IBM Watson Deep Learning: A Great Leap Forward

August 16, 2017

I read in the IBM marketing publication Fortune Magazine. Oh, sorry, I meant the independent real business news outfit Fortune, the following article: “IBM Claims Big Breakthrough in Deep Learning.” (I know the write up is objective because the headline includes the word “claims.”)

The main point is that the IBM Watson super game winning thing can now do certain computational tasks more quickly is mildly interesting. I noticed that one of our local tire discounters has a sale on a brand called Primewell. That struck me as more interesting than this IBM claim.

First, what’s the great leap forward the article touts? I highlighted this passage:

IBM says it has come up with software that can divvy those tasks among 64 servers running up to 256 processors total, and still reap huge benefits in speed. The company is making that technology available to customers using IBM Power System servers and to other techies who want to test it.

How many IBM Power 8 servers does it take to speed up Watson’s indexing? I learned:

IBM used 64 of its own Power 8 servers—each of which links both general-purpose Intel microprocessors with Nvidia graphical processors with a fast NVLink interconnection to facilitate fast data flow between the two types of chips

A couple of questions:

  1. How much does it cost to outfit 64 IBM Power 8 servers to perform this magic?
  2. How many Nvidia GPUs are needed?
  3. How many Intel CPUs are needed?
  4. How much RAM is required in each server?
  5. How much time does it require to configure, tune, and deploy the set up referenced in the article?

My hunch is that this set up is slightly more costly than buying a Chrome book or signing on for some Amazon cloud computing cycles. These questions, not surprisingly, are not of interest to the “real” business magazine Fortune. That’s okay. I understand that one can get only so much information from a news release, a PowerPoint deck, or a lunch? No problem.

The other thought that crossed my mind as I read the story, “Does Fortune think that IBM is the only outfit using GPUs to speed up certain types of content processing?” Ah, well, IBM is probably so sophisticated that it is working on engineering problems that other companies cannot conceive let alone tackle.

Now the second point: Content processing to generate a Watson index is a bottleneck. However, the processing is what I call a downstream bottleneck. The really big hurdle for IBM Watson is the manual work required to set up the rules which the Watson system has to follow. Compared to the data crunching, training and rule making are the giant black holes of time and complexity. Fancy Dan servers don’t get to strut their stuff until the days, weeks, months, and years of setting up the rules is completed, tuned, and updated.

Fortune Magazine obviously considers this bottleneck of zero interest. My hunch is that IBM did not explain this characteristic of IBM Watson or the Achilles’ heel of figuring out the rules. Who wants to sit in a room with subject matter experts and three or four IBM engineers talking about what’s important, what questions are asked, and what data are required.

AskJeeves demonstrated decades ago that human crafted rules are Black Diamond ski runs. IBM Watson’s approach is interesting. But what’s fascinating is the uncritical acceptance of IBM’s assertions and the lack of interest in tackling substantive questions. Maybe lunch was cut short?

Stephen E Arnold, August 16, 2017

Demanding AI Labels

August 16, 2017

Artificial intelligence has become a standard staple in technology driven societies.  It still feels like that statement should still only be in science-fiction, but artificial intelligence is a daily occurrence in developed nations.  We just do not notice it.  When something becomes standard practice, one thing we like to do is give it labels.  Guess what Francesco Corea did over at Medium in his article, “Artificial Intelligence Classification Matrix”?  He created terminology to identify companies that specialize in machine intelligence.

Before we delve into his taxonomy, he stated that if the framework for labeling machine intelligence companies is too narrow it is counterproductive to the sector’s purpose of maintaining flexibility.    Corea came up with four ways to classify machine intelligence companies :

i) Academic spin-offs: these are the more long-term research-oriented companies, which tackle problems hard to break. The teams are usually really experienced, and they are the real innovators who make breakthroughs that advance the field.

 

  1. ii) Data-as-a-service (DaaS): in this group are included companies which collect specific huge datasets, or create new data sources connecting unrelated silos.

 

iii) Model-as-a-service (MaaS): this seems to be the most widespread class of companies, and it is made of those firms that are commoditizing their models as a stream of revenues.

 

  1. iv) Robot-as-a-service (RaaS): this class is made by virtual and physical agents that people can interact with. Virtual agents and chatbots cover the low-cost side of the group, while physical world systems (e.g., self-driving cars, sensors, etc.), drones, and actual robots are the capital and talent-intensive side of the coin.

There is also a chart included in the article that explains the differences between high vs. low STM and high vs. low defensibility.  Machine learning companies obviously cannot be categorized into one specific niche.  Artificial intelligence can be applied to nearly any field and situation.

Whitney Grace, August 16, 2017

Chinese Sogou to Invade American Search

August 16, 2017

Having more than its fair share of the world’s population, China doesn’t do anything in small numbers. Search is no exception. It was recently announced that one of China’s most popular search engines has set its scope on the US.

