Trint Transcription

January 29, 2019

DarkCyber Annex noted “Taming a World Filled with Video and Audio, Using Transcription and AI.” The story explains a service which makes transcriptions of non text information; stated another way, voice to text.

The seminal insight, according to ZDNet, was:

The idea was that we would align the text — the machine-generated transcript and source audio — to the spoken word and do it accurately to the millisecond, so that you could follow it like karaoke, and then we had to figure out a way to correct it. That’s where it got really interesting. What we did was, we came up with the idea of merging a text editor, like Word, to an audio-video player and creating one tool that had two very distinct functions.

Users of the service include “some of the biggest media names, such as The New York Times, ABC News, Thomson Reuters, AP, ESPN, and BBC Worldwide.”

The write up helpfully omits the Trint url which is

There was no information provided about the number of languages supported, so here’s that information:

Trint currently offers language models for North American English, British English, Australian English, English – All Accents, European Spanish, European French, German, Italian, Portuguese, Russian, Polish, Finnish, Hungarian, Dutch, Danish, Polish and Swedish.

Also, Trint is a for fee service.

One key function of transcription is that it has to time connect real time streams of audio, link text chat messages and disambiguate emojis in accompanying messages, and make sense of text displayed on the screen in a picture in picture implementation.

DarkCyber Annex does not know of a service which can deliver this type of cross linked service.

Stephen E Arnold, January 29, 2019

Automatic Text Categorization Goes Mainstream

January 10, 2019

Blogger and scaling consultant Abe Winter declares, “Automatic Categorization of Text Is a Core Tool Now.” Noting that, as of last year, companies are using automatic text categorization regularly, Winter clarifies what he is, and is not, referring to here:

“I’m talking about taking a database with short freeform text fields and automatically tagging them according to a tagged sample corpus. I’m not talking about text synthesis, anything to do with speech, automatic chat, question answering, or Alexa Skills.”

Though Winter observes the trend, he is not sure why 2018 was a tipping point. He writes:

“We’ve had some of the building blocks for this kind of text processing for decades, including the stats tools and the training corpuses. Does deep learning help? I don’t know but at minimum it helps by delivering sexy headlines that keep AI in the news, which in turn convinces business stakeholders this is something they can get behind. It wasn’t magic before and it’s not magic now; the output of these algorithms still requires some amount of quality control and manual inspection. But business leaders are now willing to admit that the old manual way of doing things also had drawbacks….”

The write-up goes on to observe that, while text categorization now works well enough for the mainstream, speech and conversation interfaces still fall short of flawless functionality. He directs our attention to this Google Duplex conversation agent demo as he alludes to some troubling trends in corporate AI deployment. He closes with a word to programmers wondering whether they should add natural language processing to their toolkits:

“The part of the question I can’t answer is how big is the job pool, how long will the bubble last and how much expertise do you need to get more money than you make now? … For myself, I’m learning the basic techniques because they feel core to my industry skill set. I’m staying open to chances to apply them and to work with experts. I’m not even at the midpoint of my career and want to stay ahead of the curve.”

It does seem that natural language processing is not about to go away any time soon.

Cynthia Murrell, January 10, 2019

Ontotext Rank

December 5, 2018

Ontotext, a text processing vendor, has posted a demonstration of its ranking technology. You can find the demos at this link. The graphic below was generated by the system on December 3, 2018, at 0900 am US Eastern time. I specified the industry as information technology and the sub industry as search. Here’s what the system displayed:


A few observations:

  1. I specified 25 companies. The system displayed 10. I assume someone from the company will send me an email that the filters I applied did not have sufficient data to generate the desired result. Perhaps those data should be displayed?
  2. No Google Search nor Microsoft Bing search appeared. Google, a search vendor, has been in the news in the countries I have visited recently.
  3. RightNow appeared. The company is (I thought) a unit of Oracle.
  4. Publishers Clearing House sells magazine subscriptions. PCH does not offer information retrieval in the sense that I understand the bound phrase.

Net net: I am not sure about the size of the data set or what the categories mean.

You need to decide for yourself whether to use this service or Google Trends or a similar “popularity” or “sentiment” analysis system.

Stephen E Arnold, December 5, 2018

Digital Reasoning: From Intelligence Centric Text Retrieval to Wealth Management

November 12, 2018

Vendors of text processing systems have had to find new ways to generate revenue. The early days of entity extraction and social graphs provided customers from the US government and specialized companies like Booz, Allen & Hamilton.

Today, different economic realities have forced change.

The capitalist tool published “Digital Reasoning Brings AI To Wealth Management.” The write up does little to put Digital Reasoning in context. The company was founded in 2000. The firm accepted outside financing which now amounts to about $100 million. The firm became cozy with IBM, labored in the vineyards of the star crossed Distributed Common Ground System, and then faced a fire storm of competition from companies big and small. The reason? Entity extraction and link analysis became commodities. The fancy math also migrated into a wide range of applications.

