Palantir: A Unicorn on Steroids
June 24, 2015
Whoa. I read “Palantir Valued At $20 Billion In New Funding Round.” Palantir is not exactly pumping out the marketing collateral. The company is making sales to those who want to squeeze “nuggets” from the hydraulic flow of digital information. What’s remarkable is that the company is selling into a sector which wants to buy, yet Palantir continues to collect money from funding sources.
Is the company in the business of processing data or in the business of making presentations to venture types with open checkbooks?
According to the write up:
Palantir is raising up to $500 million in new capital at a valuation of $20 billion, people briefed on the matter told BuzzFeed News, insisting on anonymity to discuss the confidential deal. The 11-year-old company previously raised money late last year at a $15 billion valuation. The new round of funding, which has not been previously disclosed, reflects investors’ eagerness to gain access to a startup seen as one of the most successful in the world. Little is known about the details of Palantir’s business, beyond reports about its data-processing software being used to fight terror and catch financial criminals.
A couple of observations:
First, the amount of money is impressive, even by Sillycon Valley standards. The investment makes outfits like Digital Reasoning look like paupers.
Second, compared to search centric outfits like Attivio, Coveo, and others working to deliver traditional Fast Search type services, Palantir is in a different league. Attivio and Coveo combined has attracted less than $70 million or so. This amount probably is equivalent to the fees assessed on Palantir’s inflows of cash.
Third, Palantir is a bit like Google with a twist of paranoia. There are unreturned phone calls and unanswered emails. There are legal dust ups sealed away, presumably forever. There are secrets, lots of secrets.
In short, Palantir makes other content processing outfits green with envy. Green. The color of money. With a unicorn on steroids the question becomes, “Will the joints hold up?”
Stephen E Arnold, June 24, 2015
Deep Learning System Surprises Researchers
June 24, 2015
Researchers were surprised when their scene-classification AI performed some independent study, we learn from Kurzweil’s article, “MIT Deep-Learning System Autonomously Learns to Identify Objects.”
At last December’s International Conference on Learning Representations, a research team from MIT demonstrated that their scene-recognition software was 25-33 percent more accurate than its leading predecessor. They also presented a paper describing the object-identification tactic their software chose to adopt; perhaps this is what gave it the edge. The paper’s lead author, and MIT computer science/ engineering associate professor, Antonio Torralba ponders the development:
“Deep learning works very well, but it’s very hard to understand why it works — what is the internal representation that the network is building. It could be that the representations for scenes are parts of scenes that don’t make any sense, like corners or pieces of objects. But it could be that it’s objects: To know that something is a bedroom, you need to see the bed; to know that something is a conference room, you need to see a table and chairs. That’s what we found, that the network is really finding these objects.”
Researchers being researchers, the team is investigating their own software’s initiative. The article tells us:
“In ongoing work, the researchers are starting from scratch and retraining their network on the same data sets, to see if it consistently converges on the same objects, or whether it can randomly evolve in different directions that still produce good predictions. They’re also exploring whether object detection and scene detection can feed back into each other, to improve the performance of both. ‘But we want to do that in a way that doesn’t force the network to do something that it doesn’t want to do,’ Torralba says.”
Very respectful. See the article for a few more details on this ambitious AI, or check out the researchers’ open-access paper here.
Cynthia Murrell, June 24, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
HP Sales Are Slow, But CEO Says Progress
June 24, 2015
According to Computer Weekly, “HP CEO Hails Business Split Progress Amid Downbeat Q2 Revenue Slumps.” HP’s Enterprise Service has the worst revenue reports for the quarter along with several more of its business units with a seven percent net loss. The Enterprise Service saw a sixteen percent loss.
Ironically, the company’s stock rose 1 percent, mostly due to HP expanding into China due to a new partnership with Tsinghua University. The joint venture will focus on developing HP’s H3C’s technology and its China-based server business, supposedly it will have huge implications on the Chinese technology market.
Another piece of news is that HP will split up:
“[CEO Meg ] Whitman also spoke in favour of the progress the company is making with its plans to separate into two publicly traded business entities: one comprised of its consumer PC and printing operations, and the other focused on enterprise hardware, software and services.
The past six months have reinforced Whitman’s conviction that this is the right path for the company to take, and the split is still on course to occur before the end of the firm’s financial year.”
