How Long Is the Artificial Intelligence Leash?

August 17, 2018

The merger of AI technology and facial recognition software have been on the minds of the industry’s brightest thinkers lately. With developments coming at a furious clip, it seems as though there is no shortage to the benefits to this amazing prospect. We learned bout just how seamless this software is getting after reading an Inquirer story, “IBM’s Watson AI Used to Develop Multi-Faced Tracking Algorithm.”

According to the piece:

“IBM Watson researcher Chung-Ching Lin led a team of scientists to develop the technology, using a method to spot different individuals in a video sequence…. The system is also able to recognize if people leave and then re-enter the video, even if they look very different.”

Seems positive, doesn’t it? The idea of facial recognition software sweeping across a crowd and pulling out all the individuals from that sequence. However, this could become an issue if used by those with interesting intentions. For example, in some countries, excluding the US, law enforcement uses these systems for drift line surveillance.

Will governments take steps to keep AI on a shorter leash? Some countries will use a short leash because their facial recognition systems are the equivalent of a dog with a bite.

Patrick Roland, August 17, 2018

Wake Up Time: IBM Watson and Real Journalists

August 11, 2018

I read “IBM Has a Watson Dilemma.” I am not sure the word “dilemma” embraces the mindless hyperbole about Vivisimo, home brew code, and open source search technology. The WSJ ran the Watson ads which presented this Lego collection of code parts one with a happy face. You can check out the Watson Dilemma in your dead tree edition of the WSJ on page B1 or pay for online access to the story at

The needle point of the story is that IBM Watson’s push to cure cancer ran into the mushy wall composed of cancerous cells. In short, the system did not deliver. In fact, the system created some exciting moments for those trying to handcraft rules to make Dr. Watson work like the TV show and its post production procedures. Why not put patients in jeopardy? That sounds like a great idea. Put experts in a room, write rules, gather training data, and keep it update. No problem, or so the received wisdom chants.

The WSJ reports in a “real” news way:

…Watson’s recommendations can be wrong.

Yep, hitting 85 percent accuracy may be wide of the mark for some cognitive applications.

From a practical standpoint, numerical recipes can perform some tasks to spin money. Google ads work this magic without too much human fiddling. (No, I won’t say how much is “too much.”)

But IBM believed librarians, uninformed consultants who get their expertise via a Gerson Lehrman phone session, and from search engine optimization wizards. IBM management did not look at what search centric systems can deliver in terms of revenue.

Over the last 10 years, I have pointed out case examples of spectacular search flops. Yet somehow IBM was going to be different.

Sorry, search is more difficult to convert to sustainable revenues than many people believe. I wonder if those firms which have pumped significant dollars into the next best things in information access look at the Watson case and ask themselves, “Do you think we will get our money back?”

My hunch is that the answer is, “No.”

For me, I will stick to humanoid doctors. Asking Watson for advice is not something I want to do.

But if you have cancer, why not give IBM Watson a whirl. Let me know how that works out.

Stephen E Arnold, August 11, 2018

Can IBM Watermark Neural Networks?

August 8, 2018

Leave it to IBM to figure out how to put their stamp on their AI models. Of course, as with other intellectual property, AI code can be stolen, so this is a welcome development for the field. In the article, “IBM Patenting Watermark Technology to Protect Ownership of AI Models at Neowin, we learn the technology is still in development, and the company hasn’t even implemented it in-house yet. However, if all goes well, the technology may find its way into customer products someday. Writer Usama Jawad reports:

“IBM says that it showcased its research regarding watermarking models developed by deep neural networks (DNNs) at the AsiaCCS ’18 conference, where it was proven to be highly robust. As a result, it is now patenting the concept, which details a remote verification mechanism to determine the ownership of DNN models using simple API calls. The company explains that it has developed three watermark generation algorithms…

These use different methods; specifically:

  • Embedding meaningful content together with the original training data as watermarks into the protected DNNs,
  • Embedding irrelevant data samples as watermarks into the protected DNNs
  • Embedding noise as watermarks into the protected DNNs.

We learned:

“IBM says that in its internal testing using several datasets such as MNIST, a watermarked DNN model triggers an ‘unexpected but controlled response’.”

