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A new version of Atlas, designed to operate outdoors and inside buildings. It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5' 9" tall (about a head shorter than the DRC Atlas) and weighs 180 lbs.
IBM's Watson supercomputer destroys all humans in Jeopardy.
No matter what companies say, AI is not going to solve the problem of content moderation online. It’s a promise we’ve heard many times before, particularly from Facebook CEO Mark Zuckerberg, but experts say the technology is just not there — and, in fact, may never be.
Most social networks keep unwanted content off their platforms using a combination of automated filtering and human moderators. As The Verge revealed in a recent investigation, human moderators often work in highly stressful conditions. Employees have to click through hundreds of items of flagged content every day — everything from murder to sexual abuse — and then decide whether or not it violates a platform’s rules, often working on tightly-controlled schedules and without adequate training or support.
When presented with the misery their platforms are creating (as well as other moderation-adjacent problems, like perceived bias) companies often say more technology is the solution. During his hearings in front of congress last year, for example, Zuckerberg cited artificial intelligence more than 30 times as the answer to this and other issues.
“AI is Zuckerberg’s MacGuffin,” James Grimmelmann, a law professor at Cornell Tech, told The Washington Post at the time. “It won’t solve Facebook’s problems, but it will solve Zuckerberg’s: getting someone else to take responsibility.”
So what is AI doing for Facebook and other platforms right now, and why can’t it do more?
Right now, automated systems using AI and machine learning are certainly doing quite a bit to help with moderation. They act as triage systems, for example, pushing suspect content to human moderators, and are able to weed out some unwanted stuff on their own.
But the way they do so is relatively simple. Either by using visual recognition to identify a broad category of content (like “human nudity” or “guns”), which is prone to mistakes; or by matching content to an index of banned items, which requires humans to create said index in the first place.
The latter approach is used to get rid of the most obvious infringing material; things like propaganda videos from terrorist organizations, child abuse material, and copyrighted content. In each case, content is identified by humans and “hashed,” meaning it’s turned into a unique string of numbers that’s quicker to process. The technology is broadly reliable, but it can still lead to problems. YouTube’s ContentID system, for example, has flagged uploads like white noise and bird song as copyright infringement in the past.
Things become much trickier when the content itself can’t be easily classified even by humans. This can include content that algorithms certainly recognize, but that has many shades of meaning (like nudity — does breast-feeding count?) or that are very context-dependent, like harassment, fake news, misinformation, and so on. None of these categories have simple definitions, and for each of them there are edge-cases with no objective status, examples where someone’s background, personal ethos, or simply their mood on any given day might make the difference between one definition and another.
The problem with trying to get machines to understand this sort of content, says Robyn Caplan, an affiliate researcher at the nonprofit Data & Society, is that it is essentially asking them to understand human culture — a phenomenon too fluid and subtle to be described in simple, machine-readable rules.
“[This content] tends to involve context that is specific to the speaker,” Caplan tells The Verge. “That means things like power dynamics, race relations, political dynamics, economic dynamics.” Since these platforms operate globally, varying cultural norms need to be taken into account too, she says, as well as different legal regimes.
One way to know whether content will be difficult to classify, says Eric Goldman, a professor of law at Santa Clara University, is to ask whether or not understanding it requires “extrinsic information” — that is, information outside the image, video, audio, or text.
“For example, filters are not good at figuring out hate speech, parody, or news reporting of controversial events because so much of the determination depends on cultural context and other extrinsic information,” Goldman tells The Verge. “Similarly, filters aren’t good at determining when a content republication is fair use under US copyright law because the determination depends on extrinsic information such as market dynamics, the original source material, and the uploader’s other activities.”
But AI as a field is moving very swiftly. So will future algorithms be able to reliably classify this sort of content in the future? Goldman and Caplan are skeptical.
AI will get better at understanding context, says Goldman, but it’s not evident that AI will soon be able to do so better than a human. “AI will not replace [...] human reviewers for the foreseeable future,” he says.
Caplan agrees, and points out that as long as humans argue about how to classify this sort of material, what chance do machines have? “There is just no easy solution,” she says. ”We’re going to keep seeing problems.”
It’s worth noting, though, that AI isn’t completely hopeless. Advances in deep learning recently have greatly increased the speed and competency with which computers classify information in images, video, and text. Arun Gandhi, who works for NanoNets, a company that sells AI moderation tools to online businesses, says this shouldn’t be discounted.
“A lot of the focus is on how traumatic or disturbing the job of content moderator is, which is absolutely fair,” Gandhi tells The Verge. “But it also takes away the fact that we are making progress with some of these problems.”
Machine learning systems need a large number of examples to learn what offending content looks like, explains Gandhi, which means those systems will improve in years to come as training datasets get bigger. He notes that some of the systems currently in place would look impossibly fast and accurate even a few years ago. “I’m confident, given the improvements we’ve made in the last five, six years, that at some point we’ll be able to completely automate moderation,” says Gandhi.