The company wants to increase its revenue, but it needs to cut gross costs across the board. HP is confidant that it will work. Sales will continue to be slow for 2015, but they can still do investment banking things at HP.
Whitney Grace, June 24, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
New Analysis Tool for Hadoop Data from Oracle
June 23, 2015
Oracle offers new ways to analyze Hadoop data, we learn from the brief write-up, “Oracle Zeroes in on Hadoop Data with New Analytics Tool” at PCWorld. Use of the Hadoop open-source distributed file system continues to grow among businesses and other organizations, so it is no surprise to see enterprise software giant Oracle developing such tools. This new software is dubbed Oracle Big Data Spatial and Graph. Writer Katherine Noyes reports:
“Users of Oracle’s database have long had access to spatial and graph analytics tools, which are used to uncover relationships and analyze data sets involving location. Aiming to tackle more diverse data sets and minimize the need for data movement, Oracle created the product to be able to process data natively on Hadoop and in parallel using MapReduce or in-memory structures.
“There are two main components. One is a distributed property graph with more than 35 high-performance, parallel, in-memory analytic functions. The other is a collection of spatial-analysis functions and services to evaluate data based on how near or far something is, whether it falls within a boundary or region, or to process and visualize geospatial data and imagery.”
The write-up notes that such analysis can reveal connections for organizations to capitalize upon, like relationships between customers or assets. The software is, of course, compatible with Oracle’s own Big Data Appliance platform, but can be deployed on other Hadoop and NoSQL systems, as well.
Cynthia Murrell, June 23, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Non Government Statistical Atlas: Filling a Void
June 22, 2015
Short honk. Navigate to “Reviving the Statistical Atlas of the United States with New Data.” Nathan Yau, who bills himself as Superintendent of Flowing Data, produced a graphical statistical atlas. Believe me when I suggest that the US government probably do this type of report. As far as I know, the US government has not and might tie itself in knots trying to match his work. If you are interested in crops and education along with dozens of other data sets about the US, Dr. Yau’s work warrants your time. A happy quack to “some guy with a blog” for this atlas.
Stephen E Arnold, June 23, 2015
Expert Systems Acquires TEMIS
June 22, 2015
In a move to improve its product offerings, Expert System acquired TEMIS. The two companies will combine their assets to create a leading semantic provider for cognitive computing. Reuters described the acquisition in very sparse details: “Expert System Signs Agreement To Acquire French TEMIS SA.”
Reuters describes the merger as:
“Reported on Wednesday that it [Expert System] signed binding agreement to buy 100 percent of TEMIS SA, a French company offering solutions in text analytics
- Deal value is 12 million euros ($13.13 million)”
TEMIS creates technology that helps organizations leverage, manage, and structure their unstructured information assets. It is best known for Luxid, which identifies and extracts information to semantically enrich content with domain-specific metadata.
Expert System, on the other hand, is another semantically inclined company and its flagship product is Cogito. The Cogito software is designed to understand content within unstructured text, systems, and analytics. The goal is give organizations a complete picture of your information, because Cogitio actually understand what is processing.
TEMIS and Expert System have similar goals to make unstructured data useful to organizations. Other than the actual acquisition deal, details on how Expert System plans to use TEMIS have not been revealed. Expert System, of course, plans to use TEMIS to improve its own semantic technology and increase revenue. Both companies are pleased at the acquisition, but if you consider other buy outs in recent times the cost to Expert System is very modest. Thirteen million dollars underscores the valuation of other text analysis companies. Other text analysis companies would definitely cost more than TEMIS.
Whitney Grace, June 22, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Watson and Coffee Shops. Smart Software Needs k\More Than a Latte
June 19, 2015
I read “IBM Watson Analytics Helps Grind Big Data in Unmanned Coffee Shops.” I promised myself I would not call attention to the wild and wonderful Watson public relations efforts. But coffee shops?
The main idea is that:
IBM has worked with Revive Vending to create systems for unmanned coffee shops that tap into the cognitive computing technology of Watson Analytics for data analysis.
Note the verb: past tense. I would have preferred “is working” but presumably Watson is not longer sipping its latte at Revive.
