Commentary for the casual reader...
This is important stuff as it articulates what a BIG DOG sees..
Intelligence ON THE DEVICE.
Todd Mozer ( Sensory Inc. CEO) has seen the same thing for decades.
QUIK's CTO ( Dr. Saxe) must be very happy to hear this stuff from Fscebook as he too has spoken ofmore intelligence ON THE DEVICE>

It's a big pic item of some urgency now for everybody.

FPGA IP has much greater value from this new adjacent possible.

Applied Research Scientist, Machine Translation
(Menlo Park, CA)
Facebook's mission is to give people the power to share, and make the world more open and connected. Through our growing family of apps and services, we're building a different kind of company that helps billions of people around the world connect and share what matters most to them. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to make the world more open and accessible. Connecting the world takes every one of us—and we're just getting started.
Facebook is seeking an Applied Research Scientist to join our Machine Translation Team in Menlo Park. This team is part of the Applied Machine Learning organization within Facebook, working to break language barriers to create a world where everyone on Facebook can understand everyone else, no matter what languages they speak. In pursuit of this mission, the team has created machine translation systems that serve over 2 billion translations every day across 2000 different language pairs. However, this journey is still only 1% finished, and the team won't rest until they can produce human quality translations for all language directions. The ideal candidate will have a strong background in developing machine translation systems, a strong knowledge of neural networks, and have experience working with massive amounts of data. They should also have strong software engineering skills and the ability to build systems that reach Facebook scale.
Responsibilities

• Conduct research to advance the state of the art in neural networks and machine translation.
• Utilize that research to develop and deploy scalable neural network models into production to impact billions of people using Facebook.
• Contribute to helping the team develop machine learning models that help solve other language related problems, such as language identification and user language modeling.
• Collaborate with team members from other research and applied research teams working on a variety of deep learning and NLP problems.

Minimum Qualifications

• PhD/MS with relevant experience in the fields of machine translation, machine learning, deep learning, parsing or language modeling.
• 3+ years of experience in building in large-scale machine translation systems from the level of researching a prototype to the level of production.
• Experience with developing and training neural network models.
• Experience in C++ and Python.

Facebook wants the spoken word and the written word. Always with stuff like this is the sad part....those who can speak many languages are such great people to know.

There are MANY more such interesting jobs at Facebook.

On FPGA IP value...

The intrinsic scalability demonstrated by our FPGA implementation can be utilized to implement complex CNN – Convolutional Neural Networks on increasingly smaller and lower power FPGAs at the expense of some performance.



Give up the notion that QUIK don't got no IP worth a dime.
It is worth much more than you realize. 

Facebook, hmmm

1. 

   jfiebWell-Known Member

   
   

   I will store some important stuff here.
   
   They should just buy Sensory Inc?
   
   
   Applied Research Scientist, Speech Recognition Language Modeling
   (Menlo Park, CA)
   Facebook's mission is to give people the power to share, and make the world more open and connected. Through our growing family of apps and services, we're building a different kind of company that helps billions of people around the world connect and share what matters most to them. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to make the world more open and accessible. Connecting the world takes every one of us—and we're just getting started.
   Facebook is seeking a Speech Recognition Research Scientist to join our research team in Menlo Park. The ideal candidate will have research experience developing speech recognition systems in different languages. Individuals in this role should be experts in language modeling and have experience working on vast quantities of data, advanced recognition systems and perform rigorous performance evaluations. The candidate will help Facebook conduct research that support naturally spoken input in more than 70 languages.
   Responsibilities
   

   • Create language models from large corpora of text data in different languages using Hadoop/Hive
   • Work closely with other speech recognition experts and researchers on implementing algorithms that power user and developer-facing products
   • Be responsible for measuring and optimizing the quality of your algorithms

   Minimum Qualifications
   

   • Strong desire to build beautiful, expressive products that delight users in any language
   • M.S. or Ph.D. in Computer Science, Electrical Engineering, Speech/Language Processing or Machine Learning
   • Experience with scripting languages such as Perl, Python, PHP, and shell scripts
   • Experience in C/C++
   • Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable a plus
   • Fluency in at least 2 natural languages is a plus

   Last edited: 1 minute ago

   jfieb, 18 minutes agoEditReport

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2. 

   jfiebWell-Known Member

   
   

   and this one
   
   
   
   
   Typically, neural networks run on large numbers of computer servers packed into data centers on the other side of the Internet—they don’t work unless your phone is online—but with its new app, Facebook takes a different approach. The Picasso filter is driven by a neural network efficient enough to run on the phone itself. “We perceive the world in real-time,” Mehanna says. “Why wouldn’t you want the same thing from your AI?”
   
   the link
   
   https://www.wired.com/2016/11/fb-3/
   
   Really, I said to myself, holy smokes, YES! THis will be better than 10 axis fusion for indoor location..
   
