Does This Count As Irony?
May 16, 2017
Does this count as irony?
Palantir, who has built its data-analysis business largely on its relationships with government organizations, has a Department of Labor analysis to thank for recent charges of discrimination. No word on whether that Department used Palantir software to “sift through” the reports. Now, Business Insider tells us, “Palantir Will Shell Out $1.7 Million to Settle Claims that It Discriminated Against Asian Engineers.” Writer Julie Bort tells us that, in addition to that payout, Palantir will make job offers to eight unspecified Asians. She also explains:
The issue arose because, as a government contractor, Palantir must report its diversity statistics to the government. The Labor Department sifted through these reports and concluded that even though Palantir received a huge number of qualified Asian applicants for certain roles, it was hiring only small numbers of them. Palantir, being the big data company that it is, did its own sifting and produced a data-filled response that it said refuted the allegations and showed that in some tech titles 25%-38% of its employees were Asians. Apparently, Palantirs protestations weren’t enough on to satisfy government regulators, so the company agreed to settle.
For its part, Palantir insists on their innocence but say they settled in order to put the matter behind them. Bort notes the unusual nature of this case—according to the Equal Employment Opportunity Commission, African-Americans, Latin-Americans, and women are more underrepresented in tech fields than Asians. Is the Department of Labor making it a rule to analyze the hiring patterns of companies required to report diversity statistics? If they are consistent, there should soon be a number of such lawsuits regarding discrimination against other groups. We shall see.
Cynthia Murrell, May 16, 2017
Forrester Research Loses Ground with Customer Management Emphasis
May 3, 2017
Yikes, the Wave people may be swamped by red ink. The investor-targeted news site Seeking Alpha asks, “Forrester Research: Is Irony Profitable?” Posted by hedge fund manager Terrier Investing, the article observes that Forrester has been moving away from studies on business technology and toward customer-management research. The write-up reports:
The definition of irony, for $500 please? Forrester’s customers… don’t like what they’re selling. This is unfortunate, because as I explain in my Gartner write-up, selling technology research is actually a great business model in general – the value proposition to clients is strong […] and the recurring annual contracts with strong cash flow characteristics make it a hard business to kill even if you really try. To wit, while Forrester’s revenue growth and margins haven’t been anywhere near their targets for quite some time, the business hasn’t imploded and still throws off strong cash flow despite sales force issues and the ongoing product transition.
Perhaps that strong cash flow will ease the way as Forrester either pivots back toward business technology or convinces their customers to want what they’re now selling. The venerable research firm was founded back in 1983 and is based in Cambridge, Massachusetts.
Cynthia Murrell, May 3, 2017
How Data Science Pervades
May 2, 2017
We think Information Management may be overstating a bit with the headline, “Data Science Underlies Everything the Enterprise Now Does.” While perhaps not underpinning quite “everything,” the use of data analysis has indeed spread throughout many companies (especially larger ones).
Writer Michael O’Connell cites a few key developments over the last year alone, including the rise of representative data, a wider adoption of predictive analysis, and the refinement of customer analytics. He predicts, even more, changes in the coming year, then uses a hypothetical telecom company for a series of examples. He concludes:
You’ll note that this model represents a significant broadening beyond traditional big data/analytics functions. Such task alignment and comprehensive integration of analytics functions into specific business operations enable high-value digital applications ranging far beyond our sample Telco’s churn mitigation — cross-selling, predictive and condition-based maintenance, fraud detection, price optimization, and logistics management are just a few areas where data science is making a huge difference to the bottom line.
See the article for more on the process of turning data into action, as illustrated with the tale of that imaginary telecom’s data-wrangling adventure.
Cynthia Murrell, May 2, 2017
Keyword Search vs. Semantic Search for Patent Seekers
April 26, 2017
The article on BIP Counsels titled An Introduction to Patent Search, Keyword Search, and Semantic Searches offers a brief overview of the differences between keyword, and semantic search. The article is geared towards inventors and technologists in the early stages of filing a patent application. The article states,
If an inventor proceeds with the patent filing process without performing an exhaustive prior art search, it may hamper the patent application at a later point, such as in the prosecution process. Hence, a thorough search involving all possible relevant techniques is always advisable… Search tools such as ‘semantic search assistant’ help the user find similar patent families based on freely entered text. The search method is ideal for concept based search.
