In the field of science and technology, the term “artificial intelligence” plays a prominent role, and its recent advances have made AI more popular concerning the concepts of artificial intelligence and machine learning. The role of artificial intelligence has allowed machines to learn from their own experience to perform tasks more efficiently. The neural network is one of its achievements, based on the structure of the human brain, which helps computers and machines to be more human-like.
Neural networks have a remarkable ability to extract meaningful data from insignificant ones, which is used to detect trends and extract patterns that are difficult for a computer or a human to spot. A trained neural network can become an “expert” in the information that has been provided for analysis and can be used to make predictions.
The ideology of the SYPWAI project
When working with artificial intelligence, it became clear that there is a big problem: businesses often cannot allocate a separate department for data science, which will create a solution based on neural networks. Markup is time-consuming. The AI market has not yet been formed: there are no large companies and large players. In the process of building the SYPWAI platform (sypwai.com), it became clear that a totally new profession could appear – the data markup specialist. Therefore, it is impossible to choose a priority, everything is important here at once.
Expectations of companies from the SYPWAI project
The Neurosphere received a request from the public organization – a scientific group, which needed help with artificial intelligence. The company itself forms a request for solving the global problems of humankind. They talk about their main problems, their goals.
If a company is engaged in the development of prostheses, the project specialists will advise the ways to produce them in the best and cheapest way. If a public organization is trying to identify how to reduce crime in the area, experts will also train artificial intelligence to do this.
Regarding customer surveys, research and development questions are being prepared to train artificial intelligence for this company. Scientific research – helps set the vector for learning artificial intelligence. Answers to questions from living people – help to identify the real needs of people to improve products according to people’s demand.
Training neural networks based on the received data
In the earlier stages, neural networks receive a huge amount of data. Training is usually done by providing input data and teaching the network what the result should be. For example, facial recognition is the latest technology implemented by many smartphone manufacturers. Each input is collected by identifying matching data such as a person’s facial image, iris, various facial expressions, and must be trained. Providing the right answers will allow AI to adjust its internal data to find out how much better it can perform.
The rules should be defined so that each node decides what to send to the next level, given its own input from the previous level. This is done with many principles in mind, such as genetic algorithms, fuzzy logic, and Bayesian gradient-based learning. ANNs provide basic rules related to object relationships. When building the rules, you need to make the right decision.