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China has been putting Artificial Intelligence (AI) to use for a number of reasons, like using AI for a social credit score. However, a Chinese company, Squirrel AI learning, is taking AI into a different sphere: education. As covered by MIT Technology Review and Futurism, Squirrel AI is spearheading AI-enabled education in China. If you […] The post China experiments with AI in education appeared first on Technology services...
China has been putting Artificial Intelligence (AI) to use for a number of reasons, like using AI for a social credit score. However, a Chinese company, Squirrel AI learning, is taking AI into a different sphere: education.
As covered by MIT Technology Review and Futurism, Squirrel AI is spearheading AI-enabled education in China. If you think Squirrel is your typical small start-up, it is anything but.
Having trained over a million students, Squirrel is quite possibly a game-changer.
The challenge, or the main task, is to diagnose the learning gap of students that will later undergo training.
Here’s a video that explains the 5 steps of how AI works in education. Detailed post follows.
To analyze that, and to ensure a learning experience better than delivered by a human teacher, AI breaks down every subject, every topic to a granular level.
For example, the technical and scientific teams sit down with teachers and break down primary school science into, say 5,000 elements or smallest conceptual micro-topics.
Next, the student will be put through a test covering these topics. Every question is framed keeping in mind a certain micro-topic. As a result, when a student answers a question correctly, the AI system assumes the student knows this micro-topic well and places the student at a certain level for that particular micro-topic.
As the test proceeds, the AI keeps refining its judgment about how much (and what) the student knows about each micro-topics.
Based on what the system learns about the student, the AI will frame a curriculum.
Using the curriculum, the system will draw out from its bank relevant video lessons, tests and other teaching resources that are tailor-made for the student.
It’s not very difficult to figure out what is driving China is pursuing its ambitions for using AI in education in a big way. The MIT Technology Review points out three reasons:
Apart from above, here are two more reasons why China is so keen in pushing AI in education.
4. Conformity: AI can bring in conformity in terms of ideologies and pedagogy. As the rest of the world opens up to more and more wider ideas, China will have to make sure its citizens don’t grumble and toe the line when required.
5. Efficiency: You know how huge China is. Using technology to improve education can tremendously save resources for a nation that’s in a perpetual state of hurry to bring technology in everything.
Here are the six major advantages of using Artificial Intelligence in education:
Just like any other technology, the use of Artificial Intelligence in education is not without its own risks. Some of the disadvantages of using AI in education are:
Well, that sounds a strong sentence. And vague too, considering that it does not specify a time-frame.
What is certain is that Artificial Intelligence is the future of education. What is not certain is how it will turn out and how fast.
Consider this: Every child is unique and so their requirements to learn are unique too. Children have their own pace of learning.
If there’s a class of, say, 30 students, there will be likely 30 levels of understanding, since no two children may understand exactly the same kind of things.
The traditional human teacher, though extremely powerful, falls seriously short in serving the needs of each of the 30 students.
Which is where AI comes in.
AI can serve not 30, but thousands of students who have thousands of different levels of learning. And that too without losing efficiency.
Estimates vary a little too much to fully rely upon fully but here’s what it could look like this: With AI, students have a learning efficiency that’s five times of that when they’re taught by human teachers.
Squirrel AI is the leading EduTech company that’s driving AI-backed education in China. Here are some quick facts about Squirrel:
The names of top AI companies of China aren’t that well-known though the subject of Artificial Intelligence (AI) crops up in every other technology conversation. Despite their ground-breaking, and sometimes controversial, work that they do, top AI startups and companies in China aren’t as well-known to the common man as say, Facebook, Alibaba, Google, Baidu […] The post Top AI companies of China appeared first on Technology services...
The names of top AI companies of China aren’t that well-known though the subject of Artificial Intelligence (AI) crops up in every other technology conversation.
Despite their ground-breaking, and sometimes controversial, work that they do, top AI startups and companies in China aren’t as well-known to the common man as say, Facebook, Alibaba, Google, Baidu or IBM.
Some of them are very highly valued (and respected) unicorns. Others have shown tremendous inclination and enterprise in fundamental research. What remains common across all of them is their hunger to bring something innovating, something revolutionary, something fast.
Here’s the list of the 4 major AI companies from China:
Formal name: SZ DJI Technology Co., Ltd., operating as DJI
Company background: The company was founded in 2006 by Frank Wang, an alumnus of Hong Kong University of Science and Technology. Headquartered in Shenzhen, China, DJI began with drones. Even today, it is a market leader in drones with an estimated 70% market share.
Primary product: Drones
Why are we ranking a drone company in a list of China’s most valuable AI companies?
The reason is simple.
