The Dell-EMC Merger and the Googlization of IT

Yes, Joe Tucci is a great salesman and Michael Dell is the ultimate entrepreneur, but it is Google that is really behind the $67 billion merger. Tucci: “The waves of change we now see in our industry are unprecedented and, to navigate this change, we must create a new company for a new era.” In other words, we must survive in the digital natives era, ushered in by Google, and magnified by the likes of Amazon and Facebook.

To understand what Tucci calls “the new world order,” let’s take a quick tour of the old one, to better understand how the digital natives forced Dell and EMC into the largest tech acquisition in history. Dell and EMC were the two most successful U.S. stocks in the 1990s, appreciating more than any other stock over that booming decade.  They rode on a new tidal wave of digital data, unleashed by the advent of the PC and the networking of PCs in 1980s.

As a result, between 1990 and 2000, the structure of the IT industry has changed for the first—and so far, the last—time, expanding to include large vendors focused on one layer of the IT stack: Intel in semi-conductors, EMC in storage, Cisco in networking, Microsoft in operating systems, Oracle in databases. IBM—the dominant player in the previous era of vertically-integrated, “one-stop-shopping” IT vendors—saved itself from the fate of DEC, Wang, and Prime (all, like EMC, based in Massachusetts) by focusing on services.

The restructured IT industry, and specifically, the focused, “best-in-class” vendors, answered a pressing business need. Digitization and the rapid growth of data unleashed new business requirements and opportunities that called for new ways to sell and buy IT.

There were new business needs for storing much larger volumes of data, mining the data for new market insights, and providing better service to customers by making increasingly “mission-critical” computer systems available 24/7. New IT buyers, such as executives in leading-edge IT departments, business executives impatient with their IT departments, or IT  executives that were asked to take over the out-of-control IT systems acquired by the business units, eschewed the vertically-integrated IT vendors in favor of the new focused competitors, embracing enthusiastically the new “mix and match” IT mentality.

The 2000s were a decade of more-of-the-same with the industry and IT buyers recuperating for a long time from Y2K and the implosion of the dot-com bubble, and going through two recessions. IBM (minus its PC business) and HP (plus Compaq, a successful, focused, PC vendor, like Dell) were the only large “one-stop-shopping” vendors to survive (Sun Microsystems did not). Dell tried, not too successfully, to expand its business beyond PCs to become a one-stop-shopping enterprise IT vendor.

But IT was not the same. Yet another wave of digital data was unleahsed by the advent of the World Wide Web (a.k.a. “the Internet). Unlike the previous wave, this one gave rise to “digital natives,” a new breed of companies with new business models based on Web domination (i.e., mastering online advertising) and data mining (i.e., indexing, recommendations, linking, etc.).  It also gave rise to a new breed of IT buyers.

In the early 200os, Google’s business presented unprecedented IT requirements for performance, availability and scalability (IT jargon for “we have lots of data to store, process, and shuttle around”). They could buy computer storage, servers and networks from existing IT vendors but the cost was prohibitive. More important, Google’s engineers, as someone who was there at the time told me, always thought they could do a better job than anyone else. So they went ahead and built their own IT infrastructure, stringing together “commodity” (off-the shelf) hardware components, and developing innovative software to manage it.

In a recently published paper, Google’s engineers described their approach to “overcoming the cost, operational complexity, and limited scale endemic to datacenter networks a decade ago.” This was the latest in a long string of influential papers that Google has published (starting, I think, in 2006), sharing with the world its experience and expertise in building an IT infrastructure for the 21st century. Moreover, it also released some of the code it has developed as open software, available for free for anyone dealing with similar IT requirements.

Other digital natives were the first to benefit from Google’s academic-like “publish or perish” mentality. They developed Google’s ideas further or came up with their own solutions, taking a page from Google’s business model—it’s a business where IT matters a lot, IT is a core competency. A prominent example is Hadoop, originally developed at Google as a solution to a storage bottleneck standing in the way of analyzing or manipulating large amounts of data, developed further by Yahoo engineers and released by them as open source software, eventually to become a foundational technology for big data analytics.

Facebook, absorbing some top Google engineering talent, went on further to invent an IT infrastructure handling not only petabytes of data every day but also providing an online service to more than 1 billion people worldwide. And it went further than Google in influencing how IT is done everywhere, by establishing the Open Compute Project, with companies such as Goldman Sachs, Bank of America, and Fidelity as members.

