186: Artificial Intelligence (AI) & the Fake News INFODEMIC

Internet users are inundated with info every single day. Each minute, there are approximately 98,000 tweets, 160 million emails sent, and 600 videos uploaded to YouTube.

To make critical decisions in life, one requires facts. People crave a way to sort through all the information to find valuable content, they can use.

The Fake News “Infodemic”

Since anyone has the ability to publish information on the internet, false / fake news can be generated very easily and it travels fast and can have dire consequences.

World desperately needs a way to discern truth from fiction in our news and public, political and economic discussions.

World’s largest social media networks (companies such as Facebook, Twitter, and others) have come under fire for the part they play in spreading fake news.

Artificial intelligence (AI)

Artificial Intelligence is spearheading the way towards eliminating fake and toxic news.

Technology and the Artificial intelligence (AI) tools are now on the job to combat the spread of misinformation on the internet and social platforms.

AI algorithms use natural language processing to understand and analyse text.

The AI models label the credibility of the source of the content with a rating of low, medium, high, and an article as reliable or unreliable based on comparisons of similar content from more than 100,000 sources.

The algorithms are checking not only content, but metadata and images too.

The algorithms also check the toxicity of content and can block out profane and obscene content.

Man Machine Interface

While AI is able to analyse the enormous amounts of info generated daily on a scale that is impossible for humans, ultimately, humans need to be part of the process of fact-checking to ensure credibility.

Any solution for the purpose of verifying the veracity of news, images, and social discussions will have to to combine artificial intelligence and human intelligence.

Machines are adept at quickly analysing volumes of content. They can flag questionable items for review by a human fact-checker as well as become smarter over time with feedback from results.

Future Trajectory

AI-based fact checkers can be useful but they still are not fool proof.

As the pursuit of fighting fake news becomes more sophisticated, technology leaders will continue to work to find even better ways to sort out fact from fiction.

Deep learning can help automate and further refine the AI Tools that help in fake news and disinformation detection.

Bottom Line

It is still a Spy versus Spy game

Question

Will this problem be ever solved?

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References

https://www.forbes.com/sites/bernardmarr/2021/01/25/fake-news-is-rampant-here-is-how-artificial-intelligence-can-help/?sh=442daa9b48e4

https://www.mygreatlearning.com/blog/role-of-ai-in-preventing-fake-news-weekly-guide/

https://www.verdict.co.uk/social-media-hate-fake-news/

September 2, 2021December 27, 2024

181: Knowing China Better: Chinese Social Score System

China’s social credit system isn’t a world first but it is unique.

Pic courtesy:https://www.bertelsmann-stiftung.de/fileadmin/files/aam/Asia-Book_A_03_China_Social_Credit_System.pdf

The Social Credit System is part of Xi Jinping’s vision for data-driven governance.

The goal of the China social credit system is to provide a holistic assessment of an individual or a company’s trustworthiness.

The China social credit system, is an extension of existing social rankings and ratings in China which have existed for millennia.

The Social Score is a system that collects all kinds of data about citizens and companies, sorts, analyses, evaluates, interprets and implements actions based on it.

In concrete terms, this means that if you wait at a red light, you get plus points. If you pay your taxes and bills on time, you get plus points. If you are socially involved and accept the rules, you also get plus points.

If you have a good Social Score, you get unsolicited benefits for your social behaviour. These include, for example, faster visa application processing and more freedom to travel. When dating online, algorithms higher prioritize the own profile. Banks offer lower interest rates for company loans or private real estate purchases. People with a high Social Score are promoted faster and get better job offers.

However, people who go red, cut off someone while driving, spit on the street or stick their chewing gum under their seat get minus points.

Anyone who criticizes the state in social media or pays their bills too late also receives minus points.

The consequences of a poor social credit score could be serious. It may affect travel prospects, employment, access to finance, and the ability to enter into contracts. On the other hand, a positive credit score could make a range of business transactions for individuals and corporations much easier.

It is essential that any foreign business consolidating or establishing their presence in China seek professional advice for managing a social credit score. This applies both to individual scores, and the corporate social credit score.

Machine (AI) based Implementation

Every country has laws, cultural norms, social morals and social agreements. The police, courts, politicians, administrations, media and citizens are involved in a constant dialogue; it determines what we define as right or wrong.

In China, this task has partly been taken over by Artificial Intelligence based machine i.e. controlling and managing the society – with machines instead of people. The machine decides on correct and incorrect behaviour.

Inputs are obtained from:

Financial Data
Digital Data (Internet websites, apps, videos and pictures visited/browsed)
Mobile Data (Calls and messages)
Health Data

The data is used to make individual profiles (Behaviour, movement and content).

Based on the profile credit scores are allotted and reviewed.

Based on the credit score the privileges are granted or curbed.

Ethical Issues

This system raises a lot of ethical questions related to freedom and privacy.

Who monitors the score, who imports the data and who configures the system?

