The Old The Article Will Be Just a Click Away Ploy
March 31, 2018
I saw a link in one of my newsfeeds.
The starting point was a story called “8 Data and Analytics Trends to Watch” in a blog called SEOLand.in. The story ended about half way through. To see the rest of the story I had to click a link.
That second link sent me to “8 Data and Analytics Trends to Watch” on a site called Business2Community.com. The story ended about half way through. To see the rest of the story I had to click another link.
That link sent me to a MicroStrategy Web site at https://bit.ly/2ECzrtj. No story this time but I was invited to click a link in order to download a white paper.
I declined.
Several observations:
- I wonder if anyone at these firms asked themselves this question, “How will people react to this play?”
- I made a note about each of these sources. That note says, “Avoid these outfits.”
- Perhaps each of these “real news” outfits should consider shifting to a consulting service along the lines of the GSR-type of outfit?”
Oh, the trends revealed were of little interest to the deadbeats and unemployed in Harrod’s Creek. A group of MBA students from the disgraced University of Louisville could have generated a comparable list of data and analytic trends. Scary intellectual parity.
Stephen E Arnold, March 31, 2018
Stephen E
Will Google Make a China-Type Misstep in India?
March 30, 2018
India is on Google’s radar. It should be. Google has not been able to generate the type of traction it has in the US and Europe in China. Perhaps one reason is Google’s suggestion that Google “change” how it approaches digital information. That suggestion appears to have fallen on less-than-receptive ears.
Next up? India.
Google strives to make order out of chaos in search, in maps, and many other aspects of life. So, the search giant has turned its sights on a massive population that might just not be ready for Googlization: India. We learned more about how they plan to bring their order to a civilization that has thrived without Google for quite some time in a recent QZ story, “Google Maps Introduces New Features to Navigate India’s Chaotic Roads.”
According to the story, Google is tackling this challenge:
“From generating unique area-specific codes to using nearby landmarks to navigate to allowing users to add addresses that don’t appear on Maps yet, Google is attempting to bring order to the chaos. The company, which made Maps voice navigation available in Hindi in 2014, has now added six new local languages: Bengali, Gujarati, Kannada, Telugu, Tamil, and Malayalam.”
This is a big challenge, but one we are confident Google is up to the task of. Another question is why the search giant is spending so much energy on this project. That, of course, boils down to money. India has millions of users waiting and according to a recent study, over 80% of Indian internet users frequent YouTube. With dollar signs in its eyes, we are confident Google will attempt to tame some of the quirks that make India so unique.
One hopes that Google will offer policy suggestions which keep India a warm and friendly place for the GOOG.
Patrick Roland, March 30, 2018
Payoff in Shopping Speed Up
March 30, 2018
Mobile shopping is a major priority for just about anyone who does sales outside of only a brick and mortar store. The increasing attention to this economy has led to the big names online beefing up their services and the results are astounding. We learned more from a recent IT ProPortal story, “Google Reveals New Mobile Shopping Tools.”
According to the story:
“Businesses have not prioritized their mobile sites and now that more online shopping than ever is done from a smartphone, this needs to change. Google’s analysis suggests that by improving mobile load time from six to three seconds that an average site could see an increase of $255,000 in annual revenue.”
That’s an incredible number, especially for smaller businesses. But that’s not all. Oddly, some of the most impressive innovations in mobile shopping comes from the restaurant industry. Coffee shops and pizza delivery joints have led the way, but recently, places like Burger King are getting in on the action and seeing an impact. What kind of conclusion can we draw from this? Not a ton, but just that your mobile shopping experience is going to increase, but we have a hunch that along with this money that is to be made the proliferation of advertisements will soon follow in ingenuity.
Patrick Roland, March 30, 2018
Google and AI Digital Shrooms
March 30, 2018
Magic mushrooms are a delightful way to experience reality as well as hurt your body and become addicted to drugs. They were a big symbol of the 1960s-70s counterculture. Beyond their hallucinogenic properties, medical experts discovered they have medicinal uses too. Mushroom enthusiast loves the mold, but there might be a way for them to trip without breaking any laws. The International Business Times reported that “Hallucination Machine Uses Google AI, Gives Magic Mushroom-Like ‘Trip’ Without Drugs.”
The possibilities of virtual reality have been imagined for years, but only now can we fully begin to explore the possibilities. One way researchers are testing virtual reality is with the Hallucination Machine, built on Google AI and uses a virtual reality headset. The Hallucination Machine allows users to “trip” without the drugs’ harmful effects. Scientists are fascinated with hallucinations and hallucinogens because they love to study the brain’s processes when it “trips out.”
Sussex University’s Sackler Centre for Consciousness Science published a paper in the Scientific Reports journal discussing how the Hallucination Machine compares to real drug-induced hallucinations.
