Artificial intelligence, machine learning, deep learning and more

Artificial Intelligence (AI) brings a promise of true human-to-machine interaction. As machines become intelligent, they can understand requests, connect data points, and draw conclusions. They can reason, observe and plan. consider:

  • Are you going on a business trip tomorrow? Your smart device automatically provides weather reports and travel alerts for your destination city.
  • Are you planning a big birthday party? Your smart bot helps with invitations, makes reservations and reminds you to pick up the cake.
  • Are you planning a direct marketing campaign? Your AI assistant can instinctively segment your customers into groups for targeted messaging and increased response rate.

We are clearly not talking about robot shops. This is not a Hollywood movie. But we are at a new level of cognition artificial intelligence field that has grown to be really useful in our lives.

We’ll get it though. You are still confused about how all of these topics – AI, machine learning and deep learning – relate. You’re not alone. And we will help.

In this article, we explore the basic components of artificial intelligence and describe how different technologies have combined to help machines become more intelligent.

The story of AI and machine learning

So where did AI come from? Well, it didn’t spring from single-player chess games right into self-driving cars. The field has a long history of rooting in military science and statistics with contributions from philosophy, psychology, mathematics and cognitive science. Artificial intelligence initially started by making computers more useful and more capable of independent reasoning.

Most historians trace the birth of AI to a 1956 Dartmouth research project that examined topics such as problem solving and symbolic methods. In the 1960s, the US Department of Defense became interested in this type of work and increased its focus on training computers to emulate human reasoning.

Eg. Completed Defense Advanced Research Projects Agency (DARPA) street map projects in the 1970s. And DARPA produced intelligent personal assistants in 2003, long before Google, Amazon or Microsoft tackled similar projects.

This work paved the way for the automation and formal reasoning that we see in computers today.

Machine learning and deep learning are AI subfields

As a whole, artificial intelligence contains many subfields, including:

  • machine Learning automates analytical model building. It uses methods from neural networks, statistics, operational studies, and physics to find hidden insights into data without being explicitly programmed where to look or what to conclude.
  • A neural network is a kind of machine learning inspired by the human brain. It is a computer system made up of interconnected devices (like neurons) that process information by responding to external inputs, relaying information between each device. The process requires multiple passes of the data to find connections and derive meaning from undefined data.
  • Deep learning uses huge neural networks with many layers of processing units and benefits from the advances in computing power and improved training techniques to learn complex patterns in large volumes of data. Common applications include image and speech recognition.
  • Computer vision relies on pattern recognition and deep learning to recognize what’s in an image or video. When machines can process, analyze, and understand images, they can record real-time images or videos and interpret their surroundings.
  • Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next phase of NLP is natural language interaction, which allows people to communicate with computers using normal, everyday language to perform tasks.

While machine learning is based on the idea that machines need to be able to learn and adapt through experience, AI refers to a broader idea where machines can perform tasks “smart”.

Artificial intelligence uses machine learning, deep learning and other techniques to solve current problems.

Where are we today with AI?

With AI, you can ask questions about a machine – loudly – and get answers about sales, inventory, customer inventory, fraud detection and more. Your computer may also find information that you never thought to ask. It will offer a narrative overview of your data and suggest other ways to analyze it. It will also share information related to past questions from you or others who have asked similar questions. You get the answers on a screen or just in conversation.

How does this play out in the real world? In health care, treatment effectiveness can be determined more quickly. In retail, additive products can be suggested faster. In the area of ​​finance, fraud can be prevented instead of just being discovered. And so much more.

In each of these examples, the machine understands what information is needed, looks at the relationship between all variables, formulates a response – and automatically communicates it to you with options for follow-up queries.

We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-machine interactions.

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