“With big data, you have only two concerns, but they are, naturally, big ones: where the data will come from and what your company will do with it. Solve these and you have big data licked… IT projects have to be fully buzzword-compliant or they’ll fail. For a big data project, this means Hadoop. If you don’t want to invest staff time and energy learning this technology, do what my client did: Build a virtual server, install MySQL on it, and assign the name “Hadoop” to the server. When your BDSC (big data steering committee) asks if you’ve installed Hadoop, you can answer in the affirmative with a clear conscience”—Bob Lewis Continue reading
Big Data Quotes
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”—Dan Ariely
“I’m a data janitor. That’s the sexiest job of the 21st century. It’s very flattering, but it’s also a little baffling”–Josh Wills, a senior director of data science at Cloudera
“Given enough data, everything is statistically significant”–Douglas Merrill
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Big Data Observations: The Science of Asking Questions
“I am a firm believer that without speculation there is no good and original observation”—Charles Darwin
“It is the theory that determines what we can observe”—Albert Einstein
“I suspect, however, like as it is happening in many academic fields, the NSA is sorely tempted by all the data at its fingertips and is adjusting its methods to the data rather than to its research questions. That’s called looking for your keys under the light”—Zeynep Tufekci
“Large open-access data sets offer unprecedented opportunities for scientific discovery—the current global collapse of bee and frog populations are classic examples. However, we must resist the temptation to do science backwards by posing questions after, rather than before, data analysis. A scant understanding of the context in which data sets were collected can lead to poorly framed questions and results, and to conclusions that are plain wrong. Scientists intending to make use of large composite data sets need to work closely with those responsible for gathering the data. Standard scientific principles and practice then demand that they first frame the important questions, then design and execute the data analyses needed to answer them”—David B. Lindenmayer and Gene E. Likens
“The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries… I thought I was publishing an entertaining view of some data I’d extracted, but it was treated like a scientific study… I’ve enjoyed publishing a lot of data-driven stories since then, but I’ve never ceased to be disturbed at how the inclusion of numbers and the mention of large data sets numbs criticism”—Pete Warden
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Big Data Quotes: Einstein, Come Back When You’ve Got Data
“Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away”—George Dyson (quoted by Tim O’Reilly)
“If the engineers have their way, every idea, memory, and feeling—the recorded consciousness of a single lifetime—will be stored in the cloud… ‘Information overload’ once referred to the difficulty of absorbing intelligently the data produced by others. Now we face the peril of choking on our own…By remembering everything, we may become haunted by our pasts and immobilized by digital distractions—or we may gain new powers to prevent the bad and promote the good”—G. Pascal Zachary
“[I]n a world where massive datasets can be analysed to identify patterns not easily identified using simpler analogue methods, what happens to genius of the Einstein variety?
Genius is about big ideas, not big data. Analysing the attributes and characteristics of anything is guaranteed to find some patterns. It is inherently a theoretical exercise, one that requires minimal thought once you’ve figured out what you want to measure. If you’re not sure, just measure everything you can get your hands on. Since the number of observations — the size of the sample — is by definition huge, the laws of statistics kick in quickly to ensure that significant relationships will be identified. And who could argue with the data?
Unfortunately, analysing data to identify patterns requires you to have the data. That means that big data is, by necessity, backward-looking; you can only analyze what has happened in the past, not what you can imagine happening in the future. In fact, there is no room for imagination, for serendipitous connections to be made, for learning new things that go beyond the data. Big data gives you the answer to whatever problem you might have (as long as you can collect enough relevant information to plug into your handy supercomputer). In that world, there is nothing to learn; the right answer is given…
What if Albert Einstein lived today and not 100 years ago? What would big data say about the general theory of relativity, about quantum theory? There was no empirical support for his ideas at the time — that’s why we call them breakthroughs.
Today, Einstein might be looked at as a curiosity, an ‘interesting’ man whose ideas were so out of the mainstream that a blogger would barely pay attention. Come back when you’ve got some data to support your point”—Sidney Finkelstein
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Shakey, the World’s First Mobile Intelligent Robot
Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world’s first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it “could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY’s software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers.”
Read more here
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Tagged Forbes Gil Press, SRI
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Market for Industrial Robots to Reach $80 Billion in 2022
[youtube https://www.youtube.com/watch?v=rVlhMGQgDkY]
A new version of Atlas, designed to operate outdoors and inside buildings. It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5′ 9″ tall (about a head shorter than the DRC Atlas) and weighs 180 lbs.