   
   Here is some more...
   
   
   Already available in Ireland and due soon here in the States, this new Facebook app is another sign that deep neural networks will push beyond the data center and onto phones, cameras, and various other devices spread across the so-called Internet of Things. As Rick points out its just like INTC/Altera only on the other end.....
   
   
   Commentary.... ALready we have the BIG dogs of the FPGA world talking bout cloud neural networks...and Facebook talking up Todd Mozers vision...we need more intelligence ON THE DEVICE. And the article only gets better....
   
   Yes, these tools can operate without an Internet connection. And that points to a future where our smartphone apps can perform a much wider range of tasks while offline. But it also shows we’re moving towards technology that can handle more complex AI tasks with less delay. Ultimately, if you can complete a task without sending a bunch of data across the wire, it will happen quicker.
   
   Training in the cloud and inference ON THE DEVICE.....
   
   Facebook’s app doesn’t train its neural networks on your smartphone. That still happens on servers in the data center. But the phone does execute the neural net—without calling back to the data enter. That may seem like a small thing, but building a deep neural net that can so quickly execute on a phone—which offers limited processing power and memory—is no simple task.
   
   
   Am blown away this am....best find in a loooong time for 
   
   As companies like Facebook and Google continue to push neural networks onto smartphones, phone makers will start building hardware into these devices that can run neural networks with even greater speed. 
   
   
   
   QUIK, with is multi yr focus on low power, with its FPGA IP, this starts to flesh it out. QUIK has every bit and piece needed to create a ubiquitous NNLE.( neural network learning engine). 
   
   A Picasso photo filter won’t change your life. But this one points to big changes in the years to come.
   
   
   QUIK's IP just took a jump in value, likeXilinx.
   
   
   From the other recent reading, DSPs can do the training very well, FPGAs are vying for the inference part of AI.
   
   There should be a LOT of interesting talk with Dr Saxe and team,
   
   Please read this one over every couple days for incremental understanding of what it means....
   
   I have waited a LOOOOONG time to read something so profound that is good.
   
   jfieb, happy today
   
   
   
   am so blown away by implications I have to log off and do other stuff and come back l8er to look at it again and see if I got it right.
   
   Sorry bout the underlining, once I use it for a phrase it does NOT stop.
   
   Please make comments, ask questions etc. This IS HUGE.?
   
   
   Facebook has a LOT of jobs related to this...........
   

   Last edited: A moment ago

   jfieb, 14 minutes agoEditDeleteReport

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3. 

   jfiebWell-Known Member

   New
   

   
   

   https://techcrunch.com/2016/11/08/shining-light-on-facebooks-ai-strategy/
   
   Shining light on Facebook’s AI strategy
   
   a few snips
   
   Caffe2Go won’t remain limited to Style Transfer — it holds the key to deploying convolutional neural nets across Facebook’s suite of mobile apps.
   
   “For anything we build on the server, we now have a vehicle to ship it to mobile devices,” noted Schroepfer.
   
   
   
   Bringing machine intelligence to the smartphone is just step one. For the time being, the world still doesn’t have an answer for training neural networks on mobile devices. The prospect coupled with Facebook’s long-term roadmap make a compelling argument for a future where the average person could design and train a custom neural net on their own smartphone for daily use.
   
   
   Regardless of the barriers, Facebook has little choice in prioritizing AI as its competitors pour billions into beating the company to the next great breakthrough — though the mere fact that everyone is all in on the race is perhaps what makes it so interesting.
   