Ultimately the article fails to go beyond the superficial when it comes to keyword and semantic search. One almost suspects that the author (BananaIP patent attorneys) wants to send potential DIY-patent researchers running into their office for help. Yes, terminology plays a key role in keyword searches. Yes, semantic search can help narrow the focus and relevancy of the results. If you want more information than that, you may want to visit the patent attorney. But probably not the one that wrote this article.
Chelsea Kerwin, April 26, 2017
AI Might Not Be the Most Intelligent Business Solution
April 21, 2017
Big data was the buzzword a few years ago, but now artificial intelligence is the tech jargon of the moment. While big data was a more plausible solution for companies trying to mine information from their digital data, AI is proving difficult to implement. Forbes discusses AI difficulties in the article, “Artificial Intelligence Is Powerful Stuff, But Difficult To Scale To Real-Life Business.”
There is a lot of excitement brewing around machine learning and AI business possibilities, while the technology is ready for use, workers are not. People need to be prepped and taught how to use AI and machine learning technology, but without the proper lessons, it will hurt a company’s bottom line. The problem comes from companies rolling out digital solutions, without changing the way they conduct business. Workers cannot just adapt to changes instantly. They need to feel like they are part of the solution, instead of being shifted to the side in the latest technological trend.
CIO for the Federal Communications Commission Dr. David Bray said that:
The growth of AI may shift thinking in organizations. ‘At the end of the day, we are changing what people are doing,; Bray says. ‘You are changing how they work, and they’re going to feel threatened if they’re not bought into the change. It’s almost imperative for CIOs to really work closely with their chief executive officers, and serve as an internal venture capitalist, for how we bring data, to bring process improvements and organizational performance improvements – and work it across the entire organization as a whole.
Artificial intelligence and machine learning are an upgrade to not only a company’s technology but also how a company conducts business. Business processes will need to be updated to integrate the new technology, but also how workers will use and interface it. Businesses will continue facing problems if they think that changing technology, but not their procedures are the final solution.
Whitney Grace, April 21, 2017
How to Use a Quantum Computer
April 20, 2017
It is a dream come true that quantum computers are finally here! But how are we going to use them? PC World discusses the possibilities in, “Quantum Computers Are Here—But What Are They Good For?” D-Wave and IBM both developed quantum computers and are trying to make a profit from them by commercializing their uses. Both companies agree, however, that quantum computers are not meant for everyday computer applications.
What should they be used for?
Instead, quantum systems will do things not possible on today’s computers, like discovering new drugs and building molecular structures. Today’s computers are good at finding answers by analyzing information within existing data sets, but quantum computers can get a wider range of answers by calculating and assuming new data sets. Quantum computers can be significantly faster and could eventually replace today’s PCs and servers. Quantum computing is one way to advance computing as today’s systems reach their physical and structural limits.
What is astounding about quantum computers are their storage capabilities. IBM has a 5-qubit system and D-Wave’s 2000Q has 2,000 qubit. IBM’s system is more advanced in technology, but D-Wave’s computer is more practical. NASA has deployed the D-Wave 2000Q for robotic space missions; Google will use it for search, image labeling, and voice recognition; and Volkswagen installed it to study China’s traffic patterns.
D-Wave also has plans to deploy its quantum system to the cloud. IBM’s 5-qubit computer, on the other hand, is being used for more scientific applications such as material sciences and quantum dynamics. Researchers can upload sample applications to IBM’s Quantum Experience to test them out. IBM recently launched the Q program to build a 50-qubit machine. IBM also wants to push their quantum capabilities in the financial and economic sector.
Quantum computers will be a standard tool in the future, just as the desktop PC was in the 1990s. By then, quantum computers will respond more to vocal commands than keyboard inputs.
