Instagram Algorithm to Recognize Cruelty and Kindness

September 14, 2017

Instagram is using machine learning to make its platform a kinder place, we learn from the CBS  News article, “How Instagram is Filtering Out Hate.” Contributor (and Wired Editor-In-Chief) Nick Thompson interviewed Instagram’s CEO Kevin Systrom, and learned the company is using about 20 humans to teach its algorithm to distinguish naughty from nice. The article relates:

Systrom has made it his mission to make kindness itself the theme of Instagram through two new phases: first, eliminating toxic comments, a feature that launched this summer; and second, elevating nice comments, which will roll out later this year. ‘Our unique situation in the world is that we have this giant community that wants to express themselves,’ Systrom said. ‘Can we have an environment where they feel comfortable to do that?’ Thompson told ‘CBS This Morning’ that the process of ‘machine learning’ involves teaching the program how to decide what comments are mean or ‘toxic’ by feeding in thousands of comments and then rating them.

It is smarter censorship if you will. Systrom seems comfortable embracing a little censorship in favor of kindness, and we sympathize; “trolls” are a real problem, after all. Still, the technology could, theoretically, be used to delete or elevate certain ideological or political content. To censor or not to censor is a fine and important line, and those who manage social media sites will be the ones who must walk it. No pressure.

Cynthia Murrell, September 14, 2017

When Business Models Fail, Hit the Startup Casino

September 13, 2017

I read “Searching for the Next Facebook or Google: Bloomberg Helps Launch Tech Incubator.” On the surface, the write up is not too newsy. Bloomberg (the terminal folks that Thomson Reuters has not been able to kill off with hundreds of millions in cash pumped into its “innovation” efforts) is getting into the startup casino. The idea is that Bloomberg (the former mayor) is bringing incubators to New York City. The hook for the story is that Cornell University is the big fish which has been landed on Roosevelt Island, the one with the tram thing. With Bloomberg beaching Cornell and Technion (the MIT of Israel) ensnared, I have some questions floating in my rural Kentucky mind:

  1. When an innovation occurs, who will get access to that technology? The universities, the professors, the students, or Bloomberg?
  2. What will Thomson Reuters do to counter this play by the inventor of the famed and incredibly cluttered terminal for MBA clutching Red Bulls and mocha lattes?
  3. Who will be able to hire the bright sprouts who flock via tram to Roosevelt Island?
  4. Has IBM’s MIT play been “trumped” (no pun intended) because Bloomberg can play most of the numbers on the startup casino’s roulette wheel?
  5. Will Facebook and Google just buy Stanford University and leave the old school companies to the backwaters on the East coast?
  6. Which big company will fund the High Technology High School in New Jersey? (Strike that. New Jersey?)

Worth watching?

Stephen E Arnold, September 13, 2017

Tech Industry Toxicity Goes Beyond Uber

September 13, 2017

Shiny new things have distracted people from certain behaviors, and Fast Company is calling out the entire technology startup culture in, “Why Silicon Valley Can’t Call Uber an Anomaly.” Writer Austin Carr takes us briefly through Uber’s tribulations, which culminated in the departure of infamous CEO Travis Kalanick. See the article for that useful summary, but Carr’s question was whether Uber’s noxious culture is unusual. He writes:

Silicon Valley, though, is insular and guarded. In my reporting, I encountered few people willing to speak openly, let alone critically, about Uber’s troubles. Those who did (most of them, notably, women) argue that there’s an opportunity for course correction right now. It starts by acknowledging that the Valley isn’t yet the utopian meritocracy it strives to be—and that Uber’s errant system exposed some fundamental bugs in the startup economy.