   While Google first popularized algorithmic search, and Snapchat is now making augmented reality mainstream, it’s Facebook delivering artificial intelligence to the masses. AI isn’t necessarily about communicating with a conscious computer. It’s about sifting through mountains of data to make sense of the chaos. And no one has more data about us than Facebook.
   
   
   consider that in the cloud Inference is done on FPGAs. Some Soc makers will have to try it out for themselves.
   
   Facebook should just buy Sensory and be where it needs to be much much sooner and with really good stuff?
   
   Dr. Saxe you got this covered for us?  thanks in advance

Who is Andrew Ng

Discussion in 'Main Forum' started by jfieb, Today at 6:41 AM.

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1. 

   jfiebWell-Known Member

   
   

   many point to Andrew Ng’s 2012 work at Stanford as the kickstart to today’s AI obsession. Ng and his team made a breakthrough in unsupervised learning through neural networks, the underpinnings of deep learning (a series of algorithms which loosely resemble the brain). Ng, a visiting professor at Stanford (founding lead of Google Brain team, now lead of Baidu’s 1,200-person AI team) decided to test unsupervised learning, or training data without a model
   
   THis is where I will be working in early 17...a LOT of work to be done, a lot of digressive stuff that may be skipped.
   
   Facebook snip
   
   Companies like Facebook are treating AI more as a philosophy than a technology, as CTO Mike Schroepfer outlined at November’s Web Summit.
   
   ( They want it on the device, no cloud required....) They say so LOUD and very clear. ;-)
   
   
   http://venturebeat.com/2016/12/20/hbos-westworld-artificial-intelligence-then-and-now/
   
   OPen Q.... I have been reflecting on what this stuff means for a Computing biology...seems like it is a Compugen
   on steroids. Set up the learning on a massive scale and feed it...and wait for the answer to come out... I am considering that WHatever Compugen had is NOT so unique any more? May sell the shares I hold. Sure Compugen can do it too. If ALpha go can tech itself to beat the best human go players, it can outdo the world best drug receptor minds also?
   
   
   AI, and its wave has similar potential to the Internet wave of the ’90s and mobile wave of the ’00s. This movement is already being realized within image and video recognition, as well as speech recognition and translation.
   

   jfieb, Today at 6:41 AMEditReport

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2. 

   jfiebWell-Known Member

   
   

   Why a deep-learning genius left Google & joined Chinese tech shop Baidu (interview)
   JORDAN NOVET@JORDANNOVET JULY 30, 2014 8:03 AM
   
   Image Credit: Jordan Novet/VentureBeat
   SUNNYVALE, California — Chinese tech company Baidu has yet to make its popular search engine and other web services available in English. But consider yourself warned: Baidu could someday wind up becoming a favorite among consumers.
   
   The strength of Baidu lies not in youth-friendly marketing or an enterprise-focused sales team. It lives instead in Baidu’s data centers, where servers run complex algorithms on huge volumes of data and gradually make its applications smarter, including not just Web search but also Baidu’s tools for music, news, pictures, video, and speech recognition.
   
   Despite lacking the visibility (in the U.S., at least) of Google and Microsoft, in recent years Baidu has done a lot of work on deep learning, one of the most promising areas of artificial intelligence (AI) research in recent years. This work involves training systems called artificial neural networks on lots of information derived from audio, images, and other inputs, and then presenting the systems with new information and receiving inferences about it in response.
   
   Two months ago, Baidu hired Andrew Ng away from Google, where he started and led the so-called Google Brain project. Ng, whose move to Baidu followsHugo Barra’s jump from Google to Chinese company Xiaomi last year, is one of the world’s handful of deep-learning rock stars.
   
   Ng has taught classes on machine learning, robotics, and other topics at Stanford University. He also co-founded massively open online course startup Coursera.
   
   He makes a strong argument for why a person like him would leave Google and join a company with a lower public profile. His argument can leave you feeling like you really ought to keep an eye on Baidu in the next few years.
   
   “I thought the best place to advance the AI mission is at Baidu,” Ng said in an interview with VentureBeat.
   
   Baidu’s search engine only runs in a few countries, including China, Brazil, Egypt, and Thailand. The Brazil service was announced just last week. Google’s search engine is far more popular than Baidu’s around the globe, although Baidu has already beaten out Yahoo and Microsoft’s Bing in global popularity, according tocomScore figures.
   