Others would disagree, though, noting that AI systems have yet to master not only political and cultural context (which is changing month to month, as well as country to country) but also basic human concepts like sarcasm and irony. Throw in the various ways in which AI systems can be fooled by simple hacks, and a complete AI solution looks unlikely.
Sandra Wachter, a lawyer and research fellow at the Oxford Internet Institute, says there are also legal reasons why humans will need to be kept in the loop for content moderation.
“In Europe we have a data protection framework [GDPR] that allows people to contest certain decisions made by algorithms. It also says transparency in decision making is important [and] that you have a right to know what’s happening to your data,” Wachter tells The Verge. But algorithms can’t explain why they make certain decisions, she says, which makes these systems opaque and could lead to tech companies getting sued.
Wachter says that complaints relating to GDPR have already been lodged, and that more cases are likely to follow. “When there are higher rights at stake, like the right to privacy and to freedom of speech, [...] it’s important that we have some sort of recourse,” she says. “When you have to make a judgement call that impacts other people’s freedom you have to have a human in the loop that can scrutinize the algorithm and explain these things.”
As Caplan notes, what tech companies can do — with their huge profit margins and duty of care to those they employee — is improve working conditions for human moderators. “At the very bare minimum we need to have better labor standards,” she says. As Casey Newton noted in his report, while companies like Facebook do make some effort to properly reward human moderators, giving them health benefits and above-average wages, it’s often outweighed by relentless drive to better accuracy and more decisions.
Caplan says that pressure on tech companies to solve the problem of content automation could also be contributing to this state of affairs. “That’s when you get issues where workers are held to impossible standards of accuracy,” she says. The need to come up with a fix as soon as possible plays into Silicon Valley’s often-maligned “move fast and break things” attitude. And while this can be a great way to think when launching an app, it’s a terrible mindset for a company managing the subtleties of global speech.
“And we’re saying now maybe we should use machines to deal with this problem,” says Caplan, “but that will lead to a whole new set of issues.”
It’s also worth remembering that this is a new and unique problem. Never before have platforms as huge and information-dense as Facebook and YouTube existed. These are places where anyone, anywhere in the world, any time, can upload and share whatever content they like. Managing this vast and ever-changing semi-public realm is “a challenge no other media system has ever had to face,” says Caplan.
What we do know is that the status quo is not working. The humans tasked with cleaning up the internet’s mess are miserable, and the humans creating that mess aren’t much better off. Artificial intelligence doesn’t have enough smarts to deal with the problem, and human intelligence is stretched coming up with solutions. Something’s gotta give.
When is the last time you had dinner with someone without checking Facebook or Instagram on your phone? With smartphones and connected objects invading our everyday lives, it is getting harder and harder to connect with people nowadays. Rand Hindi astounds us by proposing a solution to this problem that might just change our lives.
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Why can't artificial intelligence do what humans can? Rob Miles talks about generality in intelligence.
Find out what is Neuralink, Elon Musk's newest project, which aims to develop a computer to human brain interface. This would allow us to communicate data with technology on speeds far greater then simply typing or clicking. Neuralink is Elon Musk's response to the exponential advancement of Artificial Intelligence. He wants to combine the human brain with AI, building a stable symbiosis, which would push our cognition to a superhuman level!
Yoshua Bengio, Yann LeCun, Demis Hassabis, Anca Dragan, Oren Etzioni, Guru Banavar, Jurgen Schmidhuber, and Tom Gruber discuss how and when we .
Neil understands the potential AI has to revolutionize the world; however, he also has a mission to teach people the truth about technology, informing about the dangers of AI when the revolution starts to grow faster than the regulations, putting us all at risk. Neil Deshmukh is dedicated to using technology as a superpower as a means to help people all over the world. By creating solutions to worldwide problems (even in the comfort of his bedroom), technology allows him to help people around the world. He has founded two businesses and worked on in-depth research based on the idea of Artificial Intelligence, the science and art of teaching computers to do things that we once thought only we could do. He has created VocalEyes, an IOS app which allows visually impaired users to navigate the world using technology to identify objects, text, people, and environments. It has thousands of users, and is on the App Store. PlantumAI allows farmers to diagnose their plants, and treat them before their crops are destroyed by disease. It is currently being used in areas of India to reduce over-pesticide usage. He’s also worked on many research projects utilizing the power of AI to help doctors diagnose and analyze patients more efficiently. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
In this video Scott Hanselman delivers one of the best and personal demos, he'll show you how a combined solution using technologies such as IoT devices, cloud platforms, Machine Learning and Office API's can come together to solve even the most complex problems.
The HealthClinic.biz demo used in the video can be found in the repo at https://github.com/Microsoft/HealthClinic.biz/. The samples in the repo are used to present an end-to-end demo scenario based on a fictitious B2B and multitenant system, named "HealthClinic.biz" that provides different websites, mobile apps, wearable apps, and services running on the latest Microsoft and open technologies.
