.”

He also noted the need for companies to collaborate to defend against cyberattacks.

“Mexican people need to start to work together. All the institutions need to cooperate more,” Loza said. “The main problem on cybersecurity is that we don’t share knowledge and information or talk about attacks enough. People don’t want to make details about incidents public.”

READ MORE: HOW HACKERS PULLED OFF A $20 MILLION MEXICAN BANK HEIST [Wired]

More on hacking: Hacker Figures out How to Drain $1 Million in Cash From ATM

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Here’s How Hackers Stole $15 Million From Mexican Banks

Slack Just Removed a Bunch of Hate Groups

Workplace messaging app Slack just announced that it banned 28 accounts that were known to be affiliated with hate groups.

Violating Terms

Slack, the team collaboration app commonly used to connect people within workplaces, announced Thursday that it had deleted 28 accounts that were clearly affiliated with hate groups, according to the company’s blog.

The announcement, sparse on concrete details or specifics, states that hate groups are explicitly unwelcome on the app and that Slack will continue to investigate and act on any future reports of hate speech or illegal activity.

“Today we removed 28 accounts because of their clear affiliation with known hate groups,” the statement reads. “The use of Slack by hate groups runs counter to everything we believe in at Slack and is not welcome on our platform.”

Joining the Fight

In recent years, major platforms like Facebook and Twitter have struggled to keep white supremacists and other hate groups from spreading their messages across the internet, though both ban Nazi messaging in Germany, where Holocaust denial is illegal.

Smaller scale platforms like Discord also recently started acting against hate groups, according to The Verge, which speculates that Slack’s focus on business communications instead of cultivating largescale communities may have helped the company avoid the issue of online hatemongering.

Real World Consequences

When hate speech is allowed to propagate online, it can lead to real-world violence — like the murder of Heather Heyer at a 2017 white supremacist rally. But banning hate groups and de-platforming the people behind them, as Slack claims to have done, is a successful strategy.

When right-wing activist Milo Yiannopolous was no longer permitted by online platforms to spread his racist and misogynistic viewpoints, he found himself effectively powerless and millions of dollars in debt, according to The Guardian.

“Using Slack to encourage or incite hatred and violence against groups or individuals because of who they are is antithetical to our values and the very purpose of Slack,” the company’s statement reads. “When we are made aware of an organization using Slack for illegal, harmful, or other prohibited purposes, we will investigate and take appropriate action and we are updating our terms of service to make that more explicit.”

READ MORE: Slack says it removed dozens of accounts affiliated with hate groups [The Verge]

More on content moderation: The UK Government Is Planning to Regulate Hate Speech Online

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Slack Just Removed a Bunch of Hate Groups

This Tech Could Secure Medical Implants Against Hackers

Many of today's medical implants communicate via Bluetooth, which makes them vulnerable to hacking, but a new system could change that.

Heart Hack

An implanted medical device can dramatically improve a person’s quality of life — or even save their life outright.

However, the devices come with serious security vulnerabilities, and it’s not hard to imagine the damage a person could do by hacking someone’s pacemaker, insulin pump, or brain implant.

Now, researchers from Purdue University have found a way to prevent hackers from intercepting the wireless signals used to communicate with implanted devices — and their creation could ensure the “internet of body” remains secure in the future.

Watch This

Many people monitor their implants via electronic devices, such as smart watches or smartphones, with the implants and devices communicating over Bluetooth.

Those wireless signals can extend as far as 10 meters away from a person’s body, according to the Purdue researchers – meaning someone in the vicinity of the implant owner could intercept the information — and perhaps manipulate it.

In a new paper published in the journal Scientific Reports, the researchers detail how they created a prototype watch that avoids this issue.

Short Leash

According to the researchers, their watch can receive a signal from anywhere on a person’s body, but instead of communicating over Bluetooth, the electrical signals travel through the person’s own body fluids to reach the watch, never extending more than one centimeter beyond the person’s skin.

As a bonus, the system also requires 100 times less energy than Bluetooth, according to the researchers — but its ability to protect incredibly sensitive communications could be reason enough for the technology to replace Bluetooth for implant applications in the future.

READ MORE: Your body is your internet – and now it can’t be hacked [Purdue University]

More on implants: New Brain Implant Could Translate Paralyzed People’s Thoughts Into Speech

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This Tech Could Secure Medical Implants Against Hackers

NASA: Space Travel Is Causing Astronauts’ Herpes to Flare Up

Tests show that dormant herpes viruses reactivate in more than half the astronauts who travel on the Space Shuttle and International Space station.