Drones have come far from being simply flying cameras. They are becoming smarter in maneuvering. DJI is working hard towards constantly improving its image recognition to avoid objects that a drone encounters during flight. And this – you guessed it – uses AI design principles. Its Phantom 4 drone is a good example there.
Research for its latest models, like the Phantom 4 drone, which uses image recognition to avoid objects, routinely incorporate AI design principles.
But that’s not all.
The technology that DJI is using and developing covers three areas simultaneously: drone technology, robotics and artificial intelligence. For instance, DJI is incorporating sensor technology and computer enabled vision in the both the current drones it sells and the next generation drones and devices it is working on.
Apart from the Phantom 4 done, its Mavic Pro is quite impressive as far as AI goes. Apart from the traditional remote controller, the Mavic Pro can also be controlled with gesture commands. It also has the capability to learn – deep learning, technically. Besides that, it has the ability to map the entire environment based on this learning and its image recognition technology.
As per their website, “ [their] flying and camera stabilization systems redefine camera placement and motion.”
Major products or brands: DJI’s Mavic line, The RoboMaster S1, DJI’s Phantom 4.
Recent, well-known projects: DJI has teamed up with Microsoft to collect real-time data into a computer, with DJI drones. This has various business and industrial applications, like for oil companies to track flow in pipelines or for power companies to spot faults across its power lines.
Future plans: DJI is looking at educational fighting robots. It is also believed that DJI is foraying into self-driven vehicles and advanced robotics. Considering that it has built serious expertise in image recognition and analysis powered by AI this doesn’t sound far-fetched.
Estimated valuation: US$ 15 billion
Formal name: Ubtech Robotics
Company background: Ubtech is based in Shenzhen, Guangdong Province China, a city that’s favorably called China’s Silicone Valley. It was founded in 2012. It had its own share of tough times in the early days.
It has garnered a funding of US$940 million. That’s one important reason to place it on the list of top AI companies of China.
It is world’s most highly valued AI company today.
Primary product: Robots
Ubtech has been rather plucky. The founder James Zhou started out by making the Alpha 1S robot, which were followed by Alpha 1 Pro and Alpha 2. These robots’ better motion-control got them noticed instantly.
Two partnerships took Ubtech into the big league.
One was with Apple. In a special arrangement with Apple, Ubtech gets to sell its Jimu Robot, targeted at children. Jimu is a STEM-friendly robot and is available at many Apple stores both within and outside China.
The second came in terms of strategic funding from Tencent, China’s internet-technology giant. With a US$120 million funding, Tencent is expected to receive a strong help for its own AI products.
In its search to get children more skilled and scientific in robotics right from school, Ubtech setup a separate division Ubtech Education. Partnering with Pitsco Education, aims to provide various schools access to Yanshee humanoid educational robot, under its STEM+C drive (Science, Technology, Engineering, Math + Computer science).
Major products or brands: Cruzr, Jimu, Alpha
Recent, well-known projects: Lynx, a robot with Amazon Alexa’s features.
Future plans: Ubtech is looking at creating large, human-sized robots, or humanoid robots. This need all the computer-aided vision, motion control and AI algorithm technology Ubtech can put together. Also on anvil are plans to create a robot security car. It is also planning to usher a solution for the service robot industry by 2021.
Estimated valuation: US $ 5 billion
Formal name: CloudWalk Technology Co. Ltd.
Company background: CloudWalk was founded in 2015. The founder Zhou Xi comes with a strong training in artificial intelligence and pattern recognition.
CloudWalk was incubated under the ongoing startup incubation program of Chinese Academy of Sciences (CAS), an institution that was founded way back in 1949. Currently, the CAS oversees 124 institutions and 5 universities, including the UCAS.
In its early days, CloudWalk supplied facial recognition technology to China’s border control. Today, out of the 34 provincial level authorities in China, 24 use this facial recognition terminals along with scanning door entry services.
Primary product: Facial recognition software
In a span of barely 4 years, CloudWalk has found a sizeable number of uses of its core technology – facial recognition. Banking and financial institutions have used this technology the most.
Bank of China, the world’s fourth largest bank as per assets, uses CloudWalk’s face recognition for identity authentication. This authentication is used in the device that issues resident health cards. In all, 100 large-scale banks operating in China.
The Agriculture Bank of China uses CloudWalk’s facial recognition services for efficiency, accuracy and security of its self-serving machines.
CloudWalk was tasked to build two major projects for China’s National Development and Reform Commission. The first one of this was Infrastructure Public Service Platform” while the second one was “Facial Recognition System Industrial Application Platform.”
AI, through facial recognition, helps governments and institutions in improving service efficiency, track employees and identify criminals.
CloudWalk has also designed and developed big data platforms for public security, by recognizing airline passengers.