According to the article:
IBM’s cloud-powered analytics service is used to crunch the vending machine data and form a picture of customers. Summerill [a Revive executive] explained that Watson Analytics allows Honest Café to understand which customers sit and have a drink with friends, and which ones dash in to grab a quick coffee while on the move. Transactional data is analyzed to see how people pay for their food and drinks at certain times of the day so that Honest Café can automatically offer relevant promotions and products to individual customers.
The write up also includes a balling statement from my pals at IDC, the outfit which sold my content without my permission on Amazon courtesy of the wizard Dave Schubmehl:
Miya Knights, senior research analyst at IDC, said that the mass of data generated by retailers through networked systems that cover retail activity can be used to support increasingly complex and sophisticated customer interactions.
Okay, but don’t point of sale systems (whether manual or automated) track these data? With a small operation, why not use what’s provided by the POS vendor?
The answer to the question is that IBM is chasing demo customers even to small coffee shops. IDC, ever quick to offer obvious comments without facts to substantiate the assertion, is right there. Why? Maybe IDC sells professional services to IBM?
Where are the revenue reports which substantiate Watson’s market success? Where are substantive case examples from major firms? Where is a public demonstration of Watson using Wikipedia information?
Think about these questions as you sip your cheap 7-11 coffee, gentle reader.
Ponder that there may be nothing substantive to report, so I learn about unmanned coffee shops unable to figure out who bought what without IBM Watson. Overkill? Yep.
Stephen E Arnold, June 19, 2015
Need Confidence in Your Big Data? InfoSphere Delivers Assurances
June 17, 2015
I spotted a tweet about a white paper titled “Improve the Confidence in Your Big Data with IBM InfoSphere.” The write up was a product of Information Asset LLC, a company with which I was not familiar. The link in the tweet was dead, so I located a copy of the white paper on the IBM Web site at this link, which I verified on June 17, 2015. If it is dead when you look for the white paper, take it up with IBM, not me.
The white paper is seven pages long and explains that IBM’s InfoSphere is the hub of some pretty interesting functions; specifically:
- Big Data exploration
- Enhanced 360 [degree] view of the customer
- Application development and testing
- Application efficiency
- Security and compliance
- Application consolidation and retirement
- Data warehouse augmentation
- Operations analysis
- Security/intelligence extension.
I thought InfoSphere was a brand created in 2008 by IBM marketers to group IBM’s different information management software products into one basket. The Big Data thing is a new twist for me.
The white paper takes each of these nine topics and explains them one by one. I found some interesting tidbits in several of the explanations, but I have only enough energy and good humor to tackle one category, Big Data exploration.
The notion of exploring Big Data is an interesting one. I thought one normalized, queried, and reviewed results of a query. The exploration thing is foreign to me. Big Data, by definition, are—well—big. Big collections are data are difficult to explore. I formulate queries, look at results, review clusters, etc. I suppose I am exploring, but I think of the work as routine database look ups. I am so hopelessly old fashioned, aren’t I. Some outfits like Recorded Future generate reports which illustrate certain query results, but we are back to queries, aren’t we.
Here’s what I learned about InfoSphere’s capabilities. Keep in mind that InfoSphere is a collection of discrete software programs and code systems. Data scientists need to explore and mine Big Data to uncover interesting nuggets that are relevant for better decision making. A large hospital system built a detailed model to predict the likelihood that patients with congestive heart failure would be readmitted within 30 days. Smoking status was a key variable that was strongly correlated with the likelihood of readmission. At the outset, only 25 percent of the structured data around smoking status was populated with binary yes/no answers. However, the analytics team was able to increase the population rate for smoking status to 85 percent of the encounters by using content analytics. The content analytics team was also able to use physicians’ and nurses’ notes to unlock additional information, such as smoking duration and frequency. There were a number of reasons for the discrepancy. For example, some patients indicated that they were non-smokers, but text analytics revealed the following in the doctors’ notes: “Patient is restless and asked for a smoking break,” “Patient quit smoking yesterday,” and “Quit.” IBM InfoSphere Big Insights offers strong text analytic capabilities. In addition, IBM InfoSphere Business Glossary provides a repository for key definitions such as “readmission.” IBM InfoSphere Master Data Management provides an Enterprise Master Patient Index to track readmissions for the same patient across multiple hospitals in the same network. Finally, IBM InfoSphere Data Explorer provides robust search capability across unstructured data.
Okay, search is the operative word. I find this fascinating because IBM is working hard to convince me that Watson can ingest information and figure out what it means and then answer questions automatically. For example, if a cancer doctor does not know what treatment to use, Watson will tell her.