Jawad notes one drawback as of yet—though the software works well online, it still fails to detect ownership when a model is deployed internally. From another article, “IBM Came Up With a Watermark for Neural Networks” at TheNextWeb, we spotted an  interesting tidbit—Writer Tristan Greene points out a distinct lack of code bloat from the watermark. This is an important factor in neural networks, which can be real resource hogs.

For more information, you may want to see IBM’s blog post on the subject or check out the associated research paper. Beyond Search wonders what smart software developers will use these techniques. Amazon, Facebook, Google, Oracle, Palantir Technologies? Universities with IBM research support may be more likely candidates, but that is, of course, speculation from rural Kentucky.

Cynthia Murrell, August 8, 2018

NLP, NLP, It Is for You and Me… Maybe

July 29, 2018

Natural Learning Processing is seen by many as the holy grail of artificial intelligence. The bridge this could create between high powered search and intelligence, with existing text and spoken language is staggering.

Smart software is doing a fine job matching ads to users and displaying search results because the search algorithm knows better than the user what he or she wants.

We learned more about the bridge that is being created in this world from a recent Fast AI story, Introducing State of the Art Text Classification with Universal Language Models.”

 According to the story:

“Natural language processing (NLP) is an area of computer science and artificial intelligence that deals with (as the name suggests) using computers to process natural language. Natural language refers to the normal languages we use to communicate day to day, such as English or Chinese—as opposed to specialized languages like computer code or music notation.”

Believe it or not, one area where this combination of NLP and AI is really picking up steam is in productivity. Specifically, productivity during face-to-face meetings. From assigning accountability to streamlining processes, this new world of NLP and AI is quickly bridging that human and digital gap to work quite smoothly. If NLP can make our weekly staff meetings less painful, marketers will explain that there is no limit to software’s ability to be more like humans.

If you think your industry is immune, think again. According to Forbes, NLP is poised to replace enterprise resource planning based software. This is a big deal, because it would mean designers and engineers would not be tied to laborious training and certifications from the likes of SAP and Oracle.

The one hitch, however, is that some of the “software that understands” simply falls short of the mark. Even the Harvard Business Review caustions savvy business professionals.

Harvard? Skeptical about innovations able to generate big money! Interesting.

Patrick Roland, July 29, 2018

AI: Some Problems to Address

July 28, 2018

Is all this really such a surprise? According to Quartz, “Tech Companies Just Woke Up to a Big Problem with Their AI.” Pointing to evidence of biased AIs and related legal actions against the likes of Microsoft, IBM, Google, Mozilla, Accenture, and “even Congress,” writer Dave Gershgorn ponders:

“It’s difficult to pin a reason on ‘why now?’ It could be the unexpected speed at which AI has become pervasive on tech platforms and in the real world, with demos of the disconcertingly-human-sounding Google Duplex and Amazon’s cashier-less Go store, where customers walk in, grab what they want, and walk out, with the entire affair monitored and recorded by cameras and computers. Or maybe it’s how big tech companies are suddenly seen as complicit in invasive national security projects with the US Department of Defense or Immigration and Customs Enforcement, contributing to the perception of a creeping police state. Or maybe the world is just becoming more tech-literate and conscious.”

The article supplies a list of recent announcements that support Gershgorn’s verdict. There are Google’s inclusion of checking for bias in its statement of AI principles, Congress’ assertion that tech companies must address the problem, and IBM and Microsoft’s facial-recognition technology adjustments, for example. See the write-up for more such developments. Gershgorn believes the AI industry to be undergoing a seismic shift, and we are as interested as he in where these issues go from here.

Cynthia Murrell, July 28, 2018

Google Reveals a Machine Learning Secret or Two

July 27, 2018

I read “Google AI Chief Jeff Dean’s ML System Architecture Blueprint.” Dr. Dean is a Google wizard from the good old days at the online ad search outfit. The write up is important because it reminds me that making software smart is a bit of a challenge. Amazon is trying to explain why its facial recognition pegged some elected officials as potential bad actors. IBM Watson is trying to reverse course and get its cancer treatment recommendations to save lives, not make them more interesting. Dozens upon dozens of companies are stating that each has artificial intelligence, machine learning, smart software, and other types of knowledge magic revved and ready to deploy.

