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20/02/2023

So far, most articles have suggested new techniques to improve the cutting edge of science. This article, in contrast, proves that a simple model, using the best techniques to date, can be surprisingly effective. Basically, the authors proposed a neural network evaluating human posture based solely on a basic neural network followed by three backward convolution operations. At the time, their approach produced the best COCO benchmark score, despite its simplicity.

Reason 1: Sometimes the simplest approach is the most efficient. While we would all like to try new and shiny architectures, it would be much easier to write a basic model, and it can show similar results. This article reminds us that not all good models have to be complex.

Reason 2: Science moves forward in small steps. Each new article moves the front end of science forward a little bit. However, that road doesn't have to lead only one way. Sometimes it's worth taking a little step back and turning in the other direction. "Stop Thinking with Your Head" and "Reformer" are two great examples of this approach.

Reason 3: Proper data augmentation, a training schedule, and a proper problem statement mean more than most people admit.

19/02/2023

Overview

Intelligence is the ability to take in information and store it as knowledge to construct adaptive behavior in an environment or context

This definition of intelligence from the (English-language) Wikipedia can be applied to both the organic brain and the machine. The presence of intelligence does not imply the presence of consciousness. This is a common misconception brought into the world by science fiction writers.

Try searching the internet for examples of AI and you will surely get at least one reference to IBM Watson, which uses a machine learning algorithm that became famous after winning a TV quiz called "Jeopardy" in 2011. Since then, the algorithm has undergone some changes and has been used as a template for many different commercial applications. Apple, Amazon, and Google are actively working on similar systems in our homes and pockets.

Natural language processing and speech recognition were the first examples of commercial use of machine learning. They were followed by other recognition automation tasks (text, audio, images, video, faces, etc.). Applications of these technologies are constantly growing and include unmanned vehicles, medical diagnostics, computer games, search engines, spam filters, anti-crime, marketing, robot control, computer vision, transportation, music recognition and more.

AI is so tightly embedded in the technology we use today that many people don't even think of it as "AI," that is, they don't separate it from conventional computer technology. Ask any passerby if there's artificial intelligence in his smartphone, and he'll probably answer, "No." But AI algorithms are everywhere, from predicting typed text to automatically focusing the camera. Many people think AI should come in the future. But it appeared some time ago and is already here.

The term "AI" is quite generalized. The focus of most research right now is the narrower field of neural networks and deep learning.

How our brains work

The human brain is a complex carbon-based computer, performing an estimated one billion billion operations per second (1,000 petaflops) while consuming 20 watts of energy. A Chinese supercomputer called "Tianhe-2" (the fastest in the world at the time of writing) performs 33860 trillion operations per second (33.86 petaflops) and consumes 17600000 watts (17.6 megawatts). We have a certain amount of work to do before our silicon computers can match the evolutionary carbon ones.

The exact description of the mechanism used by our brains to "think" is a subject for debate and further research (I personally like the theory that brain work is related to quantum effects, but that is a topic for a separate article). However, the mechanism of how parts of the brain work is usually modeled with

18/02/2023

AI capabilities and perspectives What does AI refer to? AI is an umbrella term for many different technologies, from chatbots to programs that create pictures or text. The IBM portal says that there are two types of AI: weak AI (narrow AI) is a system designed and trained to perform specific tasks, such as chatbots, industrial robots, and virtual assistants like Siri; strong AI, or general artificial intelligence (AGI), is programming that can replicate the cognitive abilities of the human brain. Where is artificial intelligence being used? AI is used in a variety of areas. Here are the most common examples: Speech recognition - converting speech to text, used to perform voice search or improve special features for text messages. Customer service - chatbots that answer frequently asked questions, provide personalized advice, and offer products based on requests. Computer vision - a system that extracts information from images, videos and other visual data and then takes appropriate action, such as searching the Internet for similar products. Recommendations - based on search histories, site visits, algorithms help uncover data trends that will help develop the best promotion strategy. Automated stock trading - optimizing stock portfolios. Fraud detection - helps detect suspicious transactions at banks and financial institutions. Artificial Intelligence Visualization: Pxfuel AI technology is developing rapidly, but it is still far from perfect. In the near future, more and more companies will implement artificial intelligence for specific tasks. Gartner predicts that by 2025: 50% of enterprises will use AI platforms; there will be rapid development of the AI market, which will make AI the main criterion by which infrastructure solutions will be defined (leading to a tenfold increase in computing resource requirements); 10% of governments will use artificial population with realistic behavior models for AI training, while avoiding privacy and security issues. A new AI technology worth noting is knowledge graphs. This is a semantic network, which is a network of real-world entities (objects, events, situations, concepts) and illustrates the relationship between them. This will stimulate sales strategies, recommendation mechanisms, and personalized medicine. More intuitive communication between humans and machines is expected in the near future. Artificial intelligence is developing at a rapid pace, but it is too early to talk about the full application of this technology, because only some of its elements are being used. AI will not replace humans in absolutely everything, as it is only a tool to perform routine tasks. Author: Yaroslav Bozhenko Unique Selection

16/02/2023

The Future of Artificial Intelligence
Artificial Intelligence has ceased to be science fiction and is already thoroughly entering our lives. Scientists working on the technology are making it act more and more like a real brain. Computers recognize faces, predict traffic situations, warn about health problems.

