HOW TO BECOME ONE OF WORLD'S TOP 5 ECONOMIES: JackMaNews (10/17) DAMO Academy will change the world with human values of -1 data intelligence, 2 the Internet of Things, 3 fintech, 4 quantum computing : ALQ & 5 human-machine interaction-will yours be one of the first 7 DamoCity along with AliBaba's supercity Hangzhou (inaugural 40000 person comp summit, host china g20)? - will half the innovation be led by girls? correspondence rsvp (at project of Norman Macrae Family Foundation) if you search out a superapp story worthy of worldwide youth celebrations .. 21st c logistics netpreneurs 10T MIT


cities- .jack ma's new 15 billion dollar research academy - ma's 7 most wonder cities might be toronto, bangalore, tokyo, LA, beijing, singapore & . or are 7 choices already made?

DAMO (Discovery Adentore Momentum Outlook to) attract world-class talent, build partnerships and open research laboratories in seven cities around the globe..Its research areas will cover data intelligence, the Internet of Things, fintech, quantum computing and human-machine interaction. Within those areas, it will focus on real-world applications like machine learning, network security, visual computing and Natural Language Processing. Intro to Tech

Friday, March 31, 2017

Data Intelligence

why DI and LoveQ matter-
 these are the most exciting times to be alive because  the investments and interactions between 3 generation- grandparents, parents and under 30s - are determining within the next 10 years whether our species is sustainable! if you didn't know of that challenge then its quite likely to be because of a war going on between big data big and big-data small; in the west global1.0 gave all the resources to big data big who now use these resources to fake news on how urgent this end game is -can we escape orwell's big brother syndrome; Jack also talks about the quotient of love- if you have a professional skill but find yourself in a market where all the rewards (including jobs) go to big data big it can take a lot of love to stand up for trusting big data small - this is especially so in education which so often seems to be designed for everyone one but the child's livelihoods- here's jack on this system tragedy  -here's an investigation of how education has turned against kids livelihoods in much of West