According to TechNode,

Sogou, established in 2004, is the developer of China’s most popular Chinese input method service Sogou Pinin which takes more than 60% share in the mobile market. It’s also the operator of China’s top search engine, behind market leader Baidu, providing search service for Tencent’s WeChat social media platform as well as Microsoft’ Bing for English search in China. Company CEO Wang Xiaochuan disclosed in a recent speech that the firm is pivoting its focus to AI-driven search and navigation in the future.

The company has filed for a US IPO and is now just waiting for the all clear. What will this mean for current US search engines? With their increased focus on AI, Sogou is certainly poised to go head to head with the best the US has to offer, but will it be enough to win the hearts of Americans?

Catherine Lamsfuss, August 16, 2017

AI Will Be Your New Best Friend

August 15, 2017

Technology is an important component of functioning in developed countries.  Despite large segments of the people adopting technology, there is still a huge gap with certain demographic groups based on socioeconomic and age factors.  Senior citizens cannot wrap their head around new technology, while other people cannot afford to buy expensive computers and mobile devices.  Other people are just fearful of what technology can do.  The Verge article, “Google Wants To Make Sure AI Advances Don’t Leave Anyone Behind” explains Google’s endeavors to reach all types of people despite their hesitancies.

Google launched the AI initiative PAIR (People and AI Research) that will study and redesign ways people from all levels of society interact with artificial intelligence.

It’s a broad remit, and an ambitious one. Google says PAIR will look at a number of different issues affecting everyone in the AI supply chain — from the researchers who code algorithms, to the professionals like doctors and farmers who are (or soon will be) using specialized AI tools. The tech giant says it wants to make AI user-friendly, and that means not only making the technology easy to understand (getting AI to explain itself is a known and challenging problem) but also ensuring that it treats its users equally.

One problem with AI is the type of data it is fed.  There is a reason Microsoft’s chatbot modeled after a teenage girl became a cursing racist and anti-Semitic chatbot after one day: users fed it data of this nature.  Google’s PAIR wants to fight prejudice data by using Facets Dive and Facets Overview-two new open-source tools that will allow programmers to see faults in their data.  Facets Dive is being used for facial recognition software and it is sorting testers by country origin and comparing errors with successful identification.

Artificial intelligence is not intentionally biased, human data makes it so.  Do not forget, people, that humans build machines and they reflect their creators.

Whitney Grace, August 15, 2017

Google and Microsoft AI Missteps

August 14, 2017

I read an interesting article called “Former Microsoft Exec Reveals Why Amazon’s Alexa Voice Assistant Beat Cortana.” The passage I noted as thought provoking was this one:

Qi Lu, formerly a Microsoft wizard and now a guru at Baidu allegedly said in this passage from the Verge’s article:

Lu believes Microsoft and Google “made the same mistake” of focusing on the phone and PC for voice assistants, instead of a dedicated device. “The phone, in my view, is going to be, for the foreseeable future, a finger-first, mobile-first device,” explains Lu. “You need an AI-first device to solidify an emerging base of ecosystems.”

Apparently Lu repeated what I think is a key point:

“The phone, in my view, is going to be, for the foreseeable future, a finger-first, mobile-first device,” explains Lu. “You need an AI-first device to solidify an emerging base of ecosystems.”

Several questions occurred to me:

  1. Do Google and Microsoft share a similar context for evaluating high value technologies? Perhaps these two companies are more alike in how they see the world than Amazon?
  2. Are Google and Microsoft reactive; that is, the companies act in a reflexive manner with regard to figuring out how to apply a magnetic technology?
  3. Is Amazon’s competitive advantage an ability to think about an interesting technology in terms of the technology’s ability to augment an existing revenue stream and open new revenue streams?

I don’t have the answer to these questions. If Lu is correct, Amazon has done an end run around Google and Microsoft in terms of talking to gizmos. Can Amazon sustain its technological momentum? With Microsoft floundering with Windows 10 and hardware reliability, it is possible that its applied research is mired in the Microsoft management morass. Google, on the other hand, has its hands full with Amazon taking more product search traffic at a time when Google has to figure out how to solve emotional, political, and ideological issues. Need I say “damore”?

Stephen E Arnold, August 14, 2017

Watson Powers New Translation Earpiece, No Connection Required

August 4, 2017

A start-up out of Australia is leveraging the prowess of IBM’s Watson AI to bring us a wearable translator, dubbed the Translate One2One, that does not require connectivity to function, we learn from “Lingmo Language Translator Earpiece Powered by IBM Watson” at New Atlas. Writer Rich Haridy notes that last year, Waverly Labs found success with its Pilot earpiece. That device was impressive with its near real time translation, but it did depend on a Bluetooth connection. Haridy asserts that New Atlas’ device is the first of its connection-independent kind; he writes:

Lingmo is poised to jump to the head of the class with a system that incorporates proprietary translation algorithms and IBM’s Watson Natural Language Understanding and Language Translator APIs to deal with difficult aspects of language, such as local slang and dialects, without the need for Bluetooth or Wi-Fi connectivity. …

The system currently supports eight languages: Mandarin Chinese, Japanese, French, Italian, German, Brazilian Portuguese, English and Spanish. The in-built microphone picks up spoken phrases, which are translated to a second language within three to five seconds. An app version for iOS is also available that includes speech-to-text and text-to-speech capabilities for a greater number of languages.

