News360 Confuses a Horse with Government Agency

February 14, 2018

Short honk: News360.com is a “free” headline service. You can sign up for the summary of headlines at https://news360.com. The service makes use of smart software because human editors are too darned expensive and too slow. Some human editors may be stupid.

I noted this alert on Tuesday, February 13, 2018:

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Notice that the highlight specifies the “National Security Agency.” However the headline specifies “NSA Manager of Racing Operations.” Now government agencies have many interests, but in my experience, the NSA does not spend too much time on the horse thing.

Clicking on the tile reveals this story:

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Notice that the source is a publication about race horses. The NSA is correctly interpreted by the online ad provider. But the News360 insists that NSA is the US government agency, not the National Steeplechase Association.

Put the spurs in that smart software.

Stephen E Arnold, February 14, 2018

IBM Releases Power9 AI and Machine Learning Chip

February 12, 2018

Make no mistake, the new AI processor from IBM has Watson written all over it—but it does move the software into new territory. We get a glimpse from the brief write-up, “IBM Has a New Chip for AI and Machine Learning” at IT Pro Portal. The new chip, dubbed Power9, is now available through IBM’s cloud portal and through third-party vendors and is built into the new AC9222 platform. (See here for a more detailed discussion of both Power9 and AC9222.) Writer Sead Fadilpaši? quotes market analyst Patrick Moorhead, who states:

Power9 is a chip which has a new systems architecture that is optimized for accelerators used in machine learning. Intel makes Xeon CPUs and Nervana accelerators and NVIDIA makes Tesla accelerators. IBM’s Power9 is literally the Swiss Army knife of ML acceleration as it supports an astronomical amount of IO and bandwidth, 10X of anything that’s out there today.

That is strong praise. Fadilpaši? also quotes IBM’s Brad McCredie, who observes:

Modern workloads are becoming accelerated and the Nvidia GPU is a common accelerator. We have seen this trend coming. We built a deep relationship with them and a partnership between the Power system and the GPU. We have a unique bus that runs between the processor and the GPU and has 10x peak bandwidth over competitive systems.

Will the Power9 live up to its expectations? We suspect IBM has reason to hope for success here.

Cynthia Murrell, February 12, 2018

Universal Text Translation Is the Next Milestone for AI

February 9, 2018

As the globe gets smaller, individuals are in more contact with people who don’t speak their language. Or, we are reading information written in a foreign language. Programs like Google Translate are flawed at best and it is clear this is a niche waiting to be filled. With the increase of AI, it looks like that is about to happen, according to a recent GCN article, “IARPA Contracts for Universal Text Translator.”

According to the article:

The Intelligence Advanced Research Projects Activity is a step closer to developing a universal text translator that will eventually allow English speakers to search through multilanguage data sources — such as social media, newswires and press reports — and retrieve results in English.

 

The intelligence community’s research arm awarded research and performance monitoring contracts for its Machine Translation for English Retrieval of Information in Any Language program to teams headed by leading research universities paired with federal technology contractors.

 

Intelligence agencies, said IARPA project managers in a statement in late December, grapple with an increasingly multilingual, worldwide data pool to do their analytic work. Most of those languages, they said, have few or no automated tools for cross-language data mining.

This sounds like a very promising opportunity to get everyone speaking the same language. However, we think there is still a lot of room for error. We are hedging our bets on Unibabel’s AI translation software that is backed up by human editors. (They raised $23M, so they must be doing something right.) That human angle seems to be the hinge that will be a success for someone in this rich field.

Patrick Roland, February 9, 2018

More Artificial Intelligence Fright

February 1, 2018

Is artificial intelligence a bigger development than electricity or fire? Google CEO Sundar Pichai thinks so. In fact, he warns that if not harnessed correctly, AI could be more deadly than fire. We got the full scoop from a recent Newsweek story, “What’s Bigger Than Fire and Electricity? Artificial Intelligence, Says Google Boss.”

According to the story:

“Pichai went on to warn of the potential dangers associated with developing advanced AI, saying that developers need to learn to harness its benefits in the same way humanity did with fire. My point is AI is really important, but we have to be concerned about it.

Scary stuff straight out of a sci-fi novel. Or is it? Investopedia looked deeper into the future and found a mixed bag that has us more than a little concerned. They found that we can relax, because ultimately AI is controlled by electricity and as long as we have control of power we can cut off their source of energy (Warning to power companies: Don’t give your robots the keys!). However, the story continues with a closing thought that mirrors our own—that, yeah, humans are going to push this thing as far as it will go and ultimately suffer some sort of consequence.

What might be more terrifying is the impact of ad centric search results. With more than 2,000 companies in the AI game, we wonder, “What’s winning mean?”

Patrick Roland, February 1, 2018

Artificial Intelligence Logo Map

February 1, 2018

We love logo maps. We wanted to share Venture Scanner’s latest creation. The full set of “logos” allegedly consists of 2,029 icons with data about each of the forward leaning companies to which a logo is attached.