   And Baidu co-founder and chief executive Robin Li, a frequent speaker on Stanford’s campus, has said he wants Baidu to become a brand name in more than half of all the world’s countries. Presumably, then, Baidu will one day become something Americans can use.
   
   
   Above: Baidu co-founder and chief executive Robin Li.
   
   Image Credit: Baidu
   Now that Ng leads Baidu’s research arm as the company’s chief scientist out of the company’s U.S. R&D Center here, it’s not hard to imagine that Baidu’s tools in English, if and when they become available, will be quite brainy — perhaps even eclipsing similar services from Apple and other tech giants. (Just think of how many people are less than happy with Siri.)
   
   A stable full of AI talent
   But this isn’t a story about the difference a single person will make. Baidu has a history in deep learning.
   
   A couple years ago, Baidu hired Kai Yu, a engineer skilled in artificial intelligence. Based in Beijing, he has kept busy.
   
   “I think Kai ships deep learning to an incredible number of products across Baidu,” Ng said. Yu also developed a system for providing infrastructure that enables deep learning for different kinds of applications.
   
   “That way, Kai personally didn’t have to work on every single application,” Ng said.
   
   In a sense, then, Ng joined a company that had already built momentum in deep learning. He wasn’t starting from scratch.
   
   
   Above: Baidu’s Kai Yu.
   
   Image Credit: Kai Yu
   Only a few companies could have appealed to Ng, given his desire to push artificial intelligence forward. It’s capital-intensive, as it requires lots of data and computation. Baidu, he said, can provide those things.
   
   Baidu is nimble, too. Unlike Silicon Valley’s tech giants, which measure activity in terms of monthly active users, Chinese Internet companies prefer to track usage by the day, Ng said.
   
   “It’s a symptom of cadence,” he said. “What are you doing today?” And product cycles in China are short; iteration happens very fast, Ng said.
   
   Plus, Baidu is willing to get infrastructure ready to use on the spot.
   
   “Frankly, Kai just made decisions, and it just happened without a lot of committee meetings,” Ng said. “The ability of individuals in the company to make decisions like that and move infrastructure quickly is something I really appreciate about this company.”
   
   That might sound like a kind deference to Ng’s new employer, but he was alluding to a clear advantage Baidu has over Google.
   
   “He ordered 1,000 GPUs [graphics processing units] and got them within 24 hours,” Adam Gibson, co-founder of deep-learning startup Skymind, told VentureBeat. “At Google, it would have taken him weeks or months to get that.”
   
   Not that Baidu is buying this type of hardware for the first time. Baidu was the first company to build a GPU cluster for deep learning, Ng said — a few other companies, like Netflix, have found GPUs useful for deep learning — and Baidu also maintains a fleet of servers packing ARM-based chips.
   
   
   Above: Baidu headquarters in Beijing.
   
   Image Credit: Baidu
   Now the Silicon Valley researchers are using the GPU cluster and also looking to add to it and thereby create still bigger artificial neural networks.
   
   
   
   
   
   But the efforts have long since begun to weigh on Baidu’s books and impact products. “We deepened our investment in advanced technologies like deep learning, which is already yielding near term enhancements in user experience and customer ROI and is expected to drive transformational change over the longer term,” Li said in a statement on the company’s earnings the second quarter of 2014.
   
   Next step: Improving accuracy
   What will Ng do at Baidu? The answer will not be limited to any one of the company’s services. Baidu’s neural networks can work behind the scenes for a wide variety of applications, including those that handle text, spoken words, images, and videos. Surely core functions of Baidu like Web search and advertising will benefit, too.
   
   “All of these are domains Baidu is looking at using deep learning, actually,” Ng said.
   
   Ng’s focus now might best be summed up by one word: accuracy.
   
   That makes sense from a corporate perspective. Google has the brain trust on image analysis, and Microsoft has the brain trust on speech, said Naveen Rao, co-founder and chief executive of deep-learning startup Nervana. Accuracy could potentially be the area where Ng and his colleagues will make the most substantive progress at Baidu, Rao said.
   
   Matthew Zeiler, founder and chief executive of another deep learning startup, Clarifai, was more certain. “I think you’re going to see a huge boost in accuracy,” said Zeiler, who has worked with Hinton and LeCun and spent two summers on the Google Brain project.
   
