Hal Varian on Intelligent Technology

By Hal Varian, Chief Economist, Google

Published on IMF.org

A computer now sits in the middle of virtually every economic transaction in the developed world. Computing technology is rapidly penetrating the developing world as well, driven by the rapid spread of mobile phones. Soon the entire planet will be connected, and most economic transactions worldwide will be computer mediated.

Data systems that were once put in place to help with accounting, inventory control, and billing now have other important uses that can improve our daily life while boosting the global economy.

Transmission routes

Computer mediation can impact economic activity through five important channels.

Data collection and analysis: Computers can record many aspects of a transaction, which can then be collected and analyzed to improve future transactions. Automobiles, mobile phones, and other complex devices collect engineering data that can be used to identify points of failure and improve future products. The result is better products and lower costs.

Personalization and customization: Computer mediation allows services that were previously one-size-fits-all to become personalized to satisfy individual needs. Today we routinely expect that online merchants we have dealt with previously possess relevant information about our purchase history, billing preferences, shipping addresses, and other details. This allows transactions to be optimized for individual needs.

Experimentation and continuous improvement: Online systems can experiment with alternative algorithms in real time, continually improving performance. Google, for example, runs over 10,000 experiments a year dealing with many different aspects of the services it provides, such as ranking and presentation of search results. The experimental infrastructure to run such experiments is also available to the company’s advertisers, who can use it to improve their own offerings.

Contractual innovation: Contracts are critical to economic transactions, but without computers it was often difficult or costly to monitor contractual performance. Verifying performance can help alleviate problems with asymmetric information, such as moral hazard and adverse selection, which can interfere with efficient transactions. There is no longer a risk of purchasing a “lemon” car if vehicular monitoring systems can record history of use and vehicle health at minimal cost.

Coordination and communication: Today even tiny companies with a handful of employees have access to communication services that only the largest multinationals could afford 20 years ago. These micro-multinationals can operate on a global scale because the cost of computation and communication has fallen dramatically. Mobile devices have enabled global coordination of economic activity that was extremely difficult just a decade ago. For example, today authors can collaborate on documents simultaneously even when they are located thousands of miles apart. Videoconferencing is now essentially free, and automated document translation is improving dramatically. As mobile technology becomes ubiquitous, organizations will become more flexible and responsive, allowing them to improve productivity.

Let us dig deeper into these five channels through which computers are changing our lives and our economy.

Data collection and analysis

We hear a lot about “big data” (see “Big Data’s Big Muscle,” in this issue of F&D), but “small data” can be just as important, if not more so. Twenty years ago only large companies could afford sophisticated inventory management systems. But now every mom-and-pop corner store can track its sales and inventory using intelligent cash registers, which are basically just personal computers with a drawer for cash. Small business owners can handle their own accounting using packaged software or online services, allowing them to better track their business performance. Indeed, these days data collection is virtually automatic. The challenge is to translate that raw data into information that can be used to improve performance.

Large businesses have access to unprecedented amounts of data, but many industries have been slow to use it, due to lack of experience in data management and analysis. Music and video entertainment have been distributed online for more than a decade, but the entertainment industry has been slow to recognize the value of the data collected by servers that manage this distribution (see “Music Going for a Song,” in this issue of F&D). The entertainment industry, driven by competition from technology companies, is now waking up to the possibility of using this data to improve their products.

The automotive industry is also evolving quickly by adding sensors and computing power to its products. Self-driving cars are rapidly becoming a reality. In fact, we would have self-driving cars now if it weren’t for the randomness introduced by human drivers and pedestrians. One solution to this problem would be restricted lanes for autonomous vehicles only. Self-driving cars can communicate among themselves and coordinate in ways that human drivers are (alas) unable to. Autonomous vehicles don’t get tired, they don’t get inebriated, and they don’t get distracted. These features of self-driving cars will save millions of lives in the coming years.