Carr identifies and discusses three of these bugs. First, that which makes a startup succeed often does not scale up well. For example, a confrontational culture that pits workers against each other might fuel a startup’s launch, but becomes unsustainable in a large, global corporation. The second problem is the myth of the “omniscient founder.” Though most of us realize that generating a brilliant idea does not necessarily go hand-in-hand with the capacity to run a large organization, much of the tech industry still seems taken by the foolish notion of one man at the top skillfully managing each and every aspect of the business. Carr points out that even Steve Jobs and Larry Page saw the wisdom in stepping back, and each tapped someone with more corporate experience to run their companies for a while. Not only is this hero-at-the-top attitude inefficient, it also risks the devaluation of every other employee. Talent does not stay where it is not respected.

Finally, Carr observes, the system of accountability needs an overhaul. It takes a lot of scandals to push investors to hold tech companies accountable for bad behavior, and even then board members hesitate to act. The article concludes:

If there really were healthy checks and balances, boards wouldn’t wait for public outrage to act. But to acknowledge that Uber’s system of accountability failed is to acknowledge that fundamental change—something Silicon Valley normally embraces—is necessary. If the Valley truly prides itself on moving fast and breaking things, it ought to start here.

We are curious to see how the industry will respond to such escalating criticisms.

Cynthia Murrell, September 13, 2017

IBM Watson: The US Open As a Preview of an IBM Future

September 12, 2017

I read a remarkable essay, article, or content marketing “object” called “What We Can Glean From The 2017 U.S Open to Imagine a World Powered by the Emotional Intelligence AI Can Offer.” The author is affiliated with an organization with which I am not familiar. Its name? Brandthropologie.

Let’s pull out the factoids from the write up which has two themes: US government interest in advanced technology and IBM Watson.

Factoid 1: “Throughout time, the origin of many modern-day technologies can be traced to the military and Defense Research Projects Agency (DARPA).”

Factoid 2: “Just as ARPA was faced with wide spread doubt and fear about how an interconnected world would not lead to a dystopian society, IBM, among the top leaders in the provision of augmented intelligence, is faced with similar challenges amidst today’s machine learning revolution.”

Factoid 3: “IBM enlisted its IBM Watson Media platform to determine the best highlights of matches. IBM then broadcasted the event live to its mobile app, using IBM Watson Media to watch for match highlights as they happened. It took into account crowd noises, emotional player reactions, and other factors to determine the best highlight of a match.”

Factoid 4: “The U.S. Open used one of the first solutions available through IBM Watson Media, called Cognitive Highlights. Developed at IBM Research with IBM iX, Cognitive Highlights was able to identify a match’s most important moments by analyzing statistical tennis data, sounds from the crowd, and player reactions using both action and facial expression recognition. The system then ranked the shots from seven U.S. Open courts and auto-curated the highlights, which simplified the video production process and ultimately positioned the USTA team to scale and accelerate the creation of cognitive highlight packages.”

Factoid 5: “Key to the success of this sea change will be the ability for leading AI providers to customize these solutions to make them directly relevant to specific scenarios, while also staying agilely informed on the emotional intelligence required to not only compete, but win, in each one.”

My reaction to these snippets was incredulity.

My comment about Factoid 1: I was troubled by the notion of “throughout time” DARPA has been the source of “many modern day technologies.” It is true that government funding has assisted outfits from the charmingly named Purple Yogi to Interdisciplinary Laboratories. Government funding is often suggestive and, in many situations, reactive; for example, “We need to move on this autonomous weapons thing.” The idea of autonomous weapons has been around a long time; for example, Thracians’ burning wagon assaults which were a small improvement over Neanderthals pushing stones off a cliff onto their enemies. Drones with AI is not a big leap from my point of view.

My comment about Factoid 2: I like the idea that one company, in this case IBM, was the prime mover for smart software. IBM, like other early commercial computing outfits, was on the periphery of many innovations. If anything, the good ideas from IBM were not put into commercial use because the company needed to generate revenue. IBM Almaden wizard Jon Kleinberg came up with CLEVER. The system and method influenced the Google. Where is IBM in search and information access today? Pretty much nowhere, and I am including the marketing extravaganza branded “Watson.” IBM, from my point of view, acted like an innovation brake, not an innovator. Disagree? That’s your prerogative. But building market share via wild and crazy assertions about Lucene, home brew code, and acquired technology like Vivisimo is not going to convince me about the sluggishness of large companies.

