The Fortune Magazine Channels the Onion. Is SNL in the Future of Fortune?
July 31, 2018
Perhaps ola is a good way to approach this Fortune Magazine story. Now the story is satire. Satire triggers in my mind thoughts of the Onion online service, now owned by Univision. I want to mention that Univision is trying to sell the Onion. Maybe the phrase I want is Hasta la vista?
I read “Man Terrified of Palantir, More Terrified to Explain What Palantir Is,” a write up labeled in small light blue type as “Humor. Satire from Fortune.” Yep, a million laughs.
The problem is that the write up is not particularly humorous. If the reader does not pay attention to Palantir Technologies and how it uses the Amazon Web Services platform, the article makes no sense. Therefore, the laughter quotient is small. In the calculus of humor, I would suggest that there is an infinitesimal chuckle in the story.
If one is familiar with Gotham, Metropolitan, the use cases for the software, the write up may be interpreted as an accurate “real” news story.
For me, I think Fortune has another agenda. My hunch is that Fortune wants to do a Buzzfeed dive bombing of the company. Perhaps the story ended up on a dull spike in the humming editorial hive of the magazine.
Here’s a passage I noted:
As Watts described his style of news consumption, it became clear how one man can be so fearful of a company without understanding its purpose.
With Fortune’s “real” story on ICE, I think this statement applies to much of the coverage of Palantir Technologies.
Yep, real news is satire, not fake news. Why not chase down a non fake, non humorless story about the company? Why not explain what the Gotham wheel menu provides a user? I like the wheel menu. Real news about a “wheel” subject.
Stephen E Arnold, July 31, 2018
IBM Turns to Examples to Teach AI Ethics
July 31, 2018
It seems that sometimes, as with humans, the best way to teach an AI is by example. That’s one key takeaway from VentureBeat’s article, “IBM Researchers Train Ai to Follow Code of Ethics.” The need to program a code of conduct into AI systems has become clear, but finding a method to do so has proven problematic. Efforts to devise rules and teach them to systems are way too slow, and necessarily leave out many twists and turns of morality that (most) humans understand instinctively. IBM’s solution is to make the machine draw conclusions for itself by studying examples. Writer Ben Dickson specifies:
“The AI recommendation technique uses two different training stages. The first stage happens offline, which means it takes place before the system starts interacting with the end user. During this stage, an arbiter gives the system examples that define the constraints the recommendation engine should abide by. The AI then examines those examples and the data associated with them to create its own ethical rules. As with all machine learning systems, the more examples and the more data you give it, the better it becomes at creating the rules. … The second stage of the training takes place online in direct interaction with the end user. Like a traditional recommendation system, the AI tries to maximize its reward by optimizing its results for the preferences of the user and showing content the user will be more inclined to interact with. Since satisfying the ethical constraints and the user’s preferences can sometimes be conflicting goals, the arbiter can then set a threshold that defines how much priority each of them gets. In the [movie recommendation] demo IBM provided, a slider lets parents choose the balance between the ethical principles and the child’s preferences.”
Were told the team is also working to use more complex systems than the yes/no model, ones based on ranked priorities instead, for example. Dickson notes the technique can be applied to many other purposes, like calculating optimal drug dosages for certain patients in specific environments. It could also, he posits, be applied to problems like filter bubbles and smartphone addiction.
Beyond Search wonders if IBM ethical methods apply to patent enforcement, staff management of those over 55 year old, and unregulated blockchain services. Annoying questions? I hope so.
Cynthia Murrell, July 31, 2018
DarkCyber for July 31, 2018, Is Now Available
July 31, 2018
This week’s DarkCyber video news program is available at www.arnoldit.com/wordpress and on Vimeo at https://vimeo.com/282131610 .
Produced by Stephen E Arnold and the DarkCyber research team, the weekly program covers the Dark Web and lesser known Internet services.
The July 31, 2018, program includes four stories. These are:
Chinese citizens are using the Dark Web via Tor and i2p to circumvent the Great Firewall of China. The Web surfers use hidden Internet sites and services to obtain information and engage in ecommerce. DarkCyber learned that there is an elite group of “red” hackers working for the Chinese government. These “red hat” professionals engage in cyber activities which may be viewed as “black hat” activities by those outside of China.
