Could AI Spell Doom for Marketers?
December 1, 2016
AI is making inroads into almost every domain; marketing is no different. However, inability of AI to be creative in true sense may be a major impediment.
The Telegraph in a feature article titled Marketing Faces Death by Algorithm Unless It Finds a New Code says:
Artificial intelligence (AI) is one of the most-hyped topics in advertising right now. Brands are increasingly finding that they need to market to intelligent machines in order to reach humans, and this is set to transform the marketing function.
The problem with AI, as most marketers agree is its inability to imitate true creativity. As the focus of marketing is shifting from direct product placement to content marketing, the importance of AI becomes even bigger. For instance, a clothing company cannot analyze vast amounts of Big Data, decipher it and then create targeted advertising based on it. Algorithms will play a crucial role in it. However, the content creation will ultimately require human touch and intervention.
As it becomes clear here:
While AI can build a creative idea, it’s not creative “in the true sense of the word”, according to Mr Cooper. Machine learning – the driving technology behind how AI can learn – still requires human intelligence to work out how the machine would get there. “It can’t put two seemingly random thoughts together and recognize something new.
The other school of thought says that what AI lacks is not creativity, but processing power and storage. It seems we are moving closer to bridging this gap. Thus when AI closes this gap, will most occupations, including, creative and technical become obsolete?
Vishal Ingole, December 1, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Comprehensive Search System Atlas Recall Enters Open Beta
December 1, 2016
We learn about a new way to search nearly everything one has encountered digitally from TechCrunch’s article, “Atlas Recall, a Search Engine for Your Entire Digital Live, Gets an Open Beta and $20M in Backing.” The platform is the idea of Atlas Informatics CEO, and Napster co-founder, Jordan Ritter, a man after our own hearts. When given funding and his pick of projects, Ritter says, he “immediately” chose to improve the search experience.
The approach the Atlas team has devised may not be for everyone. It keeps track of everything users bring up on their computers and mobile devices (except things they specifically tell it not to.) It brings together data from disparate places like one’s Facebook, Outlook, Spotlight, and Spotify accounts and makes the data available from one cloud-based dashboard.
This does sound extremely convenient, and I don’t doubt the company’s claim that it can save workers hours every week. However, imagine how much damage a bad actor could do if, hypothetically, they were able to get in and search for, say, “account number” or “eyes only.” Make no mistake, security is a top priority for Atlas, and sensible privacy measures are in place. Besides, the company vows, they will not sell tailored (or any) advertising, and are very clear that each user owns their data. Furthermore, Atlas maintains they will have access to metadata, not the actual contents of users’ files.
Perhaps for those who already trust the cloud with much of their data, this arrangement is an acceptable risk. For those potential users, contributor Devin Coldewey describes Atlas Recall:
Not only does it keep track of all those items [which you have viewed] and their contents, but it knows the context surrounding them. It knows when you looked at them, what order you did so in, what other windows and apps you had open at the same time, where you were when you accessed it, who it was shared with before, and tons of other metadata.
The result is that a vague search, say ‘Seahawks game,’ will instantly produce all the data related to it, regardless of what silo it happens to be in, and presented with the most relevant stuff first. In that case maybe it would be the tickets you were emailed, then nearby, the plans you made over email with friends to get there, the Facebook invite you made, the articles you were reading about the team, your fantasy football page. Click on any of them and it takes you straight there. …
When you see it in action, it’s easy to imagine how quickly it could become essential. I happen to have a pretty poor memory, but even if I didn’t, who wants to scrub through four different web apps at work trying to find that one PDF? Wouldn’t it be nice to just type in a project name and have everything related to it — from you and from coworkers — pop up instantly, regardless of where it ‘lives’?
The main Atlas interface can be integrated with other search engines like Google and Spotlight, so users can see aggregated results when they use those, too. Interested readers may want to navigate to the article and view the embedded sales video, shorter than two minutes, which illustrates the platform. If you’re interested in the beta, you can sign up here (scroll down to “When can I start using Atlas?”). Founded in 2015, Atlas Informatics is based in Seattle. As of this writing, they are also hiring developers and engineers.
Cynthia Murrell, December 01, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Microsoft: On the Bandwagon Singing Me Too
November 30, 2016
In my dead tree copy of the November 21, 2016, New York Times (which just report a modest drop in profits), I read a bit of fluff called “Microsoft Spends Big to Build a Computer Out of Science Fiction.” (If you have to pay to view the source, don’t honk at Beyond Search. Let your favorite national newspaper know directly.)
The main point of the PR piece was to make clear that Microsoft is not lagging behind the Alphabet Google thing in quantum computing. Also, Microsoft is not forking over a measly couple of hundred bucks. Nope, Microsoft is spending “big.” I learned from the write up:
There is a growing optimism in the tech world that quantum computers, super powerful devices that were once the stuff of science fiction, are possible — and may even be practical.
I think “spending” is a nice way to say “betting.”
I learned:
In the exotic world of quantum physics, Microsoft has set itself apart from its competitors by choosing a different path. The company’s approach is based on “braiding” particles known as anyons — which physicists describe as existing in just two dimensions — to form the building blocks of a supercomputer that would exploit the unusual physical properties of subatomic particles.
