Does Smart Software Understand Kid Vids?

April 26, 2018

The growth of AI and predictive analytics across the spectrum has become a universal rah rah. Super powered computers and their data crunching power is being utilized by industries great and small. However, the producers of AI technology might not be getting rich off this revolution. We learned more from a recent Market Watch story, “IBM Earnings Show AI is Not Paying Off Yet.”

According to the story:

“’The bulls were hoping for a clean modest beat on this key growth segment, which represents the underpinnings of the IBM turnaround story in 2018 and beyond,’ Ives said in a note to clients. In an email, Ives said he does not have an estimate for Watson itself. ‘It’s a major contributing factor to strategic imperatives and helping drive double-digit growth…’”

Despite these less than stellar results, the big names in tech aren’t getting scared away by AI yet. In fact, it is still a boom investment time. Intel, for one, is betting a large chunk on cash on AI. We will be watching this development closer, since we all know that AI can be the greatest product in the world, but if it keeps losing money it might just end up in the graveyard. (Unlikely, we know.)

But—and there seems to be a “but” when it comes to the capabilities of smart software—we noticed that Google seems to be relying on humans to make sure that children’s videos are not violent, chuck full of objectionable material, or inappropriate for kiddie viewing. According to “For the First Time, Parents Will Be Able to Limit YouTube Kids to Human-Reviewed Channels and Recommendations,”

The new features will allow parents to lock down the YouTube Kids app so it only displays those channels* that have been reviewed by humans, not just algorithms. And this includes both the content displayed within the app itself, as well as the recommended videos. A later update will allow parents to configure which videos and channels, specifically, can be viewed.

A few observations seem to be warranted:

  1. Google’s vaunted smart software cannot determine what’s appropriate for children. Therefore, Google is now assuming the role that old school, chain smoking, ink stained editors once performed. Back to the past?
  2. If the smart software cannot figure out what video is okay for children, how accurate is Google’s ad matching software. Is it possible that the ad matching system is able to perform in a “good enough” manner? Will advertisers lose confidence that their money is putting messages in front of the “right” eye balls?
  3. Perhaps Google has caught the same case of sniffles that IBM Watson has been suffering? The failure of smart software with regard to kid vids suggests that hyperbole is not the same as actual performance.

The kid vid matter is as significant as the Facebook Cambridge Analytica matter. Could these be different facets of the same assumption that technology is a go getter?

Stephen E Arnold, April 26, 2018

Russia and the US 2016 Elections

April 15, 2018

A Twitter storm pointed to a source claiming to have video from a Dutch TV crew showing Russians hacking the US elections. While the internet went predictably nuts, it was surprising how quickly reputable sources like Mother Jones picked up on what was later proved to be obviously fake. Steemit had a good perspective on the debacle in an article titled, “10 Reasons the Dutch-Russian Hacking Story is Fake News.”

According to the story:

“It never ceases to amaze me how intelligence agency narratives never fail to trip over their own shoelaces. How soon they forget, that one of their biggest attempts to discredit NSA whistleblower Edward Snowden, was over revelations he made about the USA hacking a major Chinese university, that were published by the South China Morning Post in June, 2013.”

Despite Mr. Zuckerberg’s confidence in artificial intelligence, smart software, Big data and algorithms teams continue to try to find digital solutions to this growing oddity, but it’s tough. The world of fake news is slippery and tough for human and AI eyes to pin down. We think people need to keep fighting this good fight, but ultimately common sense is the best fake news detector in the business. If it sounds too good or too bad to be true, it probably is. More importantly, perhaps assertions about the capabilities of smart software are themselves fake news?

Patrick Roland, April 15, 2018

Making Informed Decisions Less Like Guessing

April 12, 2018

Psychic powers may not be able to bend spoons. Hunches? Well, those are as common as microbes in one’s gut.

With just a little Internet research, it becomes easy recognize the tricks psychics use to fool unsuspecting people. Despite psychic tomfoolery, humans have not stopped for ways to predict the future and AI software has somewhat breached that capability. Computer software is as limited as the humans that program it, but Newsweek reveals that: “Human Brains Are Able To Predict The Future Before The Eye Can Tell It What Happened.” Before you start trying to develop your innate sixth sense, the article explains how eyeballs moves faster than the brain can respond, so the brain uses that gap to predict what we will see next.

Scientists at the University of Glasgow discovered how this process works. The scientist…

“…used functional magnetic resonance imaging (fMRI) and optical illusions to better understand what’s going on in our brain when we see. Whereas the eyes usually send information to the brain about what the surroundings look like, known as feedforward input, this study focused particularly on brain feedback input, the neurological process where the brain sends information to the eyes.