Personalization and customization

Twenty years ago it was a research challenge for computers to recognize pictures containing human beings. Now free photo storage systems can find pictures with animals, mountains, castles, flowers, and hundreds of other items in seconds. Improved facial recognition technology and automated indexing allow the photos to be found and organized easily and quickly.

Similarly, just in the past few years voice recognition systems have become significantly more accurate. Voice communication with electronic devices is possible now and will soon become the norm. Real-time verbal language translation is a reality in the lab and will be commonplace in the near future. Removing language barriers will lead to increased foreign trade, including, of course, tourism.

Continuous improvement

Observational data can uncover interesting patterns and correlations in data. But the gold standard for discovering causal relationships is experimentation, which is why online companies like Google routinely experiment and continuously improve their systems. When transactions are mediated by computers, it is easy to divide users into treatment and control groups, deploy treatment, and analyze outcomes in real time.

Companies now routinely use this kind of experimentation for marketing purposes, but these techniques can be used in many other contexts. For example, institutions such as the Massachusetts Institute of Technology’s Abdul Latif Jameel Poverty Action Lab have been able to run controlled experiments of proposed interventions in developing economies to alleviate poverty, improve health, and raise living standards. Randomized controlled experiments can be used to resolve questions about what sorts of incentives work best for increasing saving, educating children, managing small farms, and a host of other policies.

Contractual innovation

The traditional business model for advertising was “You pay me to show your ad to people, and some of them might come to your store.” Now in the online world, the model is “I’ll show your ad to people, and you only have to pay me if they come to your website.” The fact that advertising transactions are computer mediated allows merchants to pay only for the outcome they care about.

Consider the experience of taking a taxi in a strange city. Is this an honest driver who will take the best route and charge me the appropriate fee? At the same time, the driver may well have to worry whether the passenger is honest and will pay for the ride. This is a one-time interaction, with limited information on both sides and potential for abuse. But now consider technology such as that used by Lyft, Uber, and other ride services. Both parties can see rating history, both parties can access estimates of expected fares, and both parties have access to maps and route planning. The transaction has become more transparent to all parties, enabling more efficient and effective transactions. Riders can enjoy cheaper and more convenient trips, and drivers can enjoy a more flexible schedule.

Smartphones have disrupted the taxi industry by enabling these improved transactions, and every player in the industry is now offering such capabilities—or will soon. Many people see the conflict between ride services and the taxi industry as one of innovators versus regulators. However, from a broader perspective, what matters is which technology wins. The technology used by rideshare companies clearly provides a better experience for both drivers and passengers, so it will likely be widely adopted by traditional taxi services.

Simply being able to capture transaction history can improve contracts (see “Two Faces of Change,” in this issue of F&D). It is remarkable that I can walk into a bank in a new city, where I know no one and no one knows me, and arrange for a mortgage worth millions of dollars. This is enabled by credit rating services, which dramatically reduce risk on both sides of the transaction, making loans possible for people who otherwise could not get them.

Communication and coordination

Recently I had some maintenance work done on my house. The team of workers used their mobile phones to photograph items that needed replacement, communicate with their colleagues at the hardware store, find their way to the job site, use as a flashlight to look in dark places, order lunch for delivery, and communicate with me. All of these formerly time-consuming tasks can now be done quickly and easily. Workers spend less time waiting for instructions, information, or parts. The result is reduced transaction costs and improved efficiency.

Today only the wealthy can afford to employ executive assistants. But in the future everyone will have access to digital assistant services that can search through vast amounts of information and communicate with other assistants to coordinate meetings, maintain records, locate data, plan trips, and do the dozens of other things necessary to get things done (see “Robots, Growth, and Inequality,” in this issue of F&D). All of the big tech companies are investing heavily in this technology, and we can expect to see rapid progress thanks to competitive pressure.

