What’s next for AI: the top 4 innovations in machine learning

Advances in artificial intelligence (AI) have exceeded expectations of the past. AI is a branch of computing that deals with how a machine can display intelligent human behavior within itself.

A subset of AI, machine learning (ML), is now used in many technologies across many industries. ML uses a process in which a computer model is fed with lots of historical data in order to predict and classify new data.

Because ML requires a lot of processing power, resources, and data, developers have come up with a myriad of tools to help alleviate their workload, increase efficiency, and reduce the time it takes to ship. and deploy software out of the box. One of these tools is the AI ​​operating system cnvrg.io.

The hard work of Information Technology (IT) companies has paid off as now the world is taking advantage of a number of innovations in ML.

Machine learning innovations today

Here are the first four:


Customer service, technical support and inbound sales. These are the domains of the two types of chatbots. The way consumers interact with them is done through messaging apps, text apps, and website pop-up chat boxes (for example, the widget you can toggle, usually placed at the bottom right of the webpage. of a brand). The first type is a rules-based bot. He answers queries rigidly and is very simplistic.

On the other hand, the AI ​​chatbot is formed with thousands, if not millions, of textual conversation snippets relevant to the company’s products and policies. Customer support is more efficient and relatively more efficient with AI chatbots handling requests.

But not always. Therefore, companies would configure their customer experience workflow in such a way that the bot first tries to help the customer and has them transfer that customer to a human agent, if the problem or question is not. still not resolved.

Speech recognition

Speech recognition is how computers can convert human speech into usable text. Machine learning has been used to train it using thousands or millions of hours of audio recordings. Speech recognition has helped the deaf and hard of hearing community through captioning services.

Professionals use technology for a number of tasks. Authors, secretaries, bloggers, and some transcription roles use it for hands-free dictation. There is no need to type because the software does it automatically (called “voice writing”), and usually with minimal modifications. Thanks to this functionality, it saves a considerable amount of time.

But perhaps the most obvious use of speech recognition is for smartphones and home assistants. Using these tools, people are now used to doing research by talking to their devices (for example, “find the nearest bus stop”, or “what is the weather like today?” ). They can also give voice commands (for example, “call mom” or “schedule a meeting at 8:00 pm”).

Thanks to a smaller branch of ML called “deep learning,” speech recognition technology can now process human speech with about 95% accuracy, up from around 20% a few decades ago.

Image recognition

Image recognition aims to help computers process images and produce useful results based on them. ML helps train image recognition models for classify and identify with precision different objects based on different presentations of them in images (or videos).

One of the significant uses of image recognition is the detection of certain types of cancer, especially external ones, such as melanoma (a skin cancer). One of the ambitious, perhaps, is safer driverless cars. These vehicles not only use image recognition, but other AI components as well. For now, these two technologies are in the works.

Currently, people enjoy recognition of images through the use of their camera phones. Fun filtering apps and in-camera facial recognition (for feature enhancements when taking photos and recording videos) can brighten up your moments of meeting your friends. Make your faces and those of your friends look like zombies, fire-breathing dragons, older people, younger people, cartoons and more. You can make your moments hilarious with AI and ML.

Recommendation engines

Recommendation engines are ubiquitous, but people usually don’t know it. Whenever they do a search, listen to a song, watch a video, or buy a product online, they are participating in the collection of user data from that company which is used to fuel recommendation engines. They’re ML models responsible for the suggestions people get on their search engines, video and music streaming apps, social media, and e-commerce browsers.

Recommendation systems improve the user experience because customers see a wider selection of the products, videos or music they like. At the same time, businesses using these systems experience increased return on investment (return on investment) due to more purchases and more dynamic user engagement.

What’s next in AI:

In the near future, AI’s speculative path is towards automation. This is quite evident with the number of patents filed by many tech and financial giants in some of the developed countries today. These companies are smartphone makers, software development companies, finance companies, and electronics and robotics pioneers.

To take with

It is difficult to say exactly how different cultures will react to this next wave of deeper digitization (heavy use of electronic devices in everyday life). AI and the IT industry are active areas of research (and change). One promising result is that as the IT sector and related industries come up with exciting new technologies, the rest of the world will study it, learn from it and adapt.

Baburajan Kizhakedath

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