Voice Recognition Software Has Huge Market Reach
March 3, 2017
Voice recognition software still feels like a futuristic technology, despite its prevalence in our everyday lives. WhaTech explains how far voice recognition technology has imbedded itself into our habits in, “Listening To The Voice Recognition Market.”
The biggest example of speech recognition technology is an automated phone system. Automated phone systems are used all over the board, especially in banks, retail chains, restaurants, and office phone directories. People usually despise automated phone systems, because they cannot understand responses and tend to put people on hold for extended periods of time.
Despite how much we hate automated phone systems, they are useful and they have gotten better in understanding human speech and the industry applications are endless:
The Global Voice Recognition Systems Sales Market 2017report by Big Market Research is a comprehensive study of the global voice recognition market. It covers both current and future prospect scenarios, revealing the market’s expected growth rate based on historical data. For products, the report reveals the market’s sales volume, revenue, product price, market share and growth rate, each of which is segmented by artificial intelligence systems and non-artificial intelligence systems. For end-user applications, the report reveals the status for major applications, sales volume, market share and growth rate for each application, with common applications including healthcare, military and aerospace, communications, and automotive.
Key players in the voice recognition software field are Validsoft, Sensory, Biotrust ID, Voicevault, Voicebox Technologies, Lumenvox, M2SYS, Advanced Voice Recognition Systems, and Mmodal. These companies would benefit from using Bitext’s linguistic-based analytics platform to enhance their technology’s language learning skills.
Whitney Grace, May 3, 2017
Software Bias Is Being Addressed
February 27, 2017
Researchers are working to fix the problem of bias in software, we learn from the article, “He’s Brilliant, She’s Lovely: Teaching Computers to Be Less Sexist” at NPR’s blog, All Tech Considered. Writer Byrd Pinkerton begins by noting that this issue of software reflecting human biases is well-documented, citing this article from his colleague. He then informs us that Microsoft, for one, is doing something about it:
Adam Kalai thinks we should start with the bits of code that teach computers how to process language. He’s a researcher for Microsoft and his latest project — a joint effort with Boston University — has focused on something called a word embedding. ‘It’s kind of like a dictionary for a computer,’ he explains. Essentially, word embeddings are algorithms that translate the relationships between words into numbers so that a computer can work with them. You can grab a word embedding ‘dictionary’ that someone else has built and plug it into some bigger program that you are writing. …
Kalai and his colleagues have found a way to weed these biases out of word embedding algorithms. In a recent paper, they’ve shown that if you tell the algorithms to ignore certain relationships, they can extrapolate outwards.
And voila, a careful developer can teach an algorithm to fix its own bias. If only the process were so straightforward for humans. See the article for more about the technique.
Ultimately, though, the problem lies less with the biased algorithms themselves and more with the humans who seek to use them in decision-making. Researcher Kalai points to the marketing of health-related products as a project for which a company might actually want to differentiate between males and females. Pinkerton concludes:
For Kalai, the problem is not that people sometimes use word embedding algorithms that differentiate between gender or race, or even algorithms that reflect human bias. The problem is that people are using the algorithms as a black box piece of code, plugging them in to larger programs without considering the biases they contain, and without making careful decisions about whether or not they should be there.
So, though discoveries about biased software are concerning, it is good to know the issue is being addressed. We shall see how fast the effort progresses.
Cynthia Murrell, February 27, 2017
Gender Bias in Voice Recognition Software
February 21, 2017
A recent study seems to confirm what some have suspected: “Research Shows Gender Bias in Google’s Voice Recognition,” reports the Daily Dot. Not that this is anything new. Writer Selena Larson reminds us that voice recognition tech has a history of understanding men better than women, from a medical tracking system to voice-operated cars. She cites a recent study by linguist researcher Rachael Tatman, who found that YouTube’s auto captions performed better on male voices than female ones by about 13 percent—no small discrepancy. (YouTube is owned by Google.)
Though no one is accusing the tech industry of purposely rendering female voices less effective, developers probably could have avoided this problem with some forethought. The article explains:
’Language varies in systematic ways depending on how you’re talking,’ Tatman said in an interview. Differences could be based on gender, dialect, and other geographic and physical attributes that factor into how our voices sound. To train speech recognition software, developers use large datasets, either recorded on their own, or provided by other linguistic researchers. And sometimes, these datasets don’t include diverse speakers.
Tatman recommends a purposeful and organized approach to remedying the situation. Larson continues:
Tatman said the best first step to address issues in voice tech bias would be to build training sets that are stratified. Equal numbers of genders, different races, socioeconomic statuses, and dialects should be included, she said.
Automated technology is developed by humans, so our human biases can seep into the software and tools we are creating to supposedly to make lives easier. But when systems fail to account for human bias, the results can be unfair and potentially harmful to groups underrepresented in the field in which these systems are built.
Indeed, that’s the way bias works most of the time—it is more often the result of neglect than of malice. To avoid it requires realizing there may be a problem in the first place, and working to avoid it from the outset. I wonder what other technologies could benefit from that understanding.
Cynthia Murrell, February 21, 2017
Enterprise Heads in the Sand on Data Loss Prevention
February 16, 2017
Enterprises could be doing so much more to protect themselves from cyber attacks, asserts Auriga Technical Manager James Parry in his piece, “The Dark Side: Mining the Dark Web for Cyber Intelligence” at Information Security Buzz. Parry informs us that most businesses fail to do even the bare minimum they should to protect against hackers. This minimum, as he sees it, includes monitoring social media and underground chat forums for chatter about their company. After all, hackers are not known for their modesty, and many do boast about their exploits in the relative open. Most companies just aren’t bothering to look that direction. Such an effort can also reveal those impersonating a business by co-opting its slogans and trademarks.