   One thing is for sure: Accuracy is on Ng’s mind.
   
   
   Above: The lobby at Baidu’s office in Sunnyvale, Calif.
   
   Image Credit: Jordan Novet/VentureBeat
   “Here’s the thing. Sometimes changes in accuracy of a system will cause changes in the way you interact with the device,” Ng said. For instance, more accurate speech recognition could translate into people relying on it much more frequently. Think “Her”-level reliance, where you just talk to your computer as a matter of course rather than using speech recognition in special cases.
   
   “Speech recognition today doesn’t really work in noisy environments,” Ng said. But that could change if Baidu’s neural networks become more accurate under Ng.
   
   Ng picked up his smartphone, opened the Baidu Translate app, and told it that he needed a taxi. A female voice said that in Mandarin and displayed Chinese characters on screen. But it wasn’t a difficult test, in some ways: This was no crowded street in Beijing. This was a quiet conference room in a quiet office.
   
   “There’s still work to do,” Ng said.
   
   ‘The future heroes of deep learning’
   Meanwhile, researchers at companies and universities have been hard at work on deep learning for decades.
   
   Google has built up a hefty reputation for applying deep learning to images from YouTube videos, data center energy use, and other areas, partly thanks to Ng’s contributions. And recently Microsoft made headlines for deep-learning advancements with its Project Adam work, although Li Deng of Microsoft Research has been working with neural networks for more than 20 years.
   
   In academia, deep learning research groups all over North America and Europe. Key figures in the past few years include Yoshua Bengio at the University of Montreal, Geoff Hinton of the University of Toronto (Google grabbed him last year through its DNNresearch acquisition), Yann LeCun from New York University (Facebook pulled him aboard late last year), and Ng.
   
   But Ng’s strong points differ from those of his contemporaries. Whereas Bengio made strides in training neural networks, LeCun developed convolutional neural networks, and Hinton popularized restricted Boltzmann machines, Ng takes the best, implements it, and makes improvements.
   
   “Andrew is neutral in that he’s just going to use what works,” Gibson said. “He’s very practical, and he’s neutral about the stamp on it.”
   
   Not that Ng intends to go it alone. To create larger and more accurate neural networks, Ng needs to look around and find like-minded engineers.
   
   “He’s going to be able to bring a lot of talent over,” Dave Sullivan, co-founder and chief executive of deep-learning startup Ersatz Labs, told VentureBeat. “This guy is not sitting down and writing mountains of code every day.”
   
   And truth be told, Ng has had no trouble building his team.
   
   “Hiring for Baidu has been easier than I’d expected,” he said.
   
   “A lot of engineers have always wanted to work on AI. … My job is providing the team with the best possible environment for them to do AI, for them to be the future heroes of deep learning.”
   

3. 
   

1. 
   

   
   
   
   
   
   
   
   History[edit]
   Peel was launched in 2009 by current CEO Thiru Arunachalam[6] and co-founder Bala Krishnan, who serves as chief product officer.[5] Peel built a user base of 25 million during its first few years of operation and then more than tripled to over 70 million in the subsequent year due to agreements with major devices manufacturers Samsung[8] and HTC[9] to include Peel as a preloaded app. Today, Peel has generated over 100 billion remote commands with over 120 million registered users.[10]
   
   Perhaps the voice interface can cut down on the remote commands a few billion or so? If they use Sensory that would be great as it could be a Global audio UX as SENSORY has ALL the languages for MOST of the planet. ( Not Scottish, thats the funny part of Audio UX?).
   
   I really like the China/India & Samsung PEEL efforts. 
   
   
   I am using this first one as a mental model for those that will follow in '17.
   
   Feel free to nominate an APP that lends itself to voice, or really, really needs it. Waze is a good mental model?
   

   jfieb, 15 minutes agoEditDeleteReport

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2. 

   jfiebWell-Known Member

   New
   

   
   

   This one IS a LOT of FUN to read. Consider the implications..... with everything that is at stake in their BIG pic. + Audio UX for PEEL.
   
