While the number of Indian CEOs at Western firms is higher, Chinese prefer starting their own busine

[Shajida Khan]

hike is ahead of WeChat? What a brazen boasting. I will stop here.

You missed "In India" Part. Read more carefully.

 

[Adam WANG SHANGHAI MEGA]

Alibaba copycat? where did you read it friend?

They only copy the model of business even only partially.

Have you ever heard this:

Alibaba’s Jack Ma ranked No 3 in global tech innovation visionary survey by KPMG
http://www.scmp.com/tech/leaders-fo...-ranked-no-3-global-tech-innovation-visionary

Most Innovative Companies
Alibaba
https://www.fastcompany.com/company/alibaba



Is that all?

Reaching Mars is only tiny achievement compared to the abundant achievements that has been made by china. Do you know how many satellites that china has launched?

And witout help of european space agency you indians are totally blind and can not even find where your satelite is !!!!!

LOL..................................

 

[Sankpal]

Indians are good at housekeeping. CEO is another version of housekeeper for company stockholders.
Westerners trust Indians more than Chinese because unlike Indians, Chinese are not mentally and culturally tamed by them.

If CEO means housekeeping job then 99 percent IT people will love to do from all countries.

 

[antonius123]

Where is India's comparison?

 

[Adam WANG SHANGHAI MEGA]

And without help of european space agency you indians are totally blind and can not even find where your satelite is !!!!!

LOL..................................

Just a cover up article to hide the failure of chinese people to reach the highest positions in various sectors like education, research, corporate, engineering and medicine and fields of excellence. They can not match even India in entrepreneurship in US starting from motel business to start up in IT. Just couple of days back, UK PM said that Indians have become the backbone of UK. They are doing hard work, they are generating employments, providing foods to needy people and doing all good. You will never heard same about chinese anywhere.

 

[Adam WANG SHANGHAI MEGA]

If CEO means housekeeping job then 99 percent IT people will love to do from all countries.

They are americans thanks for your time!!!!

 

[Sankpal]

They are americans thanks for your time!!!!

But blood is Indian. Wherever they will go but will called " Indian American"

 

[Adam WANG SHANGHAI MEGA]

User base is a popular way of comparing the technical maturity of any software company. It is much harder to design and build something that scales to 100s of millions of users.

Than why Nokia and Ericsson made billions and billions dollars from you huge indian population they belong to small popualtion country,yes we china is huge as well but we have huawei that beaten shit out of them what you got!!!!

 

[antonius123]

Here’s why India is likely to lose the AI race
Sriram Sharma August 18, 2017

With Elon Musk and Mark Zuckerberg sparring over its ethics and China announcing its intention to create a $150 billion domestic industry based on it, Artificial Intelligence is perhaps the most discussed topic in the tech news cycle. It’s likely to be a talking point no matter what your favourite watering hole for tech news.

Billions of dollars have been invested by VCs in AI since 2016 with the US and China leading the race in record funding in terms of deals and dollars.

In sharp contrast, Indian startups have collectively raised less than $100 million from (2014-2017YTD), according to data from startup analytics firm Tracxn — that’s smaller than Andrew Ng’s recently launched $150 million VC fund. Another way to look at it: Grammarly, a Valley-based spell check tool raised more dollars than all of India’s AI startups put together in the past three and a half years.

According to a recent PwC report, AI is expected to contribute an additional $15.7 trillion to the global GDP by 2030, with most of the economic gains going to China and US, who will account for 70% of the global economic impact. Does India risk becoming a laggard in the AI race, and what are the potential implications, and existential risks of missing out on this wave?

FactorDaily reached out to VCs and some of the top-funded AI startups in India to hear about the challenges faced in building an AI startup out of India, in spotting AI talent, and finding the elusive product-market fit. We have also got some data on company formation, deal flow, and how it compares to leading nations, like the US, China, Israel, UK, and Canada.

The dice is loaded against startups
Artificial intelligence is an especially thorny space to be in as Indian startups lack access to large data sets. Oxymoronic as it sounds in a country of 1.3 billion, the truth is as simple as that. Tech giants such as Amazon, Google and IBM have larger data sets compared to startups, says Parag Dhol, Managing Director at Inventus Capital Partners, over a phone conversation analysing Tracxn’s data on the AI startup landscape in India.