Dormant Viruses

Tests show that dormant herpes viruses reactivate in more than half the astronauts who travel on the Space Shuttle and International Space station, according to new NASA research — a phenomenon the space agency says could pose problems for deep space missions.

“During spaceflight there is a rise in secretion of stress hormones like cortisol and adrenaline, which are known to suppress the immune system, ” said study author Satish Mehta, a researcher at Johnson Space Center, in a press release. “In keeping with this, we find that astronaut’s immune cells — particularly those that normally suppress and eliminate viruses — become less effective during spaceflight and sometimes for up to 60 days after.”

Less Effective

In research published last month in the journal Frontiers in Microbiology, Mehta and colleagues found that astronauts shed more herpes viruses in their urine and saliva than before or after space travel. The culprit, they suspect, is just the stress of spaceflight.

“NASA astronauts endure weeks or even months exposed to microgravity and cosmic radiation — not to mention the extreme G forces of take-off and re-entry,” Mehta said in the press release. “This physical challenge is compounded by more familiar stressors like social separation, confinement and an altered sleep-wake cycle.”

Minor Symptoms

Fortunately, symptoms were relatively rare. Out of 89 astronauts the team studied, only six experienced herpes breakouts in space, according to the paper — a rate of about seven percent.

The viral shedding also got worse the longer the astronauts were off Earth, leading researchers to worry the phenomenon could represent a challenge for deep space travel.

“While only a small proportion develop symptoms, virus reactivation rates increase with spaceflight duration and could present a significant health risk on missions to Mars and beyond,” reads the press release.

READ MORE: Dormant viruses activate during spaceflight [Phys.org]

More on herpes: Immune Cells Working Together To Kill Herpes Virus Captured on Video

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NASA: Space Travel Is Causing Astronauts’ Herpes to Flare Up

New Robot Hand Works Like a Venus Flytrap to Grip Objects

A team from MIT and Harvard has created a robot hand that's not only strong, but also soft — and it could usher in a new era in robotics.

Versatile Touch

If we want robots to take over more tasks for humans, we need to give them more versatile hands.

Right now, many robot hands can only complete specialized tasks. Ones that are strong often have trouble with tasks that require a delicate touch, and soft hands often don’t pack much of a punch when it comes to strength.

But now, a team of researchers from the Massachusetts Institute of Technology (MIT) and Harvard University have created a robot hand that’s not only strong, but also soft — and it could usher in a new era in robotics.

Show of Hands

The team drew inspiration for its hand from the origami magic ball. Rather than using some sort of finger-like grippers, their cone-shaped robot hand envelopes an object and then collapses around it, much like a Venus flytrap captures its prey.

The pressure applied is enough for the hand to lift objects up to 100 times its own weight, but it can also handle far more delicate, light objects. A video released by MIT demonstrates the hand’s ability to pick up everything from a soup can to a banana.

Soft, but Strong

University of California at Santa Cruz robotics professor Michael Wehner, who was not involved in the project, praised the hand, noting its novelty in an interview with MIT News.

“This is a very clever device that uses the power of 3-D printing, a vacuum, and soft robotics to approach the problem of grasping in a whole new way,” Wehner said. “In the coming years, I could imagine seeing soft robots gentle and dexterous enough to pick a rose, yet strong enough to safely lift a hospital patient.”

READ MORE: Robot hand is soft and strong [MIT News]

More on robot hands: This AI-Operated Robotic Hand Moves With “Unprecedented Dexterity”

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New Robot Hand Works Like a Venus Flytrap to Grip Objects

States Are Approving Cannabis to Fight Opioid Addiction

Risky Maneuver

So far, two U.S. states, New York and Illinois, have legalized the use of cannabis to help treat chronic pain as an alternative to addictive opioids.

Ask anyone on the street, and they would probably tell you that cannabis helps people chill out. The chemical similarities between cannabis and opioids make it seem, anecdotally, like cannabis could help reduce opioid addiction. Both drugs mitigate similar symptoms and usher in similar experiences – but cannabis is far less dangerous on its own.

But anecdotal evidence only goes so far.

Mixed Bag

While it’s hard to criticize something that could help alleviate the opioid epidemic, the physiological impact of treating either chronic pain or opioid addiction with cannabis hasn’t undergone nearly the same rigor of scientific study as other medical treatments, according to Scientific American.