Major products or brands: Face recognition terminals, infrared binoculars scanning machines
Recent, well-known projects: Cloudwalk, some time back, inked a deal with Zimbabwe that will build a country-wide facial recognition database to assist all major transportation hubs in monitoring fleet movement. The system will lead to the creation of a national facial ID database.
Future plans: CloudWalk’s research is looking at developing deeper competence in 3D face recognition and person re-identification.
Besides that, it is working with Aston Martin Rapid S and LeTV LeSee for future cars that read the drivers face and change settings accordingly.
Estimated valuation: US $ 2 billion
Formal name: SenseTime Group Limited (Shangtang Technology)
Company background: SenseTime was founded in 2014 in HongKong. Probably because one of its key founders is a professor, SenseTime encourages extensive research.
Primary product: AI service provider
Google AI companies China and SenseTime will likely appear in all the results. Multiple reasons account for why SenseTime is counted in one of the most important AI companies from China.
SenseTime remains a little ahead of its time. In 2014, it displayed its face-recognition system DeepID. This was the first system that had an accuracy rate in face recognition than human eyes (Facebook’s system came later).
Its stress on research can be gauged from the fact that it has been producing research papers consistently.
In 2016, for instance, a total of 16 papers from SenseTime were accepted at the CVPR Conference alone. What’s more, the company won the first prize in object detection and scene analysis, in addition to video object detection.
SenseTime has a number of important technological partnerships. It partnered with Qualcomm for on-device intelligence. This is believed to have advantages like real-time performance and privacy protection over cloud-only implementations.
SenseTime has partnered with E-drive along with Shanghai Municipal Corporation for building intelligent municipal transport system. The tie-up found fresh applications of face recognition – like identifying fatigue of drivers with face scanning technology.
Besides this, SenseTime has partnered with Honda for autonomous cars of the future. In February 2018, SenseTime announced joining hands with MIT to further research in AI.
Major products or brands: Algorithm provider for smart cities, smart phones, online retail and much more.
Recent, well-known projects: Shangtang has become the fifth largest national artificial intelligence open innovation platform after Aliyun, Baidu, Tencent and Keda Xunfei.(Source)
Future plans: AI Open innovation and deep learning
Estimated valuation: US$ 1.5 billion
You must have heard of everyone complaining about the lack of data privacy and the information security challenges. Search engines, social media platforms, gaming apps… you name it, they access your data, one way or the other. Aside from a handful of sites like Duck Duck Go, there aren’t too many examples where your actions […] The post 6 Challenges to data privacy appeared first on Technology services...
You must have heard of everyone complaining about the lack of data privacy and the information security challenges. Search engines, social media platforms, gaming apps… you name it, they access your data, one way or the other.
Aside from a handful of sites like Duck Duck Go, there aren’t too many examples where your actions aren’t being tracked. Looks like nothing you do is really secret.
And it’s not just what you do online. Various systems are collating your offline actions, running them through databases and using artificial intelligence (AI) to track, interpret (and occasionally influence) what you do. China is a discomfortingly wonderful example of what a nation can do once it decides to follow you with AI.
So the new question that now emerges is: in the connected world, is data privacy really possible?
The single word answer is no, it isn’t possible in the conventional sense.
To answer that, we’d need to look at the challenges to data privacy, from the point of view of the common human:
Assuming they are three of the most visited sites, their combined privacy policies runs into well over 10,000 words, all in legalese.
The average reading speed is 200 words per minute (Source), so it’d take only 50 minutes to read these three policies, right?
For one, it’s not easy for the common human to understand legal language so quickly.
For another, if people visit just 4 new websites every day, it’d mean reading and understanding over 12,000 words of legalese. Every day.
That’s nearly 4.4 million words a year.
This is the first challenge to data privacy: data privacy policies are too wordy to read and understand for the common individual.
You’ve so often heard the saying “If you are not paying for the product, you are the product.”, right?
Somewhere, the entire practice of silently harvesting data and selling, sharing or stealthily using data to promote other products came from one single assumption.
Google, Facebook, Instagram and similar companies assumed the paid model won’t work.
For some reason, these companies began by assuming their business wouldn’t work if they used a subscription model.
So how would you expect these organizations to survive, grow and make money?
They found the answer: capture data of users and use it to fuel growth.
So here’s the second data privacy challenge: companies began by assuming their paid model won’t work and the only way for them to make money would be to collect data of users in lieu of the services offered.
Let’s take Facebook, the most talked about social media platform.
Considering the number of users’ data privacy controversies the Menlo Park headquartered social media behemoth has courted in the recent years, one’d think there would have been a steep fall in the number of its users.