My comment about Factoid 3: The assertion that magic software delivered video programming is sort of true. But the reality of today’s TV production is that humans in trailers handle 95 percent of the heavy lifting. Software can assist, but the way TV production works at live events is that there are separate and unequal worlds of keeping the show moving along, hitting commercial points, and spicing up the visual flow. IBM, from my point of view, was the equivalent of salt free spices which a segment of the population love. The main course was human-intermediated TV production of the US Open. Getting the live sports event to work is still a human intermediated task. Marketing may not believe this, but, hey, reality is different from uninformed assertions about what video editing systems can do quickly and “automatically.”

My comment about Factoid 4: See my comment about Factoid 3. If you know a person who works in a trailer covering a live sports event, get their comments about smart editing tools.

My comment about Factoid 5: Conflating the idea of automated functions ability to identify a segment of a video stream with emotion detection is pretty much science fiction. Figuring out sentiment in text is tough. Figuring out “emotion” in a stream of video is another kettle of fish. True, there is progress. I saw a demo from an Israeli company’s whose name I cannot recall. That firm was able to parse video to identify when a goal was scored. The system sort of worked. Flash forward to today: Watson sort of works. Watson is a punching bag for some analysts and skeptics like me for good reason. Talk is easy. Delivering is tough.

Reality, however, seems to be quite different for the folks at Brandthropologie.

Stephen E Arnold, September 12, 2017

Technology Has Consequences

September 11, 2017

If this article is any indication, companies that can replace human workers with technology have a huge advantage over others; Recode reports, “Facebook Made $188,000 per Employee Last Quarter, Four Times as Much as Google.” As bad as that makes Google look in relation to their major competitor, the article has much broader implications. Writer Rani Molla tells us:

Silicon Valley companies are more efficient at making money than traditional industries, as evidenced by net income and revenue per employee in their latest quarterly filings. …

Facebook’s efficiency is partly because software products don’t require humans at as many steps of the production and distribution process as companies creating physical objects that need to be mass produced and delivered to stores or doorsteps. Of course, even jobs formerly assigned to humans are coming under the purview of robots — so more industries could see consolidation of labor. Companies like Amazon and its brick-and-mortar counterpart Walmart have employee counts that include part-time workers and are orders of magnitude bigger than their peers, which necessarily dilutes their profit and revenue per person. As far as tech companies, their contribution to the wider economy isn’t entirely clear. Productivity in the U.S. has been flat as we struggle to measure the economic output of internet technology, whose services are largely free.

Yes, we are in the midst of a major societal transition, and no one knows exactly where it will land us. If companies continue to replace humans with technology—and why wouldn’t they?—perhaps even those who have philosophical problems with a basic universal income will eventually view it as a necessary evil.

Oh, and about that four-fold advantage Facebook seems to hold over Google? Take it with this grain of salt: Facebook’s legion of contract workers is not reflected in their employee count. The Recode article reproduces the employee and revenue numbers for nine behemoth companies, from Facebook to Twitter, so see the write-up for those details.

Cynthia Murrell, September 11, 2017


Yet Another Digital Divide

September 8, 2017

Recommind sums up what happened at a recent technology convention in the article, “Why Discovery & ECM Haven’t, Must Come Together (CIGO Summit 2017 Recap).” Author Hal Marcus first discusses that he was a staunch challenge to anyone who said they could provide a complete information governance solution. He recently spoke at CIGO Summit 2017 about how to make information governance a feasible goal for organizations.

The problem with information governance is that there is no one simple solution and projects tend to be self-contained with only one goal: data collection, data reduction, etc. When he spoke he explained that there are five main reasons for there is not one comprehensive solution. They are that it takes a while to complete the project to define its parameters, data can come from multiple streams, mass-scale indexing is challenging, analytics will only help if there are humans to interpret the data, risk, and cost all put a damper on projects.

