Smart Software Names Cookies

December 11, 2018

Tired of McVities’ digestives, coconut macaroons, and chocolate chip cookies. Tireless researchers have trained smart software to name cookies. Next to solving death, this is definitely a significant problem.

The facts appear in “AI System Tries to Rename Classic Cookies and Fails Miserably.” You can read the original, possible check the cv of the expert who crafted this study, and inform your local patisserie that you want new names for the confections. (I assume the patisserie has not been trashed by gilets jaunes.)

Here’s an alphabetical list of the “new” names from the write up. Sorry, I don’t have the real world cookie name to which each neologism is matched. Complain to TechRadar, not me.

The names:

  • Apricot Dream Moles
  • Canical Bear-Widded Nuts
  • Fluffin coffee drops
  • Granma’s spritches
  • Hersel pump sprinters
  • Lord’s honey fight
  • Low fuzzy feats
  • Merry hunga poppers
  • Quitterbread bars
  • Sparry brunchies #2
  • Spice biggers
  • Walps

And my personal favorite:

Hand buttersacks.

I quite like the system. One can use it to name secret projects. I can envision attending a meeting and suggesting, “Our new project will be code named Quitterbread bars.”

Stephen E Arnold, December 11, 2018

SciTech Journals Resist Smart Software

December 1, 2018

The scientific community is in the throes of a battle that might sound familiar even to folks who have nothing to do with science. They are trying to overcome a glut of fake news, and are turning those weapons on themselves to do so. We discovered more in a recent Analytics India article, “How AI is Tackling Fake Academic Research That is Plaguing Scientific Community.”

According to the story:

“A decade ago, researchers Jeremy Stribling, Dan Aguayo and Max Krohna of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) built a computer science paper generator that could stitch together nonsense papers with impressive graphs and such. The papers drummed up by SCIgen software were accepted at major conferences and even by reputed journals.”

The article purports that that AI technology used to make fake articles is not being utilized in debunking them as well. According to Nature, these tools are running the gamut for academic journals, from choosing which peer reviewers to work with, to verifying statistics, and even summarizing complex articles. This is a neat tool that proves the only way to fight fire is with fire. We can only hope that we are able to keep ahead of the frauds.

Patrick Roland, December 1, 2018

Unstructured Data: Hey, Smart Software Is Supposed to Help

November 25, 2018

I read “5 Critical Steps for Identifying the Value in Your Unstructured Information.” The points in the write up are fine. In fact, anyone who has worked with unstructured data in the form of emails, tweets, Facebook posts, intercepts, etc. knows that a lot of work is required.

My problem with the write up in Datanami is that smart software keeps its nose tucked under the covers. I thought that smart software was able to perform collection (er, that’s a step not included in the list of five steps but let’s move on).

Smart software is supposed to discover important information. That’s fine but what is the process for configuring the smart software, checking to make sure that the system outputs useful or semi useful data, and presents it in a form which does not trigger another wave of manual effort? There are some systems which perform discovery; however, like today’s driverless autos, a human has to have his or her hands on the wheel. Otherwise a dead pedestrian or a dead driver can be an outcome. I recall a Tesla nuked a white truck because its LIDAR thought the truck was a cloud. Yeah, right.

The reality is that generalizations about what’s is required to make sense of unstructured data are only marginally useful. Anyone licensing a smart system from outfits like IBM, Palantir, BAE Systems, Textron, etc. must be prepared for the surprises which luck in the software.

For instance:

  1. Much of the work is manual. How does data get into Palantir Gotham?
  2. Setting up the system is iterative work. Have you ever heard about tuning?
  3. Creating and enforcing procedures for keeping data clean and happy is work. Automatic feeds and real time flows are super, but what happens when high value data is filtered and put in an exception folder?
  4. Analysis is work that needs a trained, attentive, subject matter expert. Who makes sense of the puzzle pieces and assembles them?

The real world requires that magic be confined to children’s books. Using the tools available today do not eliminate the need for manual work.

Smart software is a knee brace. The human has to carry the load. Omitting this reality creates false expectations and puts lives at risk or decision making in a higher risk setting. Smart software can do some functions well. Not all functions are smart.

Stephen E Arnold, November 25, 2018

DarkCyber for November 20, 2018, Now Available: Part Four, Amazon Poised for Policeware Growth

November 20, 2018

DarkCyber for November 20, 2018, is now available at http://www.arnoldit.com/wordpress and on Vimeo at https://vimeo.com/301440474.

In this week’s program (the fourth in the DarkCyber four part series about Amazon’s new services), Stephen E Arnold reveals how the sense making and analytics system will allow Amazon to expand its services into regulatory agencies in the US and in other countries.

Amazon’s push into policeware enables a broad market push. In addition to serving the US government, Amazon’s technology for advanced intelligence analysis allows the company to provide regulatory agencies with high value ways to fulfill their mission. The Securities & Exchange Commission and the Internal Revenue Service could become customers of the Amazon GovCloud based system.

