Algorithms Are Neutral. Well, Sort of Objective Maybe?

October 12, 2018

I read “Amazon Trained a Sexism-Fighting, Resume-Screening AI with Sexist Hiring data, So the Bot Became Sexist.” The main point is that if the training data are biased, the smart software will be biased.

No kidding.

The write up points out:

There is a “machine learning is hard” angle to this: while the flawed outcomes from the flawed training data was totally predictable, the system’s self-generated discriminatory criteria were surprising and unpredictable. No one told it to downrank resumes containing “women’s” — it arrived at that conclusion on its own, by noticing that this was a word that rarely appeared on the resumes of previous Amazon hires.

Now the company discovering that its smart software became automatically biased was Amazon.

That’s right.

The same Amazon which has invested significant resources in its SageMaker machine learning platform. This is part of the infrastructure which will, Amazon hopes, will propel the US Department of Defense forward for the next five years.

Hold on.

What happens if the system and method produces wonky outputs when a minor dust up is automatically escalated?

Discriminating in hiring is one thing. Fluffing a global matter is a another.

Do the smart software systems from Google, IBM, and Microsoft have similar tendencies? My recollection is that this type of “getting lost” has surfaced before. Maybe those innovators pushing narrowly scoped rule based systems were on to something?

Stephen E Arnold, October 11, 2018

Google and the Talking to Computers Chatbot Thing

October 3, 2018

No company wants to do customer service. Money only, please. To achieve the goal of having zero human interaction with other humans, Google continues to chug forward in the chatbot world.

The payoff is potentially huge. Imagine the number of companies eager to terminate full time, contract, and volunteer workers who field questions about products and services. Self service is not reading text pages on Web sites. Just dial a number and interact with a tireless, cheerful, intelligence software system.

Google obviously has not cracked the problem. The company has acquired Onward. This is a startup which had amassed $120,000 in funding. (Yep, I know this seems a modest sum.)

We learned of the deal in “Google Acquires AI Customer Service Startup Onward.” The write up revealed:

Onward’s enterprise chatbot platform leveraged natural language processing to extract meaning from customers’ messages. Drawing on signals like location, login status, and historical activity, it could personalize and contextualize its responses to questions. Onward’s visual bot builder, which let clients tailor answers with decision trees, afforded even greater customization.

Some of these functions have been available in Amazon’s Sagemaker for a couple of years. Like Microsoft, Google seems to recognize the threat that Amazon’s low profile approach to talking devices requires more attention.

Perhaps this will be the next big thing in getting a chatbot to explain why the caching behavior of an SSD drive causes a video render to crash. What change must I make to resolve the issue?

Maybe Siri? Maybe Watson? Maybe one of the chatbot marvels can answer the question?

And, to state the obvious, maybe not.

But $120,000 in funding. Whom can we ask?

Stephen E Arnold, October 3, 2018

Another Smart Software Milestone: An Image of a Hamburger Improved

October 2, 2018

I read an article which I don’t think was intended to make me laugh like an SNL cold open. But it did.

The article is “In Just Two Years, AI has Managed to Make a Slightly More Appetizing Cheeseburger.”

I learned:

Two years ago I alerted the world that it was now scientifically possible to generate an image of a cheeseburger. Today I have the pleasure of informing you yet again that this technique has been nearly perfected.

Okay, not a burger to eat. This is a burger image, a picture.

Here’s the method:

Have one algorithm try to generate and image, and another try to tell if that image is a real picture or fake. With the second algorithm acting as a guardrail, the first algorithm eventually learns what looks real and what doesn’t, and the resulting images approach photorealism.

I sort of wanted to eat a burger.

I don’t need a picture. Smart software does, however.

Stephen E Arnold, October 2, 2018

Scripts and Rules: The Future Is Not Fully Automatic

September 24, 2018

I wanted to capture an item of information which may be lost in the flood of marketing craziness. The subject is smart software, autonomous systems, and why humans may have to push buttons and turn knobs.

