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AI evolves! It can do IQ exams now!

 

“Does everyone still remember the news back in 2016 when AlphaGo beat the Go world champion Lee Sedol? It’s been more than two years since then, and AlphaGo has gone through even more advancements and trials including being surpassed by the AlphaGo Zero. AlphaGo’s success can be attributed to its development team DeepMind’s continuous training and improvements, today, DeepMind’s team have even started training AlphaGo to use abstract thinking in order to simulate a human’s deductive pattern. Their goal is to break the current limitations and confines of AI and deep learning, creating machine intelligence that is more and more similar to human cognition. Let us take a look at the plans of the DeepMind team!”

 

AlphaGo’s development team DeepMind have already started training artificial intelligence systems in the concept of “abstract thinking”, in hopes that future AI systems will be able to perform deductive thinking like a real person in order to answer questions. (Source)

After training its AI systems through playing games, researchers from DeepMind are also planning to let its AI challenge an IQ exam.
According to a publication in New Scientist, the London based Google DeepMind team trains its AI systems with IQ exams, allowing it to develop abstract “thinking” skills.

The training method the DeepMind team uses is based on Raven’s Progressive Matrices devised by British psychologist John Raven in 1936. By using question sets of progressive matrices from simple to complex, the AI system’s “intelligence” can be determined as its “observation” and “abstract reasoning” skills are evaluated.

As the questions in Raven’s Progressive Matrices are more than just shape matching, solving the questions will require the test taker to exercise abstract reasoning. Furthermore, a version of Coloured Progressive Matrices was even later designed for children, in addition to a version of Advanced Progressive Matrices that are targeted at people of above-average intelligence. With different versions of the test questions made for measuring the intelligence of test takers from age 5 to 75, this form of IQ exam became one of the most common methods to measure intelligence.

As explained by the DeepMind team, by using IQ tests to train AI, the goal is not to create a system that can answer these test questions, but to create an AI that can come close to simulating human thinking patterns, combining existing information with logical reasoning and abstract thinking to find answers, and even make judgments based on reflexes like a real human brain.

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Facebook has developed the technology that uses AI to determine image content.

Mash-Digi [reference]

“The Facebook team has now developed new technologies that can use images to identify the ingredients of food! If this technology gets more and more stable, in the future,  artificial intelligence and deep learning of judging pictures will become more efficient and accurate. Facebook can apply this technology to achieve the purpose of eliminating bad graphics!”

Facebook’s artificial intelligence research team FAIR said that it will be able to use the deep learning method to train the computer system to understand all kinds of food and recipe content. With this system, it can recognize a food image and immediately generate the corresponding recipe content.

After successfully generating the recipe, it means that the computer system will be able to further judge the food may include the affected components and the related calories according to the result, so that the user can know more quickly whether the food to be eaten causes the burden on the body, or let the hope Those who know how to cook the food can learn according to the recipe.

However, Facebook won’t offer this technology now. The reason is that although the technology of identification the food image can approximately getting the full recipe out, the result of artificial intelligence learning may still be inaccurate. Also, this technology is mainly focused on how to train computer systems to learn and let the computer system know more about the real world.

Allowing the computer system to correctly analyze the image content not only helps Facebook to be more efficient in content aggregation, but also more accurately pushes the appropriate content to the correct user. This will also affect the user’s viewing and adhesion, as well as the efficiency of advertising content exposure. In addition, it can effectively eliminate the bad content and fake news spread through the image. Therefore, the application of artificial intelligence technology will be strengthened. Facebook will become an important development project for more and more users and more and more complicated content.

 

 

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Facebook wants to stop Internet bullying by using Rosetta!

 

“Internet bullying has gradually become a major social problem. Nowadays, the Internet has made many people know a lot of things by just simply sliding the phone, but they can stab a person’s heart within a few words. Even some people end their lives because of cyberbullying. Now Facebook intends to use Rosetta to prevent cyberbullying from growing.”

 

As more and more pictures or videos are used to convey inappropriate remarks and are heavily reprinted and shared, Facebook plans to use artificial intelligence to block and prevent these messages from being transmitted through image recognition.

Previously stressed that it will curb Internet strikes and hate speech. Facebook expects to filter more than 1 billion videoes and video content on Facebook and Instagram service platforms. Respectively, through the artificial intelligence system code-named Rosetta. Analyzing whether the “messages” presented in various languages involve hate transmission or advocate violence.

Identify the text-content in the image by artificial intelligence, and then analyze whether the content involves false or inappropriate speech.

In addition to using Rosetta to stop network attacks and hate speech, this artificial intelligence system is expected to be used for content filterings such as Internet rumours and fake news. Once the system detects and is shared and delivered by the system, the system will automatically intervene it. Clarify whether the content involves issues such as hate speech and Internet strikes.

The reason why Rosetta is proposed is that more and more “message” transmission is presented in the form of images and videos. Therefore, the traditional way of filtering through text recognition obviously cannot prevent such content from spreading through the web platform, so Facebook hopes to borrow Cross-matching by image recognition, to prevent such malicious “messages” from continuing to spread through Facebook or Instagram service platforms.

Prior to this, Facebook has indicated its desire to completely curb the phenomenon of network stagnation. It also emphasizes the desire to prevent the impact of fake news and hate speech, and continues to reduce such problems by filtering fake accounts, reporting mechanisms, and active filtering mechanisms.

Although some of the current processing models still rely on manual inspection and screening but face thousands of information transmissions every day, Facebook believes that artificial intelligence technology should actively filter these inappropriate content, rather than passively waiting for users to report.

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