Putting it all together

Today’s mobile phones are many times more powerful and much less expensive than those that powered Apollo 11, the 1969 manned expedition to the moon. These mobile phone components have become “commoditized.” Screens, processors, sensors, GPS chips, networking chips, and memory chips cost almost nothing these days. You can buy a reasonable smartphone now for $50, and prices continue to fall. Smartphones are becoming commonplace even in very poor regions.

The availability of those cheap components has enabled innovators to combine and recombine these components to create new devices—fitness monitors, virtual reality headsets, inexpensive vehicular monitoring systems, and so on. The Raspberry Pi is a $35 computer designed at Cambridge University that uses mobile phone parts with a circuit board the size of a pack of playing cards. It is far more powerful than the Unix workstations of just 15 years ago.

The same forces of standardization, modularization, and low prices are driving progress in software. The hardware created using mobile phone parts often uses open-source software for its operating system. At the same time, the desktop motherboards from the personal computer era have now become components in vast data centers, also running open-source software. The mobile devices can hand off relatively complex tasks such as image recognition, voice recognition, and automated translation to the data centers on an as-needed basis. The availability of cheap hardware, free software, and inexpensive access to data services has dramatically cut entry barriers for software development, leading to millions of mobile phone applications becoming available at nominal cost.

The productivity puzzle

I have painted an optimistic picture of how technology will impact the global economy. But how will this technological progress show up in conventional economic statistics? Here the picture is somewhat mixed. Take GDP, for example. This is usually defined as the market value of all final goods and services produced in a given country in a particular time period. The catch is “market value”—if a good isn’t bought and sold, it generally doesn’t show up in GDP.

This has many implications. Household production, ad-supported content, transaction costs, quality changes, free services, and open-source software are dark matter as far as GDP is concerned, since technological progress in these areas does not show up directly in GDP. Take, for example, ad-supported content, which is widely used to support provision of online media. In the U.S. Bureau of Economic Analysis National Economic Accounts, advertising is treated as a marketing expense—an intermediate product—so it isn’t counted as part of GDP. A content provider that switches from a pay-per-view business model to an ad-supported model reduces GDP.

One example of technology making a big difference to productivity is photography. Back in 2000, about 80 billion photos were taken worldwide—a good estimate since only three companies produced film then. In 2015, it appears that more than 1.5 trillion photos were taken worldwide, roughly 20 times as many. At the same time the volume exploded, the cost of photos fell from about 50 cents each for film and developing to essentially zero.

So over 15 years the price fell to zero and output went up 20 times. Surely that is a huge increase in productivity. Unfortunately, most of this productivity increase doesn’t show up in GDP, since the measured figures depend on the sales of film, cameras, and developing services, which are only a small part of photography these days.

In fact, when digital cameras were incorporated into smartphones, GDP decreased, camera sales fell, and smartphone prices continued to decline. Ideally, quality adjustments would be used to measure the additional capabilities of mobile phones. But figuring out the best way to do this and actually incorporating these changes into national income accounts is a challenge.

Even if we could accurately measure the number of photos now taken, most are produced at home and distributed to friends and family at zero cost; they are not bought and sold and don’t show up in GDP. Nevertheless, those family photos are hugely valuable to the people who take them.

The same thing happened with global positioning systems (GPS). In the late 1990s, the trucking industry adopted expensive GPS and vehicular monitoring systems and saw significant increases in productivity as a result. In the past 10 years, consumers have adopted GPS for home use. The price of the systems has fallen to zero, since they are now bundled with smartphones, and hundreds of millions of people use such systems on a daily basis. But as with cameras, the integration of GPS with smartphones has likely reduced GDP, since sales of stand-alone GPS systems have fallen.

As in the case of cameras, this measurement problem could be solved by implementing a quality adjustment for smartphones. But it is tricky to know exactly how to do this, and statistical agencies want a system that will stand the test of time. Even after the quality adjustment problem is worked out, the fact that most photos are not exchanged for cash will remain—that isn’t a part of GDP, and technological improvements in that area are just not measured by conventional statistics.