How ethical and moral aspects (if any) are integrated?

Who monitors the system to prevent manipulation, and abuse of power?

What data is collected? Who has access to it?

How is the privacy of citizens and companies ensured?

Are only Chinese citizens monitored or all people on Chinese territory?

Does the government also collect data on Chinese people abroad?

End piece

Collecting data and setting up administrative systems to ensure protection, freedom and security for all concerned is a legitimate tool for states. However, as surveillance increases, privacy must be respected as long as the welfare of society is not affected.

Titbits

In China everyone’s movements are monitored continuously. In the AI based monitoring system besides face recognition, even gait recognition has been introduced to make it more fool proof.

Question

Do you approve of such a system?

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References

https://www.wired.co.uk/article/china-social-credit-system-explained

https://merics.org/en/report/chinas-social-credit-system-2021-fragmentation-towards-integration

https://nhglobalpartners.com/china-social-credit-system-explained/

https://www.bertelsmann-stiftung.de/fileadmin/files/aam/Asia-Book_A_03_China_Social_Credit_System.pdf

August 29, 2021December 27, 2024

177: Technology: Future Trajectory of Artificial Intelligence

AI is going to change the world more than anything in the history of mankind.

– Kai-Fu Lee

Artificial Intelligence (AI) has permeated into all aspects of our lives. AI tools are everywhere we look. AI is delivering tangible benefits across every industry one can think about.

Transportation: Although it could take a decade or more to perfect them, autonomous cars will one day ferry us from place to place.

Manufacturing: AI powered robots work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly.

Healthcare: In the comparatively AI-nascent field of healthcare, diseases are more quickly and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience.

Education: Textbooks are digitized with the help of AI, early-stage virtual tutors assist human instructors and facial analysis gauges the emotions of students to help determine who’s struggling or bored and better tailor the experience to their individual needs.

Media: Journalism is harnessing AI, too, and will continue to benefit from it. Bloomberg uses Cyborg technology to help make quick sense of complex financial reports. The Associated Press employs the natural language abilities of Automated Insights to produce 3,700 earning reports stories per year — nearly four times more than in the recent past.

Customer Service: Last but hardly least, Google is working on an AI assistant that can place human-like calls to make appointments at, say, your neighbourhood hair salon. In addition to words, the system understands context and nuance.

Future Trajectory

Most AI applications today are classified as “narrow” or “weak” AI, meaning that they usually carry out a specific task they are designed for.

AI is only just getting started. Computers will get smarter, quicker, and increasingly become capable of tasks that traditionally are carried out by humans, such as making complex decisions or engaging in creative thought. Truly intelligent entities would not be designed for one specific task but would be able to carry out many number of tasks.

A Quantum Powered AI

Computing power is the engine of AI.

Quantum computing, along with other next-level processing capabilities such as biological and neuromorphic computing, is likely to unlock even more possibilities.

Quantum computing is basically, ability of sub-atomic particles to exist in more than one state at the same time. It is theoretically capable of completing some calculations up to 100 trillion times faster than today’s fastest computers.

In order to continually evolve to become smarter, machine learning models will inevitably become larger.

Additionally, more processing power means we will be able to create larger amounts of “synthetic” data for training purposes, reducing the need for collecting real data to feed into algorithms for many applications.

Other technologies like neuromorphic computing would be able to mimic the “elastic” capabilities of the human brain to adapt themselves to processing new forms of information.

Creative AI

These days we can see art, music, poetry, and even computer code is being created by AI.

This has been made possible by the ongoing development of “generative” AI i.e. when Ai creates new data rather than simply analyzing and understanding existing data.

With generative AI, analyzing and understanding is the first step of the process. It then takes what it has learned and uses it to build further examples of the models that it has studied.

This ability to create synthetic data will lead us into an era where machines will be doing things we simply haven’t seen them do before.

Ethical and Accountable AI

At the moment, much of the inner workings of today’s AI is not transparent due to proprietary algorithms or complexity involved.

This creates a trust deficit and reluctance to let machines make decisions that affect people’s lives.

If AI is going to live up to its potential, then the smart machines of the near future will have to be more transparent, explainable, and accountable than the ones we’re familiar with now.

Legislative and regulatory changes are likely to be put in place in future.

Interesting

Intel recently unveiled its Loihi processing chip, packed with more than two billion transistors, which is one application that was able to identify ten different types of hazardous material by smell alone – more quickly and accurately than trained sniffer dogs.

Titbits

Adversial Model – AI vs AI

The most impressive results available today are usually obtained when this is done via an “adversarial” model – effectively, two AIs are pitted against each other, with one tasked with creating something based on existing data and the other tasked with finding flaws in the new creation. When these flaws are discovered, the creative network (known as the “generator”) learns from its mistakes and eventually becomes capable of creating data that its opponent (the “discriminator” network) finds increasingly hard to distinguish from the existing data.

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