Real time information processing and powerful analytics like cross correlation across disparate data sources can reduce costs and improve the efficiency of the agencies’ enforcement efforts.

Stephen E Arnold said, “Amazon’s push to provide services to a major US intelligence agency and to win the Department of Defense cloud computing contract worth about $5 billion are significant. Amazon’s apparent goal is to disrupt and then displace existing vendors of similar services. Amazon is well positioned to rework in a radical way the way city, county, state, and federal government agencies perform analytic and intelligence related work. Furthermore, Amazon’s platform reaches the UK law enforcement community, and it could migrate to Canada, New Zealand, and Australia as well. The impact of Amazon’s policeware is likely to be far more significant than a single JEDI contract.”

The final video in this DarkCyber series makes clear that Amazon has a strategic objective for its machine learning and advanced analytics platform.

In addition, commercial enterprises may seek to make sense of their business related data and information. Financial services firms and pharmaceutical companies are among the most information intensive businesses. Amazon could easily become a disruptive force in the traditional business intelligence market.

For more information about our for fee webinars about Amazon policeware, please, write benkent2020 at yahoo dot com.

Kenny Toth, November 20, 2018

IBM Watson: The Smart Sports Maven

November 19, 2018

The US does not follow soccer, ahem, football. The rest of the world, however, does. Whether you call it soccer or football, it is the most popular sport in the world and the World Cup requires a lot of power and technology to cover it. The Medium’s Global Editors Network explores how in the article, “Covering The World Cup Cup 2018 With AI And Automation.”

During the World Cup, fans are ravenous for information on their teams and news networks use automation and artificial intelligence to keep up with the demand. Individual networks each did something new and amazing to cover the World Cup. The UK Times launched a World Cup Alexa Skill, Fox Sports partnered with IBM Watson to make AI-powered highlight videos, and Le Figaro created automated visual summaries.

Fox Sports’s AI video highlight machine was amazing. Watson used its AI to allow users to create on-demand videos using World Cup clips from 1958 to the present.

“According to Engadget, there are 300 archived World Cup matches that Watson’s AI technology is capable of analyzing. More specifically, the IBM Watson Video Enrichment, a programmatic metadata tool, analyses the footage to create metadata that identifies what is happening in a scene at any given moment with an associated timestamp. ‘In essence, Watson Video Enrichment acts as an automatic metadata generator that is trained to use clues, such as facial characteristics, the presence of a red card, crowd noise, what’s being said by announcers and other characteristics, to create metadata that makes the massive amount of soccer video searchable’, wrote Phil Kurz on TVTechnology.”

Le Figaro’s innovation to generate World Cup visual summaries worked faster than any human. Dubbed Mondial Stories, the automated stories provide all the information someone needs to review a game as if they had watched the entire match.

Automation is a great tool, because the summaries do not require extra expenses, have low maintenance, it is an objective tool, and has potential for future sponsorships.

AI and automation cannot fully take over the human component of reporting on games, because they are just machines. However, they can enhance the viewer experience, increase commerce opportunities, and there are other ideas that have yet to be explored.

Whitney Grace, November 19, 2018

DarkCyber for November 13, 2018, Now Available: Amazon Part Three, Simplifying Intelligence Analysis

November 13, 2018

DarkCyber for November 13, 2018, is now available at www.arnoldit.com/wordpress and on Vimeo at https://vimeo.com/300178710. Amazon Policeware, Part 3. DarkCyber explains how Amazon has solved most of the problems associated with machine learning centric intelligence analysis and sense making systems.

Amazon’s approach to policeware pivots on ease of use and ready to use data.
Instead of programming a system and then undertaking expensive set up tasks, Amazon’s approach is the equivalent of heating a meal in a microwave. The time and convenience changes the landscape for advanced content processing and analytics.

With pre-curated data sets, templates, and familiar Amazon interfaces—law enforcement, military, and intelligence professionals can move from task to output in a day from weeks to months with traditional vendors’ systems. Stephen E Arnold, author of CyberOSINT: Next Generation Information Access, said: “The benefit of the Amazon approach is low cost and quicker implementation. Instead of reinventing the wheel for each case or mission, the Amazon approach is repeatable,” which slashes training, configuring, and tuning work associated with policeware systems.”

Decades ago, IBM used mainframes and their proprietary hardware and software to create a barrier to change for government agencies using the systems. Amazon’s approach is to provide a platform which makes use of open source software to allow the US government to make necessary changes to software.

Amazon also offers value added functionality ranging from hardware like the DeepLens smart surveillance devices to patented analytics for real time cross correlation of data. Government agencies using these proprietary components will find themselves dependent on Amazon despite the support for open source software.
Existing vendors have business models built on time and materials billing for each use of their systems. Amazon has changed the game to emphasize quick and easy deployment at a lower cost for greater flexibility and performance.