The number of monthly active Facebook users is like this (Source Statista)
Q1 2016: 1,654 million
Q1 2017: 1,936 million
Q1 2018: 2196 million
Q1 2019: 2,375 million
Doesn’t look like too many people are disillusioned and leaving Facebook, right?
If that’s not convincing enough, look at Facebook’s advertising revenues globally. (Source: Statista)
2015: 17, 079 million US dollars
2016: 26, 885 million US dollars
2017: 39,942 million US dollars
2018: 55,013 million US dollars
The advertising revenues have risen by a little over 222% from 2015 to 2018. Advertisers wouldn’t be spending such huge amounts if were no eyeballs, right?
It’s like people have given up. They feel they’re being spied upon anyway, so why bother.
The average user is sure they’re going to be looted of their privacy, their data. So they feel helpless about it and continue using Facebook. Information security challenges no longer sadden or discourage users.
It’s also possible that the social media platform has become an addiction like tobacco or alcohol. Despite knowing of the obvious pitfalls and risks, people can’t get off social media. They must have their daily dose.
This is the third of the key challenges in data privacy: people have either given up on data privacy or are too addicted to really care about losing their data.
“More data has been created in the past two years than in the entire previous history of the human race” wrote Bernard Marr in Forbes. That was in 2015, mind it.
The rate at which computing power has grown has exponentially increased humankind’s ability to generate, store and analyze data. Technology is getting way so sophisticated. Data security problems continue to grow and enough simply cannot be done without a proportional investment in data protection.
With more people than ever spending their time online, data mining is growing rapidly too.
Governments are trying to do their own bit by setting up commissions and enacting and implementing regulations. For instance, the European Union brought in General Data Protection Regulations (GDPR) in 2018. Most people agreed the regulations were might tough and that it would forever change the way companies used personal data of individuals.
It’d be interesting to see what GDPR has done in a year. If you’re looking for a short answer, here it is: the impact has been less than dramatic, but experts claim GDPR will soon begin showing its claws.
Whatever the case, one thing is sure, big data security issues and challenges are growing huge. Technology seems to be slowly outpacing what governments can do by way of laws.
This is the fourth of the privacy and data security challenges: technology and computing power is growing so sophisticated and so gigantic, government regulations alone may be insufficient in solving data mining privacy issues.
Makers of these apps range from one-person solopreneurs to mid-sized companies to multinationals. That means there’s no consistency in best practices beyond the basics.
Any number of apps can bungle up handling your personal data, intentionally or otherwise.
Even, unintended data breaches are happening more frequently. And somehow, governments appear slow in prosecuting erring corporates; Facebook seemed to have escaped almost unhurt, if you look at some of the questions that were asked to Mark Zuckerberg in the senate hearing.
And here’s another fine-print: a New York Times article reported that “Facebook officials said that while the social network audited partners only rarely, it managed them closely.”
So this is what happens. Facebook, or for that matter any other platform, collects your data.
Next, third-party service and apps integrate with Facebook and Facebook allows them access to your data.
One day you want to close your account and want the platform to delete your data. (Remember, you have a “right to be forgotten” under regulations like the GDPR.) So the platform agrees and deletes your data. It will also ask the third-party service to delete your data. So far so good.
The problem is your platform doesn’t conduct any audit of the third-party service. That’s why you can never know for sure if the third-party service actually deleted any or all of your data.
Here is the fifth of the major data security challenges: there are way too many apps and integrations and major data collectors like Google or Facebook don’t seem to have enough bandwidth to ensure compliance all the way through.
You remember the Strava controversy, right? Here’s a recap, in case you don’t.
The fitness app Strava released a heatmap that showed the activities of its users. Like where people are jogging, walking or doing certain exercises to stay fit and improve their helath.
That included US defence personnel sharing their activities as well.
So you could “you could find the borders of secret military outposts, as well as track patrol routes of soldiers at those bases”, as Wired put it.
Dangerously enough, it also showed locations of airstrips and locations of the US armed forced where the US was not known to have operations. In one single act, Strava let out secrets that otherwise would have taken other countries a long time to figure out.
Naturally, it became a serious security issue for the US.
Why did the soldiers share their activities in the first place?
A small part of the explanation lies in the way products and services are marketed these days. Such fitness apps, for instance, stress heavily on the minutest of muscle movement, stretching, calorie counts, and a zillion other fitness parameters.
Not all these parameters matter all that much.
But in the race to outdo competitors, one app after the other keeps adding ridiculous levels of detailing. They market these features as must have. They urge you to keep a count of the smallest kinds of exercise variations. Body fat, fitness, abs, calf-muscles, triceps, quadriceps… everything is taken to an unbelievable extreme.
Which where they encourage people to share their details. So users share their details. And then some.