   Alibaba just takes PEEL out for $$
   
   Why China Just Spent $2.3 Billion On America's Hottest Startups
   
   
   
   This story appears in the June 15, 2015 issue of Forbes. Subscribe
   Five days before closing a planned $12 million funding round in June 2013, Thiru Arunachalam and Bala Krishnan, founders of remote control app maker Peel, received an unexpected phone call. TransLink Capital, one of Peel's soon-to-be investors, asked them to consider taking an additional $1 million from a Chinese company called Alibaba , a name they barely knew.
   
   "Listen, this is too late in the game," Arunachalam recalls saying, given that it was a Monday and he anxiously wanted to close the deal by Friday. "Let's just leave them alone and move on."
   
   But their suitor would not be turned away. Within 48 hours Alibaba offered to put in $5 million--helping to pump the funding round up to $18.9 million--and sent over a two-page memo explaining its e-commerce business and ambitions in entertainment. By Thursday morning Hongping Zhang, managing director of Alibaba Capital Partners, showed up at Peel's door in Mountain View, Calif. and wooed the two cofounders over a long lunch. That night, already Friday Beijing time, Alibaba shot over the cash.
   
   "They were the last people to talk to us but ended up being the first to wire the money," Arunachalam says. "They moved like a big startup." Less than a year later Alibaba dropped another $50 million into Peel during its Series D round, helping to fund the company as its app grew to more than 100 million users.
   
   
   
   
   ADVERTISING
   This investment proved an early and tiny piece of the e-commerce giant's recent spending spree on American startups, one that speaks volumes about a trend quietly reshaping the venture capital ecosystem. Alibaba and its two giant Chinese Internet rivals--search engine Baidu and gaming/messaging firm Tencent--a trio known as BAT, are pouring money into all manner of firms at every stage from seed to late rounds. Since 2012 we count more than 50 investments totaling $2.3 billion. In the past 18 months alone Alibaba has plowed more than $1 billion into just ten U.S. firms.
   
   Many of the investments are bizarre on the surface, smacking of dumb money rushing in late in the cycle and driving up valuations for everyone. Why would an e-commerce giant spend tens of millions of dollars on a startup like Peel that's outside of its core business, not to mention its core country?
   
   
   In a word: smartphones. The three BAT companies each monopolize a sphere of China's desktop-style online behavior, but they risk falling behind in mobile. This is a problem in a country where tens of millions of people skip PCs entirely. Hence the landgrab--the Big Three don't much care where the innovations on this new intertwined platform come from or, it seems, how much they have to shell out to secure them.
   
   "In the online world, everybody has their own domain, but in mobile, everyone's competing on everyone else's turf," says Jay Eum, cofounder of TransLink Capital, the venture capital firm that introduced Alibaba to Peel and has invested in two other Alibaba-backed startups, Quixey and Tango.
   
   To some extent it's similar to the concurrent multifront competition in the U.S. among Amazon, Google GOOGL -0.75% and Facebook, and each of the three Chinese companies brings its own style. Tencent is the most quiet about its investments, reflecting the low-profile personality of its billionaire founder, Ma Huateng. Baidu focuses on research investment as well as direct funding in startups, a hybrid style similar to that of its U.S. inspiration, Google. The company is sinking $300 million into an R&D lab in Sunnyvale, Calif., the largest yet by any Chinese Internet company in California, run by Andrew Ng, cofounder of online educator Coursera and the man who set up Google's deep learning project.
   
   Meanwhile, Alibaba's venture unit operates much like its largest outside shareholder, Masayoshi Son's SoftBank. "There's always a strategic angle, but a certain level of investment returns is the bottom line," says TransLink's Eum. Alibaba's Jack Ma saw how much SoftBank and Yahoo made from investing early in his firm--now he wants his own lottery ticket.
   
   
   Bala Krishnan (left) and Thiru Arunachalam, cofounders of app maker Peel, barely knew Alibaba when the Chinese e-commerce giant offered to invest. (Eric Millette For Forbes)
   
   The competition among the three plays out in ways that often don't seem rational. Last December Baidu's chief financial officer, Jennifer Li, right-hand woman to its billionaire CEO, Robin Li, helped lead Baidu's roughly $200 million investment in Uber at a reported $41 billion valuation. A search engine buying into a car-hailing app doesn't make a ton of sense on paper. No matter, says Li: "Mobile development in China is exploding. Baidu's vision today is to connect people with services. Uber is an example of that."
   