The use of industrial robots is expected to grow exponentially in the future as their use leads to cost reduction, improved quality, increased production, and improved workplace health and safety. The global industrial robotics market is expected to reach $79.58 billion by 2022, growing at a CAGR of 11.92% between 2016 and 2022. The main drivers for this growth are the adoption of automation to ensure quality production and meet market demand, and the growing demand from small- and medium-scale enterprises in developing countries.
Collaborative Robotics Market
The global collaborative robots market is expected to grow at a CAGR of 60.04% between 2016 and 2022 from $110 Million in 2015 and is expected to reach $3.3 Billion by 2022.
The collaborative robots market is application driven; the application in the automotive sector accounted for the largest share in 2015
The global collaborative robots market is driven by application in industries such as automotive, metal and machining, furniture and equipment, food and beverages, plastic and polymers, and others.
Collaborative robots used in the automotive sector accounted for the largest share of the global collaborative robots market in 2015; this market is expected to grow at a significant rate between 2016 and 2022.
In developed regions, such as North America and Europe, growth in the collaborative robots market in the automotive sector is expected to be driven by rise in safety rated manufacturing and the growing trend of precision which were not possible due to the common human errors.
Collaborative robots are used in the furniture and equipment industry and this market is expected to witness rapid growth during the forecast period.
An acceptance and installation rate of collaborative robots in furniture and equipment industry is increasing and is expected to continue to grow rapidly during the forecast period. This growth is expected to be significant in RoW region for new fleet of applications.
Asia-Pacific is expected to hold a large share of the collaborative robots market by 2022.
Europe was the largest market in 2015, followed by Asia-Pacific and North America. Regulations have driven the market for collaborative robots to reduce the need for safety fences between human and robots and mitigate the effects of imminent collisions (accidents).
Europe was the early adopter which has resulted in a large market for collaborative robots in 2015.
The collaborative robots market in Asia-Pacific is expected to surpass that of Europe by 2018 and hold a large market share through 2022.
The major companies in the global collaborative robots market are:
ABB Ltd. (Switzerland)
KUKA AG (Germany)
FANUC Corporation (Japan)
Robert Bosch GmbH (Germany)
Universal Robots (Denmark)
Rethink Robotics (U.S.)
Energid Technologies (U.S.).
MRK-Systeme GmbH (Germany).
“Everything is about time. You’re lead timed, your speed to market, and that’s the leveling wind in our industry. Short run manufacturing means that you’re going to have a fairly high touch model. We were really looking to increase productivity and improve our delivery in our service and our quality, mainly from a standpoint of error-proofing, because there’s a number of things that have to be done 100% correctly,” explained Ron Kirscht, President of Donnelly Custom Manufacturing. “But, if that’s what your job is and you’re doing it as a person, it becomes a little mundane, and that’s when people can become inattentive.”
Baxter collaborative robots are on the job at Donnelly’s plant in Alexandria, MN, taking on those time consuming, repetitive tasks where there’s no room for errors. This includes removing parts from a conveyor belt and stacking each one on customized stacking devices. By automating these jobs with robotics, Donnelly employees are assigned to more valuable work.
Kirscht added, “Baxter has some qualities that he brings to Donnelly that creates excitement, innovation and enthusiasm, allowing people to come up with ideas in ways for utilizing Baxter. I think that the Baxter robot is a game changer in modern manufacturing, because it really creates an opportunity for people on the manufacturing floor to innovate. It spawns creativity.”
[youtube https://www.youtube.com/watch?v=ant9adbTK5M]
The Rise of Robots: Market Overview
- 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.
This is the first Adidas shoe made almost entirely by robots
[youtube https://www.youtube.com/watch?v=FVpfVdXxcCA]
The AI and Automation Buzz
CB Insights: Media buzz around AI, robotics, and automation increased significantly towards the end of 2016.
The forward march of automation technologies — which include hardware (e.g. robots, digital kiosks), software (e.g. AI), and customer self-service (e.g. mobile ordering) — continues to reshape the world economy. Automation has already begun to reshape every company’s workforce, including yours. Leaders across all roles, companies, and verticals are taking note; right now, my report The Future of Jobs, 2027: Working Side-by-Side with Robots is one of the five best-read among all reports at Forrester. We forecast a world in which automation cannibalizes 17% of US jobs by 2027, partly offset by the growth of 10% new jobs from the automation economy. Most importantly, we see human-machine teaming as a key workforce trend in the future, as more and more human employees find themselves working side-by-side with robotic colleagues.