Tag Archives: apply

#431155 What It Will Take for Quantum Computers ...

Quantum computers could give the machine learning algorithms at the heart of modern artificial intelligence a dramatic speed up, but how far off are we? An international group of researchers has outlined the barriers that still need to be overcome.
This year has seen a surge of interest in quantum computing, driven in part by Google’s announcement that it will demonstrate “quantum supremacy” by the end of 2017. That means solving a problem beyond the capabilities of normal computers, which the company predicts will take 49 qubits—the quantum computing equivalent of bits.
As impressive as such a feat would be, the demonstration is likely to be on an esoteric problem that stacks the odds heavily in the quantum processor’s favor, and getting quantum computers to carry out practically useful calculations will take a lot more work.
But these devices hold great promise for solving problems in fields as diverse as cryptography or weather forecasting. One application people are particularly excited about is whether they could be used to supercharge the machine learning algorithms already transforming the modern world.
The potential is summarized in a recent review paper in the journal Nature written by a group of experts from the emerging field of quantum machine learning.
“Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce,” they write.
“This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.”
Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
Designing these algorithms is tricky work, but the authors of the review note that there has been significant progress in recent years. They highlight multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs.
We still don’t have the hardware to implement these algorithms, but according to the researchers the challenge is a technical one and clear paths to overcoming them exist. More challenging, they say, are four fundamental conceptual problems that could limit the applicability of quantum machine learning.
The first two are the input and output problems. Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world. Translating significant amounts of classical data into the quantum systems can take so much time it can cancel out the benefits of the faster processing speeds, and the same is true of reading out the solution at the end.
The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory. A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms. But the authors note this is still a considerable engineering challenge and may not be sustainable for big data problems.
Closely related to the input/output problem is the costing problem. At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. It’s expected that on highly complex problems they will offer considerable improvements over classical computers, but it’s not clear how big problems have to be before this becomes apparent.
Finally, whether or when these advantages kick in may be hard to prove, something the authors call the benchmarking problem. Claiming that a quantum algorithm can outperform any classical machine learning approach requires extensive testing against these other techniques that may not be feasible.
They suggest that this could be sidestepped by lowering the standards quantum machine learning algorithms are currently held to. This makes sense, as it doesn’t really matter whether an algorithm is intrinsically faster than all possible classical ones, as long as it’s faster than all the existing ones.
Another way of avoiding some of these problems is to apply these techniques directly to quantum data, the actual states generated by quantum systems and processes. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
“This would enable a virtuous cycle of innovation similar to that which occurred in classical computing, wherein each generation of processors is then leveraged to design the next-generation processors,” they conclude.
Image Credit: archy13 / Shutterstock.com Continue reading

Posted in Human Robots

#430855 Why Education Is the Hardest Sector of ...

