What is machine learning and where is it used?

Computer devices have long since learned to handle tasks that were once only possible for humans. Smart machines operate space stations, cars, medical equipment, and more. They turn on lights, translate text, and can even perform some creative tasks.

All of this has become possible thanks to advances in artificial intelligence (AI). It understands language commands, can learn, respond to speech, and perform many other actions.

There are various AI technologies. One of them is machine learning (ML). In this article, we'll discuss the characteristics of machine learning, as well as where and how it's used.

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What is special about machine learning?

Artificial intelligenceArtificial Intelligence (AI) is a broad branch of computer science that focuses on creating intelligent machines that can perform intelligent tasks.

Machine learningMachine learning is a method of artificial intelligence. It solves problems in a non-directive manner, but rather by searching for patterns in data after training the algorithm on a large number of examples.

Computer systems use machine learning algorithms to process large volumes of statistical data and identify data patterns. This allows the systems to more accurately predict outcomes based on a given set of inputs.

Based on this, ML is focused on teaching AI to operate autonomously and expand its knowledge of the world to perform its assigned functions more accurately and efficiently. The goal is to make computers work without explicit programming.

Where to study ML?

The most popular course on machine learning fundamentals for beginners is a free lecture series from Stanford University (Leland Stanford Junior University) by legendary AI expert and Google Brain founder Andrew Ng. The lectures are publicly available on YouTube. Certified training is available at online.stanford.edu.

The Machine Learning specialization is also available on the Coursera educational platform. It teaches how to create intelligent applications and the fundamentals of Machine Learning through four hands-on courses. One of the course's instructors is Emily Fox, Associate Professor and Amazon Professor of Machine Learning in the Department of Statistics at the University of Washington.

You can enroll for free in the next MO course today, December 23, 2022.

How exactly is ML used in modern technologies?

We see the results of machine learning everywhere:

• Google shows us answers to our search queries;
• Netflix and other streaming platforms recommend movies and TV shows based on our preferences;
• Facebook and Instagram invite us to follow accounts of people we might know;
• YouTube generates subtitles for videos;
• Gmail recognizes spam in incoming emails and much more.

Virtual assistants

One of the most striking examples of the use of machine learning in everyday life is virtual assistants:

• Google Assistant;
• Siri from Apple;
• Alexa from Amazon.

These software agents perform a variety of tasks for us at lightning speed: setting alarms and reminders on our smartphones, calling a taxi, dialing the required number, playing music and video on demand, controlling our smart home, etc.

Each of them relies heavily on machine learning for voice recognition and natural language understanding, and utilizes a vast data base to answer queries. Products created by Microsoft, Google, Apple, and Amazon have the largest user bases.

An important feature of MO

The main difference from traditional programming is that the developer doesn't write strict code to instruct the system how to (roughly) distinguish a cat from a dog. Instead, they create a model that learns to distinguish between these animals by processing large amounts of data, specifically, a huge number of images of dogs and cats.

To solve each problem, a model is created that is theoretically capable of approaching human-level performance given the correct parameter values. During training, this model learns features that may be important for solving a specific problem, such as distinguishing an orange from an apple.

Once a machine learning algorithm has “seen” what an orange looks like many times, it can compare its external features with the studied characteristics, quickly “recognize” it, and automatically classify (identify) it.

What drives the popularity of machine learning?

The history of machine learning began in the 1950s. In 1959, American researcher and inventor Arthur Samuel created the first self-learning computer program for playing checkers. He also coined the term "machine learning."

That same year, Marvin Lee Minsky, co-founder of the MIT Artificial Intelligence Lab, created the first SNARC machine with a randomly connected neural network.

Machine learning has become widespread over the past six decades. In 2020, this artificial intelligence technology was used in virtually every software product.

Thanks to a number of breakthrough innovations, machine learning is currently experiencing a "second youth." Machine learning has set new accuracy records in two of its largest and most important fields:

1. Machine vision (CV);
2. Natural Language Processing (NLP).

The success was made possible by two factors:

• a huge amount of data for training algorithms;
• the availability of enormous parallel computing power using modern graphics processors.

In general, ML systems find application in a wide range of industries, including:

• facial recognition;
• detection of tumors on x-rays;
• the possibility of preventive maintenance of infrastructure by analyzing data from IoT sensors (sensors of the Internet of Things, in particular temperature, humidity, light, etc.).

And this is far from an exhaustive list.

Tools for Improving MO

Entire cloud clusters for machine learning have emerged. Today, anyone can use services from tech giants Microsoft, Google, or Amazon to develop their own models.

Moreover, these companies develop and produce specialized equipment designed to run and train machine learning models.

Google has created specialized tensor processing units (Google TPUs) that accelerate the training of algorithms. The company has made these neural processors available to users for free on its cloud platform.

An example of the use of ML in the field of blockchain technologies

A current example of machine learning is the Autonomous Economic Agent (AEA) technology, a new project from blockchain solutions developer Fetch.ai (UK).

AEAs are autonomous systems powered by machine learning algorithms. These agents act on behalf of their owners in various economic activities, searching for profitable deals oncryptocurrency market, book hotel rooms and parking spaces, buy concert tickets, etc.

How does this work?

• The user programs the agent's initial skills themselves. Subsequently, AEA uses the open-source Gym library from OpenAL (Open Audio Library).
• By accessing OpenAI Gym (a Python library that provides an API for developing and comparing RL algorithms with a huge number of virtual environments and a common API), the agent develops and compares reinforcement learning algorithms needed to perform the final task.

This approach allows agents to develop and learn new skills through interaction with each other. In a P2P environment, they adapt better to market conditions.

Fetch.ai's Machine Learning Products

In addition to AEA, the Fetch.ai blockchain startup's product line includes the MOBIX micromobility ecosystem, the Resonate social network, the Metalex decentralized exchange, and the Fetch Wallet.

Fetch.ai has its own native cryptocurrency, FET, which can be purchased on popular exchanges such as Binance, KuCoin, and several other platforms. In the summer of 2021, Fetch.ai launched a marketplace for non-fungible tokens, CoLearn Paint, for the creation and sale of AI-generated art.

The platform allows users to collaborate on digital collectibles (NFT) using machine learning technology.

Fetch.ai also developed the crypto service BotSwap. It also runs on AEA and allows for automated trading strategies on decentralized exchanges (DEXs) like Uniswap or PancakeSwap. The company has made demo tutorials for several AEA use cases publicly available. The goal is to demonstrate the concept of AEA and the capabilities of multi-agent interactions in various scenarios.

Conclusion

The more people use artificial intelligence, the more trust it inspires. Machine learning advances have already become an integral part of our lives. Today, ML is taking on new forms and is constantly evolving. It is built on the concept that computers can learn, i.e., can do things they weren't originally programmed to do.

Machine learning algorithms have not only become firmly established in everyday life, but are also used in business and economics, enabling reliable solutions and effective results to create a better future. High-quality machine learning helps businesses achieve a successful future.websitewith the correspondingdomainand reliablehostingRegistration will guarantee security for you and your clients.trademark (TM)and acquisitionSSL certificateThese and other goods and services can be ordered onNIC.UA.