RAVN ACE Can Help Financial Institutions with Regulatory Compliance
March 31, 2016
Increased regulations in the financial field call for tools that can gather certain information faster and more thoroughly. Bobsguide points to a solution in, “RAVN Systems Releases RAVN ACE for Automated Data Extraction of ISDA Documents Using Artificial Intelligence.” For those who are unaware, ISDA stands for International Swaps and Derivatives Association, and a CSA is a Credit Support Annex. The press release informs us:
“RAVN’s ground-breaking technology, RAVN ACE, joins elements of Artificial Intelligence and information processing to deliver a platform that can read, interpret, extract and summarise content held within ISDA CSAs and other legal documents. It converts unstructured data into structured output, in a fraction of the time it takes a human – and with a higher degree of accuracy. RAVN ACE can extract the structure of the agreement, the clauses and sub-clauses, which can be very useful for subsequent re-negotiation purposes. It then further extracts the key definitions from the contract, including collateral data from tabular formats within the credit support annexes. All this data is made available for input to contract or collateral management and margining systems or can simply be provided as an Excel or XML output for analysis. AVN ACE also provides an in-context review and preview of the extracted terms to allow reviewing teams to further validate the data in the context of the original agreement.”
The write-up tells us the platform can identify high-credit-risk relationships and detail the work required to repaper those accounts (that is, to re-draft, re-sign, and re-process paperwork). It also notes that even organizations that have a handle on their contracts can benefit, because the platform can compare terms in actual documents with those in that have been manually abstracted.
Based in London, enterprise search firm RAVN tailors its solutions to the needs of each industry it serves. The company was founded in 2011.
Cynthia Murrell, March 31, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Google Now Has Dowsing Ability
March 16, 2016
People who claim to be psychic are fakes. There is not a way to predict the future, instantly locate a lost person or item, or read someone’s aura. No scientific theory has proven it exists. One of the abilities psychics purport to have is “dowsing,” the power to sense where water, precious stones or metals, and even people are hiding. Instead of relying on a suspended crystal or an angular stick, Google now claims it can identify any location based solely on images, says The Technology Review in the article, “Google Unveils Neural Network With ‘Superhuman’ Ability To Determine The Location Of Almost Any Image.”
Using computer algorithms, not magic powers, and Tobias Weyand’s programming prowess and a team of tech savvy people, they developed a way for a Google deep-learning machine to identity location pictures. Weyand and his team designed PlaNET, the too, and accomplished this by dividing the world into 26,000 square grid (sans ocean and poles) of varying sizes depending on populous areas.
“Next, the team created a database of geolocated images from the Web and used the location data to determine the grid square in which each image was taken. This data set is huge, consisting of 126 million images along with their accompanying Exif location data.
Weyand and co used 91 million of these images to teach a powerful neural network to work out the grid location using only the image itself. Their idea is to input an image into this neural net and get as the output a particular grid location or a set of likely candidates.”
With the remaining 34 million images in the data set, they tested the PlaNET to check its accuracy. PlaNET can accurately guess 3.6% images at street level, 10.1% on city level, 28.4% country of origin, and 48% of the continent. These results are very good compared to the limited knowledge that a human keeps in their head.
Weyand believes that PlaNET is able to determine the location, because it has learned new parents to recognize subtle patterns about areas that humans cannot distinguish, as it has arguably been more places than any human. What is even more amazing is how much memory PlaNET uses: only 377 MB!
When will PlaNET become available as a GPS app?
Whitney Grace, March 16, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Artificial Intelligence Competition Reveals Need for More Learning
March 3, 2016
The capabilities of robots are growing but, on the whole, have not surpassed a middle school education quite yet. The article Why AI can still hardly pass an eighth grade science test from Motherboard shares insights into the current state of artificial intelligence as revealed in a recent artificial intelligence competition. Chaim Linhart, a researcher from an Israel startup, TaKaDu, received the first place prize of $50,000. However, the winner only scored a 59.3 percent on this series of tasks tougher than the conventionally used Turing Test. The article describes how the winners utilized machine learning models,
“Tafjord explained that all three top teams relied on search-style machine learning models: they essentially found ways to search massive test corpora for the answers. Popular text sources included dumps of Wikipedia, open-source textbooks, and online flashcards intended for studying purposes. These models have anywhere between 50 to 1,000 different “features” to help solve the problem—a simple feature could look at something like how often a question and answer appear together in the text corpus, or how close words from the question and answer appear.”