The device is expected to be available in July and can be pre-ordered now. A single unit is $179, while a two-piece pack goes for $229. Lingmo launched its first translation device in 2013 and has been refining its tech ever since. Who will be next in the field to go connection-free?

Cynthia Murrell, August 4, 2017

Lost in Translation?

August 3, 2017

Real-time translation is a reality with a host of apps. However, all these apps rely on real-time Cloud Computing for proverbial accuracy. Lingmo One2One Universal Translator seems to be different.

According to a product review published by Forbes and titled Lingmo One2One Universal Translator Preview, the reviewer says:

What gives me pause about the Lingmo, like the other universal translator devices, is the company has no track record in making hardware. Getting the translation stuff right is, I’m sure, hard enough. Getting all that to work in a portable device adds a whole other level of complexity.

Attempts have been made earlier to perfect the translation system, but so far no one has succeeded even decently. The problem is the complexity of human interactions. Though the device is powered by IBM’s AI program Watson, how it manages to store and process the humongous amount of text or voice based communication within the small box is not understandable.

Scientists have been trying to crack the natural language processing problem for a couple of years. Even with the vast amount of resources, it still looks like a distant possibility.

Vishal Ingole, August 3, 2017

Machine Learning Does Not Have the Mad Skills

July 25, 2017

Machine learning and artificial intelligence are computer algorithms that will revolutionize the industry, but The Register explains there is a problem with launching it: “Time To Rethink Machine Learning: The Big Data Gobble Is OFF The Menu.”  The technology industry is spouting that 50 percent of organizations plan to transform themselves with machine learning, but the real truth is that it is less than 15 percent.

The machine learning revolution has supposedly started, but in reality, the cannon has only be fired and the technology has not been implemented.  The problem is that while companies want to use machine learning, they are barely getting off the ground with big data and machine learning is much harder.  Organizations do not have workers with the skills to launch machine learning and the tech industry as a whole has a huge demand for skilled workers.

Part of this inaction comes down to the massive gap between ML (and AI) myth and reality. As David Beyer of Amplify Partners puts it: ‘Too many businesses now are pitching AI almost as though it’s batteries included.’ This is dangerous because it leads companies to either over-invest (and then face a tremendous trough of disillusionment), or to steer clear when the slightest bit of real research reveals that ML is very hard and not something the average Python engineer is going to spin up in her spare time.

Organizations also do not have the necessary amount of data to make machine learning feasible and they also lack the corporate culture to do the required experimentation for machine learning to succeed.

This article shares a story that we have read many times before.  The tech industry gets excited about the newest shiny object, it explodes in popularity, then they realize that the business world is not ready for implementing the technology.

Whitney Grace, July 25, 2017

China Transwarp: Can This Be a Palantir Challenger?

July 24, 2017

One of my sources provided me with a link to a write up which may be translated as “Yujialong star ring technology common to build China Palantir” or “Yu Jialong together star ring technology together to build China’s Palantir.” The link to the original article is here. “Yu Jialong” is a subsidiary of Boone Group, which may no longer be in operation. The point of the write up is that a group of Chinese wizards is working to create a “Chinese Palantir. The group is hoodek up with Six Ring Technology. TenCent is providing some financing.

image

This may be the experts who are tackling the Palantir like system.

There is the challenge of seamlessly importing the file formats used by developers of cyINT eDiscovery systems. I have added it to mist of companies engaged in moving beyond Analyst’s Notebook and Gotham systems.

Stephen E Arnold, July 24,2017

Watson Does Whiteboards

July 24, 2017

A write-up at Helge Scherlund’s eLearning News describes a very useful tool in, “World’s Smartest Active Virtual Meeting Assistant Ricoh.” The tool integrates the IBM Watson AI into an interactive whiteboard system. The press release positions the tool as the future of meetings, but we wonder whether small businesses and schools can afford these gizmos. The write-up includes a nine-minute promotional video that describes the system, so interested readers should check it out. We’re also given a list of key features.

*Easy-to-join meetings: With the swipe of a badge the Intelligent Workplace Solution can log attendance and track key agenda items to ensure all key topics are discussed.

 

*Simple, global voice control of meetings: Once a meeting begins, any employee, whether in-person or located remotely in another country, can easily control what’s on the screen, including advancing slides, all through simple voice commands.

 

*Ability to capture side discussions: During a meeting, team members can also hold side conversations that are displayed on the same Ricoh interactive whiteboard.

 

*Translation of the meeting into another language: The Cognitive Whiteboard can translate speakers’ words into several other languages and display them on screen or in transcript.

I suppose one feature here may also be a thorn in the side of some old-school business people—the system creates a transcript of everything said in each meeting, including side conversations, and sends it to each participant. Auto CYA. The process would take some getting used to, but we can see the advantages for many organizations. Headquartered in Tokyo, Ricoh’s history stretches back to 1936.

Cynthia Murrell, July 24, 2017

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