Will the promise of technology be realized?

When the entire planet is indeed connected, everyone in the world will, in principle, have access to virtually all human knowledge. The barriers to full access are not technological but legal and economic. Assuming that these issues can be resolved, we can expect to see a dramatic increase in human prosperity.

But will these admittedly utopian hopes be realized? I believe that technology is generally a force for good—but there is a dark side to the force (see “The Dark Side of Technology,” in this issue of F&D). Improvements in coordination technology may help productive enterprises but at the same time improve the efficiency of terrorist organizations. The cost of communication may drop to zero, but people will still disagree, sometimes violently. In the long run, though, if technology enables broad improvement in human welfare, people might devote more time to enlarging the pie and less to squabbling over the size of the pieces.

Posted in Digitization | Tagged | Leave a comment

Startups Disrupting Healthcare with AI and Machine Learning

CBInsights_AI_healthcare_mapCB Insights:

We identified over 90 companies that are applying machine learning algorithms and predictive analytics to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images, among other things.

A few investment highlights:

  • Increasingly crowded imaging & diagnostics: 17 out of the 22 companies under imagining & diagnostics raised their first equity funding round since January 2015 (this includes 1st Seed or Series A rounds, as well as a first round raised by stealth startup Imagen Technologies). In 2014, Butterfly Networks raised a $100M Series C, backed by Aeris Capital and Stanford University. This was the third-largest equity round to AI in healthcare companies, after China-based iCarbonX’s $154M mega-round and two $100M+ raises by oncology-focused Flatiron Health.
  • VCs invest in drug discovery: Startups are using machine learning algorithms to reduce drug discovery times, and VCs have backed 6 out of the 8 startups on the map. Andreessen Horowitz recently seed-funded twoXAR, developer of the DUMA drug discovery platform; Khosla Ventures and Data Collective backed Atomwise, which published its first findings of Ebola treatment drugs last year, and has also partnered with MERCK; Lightspeed Venture Partners invested in Numedii in 2013; Foundation Capital participated in 3 equity funding rounds to Numerate.
  • Khosla Ventures backs 5 companies: Khosla Ventures has been the most active VC investor in the space, having backed 5 unique companies: California-based Ginger.io, which focuses on patients with depression and anxiety; healthcare analytics platform Lumiata; Israel’s Zebra Medical Vision and California-based Bay Labs, which apply AI to medical imaging; as well as drug discovery startup Atomwise.
  • AI in oncology: IBM Watson Group-backed Pathway Genomics has recently started a research study for its new blood test kit, CancerIntercept Detect. The company will collect blood samples from high-risk individuals who have never been diagnosed with the disease to determine if early detection is possible. Other oncology-focused startups include Flatiron HealthCyrcadia (wearable device), CureMetrix, SkinVision, Entopsis, and Smart Healthcare.
  • Remote patient monitoring: New York-based AiCure raised $12.3M in Series A funding from investors including Biomatics Capital Partners, New Leaf Venture Partners, Pritzker Group Venture Capital, and Tribeca Venture Partners, for the use of artificial intelligence to ensure patients are taking their medications. California-based Sense.ly has developed a virtual nursing assistant, Molly, to follow up with patients post-discharge. The company claims Molly gives clinicians “20% of their day back.” Sentrian, backed by investors including frost Data Capital, analyzes biosensor data and sends patient-specific alerts to clinicians.
  • Core AI companies bring their algorithms to healthcare: Core AI startup Ayasdi, which has developed a machine intelligence platform based on topological data analysis, is bringing its solutions to healthcare providers for applications including patient risk scoring and readmission reduction. Other core AI startups looking at healthcare include H2O.ai and Digital Reasoning Systems.
Posted in AI in Healthcare, Artificial Intelligence | Tagged | Leave a comment

The U.S. Artificial Intelligence Market

Oreilley_AImkt_usecases

Oreilley_AImkt_technologies

Oreilley_AImkt_topcompanies

The New Artificial Intelligence Market  by Aman Naimat, published by O’Reilly:

There are only 1,500 companies in North America that are doing anything related to AI today, even using its narrow, task-based definition. That means less than one percent of all medium-to-large companies across all industries are adopting AI.