back to general searches on DI
Today, content is very key, but ultimately, the winning strategy has to be beyond content,” he said.
Data plays a central role here as well. Yu noted how Alibaba was able to drive box office sales in China for “A Dog’s Purpose,” a co-production of Alibaba Pictures and Steven Spielberg’s Amblin Entertainment.
Mining its massive trove of consumer data for consumers who previously purchased pet products and those who engaged with related content on Alibaba’s various media platforms, the company was able to deliver specific advertisements for the movie to its most likely audience. The end result: box office sales in China hit $88 million compared to $64 million in the U.S. “This is a good case of how we can leverage our resources and do precise marketing,” Yu said.
AI and Alibaba
Answering a question from the audience on Thursday about Alibaba’s development of artificial intelligence, Tsai noted the company is already using an AI chatbot that can handle 90 percent of customer enquiries and is spreading learning-machine technology to other areas such as logistics and product search.
“A lot of companies say they have an AI department,” Tsai said. “We don’t (say that) because AI lives in every part of our business,” he said, noting that AI “is only in the first inning of the game … We will be able to sprinkle lots of AI surprises into our core businesses to make our core businesses more valuable.”
Clear Skies Ahead for Alibaba Cloud
Investors and analysts attending the second day of Alibaba Group’s Investor Day conference were wondering if Alibaba Cloud, Alibaba’s cloud-computing subsidiary, could maintain its rapid expansion after eight consecutive quarters of triple-digit revenue growth rates.
Alibaba Cloud President Simon Hu assured attendees that, despite competition from players such as Amazon’s market-leading AWS cloud business, there are abundant blue-ocean growth opportunities globally and in China’s nascent market, which is projected to reach $20 billion by 2020, according to Bain & Co.
More than one-third of China’s top 500 companies, among them energy giant Sinopec, automaker Geely, and telecoms company China Unicom, are already Alibaba Cloud customers, as are numerous government agencies like China Customs as well as the 2022 Beijing Olympics—yet the market has barely been tapped.
“This indicates there are strong growth prospects for Alibaba Cloud going forward,” Hu said. With 15 data centers worldwide, “today our footprint is expanding across the globe, in the U.S., Japan, the Middle East and across Asia … we are truly serving a global marketplace” In the quarter ended March 2017, Alibaba Cloud reported it had 874,000 paying customers; Revenue in the quarter was $314 million, an increase of 103% compared with the same quarter of 2016.
Launched eight years ago, Alibaba Cloud is a relative latecomer to the market, but the company is already the world’s fourth-largest Internet cloud services provider in revenue and was No. 1 in China’s public cloud market last year with a market share of more than 40%, according to IDC. About 37 percent of all websites in China are hosted by Alibaba Cloud.
Hu’s positive outlook is based in part on growing demand from Chinese companies in financial services and manufacturing that are ripe for digitalization through cloud computing. China’s vast manufacturing sector wants to transition to smart manufacturing to gain greater supply chain control, yet just 1% of manufacturers have cloud capabilities today, he said.
“That’s going to go up fast,” Hu said. In addition, “many retailers want to be selling offline and online and do omni-channel marketing, this is another big opportunity for us.”
Alibaba Cloud’s innovations in big data analytics and data processing, augmented by artificial intelligence, are also expected to drive growth as the company expands its cloud services and solutions. Businesses don’t just want to store and process data in the cloud because of cost efficiencies, “businesses want to know how to use data,” Hu explained .
“What we can do in terms of technology is provide a deeper level of service. Over the course of the past year, data intelligence has become the most important strategy for Alibaba Cloud … We have 10 years of experience with (AI) algorithms and we are able to leverage that quickly.”
But will Alibaba Cloud, which is nearing profitability, continue to grow triple digits? “We believe cloud will continuously see strong growth in revenue from both more customers and increased customer spending,” said Alibaba Group CFO Maggie Wu.
“We Are A Technology Company”
Alibaba Group Chief Technology Officer Jeff Zhang wants to settle this question once and for all: is Alibaba an e-commerce company or a technology company?
“We are a technology company–we’re built on data,” Zhang said. “What all of our business units want to do with data comes down to us.” In other words, Alibaba’s massive network, data servers and computational horsepower is the fuel that powers the company’s e-commerce, not the other way around.
Not only is Alibaba a tech company, it’s one of the world’s premier tech companies, Zhang said, one that has integrated into its businesses advanced image-recognition and voice-recognition technology, artificial intelligence and “the world’s fastest cloud processing, or streaming processing, platform.”
“Our businesses are more diverse and complex than any other internet company,” he said, pointing out that Alibaba is able to drawing extensive data on shopping habits, geo-location apps, financial services and scores of other sources to help better serve customers and drive efficient merchant operations.
To cite just one example, the result of this capability can be seen in the Mobile Taobao app, which displays personalized homepages that are unique in both layout and content for millions of individual users, adding shopping recommendations from a pool of more than one billion products.
This is a bigger challenge than faced by other major internet companies, Zhang said. “Amazon and eBay are probably the biggest customers of Facebook,” he explained. “On Facebook, you see Amazon advertising and the advertising you see is different for everybody, but there are only 100,000 products listed so they are selecting one from 100,000 products to recommend to you.  So, we have a billion products; one relevant product in a billion to recommend to you requires a higher level of technology.”
While a lot of companies are investing in artificial intelligence, Alibaba has all the ingredients necessary to develop practical AI solutions and is hard at work rolling them out.
The company has massive amounts of data as fodder for learning machines, robust computational power to support real-time AI applications and process complex problems efficiently, advanced AI algorithms, and, perhaps most important, a business that is rich in opportunities to apply AI in ways that are practical and useful.
“Only if you have these things can you truly say you are an AI company,” Zhang said. “So yes, we are better at AI than other companies,” adding that “we are continuously training our systems so our platforms are becoming increasingly intelligent, and that’s our advantage.”
“At Alibaba, we are literally launching new products and new solutions each and every day,” Zhang said. Without giving details, he added that the company is investing heavily in speech-recognition technology and this summer will unveil “a whole new series” of products based on a voice-controlled intelligent platform, products for the home, entertainment, hospitality industry and education markets.