Yet we are closer to a solution:

Corporations seem to be dedicating more resources for data reduction and remediation projects, triggered largely by high profile data security breaches.

Multinationals are increasingly scrutinizing their data sharing and retention practices, spurred by the impending May 2018 GDPR deadline.

ECA for data culling is becoming more flexible and mature, supported by the growing availability and scalability of computing resources.

Discovery analytics are being offered at lower, all-you-can-eat rates, facilitating a range of corporate use cases like investigations, due diligence, and contract analysis

Tighter, more seamless and secure integration of ECM and discovery technology is advancing and seeing adoption in corporations, to great effect.

And it always seems farther away.

Whitney Grace, September 8, 2017

Audioburst Tackling Search in an Increasing Audio World

September 5, 2017

With the advent of speech recognition technology our Smart world is slowly becoming more voice activated rather than text based. One company, Audioburst, is hoping to cash in on this trend with a new way to search focusing on audio. A recent TechCrunch article examines the need for such technology and how Audioburst is going about accomplishing the task by utilizing natural language processing and speech recognition technology to identify and organize audio data.

 It…doesn’t only match users’ search queries to those exact same words when spoken, either. For example, it knows that someone speaking about the “president” in a program about U.S. politics was referring to “Donald Trump,” even if they didn’t use his name. The audio content is then tagged and organized in a way that computers understand, making it searchable…This allows its search engine to not just point you to a program or show where a topic was discussed, but the specific segment within that show where that discussion took place. (If you choose, you can then listen to the full show, as the content is linked to the source.)

This technology will allow users to never need the physical phone or tablet to conduct searches. Audioburst is hoping to begin working with car manufacturers soon to bring truly hands-free search to consumers.

Catherine Lamsfuss, September 5, 2017

Google Bashing: Two Fresh Sidewinders Launch

September 3, 2017

Here in Harrods Creek, we love the Google. The Google bashing, it seems to us near the pond filled with mine drainage, is adopting a new tactic. We call it the sidewinder. Quick and erratic, the new attack may catch the Google by surprise.

The write up “YouTube Video Captions Are More Accurate If You’re White” touches a broken tooth. The idea is that auto generated text explanation and some metadata are biased. The idea that allegedly objective functions manifest biases is an extension of the argument that if algorithms are created by humans, those human creations can reflect the biases of their creators. How does one prove that algorithms which are not easy to parse delivered  non objective results? That is a challenge, isn’t it? How will Google respond to this allegation?

The race card article asserts:

But when it came to race, both YouTube and Bing were more accurate when captioning Caucasian speakers than any other race. “The fact that they are recognized with more errors is most likely due to bias in the training data,” she wrote..

We note that Bing has the same “problem,” but the Microsofties are not associated with the “math is it” approach promulgated by Google technical papers and PR as the Google. If this type of racial argument pgets traction, life could get more exciting for the GOOG.

The seocnd tactic seems to surface in a write up not focused on Google. “Silicon Valley Has Been Humbled. But Its Schemes Are As Dangerous As Ever.” The approach here is to assert that the state of mind “Silicon Valley” has become an ideological junk yard. Sounds good, doesn’t it? The reality may ot line up with the assertion, but for making life tough for the Googley, the assertion is at least plausible.

The write up points out:

An industry once hailed for fueling the Arab spring is today repeatedly accused of abetting Islamic State. An industry that prides itself on diversity and tolerance is now regularly in the news for cases of sexual harassment as well as the controversial views of its employees on matters such as gender equality. An industry that built its reputation on offering us free things and services is now regularly assailed for making other things – housing, above all – more expensive.

Care to try to explain these allegations which seem like “factoids” away? Tough job.

The write up asks:

how could one possibly expect a bunch of rent-extracting enterprises with business models that are reminiscent of feudalism to resuscitate global capitalism and to establish a new New Deal that would constrain the greed of capitalists, many of whom also happen to be the investors behind these firms?

