Online Real Time Tracking
October 11, 2017
Online is indeed an interesting business “space.” I noted a portable GPRS GPS real time tracking locator for about $10. The features of the item include a magnet for attaching the device to a vehicle, built in microphone, and Li polymer battery. The size of the tracker is about 1.5 inches square and a half inch thick. You can find the device listed at this link. I am not sure how long this listing will be online, however. Just two years ago, this type of device was not widely available in this form factor. What are the use cases for this gizmo? Use your imagination.
Stephen E Arnold, October 11, 2017
Addicted Teens! Facebook Help Them!
October 6, 2017
I read “Teens Rebelling Against Social Media’, Say Headteachers.” Poor social media giants, one might say. Yeah, right. Real news, real facts, real phase change.
Decide for yourself.
The main point of the write up is that teens need “detox” and are embracing a cold turkey to help with withdrawl symptoms.
I noted this passage:
Chris King, chair of the HMC and Headmaster of Leicester Grammar School, said the findings were among “the first indications of a rebellion against social media”. He said they remind us that teenagers “may need help to take breaks from [social media’s] constant demands”. Some 56% of those surveyed said they were on the edge of addiction.
Hmm. Edge of addiction.
I circled this statement which was obviously based on “facts”:
Almost two-thirds of schoolchildren would not mind if social media had never been invented, research suggests.
I wonder if BBC professionals have ripped mobile devices from the addicted clutches of their own children?
Doubtful. Who wants a teen sulking and amping up the annoyance in a modern household?
Not me. Log on. Be happy. See I am not asking questions about methodology, analysis, and statistical validity. Gotta run. I have to check my social media feeds.
Stephen E Arnold, October 6, 2017
Quantum Computing: MSFT Versus Tokyo U
October 4, 2017
Quantum computing is not quite as thrilling to marketers as smart software. I noted two articles, each reporting advances in quantum computing. The first is from the folks who brought us the Windows Nokia phone. In “With New Microsoft Breakthroughs, General Purpose Quantum Computing Moves Closer to Reality,” I learned:
[Microsoft] showcased the progress it has made toward developing both a topological qubit and the ecosystem of hardware and software that will eventually allow a wide range of developers to take advantage of quantum computing’s power. That progress includes a new programming language, which is deeply integrated with Visual Studio and designed to work on both a quantum simulator and a quantum computer.
The method involves a topological method which I think means qubits are organized in a lattice. The idea is to make qubits more stable. Decoherence does not compute.
In Japan, Tokyo University professors asserted that a combination of light pulses and loop circuits would allow task switching and manipulation of the pulses. The article “University of Tokyo Pair Invent Loop-based Quantum Computing Technique” states:
Furusawa’s new approach will allow a single circuit to process more than 1 million qubits theoretically, his team said in a press release, calling it an “ultimate” quantum computing method.
Has Microsoft solved the problem? Has the Tokyo U pair prevailed? I want to wait for more tech rah rah from the Google and the myriad of other research teams trying to find a better way than Von Neumann’s.
Stephen E Arnold, October 4, 2017
Combine Humans with AI for Chatbot Success (for Now)
September 25, 2017
For once, humans are taking work from bots. The Register reports, “Dismayed by Woeful AI Chatbots, Boffins Hired Real People—And Went Back to Square One.” Today’s AI-empowered devices can seem pretty smart—as long as one sticks to the script. Until we have chatbots that can hold their own with humans in conversation, though, Chorus may give users the best of both worlds. The app taps into a human workforce through Amazon Mechanical Turk, and was developed by researchers from Carnegie Mellon, the University of Michigan, and Ariel University. A PDF of their paper can be found here. Writer Thomas Claburn reports:
It was hoped by businesses the world over that conversational software could replace face-to-face reps and people in call centers, as the machines should be far cheaper and easier to run. The problem is simply that natural language processing in software is not very good at the moment.
‘Due to the lack of fully automated methods for handling the complexity of natural language and user intent, these services are largely limited to answering a small set of common queries involving topics like weather forecasts, driving directions, finding restaurants, and similar requests,’ the paper explains. … [Researchers] devised a system that connects Google Hangouts, through a third-party framework called Hangoutsbot, with the Chorus web server, which routes queries to on-demand workers participating in Amazon Mechanical Turk.
The team acknowledges they are not the first to combine a chatbot with real people, citing the crowd-sourced app for blind iPhone users, VizWiz. Of course, employing humans brings its own set of problems. For example, they do not come equipped with an auto-timeout, and they sometimes let their emotions get the better of them. It can also be difficult to find enough workers to answer all queries quickly. Researchers see Chorus as an interim solution that, they hope, will also suggest ways to improve automated chat going forward.
Cynthia Murrell, September 25, 2017
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:
- When an innovation occurs, who will get access to that technology? The universities, the professors, the students, or Bloomberg?
- 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?
- Who will be able to hire the bright sprouts who flock via tram to Roosevelt Island?
- 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?
- Will Facebook and Google just buy Stanford University and leave the old school companies to the backwaters on the East coast?
- 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
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