“In an AI project, is it the data, the algorithm, or the processing that matters most?”
Some argue that data is like artificial intelligence gasoline, with a focus on clean data, data science, and a deep understanding of what it means.
Some people say that it is meaningless to have data without context, which can be other data, models/algorithms, or processes.
Let’s explore these AI elements concisely to discover the advantages of each perspective.
Data is the starting point because it’s a very useful asset.
True or false, data is supposed to carry knowledge, and using that knowledge will…
The Trends and Challenges of the Data Annotation Industry
At present, the commercialization of ARTIFICIAL intelligence has reached a stage of basic maturity in terms of computing power and algorithm. In order to better meet the landing needs and solve specific pain points in the industry, a large number of annotated data related to algorithm training support is still needed.
It can be said that data determines the degree of AI implementation, and more forward-looking data set products and highly customized data services have become the mainstream of the industry development.
In the next few years, the data annotation industry…
“Do you know anything about the AI industry?”
9 out of 10 people will probably say yes.
“Do you know data-annotation?”
Nine out of 10 people will probably shake their heads.
Unlike AI companies at the center of the spotlight, the data annotation industry has long been in the gray area outside the spotlight, and for a long period has been marginalized and in a low-profile existence.
However, with the change of demand brought by the development of times, the data annotation industry is also undergoing rapid changes, and it starts to move from the background to the foreground.
In 2019, the investment and financing enthusiasm of the ARTIFICIAL intelligence field has been greatly reduced, and a considerable number of AI enterprises have completely disappeared in the long river of history. “The cold wave of artificial intelligence has arrived” has even become the industry’s hot word of the year.
Compared with the boom from a few years ago when entrepreneurship and investment enthusiasm went hand in hand, 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 intelligence, the value created by…
With the maturing of computer vision technology and the increasing intelligence of travel ecology, the typical application scenario is auto autonomous driving.
1. Self-driving cars are really coming
In 2018, the world’s first driverless taxi took to the roads. This is the first driverless taxi ride in Frisco, Texas, by Silicon Valley start-up Drive.ai.
In the domestic field, Baidu is the leader in the auto autonomous driving industry. On Nov. 30, Baidu launched a trial operation of RoboTaxi in Guangzhou, the second city to test operate a self-driving car, following Baidu's pilot of a self-driving taxi in Changsha.
With the commercialization of ARTIFICIAL intelligence entering the fast lane, auto-driving, face recognition, intelligent security, and other fields have become popular application scenarios, and AI companies begin to focus on industrial landing capability.
As the foundation of the ARTIFICIAL intelligence industry, data is one of the decisive conditions for achieving this capability. Therefore, providing high-quality annotated data service for machine learning algorithm training has become one of the important conditions to determine the application height of ARTIFICIAL intelligence.
Relevant statistics show that the amount of data generated in 2025 will be as high as 163ZB, 90% of which are unstructured…
The application of NLP technology in practical scenarios may not be “extremely high”, but it is definitely not so bad either.
Chatbots, for example, can be roughly divided into “general-purpose” and “domain” bots. The Microsoft Ice chatbot mentioned by the title is a universal chatbot. Although it does have some mental retardation, 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!
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 but can only understand through facial expression. In addition to emoticons, people can record sounds to express their mood in all directions, and they can copy the recorded emoticons and send them to social media. You don’t know what to record? Maybe you can get inspired by Animoji.
Empowering Machine Learning Industry