Wednesday, March 29, 2017

from goldman sacha report on ai china

Companies such as Google and Microsoft have poured vast amounts of money into research and development to expand the horizon of what AI can achieve. Machines are fed large quantities of data and taught specific tasks, allowing companies to create software that can learn and become smarter.
While the United States is generally considered to be leading the field, other countries are catching up. China, home of internet powerhouses such as BaiduAlibaba and Tencent, is one of them.
In July, China's State Council issued guidelines on developing AI inside the country and set a goal of becoming a global innovation center for it by 2030. It expects the total output value of AI industries to surpass 1 trillion yuan ($147.80 billion).
The Council encouraged the creation of open-source computing platforms and training more AI professionals and scientists. The guidelines said the government will invest in qualified AI projects and encourage private capital investment.

Key drivers to create value in China's AI space

Goldman identified four key areas where development is needed to create value in AI: talent, data, infrastructure and computing power. The bank concluded China has the talent, data and infrastructure needed to fully embrace AI.
Because AI is a relatively new technology, finding adequate number of talented individuals is a perennial problem. Experts have argued that more needs to be done to train people in new AI-related skills.
To get around talent scarcity in any particular location, U.S. tech giants are opening research labs around the world, according to Goldman. Chinese companies are also following their lead by opening Silicon Valley research labs and offering comparable salaries, Goldman said.
Earlier this year, Baidu snagged Microsoft executive Qi Lu as part of a push into AI. Meanwhile, Tencent tapped up former Microsoft scientist Yu Dong to head up its AI research facility in Seattle.
China's vast population, much of which is connected to the internet, gives the country an advantage in generating data. Moreover, China's large internet companies have comprehensive online ecosystems increasingly penetrating more of the daily lives of the country's internet users, generating volumes of data, according to Goldman.
"China understandably generates (about) 13 percent of the digital information globally. By 2020, we expect this to grow to around 20 percent to 25 percent as China's economy emerges as the world's largest," the bank said. It predicted China would generate about 9 to 10 zettabytes of data; one zettabye is about 1 trillion gigabytes.
When it comes to infrastructure, most major companies involved in AI research have adopted open-sourced platforms to attract resources and talent into their ecosystems.
Chinese companies are also following the trend, said Goldman. For example, Baidu has an open-sourced machine-learning platform called PaddlePaddle that stands for Parallel Distributed Deep Learning. Baidu also announced project Apollo, another open-sourced platform to develop autonomous driving.
AI algorithms and their performances are also limited by computing power that depends on the processing unit. Goldman noted that China had been "heavily dependent on foreign suppliers" for processing chips, but there was some "encouraging progress" in its domestic semiconductor industry.
The bank said that it expected China's dependency on foreign suppliers to decrease over time.