“To train your machine, you need a large data set. Now, IBM walks into Manipal (Hospital), makes a nice presentation, shows off what they’ve done and gets access to Manipal’s Oncology reports for the last five years,” says Dhol. “If it was a company out of Bengaluru, can it do that? The answer is a no,”

Next is the role of patient capital, which in this instance seems to be coming from governments. US and Chinese government funds are heavy investors in AI companies. In-Q-Tel, the Central Intelligence Agency (CIA)’s VC arm, for example, has backed companies such as Palantir and ThreatMetrix, among dozens of other AI startups. The Chinese government is looking to invest billions of dollars into AI, as a part of its AI development plan, with aspirations to become a world leader in the space by 2025

https://factordaily.com/artificial-intelligence-india/

 

[antonius123]

Where is India comparison for these?

and this:

 

[Adam WANG SHANGHAI MEGA]

Your answer tells me you know nothing about what you are harping. Scalability is beyond you.


Let me know when your entire Software/IT industry is worth more than TCS, okay? Aukaat me reh kar baat karo. Pakistan ho Pakistan raho.



India already has a Big Data and ML unicorn (> $ 1 billion) it is called Mu-sigma.

Do not tell them let indians be happy in their wonder land!!!!

But blood is Indian. Wherever they will go but will called " Indian American"

Talking about blood,we are all bros and sisters come from the same ancester from Africa!!

 

[Shajida Khan]

For search engine, we have coccoc.com, which is the most used website in Vietnam.

Sorry you are wrong.
http://gs.statcounter.com/search-engine-market-share/all/viet-nam

Google rules the roost in vietnam.

 

[antonius123]

India already has a Big Data and ML unicorn (> $ 1 billion) it is called Mu-sigma.

What is the innovative breakthrough that Mu-sigma has made?




China's Rise In The Global AI Race Emerges As It Takes Over The Final ImageNet Competition

Aaron Tilley , Forbes Staff

This AI machine, named AI-MATHS, took the math portion of China's annual university entrance exam, finishing it faster than young students, but its grade was no better than the average human. (Photo credit: STR/AFP/Getty Images)

The Chinese government recently said it would invest heavily in artificial intelligence to ensure its companies, government and military dominate the field by 2030. Now there's growing evidence that China may not have that far to go to claim the AI crown.

Perhaps there's no better place to note China's rise in AI than with this year's ImageNet competition, an influential AI contest where teams from across the world compete over which algorithms can best recognize images.

Out of the 27 teams competing, more than half were Chinese-based research teams from universities or companies, and all the top performers were from China. The results were something of a repeat from last year, when Chinese scientists also dominated a field of 84 teams from around the world. To be sure, leading AI players like Google, who won top results in 2014, haven't participated in the last couple ImageNets. But China's dominance in the last two years of the competition shows just how much serious AI work is coming out of the country these days.



In this year's competitions, top results for the closely-monitored image classification challenge had an error rate of only 2.25% from a team called WMW, a small jump from the previous year's 2.99% error rate. WMW's team included two researchers from Beijing-based autonomous vehicle startup Momenta -- Jie Hu and Gang Sun -- as well as Li Shen from the University of Oxford. In an email to Forbes, the Chinese researchers said they use a technique called "squeeze and excitation," which both enhances useless feature and suppresses less useful ones of a convolutional neural network.

A big jump over the previous year happened in object detection, which refers to a computer's ability to recognize objects and identify them in an image -- there are three apples in the picture and one cat, for example. The winning team, called DBAT, achieved an accuracy of 73.1% over last year's 66.3%. The DBAT team consisted of a collection of eight researchers from China's Nanjing University and two from Imperial College London.

Since starting in 2010, ImageNet (or Large Scale Visual Recognition Challenge) has emerged as an influential event in the AI research community to track the latest advances in image recognition systems. The year 2012, in particular, is regarded as a watershed moment for AI and deep learning when a team from the University of Toronto made a major breakthrough in image recognition accuracy. Led by Alex Krizhevsky, the PhD student used a deep neural network to train a model and achieved image classification error rate of 15% -- a giant leap from the previous year's rate of around 25%. His model, called AlexNet, demonstrated the viability of deep learning systems, which had been around since at least the 1950s, but until then hadn't been taken very seriously. (Krizhevsky and his advisor, AI pioneer Geoffrey Hinton, both now work at Google's AI lab.)

“2012 was really the year when there was a massive breakthrough in accuracy, but it was also a proof of concept for deep learning models, which had been around for decades,” said Olga Russakovsky, a computer science processor at Princeton University and an ImageNet organizer. “It really was the first time these models had been shown to work in context of large-scale image recognition problems.”

Deep learning techniques have since taken off like wild fire in the AI community as well as at nearly every tech company. These AI systems very loosely resemble how the brain functions -- many neurons networked together with synapses. The systems are trained on massive sets of data and are able to pick out patterns in the data.

