Digital Transformation for Chiefs and Owners. Volume 1. Immersion. Dzhimsher Chelidze

as our favorite advertisers and marketers are satisfied… Now any, the simplest neuronetwork can proudly be called «Artificial Intelligence».

      However, artificial intelligence is still divided into strong and weak. In 2019, scientists came close to creating a strong AI, the equivalent of human consciousness. This ability not only to distinguish a pen from a pencil or a cat from a dog (according to this principle all neural networks work, it is weak AI), but also to navigate changing conditions, choose specific solutions, model and predict the development of the situation.

      A strong AI will be indispensable in intelligent transportation and transportation systems, cognitive assistants. However, this is the future, and what is now?

      Now there are learning neural networks. An artificial neural network is a mathematical model modeled on the neural networks that make up the brains of living things. Such systems learn to perform tasks by treating them without specific programming for specific applications. This can be found in Yandex Music, Tesla autopilots, referral systems for doctors and managers.

      Therefore, here are the two main trends:

      – machine learning (ML – machine learning);

      – deep learning (DL – deep learning).

      Machine learning is statistical methods that enable computers to improve the quality of the task with experience and training. So it’s about how the neural networks of living organisms work.

      Deep learning is not only learning a machine with the help of a person who says what is right and what is not, but also self-learning systems. This is the simultaneous use of different methods of training and data analysis.

      However, how do these neural networks teach? What’s the magic?

      Actually, in fact, nothing. It’s like training a dog. Neuronetworks show, for example, a picture and say that it is depicted. The neural network must then respond, and if the answer is wrong, it is corrected. An approximate algorithm is given below.

      As a result, it turns out that each «neuron» of such a network learns to recognize, refers to it this picture, or rather its part, or not.

      Example of neural network operation in image recognition

      Neural networks and machine learning apply:

      – for forecasting and decision making;

      – image recognition and generation, including «pictures» and voice recordings;

      – complex data analysis without clear relationships;

      – process streamlining.

      The application value of this can be seen in the examples of the creation of unmanned cars (decision-making), the search for illegal content (data analysis), the prediction of diseases (pattern recognition and linkage search). At the same time, on the haip it is pattern recognition and generative models (chatGPT, midjourney, etc.). However, business problems are still poorly solved. At the same time, 9 out of 10 students now go to study exactly on pattern recognition and machine vision.

      The AI + IoT link deserves special attention:

      – AI receives net big data (about them in the next section) in which there are no human factor errors to learn and search relationships;

      – IoT’s effectiveness increases as it becomes possible to create predictive (predictive) analytics and early detection of deviations.

      Okay, this is all a theory. I want to share a real example of how neuronetworks can be used in business.

      In the summer of 2021, I was approached by an entrepreneur from the realtor sector. He is engaged in the rental of real estate, including daily rent. Its goal is to increase the pool of rented apartments and change the status of an entrepreneur to a full-fledged organization. The nearest plans are to launch the site and mobile application.

      I happen to be a client myself. And at our meeting I noticed a very big problem – the long preparation of the contract: it takes up to 30 minutes for the registration of all the details and signing. This is both the limitation of the loss generating system and the inconvenience for the customer.

      Imagine that you want to spend time with a girl, but you have to wait half an hour for your passport details to be entered into the contract, checked and signed.

      Now there is only one option to eliminate this inconvenience – ask for passport photos in advance and manually enter all the data into the template of the contract. As you can imagine, that’s not very convenient either.

      How can digital tools help solve this problem, but also provide the basis for working with data and analytics?

      Neural networks. The client sends photo passports, the neural network recognizes data and enters the template or database. It remains only to print out the ready contract or to sign in electronic form. Additionally, the advantage here is that all passports are standardized. The series and the number are always printed in the same color and font, the division code too, and the list of issuing units is not very large. To teach such a neuronetwork can be easy and fast. Cope even student in the thesis. As a result, the business saves on development, and the student gets a current thesis. Besides, every time we make a mistake, the neural net gets smarter.

      As a result, instead of 30 minutes, the signing of the contract takes about 5. That is, with an eight-hour working day, 1 person will be able to conclude not 8 contracts (30 minutes for registration and 30 minutes for the road), but 13—14. Additionally, this is with a conservative approach – without electronic signature, access to the apartment through a mobile app and smart locks. However, I believe that immediately implement «fancy» solutions and do not need. There’s a high probability of spending money on something that doesn’t create value or reduce costs. This will be the next step after the client receives the result and competence.

      Restrictions

      Personally, I see the following limitations in this direction.

      – Quality and quantity of data. Neuronets are demanding on quality and quantity of source data. However, this problem is being solved. If previously it was necessary to listen to several hours of audio recordings to synthesize your speech, now only a few minutes. Additionally, the next generation will only take a few seconds. However, they still need a lot of tagged and structured data. Additionally, every mistake affects the ultimate quality of the trained model.

      – The quality of the «teachers». Neuronetworks teach people. Additionally, there are a lot of limitations: who teaches what, on what data, for what.

      – Ethical component. I mean the eternal dispute of who to shoot down the autopilot in a desperate situation: an adult, a child or a pensioner. There are countless such disputes. There is no ethics, good or evil for artificial intelligence.

      So, for example, during the test mission, the drone under the control of the AI set the task of destroying the enemy’s air defence systems. If successful, the AI would receive points for passing the test. The final decision whether the target would be destroyed would have to be made by the UAV operator. During a training mission, he ordered the drone not to destroy the target. In the end, AI decided to kill the cameraman because the man was preventing him from doing his job.

      After the incident, the AI was taught that killing the operator was wrong and points would be removed for such actions. The AI then decided to destroy the communication tower used to communicate with the drone so that the operator could not interfere with it.

      – Neural networks cannot evaluate data for reality and logic.

      – The readiness of people. We must expect a huge resistance of people whose work will be taken by the networks.

      – Fear of the unknown. Sooner or later, the neural networks will become smarter than us. Additionally,