New buzzwords appeared and gained currency. These ranged from artificial intelligence (who knows that that phrase means?) to real time data analytics (Yeah, what is “real time”?).

Digital Reasoning’s response is interesting. The company, like Attivio and Coveo, has nosed into customer support. But the intriguing play is that the Digital Reasoning system, which was text centric, is now packaging its system to help wealth management firms.

Is this text based?

Sure is. I learned:

For advisors, Digital Reasoning helps them prioritize which customers to focus on, which can be useful when an adviser may have 200 or more clients. At the management level, Digital Reasoning can show if the firm has specific advisors getting a lot of complaints so it can respond with training and intervention. At a strategic level, it can sift through communications and identify if customers are looking for a specific offering or type of product.

Interesting approach.

The challenge, of course, will be to differentiate Digital Reasoning’s system from those available from dozens of vendors.

Digital Reasoning has investors who want a return on their $100 million. After 18 years, time may be compressing as once solutions once perceived as sophisticated become more widely available and subject to price pressure.

Rumors of Amazon’s interest in this “wealth management” sector have reached us in Harrod’s Creek. That might be another reason why the low profile Digital Reasoning is stirring the PR waters using the capitalist’s tool, Forbes Magazine, once a source of “real” news.

Stephen E Arnold, November 12, 2018

Smart Software and Clever Humans

September 23, 2018

Online translation works pretty well. If you want 70 to 85 percent accuracy, you are home free. Most online translation systems handle routine communications like short blog posts written in declarative sentences and articles written in technical jargon just fine. Stick to mainstream languages, and the services work okay.

But if you want an online system to translate my pet phrases like HSSCM or azure chip consultant, you have to attend more closely. HSSCM refers to the way in which some Silicon Valley outfits run their companies. You know. Like a high school science club which decides that proms are for goofs and football players are not smart. The azure chip thing refers to consulting firms which lack the big time reputation of outfits like Bain, BCG, Booz, etc. (Now don’t get me wrong. The current incarnations of these blue chip outfits is far from stellar. Think questionable practices. Maybe criminal behavior.) The azure chip crowd means second string, maybe third string, knowledge work. Just my opinion, but online translation systems don’t get my drift. My references to Harrod’s Creek are geocoding nightmares when I reference squirrel hunting and bourbon in cereal. Savvy?

I was, therefore, not surprised when I read “AI Company Accused of Using Humans to Fake Its AI.” The main point seems to be:

[An[ interpreter accuses leading voice recognition company of ripping off his work and disguising it as the efforts of artificial intelligence.

There are rumors that some outfits use Amazon’s far from mechanical Turk or just use regular employees who can translate that which baffles the smart software.

The allegation from a former human disguised as smart software offered this information to Sixth Tone, a blog publishing the article:

In an open letter posted on Quora-like Q&A platform Zhihu, interpreter Bell Wang claimed he was one of a team of simultaneous interpreters who helped translate the 2018 International Forum on Innovation and Emerging Industries Development on Thursday. The forum claimed to use iFlytek’s automated interpretation service.

Trust me, you zippy millennials, smart software can be fast. It can be efficient. It can be less expensive than manual methods. But it can be wrong. Not just off base. Playing a different game with expensive Ronaldo types.

Why not run this blog post through Google Translate and check out the French or Spanish the system produces? Better yet, aim the system as a poor quality surveillance video or a VoIP call laden with insider talk between a cartel member and the Drug Llama?

Stephen E Arnold, September 23, 2018

Natural Language Processing: Brittle and Spurious

August 24, 2018

I read “NLP’s Generalization Problem, and How Researchers Are Tackling It.” From my vantage point in rural Kentucky, the write up seems to say, “NLP does not work particularly well.”

For certain types of content in which terminology is constrained, NLP systems work okay. But, like clustering, the initial assignment of any object determines much about the system. Examples range from jargon, code words, phrases which are aliases, etc. NLP systems struggle in a single language system.

The write up provides interesting examples of NLP failure.

The fixes, alas, are not likely to deliver the bacon any time soon. Yep, “bacon” means a technical breakthrough. NLP systems struggle with this type of utterance. I refer to local restaurants as the nasty caballero, which is my way of saying “the local Mexican restaurant on the river.”

I like the suggestion that NLP systems should use common sense. Isn’t that the method that AskJeeves tried when it allegedly revolutionized NLP question answering? The problem, of course, was the humans had to craft rules and that took money, time, and even more money.