Posted in Artificial Intelligence | Tagged | Leave a comment

Big Auto Self-Disruption

CBinsights_big-auto-August-2016

CB Insights:

Traditional automotive OEMs have begun making deals at a frantic place, seeking to remedy their shortcomings in auto tech and ride-hailing disciplines. Using CB Insights data, we mapped out the key auto tech partnerships, investments, and acquisitions of these corporations over the past three years.

We focused on auto OEMs’ private markets activity within our definition of auto tech, which includes startups that using software to improve safety, convenience, and efficiency in cars (and excludes activity in fields such as energy/powertrain, parking, and rentals/marketplaces). We also looked at their major engagements with ride-hailing companies and large tech corporations.

Scanning the timeline, the acceleration of activity seen in 2016 is immediately obvious.
[youtube https://www.youtube.com/watch?v=CMYlKBDsDtc]

 

 

Posted in Misc | Tagged | Leave a comment

Google 1 Yahoo 0

Google_YahooMany of the obituaries for Yahoo have contrasted its demise with the flourishing of Google, another Web pioneer. Why was Google’s attempt to “organize all the world’s information” vastly more successful than Yahoo’s? The short answer: Because Google did not organize the world’s information. Google got the true spirit of the Web, as it was invented by Tim Berners-Lee.

In his book Weaving the Web, Tim Berners-Lee writes:

I was excited about escaping from the straightjacket of hierarchical documentation systems…. By being able to reference everything with equal ease, the web could also represent associations between things that might seem unrelated but for some reason did actually share a relationship. This is something the brain can do easily, spontaneously. … The research community has used links between paper documents for ages: Tables of content, indexes, bibliographies and reference sections… On the Web… scientists could escape from the sequential organization of each paper and bibliography, to pick and choose a path of references that served their own interest.

With this one imaginative leap, Berners-Lee moved beyond a major stumbling block for all previous information retrieval systems: The pre-defined classification system at their core. This insight was so counter-intuitive that even during the early years of the Web, attempts were made to do just that: To classify (and organize in pre-defined taxonomies) all the information on the Web.

Google’s founders were the first to seize on Berners-Lee’s insight and build their information retrieval business on tracking closely cross-references (i.e., links between pages) as they were happening and correlate relevance with quantity of cross-references (i.e., popularity of pages as judged by how many other pages linked to them). This was what set Google apart from its competitors, including Yahoo. Having a so-called “first-mover advantage” (yet another example that there are no universal “business laws”), Yahoo worked hard and employed many people in organizing in a neat taxonomy the rapidly-growing content of the Web. It even had a Chief Ontologist on staff.

Danny Sullivan in 2010:

Google’s ranking system gave you the best of both worlds. Yahoo was a card-catalog of the web, letting you effectively search for the right “books” based on what they were titled. Google’s system let you search through all the pages of all the books in the entire library. It was far more comprehensive, plus it still managed to get good stuff to the top of the list.

Berners-Lee’s insight is frequently linked to Vannevar Bush who wrote in 1945, “Our ineptitude at getting at the record is largely caused by the artificiality of systems of indexing… Selection [i.e., information retrieval] by association, rather than by indexing may yet be mechanized.”  But I prefer to start the history of the Web (and organizing information) with what was, to my knowledge, the earliest use of cross-references.

