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#431238 AI Is Easy to Fool—Why That Needs to ...

Con artistry is one of the world’s oldest and most innovative professions, and it may soon have a new target. Research suggests artificial intelligence may be uniquely susceptible to tricksters, and as its influence in the modern world grows, attacks against it are likely to become more common.
The root of the problem lies in the fact that artificial intelligence algorithms learn about the world in very different ways than people do, and so slight tweaks to the data fed into these algorithms can throw them off completely while remaining imperceptible to humans.
Much of the research into this area has been conducted on image recognition systems, in particular those relying on deep learning neural networks. These systems are trained by showing them thousands of examples of images of a particular object until they can extract common features that allow them to accurately spot the object in new images.
But the features they extract are not necessarily the same high-level features a human would be looking for, like the word STOP on a sign or a tail on a dog. These systems analyze images at the individual pixel level to detect patterns shared between examples. These patterns can be obscure combinations of pixel values, in small pockets or spread across the image, that would be impossible to discern for a human, but highly accurate at predicting a particular object.

“An attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human.”

What this means is that by identifying these patterns and overlaying them over a different image, an attacker can trick the object recognition algorithm into seeing something that isn’t there, without these alterations being obvious to a human. This kind of manipulation is known as an “adversarial attack.”
Early attempts to trick image recognition systems this way required access to the algorithm’s inner workings to decipher these patterns. But in 2016 researchers demonstrated a “black box” attack that enabled them to trick such a system without knowing its inner workings.
By feeding the system doctored images and seeing how it classified them, they were able to work out what it was focusing on and therefore generate images they knew would fool it. Importantly, the doctored images were not obviously different to human eyes.
These approaches were tested by feeding doctored image data directly into the algorithm, but more recently, similar approaches have been applied in the real world. Last year it was shown that printouts of doctored images that were then photographed on a smartphone successfully tricked an image classification system.
Another group showed that wearing specially designed, psychedelically-colored spectacles could trick a facial recognition system into thinking people were celebrities. In August scientists showed that adding stickers to stop signs in particular configurations could cause a neural net designed to spot them to misclassify the signs.
These last two examples highlight some of the potential nefarious applications for this technology. Getting a self-driving car to miss a stop sign could cause an accident, either for insurance fraud or to do someone harm. If facial recognition becomes increasingly popular for biometric security applications, being able to pose as someone else could be very useful to a con artist.
Unsurprisingly, there are already efforts to counteract the threat of adversarial attacks. In particular, it has been shown that deep neural networks can be trained to detect adversarial images. One study from the Bosch Center for AI demonstrated such a detector, an adversarial attack that fools the detector, and a training regime for the detector that nullifies the attack, hinting at the kind of arms race we are likely to see in the future.
While image recognition systems provide an easy-to-visualize demonstration, they’re not the only machine learning systems at risk. The techniques used to perturb pixel data can be applied to other kinds of data too.

“Bypassing cybersecurity defenses is one of the more worrying and probable near-term applications for this approach.”

Chinese researchers showed that adding specific words to a sentence or misspelling a word can completely throw off machine learning systems designed to analyze what a passage of text is about. Another group demonstrated that garbled sounds played over speakers could make a smartphone running the Google Now voice command system visit a particular web address, which could be used to download malware.
This last example points toward one of the more worrying and probable near-term applications for this approach: bypassing cybersecurity defenses. The industry is increasingly using machine learning and data analytics to identify malware and detect intrusions, but these systems are also highly susceptible to trickery.
At this summer’s DEF CON hacking convention, a security firm demonstrated they could bypass anti-malware AI using a similar approach to the earlier black box attack on the image classifier, but super-powered with an AI of their own.
Their system fed malicious code to the antivirus software and then noted the score it was given. It then used genetic algorithms to iteratively tweak the code until it was able to bypass the defenses while maintaining its function.
All the approaches noted so far are focused on tricking pre-trained machine learning systems, but another approach of major concern to the cybersecurity industry is that of “data poisoning.” This is the idea that introducing false data into a machine learning system’s training set will cause it to start misclassifying things.
This could be particularly challenging for things like anti-malware systems that are constantly being updated to take into account new viruses. A related approach bombards systems with data designed to generate false positives so the defenders recalibrate their systems in a way that then allows the attackers to sneak in.
How likely it is that these approaches will be used in the wild will depend on the potential reward and the sophistication of the attackers. Most of the techniques described above require high levels of domain expertise, but it’s becoming ever easier to access training materials and tools for machine learning.
Simpler versions of machine learning have been at the heart of email spam filters for years, and spammers have developed a host of innovative workarounds to circumvent them. As machine learning and AI increasingly embed themselves in our lives, the rewards for learning how to trick them will likely outweigh the costs.
Image Credit: Nejron Photo / Shutterstock.com Continue reading

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#428367 Fusion for Energy signs multi-million ...

