- \n
- The Russian Ministry of Economic Development defined criteria for AI projects.<\/li>\n
- A facial-recognition payment system began operating on the Filyovskaya Line of the Moscow Metro.<\/li>\n
- Microsoft developed its own language model for the Bing search engine.<\/li>\n
- An Australian court recognised AI’s right to patent inventions.<\/li>\n
- In China, a lift confused a corgi with an electric scooter and refused to transport passengers.<\/li>\n
- An enthusiast developed a miniature version of OpenAI’s DALL-E that generates images from text descriptions.<\/li>\n<\/ul>\n<\/div>\n
The Russian Ministry of Economic Development defines what counts as AI projects<\/strong><\/h2>\n
The Russian Ministry of Economic Development defined<\/a> criteria for AI projects.<\/p>\n
According to the document, AI includes solutions aimed at the development, creation and deployment of technologies in the following areas:<\/p>\n
- \n
- computer vision;<\/li>\n
- natural language processing;<\/li>\n
- speech recognition and synthesis;<\/li>\n
- intelligent decision-support systems;<\/li>\n
- advanced AI methods.<\/li>\n<\/ul>\n
Such projects may qualify for grants from the Innovation Support Foundation and the Skolkovo Foundation, with a total amount of 12 billion rubles.<\/p>\n
Representatives of organisations have already launched a competition for the relevant programs.<\/p>\n
Face-recognition fare payment rolled out on the Moscow Metro<\/strong><\/h2>\n
The Moscow Metro launched<\/a> testing of fare payment using facial recognition Face Pay.<\/p>\n
The capital’s transport department explained how the service works. First, the system scans the passenger’s face with a camera installed at the turnstile. It then compares the image with biometric data the person has on file with their bank and deducts the fare from their account.<\/p>\n
In the first phase, the experiment was rolled out on the Filyovskaya Line for 1,000 volunteers.<\/p>\n
\n
\u201cWe will gradually connect all lines to the test and expand the focus group, and by the end of the year the service will be available on all stations, as we promised,\u201d the mayor’s office said.<\/p>\n<\/blockquote>\n
Media: Chinese regulator delays review of Nvidia-ARM deal<\/strong><\/h2>\n
The Chinese antitrust regulator delayed<\/a> review of Nvidia’s and ARM’s deal for an indefinite period.<\/p>\n
Regulators have been in talks with the companies since early 2021, but an official review has yet to begin. This is likely related to the ongoing US-China trade war.<\/p>\n
Earlier, the European Commission suspended<\/a> review of the merger of the companies, as the regulator went on vacation. British authorities also intervened<\/a> in the multi-billion deal on national-security grounds.<\/p>\n
Microsoft developed its own language model for the Bing search engine<\/strong><\/h2>\n
Microsoft developed<\/a> the language model Make Every Feature Binary (MEB) with 135 billion parameters and integrated it into the Bing search engine to improve results.<\/p>\n
It analyzes queries and helps identify the most relevant pages on the web. MEB performs this task alongside other ML algorithms embedded in the search system.<\/p>\n
Example of the model’s processing of the query \u201cMicrosoft Windows\u201d. Data: Microsoft.<\/figcaption><\/figure>\n According to company representatives, the language model handles 100% of Bing queries across all regions and languages.<\/p>\n
\n
\u201cThis is the largest universal model created at Microsoft, and it demonstrates an excellent ability to remember facts with robust continual learning on vast amounts of data,\u201d the blog says.<\/p>\n<\/blockquote>\n
For comparison: Google’s language model BERT, used in its search engine, contains 340 million parameters, and OpenAI’s GPT-3 has 175 billion parameters.<\/p>\n
Yandex\u2019s autonomous-driving business valued at $7 billion<\/strong><\/h2>\n
Yandex\u2019s autonomous-vehicle division valued<\/a> at $7 billion and named among the leaders in the global market.<\/p>\n
According to a Morgan Stanley report, sustaining this status requires increasing investments in this direction.<\/p>\n
Researchers use AI to create a 3D map of a macaque brain<\/strong><\/h2>\n
Researchers from the University of Science and Technology of China created<\/a> a 3D map of a monkey brain with 1 \u00b5m resolution using artificial intelligence.<\/p>\n
Scientists introduced a genetically modified virus into the macaque, which causes neurons to express a glowing protein. After the animal’s death, they produced detailed images from 250 brain slices.<\/p>\n
Together with developers from MIT and the University of California, Los Angeles, they added AI that stitched the photos into a single image and compressed it.<\/p>\n
Example of the macaque brain structure captured at high resolution. Data: SCMP.<\/figcaption><\/figure>\n According to the researchers, this method could be applied to other organs and body parts.<\/p>\n
Australian court rules AI can patent inventions<\/strong><\/h2>\n
Artificial intelligence can be an inventor under Australian patent law. The precedent was set by a local federal judge.<\/p>\n
Shortly before, British researchers filed patent applications for two inventions attributed to the neural network Dabus in several countries. Regulators, however, rejected the applications, stating that the inventor must be a human, not a machine.<\/p>\n
Nevertheless, the Australian court overturned the patent office’s decision and sent the application back for reconsideration.<\/p>\n
In China, an object-recognition system in an elevator mistook a dog for a scooter<\/strong><\/h2>\n
In Shandong province a lift identified<\/a> a corgi dog as an electric scooter. It told the owner that entry with a mobile vehicle was prohibited.<\/p>\n
The object-recognition algorithm proved insufficiently trained.<\/p>\n
Researchers create a service to predict future technological breakthroughs<\/strong><\/h2>\n
MIT researchers have developed an online system<\/a> powered by AI to predict major technological breakthroughs.<\/p>\n
They analysed 97% of the US patent system. This data underpinned an algorithm that found software to be the fastest-growing domain.<\/p>\n
The developers claim their system will give entrepreneurs, researchers, investors and policymakers hints about future opportunities in technology.<\/p>\n
Enthusiast builds a smaller version of OpenAI’s DALL-E that generates images from text<\/strong><\/h2>\n
The developer known as Boris Dayma created<\/a> the DALL-E mini model for generating images from any text description and published an interactive demo<\/a>.<\/p>\n
Example of the algorithm’s output for the query \u2018A chair made of avocado flies into space\u2019.<\/figcaption><\/figure>\n The project is based on OpenAI’s original DALL-E, but its architecture is 27 times smaller. According to the author, it used far fewer computing resources and took three days to create.<\/p>\n
The source code is available on GitHub.<\/p>\n
Read on ForkLog:<\/p>\n
- \n
- The Pentagon tested a decision-making system<\/a> for competing with Russia and China.<\/li>\n
- Google developed a mobile processor with tensor cores for machine learning<\/a>.<\/li>\n
- Amazon launched a system for tracking delivery drivers<\/a> with AI cameras.<\/li>\n
- DeepMind developed a universal architecture for creating artificial intelligence<\/a>.<\/li>\n
- The neural network learned to fool identification systems<\/a> using a \u201cuniversal\u201d face.<\/li>\n
- Apple deployed AI to detect illegal content on iPhone<\/a> users.<\/li>\n<\/ul>\n
- Google developed a mobile processor with tensor cores for machine learning<\/a>.<\/li>\n
- The Pentagon tested a decision-making system<\/a> for competing with Russia and China.<\/li>\n
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