This was Ephraim Chambers’ Cyclopaedia, published in London in 1728. While lacking the worldwide platform for “crowd-sourcing” references that Berners-Lee invented, Chambers shared with him (and Bush) a dislike for hierarchical, alphabetical, indexing systems. Here’s how Chambers explained in the Preface his innovative system of cross-references:

Former lexicographers have not attempted anything like Structure in their Works; nor seem to have been aware that a dictionary was in some measure capable of the Advantages of a continued Discourse. Accordingly, we see nothing like a Whole in what they have done…. This we endeavoured to attain, by considering the several Matters [i.e., topics] not only absolutely and independently, as to what they are in themselves; but also relatively, or as they respect each other. They are both treated as so many Wholes, and so many Parts of some greater Whole; their Connexion with which is pointed out by a Reference. So that by a Course of References, from Generals to Particulars; from Premises to Conclusions; from a Cause to Effect; and vice versa, i.e., in one word, from more to less complex, and from less to more: A Communication is opened between the several parts of the Work; and the several Articles are in some measure replaced in their natural Order of Science, out of which the Technical or Alphabetical one had remov’d them.

Chambers’ Cyclopaedia was the earliest attempt to link by association all the articles in an Encyclopedia or, in more general terms, of everything we know at a given point in time. And like the World Wide Web, it moved some people to voice their concern about what Google is doing to our brains. The supplement to the 1758 edition of the Cyclopaedia says:

Some few however condemn the use of all such dictionaries, on the first pretence, that, by lessening the difficulties of attaining knowledge, they abate our diligence in the pursuit of it; and by dazzling our eyes with superficial shew, seduce us from digging solid riches in the mine itself.

The fear of what tools for organizing information could do to our thinking (and livelihood) was renewed many-fold with the advent of modern computers. “They can’t build a machine to do our job; there are too many cross-references in this place,” says the head librarian (Katharine Hepburn) to her anxious colleagues in the research department when a “methods engineer” (Spencer Tracy) is hired to “improve workman-hour relationship” in a large corporation. By the end of the film, Desk Set (released in 1957), she proves her point by winning, not only the engineer’s heart, but also a contest with the ominous looking “Electronic Brain” (aka Computer).

Automation—replacing librarians and their card catalogues—has been at the heart of Google’s success and obsession with “scale” (and “at scale” has become an obsession for Silicon Valley). But this automation has led to augmentation, to supporting our thinking by creating a new way to organize the world’s information, one that is more in line with our thought process and more in line with the impossible-to-catalogue current volume of (valuable and useless) information. As Vannevar Bush wrote:

The human mind… operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain … One cannot hope to equal the speed and flexibility with which the mind follows an associative trail, but it should be possible to beat the mind decisively in regard to the permanence and clarity of the items resurrected from storage.

Originally published on Forbes.com

Posted in Misc | Tagged , | Leave a comment

Inherited Wealth in Europe and the U.S.

INheritedWealth

Bloomberg:

More than one-third of Italy’s richest people inherited their fortunes, compared with just 29 percent in the U.S. and 2 percent in China, according to a 2014 study of the world’s billionaires by the Peterson Institute for International Economics. Germany has the highest share of inheritor-billionaires among developed economies, 65 percent. Overall, heirs and heiresses make up about half of Western Europe’s billionaires.

Posted in Misc | Tagged | Leave a comment

30% of internet data usage at home comes from phones and tablets, up from 9% in 2012

mobile data usage at home - sandvine

Recode:

Sandvine, a broadband services company, says that 30 percent of internet data usage at home comes from phones and tablets. That’s up from 20 percent in 2013 and 9 percent in 2012.

Posted in Misc | Tagged | Leave a comment

China: Increasing Investments in AI, Big Data and Digital Health

Gartner_hc-ict-china-2016

Gartner Hype Cycle for ICT in China, 2016

Gartner:

Despite the slowdown in GDP growth to 6.9 percent in 2015, China is still making aggressive investments to drive the adoption of high technology by local enterprises and organizations, according to Gartner, Inc…

The massive consumer base and the number of internet users in China (estimated at 650 million internet and 980 million mobile internet users in 2016) present the most-promising big data opportunities. Led by hyperscale internet companies such as Baidu (internet traffic data), Alibaba (supply chain and transaction data) and Tencent (social data), approximately 25 percent of businesses have been pursuing the value of big data.