Whitney Grace, April 20, 2017
Smart Software, Dumb Biases
April 17, 2017
Math is objective, right? Not really. Developers of artificial intelligence systems, what I call smart software, rely on what they learned in math school. If you have flipped through math books ranging from the Googler’s tome on artificial intelligence Artificial Intelligence: A Modern Approach to the musings of the ACM’s journals, you see the same methods recycled. Sure, the algorithms are given a bath and their whiskers are cropped. But underneath that show dog’s sleek appearance, is a familiar pooch. K-means. We have k-means. Decision trees? Yep, decision trees.
What happens when developers feed content into Rube Goldberg machines constructed of mathematical procedures known and loved by math wonks the world over?
The answer appears in “Semantics Derived Automatically from Language Corpora Contain Human Like Biases.” The headline says it clearly, “Smart software becomes as wild and crazy as a group of Kentucky politicos arguing in a bar on Friday night at 2:15 am.”
Biases are expressed and made manifest.
The article in Science reports with considerable surprise it seems to me:
word embeddings encode not only stereotyped biases but also other knowledge, such as the visceral pleasantness of flowers or the gender distribution of occupations.
Ah, ha. Smart software learns biases. Perhaps “smart” correlates with bias?
The canny whiz kids who did the research crawfish a bit:
We stress that we replicated every association documented via the IAT that we tested. The number, variety, and substantive importance of our results raise the possibility that all implicit human biases are reflected in the statistical properties of language. Further research is needed to test this hypothesis and to compare language with other modalities, especially the visual, to see if they have similarly strong explanatory power.
Yep, nothing like further research to prove that when humans build smart software, “magic” happens. The algorithms manifest biases.
What the write up did not address is a method for developing less biases smart software. Is such a method beyond the ken of computer scientists?
To get more information about this question, I asked on the world leader in the field of computational linguistics, Dr. Antonio Valderrabanos, the founder and chief executive officer at Bitext. Dr. Valderrabanos told me:
We use syntactic relations among words instead of using n-grams and similar statistical artifacts, which don’t understand word relations. Bitext’s Deep Linguistics Analysis platform can provide phrases or meaningful relationships to uncover more textured relationships. Our analysis will provide better content to artificial intelligence systems using corpuses of text to learn.
Bitext’s approach is explained in the exclusive interview which appeared in Search Wizards Speak on April 11, 2017. You can read the full text of the interview at this link and review the public information about the breakthrough DLA platform at www.bitext.com.
It seems to me that Bitext has made linguistics the operating system for artificial intelligence.
Stephen E Arnold, April 17, 2017
Forrester: Enterprise Content Management Misstep
April 14, 2017
I have stated in the past that mid tier consulting firms—that is, outfits without the intellectual horsepower of a McKinsey, Bain, or BCG—generate work that is often amusing, sometimes silly, and once in a while just stupid. I noted an error which is certainly embarrassing to someone, maybe even a top notch expert at mid tier Forrester. The idea for a consulting firm is to be “right” and to keep the customer (in this case Hyland) happy. Also, it is generally good to deliver on what one promises. You know, the old under promise, over deliver method.
How about being wrong, failing, and not delivering at all? Read on about Forrester and content management.
Context
I noted the flurry of news announcements about Forrester, a bigly azure-chip consulting firm. A representative example of these marketing news things is “Microsoft, OpenText, IBM Lead Forrester’s ECM Wave in Evolving Market.” The write up explains that the wizards at Forrester have figured out the winners and losers in enterprise content management. As it turns out, the experts at Forrester do a much better job of explaining their “perception” of content management that implementing content management.
How can this be? Paid experts who cannot implement content management for reports about content management? Some less generous people might find this a minor glitch. I think that consultants are pretty good at cooking up reports and selling them. I am not too confident that mid tier consulting firms and even outfits like Booz, Allen has dotted their “i’s” and crossed their “t’s.”
Let me walk you through this apparent failure of Forrester to make their reports available to a person interested in a report. This example concerns a Forrester reviewed company called Hyland and its OnBase enterprise content management system.
The deal is that Hyland allows a prospect to download a copy of the Forrester report in exchange for providing contact information. Once the contact information is accepted, the potential buyer of OnBase is supposed to be able to download a copy of the Forrester report. This is trivial stuff, and we are able to implement the function when I sell my studies. Believe me. If we can allow registered people to download a PDF, so can you.