Following the 2012 contest, large tech companies like Google and Microsoft began taking part in ImageNet to show off their latest advances in deep learning-based image recognition systems. In 2014, Google entered the competition with a team called GoogLeNet, and made a big breakthrough in object detection accuracy: 43.9% from the previous year's 22.6% accuracy. ImageNet makes for good marketing: In 2013, AI research Matt Zeiler launched his AI startup, Clarifai, while achieving top results at ImageNet in image classification -- a jump to 12% error rate from Krizhevsky's 15% the year before.

ImageNet's organizers wanted to stop running the classification challenge in 2014 and focus more on object localization and detection as well as video later on, but the tech industry continued to track classification closely throughout the years.

Now, ImageNet is shutting down because of performance saturation in challenges like image classification, said Alex Berg, a computer science professor at the University of North Carolina at Chapel Hill and an ImageNet organizer. "There's not a lot of room on the top," he said.

"I think ImageNet is still making massive progress," added Russakovsky. "But it's healthy for the community to start focusing on perhaps other tasks, challenges or datasets."

Some in the AI community are wondering what research-focused AI challenges might take ImageNet's place. One possible contender Russakovsky points to is the COCO (or Common Objects in Context) contest. Berg is also working on putting together a challenge for image recognition based strictly on real world data using smartphone cameras. One contest, called WebVision, requires teams to train their models on images culled from the internet that haven't been exhaustively labeled, like ImageNet's dataset.

The results for the WebVision challenge were recently announced and the top performer was Shenzhen-based Malong Technologies, maker of AI developer tools for image recognition tasks. Malong achieved a 94.78% accuracy rate in classifying the web images. Malong is a private business, but it opened a joint AI research lab with Tsinghua University with official sponsorship from the Shenzhen government, which is making offers of $1 million to any AI efforts kickstarted there.

"AI is so fierce now, you need any competitive advantage you can get," said Matt Scott, cofounder and chief technology officer at Malong. "Government support is one of the very helpful things going on in China."

Click here for details on how to send me information anonymously. Follow me on Twitter @aatilley or send me an email: atilley@forbes.com

https://www.forbes.com/sites/aarontilley/2017/07/31/china-ai-imagenet/#4c485f1b170a

 

[antonius123]

Cheap copy of Boston Dynamics BigDog project from 2006-07.

China and copying... the usual

Cheap? how do you know it cheap?
Copy? in what way it copies from Boston dynamic?

Can India make it?

Has USA ever made the first dentist robot?

 

[antonius123]

Where is India's comparison for this:

China Now Leads the Server Race: Meet the Phytium MARS Processor

August 29, 2016 6

Two decades ago, the US high end microprocessor industry was a lively, diverse market where about five various instruction set architectures battled it out across the workstation and server fields. You had choices like DEC’s Alpha – the speed leader; MIPS – the Silicon Graphics heart; SPARC from Sun Microsystems, IBM POWER, HP PA, the nascent X86, and a few custom architectures for MPP massive parallel processing, for instance. The rest of the world pretty much had nothing – British Transputer and German Hyperstone platforms died out due to lack of funding, while ARM was still keeping to the low end embedded arena after the end of the Acorn RISC Machine, a follow up home desktop BBC Micro. The Far East had nothing save NEC / Fujitsu / Hitachi custom vector processors for niche high end machines.

Come back to today: The US server market diversity is a long gone history, right up there in the forgotten mist of lost antediluvian civilizations and such. Basically, during the past decade or so, all you got is X86, specifically Intel Xeons, at whatever development and pricing points Intel decides to offer them. IBM POWER and Oracle SPARC do have some token presence, while ARM, the weakest RISC architecture compute-wise, has some nascent but still immature entries in the server field, mostly far behind Xeons in compute power or bandwidth.

Cross the Big Pond to Middle Kingdom, which was nowhere on the radar two decades ago… What a change: you got about everything the USA had in the Golden ‘90s – there’s Alpha development, right at its rightful No.1 TOP500 place through ShenWei Alpha. There’s LoongSon MIPS in appliance servers, backdoor-free (at least no Western backdoors, that is). There’s PowerCore CP1 (full POWER 8) and coming CP2 (POWER9 with China-specific mods). Then, there are SPARC (FeiTeng) and ARM (Phytium MARS) efforts, both linked to some of China’s leading supercomputers. And to leave this Chinese sugar for the end, Tianjin also now has a company designing AMD Zen derivatives coming soon as well, likely a target for China’s own X86 supercomputer in due course.

https://vrworld.com/2016/08/29/china-now-leads-server-race-meet-phytium-mars-processor/