One problem. The Google DWave gizmos are not exactly ready for use in your mobile phone. The Microsoft approach is the anyon, and it is anyone’s guess if the Microsofties can make the gizmo do something useful for opening Word or, like IBM, treat cancer or, like Google, “solve death.”
Where on the journey to the anyon is Microsoft? It seems that this sentence suggests that Microsoft is just about ready to start thinking about planning a trip down computing lane:
Once we get the first qubit figured out, we have a road map that allows us to go to thousands of qubits in a rather straightforward way,” Mr. Holmdahl [a Microsoftie who has avoided termination] said.
Yep, get those qubits working and then one can solve problems in quantum physics or perhaps get Microsoft Word’s auto numbering system to work. Me too, me too. Do you hear the singing? I do.
Stephen E Arnold, November 30, 2016
Emphasize Data Suitability over Data Quantity
November 30, 2016
It seems obvious to us, but apparently, some folks need a reminder. Harvard Business Review proclaims, “You Don’t Need Big Data, You Need the Right Data.” Perhaps that distinction has gotten lost in the Big Data hype. Writer Maxwell Wessel points to Uber as an example. Though the company does collect a lot of data, the key is in which data it collects, and which it does not. Wessel explains:
In an era before we could summon a vehicle with the push of a button on our smartphones, humans required a thing called taxis. Taxis, while largely unconnected to the internet or any form of formal computer infrastructure, were actually the big data players in rider identification. Why? The taxi system required a network of eyeballs moving around the city scanning for human-shaped figures with their arms outstretched. While it wasn’t Intel and Hewlett-Packard infrastructure crunching the data, the amount of information processed to get the job done was massive. The fact that the computation happened inside of human brains doesn’t change the quantity of data captured and analyzed. Uber’s elegant solution was to stop running a biological anomaly detection algorithm on visual data — and just ask for the right data to get the job done. Who in the city needs a ride and where are they? That critical piece of information let the likes of Uber, Lyft, and Didi Chuxing revolutionize an industry.
In order for businesses to decide which data is worth their attention, the article suggests three guiding questions: “What decisions drive waste in your business?” “Which decisions could you automate to reduce waste?” (Example—Amazon’s pricing algorithms) and “What data would you need to do so?” (Example—Uber requires data on potential riders’ locations to efficiently send out drivers.) See the article for more notes on each of these guidelines.
Cynthia Murrell, November 30, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Wisdom from the First OReilly AI Conference
November 28, 2016
Forbes contributor Gil Press nicely correlates and summarizes the insights he found at September’s inaugural O’Reilly AI Conference, held in New York City, in his article, “12 Observations About Artificial Intelligence from the O’Reily AI Conference.” He begins:
At the inaugural O’Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. … Here’s a summary of what I heard there, embellished with a few references to recent AI news and commentary.
Here are Press’ 12 observations; check out the article for details on any that spark your interest: “AI is a black box—just like humans”; “AI is difficult”; “The AI driving driverless cars is going to make driving a hobby. Or maybe not”; “AI must consider culture and context”; “AI is not going to take all our jobs”; “AI is not going to kill us”; “AI isn’t magic and deep learning is a useful but limited tool”; “AI is Augmented Intelligence”; “AI changes how we interact with computers—and it needs a dose of empathy”; “AI should graduate from the Turing Test to smarter tests”; “AI according to Winston Churchill”; and “AI continues to be possibly hampered by a futile search for human-level intelligence while locked into a materialist paradigm.”
It is worth contemplating the point Press saved for last—are we even approaching this whole AI thing from the most productive angle? He ponders:
Is it possible that this paradigm—and the driving ambition at its core to play God and develop human-like machines—has led to the infamous ‘AI Winter’? And that continuing to adhere to it and refusing to consider ‘genuinely new ideas,’ out-of-the-dominant-paradigm ideas, will lead to yet another AI Winter? Maybe, just maybe, our minds are not computers and computers do not resemble our brains? And maybe, just maybe, if we finally abandon the futile pursuit of replicating ‘human-level AI’ in computers, we will find many additional–albeit ‘narrow’–applications of computers to enrich and improve our lives?
I think Press is on to something. Perhaps we should admit that anything approaching Rosie the Robot is still decades away (according to conference presenter Oren Etzioni). At this early date, we may do well to accept and applaud specialized AIs that do one thing very well but are completely ignorant of everything else. After all, our Roombas are unlikely to attempt conquering the world.
Cynthia Murrell, November 28, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Machine Learning Does Not Have All the Answers
November 25, 2016
Despite our broader knowledge, we still believe that if we press a few buttons and press enter computers can do all work for us. The advent of machine learning and artificial intelligence does not repress this belief, but instead big data vendors rely on this image to sell their wares. Big data, though, has its weaknesses and before you deploy a solution you should read Network World’s, “6 Machine Learning Misunderstandings.”