Companies who wish to go beyond the bare minimum will need to expand their monitoring to the dark web (and expand their data-processing capacity). From “shady” social media to black markets to hacker libraries, the dark web can reveal much about compromised data to those who know how to look. Parry writes:
Yet extrapolating this information into a meaningful form that can be used for threat intelligence is no mean feat. The complexity of accessing the dark web combined with the sheer amount of data involved, correlation of events, and interpretation of patterns is an enormous undertaking, particularly when you then consider that time is the determining factor here. Processing needs to be done fast and in real-time. Algorithms also need to be used which are able to identify and flag threats and vulnerabilities. Therefore, automated event collection and interrogation is required and for that you need the services of a Security Operations Centre (SOC).
The next generation SOC is able to perform this type of processing and detect patterns, from disparate data sources, real-time, historical data etc. These events can then be threat assessed and interpreted by security analysts to determine the level of risk posed to the enterprise. Forewarned, the enterprise can then align resources to reduce the impact of the attack. For instance, in the event of an emerging DoS attack, protection mechanisms can be switched from monitoring to mitigation mode and network capacity adjusted to weather the attack.
Note that Parry’s company, Auriga, supplies a variety of software and R&D services, including a Security Operations Center platform, so he might be a tad biased. Still, he has some good points. The article notes SOC insights can also be used to predict future attacks and to prioritize security spending. Typically, SOC users have been big businesses, but, Parry advocates, scalable and entry-level packages are making such tools available to smaller companies.
From monitoring mainstream social media to setting up an SOC to comb through dark web data, tools exist to combat hackers. The question, Parry observes, is whether companies will face the growing need to embrace those methods.
Cynthia Murrell, February 16, 2017
Hewlett Packard Enterprise Releases Q4 Earnings of First Year After Split from HP
January 30, 2017
The article on Business Insider titled Hewlett Packard Enterprise Misses Its Q4 Revenue Expectations But Beats on Profit discusses the first year of HPE following its separation from HP. The article reports fiscal fourth quarter revenue of $12.5B, just short of the expected $12.85B. The article provides all of the nitty gritty details of the fourth quarter segment results, including,
Software revenue was $903 million, down 6% year over year, flat when adjusted for divestitures and currency, with a 32.1% operating margin. License revenue was down 5%, down 1% when adjusted for divestitures and currency, support revenue was down 7%, up 1% when adjusted for divestitures and currency, professional services revenue was down 7%, down 4% adjusted for divestitures and currency, and software-as-a-service (SaaS) revenue was down 1%, up 11% adjusted for divestitures and currency.
Additionally, Enterprise Services revenue was reported as $4.7B, down 6% year over year, and Enterprise Group revenue was down 9% at $6.7B. Financial Services revenue was up 2% at $814M. According to HPE President and CEO Meg Whitman, all of this amounts to a major win for the standalone company. She emphasized the innovation and financial performance and called FY16 a “historic” year for the company.
Chelsea Kerwin, January 30, 2017
The Software Behind the Web Sites
January 17, 2017
Have you ever visited an awesome Web site or been curious how an organization manages their Web presence? While we know the answer is some type of software, we usually are not given a specific name. Venture Beat reports that it is possible to figure out the software in the article, “SimilarTech’s Profiler Tells You All Of The Technologies That Web Companies Are Using.”
SimilarTech is a tool designed to crawl the Internet to analyze what technologies, including software, Web site operators use. SimiliarTech is also used to detect which online payment tools are the most popular. It does not come as a surprise that PayPal is the most widely used, with PayPal Subscribe and Alipay in second and third places.
Tracking what technology and software companies utilize for the Web is a boon for salespeople, recruiters, and business development professionals who want a competitive edge as well as:
Overall, SimilarTech provides big data insights about technology adoption and usage analytics for the entire internet, providing access to data that simply wasn’t available before. The insights are used by marketing and sales professionals for website profiling, lead generation, competitive analysis, and business intelligence.
SimiliarTech can also locate contact information for personnel responsible for Web operations, in other words new potential clients.
This tool is kind of like the mailing houses of the past. Mailing houses have data about people, places, organizations, etc. and can generate contact information lists of specific clientele for companies. SimiliarTech offers the contact information, but it does one better by finding the technologies people use for Web site operation.
Whitney Grace, January 17, 2016
Ricochet Software Empowers Whistleblowers to Message Journalists Securely and Anonymously
December 18, 2016
The article titled Ricochet Uses Power of the Dark Web to Help Journalists, Sources Dodge Metadata Laws on The Age announces the completion of a formal security audit that gives would-be users of the software the go-ahead. Ricochet is secure messaging resource intended to enable whistleblowers and human rights activists to communicate with journalists without fear of being exposed. The article explains,
Ricochet… would be able to deliver a level of security and anonymity that isn’t possible with current messaging software, including Wickr — the self-destructing message platform… “The key difference between Ricochet and anything else that’s out there is that it does not use a server. It uses the same technology that ran Silk Road, it uses dark web technology,” Mr Gray said, referring to the notorious online black on which drug dealers thrived until the FBI shut it down in November 2014.
The article does address concerns that software such as this might be useful to terrorist operations in addition to its stated purpose. The makers point out that Ricochet is designed for one-on-one communication, which is not very appealing to the terrorists who have been more focused on reaching many people to coordinate their activities. At the same time, they accept that it might be used by a criminal element and state that such uses don’t negate the positive potential of the software.
Chelsea Kerwin, December 18, 2016
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