But even as we see impressive projects proving that AI integration makes life more convenient and safer, the future of artificial intelligence lies ahead. Engineers believe that the current level of AI use does not even come close to reflecting its full potential, and the industry is in its infancy.

1. Automated transport

We are witnessing the advent of automated driving. Yes, safety still requires a human in the cabin, but the industry is growing and developing further. And theory and practice show that there are many prospects for unmanned cars - a robot makes fewer mistakes than a human, which means its driving is more practical. The future of artificial intelligence is primarily associated with vehicles that operate on autopilot.

The pioneer of the industry was Google, whose special department failed the task on robotic cars in 2005. Five years later, the same developers created Waymo and tried more - it worked, and interest in these technologies spread around the world.

In the last 10 years, all the major car and technology players have been active in the field of unmanned cars:

- BMW managed to make a big breakthrough with the launch of the i3 and i8 hybrid models in 2013-2014.

- Tesla promotes two technology products in one: In addition to conventional Tesla electric cars, in early 2018 Musk unveiled the Tesla Semi self-driving truck.

- Nissan is developing unmanned cars with Renault, with high hopes for Leaf electric cars.

- Yandex.Taxi uses drones in Skolkovo, and recently announced the launch of the first such cab in Europe.

- "Kamaz is keeping up with the trend and is working with Cognitive Technologies to design its "smart" truck based on the Kamaz 5350.

Cars are not the only type of transport that robotization is changing, but one of the most sophisticated. There are also unmanned trains that operate on and under the ground. There are plans to launch them in the Moscow subway in the next few years. The trains are even easier to "friend" each other. Their transport network works on schedule and has few participants in traffic.

2. Cyborgs

Researchers believe that in the future people will use computers and robotic devices to preserve and improve body and brain abilities. Some designs will be produced for comfort, while others will be produced to bring back important functions. For example, bionic prostheses for people with amputated limbs.

In this area, artificial intelligence is responsible for helping the brain and devices understand each other. It transforms incoming signals, transmits information about the position of limbs in space and the state of the external environment.

- Newcastle University made an additional module for hand prostheses equipped with a camera. It "views" the object in front of it and helps to grasp it more firmly and comfortably.

- Leaders in bioprosthetics - Ossur company from Iceland has created independent robotic prostheses that study the environment, guess human actions and adjust to human behavior.

- Dustin Tyler's group from Cleveland is developing palm prostheses so that the user can really feel objects and surfaces.

15/02/2023

Artificial intelligence is the property of artificial intelligence systems to perform creative functions that are traditionally considered the prerogative of man (not to be confused with artificial consciousness); the science and technology of building intelligent machines, especially intelligent computer programs.

Artificial intelligence is related to the similar task of using computers to understand human intelligence, but is not necessarily limited to biologically plausible methods.

Intelligent systems that exist today have rather narrow areas of application. For example, programs that can beat a person at chess cannot answer questions.

15/02/2023

How are Big Data and machine learning connected?

Machine learning models require numerical, textual, photographic, video, audio, and other data in various cases. In order to store and analyze this information, there is a whole area of technology - Big Data. For optimal accumulation of data and its analysis create "data lakes" (Data Lake) - special distributed storages for large volumes of weakly structured information based on Big Data technologies.

Digital twin as an electronic passport

Digital twin - a virtual copy of the real material object, process or organization, which allows you to simulate the behavior of the object / process under study. For example, you can preview the results of changes in the chemical composition in production after changing the settings of production lines, changes in sales after an advertising campaign with certain characteristics, etc. At the same time, predictions are made by a digital twin based on accumulated data, and scenarios and future situations are modeled by machine learning methods, among others.

What do you need for quality machine learning?

Data Scientists! They are the ones who create the predictive algorithm: they study the available data, make hypotheses, build models based on Data Set. They must have three basic skill sets: IT literacy, mathematical and statistical knowledge, and meaningful domain expertise.

Machine learning stands on three pillars

Obtaining Data.
Data from related systems can be used: work schedule, sales plan. The data can also be enriched with external sources: exchange rates, weather, calendar of holidays, etc. A method for working with each type of data needs to be developed, and a pipeline for converting it into a machine learning model format (a set of numbers) needs to be thought through.

Feature construction.
Conducted in conjunction with experts from the required domain. It helps to calculate data that is good for forecasting purposes: statistics and changes in the number of sales over the last month for market forecasting.

Machine Learning Model
The method for solving the business problem is chosen by the data scientist himself based on his experience and the capabilities of various models. A separate algorithm must be selected for each specific task. The selected method directly affects the speed and accuracy of the result of initial data processing.

15/02/2023

Il nostro team fornisce assistenza tecnica all'IA di CommonWealth. Inoltre, aiutiamo i nostri clienti a imparare a utilizzare la nostra piattaforma e li guidiamo durante l'intero periodo di collaborazione.

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