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Venture Scanner sells a report. Contact the firm at www.venturescanner.com. That’s a lot of AI logos.

Stephen E Arnold, February 1, 2018

Palantir: Accused of Hegelian Contradictions

January 29, 2018

I bet you have not thought about Hegel since you took that required philosophy course in college. Well, Hegel and his “contradictions” are central to “WEF 2018: Davos, Data, Palantir and the Future of the Internet.”

I highlighted this passage from the essay:

Data is the route to security. Data is the route to oppression. Data is the route to individual ideation. Data is the route to the hive mind. Data is the route to civic wealth. Data is the route to civic collapse.

Thesis, antitheses, synthesis in action I surmise.

The near term objective is synthesis. I assume this is the “connecting the dots” approach to finding what one needs to know.

I learned:

The stakes for big data couldn’t be bigger.

Okay, a categorical in our fast changing, diverse economic and political climate. Be afraid seems to be the message.

Palantir’s point of operations in Davos is described in the write up as “a pimped up liquor store.” Helpful and highly suggestive too.

The conclusion of the essay warranted a big red circle:

So next time you hear the names Palantir or Alex Karp, stop what you’re doing and pay attention. The future – your future – is under discussion. Under construction. This little first draft of history of which you’ve made it to the end (congratulations and thanks) – the history of data – is of a future that will in time come to be seen for what it is: digital that truly matters.

Several observations:

  • The author wants me to believe that Palantir is not a pal.
  • The big data thing troubles the author because Palantir is one of the vendors providing next generation information access.
  • The goal of making Palantir into something unique is best accomplished by invoking Fancy Dan ideas.

I would suggest that knowledge about companies like Gamma Group FinFisher, Shoghi, Trovicor, and some other interesting non US entities might put Palantir in perspective. Palantir has an operational focus; some of the other vendors perform different information services.

Palantir is an innovator, but it is part of a landscape of data intercept and analysis organizations. I could make a case that Palantir is capable but some companies in Europe and the East are actually more technologically advanced.

But these outfits were not at Davos. Why? That’s a good question. Perhaps they were too busy with their commercial and government work. My hunch is that a few of these outfits were indeed “there”, just not noticed by the expert who checked out the liquor store.

Stephen E Arnold, January 29, 2019

Are There Only 10,000 Machine Learning Experts? LinkedIn Offers a Different Number, 651,627

January 18, 2018

I read in the dead tree edition of the New York Times (still not a tabloid sized “real” journalism delivery vehicle) that there are 10,000 machine learning experts in the world. You can find a version of this story at this link.

Just to check the validity of this magical number, which reinforces the notion of elitism, the one percent of the one percent, and the complexity of the Dark Arts of smart software, I did some research.

I turned to LinkedIn, entered the phrase “machine learning” and this is what I learned from the Microsoft professional social media search system:

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I realize that the low key colors and gray type are unreadable, but contact Microsoft LinkedIn, not me.

There are more than 38,000 jobs open for experts in machine learning.

What’s the talent pool?

The number is 651,627.

Now I understand that if one is making a list of top anything, the peak of the pyramid will be, by definition, one. For music, you may have disagreements. For machine learning, it’s different.

Since machine learning and other smart software jargon is pretty vague, mostly incorrect, and generally misunderstood, the New York Times’ story missed the mark by a mere 641,627 “experts.” Keep in mind anyone can say one is an expert in anything unless the government regulates via licenses like those issued to doctors, lawyers, and beauticians. Beauticians? Yep.

Ah, you say. LinkedIn is for marketers and headhunters.

Yes, I respond.

But the point is that in jargon charged disciplines, it is tough to convince me that there are 10,000 machine learning experts in the world. My hunch is that the cream of the crop will be a handful of people, assuming that one can define what it takes to be an expert; for instance:

  1. Math skills that go beyond the required course in computer science with an emphasis on artificial intelligence
  2. Math skills which nose into the territory of Kolmogorov and his cronies (yep, my uncle, the crony)
  3. Database skills tuned to deal with machine learning
  4. Linguistics capabilities to cope with multi lingual content
  5. Engineering skills tuned to the peculiar demands of a real time stream of intercepted data from an outfit like WebHose
  6. Subject matter experts with knowledge of such exciting topics as Bayesian “drift” and how to make necessary human interventions to get the statistical ship back on course
  7. Operations experts who can get something useful from a ML-infused application like creating a smart home appliance which does not burn the roast chicken which must be well done for an ageing boxer.

I could go on.

Right now, anyone can claim to be an expert in machine learning. The problem is that machine learning is not one thing. Google is bundling up a bunch of stuff and making it available to LinkedIn type experts.

What could possibly go wrong? Let’s hope the New York Times knows exactly which type of expert in the components of machine learning to have a reasonable shot of reporting on the event that catches a “real” newsperson’s attention.