Overall, scientists have faced many challenges when it comes to experimenting with cannabis. Though Scientific American reports that some clinical research is finally starting to support it, overall, there’s just not a lot of evidence backing up that anecdotal hunch.

But because other opioid addiction treatments like methadone already work, and because cutting people off of them can be dangerous, scientists argued that switching people already taking prescription opioids over to a prescription of cannabis could actually be dangerous in a perspective letter recently published to the Journal of the American Medical Association.

Pain Factor

The big question is whether cannabis will not only be able to help people already addicted to opioids, but also the chronic pain that the opioids may have been for in the first place.

In this case, research is once more limited. Plenty of studies suggest that cannabis treats pain, but a research paper published in European Archives of Psychiatry and Clinical Neuroscience earlier this year found that most cannabis pain studies had severe limitations, calling their findings into question.

Legalizing marijuana could help people find all sorts of new treatments. And while exploring new tools to help treat people affected by the opioid epidemic is commendable, cannabis likely won’t end up being the answer.

READ MORE: Can Cannabis Solve the Opioid Crisis? [Scientific American]

More on cannabis: New Senate Bill Would Legalize Marijuana Nationwide

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The Pentagon Wants an Orbital Space Weapon to Blast Enemy Missiles

Space Laser

You know the scene in “Akira” where Tetsuo rips a satellite space weapon out of orbit?

Now the U.S. military wants to try something similar, according to Defense One. The Pentagon is requesting hundreds of millions of dollars to ramp up space-based weaponry including particle beams and space lasers that’ll fire downward at Earthly targets — a dark vision of the militarization of space.

Ballistic Missiles

According to Defense One, a Missile Defense Agency document released this week describes the military’s ambitions to disable ballistic missiles right as they launch.

“The addition of the neutral particle Beam effort will design, develop, and conduct a feasibility demonstration for a space-based Directed Energy Intercept layer,” it reads. “This future system will offer new kill options for the [Ballistic Missile Defense System] and adds another layer of protection for the homeland.”

Orbital Weaponry

Several contractors have prepared prototype orbital weaponry designs for the Pentagon over the years, according to Defense One, but they’ve been enormous and impractical. But now officials hope advances could make such a weapon feasible for a test by 2023.

“We’ve come a long way in terms of the technology we use today to where a full, all-up system wouldn’t be the size of three of these conference rooms, right?” said a senior defense official, according to Defense One. “We now believe we can get it down to a package that we can put on as part of a payload to be placed on orbit.”

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The Pentagon Wants an Orbital Space Weapon to Blast Enemy Missiles

Computer Fraud Laws are Flawed, this Lawyer is Fighting Against Them

Tor Ekeland, hacker lawyer, fights back against the harsh punishments decreed using the Computer Fraud and Abuse Act. And one of those fights can be seen in “Trust Machine,” available now at Breaker.io.

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NASA Engineer: Humans Should Consider Settling Saturn’s Moon Titan

nasa-engineer-humans-settling-titan

Destination: Titan

If the Earth becomes inhabitable, a NASA engineer named Janelle Wellons says we should think about settling Saturn’s moon Titan, and she has a laundry list of reasons why — including that you might be able to fly by flapping your arms.

“It has a thick atmosphere that could help protect us from space radiation,” Wellons wrote on Reddit. “It is so dense that we could actually attach wings to our arms and fly on this moon. I don’t know, it just seems like an awesome place to live.”

Largest Moon

Wellon’s comments came in a Reddit appearance in which NASA engineers, scientists and pilots fielded questions from the public. One Redditor asked where the team would recommend settling if conditions on Earth became untenable, and Wellons chimed in with what she said was a “more interesting answer than the standard Mars or Moon response.”

“How about we consider one of the water worlds in our solar system — Titan,” she wrote. “Titan is the largest moon of Saturn, larger than the planet Mercury even, so I think we could settle with plenty room.”

Swim Good

In spite of Wellon’s enthusiasm, there are definite downsides to Titan. It only gets about one percent of the sunlight Earth does, and according to NASA’s research its maximum temperature is a wintry -292 degrees Fahrenheit.

But Wellon is still a fan.

“Now as for the conditions on the surface — not as rough as you may think,” she wrote. “Titan is the only place besides Earth known to have liquids in the form of lakes and seas on its surface. These liquids are made of methane but, armed with the right kind of protective gear, one could theoretically be able to swim without harm!”

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New Rocket Engine Could Whip You From London to Sydney in 4 Hours

The makers of a new hypersonic rocket engine say it could whip flights from London to Sydney in just four hours, traveling at five times the speed of sound.

