Is Your Fish Tank Spying on You?
August 17, 2017
The search for information never ends. We learned in the Darktrace Global Threat Report about a hacked fish tank. A smart fish tank was compromised. The fish tank was hacked. Darktrace’s technology speared the attempt. The bad guys have not yet been been converted to sushi.
Stephen E Arnold, August 17, 2017
Gaze into the Search Crystal Ball
August 17, 2017
The way we consume Internet content has drastically changed. We are no longer tethered to hulking desk top computers, instead we can browse the Web as easily as drive a car. It goes without saying that the way we search and consume content will change. We have already seen changes, such as more ads appearing on movie, social media, and news Web sites. Google Answers and Google My Business are also affecting how we access content without needing to visit a Web site. Entrepreneur shares predictions for search and content in the article, “How Changes To The Way We Search Will Impact Businesses.”
While the majority of us still type our search queries, the rise of digital assistants has made vocal search gain traction. Vocal search means that we are using natural language to ask digital assistants queries. This is actually better for search results, because we tend to simplify questions when we talk and search engines like simple search queries. Digital assistants also change how we interact/consume our information. Instead of delving into the results ourselves, we rely on a third-party device to provide it to us. It will also change how we shop, especially if Amazon or another shopping site has a digital assistant.
Users are also seeking out an “everything-in platform,” where all the services they need from payments, shopping, and even ordering a sandwich are in one application.
Facebook Instant Articles and Google AMP don’t take users too far away from the originating platform source, enabling them to return to whatever they were doing before something caught their eye. Solutions like Facebook Store integrate products for an in-platform shopping experience, tightening the gap between product discovery and purchase, while directing users away from Google’s fairly limitless shopping mall of possibilities.
Hyper-personalization might be the creepiest and have the most impact. Search engines already gather information about us from our queries and then target us with related ads. However, it can get even worse with beacon technology that can track and recommend services/products to us based on a store we just visited or where we are traveling too. It will be the capitalist version of Big Brother.
Whitney Grace, August 17, 2017
Google Amps Ads to New Annoying Levels
August 17, 2017
Today, Google is synonymous with search, as they’ve worked very hard to ensure. But search has changed, and not always for the good. One of Google’s hallmark principles at the beginning of their existence was to provide an unbiased search engine with any additions only being to enhance the user experience. Nowadays, though, it seems like Google looks like every other search engine, littered with Ads and flashing videos.
Not impressed with these changes, Wired recently called the search giant out on their recent addition of automatically-playing movie trailers, saying ‘enough is enough’.
Showing a few ads in the image search system isn’t a bad thing. But it shows just how much Google’s thinking has changed. Google’s not a scrappy startup anymore. It’s the world’s most valuable company, and its investors want results. And without much serious competition, the risk of customers bolting for another search engine is pretty low.
Wired is spot on, of course, but what if customers did start trickling out to other search engines that adhere to Google’s original principles and ideologies?
Catherine Lamsfuss, August 17, 2017
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:
- How much does it cost to outfit 64 IBM Power 8 servers to perform this magic?
- How many Nvidia GPUs are needed?
- How many Intel CPUs are needed?
- How much RAM is required in each server?
- 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.
- 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.
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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
HonkinNews for August 15, 2017 Now Available
August 15, 2017
This week’s HonkinNews has been whipped by the Google memo cyclone. The search and content processing news has been slammed into the emotional, subjective, political malestrom of Google management policies. We tackle this issue from the point of view of a science club member. How did Google respond to an emotional issue? Why did an unknown Googler become the digital equivalent of Lady Gaga? What do really smart people “love”? This week’s program answers these questions. Plus, you will learn about a quick and painless way to get your IBM Watson system running as quickly as a young Hussain Bolt. We reveal the fture of search. Hint: You will not have to read, which is great if you have the use of your eyeballs. If not, well, we don’t have an answer to that based on what we learned about the future of search. We reported that a blog about “real” publishers revealed what newspapers really want and need. Will a Chrysler-style bail out do the trick for this pround crew? What about a handful of SNAP cards? Even more interesting is access to at least one SOCOM team of skilled operators. Ah, newspaper publishers. Ever fascinating. We provide an insiders’ tip about Internet-enabled fish tanks. What’s fish got to do with anything? Watch and find out in the August 15, 2017, edition of HonkinNews. You can view this week’s program at this link.
Kenny Toth, August 15, 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
Bannon Threatens Antitrust on Google and Facebook
August 15, 2017
During a time when the left and right seem further apart than ever before an odd, unexpected leak from within the white house has emerged. According to The Atlantic,
Steve Bannon, the chief strategist to President Donald Trump, believes Facebook and Google should be regulated as public utilities, according to an anonymously sourced report in The Intercept. This means they would get treated less like a book publisher and more like a telephone company. The government would shorten their leash, treating them as privately owned firms that provide an important public service.
Previously, only the far left has voiced such opinions making this questionable. Are the motives altruistic or monetary in nature? If such a move actually were to happen the way business is done at Google and Facebook would drastically change.
The article goes on to point out why and how Bannon’s musings on tech giants will never happen under the current administration, but regardless of one’s political ways, the fact that antitrust and online giants are being discussed together might signal the end of an era.
Catherine Lamsfuss, 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:
- 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?
- 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?
- 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
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