Companies to watch out for

Goldman said it expected the initial benefits of AI will go to China's so-called BAT: Baidu, Alibaba and Tencent. That's because these companies have substantial and unique data sets and have the right size of resources to take advantage of the the technology.
Another company to watch is Chinese on-demand services provider Meituan-Dianping. It uses big data analytics to generate the most efficient delivery route in less than 100 milliseconds, said Goldman.
China's largest ride-hailing app Didi Chuxing is also working on deep learning, human-machine interaction, computer vision and intelligent driving technologies. It processes over 4,500 terabyte of data, receives over 20 billion route requests and handles more than 20 million orders on average on a daily basis, according to Goldman.
iFLYTek is a company that focuses on speech and language recognition. The report said it has the largest market share in China's intelligent speech industry.
Hikvision is a technology company that uses AI for surveillance products, including intelligent cameras.
The report said some of the other companies to watch out for included Mobvoi, which is involved in speech and natural language and SenseTime, which focuses on computer vision and deep learning. Also included were drone maker DJI and humanoid-robot maker UBTECH.
Mobile Taobao is not just about consumption, it’s also about community and content, according to the app’s manager, Jiang Fan.
Addressing Alibaba investors in Hangzhou last week, Fan said the Alibaba mobile shopping application is “the world’s largest destination for everyday shopping, entertainment, lifestyle and community.” And it’s all driven by big data and artificial intelligence.
Just how big is that community? Some 468 million users log onto Mobile Taobao every month. And 189 million launch the app every day, with the average user doing so 7.8 times in a 24-hour period.
Mobile Taobao
Increasing usage of mobile app
They’re not doing so simply to purchase apparel, smartphones or even baby food. In fact, China’s shoppers are also taking to the mobile site for the same reasons Americans take to Facebook, YouTube or Twitter: to engage with other users and consume content.
Some of the most-popular types of content right now are live-streaming and short-form video, which allow brands to tell their stories in ways that merely placing their goods on a virtual shelf does not. Those stories are working: Users spend an average of 18 minutes a day watching live-streamed video on Mobile Taobao, Fan said, and 50 percent of watchers click through to a brand’s flagship store during a broadcast.
Live Streaming
A 24-hour online TV station
Live-streaming has also enabled the rise of “key opinion leaders,” or celebrity influencers who interact with consumers in real time and recommend products for sale. KOLs have become so central to consumption in China that Mobile Taobao has launched three types of recommendation features—Crazy About Shopping, Good Find and Wishlist—all of which driven by these celebrities.
“So, if search was the primary route to purchase in the past, today guided purchase is becoming more and more important,” Fan said.
Short-form video is also on an uptick, with 500,000 short-form videos currently on Taobao. As video-product costs come down, and with the expansion of brand-building opportunities they offer, Fan predicted that most products sold on Taobao would have a short video within five years.
“You’ll be browsing products through the short-video format, and it’s a better format to present a product or to present the story of a shop, of a merchant,” Fan said.
Also in the content space, Mobile Taobao features Weitao, a micro-blogging service for brands, the news feed Taobao Headlines and question-and-answer forum “Your Advice Please” have given consumers even more things to consume. Weitao has proved the most popular, with over 5,000 brands, media outlets and cyber celebrities using the service to reach shoppers. Fan likened these features and their popularity to other apps in the West, such as Instagram or Pinterest.
Vs Other Apps
Beating the competition
While the content and community are visible to consumers on Mobile Taobao, what they can’t see is the big data and artificial intelligence powering the platform. The most-obvious example of these tools at work, Fan said, is in customer recommendations.
The company collects data related to everything from a shopper’s purchase history to the pages they view to products they bookmark. The level of detail is so great that Mobile Taobao divides its customers into 500 different segments. Then it couples those segments with the information it has on the more than 1 billion products being sold on the site before putting artificial intelligence to work to generate the most accurate recommendations possible.
Anticipating Customer Needs
Mobile Taobao anticipates customer needs.
“We can analyze their attributes, their characteristics, their past purchase history, their purchasing behavior, [and] their perceived purchasing intention so we can make the recommendations more relevant and more targeted,” Fan said.
Merchants can be segmented, too. Mobile Taobao uses those segments, which comprise different sectors, business models and even the different needs of a merchant, to develop and deliver tools that allow them to better serve their customers. In the past, these assessments were done by human intelligence, but now AI has taken the lead, Fan said.
Intelligent Merchants
Something for every merchant
One such tool designs banner advertisements for merchants. Powered by AI, Mobile Taobao’s system can create a million different ads, each targeted to a different consumer according to his or her profile. This cuts the costs of having a human design the banner, to say nothing of AI’s ability to far outproduce what a human designer could make. Moreover, the AI can track the click-through rate of the ads to judge their effectiveness and switch out the less engaging ones in real time in favor of others that may be more engaging.
Other AI-powered tools, such as a customer-service chat botand a shopping assistant, are also being used to help both merchants and customers. Indeed, Fan notes that all of the operation models underlying consumer behavior on the platform are underpinned by AI.
“You could say that Taobao is the largest use case for artificial intelligence in the world,” he said.