Rocket Plane

The makers of a new hypersonic rocket engine say it could whisk flights from London to Sydney in just four hours, traveling at five times the speed of sound. That’s a flight that can take 20 hours on a conventional jetliner.

According to the BBCUK company Reaction Engines says it’s gearing up to test the futuristic craft in Colorado — a startling vision of the future of transportation that could also, if the engine lives up to the hype, inform the future of spaceflight.

Screaming Fist

Reaction Engines, which has backing from the Rolls-Royce and Boeing, calls the new rocket engine the Sabre. It inhales air at lower altitudes, but works more like a rocket when it gets higher up.

“The core can be tested on the ground, but it’s the core that gets you air-breathing from the ground up to the edge of space, at which point there is no more oxygen to breathe and the system transitions to the pure rocket mode,” said Shaun Driscoll, Reaction Engines’ program director, according to the BBC.

Orbiter

The company also says the Sabre engine could push the frontiers of spaceflight, by sending crafts straight into orbit without multiple propellant stages, according to the BBC, which also reported that the the European Space Agency recently signed off on a design review for the Sabre engine.

“The positive conclusion of our Preliminary Design Review marks a major milestone in Sabre development,” ESA’s head of propulsion engineering Mark Ford told the broadcaster. “It confirms the test version of this revolutionary new class of engine is ready for implementation.”

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Astronomers Just Found 83 Giant Black Holes at Universe’s Edge

An international team of researchers says it's found 83 new supermassive black holes at extreme end of the visible universe.

Hole Story

An international team of researchers says it’s found 83 new supermassive black holes at extreme end of the visible universe — by looking at light that took so long to reach Earth that it dates from the early universe.

“It is remarkable that such massive dense objects were able to form so soon after the Big Bang,” said Michael Strauss, a professor of astrophysical sciences at Princeton University involved in the research, in a press release. “Understanding how black holes can form in the early universe, and just how common they are, is a challenge for our cosmological models.”

Squad Goals

The discovery was made by 48 astronomers around the world who described the findings in five new papers in The Astrophysical Journal and the Publications of the Astronomical Observatory of Japan.

The finding was based on data taken with the Hyper Suprime-Cam, a “cutting-edge instrument” at the Subaru Telescope at the National Astronomical Observatory of Japan, in Hawaii, which the researchers combined with readings from three more powerful telescopes around the world.

Quasar Theory

The newly-discovered black holes are quasars, which shoot out matter in powerful jets. The researchers are hoping that more datagathering and analysis will shed light onto how some of the earliest quasars in the universe formed.

“The quasars we discovered will be an interesting subject for further follow-up observations with current and future facilities,” said Yoshiki Matsuok, a researcher at Ehime University who worked on the discovery. “We will also learn about the formation and early evolution of [super massive black holes], by comparing the measured number density and luminosity distribution with predictions from theoretical models.”

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Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] More specifically, Kaplan and Haenlein define AI as a systems ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.[2] Colloquially, the term “artificial intelligence” is used to describe machines that mimic “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

As machines become increasingly capable, tasks considered to require “intelligence” are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler’s Theorem says “AI is whatever hasn’t been done yet.”[4] For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.[5] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[7] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[2] Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[8][9] followed by disappointment and the loss of funding (known as an “AI winter”),[10][11] followed by new approaches, success and renewed funding.[9][12] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[13] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[14] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[15][16][17] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[13]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14] General intelligence is among the field’s long-term goals.[18] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[19] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[20] Some people also consider AI to be a danger to humanity if it progresses unabated.[21] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[22]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[23][12]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[24] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[25] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[20]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[26] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[27] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[29] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[30] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[32] (and by 1959 were reportedly playing better than the average human),[33] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[34] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[35] and laboratories had been established around the world.[36] AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation… the problem of creating ‘artificial intelligence’ will substantially be solved”.[8]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”,[10] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[38] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S and British governments to restore funding for academic research.[9] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[11]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[23] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[39] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[42] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[43] as do intelligent personal assistants in smartphones.[44] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.[7][45] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[46] who at the time continuously held the world No. 1 ranking for two years.[47][48] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[49] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[12] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[49] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[50][51] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[52][53]