The suggestion to “Evaluate unseen distributions and unseen tasks.” That’s interesting as well. The challenge is the one that systems like IBM Watson face. Humans have to make decisions about dicey issues like clustering, then identify relevant training data, and index the text with metadata.

Same problem: Time and money.

For certain applications, NLP can be helpful. For other types of content comprehension, one ends up with the problem of getting Gertie (the NLP system) up and running. Then after a period of time (often a day or two), hooking Gertie to the next Star Trek innovation from Sillycon Valley.

How do you think NLP systems handle my writing style? Let’s ask some NLP systems? DR LINK? IBM Watson? Volunteers?

Stephen E Arnold, August 24, 2018

The Social Vendor ATM: Governments Want to Withdraw Cash

August 21, 2018

I read “Social Networks to Be Fined for Hosting Terrorist Content.” My first reaction is, “Who is going to define terrorist content?” Without an answer swirling into my mind, I looked to the article for insight.

I learned:

,,, the EC’s going to follow through on threats to fine companies like Twitter, Facebook and YouTube for not deleting flagged content post-haste. The commission is still drawing up the details…

I assume that one of the details will be a definition of terrorist content.

How long will a large, mostly high school science club type company have to remove the identified content?

The answer:

One hour for platforms to delete terrorist content.

My experience, thought hardly representative, is that it is difficult to get much accomplished in one hour in my home office. A 60 minute turnaround time may be as challenging for a large outfit operating under the fluid principles of high school science club management.

Programmers sort of work in a combination of intense focus and general confusion. My hunch it may be difficult to saddle up the folks at a giant social vendor to comply with a take down request in 3,600 seconds.

My thought is that the one hour response time may be one way to get the social media ATM to eject cash.

By the way, some of Google’s deletion success can be viewed at this page on YouTube. Note that there are some interesting videos which are not deleted. One useful way to identify some interesting videos is to search for the word “nashid” or “nasheed.”

The results list seems to reveal at least one facet of terrorism’s definition.

Stephen E Arnold, August 21, 2018

Chatbots: Yak, Yak, Yak

May 24, 2018

We want to keep an open mind about smart software and the go-to application designed to terminate the folks with thrilling phone and email customer support jobs.

Just the name, “chatbot” is likely to elicit eyerolls from readers. While we have frequently been told these online oddities will be stepping up into the big leagues of usability, they don’t seem to have really found their niche. That’s what made it all the more surprising when their creators began demanding a little respect in a recent Qrius piece, “Chatbots Deserve More Than Being a Joke, Here’s Why.”

“In the most successful (and useful) applications we were able to schedule meetings and order pizza. …

“[But] We remember the failures. And when Microsoft’s Tay turned into a racist within 24 hours of release, we all laughed. If one of the biggest technology companies in existence couldn’t prevent a chatbot from becoming an anti-semite, what hope was there for the technology writ large?”

The reason we remember the failures and not the successes is because the benefits of one are outweighed by the regret of the other. However, more and more businesses are aiming to change this. Forbes recently reported on how AI was helping make chatbots more useful (go figure!). It’s a compelling point and maybe one that is finally on the verge of becoming relevant. Relevant is not the same as annoying and sometimes very, very dumb.

Patrick Roland, May 25, 2018

Short Honk: Online Translation Services

May 10, 2018

I read “Five of the Best Free Online Translators to Translate Foreign Languages.” Not a great headline, but I pulled out the list of services. Here they are:

I would suggest that you take a look at SDL’s service at Sometimes useful.

For accurate translations, one needs a native language speaker. Software is okay, but it does not do well with jargon, insider lingo, and words with loaded meanings.

Stephen E Arnold, May 10, 2018

Houston, We May Want to Do Fake News

May 2, 2018

The fake news phenomenon might be in the public eye more, thanks to endless warnings and news stories, however that has not dulled its impact. In fact, this shadowy form of propaganda seems to flourish under the spotlight, according to a recent ScienceNews story, “On Twitter, The Lure of Fake News is Stronger than Truth.”

According to the research:

“Discussions of false stories tended to start from fewer original tweets, but some of those retweet chains then reached tens of thousands of users, while true news stories never spread to more than about 1,600 people. True news stories also took about six times as long as false ones to reach 1,500 people. Overall, fake news was about 70 percent more likely to be retweeted than real news.”

That’s an interesting set of data. However, anyone quick to blame spambots for this amazing proliferation of fake news needs to give it a second look. According to research, bots are not as much to blame for this trend than humans. This is actually good news. Ideally, changes can be made on the personal level and we can eventually stamp out this misleading trend of fake news.

But if fake news “works”, why not use it? Not even humans can figure out what’s accurate, allegedly accurate, and sort of correct but not really. Smart software plus humans makes curation complex, slow, and costly.

That sounds about right or does it?

Patrick Roland, May 2, 2018

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