Wednesday, March 22, 2017


Technology companies need to embrace artificial intelligence and put it at their core to be successful, Joe Tsai said Saturday.
“Technology companies today should think about how to solve problems, rather than how to connect the world and all those very large, philosophical questions. I think we are here to solve problems, and AI is definitely the tool,” the Alibaba Executive Vice Chairman told the audience attending a Singapore Summit panel discussion.
Like the “Intel Inside” advertising campaign of the early 1990s, Tsai said AI is “very much inside everything” Alibaba does.
For example, Tsai said Alibaba is helping farmers in China establish a visual early-warning system to detect whether their crops are infected by disease or have other problems. The program uses drones to take aerial photos and computers to learn growth characteristics of each crop. It then uses image recognition to spot issues.
Alibaba is also working with GCL, the world’s largest maker of solar cells, to ease some of the challenges in cutting the panels it manufactures to the precise parameters required. Alibaba feeds key manufacturing parameters, such as the optimal temperature and angle to cut at, and the AI feeds back the best ways to improve the production process.
“This is all about data. This is all about collecting and being able to manipulate and manage data in the factory-manufacturing context,” Tsai said.
Tsai suggested viewing data as a nutrient, “food for the machine.” Feed data into a computer, and it can generate outcomes, offering real-time feedback. There are different kinds of data, with different value, Tsai said, noting “historical data may not be as important as real-time data.”
The distinction is important, he said, because real-time data can provide highly positive and relevant experiences for consumers, as Alibaba has found on its world-leading e-commerce platforms.
“When you are scrolling real-time, that is the data that we’re capturing. And through a process that we call ‘reinforcement learning,’ we are feeding you real-time as to what the next item you should be seeing on the app,” Tsai said.
Regarding security and privacy concerns, Tsai said it’s important “to understand that everything is a balance” and that data only has value “when it’s aggregated, analyzed and, to some extent, shared.” He added that Alibaba, while it has a lot of data about its hundreds of millions of users, “we will never sell our data,” and “‘share’ is not ‘exchange.’”
To emphasize the distinction, Tsai talked about the partnership Alibaba has with a rural cooperative that lends money to farmers.
“We’re able to use their data set and our data set, put them together to develop a lending model that manages the credit risk. In that context, neither of us can see each other’s data. Everything is encrypted. You have ways to protect data,” Tsai said.
It’s ultimately on companies to do the right, ethical thing, though.
“How companies behave, how management behaves…They need to establish trust with the community, with the people who entrust us with the data,” Tsai said.
Tsai also touched on some of the fears that widespread adoption of AI will result in job losses for humans. His conclusion was that AI is better at some things than humans, but that “human brains will always be running faster than machines.”
“AI is good at complex, massive, mathematical computational problems that the human mind just cannot handle in a short period of time, and the speed and complexity in which algorithms can manage and spit out outcomes is very impressive and frankly better than the human mind,” Tsai said.
But thoughts that humans will be supplanted by computers— or one computer using AI—are more philosophical than practical. He said a lot of those prognostications are based on a faulty assumption that we already know the limits of the human brain.
“You’ll be underestimating your own brain power if you believe that there’s going to be a single computer that rules the world,” Tsai said.

