Practical application of ML technologies
Machine learning is already being applied in all areas of human activity. Back in 2017, Stanford University launched a new AI100 index to track AI dynamics. According to data obtained by the university, the number of startups grew 14 times from 2000 to 2018. Consider the areas in which technological breakthroughs await us thanks to ML. Machine learning service provider.
In the future, robots will self-learn the tasks previously assigned to them. For example, they will be able to work on the extraction of minerals – oil, gas, and others. They will be able, for example, to study the depths of the sea, extinguish fires. Programmers may not write massive and complex programs on their own for fear of making a mistake in the code. AI will also affect the improvement of the quality of a person’s private life: we already have unmanned vehicles, robotic vacuum cleaners, sleep trackers, physical activity and health trackers, and other Internet behavior products.
The most obvious example of the use of machine learning in marketing is the Google and Yandex search engines, which use it to control the relevance of advertisements. Social networks Facebook, VKontakte, Instagram, and others use their own analytical machines to research user interests and improve the personalization of the news feed. Marketing research leading up to the development and release of a company’s products will be easier to implement, and the bottom line will be more accurate. Allocation of clusters in groups with similar parameters will turn customized proposals into reality – it will be possible to solve the problems of not groups of consumers, but each individually.
The modern security industry cannot be imagined without machine learning. Face recognition systems in the subway and the use of cameras that scan faces and license plates when driving on highways have become an integral part of human life and indispensable assistants for the police in finding criminals and lost people.
Financial sector and insurance
More accurate stock market forecasts and brand capitalization, decisions on the issuance of credit products to individuals and businesses, determining the cost and feasibility of insurance, and even reducing office queues while reducing personnel costs are just a part of the opportunities that will become available in this area.
On the basis of Big Data, special offers for guests are developed, taking into account the loading of seats in restaurants and cafes, and there are procurement planning services for chefs.
In medical institutions, machine learning allows you to quickly process patient data, make preliminary diagnostics and select an individual treatment, based on information about the patient’s diseases from the database. ML also allows you to automatically identify risk groups when new strains of viral diseases appear.
5 Impressive Deep Learning Applications:
- We erase the language barrier Instant translation app Google Translate app now adopts Deep Learning technology for visual translation. How it works? The app uses deep neural networks to recognize text when scanning a picture. To put it quite simply, Deep Learning technology allows you to determine if there are letters in the picture, then, when the letters are identified and the words are recognized, the application translates the inscription from the picture into your native language.
- Super search Deep Learning technology allows you to move from recognition of inscriptions in pictures even further – to video analysis. The Oxford Visual Geometry group has launched a neural network-based service for finding relevant BBC news. The program allows you to select the videos you are interested in by the keyword that appeared in the video, even many years ago.
- Unlimited possibilities for working with images Systems based on Deep Learning provide a lot of possibilities for image processing, with their help it is already possible: add effects (for example, make a picture out of a regular photo in the style of a famous artist), increase the clarity of an image, etc.
- One step closer to communicating with machines In 2016, Google released WaveNet, a deep neural network-based system that can convert text to audio. Unlike voice assistants such as Siri, WaveNet allows you to create much more realistic sounding voices by sampling real human speech and directly modeling the signals.
- Speech recognition With Deep Learning, machines can not only speak but also understand what you are saying. A striking example of this is the LipNet system, developed using neural network technology by scientists at Oxford University. LipNet became the first system in the world that can recognize speech by lips, and not just single words, but whole sentences at once. For this, the system processes the video sequence, dividing it into many fragments and layers.