The key part of the write up in my opinion boils down to this list of six “concerns”:

  • Training
  • Batch Size
  • Sparsity and Embeddings
  • Quantization and Distillation
  • Networks with Soft Memory
  • Learning to Learn (L2L)

The list identifies some hurdles. But underpinning these concerns is one significant “separate the men from the boys” issue; to wit:


What’s this suggest? Three things from my vantage point in rural Kentucky:

First, Google is spending big money on smart software, and others should get with the program and use its technology. The object of course is to generate lock in and produce revenue for the Google.

Second, make Google’s method “the method.” Innovation using Google’s approach is better, faster, and cheaper.

Third, Google is the leader in machine learning and smart software. Keep in mind, however, that these technologies may not be available to law enforcement, to governments which wish to use the approach for warfighting, or certain competitors.

Worth reading this Google paper. One downside: The diagrams are somewhat difficult to figure out. But that may not matter. Google has you covered.

Stephen E Arnold, July 27, 2018

Journalists: Smart Software Is Learning How to Be a Real Journalist

July 15, 2018

I read “Why Bots Taking Over (Some) Journalism Could Be a Good Thing.” I love optimists who lack a good understanding of how numerical recipes work. The notion of “artificial intelligence” is just cool like something out of science fiction like “Ralph 124C 41+” except for the wrong headed predictions. In my 50 year work career, technologies are not revolutions. Technologies appear, die, reform, and then interact, often in surprising ways. Then one day, a clever person identifies a “paradigm shift” or “a big thing.”

The problem with smart software which seems obvious to me boils down to:

  • The selection of numerical recipes to use
  • The threshold settings or the Bayesian best guesses that inform the system
  • The order in which the processes are implemented within the system.

There are other issues, but these provide a reasonable checklist. What does on under the kimono is quite important.

The write up states:

If robots can take over the grunt work, which in many cases they can, then that has the potential to lower media organizations’ costs and enable them to spend a greater proportion of their advertising income on more serious material. That’s terrible news for anybody whose current job is to trawl Twitter for slightly smutty tweets by reality TV show contestants, but great news for organizations funding the likes of Guardian journalist Carole Cadwalladr, who broke the Facebook / Cambridge Analytica scandal. Isn’t it?

Good question. I also learned:

Technology can help with a lot of basic reporting. For example, the UK Press Association’s Radar project (Reporters And Data And Robots) aims to automate a lot of local news reporting by pulling information from government agencies, local authorities and the police. It’ll still be overseen by “skilled human journalists”, at least for the foreseeable future, but the actual writing will be automated: it uses a technology called Natural Language Generation, or NLG for short. Think Siri, Alexa or the recent Google Duplex demos that mimic human speech, but dedicated to writing rather than speaking.

I recall reading this idea to steal:

In fact, human reporters will continue to play a vital role in the process, and Rogers doesn’t see this changing anytime soon. It’s humans that make the decision on which datasets to analyze. Humans also “define” the story templates – for example, by deciding that if a certain variable in one region is above a particular threshold, then that’s a strong indicator that the data will make a good news story.

Now back to the points in the checklist. In the mad rush to reduce costs, provide more and better news, and create opportunities to cover certain stories more effectively, who is questioning the prioritization of content from an available stream, the selection of items from the stream, and the evaluation of the data pulled from the stream for automatic story generation?

My thought is that it will be the developers who are deciding what to do in one of those whiteboard meetings lubricated with latte and fizzy water.

The business models which once sustained “real” journalism focused on media battles, yellow journalism, interesting advertising deals, and the localized monopolies. (I once worked for such an outfit.)

With technology concentration a natural consequence of online information services, I would not get too excited about the NLG and NLP (natural language generation and natural language processing services). These capabilities for smart software will arrive. But I think the functionality will arrive in dribs and drabs. One day an MBA or electrical engineer turned business school professor will explain what happened.

What’s lost? Typing, hanging out in the newspaper lunch room, gossip, and hitting the bar a block from the office. Judgment? Did I leave out judgment. Probably not important. What’s important that I almost forgot? Getting rid of staff, health coverage, pensions, vacations, and sick leave. Software doesn’t get sick even though it may arrive in a questionable condition.