Fusion for Energy signs multi-million deal with Airbus Safran Launchers, Nuvia Limited and Cegelec CEM to develop robotics equipment for ITER
The contract for a value of nearly 100 million EUR is considered to be the single biggest robotics deal to date in the field of fusion energy. The state of the art equipment will form part of ITER, the world’s largest experimental fusion facility and the first in history to produce 500 MW. The prestigious project brings together seven parties (China, Europe, Japan, India, the Republic of Korea, the Russian Federation and the USA) which represent 50% of the world’s population and 80% of the global GDP.
The collaboration between Fusion for Energy (F4E), the EU organisation managing Europe’s contribution to ITER, with a consortium of companies consisting of Airbus Safran Launchers (France-Germany), Nuvia Limited (UK) and Cegelec CEM (France), companies of the VINCI Group, will run for a period of seven years. The UK Atomic Energy Authority (UK), Instituto Superior Tecnico (Portugal), AVT Europe NV (Belgium) and Millennium (France) will also be part of this deal which will deliver remotely operated systems for the transportation and confinement of components located in the ITER vacuum vessel.
The contract carries also a symbolic importance marking the signature all procurement packages managed by Europe in the field of remote handling. Carlo Damiani, F4E’s Project Manager for ITER Remote Handling Systems, explained that “F4E’s stake in ITER offers an unparalleled opportunity to companies and laboratories to develop expertise and an industrial culture in fusion reactors’ maintenance.”
Cut-away image of the ITER machine showing the casks at the three levels of the ITER machine. ITER IO © (Remote1 web). Photo Credit: f4e.europa.euIllustration of lorry next to an ITER cask. F4E © (Remote 2 web). Photo Credit: f4e.europa.euAerial view of the ITER construction site, October 2016. F4E © (ITER site aerial Oct). Photo Credit: f4e.europa.eu

Why ITER requires Remote Handling?
Remote handling refers to the high-tech systems that will help us maintain and repair the ITER machine. The space where the bulky equipment will operate is limited and the exposure of some of the components to radioactivity, prohibit any manual intervention inside the vacuum vessel.

What will be delivered through this contract?
The transfer of components from the ITER vacuum vessel to the Hot Cell building, where they will be deposited for maintenance, will need to be carried out with the help of massive double-door containers known as casks. According to current estimates, 15 of these casks will need to be manufactured and in their largest configuration they will measure 8.5 m x 3.7 m x 2.6 m approaching 100 tonnes when transporting the heaviest components. These enormous “boxes”, resembling to a conventional lorry container, will be remotely operated as they move between the different levels and buildings of the machine. Apart from the transportation and confinement of components, the ITER Cask and Plug Remote Handling System will also ensure the installation of the remote handling equipment entering into the vacuum vessel to pick up the components to be removed. The technologies underpinning this system will encompass a variety of high-tech skills and comply with nuclear safety requirements. A proven manufacturing experience in similar fields and the development of bespoke systems to perform mechanical transfers will be essential.

Background information
MEMO: Fusion for Energy signs multi-million deal with Airbus Safran Launchers, Nuvia Limited and Cegelec CEM to develop robotics equipment for ITER
Multimedia
To see how the ITER Remote Handling System will operate click on clip 1 and clip 2
To see the progress of the ITER construction site click here
To take a virtual tour on the ITER construction site click here

Image captions
Cut-away image of the ITER machine showing the casks at the three levels of the ITER machine. ITER IO © (Remote1 web)

Illustration of lorry next to an ITER cask. F4E © (Remote 2 web)

Aerial view of the ITER construction site, October 2016. F4E © (ITER site aerial Oct)

The consortium of companies
The consortium combines the space expertise of Airbus Safran Launchers, adapted to this extreme environment to ensure safe conditions for the ITER teams; with Nuvia comes a wealth of nuclear experience dating back to the beginnings of the UK Nuclear industry. Nuvia has delivered solutions to some of the world’s most complex nuclear challenges; and with Cegelec CEM as a specialist in mechanical projects for French nuclear sector, which contributes over 30 years in the nuclear arena, including turnkey projects for large scientific installations, as well as the realisation of complex mechanical systems.

Fusion for Energy
Fusion for Energy (F4E) is the European Union’s organisation for Europe’s contribution to ITER.
One of the main tasks of F4E is to work together with European industry, SMEs and research organisations to develop and provide a wide range of high technology components together with engineering, maintenance and support services for the ITER project.
F4E supports fusion R&D initiatives through the Broader Approach Agreement signed with Japan and prepares for the construction of demonstration fusion reactors (DEMO).
F4E was created by a decision of the Council of the European Union as an independent legal entity and was established in April 2007 for a period of 35 years.
Its offices are in Barcelona, Spain.
http://www.fusionforenergy.europa.eu
http://www.youtube.com/user/fusionforenergy
http://twitter.com/fusionforenergy
http://www.flickr.com/photos/fusionforenergy

ITER
ITER is a first-of-a-kind global collaboration. It will be the world’s largest experimental fusion facility and is designed to demonstrate the scientific and technological feasibility of fusion power. It is expected to produce a significant amount of fusion power (500 MW) for about seven minutes. Fusion is the process which powers the sun and the stars. When light atomic nuclei fuse together form heavier ones, a large amount of energy is released. Fusion research is aimed at developing a safe, limitless and environmentally responsible energy source.
Europe will contribute almost half of the costs of its construction, while the other six parties to this joint international venture (China, Japan, India, the Republic of Korea, the Russian Federation and the USA), will contribute equally to the rest.
The site of the ITER project is in Cadarache, in the South of France.
http://www.iter.org

For Fusion for Energy media enquiries contact:
Aris Apollonatos
E-mail: aris.apollonatos@f4e.europa.eu
Tel: + 34 93 3201833 + 34 649 179 42
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