And here is the sixty of the major data security challenges: some marketers take their marketing message to the extreme and manipulate users into divulging too many personal details.
Ask any expert about where Europe stands in the race to dominate Artificial Intelligence (AI) globally. Chances are, they’ll tell you the real competition is between China and the US; Europe isn’t even close to winning the bronze medal, as Kai-fu Lee said in an interview. Lee isn’t alone. Many observers and experts, both inside […] The post 11 reasons why Europe could win the AI war appeared first on Technology services...
Ask any expert about where Europe stands in the race to dominate Artificial Intelligence (AI) globally. Chances are, they’ll tell you the real competition is between China and the US; Europe isn’t even close to winning the bronze medal, as Kai-fu Lee said in an interview.
Lee isn’t alone. Many observers and experts, both inside Europe and beyond, believe Europe has missed the AI bus. There’s nothing to suggest Europe is a serious contender for the winner’s throne in AI.
And that’s not inaccurate. Europe has made very little contribution, or rather disruption, to the AI industry. No major breakthroughs in artificial intelligence have been reported in Europe. Europe is nowhere close to being the startupper’s darling when it comes to AI. There hasn’t been any significant invention that has come out of Europe in the field of AI.
A commonly held belief is that the US will use AI for profits and China will, among other things, build a social credit system with AI. Europe, they say, isn’t clear what it wants out of AI.
In short, artificial intelligence in Europe doesn’t appear to be a top priority of policy makers.
Yes, there are a lot of things stacked up against Europe.
And yet we see a silver lining. We believe that it’s not right to write off Europe so fast.
Despite everything that experts say about AI and Europe (and mostly their observations have been correct), there are certain strong factors that work in favor of Europe.
It may surprise you, but Europe has a bigger number of professional developers as compared to the USA.
McKinsey estimates that Europe has close to six million professional developers which is more than what the US has.
Because Europe isn’t a single country like China or the US, different AI companies in Europe have grown in different ways in different countries. For instance, Finland has some amazing human capital while the UK is a force to reckon with in innovation.
The end result is Europe has built a wider set of AI capabilities than you’d believe. And because AI has applications in practically everything, this advantage will go a long way.
Europe has a unique leadership trait of setting standards (remember emission norms?). As AI matures everywhere, there will be a need for measurements, standardization and certifications.
The AI development in Europe, along with the tradition of setting benchmarks, will help Europe establish leadership where no other economies possibly can: providing credibility.
The AI startups in Europe are far bigger in number than you’d probably give them credit for.
While Europe isn’t famous for startups, the numbers are quite encouraging. Of the 2,451 AI startups that Statista reports as on 2018, 675 belong to European countries (the UK alone has 245).
Current European Commission Vice President Andrus Ansip, in an official statement, disclosed how Europe is planning to ‘develop common data spaces’.
These common data spaces, for areas like energy, manufacturing and healthcare, will make data sharing between European countries easier without compromising data security. AI development in Europe will hugely benefit from this, because successful AI can be built only through huge amount of data.
The Prairie (PaRis Artificial Intelligence Research InstitutE) has been conceived of as an institute of institutes. That’s because “it brings together five academic institutions, 16 major corporations and a network of international partners.”
PRAIRIE has no magic wand so they’ve set themselves a period of five years in which to be a world leader in AI research. The institute assumes importance increases because it will conduct both fundamental research as well as interdisciplinary research. (Reference)
Another thrust that AI in Europe will receive in a positive direction is the DECODE (DEcentralized Citizen Owned Data Ecosystem).
With this system, citizens can proactively exercise their right to choose if and how their personal data will be used. It also gives them the power to who can use their personal data. This is in tune with the GDPR regulations that the European Union introduced last year (Read more about the GDPR here).
Europe doesn’t have to fight every single AI battle to win the war. There are areas where Europe could be fighting a losing battle too.
But there are ample number of fronts on which top AI companies in Europe might easily be clear winners. It, for instance, already an edge in B2B and industrial robotics. That, and a pan-Europe network of AI based innovation hubs could be more than what China or the USA could possibly handle.
For all the big news you read about AI in China or AI in the USA, there are successful AI companies in the European Union that are winners long since.
AI adoption in Europe, particularly in north European and Anglo-Saxon countries is already high. These countries lead Europe, are ahead of China and not too far behind the US. While they haven’t garnered publicity, they would be a major force to reckon with at the time of the final tally.
AI4EU is an on-demand AI platform. It pools together 80 partners across 21 countries.
Funded with Euro 20 million, AI4EU is a project that will run for three years. Its actions will focus on the use of AI for robotics, healthcare, cybersecurity, agriculture and IoT, among other things. AI in healthcare in Europe is a great promise, and when built with agriculture, it could change a number of things.