The write up is “Deep Learning Is Inferior to Scripting for Teaching Robots.” The main idea, as I understood the article, is that creating useful robots (hardware and software) may benefit from old school methods.

The method is for one or more smart humans to create data sets and train robots. But the humans are not out of a job. The robots have to be retrained with more rules, updated rules, and fresh data sets.

The article points out:

They [A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma and James Bergstra] conclude that deep learning lags behind the traditional way of training robots with scripts ‘by a large margin in some tasks, where such solutions were well established or easy to script‘. However, RL was ‘more competitive’ in complex tasks (for instance, docking to a charging station). The report also shows that the RL algorithms need careful tuning to their variables before being useful for any task – but after tuning, those same variables were applicable across a range of tasks.

In short, autonomous methods are not as efficient as humans doing the rule work and creating scripts or training sets.

The point is an important one. Smart software is not the cost and accuracy silver bullet that marketers have described as a reality.

I am not disputing that for certain specific operations on bounded data smart software can be magical.

But bootstrapping intelligence and learning with zeros and ones has not yet docked at the port, unloaded, and moved the goods to the user.

Stephen E Arnold, September 24, 2018

Deep Learning Helps Bing Spotlight Aggregate Breaking News

September 20, 2018

News aggregators sift through the vast number of news stories out there to focus on the content users want to see (and lead to filter bubbles, but that is another topic.) Now, Microsoft has built an aggregator for breaking content right into its browser. VentureBeat reports, “Bing Spotlight Uses AI to Highlight Developing News Stories.” Writer Kyle Wiggers informs us:

“A spokesperson told VentureBeat via email that Bing Spotlight is an ‘evolving feature,’ and that the team will evaluate options based on feedback. Bing Spotlight spots trending topics with the help of deep learning algorithms that ingest millions of search queries and news articles every day. Leaning on a web graph of ‘hundreds of millions’ of websites, it factors in signals such as browser logs, the number of publishers covering a story, and how prominently each publisher featured their respective stories on their sites.’ Articles have to be ‘original, readable, newsworthy, and transparent’ before they’re considered for a top spot, and must demonstrate ‘sound journalistic practices’ such as identify sources and authors, giving attribution, and labeling opinion and commentary.”

Wiggers reproduces a diagram that illustrates the sections of a Spotlight results page—a carousel at the top revolves through related stories; a section titled Perspectives offers various points of view on the topic; the Rundown presents the story’s development over time; and, of course, there’s a section that shares related social media posts. ­­­Notably, this development comes on the heels of a similar move from Google—that company recently retooled their Google News app for smartphones. I suppose all users must do is decide who they want assembling their news for them.

Cynthia Murrell, September 20, 2018

Artificial Intelligence: Oversold?

September 18, 2018

I read “Big Tech Is Overselling AI As the Solution to Online Extremism.” Phys.org strikes me as a semi-reliable outfit. I cannot, however, overlook the write up’s failure to define “extremism.” Physicists these days cannot define dark matter, so I suppose I will have to accept the non definition.

Assuming that one can define extremism, Phys.org is holding “big tech” to a higher standard than it holds physicists who disagree that the undefined dark matter does not exist. I know it is a lazy rhetorical trick, but these folks are physicists and deal with uncertainty, in theory at least, every day.

Nevertheless, I found this statement in the Phys.org article thought provoking:

In 2017, 250 companies suspended advertising contracts with Google over its alleged failure to moderate YouTube’s extremist content. A year later, Google’s senior vice president of advertising and commerce, Sridhar Ramaswamy, says the company is making strong progress in platform safety to regain the lost confidence of its clients. However, a recent study by the NGO Counter Extremism Project refutes the effectiveness of the company’s effort to limit and delete extremist videos. More transparency and accountability from YouTube is needed, given that the study found that over 90 per cent of ISIS videos were uploaded more than once, with no action taken against the accounts that violated the company’s terms of service.