Stephen E Arnold, July 15, 2018

AI: Useful to Major Major Major of In and Out Fame

July 13, 2018

While the capacity for work and accuracy of artificial intelligence is pretty hard to argue with, the expense of starting a machine learning program from the ground up is pretty easy to argue. In some cases, it is more expensive to teach a machine to act like a human than to actually hire a human, we discovered after reading a recent Guardian story, “The Rise of Pseudo-AI: How Tech Firms Quietly Use Humans to Do Bots’ Work.”

One example they gave was:

“In the case of the San Jose-based company Edison Software, artificial intelligence engineers went through the personal email messages of hundreds of users – with their identities redacted – to improve a “smart replies” feature. The company did not mention that humans would view users’ emails in its privacy policy.”

How do you get around this modern day Catch-22? Some think the answer lies in blocks. By using Blockchain technology, development costs for AI could drastically be reduced, some experts think. This is because one of the great costs of AI is data management and sorting. If that process is simplified by Blockchain, the reasoning is that the cost of the program would go down. Finally, we can relieve those poor humans from doing a machine’s job.

When it’s in, it’s out. When it’s out, it’s in. Poetic, no?

Patrick Roland, July 13, 2018

Amazon: Its Artificial Intelligence Is Not Up to Snuff

July 10, 2018

I read “AI Is The Weakness In Amazon’s Push To Take On Google And Facebook.” I am not sure I can hop on board this train. One reason is technologies like Amazon’s Integration Based Anomaly Detection Service. I do not want to slog through this particular artifact from 2011, but it does reveal that the online bookstore has some reasonably sophisticated smart software.

The capitalist tool, however, takes a different viewpoint. I learned from the article:

Amazon has made a good start but to really move forward it will need to make its targeting much more effective. There are many users who have been on the receiving end of Amazon advertisements for products that they have already purchased. If Amazon’s advertising system is not even able to get this bit right, it will be a long time before it can really understand user behavior and make its advertising that much more effective.

Okay, Amazon does not use smart software the way Facebook and Google do. I think I understand.

The article continues:

This comes down the quality of the AI algorithms that it uses to understand its users and work out what products and services they are more likely to respond to. When it comes to this, Amazon is way behind Google but ahead of Facebook meaning that advertisers currently using Facebook might be lured away more easily. That being said, Amazon has been losing some sellers to Instagram (see here) where product discovery is easier given the lower volume of sellers and where the costs and conditions of selling are not nearly as onerous. Hence, for the simple stuff on its own website, Amazon’s advertising revenues should continue to grow nicely.

I like the idea that Amazon’s approach lacks the quality of Facebook’s and Google’s approach. Nifty assertion. I would suggest that perhaps Amazon offers advertisers a different value proposition based on cross correlation and specific real time browsing and purchasing behavior.

Probabilities are useful. But knowing what a person wants to buy at a particular point in time might cause some advertisers to sit up and take notice.

Artificial intelligence hoo-hah is sort of fun, just not as compelling as real time streaming data about specific user intent and actions.

Stephen E Arnold, July 10, 2018

Digital Assistants Working Hard to Be More Human

July 8, 2018

Whether you use Alexa, or Siri, or Cortana, or a host of other AI-infused digital assistants, the producers of that technology have something in common: they want those electronic personalities to be more human. Interesting moves are being made in this world to make that happen, according to a recent Inquirer story, “Microsoft Snaps Up Semantic to Make Cortana Seem a Bit Less Robotic.”

According to one Microsoft exec:

“Combining Semantic Machines’ technology with Microsoft’s own AI advances, we aim to deliver powerful, natural and more productive user experiences that will take conversational computing to a new level.”

The story continued:

“Google, of course, has Duplex which can make natural sounding voice calls on your behalf. It has also suggested it is looking into the idea of giving Assistant a back story.”

However, this comes with a consequence. As Wired pointed out, as these assistants get more comfortable with inflection and reading our voices, the opportunity for manipulation becomes eerily more present. These near-human tools are not to that point yet, but we don’t doubt that it’ll arrive soon. Who wants to type when one can talk, think a human is on the other end of the connection, and be so much more efficient.

Patrick Roland, July 10, 2018



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