Basically, it seeks to make the benefits of AU available to all.
The European Commission is trying to bring together various entities to build digital innovation hubs. PwC, Carsa and Innovalia are joining hands in this effort.
They will build a network of collaborations across the EU and work synergistically. That would also boost the AI jobs in Europe.
No. Nothing can.
AI is too complex to say this so soon.
Here’s what the post is trying to say: it’d be a capital mistake on the part of China, the US or anyone else to say Europe is nowhere in the AI race.
Europe has repeatedly made clear that it wants a safe AI, an AI with ethics. So Europe may be fighting a few battles that China or the US aren’t.
One thing is sure: Europe is giving a tough fight in Artificial Intelligence.
Despite wielding almost unbelievable power across the world, Google is contesting a number of litigation cases in a number of countries. Not long ago, there was some upsetting news that Google reads your gmail messages. The following Google court cases mostly include instances of data privacy violations. Google antitrust incidents aren’t that well-known. Alternatively, here’s […] The post Cases against Google in different countries appeared first on Technology services...
Despite wielding almost unbelievable power across the world, Google is contesting a number of litigation cases in a number of countries.
Not long ago, there was some upsetting news that Google reads your gmail messages.
The following Google court cases mostly include instances of data privacy violations. Google antitrust incidents aren’t that well-known.
Alternatively, here’s an infographic on Google court cases in different countries.
On August 25, 2005, the US Department of justice ordered Google to follow with a subpoena. The subpoena required Google to share “the text of each search string entered onto Google’s search engine”
Outcome: The court ruled in Google’s favor, considering the privacy implications of sharing the search terms.
Charges: Privacy violations on the grounds of a video that showed a physically challenged minor being bullied by the minor’s classmates.
What it led to: The minor’s parents accepted an undisclosed sum as financial compensation. Three Google managers were suspended.
Germany has asked Google to hand over harvested data, the one Google Street View collected from private wi-fi networks by May 26, 2010. Google failed to adhere to the deadline.
Outcome: Google handed over the data in June 2010.
What happened: The Czech Office for Personal Data Protection prevented Google Street View from taking pictures beyond “ordinary sight from a street”.
Outcome: Google prevailed, but with some restrictions. In 2012, Google could restart taking pictures, albeit conditions apply.
One of the more well-known Google litigation incidences. The Electronic Privacy Information Center (EPIC) filed a complaint against Google before the Federal Trade Commission (FTC). Consequently FTC filed administrative proceedings against Google.
Charges against Google: The FTC claimed Google had violated the FTC Act by “convert(ing) the private, personal information of Gmail subscribers into public information for the company’s social network service Google Buzz…”.
The FTC felt Google had “misrepresented to users of its Gmail email service” and violated its own privacy promise.
Outcome: Google agreed to pay a civil penalty of over $20Mn but it denied was guilty.
What happened: Google had bypassed privacy settings of Apple’s Safari browser and was tracking users without their knowledge.
Outcome: This one went beyond fines. It made Google agree to things like not bypassing a browser’s cookie settings and ensuring the cookies would expire. Again, Google did not confess being guilty.
Situation: An individual requested Google Spain that his name be removed from search results that appeared in connection with forced sales, saying the event had appeared long back and was no longer current or relevant.
Outcome: The Court of Justice of European Union ruled in favor of the individual, upholding his right to be forgotten.
Ruling by the Court of Appeal allowed British consumers the right to sue Google in the UK on the grounds of misuse of private information.
Major source: Wikipedia
There’s absolutely no two opinions that whichever country or region masters Artificial Intelligence (AI) will dominate business and pretty much everything else for a long time to come. That’s absolutely why China has been working so hard on AI. China Artifical Intelligence Plan is a hot topic through and through. That, despite the fact that […] The post Microsoft’s report on AI in Europe, with findings and criticism appeared first on Technology services...
There’s absolutely no two opinions that whichever country or region masters Artificial Intelligence (AI) will dominate business and pretty much everything else for a long time to come.
That’s absolutely why China has been working so hard on AI. China Artifical Intelligence Plan is a hot topic through and through. That, despite the fact that AI has the potential to cause job losses (or rather, a radical shift in job profiles). And job losses is something that China as the world’s most populous country can’t afford.
A lot has been said and analyzed about the way China has been trying to leverage AI. In fact, China’s strong social credit system is being built using significantly the tools that only AI can give.
And in all that din, even industry observers almost forgot Europe.
So what’s the status of Artificial Intelligence in Europe? What is the EU Artificial Intelligence strategy?
Today, we analyze Microsoft’s report on AI in Europe.