We’ve all heard the warning cries: automation will disrupt entire industries and put millions of people out of jobs. In fact, up to 45 percent of existing jobs can be automated using current technology.
However, this may not necessarily apply to the education sector. After a detailed analysis of more than 2,000-plus work activities for more than 800 occupations, a report by McKinsey & Co states that of all the sectors examined, “…the technical feasibility of automation is lowest in education.”
There is no doubt that technological trends will have a powerful impact on global education, both by improving the overall learning experience and by increasing global access to education. Massive open online courses (MOOCs), chatbot tutors, and AI-powered lesson plans are just a few examples of the digital transformation in global education. But will robots and artificial intelligence ever fully replace teachers?
The Most Difficult Sector to Automate
While various tasks revolving around education—like administrative tasks or facilities maintenance—are open to automation, teaching itself is not.
Effective education involves more than just transfer of information from a teacher to a student. Good teaching requires complex social interactions and adaptation to the individual student’s learning needs. An effective teacher is not just responsive to each student’s strengths and weaknesses, but is also empathetic towards the student’s state of mind. It’s about maximizing human potential.
Furthermore, students don’t just rely on effective teachers to teach them the course material, but also as a source of life guidance and career mentorship. Deep and meaningful human interaction is crucial and is something that is very difficult, if not impossible, to automate.
Automating teaching is an example of a task that would require artificial general intelligence (as opposed to narrow or specific intelligence). In other words, this is the kind of task that would require an AI that understands natural human language, can be empathetic towards emotions, plan, strategize and make impactful decisions under unpredictable circumstances.
This would be the kind of machine that can do anything a human can do, and it doesn’t exist—at least, not yet.
We’re Getting There
Let’s not forget how quickly AI is evolving. Just because it’s difficult to fully automate teaching, it doesn’t mean the world’s leading AI experts aren’t trying.
Meet Jill Watson, the teaching assistant from Georgia Institute of Technology. Watson isn’t your average TA. She’s an IBM-powered artificial intelligence that is being implemented in universities around the world. Watson is able to answer students’ questions with 97 percent certainty.
Technologies like this also have applications in grading and providing feedback. Some AI algorithms are being trained and refined to perform automatic essay scoring. One project has achieved a 0.945 correlation with human graders.
All of this will have a remarkable impact on online education as we know it and dramatically increase online student retention rates.

Any student with a smartphone can access a wealth of information and free courses from universities around the world. MOOCs have allowed valuable courses to become available to millions of students. But at the moment, not all participants can receive customized feedback for their work. Currently, this is limited by manpower, but in the future that may not be the case.
What chatbots like Jill Watson allow is the opportunity for hundreds of thousands, if not millions, of students to have their work reviewed and all their questions answered at a minimal cost.
AI algorithms also have a significant role to play in personalization of education. Every student is unique and has a different set of strengths and weaknesses. Data analysis can be used to improve individual student results, assess each student’s strengths and weaknesses, and create mass-customized programs. Algorithms can analyze student data and consequently make flexible programs that adapt to the learner based on real-time feedback. According to the McKinsey Global Institute, all of this data in education could unlock between $900 billion and $1.2 trillion in global economic value.
Beyond Automated Teaching
It’s important to recognize that technological automation alone won’t fix the many issues in our global education system today. Dominated by outdated curricula, standardized tests, and an emphasis on short-term knowledge, many experts are calling for a transformation of how we teach.
It is not enough to simply automate the process. We can have a completely digital learning experience that continues to focus on outdated skills and fails to prepare students for the future. In other words, we must not only be innovative with our automation capabilities, but also with educational content, strategy, and policies.
Are we equipping students with the most important survival skills? Are we inspiring young minds to create a better future? Are we meeting the unique learning needs of each and every student? There’s no point automating and digitizing a system that is already flawed. We need to ensure the system that is being digitized is itself being transformed for the better.
Stock Media provided by davincidig / Pond5 Continue reading

Posted in Human Robots

#430743 Teaching Machines to Understand, and ...