Thursday, March 9, 2017
Alibaba Cloud, Alibaba Group’s cloud-computing subsidiary, was recently ranked as a “visionary” cloud company in a report by influential technology research firm Gartner. One of the Alibaba Cloud executives behind that vision is Dr. Min Wanli, who is leading the company’s development of cloud-based artificial intelligence (AI) technology.
A former researcher at IBM’s T. J. Watson Research Center and a senior statistician Google. Min, who holds a Ph.D in statistics from the University of Chicago, oversees Alibaba Cloud’s AI projects, which aim to make learning machines accessible to businesses, manufacturers, governments and other organizations through an as-yet undeveloped array of AI-enabled solutions and services. Along with Alibaba Cloud Chief Scientist Jingren Zhou, Min was recently named by Forbes magazine as one of 20 leading technologists driving China’s AI revolution.
Among Min’s pioneering projects is Alibaba Cloud’s “ET Medical Brain,” a platform that harnesses the company’s massive computational capacity to drive the creation of new AI medical applications. Although efforts are just getting off the ground, Alibaba Cloud is already working with hospitals to train AI software to diagnose thyroid nodules by scanning ultrasound images. Early results indicate AI is making an accurate diagnosis in 85% of cases, a 15% improvement over the average accuracy rate of doctors.
Alibaba Cloud is also partnered with a private medical device maker, Wuhan Landing Medical High-tech Co., on a system that leverages AI and visual computation technologies to detect early stage cervical cancer by identifying DNA abnormalities in images of cell samples. But perhaps most ambitious is an Alibaba Cloud-backed project to train machines to detect lung cancer using high-resolution chest CT scans; for this project, the company partnered with chipmaker Intel and oncology big-data company LinkDoc to hold a competition to find the best real-world solution. More than 3,000 participants, including doctors and technicians, are competing by developing algorithms and models to analyze CT scans and improve the accuracy of early lung cancer detection while helping to pioneer the integration of machine learning with traditional medicine.
Alizila recently talked with Min about the project, and how AI can play a role in making healthcare more efficient. Here’s what he had to say:
AI is getting a lot of attention in the media right now. Where would you say Alibaba is at in the development of this technology for healthcare?
I would say we are still in a pretty early stage. I emphasize we are not trying to replace doctors or experts. We are trying to help doctors or experts do a better job, a more efficient job in diagnosing with medical imaging.
We started looking into this almost one year ago. It took us quite awhile to identify the specialized area to focus on in which AI could be useful. In the end, we determined that medical imaging could be a pretty strong use case. It requires a special expertise to do a diagnosis by reading medical imaging, and there is a big shortage of experts who can do the work. This imbalance in supply versus demand is a huge opportunity for a new technology.
How did the lung cancer project get started?
First we had to convince hospitals to join forces with us, to provide us with medical-image samples we could use to begin to teach our machine, our AI program. The samples needed to be annotated by specialists, because if a sample doesn’t have any annotation we don’t know if this is a healthy person or if it’s a sample from a sick person, so we needed expert diagnosis with each sample.
This was a pretty important step. To convince hospitals to collaborate with us took so much effort because at the beginning they were pretty reluctant. They thought we are trying to replace doctors. After we clearly elaborated our approach, our way of thinking, and our expected business model for future collaboration moving forward, we identified a couple of hospitals and also a company in medical information service area and they decided to test the waters with us, as a first mover. That’s how we started the whole Medical Brain business effort.
What came next?
It took two months to clean up the samples, after that we hired some experts to develop an AI program to analyze the images and to prove this was something doable. At the beginning, nobody knew if medical images are too complex for a machine to read or to understand. So we had to hire some technical people to do a deep dive, looking into medical imaging in much more detail. In the end, we concluded the annotated samples were of pretty good quality and quantity. We started with a first batch of more than 1,000 cases. That was enough to decide to go ahead.
I understand you actually started a competition to come up with solutions.
That’s correct. We invited anyone and everyone who might be interested to compete to come up with the best AI solution. This is an ongoing effort right now, but let me give you some interesting statistics. By the end of last week, we had roughly 3,000 participants registered for this competition. And out of the 3,000 participants, we have 30 plus people from hospitals, real doctors who want to improve their own expertise by participating in this competition and find the best solution. The first season (round) of the competition will be concluded by this September, then we will have additional seasons.
Explain how this early work on the Medical Brain will translate into a market opportunity for Alibaba Cloud.
Imagine if this Medical Brain is successful from a technical perspective. Then we deploy this as a service on the cloud. We can enable and empower hospitals in remote areas in the countryside; if they can connect to the internet, they can enjoy this AI service. Anybody, anywhere might be able to get a diagnosis by this AI service. We can monetize by charging a subscription fee or by charging per usage. There are many differently innovative models; if you look at how mobile carriers charge, we can use a very similar model.
So let me understand the Medical Brain itself. Although you are initially experimenting with lung cancer, the Brain is not a specific medical application, it’s actually a platform upon which AI solutions can run. And those solutions may be developed by others, correct?
Precisely.  At the end of the day, Alibaba Cloud is not a medical company, so we cannot offer a total medical solution. Rather, we can offer the critical technical components to enable medical solutions powered by new technology. We can enable anybody, any hospital anywhere, as long as you have access to the internet and to our cloud. Essentially it brings equal opportunity to any medical hospital or unit. So to a certain extent this could alleviate the shortage of experts who can read medical images.
How would you describe the Medical Brain to a layperson? Is it a program, is it database connected to an AI solution?
It’s a combination of what you just said. You could describe it as software that accumulates the knowledge of many experts in a particular field. Essentially it evaluates the samples we provide very carefully and very thoroughly, and in the end it becomes a super expert in a certain domain, for example in cancer diagnosis.
At the end of the day, it’s just a program that is trying to help the actual doctor or expert do a better job and expedite their diagnosis process. Its advantage is, as opposed to an individual expert, this program never retires, it never takes a vacation, it works seven days, 24 hours a day, and moreover it keeps getting smarter and smarter and smarter, because it has a cumulative way of learning.
Which is the essence of machine learning …
Yes. It’s a self-evolving program. But it requires data input for an initial kick-start, you need a good amount of samples annotated by experts. Once you have that, gradually the number of samples keeps going up.
This knowledge will reside on Alibaba Cloud servers, but who will it belong to? I’m thinking about specific applications. There are any number of scenarios you can imagine in the diagnostic area. But Alibaba won’t necessarily be building them.
No, because the data doesn’t belong to us.
So what Alibaba Cloud is really providing is connectivity, data storage, computing horsepower and AI expertise?
Exactly. Let me give you an analogy: Suppose we have a manufacturer on Alibaba Cloud, a bread factory. We have our own recipe to produce some premium bread. We only sell the recipe, but we do not sell the raw material, the flour or wheat. That belongs to the manufacturer. We just offer the service.
What are the range of applications that you are looking at? What are the diseases you are looking to provide diagnosis for?
At this moment, our current focus is on lung cancer, and gradually we are looking into some related areas which rely on medical imaging. This is a journey that will take many years to complete, so we want to do this step-by-step, slowly and gradually.
At the same time, you are working with companies like Wuhan Landing Medical, which is commercializing an AI-enhanced system for cervical cancer screening.
Yes, we are seeing some start-up companies that realize the computation horsepower and the AI technique we have could help them to accelerate their own businesses. This could help us bridge out to other vertical domains by supporting applications in a variety of medical specialties.
Are you getting good results? How accurate is diagnosis by AI?
I would say pretty positive results. As I mentioned to you earlier, we hired our own people to build a baseline solution for the competition. Some of the participants have delivered results that are much better than baseline. It’s encouraging. I expect by the end of the first (round) we will see some pretty cutting-edge results.
Can you characterize what cutting edge would be? Say 90% accuracy? Better than human experts?
I can’t give a specific number right now because the sample size of only 1,000 is too small, there could be a high chance for random error. I would say wait until end of the first (round).
In the future, what do you think the impact of AI will be on patient care?
There could be both positive and negative effects. On the positive side, because of AI technology, it could be the case that anybody who needs healthcare, they might have care on demand, within their reach. They don’t have to wait a couple of days for test results, they don’t have to travel from the countryside to the big city to get medical service.
On the negative side, there might be a risk that the AI program itself could be erratic—by that I mean somebody might hack into the program, or you could suddenly have a power outage. This would be a rare event, but the results could be disastrous. Like any new technology, people can get dependent on it and they become vulnerable to any type of breakdown. But this is a common phenomenon, not necessarily unique to AI.