Some time back, Microsoft commissioned Ernst & Young (E&Y) to carry out a study on the status of AI in Europe with Sweden as a special focus. This report, formally titled “Artificial Intelligence in Europe How 277 Major Companies Benefit from AI Sweden Outlook for 2019 and Beyond”, is the summary of the findings of the survey.
The study was carried out in 15 countries of EU and 277 companies participated in the study. Most findings are classified in two parts: 15 European markets and Sweden.
It’s best to treat this report as a study that shows you the overall picture but isn’t highly accurate at the granular level. Also, it wouldn’t be a good idea to treat the findings as truly representational of the entire European Union (EU).
Note: There are other reports from Microsoft too that center around different countries of Europe. The one we are discussing is focused on Sweden.
Like almost all surveys, this survey too suffers from biases.
What’s more, there are certain interpretations in the study that not everyone would agree with; for instance, it calls 44% a ‘majority’ whereas common sense requires you call something a ‘majority’ only when the percentage crosses the half-way mark, i.e. 50%
But that doesn’t mean the report is of no value – it provides great insights into the state of affairs of artificial intelligence in the countries surveyed.
It’s a wonderful peep into what goes inside the participating companies, what’s their stand as regards AI, how equipped they are, what all things are at stake, what level of preparedness the companies are, how these companies view the various challenges and opportunities ahead in the light of AI and much more.
The report is divided into five major sections excluding the preface.
The first section begins by offering an executive summary of the findings. It lists out the participating companies and the research methodology. It moves on to defining what all technologies are included in the study and concludes with an overview of investments in the field of AI in Europe.
The second section deals with the role of AI in European markets. It begins by showing at what level of the participating companies is the AI dialogue taking place.
Next, it explores the maturity and preparedness levels of AI within these companies based on what stage these companies are in their pursuit of building a competitive advantage through AI.
Finally, it concludes with laying out where AI is deployed across the companies.
The third section starts off by spelling out the expectations the companies have from AI over the next 5 years and how closely those expectations are related to the core business of the companies today.
The next questions posed are key: what is a good framework to milk the benefits of AI and what are the sector-wise benefits from AI. The last part talks about the risks involved with AI.
The fourth section defines exactly eight competencies companies would need to really leverage the true potential of AI. Each of the competencies were discuses on the basis of how the companies view their own readiness with respect to these competencies.
The fifth and the final section is the shortest – it analyzes how the companies can take AI further.
Here are the top 7 findings of the Microsoft’s report on AI in Europe:
1.Data: A total of 71% companies reported that Artificial Intelligence is an important topic at the executive management level (or C-level). Against this, 28% of the companies reported that the AI was an important topic at the non-managerial or employee level.
Interpretation: AI is still largely top-down rather than bottom-up. A great deal more people at the top than at the junior level believe AI is important.
2. Data: The UK, France, and Germany have attracted 87% of investment in AI companies over the past decade.
Interpretation: The AI scene in Europe is nowhere close to even growth when you see 3 of the countries covered attracting nearly 9 out every 10 dollar invested.
3. Data: Private Equity and Venture Capitals (PE & VC) account for 75% of the total investment that has poured in over the past ten years.
Interpretation: One, AI is a high risk high return business, since more PE & VC firms than established corporates are investing in AI. Two, these established corporates, at this stage, don’t believe investing in AI must be their top-priority.
4. Data: Of the companies that responded, 4% claimed (in “At a Glance”) they were at an advanced stage in AI, meaning that for these companies, Artificial Intelligence was “contributing to many processes and …. enabling quite advanced tasks”. The same number was 45% for Sweden.
Interpretation: Sweden seems to be doing a far better job at AI. That’s not surprising, since TechCrunch calls it the technological superstar of the North. The only question, however, is how consistent is the interpretation of the term ‘Advanced’ in different countries.
5. Data: AI is deployed the most in the IT department (47%), while it’s least deployed in general management (4%) and HR (7%). Deployment in commercial activities (Sales, Marketing, Customer Services) is around 20%.
Interpretation: The relatively low deployment in Sales (19%), Marketing (22%) and Customer Services (24%) is a surprise, considering that chatbots have been a sort of rage all through 2018.
6. Data: Of the surveyed companies, 81% believe AI will have a high or significant impact on their industry over the next five years.
Interpretation: This is well corroborated by another data in the report: 21% of the companies believe AI is not important or only slightly important among their digital priorities.
7. Data: 74% of the companies surveyed expect to use AI to predict things about their business.
Interpretation: As we earlier noted, less than 1 in 5 (19%) companies are deploying AI in Sales. So here you have a paradox: On the one hand, you have about 3 out of every 4 (74%) companies expecting to use AI to predict things. On the other, your deployment in predicting and finding more about future sales trends is at a low 19%. Simply put, companies are using AI predictions a lot more for other things than for predicting sales trends.