The second and third place winners scored just around one percent behind Linhart’s robot. This may suggest a competitive market when the time comes. Or, perhaps, as the article suggests, nothing very groundbreaking has been developed quite yet. Will search-based machine learning models continue to be expanded and built upon or will another paradigm be necessary for AI to get grade A?
Megan Feil, March 3, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Search Vendor RAVN Systems Embraces Buzzwords
February 19, 2016
The article titled RAVN Systems Releases RAVN ACE for Automated Data Extraction of ISDA Documents Using Artificial Intelligence on BobsGuide details the needs of banks and other members of the derivatives market. Risk mitigation leads to ongoing negotiations that result in major documentation issues to keep up with the changes. The article explains how RAVN has met these challenges,
“RAVN ACE can extract the structure of the agreement, the clauses and sub-clauses, which can be very useful for subsequent re-negotiation purposes. It then further extracts the key definitions from the contract, including collateral data from tabular formats within the credit support annexes. All this data is made available for input to contract or collateral management and margining systems or can simply be provided as an Excel or XML output for analysis.”
Not only does RAVN ACE do the work in a fraction of the amount of time it would take a person, the output is also far more accurate, always good news when handling legal documents. The service also includes an audit service that compares terms from the documents with the manual abstraction. By doing so, RAVN ACE is able to analyze the risks and even estimate the amount of negotiating necessary to complete the contract.
Chelsea Kerwin, February 19, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Strong and Loud or Quiet and Weak, Googles Robot Grandkids Fail to Impress the Marines
January 15, 2016
The article titled Why the Marines Don’t Want Google’s Robot Soldiers in Combat on Fortune discusses the downside of the Google-owned company Boston Dynamics’ robots. You might guess, moral concerns, or more realistically, funding. But you would be wrong, since DARPA already shelled out over $30 million for the four-legged battle bots. Instead, the issue is that a single robot, which looks like a huge insect wearing a helmet and knee and elbow pads, emits a noise akin to a motorcycle revving, or a jackhammer drilling, for small movements. The article explains,
“Anyone who’s seen Boston Dynamics’ four-legged robots in action typically is wowed by their speed, strength, and agility, but also note how loud they are. They sound like chainsaws on steroids. And that decibel level is apparently a problem for potential customers, namely the U.S. military.
For Marines who took the robot out for a spin, that noise is apparently a deal breaker. “They took it as it was: a loud robot that’s going to give away their position.”
The reason for all this hullaballoo on the part of the robot is its gas engine, intended for increased robustness. The military was looking for a useful helpmate capable of carrying heavy loads of up to 400 lbs. There has been some back and forth between military representatives and Boston Dynamics, but the current state of affairs seems to be a quieter, and weaker, robot. Not ideal.
Chelsea Kerwin, January 15, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Feeding the Google AI Beast and Keeping in Mind, You Are What You Eat
January 13, 2016
The article titled We are All SkyNet in the Googlesphere on Disinformation refers to the Terminator’s controlling A.I., SkyNet, who determines the beginning of a machine age in the movie, and the conspiracy that Google is taking on that role in reality. Is it easy to understand the fear of Google’s reach, it does sometimes seem like a gigantic arm with a thousand hands groping about in cyberspace, and collecting little pieces of information that on their own seem largely harmless. The article discusses cloud computing and its relationship to the conspiracy,
“When you need your bits of info, your computer gathers them from the cloud again. The cloud is SkyNet’s greatest line of defense, as you can’t kill what is spread out over an entire network. Since the magnificent expose of the NSA and their ability to (at least) access every keystroke, file or phone call and Google’s (at minimum) complicity in managing the data, that is to say, nearly all data being collected, it’s hard to imagine the limitations to what any such Google AI program could learn.”
The article ends philosophically with the suggestion that the nature of a modern day SkyNet will depend on the data that it gathers from us, that we will create the monster in our likeness. This may not be where we expected the article to go, but it does make sense. Google as a company will not determine it, at least if literature has taught us anything.
Chelsea Kerwin, January 13, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Rethinking the J.D. As Artificial Intelligence Takes over Lawyers Work
January 5, 2016
The article titled Report: Artificial Intelligence Will Cause “Structural Collapse” of Law Firms by 2030 on Legal Futures posits that AI will take over legal practice in the near future. Jomati Consultants LLP released the report “Civilization 2030: The Near Future for Law Firms” which estimates that as population growth slows, legal work will be directed mainly toward the arena of geriatric advice and litigation. The article states,
“The report’s focus on the future of work contained the most disturbing findings for lawyers… By [2030], ‘bots’ could be doing “low-level knowledge economy work” and soon much more. “Eventually each bot would be able to do the work of a dozen low-level associates. They would not get tired. They would not seek advancement. They would not ask for pay rises. Process legal work would rapidly descend in cost.” The human part of lawyering would shrink.”