I admire “have you stopped beating your dog” questions.

Now these sidewinder attacks are used to question Google’s objectivity and damning the company  because it is a keystone of the Silicon Valley’s fragile arch.

Despite our backwoods understanding of these big city issues, we surmise that more sidewinder tactics will be fired against a company we embrace, nay, love. A “star wars defense” is needed. And fast even inInternet time.

Stephen E Arnold, September 3, 2017


Accenture Makes Two Key Acquisitions

August 29, 2017

Whither search innovation? It seems the future of search is now about making what’s available work as best it can. We observe yet another effort to purchase existing search technology and plug it into an existing framework; DMN reports, “Accenture Acquires Brand Learning and Search Technologies.” Brand Learning is a marketing and sales consultancy, and Search Technologies is a technology services firm. Will Accenture, a professional-services firm, work to improve the search and analysis functionalities within their newly acquired tools? DMN’s Managing Editor Elyse Dupre reports:

A press release states that Brand Learning’s advisory team will join the management consulting and industry specialists within Accenture’s Customer and Channels practice. The partnership, according to the press release, will enhance Accenture’s offerings in terms of marketing and sales strategy, organizational design, industry-specific consulting, and HR and leadership.

It is unclear whether the “advisory team” includes any of the talent behind Brand Learning’s software. As for the Search Technologies folks, the article gives us more reason to hope for further innovation. Citing another press release, Dupre notes that company’s API-level data connectors will greatly boost Accenture’s ability to access unstructured data, and continues:

Search Technologies will join the data scientists and engineers within Accenture Analytics. According to the press release, this team will focus on creating solutions that make unstructured content (e.g. social media, video, voice, and audio) easily searchable, which will support data discovery, analytics, and reporting. Accenture’s Global Delivery Network will also add a delivery center in Costa Rica, the release states, which will serve as the home-base for the more than 70 Search Technologies big data engineers who reside there. This team focuses on customer and content analytics, the release explains, and will work with Accenture Interactive’s digital content production and marketing services professionals.


Furthermore, Kamran Khan, president and CEO of Search Technologies, will now lead a new content analytics team that will reside within Accenture Analytics.

Let us hope those 70 engineers are given the freedom and incentive to get creative. Stay tuned.

Cynthia Murrell, August 29, 2017

An Automatic Observer for Neural Nets

August 25, 2017

We are making progress in training AI systems through the neural net approach, but exactly how those systems make their decisions remains difficult to discern. Now, Tech Crunch reveals, “MIT CSAIL Research Offers a Fully Automated Way to Peer Inside Neural Nets.” Writer Darrell Etherington recalls that, a couple years ago, the same team of researchers described a way to understand these decisions using human reviewers. A fully automated process will be much more efficient and lead to greater understanding of what works and what doesn’t. Etherington explains:

Current deep learning techniques leave a lot of questions around how systems actually arrive at their results – the networks employ successive layers of signal processing to classify objects, translate text, or perform other functions, but we have very little means of gaining insight into how each layer of the network is doing its actual decision-making. The MIT CSAIL team’s system uses doctored neural nets that report back the strength with which every individual node responds to a given input image, and those images that generate the strongest response are then analyzed. This analysis was originally performed by Mechanical Turk workers, who would catalogue each based on specific visual concepts found in the images, but now that work has been automated, so that the classification is machine-generated. Already, the research is providing interesting insight into how neural nets operate, for example showing that a network trained to add color to black and white images ends up concentrating a significant portion of its nodes to identifying textures in the pictures.

The write-up points us to MIT’s own article on the subject for more information. We’re reminded that, because the human thought process is still largely a mystery to us, AI neural nets are based on hypothetical models that attempt to mimic ourselves. Perhaps, the piece suggests, a better understanding of such systems could inform the field of neuroscience. Sounds fair.

Cynthia Murrell, August 25, 2017

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