Amazon not only built an IT infrastructure for the 21st century, but went even further than Google and Facebook by making it available to the world for a fee, establishing the concept of IT-on-demand or cloud computing on a solid footing. In the process, it has convinced many digital natives, such as Netflix, to run their entire demanding IT infrastructure on Amazon Web Services.  Now, Amazon is ready to take over the enterprise IT market, making clear at AWS:reinvent 2015 that it is going after the legacy IT business.

This is the supply side of the equation that forced Dell and EMC into this merger. But the demand side is no less important. Just like in the early 1990s, when cheaper hardware and software allowed business executives to do their own computing, by-passing the central IT department, we see today the rise of business executives building their fame and fortunes by buying computer services directly from cloud computing providers.

But the Googlization or Amazonization of IT is not limited to business executives.  It is impossible to overstate the impact Google and other digital natives had on IT executives. The new breed of IT executives is ready to “mix and match,” to buy “best-of-breed,” to experiment with off-the-shelf hardware and open source software.

All of this explains why Dell and EMC are merging but also hints at the enormous challenges they will have in convincing IT buyers to buy into their “back-to-the-future” strategy, that a business model that stopped working in the 1990s is the answer to winning in “a new world order.” All the Google-derived talk about “software-defined-everything” and “converged infrastructure” may not be enough for IT buyers looking to take charge of what is increasingly becoming, if not a core competency, a competitive differentiator and a new source of revenues for many companies. All businesses are now digital businesses and their IT requirements are starting to resemble those Google encountered a decade ago.

IBM, HP, Oracle, and Cisco also need to articulate why “one-stop-shopping” is the way forward for IT buyers. Their task is not made easier by the industry’s influential opinion makers, such as Gartner. In its recent Symposium, Gartner told the more than 8,000 CIOs and senior IT executives in attendance to choose as partners “digital accelerators” such as Amazon and Google, not “digital inhibitors” such as Dell and EMC.

Gartner, however, put VMware, the crown jewel in the EMC “federation,” somewhat ahead of the legacy vendors. Will the company that made cloud computing a reality (there will be no cloud computing without server virtualization) save the biggest technology-industry takeover ever?

Originally published on Forbes.com

Posted in Misc | Tagged , , , , , | Leave a comment

Internet Of Things By The Numbers: Results from New Surveys

comptia-iot-slide-2

Things are looking up for the Internet of Things. 80% of organizations have a more positive view of IoT today compared to a year ago, according to a survey of 512 IT and business executives by CompTIA. “This reflects greater levels of attention from the C-suite and a better understanding of how the many different elements of the IoT ecosystem are starting to come together,” says CompTIA. Here are the highlights from this and other recent surveys:

How big is the IoT and how fast is it growing? The number of connected things, from computers to household monitors to cars, is projected to grow at an annual compound rate of 23.1% between 2014 to 2020, reaching 50.1 billion things in 2020.

What is the IoT? In the minds of the business and IT executives surveyed, the IoT is associated with “ever-greater levels of connectivity; more intelligence built into devices, objects, and systems; and a strong data and applied learning orientation.” These views “sync-up well with the macro trends of more powerful and pervasive computing and storage, the further blurring of the physical and the virtual and the harnessing of big data for real-world functional activities.”

comptia-iot_associations

What is the current level of IoT adoption? 60% of organizations have started an IoT initiative, 45% of which were funded by a new budget allocation. An additional 23% of companies plan to start an IoT initiative within a year. About 90% of the 500 executives Bain surveyed remain in the planning and proof-of-concept stage, and only about 20% expect to implement solutions at scale by 2020.

comptia-iot_slide3

What is the perceived impact of the IoT compared to other new technologies? The IoT leads other much-discussed technologies, including robotics and artificial intelligence, as the technology that is having the most impact on the business.

comptia_iot_impact

What are the expected benefits from IoT and how do they relate to existing activities and operations? The top 5 expected benefits are:

  1.  Cost savings from operational efficiencies
  2.  New/better streams of data to improve decision-making
  3. Staff productivity gains
  4.  Better visibility/monitoring of assets throughout the organization
  5.  New/better customer experiences.

While the expected benefits are roughly split between existing operations and new products or revenue streams, a majority of businesses (61%) report having their IoT initiative as “enabling and extending” technology as opposed to regarding it as a separate and distinct activity (37%).

