Data, Algorithms, and Processing are Three indispensable Elements of AI

The question is: which matters most?

Some argue that data is like artificial intelligence gasoline, some say that it is meaningless to have data without context.

Let’s explore these AI elements concisely to discover the advantages of each perspective.

Data

Data is the starting point.

New Trends and Challenges in Data Annotation Industry

Data Annotation

Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, and many other AI and robotics applications.

Data Annotation Market Size

The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to Research And Markets’ report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future.

The data annotation industry…

Data Annotation Service — From the Backstage to the Front Stage

“Have you heard about the AI industry?”

9 out of 10 people will probably say yes.

“Do you know data-annotation?”

This time, 9 out of 10 people will probably shake their heads.

Unlike AI companies at the center of the spotlight, the data annotation industry has been in the gray area for a long time, in a low-profile status.

However, with the increasing of refined demands, the data annotation industry is undergoing rapid changes, moving from the background to the foreground.

Annotation service

Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical…

Data Annotation Industry Needs to Take the Lead in Reform as AI is Difficult to Break the Ground

AI landing has become a difficulty

Two years ago, the investment and financing enthusiasm of the artificial intelligence field has been greatly reduced, and a considerable number of AI enterprises have completely disappeared. “The cold wave of artificial intelligence has arrived” has even become the industry’s hot word in 2019.

Compared with the boom a few years ago when entrepreneurship and investment enthusiasm went forward together, the AI industry has suffered a lot recently.

The reason is that “AI landing has become a difficulty”.

From the age of automation to the age of AI, the value created by artificial intelligence is constantly increasing. Meanwhile, the refinement…

Data Annotation, an “Engine” for Self-Driving Cars

With the development of computer vision technology and the increasing intelligence of travel eco-system, the typical application scenario is autonomous driving.

Self-driving cars are really coming

In 2018, the world’s first driverless taxi was on the roads. This is the first driverless taxi ride in Frisco, Texas, by Silicon Valley start-up Drive.ai.

In China, Baidu is the leader in the auto autonomous driving industry. On 30 Nov 2019, Baidu launched a trial operation of RoboTaxi in Guangzhou, the second biggest city in China.

Technical support behind self-driving cars

In the process of autonomous driving, the car itself needs to have…

Four Customer Pain Points in Data Labeling Service

With the commercialization of AI products, auto-driving, face recognition, security, and other fields have become popular scenarios, and AI companies begin to focus on scenario-based landing capability.

As the basis of the AI industry, high-quality training data is one of the decisive elements of the model launching.

Relevant statistics show that the amount of data generated in 2025 will be as high as 163ZB, 90% of which are unstructured data. These unstructured data can only be “awakened” by cleaning and labeling. The potential and large demands allow the data labeling service to keep booming and expanding.

The application of NLP technology in practical scenarios may not be “extremely extended”, but it is definitely not so far away from our daily life.

Chatbots

For example, chatbots can be roughly divided into “general-purpose” and “special domain” bots. The Microsoft Ice chatbot is a universal chatbot. Although it does have some lag, it does not prevent bored people from chatting with her all night long. Some domain-type chatbots, such as Amazon’s customer service and medical consultation apps, have really facilitated our lives and reduced labor costs for stores and hospitals.

NLP can be profitable. Take machine translation as an example…

The Animoji feature in the text message, of course!

Source:https://www.thurrott.com/microsoft/209813/microsoft-copied-the-iphones-animoji-and-made-it-more-accessible

Simply put, it is through face recognition technology, using the image provided by the system to record their own exclusive dynamic expression.

This amazing feature helps us express things that we don’t know how to express through words. In addition to emoticons, people can record sounds to express their mood, and they can copy the recorded emoticons and send them to social media.

How do these dynamic expressions, which you can’t stop playing, come into being?

Bytebridge.io

Empowering Machine Learning Industry

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