The report, while carefully researched and put together, certainly has its set of drawbacks. Some data could have been presented better while there are some interpretations that you’d not fully agree with.
It’s important, therefore, to look at the report with a healthy pinch of criticism.
Here are the four major weaknesses of the report:
We reached out to E & Y as well as Microsoft regarding the following criticisms, using the email addresses provided at the end of the report.
Microsoft didn’t respond; E & Y did. We’ve included their side of the story wherever relevant.
A pie-chart on page 15 of the report shows the number of online surveyed companies per country. While most other countries have about 20 companies each in the survey, UK, France and Germany have a total of 15 companies.
Page 21 of the same report mentions these three countries have attracted 87% of investment in AI companies over the past decade.
Effectively, countries attracting 87% of the total investment get less than 5.6% of the total representation in the study.
Question unanswered: How can you explain why countries with an overwhelmingly high proportion of investment are so heavily under-represented?
E & Y wrote to us: “…the focus of these reports was on geographic western European countries that do not have recent reports. There has already been more focus on UK, France, and Germany, so, one of our goals was to see what is going on in the other countries.”
Our view: In that case, may be it would have been best to drop the top three performing countries. Including these three countries and keeping them under-represented doesn’t appear to contribute any additional value to the report.
The bar-chart on page 21 says TMT (Technology, Media, Telecom) is most active, just behind PE & VC when it comes to investment.
However, a closer look reveals a significantly different picture.
The value per deal of PE & VC or TMT $7.2Mn/deal and $8.3Mn/deal is nowhere close to the top. Life Science, for instance, booked 12.09Mn/deal, nearly 50% more than that of TMT.
At $30.6Mn/deal, the average investment per deal is the highest with Infrastructure.
Question: What are the grounds of labeling TMT most active, just after PE & VC, when the value per deal is much higher in Infrastructure, Industrial Products and Life Sciences?
E & Y wrote to us: “The number of deals is a measure of activity in the market, hence the ”most active” designator”
Our view: On one part, we stand corrected; earlier we had mentioned this pertained to Sweden. That’s wrong, it covers all the 15 countries.
We appreciate E&Y’s view that number of deals is a measure of activity in the market. On our part, we strongly believe that emerging technologies like AI are driven heavily by talent and capital (putting the mouth where the money is).
In that context, we prefer to believe it’s the value of deal rather than the number of deals that might be a better indicator.
Our view, however, does not refute that the fact number of deals is a strong measure of how widespread the overall technology push is.
On page 28 of the report, it says “The majority consider AI to be important” pointing to a small graph below. The number this caption points to is 44%. Traditionally, one uses the term majority only when the percentage is more than 50%.
Question: Any specific reasons the report calls 44% a “majority”?
E & Y wrote to us: “For the 15 European markets, 44% + 28% + 7% (= 79%) consider it, ”important”, ”between important and most important” and ”most important”, which is a majority. By putting the marker at ”important” it signifies that as the threshold above which there is a majority (79%) “
Our view: By the same logic, 44% + 12% + 9% = 65% consider it “Not important”, between “Not important” and “important”, and “important”, which is a majority too. That could have been worded as 65% (or majority) believe AI is “important” to “not important”.
That said, we believe the 44% that believes AI is “important” is a good sign going forward.
On page 61 of the report, it says, “A large proportion of companies consider themselves to have limited or no AI Leadership competency”.
Well, there’s another way of looking at the same data.
And the interpretation would come out exactly the opposite.
In the same graph a total of 64% (32% + 23% + 9%) of the companies rate themselves as Moderately (or higher) competent.
Question: What could explain the basis of saying a large number of companies consider themselves to have limited or no AI competency, when the converse is more accurate?
E & Y wrote to us: “In that same graph, 34% rate themselves as ”not competent” or ”between not competent and moderately competent”, which is a large portion of the distribution.“
Our view: Agree – 34% can qualify as a large portion, especially when the other competency ratings are lower too, as E & Y further pointed out to us. And more in spirit than in letter.
That stand, however, isn’t consistent with the stand the report took in the earlier point (#3, right above this one). There, they chose to go with the upper-bound, or an optimistic POV while here they chose the lower-bound.
The report is quite both exhaustive and educative. If the purpose was to take a general overview of the AI scene, it’d be accurate to say the report has achieved it.
We do concur with the general view that reports, especially on emerging technologies, are fraught with risks since there are too many grey areas.
In conclusion, we thank the E&Y team for having engaged in a dialogue following the publication of this post and sharing their side of the story.
The post Microsoft’s report on AI in Europe, with findings and criticism appeared first on Technology services news.
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