Study co-author Gracie Edwards, who specializes in neuroscience and psychology at the University of Glasgow, explained that the brain creates predictions based on memories of similar actions. ‘Feedforward and feedback information interact with one another to produce the visual scene we perceive every day,’ said Edwards.”

It sounds like the scientists discovered an explanation for déjà vu, but humans experience this process more regularly than that odd “done it before” feeling. AI programs are actually adapted from how neuroscience. AI algorithms already have the feedforward mechanism, but they lack the feedforward predictive mechanism.

Elon Musk’s fear of smart software may be an example of the grip of guessing. Season with predictive analytics and one can peer into the future with renewed confidence.

Whitney Grace, April 12, 2018

Would This Employee Protest Get Traction in China?

April 10, 2018

I noted this headline and variants in my newsfeeds in the last four days: “Thousands of Google Employees Protest Company’s Involvement in Pentagon AI Drone Program.” I think this may be a management challenge for the Google executives.

What interested me is that Google wants to do more work for the US government. Amazon is working on this revenue angle as well. One company uses technology to sell ads; the other uses technology to sell products to consumers.

The contrast which struck me is that smart software is a booming business.

In “Forget the Trade War, China Wants to Win the Arms Race in Computing,” the article asserts:

While overall spending by China is unknown, its government is building a US$10 billion National Laboratory for Quantum Information Sciences in Hefei, Anhui Province, which is slated to open in 2020. US-funded research in quantum is about US$200 million a year, according to a July 2016 government report, and some researchers and companies don’t believe that’s enough.

With spending chugging along at a pace which makes the hare and tortoise race seem an apt metaphor, will Chinese employees protest the use of smart software? Will China’s newspapers publicize the apparent discord which seems to challenge Google management authority?

I am not sure what to make of this employee pushback as China pushes forward. My initial reaction is this may be an issue to consider with regards to where the hot spot in smart software may be located. And that location may not accommodate public employee protests.

Stephen E Arnold, April 10, 2018

Is Chinese Smart Software Different from US and UK AI?

April 9, 2018

I read “China Now Has the Most Valuable AI Startup in the World.” That’s one difference: Valuation. According to the Bloomberg news report:

Backed by Qualcomm Inc., it underscores its status as one of a crop of homegrown firms spearheading Beijing’s ambition to become the leader in AI by 2030. And it’s a contributor to the world’s biggest system of surveillance: if you’ve ever been photographed with a Chinese-made phone or walked the streets of a Chinese city, chances are your face has been digitally crunched by SenseTime software built into more than 100 million mobile devices.

Yep, another difference. Instead of pushing ads, the Chinese AI outfit seems to be focused on surveillance. Instead of operating in stealth mode, it seems as if someone affiliated with the company is delighted to make the link between smart software and mass surveillance.

Is there a third difference?

Based on the information dribbling in via my open source news stream, yes, there is another difference:

Engineers, computer scientists, and data management professionals.

US universities crank out top notch folks. But China goes for the Daily Double: Lots of engineers. Many high quality engineers, computer scientists, and data management professionals.

Those differences matter in my view.

Stephen E Arnold, April 9, 2018

Insight into the Value of Big Data and Human Conversation

April 5, 2018

Big data and AI have been tackling tons of written material for years. But actual spoken human conversation has been largely overlooked in this world, mostly due to the difficulty of collecting this information. However, that is on the cusp of changing as we discovered from a white paper from the Business and Local Government Resource Center,The SENSEI Project: Making Sense of Human Conversations.”

According to the paper:

“In the SENSEI project we plan to go beyond keyword search and sentence-based analysis of conversations. We adapt lightweight and large coverage linguistic models of semantic and discourse resources to learn a layered model of conversations. SENSEI addresses the issue of multi-dimensional textual, spoken and metadata descriptors in terms of semantic, para-semantic and discourse structures.”

While some people are excited about the potential for advancement this kind of big data research presents, others are a little more nervous; for example, one or two of the 87 million individuals whose Facebook data found its way into the capable hands of GSR and Facebook.

In fact, there is a growing movement, according to the Guardian, to scale back big data intrusion. What makes this interesting is that advocates are demanding companies that harvest our information for big data purposes give some of that money back to the people whom the info originate, not unlike how songwriters are given royalties every time their music is used for film or television. Putting a financial stipulation on big data collection could cause SENSEI to top its brake pedal. Maybe?

Patrick Roland, April 5, 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.

Image result for speed bump

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.

Read more

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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