The U.S. Artificial Intelligence Market

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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.

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Big Auto Self-Disruption

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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.

 

 

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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

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Inherited Wealth in Europe and the U.S.

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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.

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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.

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

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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.

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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, Big Data Analytics, China, healthcare | Tagged , | 1 Comment

Give Me ‘Disruptive’

Disruptive

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