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
Lucidworks: The Future of Search Which Has Already Arrived
August 24, 2017
I am pushing 74, but I am interested in the future of search. The reason is that with each passing day I find it more and more difficult to locate the information I need as my routine research for my books and other work. I was anticipating a juicy read when I requested a copy of “Enterprise Search in 2025.” The “book” is a nine page PDF. After two years of effort and much research, my team and I were able to squeeze the basics of Dark Web investigative techniques into about 200 pages. I assumed that a nine-page book would deliver a high-impact payload comparable to one of the chapters in one of my books like CyberOSINT or Dark Web Notebook.
I was surprised that a nine-page document was described as a “book.” I was quite surprised by the Lucidworks’ description of the future. For me, Lucidworks is describing information access already available to me and most companies from established vendors.
The book’s main idea in my opinion is as understandable as this unlabeled, data-free graphic which introduces the text content assembled by Lucidworks.
However, the pamphlet’s text does not make this diagram understandable to me. I noted these points as I worked through the basic argument that client server search is on the downturn. Okay. I think I understand, but the assertion “Solr killed the client-server stars” was interesting. I read this statement and highlighted it:
Other solutions developed, but the Solr ecosystem became the unmatched winner of the search market. Search 1.0 was over and Solr won.
In the world of open source search, Lucene and Solr have gained adherents. Based on the information my team gathered when we were working on an IDC open source search project, the dominant open source search system was Lucene. If our data were accurate when we did the research, Elastic’s Elasticsearch had emerged as the go-to open source search system. The alternatives like Solr and Flaxsearch have their users and supporters, but Elastic, founded by Shay Branon, was a definite step up from his earlier search service called Compass.
In the span of two and a half years, Elastic had garnered more than a $100 million in funding by 2014and expanded into a number adjacent information access market sectors. Reports I have received from those attending Elastic meetings was that Elastic was putting considerable pressure on proprietary search systems and a bit of a squeeze on Lucidworks. Google’s withdrawing its odd duck Google Search Appliance may have been, in small part, due to the rise of Elasticsearch and the changes made by organizations trying to figure out how to make sense of the digital information to which their staff had access.
But enough about the Lucene-Solr and open source versus proprietary search yin and yang tension.
Attack Planes Soon to Be Equipped with Lasers
August 24, 2017
The US Air Force soon will be equipping its attack planes with laser weapons to fight UAVs that terrorist organizations may use for launching attacks.
According to an op-ed published by Defense One and titled The Future of the Air Force, the author says:
We are currently investing in the hardware to ensure space superiority; in the near future we will need to grow the number of space airmen and the accompanying infrastructure much like we did for the combat Air Force 40 years ago.
Wars in the future will be fought on multiple fronts, including space. As per the op-ed, the US Air Force needs to be equipped sufficiently to fight these battles without putting people on the front line.
The op-ed also says about the acquisition of an Israeli company that enables attack planes using lasers to fend off drones that are used for dropping bombs and other weapons. The acquisition does not come as a surprise as Pentagon had been researching use of lasers as tactical weapons since long. It seems the days of Star Wars are very near.
Vishal Ingole, August 24, 2017
Demanding AI Labels
August 16, 2017
Artificial intelligence has become a standard staple in technology driven societies. It still feels like that statement should still only be in science-fiction, but artificial intelligence is a daily occurrence in developed nations. We just do not notice it. When something becomes standard practice, one thing we like to do is give it labels. Guess what Francesco Corea did over at Medium in his article, “Artificial Intelligence Classification Matrix”? He created terminology to identify companies that specialize in machine intelligence.
Before we delve into his taxonomy, he stated that if the framework for labeling machine intelligence companies is too narrow it is counterproductive to the sector’s purpose of maintaining flexibility. Corea came up with four ways to classify machine intelligence companies :
i) Academic spin-offs: these are the more long-term research-oriented companies, which tackle problems hard to break. The teams are usually really experienced, and they are the real innovators who make breakthroughs that advance the field.
- ii) Data-as-a-service (DaaS): in this group are included companies which collect specific huge datasets, or create new data sources connecting unrelated silos.
iii) Model-as-a-service (MaaS): this seems to be the most widespread class of companies, and it is made of those firms that are commoditizing their models as a stream of revenues.
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iv) Robot-as-a-service (RaaS): this class is made by virtual and physical agents that people can interact with. Virtual agents and chatbots cover the low-cost side of the group, while physical world systems (e.g., self-driving cars, sensors, etc.), drones, and actual robots are the capital and talent-intensive side of the coin.
There is also a chart included in the article that explains the differences between high vs. low STM and high vs. low defensibility. Machine learning companies obviously cannot be categorized into one specific niche. Artificial intelligence can be applied to nearly any field and situation.
Whitney Grace, August 16, 2017
Doctors Fearful of Technology? Too Bad for Them and Maybe the Patients?
July 27, 2017
IBM, Google, and other outfits want doctors to get with the technology program. Sure, docs use mobile phones, but email and such wonderful innovations as selfies from the operating theatre have not yet caught on. Watching my doc fumble with the required online medical record system is interesting. Try it sometime. Puzzled expressions, eye squinting, and sloooow keyboarding are part of the show. One of my docs expressed interest in my Dark Web Notebook. I sent him a link so he could download a comp copy. Guess what? He couldn’t figure out how to download the book. Amazing expertise.