Hallucinations help scientists focus their study on areas of the brain that are affected when there is an altered reality. Using hallucinogens alters the chemical composition of the brain, which makes it hard to isolate just the visual effects. So the team used Google’s DeepDream system, which uses a neural network approach to try and identify patterns and features in images. You can actually try it out for yourself online. DeepDream works by creating patterns and over emphasizing on certain recurring details that helps put our brain into perception overdrive, so much so that it starts to imagine stuff that isn’t actually there.
The Sackler Center conducted two tests. The first exposed participants to DeepDream and users experienced hallucinations similar to those caused by magic mushrooms. The second tested participants’ time perception, but the Hallucination Machine cannot recreate that psychedelic experience yet.
Replicating the magic mushroom’s tripping experience is still in the development phases, but give it a few more years and this will probably be a popular virtual reality program.
Whitney Grace, March 30, 2018
Speeding Up Search: The Challenge of Multiple Bottlenecks
March 29, 2018
I read “Search at Scale Shows ~30,000X Speed Up.” I have been down this asphalt road before, many times in fact. The problem with search and retrieval is that numerous bottlenecks exist; for example, dealing with exceptions (content which the content processing system cannot manipulate).
Those who want relevant information or those who prefer superficial descriptions of search speed focus on a nice, easy-to-grasp metric; for example, how quickly do results display.
May I suggest you read the source document, work through the rat’s nest of acronyms, and swing your mental machete against the “metrics” in the write up?
Once you have taken these necessary steps, consider this statement from the write up:
These results suggest that we could use the high-quality matches of the RWMD to query — in sub-second time — at least 100 million documents using only a modest computational infrastructure.
The path to responsive search and retrieval is littered with multiple speed bumps. Hit any one when going to fast can break the search low rider.
I wish to list some of the speed bumps which the write does not adequately address or, in some cases, acknowledge:
- Content flows are often in the terabit or petabit range for certain filtering and query operations., One hundred million won’t ring the bell.
- This is the transform in ETL operations. Normalizing content takes some time, particularly when the historical on disc content from multiple outputs and real-time flows from systems ranging from Cisco Systems intercept devices are large. Please, think in terms of gigabytes per second and petabytes of archived data parked on servers in some countries’ government storage systems.
- Populating an index structure with new items also consumes time. If an object is not in an index of some sort, it is tough to find.
- Shaping the data set over time. Content has a weird property. It evolves. Lowly chat messages can contain a wide range of objects. Jump to today’s big light bulb which illuminates some blockchains’ ability house executables, videos, off color images, etc.
- Because IBM inevitably drags Watson to the party, keep in mind that Watson still requires humans to perform gorilla style grooming before it’s show time at the circus. Questions have to be considered. Content sources selected. The training wheels bolted to the bus. Then trials have to be launched. What good is a system which returns off point answers?
I think you get the idea.
DarkCyber Explores the Cambridge Analytica Matter
March 29, 2018
Short honk: The April 3, 2018, DarkCyber devotes the program to the Cambridge Analytica Matter. What makes this program different is the DarkCyber approach. The DarkCyber researchers examined open source information for factoids about how Cambridge Analytica created their “actionable” information for political clients. If you want to see a social media survey question can generate “triggers” to cause action via an image, a tweet, or blog post — tune in on April 3, 2018. Plus the program provides a link so you can download an application which can be used to generate “centers of influence”. Who knows? You could become the next big thing in content analysis and weaponizing information.
Make a note. On Tuesday, April 3, 2018, You will be able to view the video at www.arnoldit.com/wordpress or on Vimeo.
Kenny Toth, March 29, 2018
Artificial Intelligence: Tiny Ears May Listen Well
March 29, 2018
The allegations that Facebook-type companies can “listen” to one’s telephone conversations or regular conversations may be “fake” news. But the idea is worth considering.
Artificial intelligence’s ability to process written data is unparalleled. However, the technology has always lagged pretty severely when it comes to spoken words. Soon, that will be a thing of the past if this recent article is to be believed. We learned more from the Smart Data Collective piece, “Natural Language Processing: An Essential Element of Artificial Intelligence.”
According to the story:
“Natural Language Processing (NLP) is an important part of artificial intelligence which is being researched upon to aid enterprises and businesses in the quick, speedy and fast retrieval of both structured and unstructured organizational data when needed. In simple terms, natural language processing (NLP), is the skill of a machine to understand and process human language within the context in which it is spoken.”
This technology is really taking off in the food industry. According to sources, shoppers in London are the first to use language processing apps to help them determine what vitamins their body may be lacking. It may sound like a stretch, but this is the sweet spot where AI really soars. The technology seems to really take off in industries that previously felt like it needed no help. Watch for language processing to begin bleeding into everyday life elsewhere, too. If one is carrying a mobile phone, is it listening and recording, converting text to speech, and indexing that content for psychographic analysis?