We humans are swamped with text. It’s not just news and other timely information: Regular people are drowning in legal documents. The problem is so bad we mostly ignore it. Every time a person uses a store’s loyalty rewards card or connects to an online service, his or her activities are governed by the equivalent of hundreds of pages of legalese. Most people pay no attention to these massive documents, often labeled “terms of service,” “user agreement,” or “privacy policy.”
These are just part of a much wider societal problem of information overload. There is so much data stored—exabytes of it, as much stored as has ever been spoken by people in all of human history—that it’s humanly impossible to read and interpret everything. Often, we narrow down our pool of information by choosing particular topics or issues to pay attention to. But it’s important to actually know the meaning and contents of the legal documents that govern how our data is stored and who can see it.
As computer science researchers, we are working on ways artificial intelligence algorithms could digest these massive texts and extract their meaning, presenting it in terms regular people can understand.
Can computers understand text?
Computers store data as 0s and 1s—data that cannot be directly understood by humans. They interpret these data as instructions for displaying text, sound, images, or videos that are meaningful to people. But can computers actually understand the language, not only presenting the words but also their meaning?
One way to find out is to ask computers to summarize their knowledge in ways that people can understand and find useful. It would be best if AI systems could process text quickly enough to help people make decisions as they are needed—for example, when you’re signing up for a new online service and are asked to agree with the site’s privacy policy.
What if a computerized assistant could digest all that legal jargon in a few seconds and highlight key points? Perhaps a user could even tell the automated assistant to pay particular attention to certain issues, like when an email address is shared, or whether search engines can index personal posts. Companies could use this capability, too, to analyze contracts or other lengthy documents.
To do this sort of work, we need to combine a range of AI technologies, including machine learning algorithms that take in large amounts of data and independently identify connections among them; knowledge representation techniques to express and interpret facts and rules about the world; speech recognition systems to convert spoken language to text; and human language comprehension programs that process the text and its context to determine what the user is telling the system to do.
Examining privacy policies
A modern internet-enabled life today more or less requires trusting for-profit companies with private information (like physical and email addresses, credit card numbers and bank account details) and personal data (photos and videos, email messages and location information).
These companies’ cloud-based systems typically keep multiple copies of users’ data as part of backup plans to prevent service outages. That means there are more potential targets—each data center must be securely protected both physically and electronically. Of course, internet companies recognize customers’ concerns and employ security teams to protect users’ data. But the specific and detailed legal obligations they undertake to do that are found in their impenetrable privacy policies. No regular human—and perhaps even no single attorney—can truly understand them.
In our study, we ask computers to summarize the terms and conditions regular users say they agree to when they click “Accept” or “Agree” buttons for online services. We downloaded the publicly available privacy policies of various internet companies, including Amazon AWS, Facebook, Google, HP, Oracle, PayPal, Salesforce, Snapchat, Twitter, and WhatsApp.
Summarizing meaning
Our software examines the text and uses information extraction techniques to identify key information specifying the legal rights, obligations and prohibitions identified in the document. It also uses linguistic analysis to identify whether each rule applies to the service provider, the user or a third-party entity, such as advertisers and marketing companies. Then it presents that information in clear, direct, human-readable statements.
For example, our system identified one aspect of Amazon’s privacy policy as telling a user, “You can choose not to provide certain information, but then you might not be able to take advantage of many of our features.” Another aspect of that policy was described as “We may also collect technical information to help us identify your device for fraud prevention and diagnostic purposes.”

We also found, with the help of the summarizing system, that privacy policies often include rules for third parties—companies that aren’t the service provider or the user—that people might not even know are involved in data storage and retrieval.
The largest number of rules in privacy policies—43 percent—apply to the company providing the service. Just under a quarter of the rules—24 percent—create obligations for users and customers. The rest of the rules govern behavior by third-party services or corporate partners, or could not be categorized by our system.

The next time you click the “I Agree” button, be aware that you may be agreeing to share your data with other hidden companies who will be analyzing it.
We are continuing to improve our ability to succinctly and accurately summarize complex privacy policy documents in ways that people can understand and use to access the risks associated with using a service.

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

Posted in Human Robots

#428574 6 Big Ways Tech Is Rewriting Society’s ...

Technology is advancing so rapidly that we will experience radical changes in society not only in our lifetimes but in the coming years. We have already begun to see ways in which computing, sensors, artificial intelligence and genomics are reshaping entire industries and our daily lives. As we undergo this rapid change, many of the old assumptions that we have relied on will no longer apply. Technology is creating a new set of rules that will… read more Continue reading

Posted in Human Robots

#428433 UK Robotics Week To Return – 24th June ...

