The trend of machine learning and the future of artificial intelligence

Each company is now a data company that can use machine learning to deploy smart applications in the cloud to a certain extent, thanks to three machine learning trends: data flywheels, algorithmic economy, and smart cloud hosting.

With machine learning models, companies can now quickly analyze large, complex data and provide faster, more accurate insights without the high cost of deploying and maintaining machine learning systems.

"Every successful new application built today will be a smart application, and smart building blocks and learning services will be the brain behind the application."

Below are three new machine paradigms that lead to a new paradigm, and each application has the potential to become an overview of a smart application.

Data flywheel

Digital data and cloud storage follow Moore's Law: data worldwide doubles every two years, while the cost of storing data drops at roughly the same rate. A lot of data makes more features, and a better machine learning model is created.

"In the world of smart applications, data will be kings, and services that generate the highest quality data from their data flywheels will have an unfair advantage - more data leads to better models, bringing better User experience, bringing more users and bringing more data."

For example, Tesla has collected 780 million miles of driving data and added millions of miles every 10 hours.

These data are sent to the autopilot, their driver assistance program uses ultrasonic sensors, radar, and camera guidance, lane change, and avoids small human-computer interaction collisions. Ultimately, the data will be the basis for the driverless cars they plan to release in 2018.

It has accumulated more than 1.5 million miles of driving data compared to Google's driverless program. Tesla's data flywheels play a full role.

Algorithm economy

If you can't take advantage of it, all the data in the world is not very useful. Algorithms are how you can effectively extend the manual management of business processes.

This creates an algorithmic economy, and the role of the algorithmic market is as a global meeting place for researchers, engineers, and organizations to create, share, and intelligently blend algorithms with a certain degree of intelligence. As combinable building blocks, algorithms can be stacked together to manipulate data and extract key insights.

In the algorithmic economy, the latest research turns into practical, running code that is available to others. Smart applications demonstrate the abstraction layer and form the building blocks needed to create smart applications.

“The algorithmic market is similar to the mobile app store, which creates an application economy. The essence of the app economy is to allow a wide variety of individuals to distribute and sell software globally, without having to sell their ideas to investors or build their own sales, marketing. And distribution channels."

Intelligent cloud hosting

In order to gain insight into their business, a company that uses algorithmic machine intelligence to iteratively learn their data is the only scalable way. Historically it has been an expensive upfront investment and there is no guarantee of a clear return.

“Today's analysis and data science is like a tailor 40 years ago. It takes a long time and a lot of effort.”

For example, an organization needs to first collect custom data, hire a team of data scientists, continually develop models, and optimize them to keep up with rapid changes and ever-increasing amounts of data—this is just the beginning.

As more and more data is available, and the cost of storing data drops, machine learning begins to move to the cloud, where the extensible Web service is an API call. Data scientists will no longer need to manage infrastructure or implement custom code. The system will measure them, dynamically generate new models, and deliver faster, more accurate results.

“When efforts to build and deploy machine learning models are becoming less and less—when you can 'manufacture' it in bulk—the data that is done that is widely used in the cloud.”

Emerging machine intelligence platform hosting pre-trained machine learning model service will enable companies to start using ML, allowing them to quickly move applications from models to products.

“As companies adopt a microservices model, the ability to plug and play different machine learning models and services to deliver specific functionality becomes more and more interesting.”

When open source machine learning and deep learning frameworks run in the cloud, like Scikit-Learn, NLTK, Numpy, Caffe, TensorFlow, Theano, or Torch, companies will be able to easily leverage pre-training, host tag image models, recommend products, and do General natural language processing tasks.

Summary of machine learning trends

“Our worldview is that every company today is a data company, and each application is a smart application. How does the company gain insight and learn from the vast amount of data? This is given to every organization in the world. ”

As the data flywheel begins to spin, the cost of acquiring, storing, and computing data will continue to decline. This creates an algorithmic economy in which the building blocks of machine intelligence are stored in the cloud. Pre-training, managed machine learning models allow each application to take advantage of algorithmic intelligence to some degree.

The convergence of data flywheels, algorithmic economy, and intelligent cloud hosting means:

1. Now every company can become a data company.
   

2. Now every company can access intelligent algorithms

3. Now every application can be a smart application