Patrick Roland, March 29, 2018
What Has Cambridge Analytica Done to Crowdsourcing As a Way to Identify Fake News?
March 29, 2018
The battle to counteract fake news is hitting a strange, new chapter. Where, once, the falsification of news was the source of much ridicule and scrutiny, now it’s the solution to the problem. Social media titans are suggesting solutions, but the public isn’t so sure, as we discovered in a recent Slate story, “A Surprising New Study Shows That Facebook’s Ridiculed Plan to Rate The Media Could Actually Work.”
According to the story:
“In at least one plausible interpretation of the survey results, the respondents distinguished the credible outlets from the sketchy ones with near-perfect accuracy. Nineteen of the 20 mainstream news outlets in the sample were trusted more by both Democrats and Republicans than any of the other 40 outlets were trusted by respondents of either party.”
According to the Neiman Lab Facebook’s ratings system has a flaw built into the system. It seems like a great idea to rate the trustworthiness of news sources, but the fact that the system will be crowdsourced pretty much ensures that nobody will trust the results. In a world filled with hackers and bots that can funnel millions of votes toward crowdsourced content, it’s going to be hard to trust the trustworthiness of Facebook’s venture. Certainly, they will have filters in place to try to prevent such corruption, but the public will likely always be a little weary.
Beyond Search wonders if the information about this initiative is itself either fake news or an example of how some individuals issue a report in order to shape perception. Isn’t this the core method of the GSR Cambridge Analytica matter?
Patrick Roland, March 29, 2018
The AI Spy Who Photographed Me
March 29, 2018
Artificial intelligence is one of the of the tools that law enforcement is using to thwart potential terrorist attacks and other illegal activities. Applications use AI to run data analysis, scan the Dark Web, and monitor identity theft. One major use for AI is image analysis and facial recognition. IEEE Spectrum takes a look at how there is a huge demand for more accurate image AI, “Wanted: AI That Can Spy.” While fear over spy satellites is not much a plot point anymore, the US has hundreds of satellites orbiting the planet capturing photographic data. Humans are only capable of observing so many photographic data and the US government has FOMO “fear of missing out” on something important.
US intelligence officials sponsored an AI challenge to identify objects of interest in satellite images. The entire goal is to improve AI standards and capabilities:
Since July, competitors have trained machine-learning algorithms on one of the world’s largest publicly available data sets of satellite imagery—containing 1 million labeled objects, such as buildings and facilities. The data is provided by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The 10 finalists will see their AI algorithms scored against a hidden data set of satellite imagery when the challenge closes at the end of December.
The agency’s goal in sponsoring the Functional Map of the World Challenge aligns with statements made by Robert Cardillo, director of the U.S. National Geospatial-Intelligence Agency, who has pushed for AI solutions that can automate 75 percent of the workload currently performed by humans analyzing satellite images.
Lockheed research scientist Mark Pritt guessed that the US government wants to automatically generate maps, instead of relying on manual labor. Pritt’s Lockheed team is one of the many teams competing for the $100,000 prize to develop the best deep-learning algorithm that can recognize specific patterns and identify objects of interest in satellite images. Satellite images are more complex than other images because they are shot from multiple angles, cloud coverage is a problem, and a variety of resolutions.
Even if a deep-learning algorithm was developed it would not be enough, because the algorithm lacks the ability for refinement. Think sentimental analysis, except with images. The perfect solution for the moment is a combination of AI and human interaction. The AI does the bulk of the work, while humans examine flagged photos for further investigation.
Whitney Grace, March 29, 2018
Who Guesses Better: Humans or Smart Software
March 28, 2018
MBAs are likely to pay close attention to smart software which makes decisions about which start up or stock to back.
With all the hand wringing about how artificial intelligence is going to put a lot of people out of work and drastically change our future landscape, it’s almost as if commentators are making it a given that humans are inferior. These writers and thinkers don’t seem to have any faith that our brains can do the heavy lifting to. CNBC recently found a niche where maybe we simple men and women can keep up thanks to…research, of course. We learned more in the article, “Doing Your Homework Does Lead to Better Investing returns.”
According to the story:
“…sophisticated hedge-fund managers are simply more skilled at processing swaths of information and data, their advantage may be more in their ability to match private data with public disclosures and SEC filings. ‘We look at the people who do robotic downloading. The people who use it suggests that hedge funds are going out and that they’re getting public information whenever they need.’”
It’s a great angle, for sure. That with endless hours of research, our investments can turn to gold. However, this overlooks the idea that there may be flaws with the data itself. What if you are using biased info or downright bad data?
Perhaps the humans are better at picking winners than smart software. Data are not created equal. Smart software may incur a penalty because of flawed inputs. Bad data can cripple some data analytics outputs.
Net net, as the MBAs say, data have to be reliable. For now, bet on the human when it comes to deciding about investments.
Patrick Roland, March 28, 2018
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