Stephen E Arnold, January 18, 2017

Amazon Cloud Injected with AI Steroids

January 17, 2018

Amazon, Google, and Microsoft are huge cloud computing rivals.  Amazon wants to keep up with the competition, says Fortune, in the article, “Amazon Reportedly Beefing Up Cloud Capabilities In The Cloud.”  Amazon is “beefing up” its cloud performance by injecting it with more machine learning and artificial intelligence.   The world’s biggest retailer is doing this by teaming up with AI-based startups Domino Data Lab and DataRobot.

Cloud computing is mostly used by individuals as computer backups and the ability to access their files from anywhere.  Businesses use it to run their applications and store data, but as cloud computing becomes more standard they want to run machine learning tasks and big data analysis.

Amazon’s new effort is code-named Ironman and is aimed at completing tasks for companies focused on insurance, energy, fraud detection, and drug discovery, The Information reported. The services will be offered to run on graphic processing chips made by Nvidia as well as so-called field programmable gate array chips, which can be reprogrammed as needed for different kinds of software.

Nvidia and other high-performing chip manufacturers such as Advanced Micro Devices and Intel are ecstatic about the competition because it means more cloud operators will purchase their products.  Amazon Web Services is one of the company’s fastest growing areas and continues to bring in the profits.

Whitney Grace, January 17, 2018

Averaging Information Is Not Cutting It Anymore

January 16, 2018

Here is something interesting that comes after the headline of “People From Around The Globe Met For The First Flat Earth Conference” and beliefs that white supremacists are gaining more power.  The Frontiers Media shares that, “Rescuing Collective Wisdom When The Average Group Opinion Is Wrong” is an article that pokes fun at the fanaticism running rampant in the news.  Beyond the fanaticism in the news, there is a real concern with averaging when it comes to data science and other fields that heavily rely on data.

The article breaks down the different ways averaging is used and the different theorems that are developed from it.  The introduction is a bit wordy but it sets the tone:

The total knowledge contained within a collective supersedes the knowledge of even its most intelligent member. Yet the collective knowledge will remain inaccessible to us unless we are able to find efficient knowledge aggregation methods that produce reliable decisions based on the behavior or opinions of the collective’s members. It is often stated that simple averaging of a pool of opinions is a good and in many cases the optimal way to extract knowledge from a crowd. The method of averaging has been applied to analysis of decision-making in very different fields, such as forecasting, collective animal behavior, individual psychology, and machine learning. Two mathematical theorems, Condorcet’s theorem and Jensen’s inequality, provide a general theoretical justification for the averaging procedure. Yet the necessary conditions which guarantee the applicability of these theorems are often not met in practice. Under such circumstances, averaging can lead to suboptimal and sometimes very poor performance. Practitioners in many different fields have independently developed procedures to counteract the failures of averaging. We review such knowledge aggregation procedures and interpret the methods in the light of a statistical decision theory framework to explain when their application is justified. Our analysis indicates that in the ideal case, there should be a matching between the aggregation procedure and the nature of the knowledge distribution, correlations, and associated error costs.

Understanding how data can be corrupted is half the battle of figuring out how to correct the problem.  This is one of the complications related to artificial intelligence and machine learning.  One example is trying to build sentiment analysis engines.  These require huge data terabytes and the Internet provides an endless supply, but the usual result is that the sentiment analysis engines end up racist, misogynist, and all around trolls.  It might lead to giggles but does not very accurate results.

Whitney Grace, January 17, 2018

Will Mobile Be Microsoft Downfall in AI Field?

January 12, 2018

We are startled to see Computerworld levy such a blow to Microsoft, but here we go— see their article, “The Missing Link in Microsoft’s AI Strategy.” Writer Preston Gralla insists that the company’s weakness lies in mobile tech—and it could prove to be a real problem as Microsoft competes against the likes of Google, Apple, Facebook, and Amazon in the growing field of AI. Galla acknowledges Microsoft’s advantages here—its vast quantities of valuable data and its AI system, Cortana, already built into Windows. However, she writes:

Microsoft is missing something very big in A.I. as well: a significant mobile presence. Google and Apple, via Android and iOS, gather tremendous amounts of useful data for their A.I. work. And gathering the data is just the starting point. Hundreds of millions of people around the world use the A.I.-powered Siri, Google Assistant and Google Now on their mobile devices. So Google and Apple can continue to improve their A.I. work, based on how people use their devices. Given that the future (and to a great extent, the present) is mobile, all this means serious problems for Microsoft in A.I. A.I. is likely a big part of the reason that Microsoft kept Windows Phone on life support for so many years, spending billions of dollars while it died a slow, ugly, public death.

The article outlines a few things Microsoft has been doing to try to catch up to its rivals, like developing (little-used) versions of Cortana for iOS and Android, working with hardware makers on Cortana-powered speakers, and partnering with Amazon’s Alexa for any tasks Cortana is not quite up to (yet). Will this need to play catch-up seriously hamper Microsoft’s AI prominence? We shall see.

Cynthia Murrell, January 12, 2018

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