The article goes on in great detail about who will be affected. Partners will come out on top (no surprises there) but associates, particularly those doing billable work rather than client-facing work, will be in much less demand. This may be difficult for the hoards of young law school students produced each year as their positions are increasingly taken over by AI technology. Time to rethink that law degree and consider a career path tailored to human skills.
Chelsea Kerwin, January 5, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Machine Learning Used to Decipher Lute Tablature
December 23, 2015
The Oxford Journal’s Early Music publication reveals a very specialized use of machine learning in, “Bring ‘Musicque into the Tableture’: Machine-Learning Models for Polyphonic Transcription of 16th-Century Lute Tablature” by musical researchers Reinier de Valk and Tillman Weyde. Note that this link will take you to the article’s abstract; to see the full piece, you’ll have to subscribe to the site. The abstract summarizes:
“A large corpus of music written in lute tablature, spanning some three-and-a-half centuries, has survived. This music has so far escaped systematic musicological research because of its notational format. Being a practical instruction for the player, tablature reveals very little of the polyphonic structure of the music it encodes—and is therefore relatively inaccessible to non-specialists. Automatic polyphonic transcription into modern music notation can help unlock the corpus to a larger audience and thus facilitate musicological research.
“In this study we present four variants of a machine-learning model for voice separation and duration reconstruction in 16th-century lute tablature. These models are intended to form the heart of an interactive system for automatic polyphonic transcription that can assist users in making editions tailored to their own preferences. Additionally, such models can provide new methods for analysing different aspects of polyphonic structure.”
The full article lays out the researchers’ modelling approaches and the advantages of each. They report their best model returns accuracy rates of 80 to 90 percent, so for modelers, it might be worth the $39 to check out the full article. We just think it’s nice to see machine learning used for such a unique and culturally valuable project.
Cynthia Murrell, December 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
The Importance of Google AI
December 23, 2015
According to Business Insider, we’ve all been overlooking something crucial about Google. Writer Lucinda Shen reports, “Top Internet Analyst: There Is One Thing About Google that Everyone Is Missing.” Shen cites an observation by prominent equity analyst Carlos Kirjner. She writes:
“Kirjner, that thing [that everyone else is missing] is AI at Google. ’Nobody is paying attention to that because it is not an issue that will play out in the next few quarters, but longer term it is a big, big opportunity for them,’ he said. ‘Google’s investments in artificial intelligence, above and beyond the use of machine learning to improve character, photo, video and sound classification, could be so revolutionary and transformational to the point of raising ethical questions.’
“Even if investors and analysts haven’t been closely monitoring Google’s developments in AI, the internet giant is devoted to the project. During the company’s third-quarter earnings call, CEO Sundar Pichai told investors the company planned to integrate AI more deeply within its core business.”
Google must be confident in its AI if it is deploying it across all its products, as reported. Shen recalls that the company made waves back in November, when it released the open-source AI platform TensorFlow. Is Google’s AI research about to take the world by storm?
Cynthia Murrell, December 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Data Analytics Is More Than Simple Emotion
November 6, 2015
Hopes and Fears posted the article, “Are You Happy Now? The Uncertain Future Of Emotion Analytics” discusses the possible implications of technology capable of reading emotions. The article opens with a scenario from David Collingridge explaining that the only way to truly gauge technology’s impact is when it has become so ingrained into society that it would be hard to change. Many computing labs are designing software capable of reading emotions using an array of different sensors.
The biggest problem ahead is not how to integrate emotion reading technology into our lives, but what are the ethical concerns associated with it?
Emotion reading technology is also known as affective computing and the possible ethical concerns are more than likely to come from corporation to consumer relationships over consumer-to-consumer relationships. Companies are already able to track a consumer’s spending habits by reading their Internet data and credit cards, then sending targeted ads.
Consumers should be given the option to have their emotions read:
“Affective computing has the potential to intimately affect the inner workings of society and shape individual lives. Access, an international digital rights organization, emphasizes the need for informed consent, and the right for users to choose not to have their data collected. ‘All users should be fully informed about what information a company seeks to collect,’ says Drew Mitnick, Policy Counsel with Access, ‘The invasive nature of emotion analysis means that users should have as much information as possible before being asked to subject [themselves] to it.’”
While the article’s topic touches on fear, it ends on a high note that we should not be afraid of the future of technology. It is important to discuss ethical issues right now, so groundwork will already be in place to handle affective computing.
Whitney Grace, November 6, 2015
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