Pulling from Juniper Networks’s security intelligence software engineer Roman Sinayev explains some of the pitfalls to avoid before implementing big data technology. It is important not to take into consideration all the variables and unexpected variables, otherwise that one forgotten factor could wreck havoc on your system. Also, do not forget to actually understand the data you are analyzing and its origin. Pushing forward on a project without understanding the data background is a guaranteed fail.
Other practical advice, is to build a test model, add more data when the model does not deliver, but some advice that is new even to us is:
One type of algorithm that has recently been successful in practical applications is ensemble learning – a process by which multiple models combine to solve a computational intelligence problem. One example of ensemble learning is stacking simple classifiers like logistic regressions. These ensemble learning methods can improve predictive performance more than any of these classifiers individually.
Employing more than one algorithm? It makes sense and is practical advice why did that not cross our minds? The rest of the advice offered is general stuff that can be applied to any project in any field, just change the lingo and expert providing it.
Whitney Grace, November 25, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
The Noble Quest Behind Semantic Search
November 25, 2016
A brief write-up at the ontotext blog, “The Knowledge Discovery Quest,” presents a noble vision of the search field. Philologist and blogger Teodora Petkova observed that semantic search is the key to bringing together data from different sources and exploring connections. She elaborates:
On a more practical note, semantic search is about efficient enterprise content usage. As one of the biggest losses of knowledge happens due to inefficient management and retrieval of information. The ability to search for meaning not for keywords brings us a step closer to efficient information management.
If semantic search had a separate icon from the one traditional search has it would have been a microscope. Why? Because semantic search is looking at content as if through the magnifying lens of a microscope. The technology helps us explore large amounts of systems and the connections between them. Sharpening our ability to join the dots, semantic search enhances the way we look for clues and compare correlations on our knowledge discovery quest.
At the bottom of the post is a slideshow on this “knowledge discovery quest.” Sure, it also serves to illustrate how ontotext could help, but we can’t blame them for drumming up business through their own blog. We actually appreciate the company’s approach to semantic search, and we’d be curious to see how they manage the intricacies of content conversion and normalization. Founded in 2000, ontotext is based in Bulgaria.
Cynthia Murrell, November 25, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Keeping Current with Elastic.co
November 24, 2016
Short honk. If you want to keep up with Elastic and Elasticsearch, the company’s “This Week in Elasticsearch and Apache Lucene” may be of interest. The weekly posting includes information about commits, releases, and training. Unlike the slightly crazed, revenue challenged open source search vendors, Elastic.co provides factual information about the plumbing for the search and retrieval system. We found the “Ongoing Changes” section useful and interesting. The idea is that one can keep track of certain features, methods, and issues by scanning a list. The short description of an issue, for instance, includes a link to additional information. Highly recommended for those hooked on Elastic.co’s free and open source solution or the for fee products and services the company offers.
Stephen E Arnold, November 24, 2016
Do Not Forget to Show Your Work
November 24, 2016
Showing work is messy, necessary step to prove how one arrived at a solution. Most of the time it is never reviewed, but with big data people wonder how computer algorithms arrive at their conclusions. Engadget explains that computers are being forced to prove their results in, “MIT Makes Neural Networks Show Their Work.”
Understanding neural networks is extremely difficult, but MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a way to map the complex systems. CSAIL figured the task out by splitting networks in two smaller modules. One for extracting text segments and scoring according to their length and accordance and the second module predicts the segment’s subject and attempts to classify them. The mapping modules sounds almost as complex as the actual neural networks. To alleviate the stress and add a giggle to their research, CSAIL had the modules analyze beer reviews:
For their test, the team used online reviews from a beer rating website and had their network attempt to rank beers on a 5-star scale based on the brew’s aroma, palate, and appearance, using the site’s written reviews. After training the system, the CSAIL team found that their neural network rated beers based on aroma and appearance the same way that humans did 95 and 96 percent of the time, respectively. On the more subjective field of “palate,” the network agreed with people 80 percent of the time.
One set of data is as good as another to test CSAIL’s network mapping tool. CSAIL hopes to fine tune the machine learning project and use it in breast cancer research to analyze pathologist data.
Whitney Grace, November 24, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Dawn of Blockchain Technology
November 24, 2016
Blockchain technology though currently powers the Bitcoin and other cryptocurrencies, soon the technology might find takers in mainstream commercial activities.
Blockgeeks in an in-depth article guide titled What Is Blockchain Technology? A Step-By-Step Guide for Beginners says:
The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.
Without getting into how the technology works, it would be interesting to know how and where the revolutionary technology can be utilized. Due to its inherent nature of being incorruptible due to human intervention and non-centralization, blockchain has numerous applications in the field of banking, remittances, shared economy, crowdfunding and many more, the list is just endless.
The technology will be especially helpful for people who transact over the Web and as the article points out:
Goldman Sachs believes that blockchain technology holds great potential especially to optimize clearing and settlements, and could represent global savings of up to $6bn per year.
Governments and commercial establishment, however, are apprehensive about it as blockchain might end their control over a multitude of things. Just because blockchain never stores data at one location. This also is the reason why Bitcoin is yet to gain full acceptance. But, can a driving force like blockchain technology that will empower the actual users can be stopped?
Vishal Ingole, November 24, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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