The Failure
I wanted a copy of “The Forrester Wave: ECM Business Content Services.” May I illustrate how Forrester’s enterprise content management system fails its paying customers and those who register to download these high value, completely wonderful documents.
Step 1: Navigate to this link for OnBase by Hyland, one of the vendors profiled in the allegedly accurate, totally object Forrester report
Step 2: Fill out the form so Hyland’s sales professionals can contact you in hopes of selling you the product which Forrester finds exceptional
Note the big orange “Download Now” button. I like the “now” part because it means that with one click I get the high-value, super accurate report.
Step 3: Click on one of these two big green boxes:
I tested both, and both return the same high value, super accurate, technically wonderful reports—sort of.
A Peek at the DeepMind Research Process
April 14, 2017
Here we have an example of Alphabet Google’s organizational prowess. Business Insider describes how “DeepMind Organises Its AO Researchers Into ‘Strike Teams’ and ‘Frontiers’.” Writer Sam Shead cites a report by Madhumita Murgia as described in the Financial Times. He writes:
Exactly how DeepMind’s researchers work together has been something of a mystery but the FT story sheds new light on the matter. Researchers at DeepMind are divided into four main groups, including a ‘neuroscience’ group and a ‘frontiers’ group, according to the report. The frontiers group is said to be full of physicists and mathematicians who are tasked with testing some of the most futuristic AI theories. ‘We’ve hired 250 of the world’s best scientists, so obviously they’re here to let their creativity run riot, and we try and create an environment that’s perfect for that,’ DeepMind CEO Demis Hassabis told the FT. […]
DeepMind, which was acquired by Google in 2014 for £400 million, also has a number of ‘strike teams’ that are set up for a limited time period to work on particular tasks. Hassabis explained that this is what DeepMind did with the AlphaGo team, who developed an algorithm that was able to learn how to play Chinese board game Go and defeat the best human player in the world, Lee Se-dol.
Here’s a write-up we did about that significant AlphaGo project, in case you are curious. The creative-riot approach Shead describes is in keeping with Google’s standard philosophy on product development—throw every new idea at the wall and see what sticks. We learn that researchers report on their progress every two months, and team leaders allocate resources based on those reports. Current DeepMind projects include algorithms for healthcare and energy scenarios.
Hassabis launched DeepMind in London in 2010, where offices remain after Google’s 2014 acquisition of the company.
Cynthia Murrell, April 14, 2017
Motivations for Microsoft LinkedIn Purchase
April 13, 2017
We thought the purchase was related to Microsoft’s in-context, real-time search within an Office application. However, according to BackChannel’s article, “Now We Know Why Microsoft Bought LinkedIn,” it’s all about boosting the company’s reputation. Writer Jessi Hempel takes us back to 2014, when CEO Satya Nadella was elevated to his current position. She reminds of the fiscal trouble Microsoft was having at the time, then continues:
It also had a lousy reputation, particularly in Silicon Valley, where camaraderie and collaboration are hallmarks of tech’s evolution and every major player enjoys frenemy status with its adversaries. Microsoft wasn’t a company that partnered with outsiders. It scorned the open-source community and looked down its nose at tech upstarts. In a public conversation with Marc Andreessen in October 2014, investor Peter Thiel called Microsoft a bet ‘against technological innovation.’
The write-up goes on to detail ways Nadella has turned the company around financially. According to Hempel, the LinkedIn purchase, and the installation of its founder Reid Hoffman on the board, are in an effort to boost Microsoft’s reputation. Hembel observes:
As a board member, Hoffman will be Microsoft’s ambassador in the Valley. Among a core group of constituents for whom Microsoft may not factor into conversation, Hoffman will work to raise its profile. The trickle-down effect has the potential to be tremendous as Microsoft competes for partners and talent.
See the article for more information on the relationship between the Nietzsche-quoting Nadella and the charismatic tech genius Hoffman, as well as changes Microsoft has been making to boost both its reputation and its bottom line.
Cynthia Murrell, April 13, 2017
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