Bain also found high expectations of the potential benefits of the IoT, including improving the quality of products or services, improving the productivity of the workforce, and increasing the reliability of operations.

comptia_iot_benefits

comptia_iot_fit

Are they too optimistic or too pessimistic? 57%  of respondents believe their organization is very  well equipped or mostly well equipped to manage the security component of          IoT. “Given the number of security unknowns with IoT,” says CompITA, “especially in areas that may be beyond the control of the operator, this confidence may be misplaced.” Indeed, Bain found security at the top of the list of concerns about IoT, with 45% of respondents citing it as one of the top three barriers to IoT implementation.  Similarly, when Forrester surveyed 232 companies developing IoT products it found that 38% anticipated security to be the biggest challenge to IoT implementation, more than any other issue and 64% cited data and device security as the most important capability for their IoT product. Finally, a Tripwire survey of 220 security professionals found that only 30% felt their organizations were prepared for security threats related to IoT devices.

comptia_iot_security

Originally published on Forbes.com

Posted in Internet of Things | Tagged , , , | Leave a comment

Gartner Hype Cycle for Emerging Technologies 2016: Deep Learning Still Missing

gartner_emerging-tech-hc-2016

For the 22nd year, Gartner has released its much-discussed hype cycle report on emerging technologies, “providing a cross-industry perspective on the technologies and trends that business strategists, chief innovation officers, R&D leaders, entrepreneurs, global market developers and emerging-technology teams should consider in developing emerging-technology portfolios.”

Reacting to last year’s hype cycle report (see below), I made the following comment:

Machine learning is making its first appearance on the chart this year, but already past the peak of inflated expectations. A glaring omission here is “deep learning,” the new label for and the new generation of machine learning, and one of the most hyped emerging technologies of the past couple of years.

gartner-hype-2015

Source: Gartner, August 2015

This year, Gartner has moved machine learning back a few notches, putting it at the peak of inflated expectations, still with 2 to 5 years until mainstream adoption. Is machine learning an emerging technology and is there a better term to describe what most of the hype is about nowadays in tech circles?

Machine learning is best defined as the transition from feeding the computer with programs containing specific instructions in the forms of step-by-step rules or algorithms to feeding the computer with algorithms that can learn from data and can make inferences “on their own.” The computer is “trained” by data which is labeled or classified based on previous outcomes, and its software algorithms “learns” how to predict the classification of new data that is not labeled or classified. For example, after a period of training in which the computer is presented with spam and non-spam email messages, a good machine learning program will successfully identify, (i.e., predict,) which email message is spam and which is not without human intervention. In addition to spam filtering, machine learning has been applied successfully to problems such as hand-writing recognition, machine translation, fraud detection, and product recommendations.

Indeed, machine learning has been around for quite a while. In 1959, per Wikipedia, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed.” Yes, 1959—not exactly what one would call “an emerging technology.”

“Artificial Neural Networks” and “Deep Learning” (and variations thereof) are the most hyped buzzwords today, more than any other tech buzzword, I would argue. They  have also been around for a long time, but advances made by The Canadian Mafia (and others) over the last decade in training computers with big data using specialized processors have generated “the latest craze.” The tipping point(s), to borrow another buzzword, came in 2012 when two much-publicized breakthroughs occurred: The Google “Brain Team” has trained a cluster of 16,000 computers to train itself to recognize an image of a cat after processing 10 million digital images taken from YouTube videos. In the same year, a deep neural network achieved 16% error rate at the annual Imagenet Large Scale Visual Recognition Challenge (ILSVCR), a competition where research teams submit programs that classify and detect objects and scenes, a significant improvement over previous results. The rapid progress this method has exhibited over the last four years has prompted countless PhD candidates to switch the focus of their research to the newly promising field, the application of deep neural nets to other areas requiring the processing and analysis of unstructured data, vastly increased funding from VCs, and a fierce competition for artificial intelligence startups.

Deep learning is not the only approach to machine learning being pursued today—Pedro Domingos suggests five fundamental approaches, but the variations and combinations are numerous. It gets most of the attention today, particularly after the triumph earlier this year of Deep Mind Technologies neural net over one of the best Go players in the world, but other approaches (or a combination of approaches) may prove even more promising or more successful in solving other types of challenges. So maybe using “deep learning” in a hype cycle chart, regardless of the hype, is indeed not a good idea. Instead, maybe Gartner should have used “advanced machine learning” to describe the emerging technology or technologies that are at the core of the hype, excitement, and interest in machine learning (or “artificial intelligence” which is also an old term and very ambiguous one).