Watch for the final segment of this four part series. The video will be released on November 20, 2018

Google AI: One Model to Learn Everything. Yep, Everything

November 8, 2018

What’s an online advertising company with interesting ethical norms doing with artificial intelligence? Part of the answer appears in the ad stuffed The Stanford Daily’s story “Jeff Dean Discusses Google’s Current Efforts around AI.”

I noted this interesting point:

The future, he said, lies not in creating lots of models and algorithms for distinct tasks but rather in having one model that can learn everything.

Makes sense. A monopoly on information. Now about the ethics part from a company with a founder who dallied with a Glass wearer, a lawyer who sired a Googler at Google, and a Google VP who ended up dead on a yacht from a needle mishap involving a contract worker and a controlled substance.

Will the magic algorithm operate without bias or unusual tendencies?

Yeah, one model to learn everything. Universal. Perfect right?

Sounds good. But Google is not your mother’s AltaVista, is it?

Stephen E Arnold, November 8, 2018

References for those who do not link these ideas:

The Google Glass affair: https://www.bizjournals.com/sanfrancisco/blog/2013/08/sergey-brin-and-susan-wojcicki-split.html

The productive Google lawyer reference: https://www.theatlantic.com/technology/archive/2018/10/your-move-google-board/574036/

The dead Googler and alleged drug explorer: https://www.forbes.com/sites/ellenhuet/2014/07/09/google-executive-yacht-overdose/#ae67e3e9e255

MIT: IBM a Go To Player in AI School

November 8, 2018

I found this item from AI Dreams quite interesting. I learned:

“As MIT’s partner in shaping the future of AI, IBM is excited by this new initiative,” says Ginni Rometty IBM chairman, president, and CEO. “The establishment of the MIT Schwarzman College of Computing is an unprecedented investment in the promise of this technology. It will build powerfully on the pioneering research taking place through the MIT-IBM Watson AI Lab. Together, we will continue to unlock the massive potential of AI and explore its ethical and economic impacts on society.”

MIT = Big Blue?

Stephen E Arnold, November 8, 2018

IBM Watson Perfume: The Odor of Burned Cash?

November 7, 2018

Some scents are elusive. For example, what’s the odor of burned hundred dollar bills? A team locker room after a devastating loss? A failed start up’s empty cube?

The problem of elusive odors may have been solved. I learned in “Is AI the Future of Perfume? IBM Is Betting on It” that:

IBM has developed a scent algorithm, and it’s coming for the fragrance aisle.

Enticing? You bet. The write up explains:

IBM developed an algorithm that studies existing fragrance formulas and then compares the ingredients to other data sets, like geography and customer age. This algorithm, which was created in IBM’s Thomas J. Watson Research Center and which the company has named Philyra, can now develop new perfumes that will target very specific market segments.

Whether the IBM systems works or not, the idea may be that algorithms provide a way to emulate a scent and point to the math, not a human “nose” able to duplicate a competitor’s fragrance. I noted this statement:

Applying machine learning to the fragrance industry, for instance, could help companies dupe highly coveted scents without violating trade secrets by using an algorithm to simply tweak the formula slightly.

What’s the fragrance generated by RedHat employees who find that IBM is different from the pre acquisition RedHat?

Spicy, I would wager.

Stephen E Arnold, November 7, 2018

DarkCyber for November 6, 2018, Is Now Available: Part Two, Amazon’s Disruptive Thrust

November 6, 2018

DarkCyber for November 6, 2018, is now available at www.arnoldit.com/wordpress and on Vimeo at https://vimeo.com/298831585

In this program, DarkCyber explains how Amazon is using open source software and proprietary solutions to reinvent IBM’s concept of vendor lock in.

Decades ago, IBM used mainframes and their proprietary hardware and software to create a barrier to change for government agencies using the systems. Amazon’s approach is to provide a platform which makes use of open source software to allow the US government to make necessary changes to software.

Amazon also offers value added functionality ranging from hardware like the DeepLens smart surveillance devices to patented analytics for real time cross correlation of data. Government agencies using these proprietary components will find themselves dependent on Amazon despite the support for open source software. Stephen E Arnold, author of CyberOSINT, said: “Amazon’s use of open source makes it easy for customers to make changes to the Amazon policeware system. However, Amazon’s value adding proprietary software allows Amazon to lock in government agencies who want access to Amazon’s most advanced services, features, and functions. Amazon wants to reinvent IBM’s approach to lock in for the 21st century.”

An added twist is that many of the providers of policeware and advanced intelligence systems use the Amazon cloud platform to deliver their products and services to US government agencies. Examples include Palantir Technologies, 4iQ and Webhose. Companies leveraging Amazon’s platform have an advantage over firms which use other cloud solutions. However, in the longer terms, Amazon can exercise control over vendors, partners, and integrators as part of a lock in strategy tuned to the 21st century computing realities.

Watch for the third part of this four part series on November 13, 2018.

Kenny Toth, November 6, 2018

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