Today marks official launch of the second UK Robotics Week; entries now open in Surgical Robot, Autonomous Driving and School Robot Challenges
London, UK, 7th November 2016. – UK Robotics Week 2017 officially launches today, with a range of robotics activities and challenges open to schools, academic institutions and industry sectors. These activities culminate in a national week of celebration being held 24th – 30th June 2017. The second annual UK Robotics Week is set to be even bigger and better, building on the huge success of the inaugural event. Any institutions or organisations planning to hold their own robotics events – either in the run-up to and during the UK Robotics Week – can also apply now to be included in the official Programme of Activities (please visit www.roboticsweek.uk for details of how to register).
The first ever UK Robotics Week proved a huge success, encompassing a host of events up and down the UK, including public lectures, open labs, hackathons, tech weekends, conferences, and a state-of-the-art robotics showcase held on the last day. The UK Robotics Week initiative is jointly spearheaded by founding supporters, the Engineering and Physical Sciences Research Council (EPSRC), The Royal Academy of Engineering, the Institution of Engineering and Technology, the Institution of Mechanical Engineers and the UK-RAS Special Interest Group, and is being coordinated by the EPSRC UK-RAS network.
As part of the official launch, this year’s School Robot Challenge is now open for entries to all schools nationwide. The competition offers schoolchildren the opportunity to design their own virtual robot bug and teach it to move, with the option of printing their bug in 3D. The challenge aims to develop children’s interest and skills in digital technology, design, science, engineering and biology. This year’s competition has been split into two age group categories – 4-12 years and 13-18 years – with top prizes to be awarded in each. School are actively encouraged to register their interest on the website now to access the information packs and software at http://www.roboticsweek.uk/schoolrobotchallenge.htm
The first Surgical Robot Challenge attracted participation from the world’s leading institutions, with top robotics research teams travelling to the UK to demonstrate their outstanding innovations during last year’s competition finals. The 2017 competition is now open for entry, and any international researchers interested in participating in this prestigious challenge can download all the competition information at http://www.roboticsweek.uk/surgicalrobotchallenge.htm
The second Autonomous Driving Challenge is also launched today. This is an international competition to inspire the next generation of designers and engineers, and involves designing your own vehicle and teaching it to drive autonomously. The challenge is open to everyone: children and adults, amateurs and professionals.
Commenting on today’s official launch, Professor Guang-Zhong Yang PhD, FREng, Director and Co-founder of the Hamlyn Centre for Robotic Surgery, at Imperial College London and Chair of the UK-RAS Network, said: “We have been delighted with the response to UK Robotics Week, which looks set to become one of the key highlights in the science and technology calendar. This is a unique opportunity to celebrate the UK’s technology leadership in robotics and autonomous systems, and for individuals and institutions to get involved – hands-on – with robotics development.”
Professor Philip Nelson, Chief Executive of EPSRC, added: “From inspiring the nation’s budding engineers in STEM subjects to engaging people of all ages in a national debate about the contribution robotic technology can make to society and our economy, we’re looking forward to creating even more of a buzz with UK Robotics Week this year, and shining an even bigger spotlight on the fantastic robotics innovation being driven from the UK.”
For full information about all the activities planned for UK Robotics Week, please visit the website: www.roboticsweek.uk and follow UK Robotics Week on Twitter (@ukroboticsweek)
About the EPSRC UK-RAS Network (http://www.uk-ras.org) : The EPSRC UK Robotics and Autonomous Systems Network (UK-RAS Network) is dedicated to robotics innovation across the UK, with a mission to provide academic leadership in Robotics and Autonomous Systems (RAS), expand collaboration with industry, and integrate and coordinate activities at eight Engineering and Physical Sciences Research Council (EPSRC) funded RAS capital facilities and Centres for Doctoral Training (CDTs) across the country.
PRESS CONTACT:
Nicky Denovan
EvokedSet
Email: nicky[@]evokedset[dot]com
Mobile: +44 (0)7747 017654
The post UK Robotics Week To Return – 24th June to 30th June 2017 appeared first on Roboticmagazine. Continue reading

Posted in Human Robots