Here’s a definition of Advance Machine Learning:

In advanced machine learning, deep neural nets (DNNs) move beyond classic computing and information management to create systems that can autonomously learn to perceive the world, on their own. The explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomic. DNNs automate these tasks and make it possible to address key challenges related to the information of everything trend.

DNNs (an advanced form of machine learning particularly applicable to large, complex datasets) is what makes smart machines appear “intelligent.” DNNs enable hardware- or software-based machines to learn for themselves all the features in their environment, from the finest details to broad sweeping abstract classes of content. This area is evolving quickly, and organizations must assess how they can apply these technologies to gain competitive advantage.

Excellent definition (and advice), distinguishing what is “emerging” from “artificial intelligence” (coined in 1955 to describe human-like intelligence displayed by a computer program) and “machine intelligence. Oh, the source of this excellent definition of “Advanced Machine Learning” emerging technologies? Check out Gartner Identifies the Top 10 Strategic Technology Trends for 2016, published in October 2015.

In the press release accompanying the new hype cycle chart, Gartner states categorically:

Smart machine technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks [italics mine] that will allow organizations with smart machine technologies to harness data in order to adapt to new situations and solve problems that no one has encountered previously.

Originally published on Forbes.com

Posted in AI, deep learning, Machine Learning | Tagged | Leave a comment

Top 19 Artificial Intelligence Movies

ai-topmovies

Posted in AI, robots | Tagged | Leave a comment

Timeline of Artificial Intelligence

ai_timeline

Source: Live Science

Posted in AI | Tagged | Leave a comment

Smart Cars: The Next 10 Years

futurism_car-tech-forecast

Posted in Misc | Tagged | Leave a comment

Artificial Intelligence: Market Overview

ai_lanscape_venturescanner

Venture Scanner:

Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data.

Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases. Examples include using machine learning technology to detect banking fraud or to identify the top retail leads.

Natural Language Processing (General): Companies that build algorithms that process human language input and convert it into understandable representations. Examples include automated narrative generation and mining text into data.

Natural Language Processing (Speech Recognition): Companies that process sound clips of human speech, identify the exact words, and derive meaning from them. Examples include software that detects voice commands and translates them into actionable data.

Computer Vision/Image Recognition (General): Companies that build technology that process and analyze images to derive information and recognize objects from them. Examples include visual search platforms and image tagging APIs for developers.

Computer Vision/Image Recognition (Applications): Companies that utilize technology that process images in vertically specific use cases. Examples include software that recognizes faces or enables one to search for a retail item by taking a picture.

Gesture Control: Companies that enable one to interact and communicate with computers through their gestures. Examples include software that enables one to control video game avatars through body motion, or to operate computers and television through hand gestures alone.

Virtual Personal Assistants: Software agents that perform everyday tasks and services for an individual based on feedback and commands. Examples include customer service agents on websites and personal assistant apps that help one with managing calendar events, etc.

Smart Robots: Robots that can learn from their experience and act autonomously based on the conditions of their environment. Examples include home robots that could react to people’s emotions in their interactions and retail robots that help customers find items in stores.

Recommendation Engines and Collaborative Filtering: Software that predicts the preferences and interests of users for items such as movies or restaurants, and delivers personalized recommendations to them. Examples include music recommendation apps and restaurant recommendation websites that deliver their recommendations based on one’s past selections.

Context Aware Computing: Software that automatically becomes aware of its environment and its context of use, such as location, orientation, lighting and adapts its behavior accordingly. Examples include apps that light up when detecting darkness in the environment.

Speech to Speech Translation: Software which recognizes and translates human speech in one language into another language automatically and instantly. Examples include software that translates video chats and webinars into multiple languages automatically and in real-time.

Video Automatic Content Recognition: Software that compares a sampling of video content with a source content file to identify the content through its unique characteristics. Examples include software that detects copyrighted material in user-uploaded videos by comparing them against copyrighted material.

ai_venturescanner

dilbert_ai

dilbert_ai

Posted in AI | Tagged | Leave a comment

The Rise of Robots: Market Overview

cbinsights_robotics

CB Insights:

  • Social: Startups here are building consumer-focused companion and entertainment robots. The most well-funded startup on this list is Anki, with $157M in equity funding from investors including Andreessen Horowitz, Two Sigma Ventures, and JPMorgan Chase & Co. China-based humanoid robotics startup UBTECH raised a $100M Series B round in Q3’16 and joined the Unicorn Club with a $1B valuation. More recently, UK-based Olly, which focused on building a personal, interactive robot, raised $10M in Series A funding from Alliance Capital Ventures and China-based Lightning Capital. Social robots differ from service robots (listed below), which perform household chores.
  • Bionics/Rehab: Startups in this sector include those building exoskeletons, a type of body armor that aids in movement, as well as aiding patients with rehabilitation services. One of the more well-funded companies is California-based AlterG, which has raised over $35M in equity funding so far from investors including Oxford Finance, Silicon Valley Bank, and Versant Ventures, and has developed a wearable bionic leg.
  • Surgical: This category includes startups building robotics surgery-assistance technology. Auris Surgical Robots is one of the most well-funded robotics companies, having raised over $230M in growth equity from investors including Lux Capital, Highland Capital Partners, and Mithril Capital Management. This year, they also made a public-to-private acquisition of Hansen Medical, a medical robotics startup that was previously funded by VCs including Skyline Ventures, Prospect Venture Partners, and De Novo Ventures.
  • Industrial: Our industrial robotics category includes manufacturing, warehouse, packaging, sorting, inspection, and quality testing robotics. Industrial robotics is the most crowded category, as we mentioned in our market map of 80+ robotics startups. Pittsburgh-based Seegrid raised a $14M round this year, followed by $12M corporate minority round from Pittsburgh-based supermarket Giant Eagle. Other startups that raised equity funds this year include Japan-based Life Robotics and China-based Quotient Kinematics Machine.
  • Drones/UAVs: This category includes drones for inspection and delivery. Some of the most well-funded drone startups are 3D Robotics, which built the site scanning drone Solo for site inspections, and China-based DJI Innovations, which caters to industries including agriculture and filmmaking.
  • Education: Robots in this category are focused on teaching children how to code. California-based Wonder Workshop raised $20M in Series B in Q3’16 from VCs including CRV, Learn Capital, and Madrona Venture Group. With $40M in equity funding, it is the most well-funded educational robotics startup, with backing from VCs from China (TCL Capital) and Hong Kong (Bright Success Capital) as well.
  • Service (Consumer): Startups here include those developing consumer-focused service robots that perform household chores like cleaning and cooking. It also includes China-based personal transportation robot Ninebot (which acquired US-based Segway), and robotic infant seat maker 4Moms (which raised over $40M in Series F in Q3’14 from investors including Bain Capital Ventures and Castanea Partners).
  • Service (Medical): This category includes hospital cleaning robot Xenex Disinfection Services, and Pennsylvania-based Aethon, which has developed a transportation robot for hospitals.
  • Service (Other): This category includes Intel Capital-backed Savioke, which has developed a service robot for the hospitality industry; robotic restaurant Spyce Kitchen, which raised $2.6M this year from Rough Draft Ventures; and ground delivery robot Marble, which was seed-funded this year by Eclipse Ventures, Lemnos Labs and Promus Ventures.
  • Security: Rapyuta Robotics is building a “multi-robotic system” with machines that can interact with each other to prevent crime. It is backed by corporate venture capital group Fuji Startup Ventures in Japan, and recently raised $10M in Series A from Japan-based asset management firm SBI Investment. Another startup, California-based Knightscope, raised $5M in Series B funding in Q4’15.
  • VC-backed exits: This category only includes 1st exits since 2012. Amazon acquired Kiva Systems in 2012. The same year, the SoftBank Group acquired a majority stake in France’s Aldebaran Robotics. A detailed timeline of major robotics M&A can be found here.
  • Most active VCs: The most active VC in robotics since 2012 has been High-Tech Gruenderfonds. The Germany-based VC has backed more than 5 unique companies during this period, including rehabilitation robot Reactive Robotics and industrial robots REVOBOTIK and Bionic Robotics. Eclipse Ventures is the 2nd most active VC on our list, having backed companies like Modbot, Rise Robotics, and Clearpath Robotics.

See also

How to build a robot that “sees” with $100 and TensorFlow

Architecture of the object-recognizing robot. Image courtesy of Lukas Biewald.

Architecture of the object-recognizing robot. Image courtesy of Lukas Biewald.

This is the first Adidas shoe made almost entirely by robots

[youtube https://www.youtube.com/watch?v=FVpfVdXxcCA]

 

scottaams_robots

Posted in Robotics, robots | Tagged | Leave a comment

Disrupting Hotels: Airbnb by the Numbers

raconteur_-future_of_hospitality

Source: Raconteur

Posted in Misc | Tagged | Leave a comment

The News About AI (Infographic)

ai-infographic-dooley

Source: Singular Vision

Posted in AI | Tagged | Leave a comment