“The government-sponsored strategy ‘Internet Plus’ is targeted at boosting economic growth through digital transformation,” said Jie Zhang, research director at Gartner. “It has issued a detailed action plan for 11 key industries in 2015, mandating the necessity of digital business transformation by leveraging big data and cloud technologies.”

Wall Street Journal:

The biggest buzz in China’s internet industry isn’t about besting global tech giants by better adapting existing business models for the Chinese market. Rather, it’s about competing head-to-head with the U.S. and other tech powerhouses in the hottest area of technological innovation: artificial intelligence.

Venture capitalists have been pouring money into startups focused on AI, which broadly refers to efforts to make computers emulate human cognitive functions such as recognizing speech or images. Chinese tech companies such as search giant Baidu have been investing heavily in the technology, and poaching high-level talent from foreign rivals.

Enthusiasts of the technology in China say those resources, along with some particular advantages in China, such as the sheer volume of data generated by its enormous population of internet users, makes this an area where China can excel.

“China is poised to be a leader in AI because of its great reserve in AI talent, excellent engineering education and massive market for AI adoption,” says Kai-Fu Lee, a former Microsoft and Google executive who is now chief executive of Sinovation Ventures. The firm, formerly known as China’s Innovation Works, has invested $100 million in 25 AI-related startups in the U.S. and China in the past three years.

CBInsights_chinadigitalhealth

CB Insights:

In total, over $1.1B has been deployed across 21 deals to Chinese digital health companies in the first six months of the year. It’s worth noting, though, that three investments each totaled over $100M in financing over the period including Ping An Insurance-backed medical services app Ping An Good Doctor, Beijing-based mobile healthcare app maker Spring Rain Software, and health data mining startup iCarbonX.

The chart above highlights how mega-rounds have propelled China’s digital health investment since 2012. Deal activity in the first half of 2016 was nearly equivalent with that of all of 2015.

 

Posted in AI in Healthcare, Artificial Intelligence, Big Data | Tagged , | Leave a comment

Give Me ‘Disruptive’

Disruptive

Posted in Misc | Leave a comment

Robotics: From Tesla in 1898 to Exits in 2011-2016

CBInsights_robotics_MnA_2016

 

Submerged version of Nikola Tesla's remote-controlled boat

Submerged version of Nikola Tesla’s remote-controlled boat

PBS:

[Nikola] Tesla wanted an extraordinary way to demonstrate the potential of his system for wireless transmission of energy [radio]. In 1898, at an electrical exhibition in the recently completed Madison Square Garden, he made a demonstration of the world’s first radio-controlled vessel. Everyone expected surprises from Tesla, but few were prepared for the sight of a small, odd-looking, iron-hulled boat scooting across an indoor pond (specially built for the display). The boat was equipped with, as Tesla described, “a borrowed mind.”

“When first shown… it created a sensation such as no other invention of mine has ever produced,” wrote Tesla. As happened fairly often with his inventions, many of those present were unsure how to react, whether to laugh or take flight. He had cleverly devised a means of putting the audience at ease, encouraging onlookers to ask questions of the boat. For instance, in response to the question “What is the cube root of 64?” lights on the boat flashed four times. In an era when only a handful of people knew about radio waves, some thought that Tesla was controlling the small ship with his mind. In actuality, he was sending signals to the mechanism using a small box with control levers on the side.

Tesla’s U.S. patent number 613,809 describes the first device anywhere for wireless remote control. The working model, or “teleautomaton,” responded to radio signals and was powered with an internal battery.

Tesla did not limit his method to boats, but generalized the invention’s potential to include vehicles of any sort and mechanisms to be actuated for any purpose. He envisioned one operator or several operators simultaneously directing fifty or a hundred vessels or machines through differently tuned radio transmitters and receivers.