A typical AI analyses its environment and takes actions that maximize its chance of success.[1] An AI’s intended utility function (or goal) can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do mathematically similar actions to the ones succeeded in the past”). Goals can be explicitly defined, or induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems, similarly to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[56]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world. These learners could therefore, derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad range of possibilities that are unlikely to be beneficial.[58] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[60]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[61] the best approach is often different depending on the problem.[63]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][66][67][68]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[71][72][73] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[74][75][76]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[14]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[77] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[78]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[58] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[79]

Knowledge representation[80] and knowledge engineering[81] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[82] situations, events, states and time;[83] causes and effects;[84] knowledge about knowledge (what we know about what other people know);[85] and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[86] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[87] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[88] scene interpretation,[89] clinical decision support,[90] knowledge discovery (mining “interesting” and actionable inferences from large databases),[91] and other areas.[92]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[99] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[100]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[101] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[102]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[103]

Machine learning, a fundamental concept of AI research since the field’s inception,[104] is the study of computer algorithms that improve automatically through experience.[105][106]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[107] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[106] Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[108] In reinforcement learning[109] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[110] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[111] and machine translation.[112] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[113]

Machine perception[114] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[115] facial recognition, and object recognition.[116] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[117]

AI is heavily used in robotics.[118] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[119] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[121][122] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[123][124] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[125]

Moravec’s paradox can be extended to many forms of social intelligence.[127][128] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[129] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[133]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[134] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[135]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[136] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[18][137] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[138][139][140] Besides transfer learning,[141] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[143][144]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[145] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[15]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[16]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[146] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[147] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[148]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[149][150]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless of whether people used the same algorithms.[15] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[151] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[152]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[153] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[16] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[154]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[155] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[38] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[156] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[17] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[157] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[158][159]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[162] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[163]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[39][164] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[173] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[174] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[175] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[119] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[176] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use “heuristics” or “rules of thumb” that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for the path on which the solution lies.[177] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[178]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[179] Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[180][181]

Logic[182] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[183] and inductive logic programming is a method for learning.[184]

Several different forms of logic are used in AI research. Propositional logic[185] involves truth functions such as “or” and “not”. First-order logic[186] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a “degree of truth” (between 0 and 1) to vague statements such as “Alice is old” (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][188][189]

Default logics, non-monotonic logics and circumscription[94] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[82] situation calculus, event calculus and fluent calculus (for representing events and time);[83] causal calculus;[84] belief calculus;[190] and modal logics.[85]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[192]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[193]

Bayesian networks[194] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[195] learning (using the expectation-maximization algorithm),[f][197] planning (using decision networks)[198] and perception (using dynamic Bayesian networks).[199] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[199] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[200] and information value theory.[100] These tools include models such as Markov decision processes,[201] dynamic decision networks,[199] game theory and mechanism design.[202]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[203]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[204] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[206]k-nearest neighbor algorithm,[g][208]kernel methods such as the support vector machine (SVM),[h][210]Gaussian mixture model,[211] and the extremely popular naive Bayes classifier.[i][213] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as “naive Bayes” on most practical data sets.[214]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[217][218]

The study of non-learning artificial neural networks[206] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[219] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[220]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[221][222] and was introduced to neural networks by Paul Werbos.[223][224][225]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[226]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[227]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[228] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[229][230][228]

According to one overview,[231] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[232] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[233] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[234][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[235] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[237]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[238] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[239]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[228]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[240]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[241] which are in theory Turing complete[242] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[228] RNNs can be trained by gradient descent[243][244][245] but suffer from the vanishing gradient problem.[229][246] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[247]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[248] LSTM is often trained by Connectionist Temporal Classification (CTC).[249] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[250][251][252] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[253] Google also used LSTM to improve machine translation,[254] Language Modeling[255] and Multilingual Language Processing.[256] LSTM combined with CNNs also improved automatic image captioning[257] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[258] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[259][260] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[261] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[125]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[262][263] E-sports such as StarCraft continue to provide additional public benchmarks.[264][265] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[266]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[267] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[269][270]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[273] prediction of judicial decisions[274] and targeting online advertisements.[275][276]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[277] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[278]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[279]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[280] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[281] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[282] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[283]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[284] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[285] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[286]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[287]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[288]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[289] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[290]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[291] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[292]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[293] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[294] In August 2001, robots beat humans in a simulated financial trading competition.[295] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[296]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[297] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[298][299]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[300][301] Military drones capable of autonomous action are widely considered a useful asset. Many artificial intelligence researchers seek to distance themselves from military applications of AI.[302]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[303]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[304] A documented case reports that online gambling companies were using AI to improve customer targeting.[305]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[306]

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