What’s at fault? The Google type outfits which cannot get software to figure out human utterance in a way that nails extremism, which if not defined, can be a tough task. Or is the problem that smart software does not work as some big tech folks assert and dearly hope is correct?

My hunch is that artificial intelligence is the equivalent of a cowboy throwing sand in the eyes of the gun toting bad guy who wants to shoot the person in the white hat. Note: the hat is a real Western thing, not a beanie with a propeller like those I spotted in a 2016 video recently.

I know that tech yip yap with mouthfuls of jargon can send some intellectual blood hounds chasing chimera.

You decide. I think I smell a red herring.

Stephen E Arnold, September 18, 2018

Machine Learning Frameworks: Why Not Just Use Amazon?

September 16, 2018

A colleague sent me a link to “The 10 Most Popular Machine Learning Frameworks Used by Data Scientists.” I found the write up interesting despite the author’s failure to define the word popular and the bound phrase data scientists. But few folks in an era of “real” journalism fool around with my quaint notions.

According to the write up, the data come from an outfit called Figure Eight. I don’t know the company, but I assume their professionals adhere to the basics of Statistics 101. You know the boring stuff like sample size, objectivity of the sample, sample selection, data validity, etc. Like information in our time of “real” news and “real” journalists, some of these annoying aspects of churning out data in which an old geezer like me can have some confidence. You know like the 70 percent accuracy of some US facial recognition systems. Close enough for horseshoes, I suppose.

miss sort of accurate

Here’s the list. My comments about each “learning framework” appear in italics after each “learning framework’s” name:

  1. Pandas — an open source, BSD-licensed library
  2. Numpy — a package for scientific computing with Python
  3. Scikit-learn — another BSD licensed collection of tools for data mining and data analysis
  4. Matplotlib — a Python 2D plotting library for graphics
  5. TensorFlow — an open source machine learning framework
  6. Keras — a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
  7. Seaborn — a Python data visualization library based on matplotlib
  8. Pytorch & Torch
  9. AWS Deep Learning AMI — infrastructure and tools to accelerate deep learning in the cloud. Not to be annoying but defining AMI as Amazon Machine Learning Interface might be useful to some
  10. Google Cloud ML Engine — neural-net-based ML service with a typically Googley line up of Googley services.

Stepping back, I noticed a handful of what I am sure are irrelevant points which are of little interest to a “real” journalists creating “real” news.

First, notice that the list is self referential with python love. Frameworks depend on other python loving frameworks. There’s nothing inherently bad about this self referential approach to shipping up a list, and it makes it a heck of a lot easier to create the list in the first place.

Second, the information about Amazon is slightly misleading. In my lecture in Washington, DC on September 7, I mentioned that Amazon’s approach to machine learning supports Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras. I found this approach interesting, but of little interest to those creating a survey or developing an informed list about machine learning frameworks; for example, Amazon is executing a quite clever play. In bridge, I think the phrase “trump card” suggests what the Bezos momentum machine has cooked up. Notice the past tense because this Amazon stuff has been chugging along in at least one US government agency for about four, four and one half years.

Third, Google brings up dead last. What about IBM? What about Microsoft and its CNTK. Ah, another acronym, but I as a non real journalist will reveal that this acronym means Microsoft Cognitive Toolkit. More information is available in Microsoft’s wonderful prose at this link. By the way, the Amazon machine learning spinning momentum thing supports the CNTK. Imagine that? Right, I didn’t think so.

Net net: The machine learning framework list may benefit from a bit of refinement. On the other hand, just use Amazon and move down the road to a new type of smart software lock in. Want to know more? Write benkent2020 @ yahoo dot com and inquire about our for fee Amazon briefing about machine learning, real time data marketplaces, and a couple of other most off the radar activities. Have you seen Amazon’s facial recognition camera? It’s part of the Amazon machine learning imitative, and it has some interesting capabilities.

Stephen E Arnold, September 16, 2018

Financial Tremors?

September 12, 2018

The folks with crypto currency may be having a bit of a thrill. The volatility suggests that bits and bytes may not be as stable as owning a chunk of real estate in Tokyo.

We have also noted rumblings elsewhere. Smart software, for example. Many hopes, of course, but there may be some downstream consequences. Salmon finding life difficult may be one metaphor.

It has become a weekly, maybe even daily, routine: some alarmist talks about the dangers of AI on a particular industry, we get scared, the news cycle moves on, and everyone forgets. However, a warning is lurking that has the potential to have some staying power. We learned more from a recent Technology Review story, “The World Economic Forum Warns That AI Might Destabilize The Financial System.”

We learned:

[A]rtificial intelligence will disrupt the industry by allowing early adopters to outmaneuver competitors. It also suggests that the technology will create more convenient products for consumers, such as sophisticated tools for managing personal finances and investments.

We also noted:

But most notably, the report points to the potential for big financial institutions to build machine-learning-based services that live in the cloud and are accessed by other institutions.

This is a very volatile situation, especially as so much finance is starting to hinge on machine learning. For example, many retirement plans are shifting funds around based on AI insights. But take hope for what it is. Quantum computing may be just around the corner.

Patrick Roland, September 13, 2018

No Surprise: Some Analytic Jobs are Still Done By Humans

September 8, 2018

Once in a while we stumble across something that truly surprises us. Like how some jobs you thought could be erased by artificial intelligence or machine learning in about five seconds, still exist. Take, for example, this handy list of airport WiFi passwords from around the globe that we discovered at FoxNoMad, entitled (yep, you guessed it) “A Map of Wireless Passwords From Airports and Lounges Around the World.”

According to the page:

“Finding an open wireless connection in many airports is not always easy, or possible, without a password (or local phone number which is stupid). The difficulty of getting online is why I asked you for and created an always-up-to-date list of airport wireless passwords around the world. You’ve been sending me your tips regularly and I post on the foXnoMad Facebook page when there’s a new password or airport added.”

One look is like stepping into a digital time machine. There’s something quaint about this simple map updated by individuals on their own after they use an airports Internet. It’s shocking that an algorithm has not done this all by now. Much like the job of quality assurance tester mentioned here there is still a shocking amount of work out there that AI has not done. Soak up the good news while you can, we say.

Patrick Roland, September 8, 2018

Is An Operating System Needed For Collective Intelligence?

September 6, 2018

Humans love labels, organizing, and order.  The more the Internet becomes commonplace in our daily lives, the more organization becomes important.  The Let’s Talk Bitcoin Network has an interesting discussion on the need for an operating system for humanity’s collective intelligence in the article, “#237 Matan Field: DAOstack-An Operating System For Collective Intelligence.”

Looking back through history, humans cooperating and acting in collective organization has had an important impact at historical milestones.  Think the discovery of fire, invention of the wheel, war, Industrial Revolution, etc.  But what does it mean for humanity when the Internet runs more of our lives?

“As we move towards an increasingly connected and automated society and economy, there will become a need for decentralized infrastructure which enables companies and markets to make fast decisions at scale.  We’re joined by Matan Field, CEO of DAOstack, a new platform that aims to become the operating system for collective intelligence. DAOstack is building a toolset to allow decentralized governance and building self-organizing collectives at scale.”

This podcast focuses on how a DAOstack would enable a collective intelligence operating system, its importance, and why we are going to need one.

I can see the importance of a centralized operating system, but then my science fiction imagination comes into play.  I have visions of HAL 2000 and its many descendants taking over the world.  Or robots.  Robots taking over the world.  What about the Borg?  There is them too.  Would it not be more intuitive to create a bunch of smaller collective operating systems that sync up than putting all the eggs…ahem…data in one basket?

Whitney Grace, September 6, 2018

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