I read a Thomson Reuters’ article which seems to stray dangerously close to my view of technology in the medical profession. Mind you, here in Louisville sales people are in the operating room to provide information to a doc who may not be familiar with a new gadget. Get enough gadgets and peddlers in the facility and the patients may have to rest on gurneys in the hall.
But I digress. The write up i noticed was “Doctors View Technology as Largely Problematic.” I highlighted this “real” news statement:
69 percent of the 100 doctors in the audience said increased reliance on technology and electronic health records only served to separate them from their patients….But the biggest problem stemming from technology for the doctors, and the bane of many doctors’ existence, is the electronic health record, also known as an EHR.
Now think about the over the top marketing from IBM about Watson’s ability in a narrow field like bladder cancer. Put that Anderson affair out of your main. Google continues to push forward with an even more interesting approach. I recall the phrase was “solving death.” And there are other outfits which believe that their technologists can make life so much better for doctors.
Seems like the revolution may take a bit more time. The good news is that since Google has not solved death, the doubting docs will die. Their replacements may be more into the IBM, Google, et al approach to health care.
No worries in Harrod’s Creek. We just use a mixture of black powder and bourbon to cure all manner of ills.
Stephen E Arnold, July 27, 2017
Shopping List of Technologies That Will Make Life Really Good… Well, Some Lives’ Lives
July 26, 2017
I read or rather scanned an infographic called “Things to Come: A Timeline of Technology.” Sci fi? Nope, the stuff that makes VCs’ ice cold blood flow just a little bit faster. In terms of search, two entries seem to suggest that finding information will be a semi big thing:
- 2036, The next evolution of AI. Okay, but won’t today’s whiz bang systems get better really rapidly? According to the “Things to Come”, AI has its booster rocker fired in 19 years. Make your hotel reservation the IBM Watson B&B today.
- 2040. Genetic computing. Okay, more than a GIF in a protein chain and less than a quantum computer maybe?
- 2045, Algorithmic advances. Hmm. Algorithms are “evolving” if one is into the genetic algorithms. For the rule-based approaches, that seems to move less rapidly. But advances down the road in 28 years. Seems like a made up target date. But that’s just the view of an old guy in rural Kentucky
What I did like about the VC blood enhancer was this list of buzzwords. How many can you define?
- Eye controlled technology
- Paper diagnostics
- Designer antibiotics
- Ingestible robots
- Smart clothing
- Photonics in space
- Volcanic mining
- Spintronics revolution
- Carbon breathing batteries
- Super antivirals
- Diamond batteries
- Optogenetics
- Nano feasibility
- Unhackable quantum Internet
- Cheap solar power
- Biomimetic materials
- 3D printing in every home
- Fully immersive computer interface
- Self sufficient energy ecosystem
- Germ line genetic modification
- Holograpic pets
- Rapid genetic screening
- Microwave rockets
- Space based solar energy
- Fusion power
- Evolutionary enhancement
- Carbon sequestration
- Geoengineering
- Wavetop and undersea cities
- Geoneutrino satellites.
This list reminds me of my eavesdropping on a group of PC Magazine editors cooking up a list of the Top 10 trends in personal computers or the brainstorming sessions at Booz, Allen when a Top Dog said, “We need to identify hot business trends.”
Anyway, you can use these buzzwords in your LinkedIn résumé or in your next tweet. Better yet, print out the list and look for mid tier consulting firms at the next conference you attend. You can pitch your expertise in wave top and undersea cities and hope the individual with whom you are speaking has never watched the film Waterworld.
Stephen E Arnold, July 26, 2017
Machine Learning Does Not Have the Mad Skills
July 25, 2017
Machine learning and artificial intelligence are computer algorithms that will revolutionize the industry, but The Register explains there is a problem with launching it: “Time To Rethink Machine Learning: The Big Data Gobble Is OFF The Menu.” The technology industry is spouting that 50 percent of organizations plan to transform themselves with machine learning, but the real truth is that it is less than 15 percent.
The machine learning revolution has supposedly started, but in reality, the cannon has only be fired and the technology has not been implemented. The problem is that while companies want to use machine learning, they are barely getting off the ground with big data and machine learning is much harder. Organizations do not have workers with the skills to launch machine learning and the tech industry as a whole has a huge demand for skilled workers.
Part of this inaction comes down to the massive gap between ML (and AI) myth and reality. As David Beyer of Amplify Partners puts it: ‘Too many businesses now are pitching AI almost as though it’s batteries included.’ This is dangerous because it leads companies to either over-invest (and then face a tremendous trough of disillusionment), or to steer clear when the slightest bit of real research reveals that ML is very hard and not something the average Python engineer is going to spin up in her spare time.
Organizations also do not have the necessary amount of data to make machine learning feasible and they also lack the corporate culture to do the required experimentation for machine learning to succeed.
This article shares a story that we have read many times before. The tech industry gets excited about the newest shiny object, it explodes in popularity, then they realize that the business world is not ready for implementing the technology.
Whitney Grace, July 25, 2017
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