When a New York Times writer suggested that Tesla could make the boat submerge and carry dynamite as a weapon of war, the inventor himself exploded. Tesla quickly corrected the reporter: “You do not see there a wireless torpedo, you see there the first of a race of robots, mechanical men which will do the laborious work of the human race.”

Tesla’s device was literally the birth of robotics, though he is seldom recognized for this accomplishment. The inventor was trained in electrical and mechanical engineering, and these skills merged beautifully in this remote-controlled boat. Unfortunately, the invention was so far ahead of its time that those who observed it could not imagine its practical applications.

Smithsonian.com

[Nikola] Tesla’s work in robotics began in the late 1890s when he patented his remote-controlled boat, an invention that absolutely stunned onlookers at the 1898 Electrical Exhibition at Madison Square Garden. [Tesla said in 1935:]

At present we suffer from the derangement of our civilization because we have not yet completely adjusted ourselves to the machine age. The solution of our problems does not lie in destroying but in mastering the machine.

Innumerable activities still performed by human hands today will be performed by automatons. At this very moment scientists working in the laboratories of American universities are attempting to create what has been described as a ” thinking machine.” I anticipated this development.

I actually constructed ” robots.” Today the robot is an accepted fact, but the principle has not been pushed far enough. In the twenty-first century the robot will take the place which slave labor occupied in ancient civilization. There is no reason at all why most of this should not come to pass in less than a century, freeing mankind to pursue its higher aspirations.

CB Insights:

Last year was the busiest year for robotics M&A activity, with the industry seeing nearly 15 acquisitions of private companies (we excluded reverse mergers and unit acquisitions). This year there have been three M&A deals involving private robotics companies year-to-date.

  • Around 50% of acquisitions were in industrial robotics: A majority of the acquisitions in the last 5 years have been in the industrial robotics sector. A notable deal was Teradyne’s acquisition of Denmark-based Universal Robotics in Q2’15. The startup, with subsidiaries in countries including China and Singapore, makes robotics arms for application in a variety of industries including electronics, agriculture, and pharmaceuticals.
  • VC-backed acquisitions: US-based Segway, backed by investors including smart money VC Kleiner Perkins Caufield & Byers, was acquired in 2013 by Summit Strategic Investments, and later acquired by China-based personal transportation startup Ninebot in 2015. Other VC-backed startups that were acquired include Aldebaran Robotics and VGo Communications.
  • Canada-based acquirers: This year, Ontario-based medical device and robotics corporation, Bionik Laboratories, acquired Massachusetts-based Interactive Motion Technologies, a startup providing robotic technology for neuro-rehabilitation. Two other Ontario-based corporations, Great Rock Development and Ross Video, acquired autonomous robotic startup Cyberworks and robotic camera startup FX-Motion, respectively.
  • Google dominates 2013: Google acquired 7 robotics startups in 2013 — including US Department of Defense grantee Boston Dynamics, Japan-based SCHAFT, and robotics motion control startup Bot & Dolly — as part of an ambitious plan to boost its robotics division. But Google has now put its Boston Dynamics unit for sale amid leadership changes and management issues, Bloomberg reported, listing Amazon and Toyota Research Institute as potential acquirers.
  • Amazon makes key industrial robotics acquisition in 2012: The previously-mentioned Kiva robots are now used at Amazon’s warehouse and fulfillment center. Post the acquisition, “there has been a scramble of new providers [like Iam Robotics, Locus Robotics, and 6 River Systems] to fill the void left by Kiva’s technology,” Frank Tobe wrote in The Robot Report.
  • Aldebaran rebrands as SoftBank Robotics: In 2012, Japan-based SoftBank Group bought a majority stake in Paris-based Aldebaran Robotics, the makers of NAO, Pepper, and Romeo robots. Aldebaran was rebranded earlier this year as SoftBank Robotics.

 

Posted in Robotics | Tagged | Leave a comment