The topic of AI and machine learning is now actual in high-tech industry. Probably, the artificial intelligence (AI) has a bigger influence on business all over the world than our daily affairs. In 2014 AI startups got more than 300 million USD of venture capital which is in 300% more than the previous year result.

AI is everywhere: in consoles and in systems of keeping of complicated business data. Computer engineers do their best to improve these systems, to make them thinking and reacting in a real-time mod. AI, which used to be a theory, is in an early stage of business adoption now.

Such technological giants as Google and Facebook use AI and machine learning in their products. However, this is just a beginning: in coming years we will see how AI is adopted in more and more products.

To clarify, artificial intelligence (AI) and machine learning (ML) are different things.

  • Artificial intelligence – is a science such as mathematics or biology. It learns the ways of developing intellectual programs and devices which will be able to complete tasks in a creative way. It always used to be a preserve of humans.
  • Machine learning – is a part of AI which allows the systems to learn automatically and to upgrade themselves. It is based on experience without any programming. ML has several algorithms which help to solve this problem

We will give more details.

What is AI

According to John McCarthy, a researcher from Stanford, AI is a science and a technique of developing intellectual devices and especially computer programs. AI is connected with a similar task of using computers for understanding human intellect, but AI should not be limited by biological methods.

Speaking in a simpler way, AI’s task is to upgrade programs with giving a capability of imitating human behavior.

Engineering knowledge is an important part of AI researching. Devices and programs must have enough data about the world to act and react as humans.

AI must have an access to features, categories, objects and their relations to realize engineering knowledge of AI. AI stimulates common sense, problems solving, capability to analytical reasoning and thinking of devices which is a complicated and exhausting job.

AI services can be divided into vertical and horizontal ones.

What is vertical AI

These are services oriented on one type of tasks: either meetings planning or automatizing of repeating work. Vertical AI bots can do only one type of work but in a such good level that we can mistake it with a work of human.

What is horizontal AI

These services can complete several tasks in the same time. There is no single-application work. Siri, Cortana, Alice (Yandex bot), Marusia (VK bot) – some examples of horizontal AI. The work diapason of these services is wider and they can answer the user’s questions like ‘What temperature is in Moscow now?’.

They work for several task but not for the only one and entirely.

AI is achieved by analysis of human brain while solving a problem and by further usage of these analysis methods of tasks completing for making sophisticated algorithms while AI system is working.

AI is an automatized system of decisions accepting which is always learning, adopting, suggesting and solving the problems itself. Actually, the need for algorithms which can learn themselves by the experience of the whole system. Here machine learning comes in.

What is Machine Learning

Nowadays AI and ML are very popular but complicated concepts.

Machine Learning (ML) is a part of AI. ML is a science of development of algorithms which can learn by past cases. If there were any behavior cases before, they can be predicted. Also it means that without cases there cannot be any projection.

ML can be used for complicated tasks e.g. credit cards fraud detection, self-driving cars starting and face recognition.

ML uses sophisticated algorithms which check big data amount, analyze data patterns, and help the devices to react on cases which were not planned in their code.

Devices learn on history to give reliable results. ML algorithms use informatics and statistics for rational forecasting.

There are 3 ML directions:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning

During the ML process the sets of learning data are provided. Algorithms of supervised learning analyze data and create intended function. The final decision can be used for making new examples.

Credit card fraud detection is a common example of supervised learning.

Unsupervised learning

Unsupervised learning algorithms are more complex because the data is not clustered. The aim is that the device should learn itself with no control. The correct problem solving is not provided.

The algorithm searches for patterns in data. One of unsupervised ML example is recommendations in commercial services and in Facebook.

Reinforcement learning

This kind of algorithm provides ideal behavior automatically to maximize the productivity. Reinforcement learning is determined by problem’s characteristics and not by learning methods.

We consider each method which is appropriate for a case as a reinforced learning. Reinforced learning means that program agent (app or bot) connects to a dynamic environment to complete its goal. This method chooses the most efficient action.

Artificial Intelligence and Machine learning always amaze by their innovations. Nowadays AI is adopted in client service, e-commerce, finances and in other spheres.

Our company will make a strong and modern AI or neural network for your project. We will produce a ML prototype with a regular development and optimization aim. This will help to automatize and make easier the work processes.

Our clients can choose both separate services and long-term strategical partnership. It all means a complex approach: from requirements detailing to outsourcing.

You can be interested in further services: Business digitalizing, Agile methodology development services.