Google DeepMind Acquires Healthcare App
April 5, 2016
What will Google do next? Google’s London AI powerhouse has set up a new healthcare division and acquired a medical app called Hark, an article from Business Insider, tells us the latest. DeepMind, Google’s artificial intelligence research group, launched a new division recently called DeepMind Health and acquired a healthcare app. The article describes DeepMind Health’s new app called Hark,
“Hark — acquired by DeepMind for an undisclosed sum — is a clinical task management smartphone app that was created by Imperial College London academics Professor Ara Darzi and Dr Dominic King. Lord Darzi, director of the Institute of Global Health Innovation at Imperial College London, said in a statement: “It is incredibly exciting to have DeepMind – the world’s most exciting technology company and a true UK success story – working directly with NHS staff. The types of clinician-led technology collaborations that Mustafa Suleyman and DeepMind Health are supporting show enormous promise for patient care.”
The healthcare industry is ripe for disruptive technology, especially technologies which solve information and communications challenges. As the article alludes to, many issues in healthcare stem from too little conveyed and too late. Collaborations between researchers, medical professionals and tech gurus appears to be a promising answer. Will Google’s Hark lead the way?
Megan Feil, April 5, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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
Slack Hires Noah Weiss
March 29, 2016
One thing you can always count on the tech industry is talent will jump from company to company to pursue the best and most innovating endeavors. The latest tech work to jump ship is Eric Weiss, he leaps from Foursquare to head a new Search, Learning, & Intelligence Group at Slack. VentureBeat reports the story in “Slack Forms Search, Learning, & Intelligence Group On ‘Mining The Chat Corpus.’” Slack is a team communication app and their new Search, Learning, & Intelligence Group will be located in the app’s new New York office.
Weiss commented on the endeavor:
“ ‘The focus is on building features that make Slack better the bigger a company is and the more it uses Slack,” Weiss wrote today in a Medium post. “The success of the group will be measured in how much more productive, informed, and collaborative Slack users get — whether a company has 10, 100, or 10,000 people.’”
For the new group, Weiss wants to hire experts who are talented in the fields of artificial intelligence, information retrieval, and natural language processing. From this talent search, he might be working on a project that will help users to find specific information in Slack or perhaps they will work on mining the chap corpus.
Other tech companies have done the same. Snapchat built a research team that uses artificial intelligence to analyze user content. Flipboard and Pinterest are working on new image recognition technology. Meanwhile Google, Facebook, Baidu, and Microsoft are working on their own artificial intelligence projects.
What will artificial intelligence develop into as more companies work on their secret projects.
Whitney Grace, March 29, 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
Startup Semantic Machines Scores Funding
February 26, 2016
A semantic startup looks poised for success with experienced executives and a hefty investment, we learn from “Artificial Intelligence Startup Semantic Machines Raises $12.3 Million” at VentureBeat. Backed by investors from Bain Capital Ventures and General Catalyst Partners, the enterprise focuses on deep learning and improved speech recognition. The write-up reveals:
“Last year, Semantic Machines named Larry Gillick as its chief technology officer. Gillick was previously chief speech scientist for Siri at Apple. Now Semantic Machines is looking to go further than Siri and other personal digital assistants currently on the market. ‘Semantic Machines is developing technology that goes beyond understanding commands, to understanding conversations,’ the startup says on its website. ‘Our Conversational AI represents a powerful new paradigm, enabling computers to communicate, collaborate, understand our goals, and accomplish tasks.’ The startup is building tools that third-party developers will be able to use.”
Launched in 2014, Semantic Machines is based in Newton, Massachusetts, with offices in Berkeley and Boston. The startup is also seeking to hire a few researchers and engineers, in case anyone is interested.
Cynthia Murrell, February 26, 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
AI Startups Use Advanced AI Technology to Improve Daily Chores
February 11, 2016
The article on e27 titled 5 Asian Artificial Intelligence Startups that Caught Our Eye lists several exciting new companies working to unleash AI technology, often for quotidian tasks. For example, Arya.ai provides for speeder and more productive decision-making, while Mad Street Den and Niki.ai offers AI shopping support! The article goes into detail about the latter,
“Niki understands human language in the context of products and services that a consumer would like to purchase, guides her along with recommendations to find the right service and completes the purchase with in-chat payment. It performs end-to-end transactions on recharge, cab booking and bill payments at present, but Niki plans to add more services including bus booking, food ordering, movie ticketing, among others.”
Mad Street Den, on the other hand, is more focused on object recognition. Users input an image and the AI platform seeks matches on e-commerce sites. Marketers will be excited to hear about Appier, a Taiwan-based business offering cross-screen insights, or in layman’s terms, they can link separate devices belonging to one person and also estimate how users switch devices during the day and what each device will be used for. These capabilities allow marketers to make targeted ads for each device, and a better understanding of who will see what and via which device.
Chelsea Kerwin, February 11, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Big Data Is so Last Year, Data Analysts Inform Us
February 1, 2016
The article on Fortune titled Has Big Data Gone Mainstream? asks whether big data is now an expected part of data analysis. The “merger” as Deloitte advisor Tom Davenport puts it, makes big data an indistinguishable aspect of data crunching. Only a few years ago, it was a scary buzzword that executives scrambled to understand and few experts specialized in. The article shows what has changed lately,
“Now, however, universities offer specialized master’s degrees for advanced data analytics and companies are creating their own in-house programs to train talent in data science. The Deloitte report cites networking giant Cisco CSCO -4.22% as an example of a company that created an internal data science training program that over 200 employees have gone through. Because of media reports, consulting services, and analysts talking up “big data,” people now generally understand what big data means…”
Davenport sums up the trend nicely with the statement that people are tired of reading about big data and ready to “do it.” So what will replace big data as the current mysterious buzzword that irks laypeople and the C-suite simultaneously? The article suggests “cognitive computing” or computer systems using artificial intelligence for speech recognition, object identification, and machine learning. Buzz, buzz!
Chelsea Kerwin, February 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Measuring Classifiers by a Rule of Thumb
February 1, 2016
Computer programmers who specialize in machine learning, artificial intelligence, data mining, data visualization, and statistics are smart individuals, but they sometimes even get stumped. Using the same form of communication as reddit and old-fashioned forums, Cross Validated is a question an answer site run by Stack Exchange. People can post questions related to data and relation topics and then wait for a response. One user posted a question about “Machine Learning Classifiers”:
“I have been trying to find a good summary for the usage of popular classifiers, kind of like rules of thumb for when to use which classifier. For example, if there are lots of features, if there are millions of samples, if there are streaming samples coming in, etc., which classifier would be better suited in which scenarios?”
The response the user received was that the question was too broad. Classifiers perform best depending on the data and the process that generates it. It is kind of like asking the best way to organize books or your taxes, it depends on the content within the said items.
Another user replied that there was an easy way to explain the general process of understanding the best way to use classifiers. The user directed users to the Sci-Kit.org chart about “choosing the estimator”. Other users say that the chart is incomplete, because it does not include deep learning, decision trees, and logistic regression.
We say create some other diagrams and share those. Classifiers are complex, but they are a necessity to the artificial intelligence and big data craze.
Whitney Grace, February 1, 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
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