I must tell you that this white paper illustrates the fuzzy thinking that characterizes many firms’ approach to information challenges. Remember. The InfoSphere Big Data explorer is just one of nine capabilities of a marketing label.
Useful? Just ring up your local IBM regional office and solve nine problems with that phone call. Magic. Big insights too.
Stephen E Arnold, June 17, 2015
Predictive Analytics Applied to Marketing As a Service
June 17, 2015
In the good old days, content processing provided outputs to those who knew how to ask quite specific questions. Today analytics are predictive and the outputs are packaged to beckon to marketers who are struggling to generate leads and sales.
I read “The Story Behind Syntasa: A Rising Data Analytics Startup With DoD Contractor Roots.” The article is a success story with a dash of emotion and gloss of cheerleading. The company profiled is “a new species of data analytics company, combining national defense expertise with big data marketing technology.” That is an interesting combination.
According to the write up:
The digital marketing and data analytics tech startup offers the “very latest predictive behavioral analytics technology to help enterprises use their large amounts of data and identify actions and outcomes,” said Marwaha, “We do that by providing software that goes through mountains of consumer data gathered by each brand and analyzing click strokes to understand and predict online consumer behavior while they peruse the sites of particular brands.” Syntasa’s CEO added,”[the company] has taken off as more and more enterprises are moving to open source tools like Hadoop and Apache Spark, which can handle large amounts of data. We’ve brought the expertise once used in the federal government’s efforts to fight national security threats through intelligence gathering online, and unleashed it at the enterprise level.”
I noted this passage as well:
When I [author of the article] asked Syntasa’s CEO whether he believes we will begin to see other cyber security companies and intelligence experts expand and/or pivot into marketing/advertising ventures, he offered an interesting counter response: “The converse is more likely. There is a sense of behavioral analytics taking shape in the cyber security market in order to proactively predict where an attack may occur. Which comes first isn’t really the point. The two markets are BOTH now leveraging the power of big data and machine learning to predict events — whether it is leading to a potential threat or a potential customer.”
If you are looking for an outfit with predictive marketing analytics, perhaps Syntasa’s capabilities are spot on for you.
Stephen E Arnold, June 17, 2015
Video and Image Search In the News
June 17, 2015
There’s been much activity around video and image search lately. Is it all public-relations hype, or is there really progress to celebrate? Here are a few examples that we’ve noticed recently.
Fast Company reports on real-time video-stream search service Dextro in, “This Startup’s Side Project Scans Every Periscope Video to Help You Find the Best Streams.” Writer Rose Pastore tells us:
“Dextro’s new tool, called Stream, launches today as a mobile-optimized site that sorts Periscope videos by their content: Cats, computers, swimming pools, and talking heads, to name a few popular categories. The system does not analyze stream text titles, which are often non-descriptive; instead, it groups videos based only on how its algorithms interpret the visual scene being filmed. Dextro already uses this technology to analyze pre-recorded videos for companies … but this is the first time the two-year-old startup has applied its algorithms to live streams.”
Meanwhile, ScienceDaily reveals an interesting development in, “System Designed to Label Visual Scenes Turns Out to Detect Particular Objects Too.” While working on their very successful scene-classification tool, researchers at MIT discovered a side effect. The article explains that, at an upcoming conference:
“The researchers will present a new paper demonstrating that, en route to learning how to recognize scenes, their system also learned how to recognize objects. The work implies that at the very least, scene-recognition and object-recognition systems could work in concert. But it also holds out the possibility that they could prove to be mutually reinforcing.”
Then we have an article from MIT’s Technology Review, “The Machine Vision Algorithm Beating Art Historians at Their Own Game.” Yes, even in the highly-nuanced field of art history, the AI seems to have become the master. We learn:
“The challenge of analyzing paintings, recognizing their artists, and identifying their style and content has always been beyond the capability of even the most advanced algorithms. That is now changing thanks to recent advances in machine learning based on approaches such as deep convolutional neural networks. In just a few years, computer scientists have created machines capable of matching and sometimes outperforming humans in all kinds of pattern recognition tasks.”
Each of these articles is an interesting read, so check them out for more information. It may be a good time to work in the area of image and video search.
Cynthia Murrell, June 17, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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