Prof Pantic from Imperial College London at The Future of AI and ML in Healthcare Conference
Listen on SoundCloud: https://soundcloud.com/user-547260463
Bloomberg's Hello World host Ashlee Vance recently traveled to Osaka University to see Professor Hiroshi Ishiguro’s latest creation, an android named Erica that's designed to work, one day, as a receptionist or personal assistant. The android has lifelike skin and facial gestures and uses artificial intelligence software to listen to and respond to requests. Is Erica creepy? To Vance she is, but not to Professor Ishiguro, who considers her nearly indistinguishable from a human.
Based off of http://www.youtube.com/watch?v=kBKr8YLuVgs
An excerpt from the 1968 film "2001: A Space Odyssey" directed by Stanley Kubrick.
The Most Advanced Quadruped Robot on Earth
BigDog is the alpha male of the Boston Dynamics family of robots. It is a quadruped robot that walks, runs, and climbs on rough terrain and carries heavy loads. BigDog is powered by a gasoline engine that drives a hydraulic actuation system. BigDog's legs are articulated like an animal's, and have compliant elements that absorb shock and recycle energy from one step to the next. BigDog is the size of a large dog or small mule, measuring 1 meter long, 0.7 meters tall and 75 kg weight.
BigDog handles heavy objects. The goal is to use the strength of the legs and torso to help power motions of the arm. This sort of dynamic, whole-body approach to manipulation is used routinely by human athletes and will enhance the performance of advanced robots. Boston Dynamics is developing the control and actuation techniques needed for dynamic manipulation. The cinderblock weighs about 35 lbs and the best throw is a bit more than 17 ft. The research is funded by the Army Research Laboratory's RCTA program.
MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston
MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
Instructor: Patrick Winston
What do you get when you give a design tool a digital nervous system? Computers that improve our ability to think and imagine, and robotic systems that come up with (and build) radical new designs for bridges, cars, drones and much more — all by themselves. Take a tour of the Augmented Age with futurist Maurice Conti and preview a time when robots and humans will work side-by-side to accomplish things neither could do alone.
New tech spawns new anxieties, says scientist and philosopher Grady Booch, but we don't need to be afraid an all-powerful, unfeeling AI. Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how we'll teach, not program, them to share our values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial intelligence will enhance human life.
A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead?
Richard Socher is an adjunct professor at the Stanford Computer Science Department where he obtained his PhD working on deep learning with Chris Manning and Andrew Ng. He won the best Stanford CS PhD thesis award.
He is now Chief Scientist at Salesforce where he leads the company’s research efforts in artificial intelligence. He previously founded MetaMind, a deep learning AI platform that analyzes, labels and makes predictions on image and text data.
Richard Socher is Chief Scientist at Salesforce and an adjunct professor at the Stanford Computer Science Department. At Salesforce he leads the company’s research efforts and brings state of the art artificial intelligence solutions to Salesforce.
Prior to Salesforce, Richard was the CEO and founder of MetaMind, a startup acquired by Salesforce in April 2016. Richard obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and won the best Stanford CS PhD thesis award. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
A.I. researcher and rising high school senior Animesh Koratana envisions a world where medical research on the human brain for cures to diseases ranging from depression to bi-polar to schizophrenia can be done on artificially created brains that think and behave just like human brains. He shares the first breakthrough in this short talk.
Artificial Intelligence Scientist. Scientific Director of the Swiss AI Lab, IDSIA, DTI, SUPSI; Prof. of AI, Faculty of Informatics, USI, Lugano; Co-founder & Chief Scientist, NNAISENSE, Switzerland. Prof. Jürgen Schmidhuber has been called the father of modern Artificial Intelligence. His lab's deep learning methods have revolutionized machine learning and are now available on 3 billion smartphones, and used billions of times per day, e.g. for Facebook's automatic translation, Google's speech recognition, Apple's Siri & QuickType, Amazon's Alexa, etc. His research group also established the field of mathematically rigorous universal AI and optimal universal problem solvers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He is recipient of numerous awards including the 2016 IEEE Neural Networks Pioneer Award "for pioneering contributions to deep learning and neural networks". Scientific Director of the Swiss AI Lab, IDSIA, DTI, SUPSI; Prof. of AI, Faculty of Informatics, USI, Lugano; Co-founder & Chief Scientist, NNAISENSE, Switzerland. Prof. Jürgen Schmidhuber has been called the father of modern Artificial Intelligence. His lab's deep learning methods have revolutionized machine learning, are now available on 3 billion smartphones, and used billions of times per day, e.g. for Facebook's automatic translation, Google's speech recognition, Apple's Siri & QuickType, Amazon's Alexa, etc. His research group also established the field of mathematically rigorous universal AI and optimal universal problem solvers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He is recipient of numerous awards including the 2016 IEEE Neural Networks Pioneer Award "for pioneering contributions to deep learning and neural networks". This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx