Quote to Note: The Role of US AI Innovators

March 24, 2017

I read “Opening a New Chapter of My Work in AI.” After working through the non-AI output, I concluded that money beckons the fearless leader, Andrew Ng. However, I did note one interesting quotation in the apologia:

The U.S. is very good at inventing new technology ideas. China is very good at inventing and quickly shipping AI products.

What this suggests to me is that the wizard of AI sees the US as good at “ideas”, and China an implementer. A quick implementer at that.

My take is that China sucks up intangibles like information and ideas. Then China cranks out products. Easy to monetize things, avoiding the question, “What’s the value of that idea, pal?”

Ouch. On the other hand, software is the new electricity. So who is Thomas Edison? I wish I “knew”.

Stephen E Arnold, March 24, 2017

MBAs Under Siege by Smart Software

March 23, 2017

The article titled Silicon Valley Hedge Fund Takes Over Wall Street With AI Trader on Bloomberg explains how Sentient Technologies Inc. plans to take the human error out of the stock market. Babak Hodjat co-founded the company and spent the past 10 years building an AI system capable of reviewing billions of pieces of data and learning trends and techniques to make money by trading stocks. The article states that the system is based on evolution,

According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls “genes.” These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation… Sentient can squeeze 1,800 simulated trading days into a few minutes.

Hodjat believes that handing the reins over to a machine is wise because it eliminates bias and emotions. But outsiders wonder whether investors will be willing to put their trust entirely in a system. Other hedge funds like Man AHL rely on machine learning too, but nowhere near to the extent of Sentient. As Sentient bring in outside investors later this year the success of the platform will become clearer.

Chelsea Kerwin, March 23, 2017

IBM Out-Watsons Watson PR

March 15, 2017

I noted that IBM can store data in an atom. I marveled at IBM’s helping with arthritis research. I withdrew my life savings to bet on IBM Watson’s predictions for the next big thing. Wow. Busy that Watson smart software is. Versatile too.

What I found interesting is that IBM has announced that it has knocked the cover off the ball with its speech recognition capabilities. Too bad Amazon, Google, Microsoft, and Nuance think they know how to perform this Star Trek-type function trick. Clueless pretenders if the IBM assertion is accurate.

Navigate to another IBM “real” journalistic revelation in “Why IBM’s Speech Recognition Breakthrough Maters for AI and IoT.”

I learned:

IBM recently announced that its speech recognition system achieved an industry record of 5.5% word error rate, coming closer to human parity.

Yep, an announcement. Remember. Google’s speech recognition is on lots of mobile phones. Dear old Microsoft, despite the missteps of Tay, landed a deal with the dazed and confused UK National Health Service. And Amazon. Well, there is that Alexa Echo and Dot product line. And IBM? Well, an announcement.

The write up reveals that a blog post makes clear that IBM is improving its speech recognition. As proof, the write up points out that IBM’s error rate declined. IBM does that with its revenues, so maybe this is a characteristic of the Big Blue machine.

But I particularly enjoyed this bit of analysis:

Reaching human-level performance in AI tasks such as speech or object recognition remains a scientific challenge, according to Yoshua Bengio, leader of the University of Montreal’s Montreal Institute for Learning Algorithms (MILA) Lab, as quoted in the blog post. Standard benchmarks do not always reveal the variations and complexities of real data, he added. “For example, different data sets can be more or less sensitive to different aspects of the task, and the results depend crucially on how human performance is evaluated, for example using skilled professional transcribers in the case of speech recognition,” Bengio said.

Isn’t this the outfit which Microsoft relies upon for some of its speech wizardry. So what exactly is IBM doing? Let’s ask Alexa?

Stephen E Arnold, March 15, 2017

Significant Others: Salesforce Einstein and IBM Watson

March 13, 2017

The flow of semi-smart software publicity continues. Keep in mind that most smart software is little more than search with wrappers performing special operations.

The proud parents of Einstein and Watson announced that one another’s smart software systems have become a thing. Salesforce has scripts and numerical recipes to make it easier to figure out if a particular client really wants to drop the service. Watson brings Jeopardy type question answering and lots of data training to the festive announcement party.

I enjoyed “Salesforce Will Be Using IBM Watson to Make its Einstein AI Service Even Smarter.” The write up strikes me as somewhat closer to the realities of the tie up than the inebriated best wishes emanating from many other “real” journalists. For example, the write up asserts:

By bringing its all-important Watson service to Salesforce and Einstein customers, IBM is determined to double-down on that huge Salesforce consulting market, not compete with it.

IBM cannot “become” Salesforce. But Salesforce generates a need for services in many large companies. The idea is that Einstein does its thing to help a sales professional close a deal, and IBM Watson can do its thing to make “sense” of the content related to the company paying Salesforce for an integrated sales prospecting and closing system.

My take is that this is not much more than a co-publicity set up with the hope that the ability of Salesforce to talk about its tie up with IBM will generate sales and buzz. IBM hopes that its PR capabilities will produce some mileage for the huffing and puffing Watson “solution.”

In my opinion, IBM is turning cartwheels to get substantial, evergreen revenue from the Watson thing. But IBM may be pushing another fantasy animal into the revenue race. Quantum computing as a service is the next big thing. Now is quantum computing something one can actually use?

Nah, but the point is that revenue is not news at IBM. Quantum computing gives the IBM marketers another drum to bang. Moving in with Salesforce provides a way to sell something, anything, maybe.

Stephen E Arnold, March 13, 2017

Alphabet Google Smart Software Cannot Define Hate

March 7, 2017

Again. More artificial intelligence news. Or is it “fake news.” Many people want smart software to generate big revenues. Salesforce has Einstein. Vendors of keyword search centric information retrieval systems suddenly have smart software. At a meeting this week, a vendor of plastic piping said his new inventory system was intelligent. Yep, plastic pipes.

I read “Alphabet’s Hate Fighting AI Doesn’t Understand Hate Yet.” That struck me as odd. I learned in “Google’s AI Learned to Be Highly Aggressive When Stressed.” I assumed that an aggressive AI would take on an online dictionary, wrest the definition of hate from the Web site, and stuff the bits into the voracious multi petabyte storage system available to Deep Mind.

The issue of hate is relevant to hate speech. I think this is a gentle way of saying that unless text has some cue like a known entity which outputs nasty grams or a list of words likely to be used to convey hate, the smart software is performing like most smart software; that is, somewhere in the 40 to 65 percent accuracy range. Toss in human help, assorted dictionaries, a curated set of hateful content objects, and patient tuning and smiles all around.

The write up sidesteps my views and offered:

Google and its sister Alphabet company Jigsaw announced Perspective, a tool that uses machine learning to police the internet against hate speech.

And then noted:

Computer scientists and others on the internet have found the system unable to identify a wide swath of hateful comments, while categorizing innocuous word combinations like “hate is bad” and “garbage truck” as overwhelmingly toxic.

We know about Google’s track record in releasing early versions of software which maybe will sort of work a little bit someday.

The Googlers are busy doing what Autonomy Software did in the 1990s and other vendors of smart software have done in the subsequent quarter century: Teach the software by spoon feeding information into the system.

The write up points out:

Like all machine-learning algorithms, the more data the Perspective API has, the better it will work. The Alphabet subsidiary worked with partners like Wikipedia and The New York Times to gather hundreds of thousands of comments, and then crowd sourced 10 answers for each comment on whether they were toxic or not. The effort was intended to kick start the deep neural network that makes the backbone of the Perspective API.

I love the “all.” The truth of the matter is that self learning software just doesn’t work as well as the more narrowly defined artificial intelligence systems. Buy a book and Amazon looks in its database to see what other book buyers with a statistically generated profile seem to have bought. Bang. A smart recommendation. Skip the fact that books already purchased and stored in an Amazon database appear again and again on my list of recommended books. Smart but stupid, and that’s reasonably good implementation of smart software.

The write up works through examples of hate speech. Consult the source document for the lists. The write up works overtime to paint the lily gold and put some stage make up on what seems to be a somewhat dowdy implementation of Deep Mind / Google’s artificial intelligence.

Hey, I don’t want to drag another cat into the kitchen, but why not ask Watson what hate means. My hunch is that either the Google / Deep Mind engineers or the IBM Watson engineers will have a laugh over that idea. The smart software, on the other hand, might try to knock some sense into the competitor’s system.

Stephen E Arnold, March 7, 2017

Take the Time for Alexa

March 6, 2017

In the new digital assistant line up, Alexa responds better than Cortana and Siri, because it can provide better and more intelligent services that the smartphone based app.  As an Amazon product, as with Amazon Web Services, developers can learn how to build apps and other products for Alexa.  The question is how to get started?  HeroTurko created a learning tutorial for interested Alexa developers and it can be checked out at, “Amazon Alexa Development From Beginner To Intermediate.”

Voice-based apps are a growing sector in the technology industry and will only get bigger as the demand for voice-controlled technology increases.  The tutorial is designed to teach developers how to design voice apps and then launch them on the Amazon Echo.  Building your Alexa skills is a necessary step, so the course says, to get an edge on the voice app market:

The biggest industries in technology are surrounded by AI, Bots, and Voice technology. Voice technology I believe will be the new 21st user interface that will not only understand basic commands, but will be so smart to understand anything you tell it. This is why Amazon is making a big bet with Alexa, which it plans to generate close to $11 billion dollars by 2020. They know something about Amazon Echo, which is why now is the best time to learn these skills before the mainstream starts developing applications. We all know the story about apps for the smartphones, this is the same thing.

This course contains over 50 lectures and 1.5 hrs of content. It’s designed for beginners to play with new platforms in the voice space. You’ll learn the tools needed to build the Alexa Skills, how Alexa Skills work, and publish a skill to Amazon’s Alexa store.

Learning how to use Alexa is the precursor to designing other voice app and will probably segway into NLP.  If you want to learn where the IT market is going beyond machine learning and artificial intelligence, this is one of the places to start.

Whitney Grace, March 6, 2017

Amazon, the Walmart of Digital Selling, Is into Smart Software Too

March 3, 2017

Big day. Amazon, the company that reports its financials in a remarkably weird way, is now explaining that it is a player in the smart software poker game. Navigate to “Welcome to the New AWS AI Blog!” Now the DWs (digital Walmarters) are explaining that artificial intelligence, was, is, and will be the future of the mall killer.

How many AI and smart software services does the Main Street shopping pillager offer. Check out this chart:


There are “engines.” One can pay money to use this devices. Some of the names are semi familiar like TensorFlow; others may be unfamiliar to the shopping crowd; for instance, CNTK. Love that acronym.

There are complete platforms. Some of these are open sourcey, but when vendor lock in is one of the possible consequences of the cloud approach to software, one never knows, does one? I like Amazon machine learning and “EMR”, an acronym which means a variant of Google’s old school batch processing thing. Yikes! Batch processing in a zip zip world of real time flows.

The third layer is Amazon AI services. I noted the inclusion of Lex, Amazon’s home companion. Alexa, what’s the weather?

The idea is that Amazon is every bit as robust as some of the other smart software outfits. No less an authority than Mark Cuban is pressed into duty as an objective supporter of Amazon’s freshly repackaged collection of oddments.

The blog, I assume, will explain how those in search of smart software can order up machine learning along with an order of dog food.

Who should be nervous about Amazon’s repackaging of its industry leading cloud services? I would suggest that cross town pal Microsoft might be checking out Amazon’s AI chart. Then, of course, there are the wizards at Hewlett Packard Enterprise. I wonder of Amazon’s services will be of use to HPE when it meets up with Autonomy in court later this year. And the number one outfit likely to be consulting tea leaves for hints of Amazon’s AI impact? Good old IBM. Fresh from another quarter of declining revenues and more IBM Watson hyperbole, IBM might be wondering, “How did a digital Walmart get from used CDs to AI?”

And the Google? My hunch is that the Google  may note Amazon’s blog. But the company is in the ad business, has been, and probably will be for the foreseeable future. Amazon is too diversified to the Google to see many parallels.

My hunch is that Amazon does see search as vulnerable. Another Main Street? Perhaps?

Stephen E Arnold, March 3, 2017

Parlez Vous Qwant, Nest-Ce Pas?

March 2, 2017

One of Google’s biggest rivals is Yandex, at least in Russia.  Yandex is a Russian owned and operated search engine and is more popular in Russia than the Google, depending on the statistics.  It goes to say that a search engine built and designed by native speakers does have a significant advantage over foreign competition, and it looks like France wants a chance to beat Google.  Search Engine Journal reports that, “Qwant, A French Search Engine, Thinks It Can Take On Google-Here’s Why.”

Qwant was only founded in 2013 and it has grown to serve twenty-one million monthly users in thirty countries.  The French search engine has seen a 70% growth each year and it will see more with its recent integration with Firefox and a soon-to-be launched mobile app.  Qwant is very similar to DuckDuckGo in that it does not collect user data.  It also boasts mote search categories than news, images, and video and these include, music, social media, cars, health, music, and others.  Qwant had an interesting philosophy:

The company also has a unique philosophy that artificial intelligence and digital assistants can be educated without having to collect data on users. That’s a completely different philosophy than what is shared by Google, which collects every bit of information it can about users to fuel things like Google Home and Google Allo.

Qwant still wants to make a profit with pay-per-click and future partnerships with eBay and TripAdvisor, but they will do without compromising a user’s privacy.  Qwant has a unique approach to search and building AI assistants, but it has a long way to go before it reaches Google heights.

They need to engage more users not only on laptops and computers but also mobile devices.  They also need to form more partnerships with other browsers.

Bon chance, Qwant!  But could you share how you plan to make AI assistants without user data?

Whitney Grace, March 2, 2017


Chan and Zuckerberg Invest in Science Research Search Engine, Meta

March 1, 2017

Mark Zuckerberg and his wife Priscilla Chan have dedicated a portion of their fortune to philanthropy issues through their own organization, the Chan Zuckerberg InitiativeTech Crunch shares that one of their first acquisitions is to support scientific research, “Chan Zuckerberg Initiative Acquires And Will Free Up Science Search Engine Meta.”

Meta is a search engine dedicated to science research papers and it is powered by artificial intelligence.  Chan and Zuckerberg plan to make Meta free in a few months, but only after they have enhanced it.  Once released, Meta will help scientists find the latest papers in their study fields, which is awesome as these papers are usually blocked behind paywalls.  What is even better is that Meta will also assist funding organizations with research and areas with potential for investment/impact.  What makes Meta different from other search engines or databases is quite fantastic:

What’s special about Meta is that its AI recognizes authors and citations between papers so it can surface the most important research instead of just what has the best SEO. It also provides free full-text access to 18,000 journals and literature sources.

Meta co-founder and CEO Sam Molyneux writes that “Going forward, our intent is not to profit from Meta’s data and capabilities; instead we aim to ensure they get to those who need them most, across sectors and as quickly as possible, for the benefit of the world.

CZI invested $3 billion dedicated to curing all diseases and they already built the Biohub in San Francisco for medical research.  Meta works like this:

Meta, formerly known as Sciencescape, indexes entire repositories of papers like PubMed and crawls the web, identifying and building profiles for the authors while analyzing who cites or links to what. It’s effectively Google PageRank for science, making it simple to discover relevant papers and prioritize which to read. It even adapts to provide feeds of updates on newly published research related to your previous searches.

Meta is an ideal search engine, because it crawls the entire Web (supposedly) and returns verified information, not to mention potential research partnerships and breakthroughs.  This is the type of database researchers have dreamed of for years.  Would CZI be willing to fund something similar for fields other than science?  Will they run into trouble with other organizations less interested in philanthropy?

Whitney Grace, March 1, 2017

When AI Spreads Propaganda

February 28, 2017

We thought Google was left-leaning, but an article at the Guardian, “How Google’s Search Algorithm Spreads False Information with a Rightwing Bias,” seems to contradict that assessment. The article cites recent research by the Observer, which found neo-Nazi and anti-Semitic views prominently featured in Google search results. The Guardian followed up with its own research and documented more examples of right-leaning misinformation, like climate-change denials, anti-LGBT tirades,  and Sandy Hook conspiracy theories. Reporters Olivia Solon and Sam Levin tell us:

The Guardian’s latest findings further suggest that Google’s searches are contributing to the problem. In the past, when a journalist or academic exposes one of these algorithmic hiccups, humans at Google quietly make manual adjustments in a process that’s neither transparent nor accountable.

At the same time, politically motivated third parties including the ‘alt-right’, a far-right movement in the US, use a variety of techniques to trick the algorithm and push propaganda and misinformation higher up Google’s search rankings.

These insidious manipulations – both by Google and by third parties trying to game the system – impact how users of the search engine perceive the world, even influencing the way they vote. This has led some researchers to study Google’s role in the presidential election in the same way that they have scrutinized Facebook.

Robert Epstein from the American Institute for Behavioral Research and Technology has spent four years trying to reverse engineer Google’s search algorithms. He believes, based on systematic research, that Google has the power to rig elections through something he calls the search engine manipulation effect (SEME).

Epstein conducted five experiments in two countries to find that biased rankings in search results can shift the opinions of undecided voters. If Google tweaks its algorithm to show more positive search results for a candidate, the searcher may form a more positive opinion of that candidate.

This does add a whole new, insidious dimension to propaganda. Did Orwell foresee algorithms? Further complicating the matter is the element of filter bubbles, through which many consume only information from homogenous sources, allowing no room for contrary facts. The article delves into how propagandists are gaming the system and describes Google’s response, so interested readers may wish to navigate there for more information.

One particular point gives me chills– Epstein states that research shows the vast majority of readers are not aware that bias exists within search rankings; they have no idea they are being manipulated. Perhaps those of us with some understanding of search algorithms can spread that insight to the rest of the multitude. It seems such education is sorely needed.

Cynthia Murrell, February 28, 2017



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