The second story updates viewers about the legal challenges several SEA members face in US courts. DarkCyber provides brief descriptions of two reports about the SEA’s hacking activities in the US and elsewhere. These reports contain high value information about systems and methods used by these individuals. Links to these reports are included in the video plus a pointer to an SEA recruiting video available on YouTube. Stephen E Arnold, author of Dark Web Notebook, said: “Technical information compiled by analysts provides a road map for cyber security professionals. On the other hand, the availability of information warfare techniques makes it easier for bad actors to improve their digital attack methods. A cat and mouse game with significant stakes is escalating.”
The third story explains that Russia’s new surveillance and data retention regulations are now in effect. Mobile vendors, ISPs, and similar companies have to retain index data and content for six months. The influence of the Russian Internet crackdown has diffused to Kazakhstan. That Russian neighbor throttles the Internet and blocks access when opposition political voices stream via the Internet.
The final story directs viewers to the free Dark Web scanning service provided by Capitol One. The new service looks for individuals social security numbers, emails, and other personal information. Automatic alerts are sent to registered users when sensitive information is discovered.
You can view the video at this link.
Kenny Toth, July 31, 2018
Facial Recognition: Not for LE and Intel Professionals? What? Hello, Reality Calling
July 30, 2018
I read “Facial Recognition Gives Police a Powerful New Tracking Tool. It’s Also Raising Alarms.” The write up is one of many pointing out that using technology to spot persons of interest is not a good idea. The Telegraph has a story which suggests that Amazon is having some doubts about its Rekognition facial recognition system. What? Hello, reality calling.
The “Raising Alarms” story makes this statement, obtained from an interview with an outfit called Kairos. I circled these statements:
“Time is winding down but it’s not too late for someone to take a stand and keep this from happening,” said Brian Brackeen, the CEO of the facial recognition firm Kairos, who wants tech firms to join him in keeping the technology out of law enforcement’s hands. Brackeen, who is black, said he has long been troubled by facial recognition algorithms’ struggle to distinguish faces of people with dark skin, and the implications of its use by the government and police. If they do get it, he recently wrote, “there’s simply no way that face recognition software will be not used to harm citizens.”
The write up points out:
Many law enforcement agencies — including the FBI, the Pinellas County Sheriff’s Office in Florida, the Ohio Bureau of Criminal Investigation and several departments in San Diego — have been using those databases for years, typically in static situations — comparing a photo or video still to a database of mug shots or licenses. Maryland’s system was used to identify the suspect who allegedly massacred journalists at the Capital Gazette newspaper last month in Annapolis and to monitor protesters following the 2015 death of Freddie Gray in Baltimore.
Yep, even the Hollywood gangster films have featured a victim flipping through a collection of mug shots. The idea is pretty simple. Bad actors who end up in a collection of mug shots are often involved in other crimes. Looking at images is one way for LE and intel professionals to figure out if there is a clue to be followed.
Now what’s the difference between having software look for matches? Software can locate similar fingerprints. Software can locate similar images, maybe even the image of the person who committed a crime. The idea of a 50 year old man robbed at an ATM flipping through images of bad actors in a Chicago police station is, from my point of view, a bridge too far. The 50 year old will either lose concentration or just point at some image and say, “Yeah, yeah, that looks like the guy.”
Let’s go with software because there are a lot of bad actors, there are some folks on Facebook who are bad actors, and there are bad actors wandering around in a crowd. Don’t believe me. Go to Rio, stay in a fancy hotel, and wander around on a Saturday night. How long before you are robbed? Maybe never, but maybe within 15 minutes. Give this test a try.
Software, like humans, makes errors. However, it seems to make sense to use available technology to take actions required by government rules and regulations. That means that big companies are going to chase government contracts. That means that stopping companies from providing facial recognition technology is pretty much impossible.
I would suggest that the barn is on fire, the horses have escaped, and Costco built a new superstore on the land. Well, maybe I will suggest that this has happened.
Facial recognition systems are tools which have been and will continue to be used. Today’s systems can be fooled. I showed a pair of glasses which can baffle most facial recognition systems in my DarkCyber video a couple of months ago.
The flaws in the algorithms will be improved slowly. The challenge of crowds, lousy lightning, disguises, hats, shadows, and the other impediments to higher accuracy will be reduced slowly and over time.