   Plus, she needed to respond to her two rivals. As Uber set foot in China, Tencent and Alibaba were already backing two Uber clones, Didi Dache and Kuaidi Dache, respectively. Now the Chinese can hail Uber on their phones through Baidu Maps. The search engine is also hoping to leverage Uber in Baidu Wallet's uphill battle against Alibaba's Alipay and Tencent's WeChat Payment. Not to be outflanked, Tencent and Alibaba merged their ride-share shops, creating a near monopoly worth $8.75 billion. And the battle rages on.
   
   Kevin Chou could do that math. The CEO of San Francisco-based mobile-gaming startup Kabam hit the road in China for his latest funding round last spring, hopeful for access to that market. "Entering China is a pretty complicated affair," he says. "It operates in a very different fashion than any other market in the world because of such a strong presence of the major Chinese Internet players." For the next few months the 35-year-old Chinese-American, who cofounded his company in downtown Mountain View above a dim sum restaurant, sought out the BAT trio, among others, and ended up with five term sheets. "It's a lot of dinners, a lot of baijiu ," grins Chou, referring to a potent Chinese liquor.
   
   By July Kabam had its number--a valuation that shot it past the $1 billion "unicorn" threshold--from Alibaba, which plunked down $120 million for just over a 10% stake, as well as instant credibility and relationships in China. "If you don't know who you're working with, you can really get yourself into trouble," says Chou.
   
   Alibaba, for its part, paid the premium with a clear eye on Tencent, the world's largest gaming company, which has $7.2 billion in revenue from online gaming alone last year. The deal came with an initial commitment by Kabam to launch ten games during the next three years through Alibaba, including ones related to Hollywood blockbuster movies like Lord of the Rings , Hunger Games and Fast and Furious . Expect Alibaba to leverage its investment by using its PayPal clone, Alipay, for mobile micropayments and by promoting Kabam through the Alibaba-backed video site Youku Tudou.
   
   
   Kabam CEO Kevin Chou had his pick of Chinese money and took Alibaba’s $120 million plus its promise to help him crack China’s market. (Timothy Archibald for Forbes)
   
   "While they care about the financial returns, the amount of money they put into Kabam is a rounding error," says Chou. "They don't stress out anytime when there's a small problem." Alibaba even organizes an annual "Ali-family" party for its portfolio companies with the hope of fostering synergies. San Mateo-based ShopRunner, which raised $206 million from Alibaba in 2013, was one of the first partners of Alipay's ePass program, which enables U.S. retailers to sell directly to Chinese consumers. "It came up afterwards, after we started to understand more about each other," CEO Scott Thompson says. "Now it's obvious that there's something else we can do together, and it's likely to be a really interesting big business at some point."
   
   Such synergies (and enormous valuations) make the BAT troika today's investors of choice. Silicon Valley used to look askance at money coming from a place where the central government blocks everything from Facebook to Gmail. "When Alibaba first invested, we took it with a huge grain of salt," says Peel's Krishnan. Now companies like Snapchat look eagerly to China--the disappearing-message app is reportedly raising $200 million from Alibaba, which again seems to be targeting Tencent, specifically its WeChat service. No matter that Snapchat is banned in China (or that Tencent also invested in a 2013 round).
   
   "They're playing a global game of domination on the Internet," says Patrick Riley, founder and CEO of search engine Ark, for which Tencent participated in a $4.2 million seed round. "It goes entirely against the reputation, what the United States thinks of Chinese companies, which is just copy-paste." With BAT leading the trend, other tech companies, from mobile giant Xiaomi to Baidu rival Qihoo, are following in their footsteps with smaller deals. With the right bets in Silicon Valley, Chinese companies can redeploy the U.S. startups' technology and talent for their immediate next steps: expansion into other emerging economies such as India and Brazil. Eventually they'll grab a piece in the saturated American market.
   
   "Everyone is competing on everything," says Kabam's Chou. Advantage, entrepreneurs.
   
   
   Audio UX looms large for China. We want the PEEL integration to be GLOBAL....China/India.
   If it works really well imagine how a device starts to be different that one that does NOT have this Voice PEEL?