But let’s get down to basics: The facial recognition systems are here to stay. In the US, the UK, and most countries on the planet. Go to a small city in Ecuador. Guess what? There is a Chinese developed facial recognition system monitoring certain areas of most cities. Why? Flipping through a book with hundreds of thousands of images in an attempt to identify a suspect doesn’t work too well. Toss in Snapchat and YouTube. Software is the path forward. Period.
Facial recognition systems, despite their accuracy rates, provide a useful tool. Here’s the shocker. These systems have been around for decades. Remember the Rand Tablet. That was in the 1960s. Progress is being made.
Outrage is arriving a little late.
Stephen E Arnold, July 30, 2018
Silos Are a Natural Consequence of Information: Learn to Love Them
July 30, 2018
How To Eradicate Unnecessary Data Silos
A piece at the SmartDataCollective explains “How to Eliminate Silos in Company-Wide Data Analytics.” Writer Larry Alton explains:
“Silos emerge when a cluster of individuals in your company (usually within a specific department) have trouble communicating with, or collaborating with another cluster of individuals in your company (usually within another department). In some ways, this is a natural result of building a company; if you want your sales team to focus on sales and your marketing team to focus on marketing, eventually, it will be difficult for your sales and marketing staff to collaborate on a mutual problem. But if you want your company’s data to be streamlined, accessible, and impactful to your organization’s bottom line, you’ll need to eliminate these silos, or at least mitigate their development.”
The piece lists the reasons silos are to be avoided and we agree, in general, with Alton’s points. However, we observe that data isolation by department is required in some sectors—intelligence, law enforcement, and pharmaceuticals, for example. Alton offers specific advice in his list, “How to Break Silos Down,” so see the piece for that info.
The problem, however, is that data silos are a fact of life in many organizations. Examples range from the 23andMe data now shared with a major pharmaceutical company to information in the possession of an attorney allegedly bound by confidentiality obligations. The idea that federating a wide range of data is a natural condition goes against individual and corporate behavior.
Talk about data silos is one thing. Delivering a giant data lake with open access to those with permission to view the data is another. When a new project gets off the ground, how are the data handled? The answer, “In a silo.” Toss in a government requirement for secrecy or a corporate rule about secret drug research, and you have silos.
Who doesn’t want silos?
Cynthia Murrell, July 30, 2018
Amazon Joins Visual Search Parade
July 30, 2018
Text search is long past done as a frontier. Verbal search is already being nailed down by more startups and tech giants than you can tell Alexa to shake a stick at. So, the new frontier? It’s visual search and, you guessed it, one of the biggest names in the industry is already working their way in, as we discovered in a recent Fortune story, “Snapchat and Amazon are Working On Visual Search Feature.”
According to the story:
“Snap appears to be laying the groundwork for a partnership with e-commerce giant Amazon. “According to TechCrunch, a version of Snapchat being developed for Android phones includes code for a new feature called “Visual Search” that can use Snapchat’s camera to send images of a product or a barcode scan to Amazon, which then display search results.”
Amazon is not alone, however. Microsoft is also developing a visual search tool that can simply look at items and begin shopping for them. The controversy about the accuracy of Amazon’s Rekognition system may inhibit some of Amazon’s plans for image centric features and functions. I I search for a product with my mobile phone and Amazon returns matches which are incorrect, what happens to consumer confidence?
Error rates are likely to matter, probably more when looking for a shirt than when trying to figure out which elected official is a bad actor. Shirts are different. Bad actors not so much, some may suggest.
Patrick Roland, July 30, 2018
Amazon Rekognition: The View from Harrods Creek
July 29, 2018
I read the stories about Amazon’s facial recognition system. A representative example of this genre is “Amazon’s Facial Recognition Tool Misidentified 28 Members of Congress in ACLU Test.” The write up explains the sample. The confidence level was set at 80 percent. Amazon recommends 95 percent.
The result? Twenty eight individuals were misidentified.
At a breakfast meeting this morning (Sunday, July 29, 2018) one uninformed Kentucky resident asked:
What if these individuals are criminals?
Another person responded:
Just 28?
I jotted down the remarks on my mobile phone. Ah, the Bluegrass state.
Stephen E Arnold, July 29, 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:
Cost
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
-
- Subscribe to Beyond Search
Feature archive
News archive
-
