The first step into artificial intelligence: the three most common applications in enterprises

Artificial intelligence is becoming the cornerstone of all walks of life, but many companies still don’t know how to get involved.

Some people look at what AI-driven companies like Amazon, Microsoft, and Google Cloud are doing and worry that they don’t have the wherewithal or the best-trained employees to emulate these leaders.

The good news is that thanks to advances in hardware and software, almost any company can start an AI project. And they're not alone: ​​The global artificial intelligence market is expected to grow from $93.5 billion in 2021 to $641.3 billion in 2028.

For companies looking to grow their business, the ideal first step is to start with the three most common applications: chatbots, image classification, and price prediction.

Chatbots: The Rise of Artificial Intelligence Conversations

Chatbots refer to customer service agents powered by artificial intelligence. A customer asks a question to the chatbot, and the chatbot looks up information from numerous systems and gives the customer an answer.

Chatbots have tried to please consumers before, but the results were unsatisfactory. But today, chatbots have helped improve customer service and satisfaction, and saved companies in the industry a lot of money. Juniper Research estimates that chatbots will save businesses up to $8 billion annually.

Ping An of China is a major financial services provider headquartered in China. Ping An was an early pioneer in the use of chatbots. Developed and trained based on artificial intelligence, conversational chatbots have high levels of understanding and improved accuracy and are capable of completing millions of customer queries every day, thus not only providing significant cost savings but also Importantly, customer service levels can be improved by reducing call center wait times.

Core Areas of Conversational Artificial Intelligence Applications

• Automatic Speech Recognition (ASR): When we speak to a virtual assistant at home or speak to our mobile phones , automatic speech recognition enters the working state, converting speech into text, and users do not need to use traditional key input.
• Natural Language Processing (abbreviated as NLP in English): A step further than automatic speech recognition, NLP can be used to build applications that provide seamless human-technology interaction.
• Text-to-speech based on speech synthesis technology: The chatbot uses text-to-speech to answer customer questions through voice.

Deploying a successful chatbot requires speed, accuracy, customizable speech and language, and the chatbot also needs to be scalable so that it can serve hundreds or thousands if the need arises. A customer requested services.

It sounds very simple, so what is the difficulty? The difficulty is that the whole process is not once and for all. Developing accurate, fast software requires constant adjustments, which can severely impact the work of data science teams if they do it manually. Thankfully, a growing number of software tools are now available to reduce the time required to develop a robust chatbot, which once took months and can now be completed in days.

Teams can also upskill first, using a pre-trained model as a starting point, and then build a chatbot from scratch.

Image Classification See the Whole Picture

Computer vision (image classification) refers to the process of using artificial intelligence to group and classify images with the goal of increasing accuracy, improving security, and accelerating new projects. For example, various situations encountered when planning a journey or controlling the timing of traffic lights require real-time perception and real-time solutions based on changing data points. Computer vision helps the physical world meet the virtual world.

Deploying image classification requires a trained AI model that runs the inference workload in production and makes predictions.

The following three stages of segmentation, classification and detection come together in a matter of milliseconds while the system is running inference.

• A typical image classification system involves image segmentation
• Various parts of the image are divided into different categories
• Any anomalies detected are flagged , provided to operators

In three areas, including medical imaging, autonomous vehicles, and traffic control systems, image classification can help the industry improve safety, security, and accuracy. To achieve these goals, AI inference needs to be fast, able to achieve accurate results, and needs to be retrained regularly.

Businesses can develop skills to build image classification systems in managed labs and explore how to create an end-to-end data science workflow that can deploy models in production while running inference.

Learn Why Price Forecasting is Key

Raw material price forecasting is becoming increasingly relevant in nearly every industry due to unforeseen events related to the pandemic, politics, and extreme weather challenge.

These variables are constantly changing, and price predictions driven by artificial intelligence can help enterprises overcome challenges, bring a stable operating environment to enterprises, and help maximize profits.

Artificial intelligence price prediction models need to evaluate a number of data points, and these data points will vary with different applications.

• Rideshare price prediction models may consider factors such as time of day, weather, and geographic area routes.
• Models that predict future wheat prices may include data on seasonal demand, weather and political activity.

Training an AI model to predict prices involves basic data science work, including preparing the data to be processed. In the ride-sharing example, building a price prediction model would involve the evaluation of data sets including pick-up and drop-off points, fare amounts, number of passengers, ride demand and possibly even weather.

Similarly, price prediction models require access to large data sets, which must be processed quickly before the information becomes stale and outdated. Both accuracy and efficiency require accelerated calculations to ensure predictions are correct. If accelerating data science is a new workload for your business, there are labs that can help teams hone their skills at speed.

Start Your First Artificial Intelligence

Projects Where can a company start on its AI journey? Developing the skills to run these and other critical AI workloads doesn’t have to be expensive , and there is no need to return to academia.

Businesses eager to expand their AI capabilities can invest in existing team skills, or they can hone themselves in a variety of virtual tests and company-sponsored or third-party "learning labs" around the world. Ability.

A good, hands-on lab experience allows users to see, learn about, and try out various types of AI applications that may be most beneficial to their specific industry. Artificial intelligence can have a huge impact on almost any industry or organization, such as developing a new, time-saving chatbot for an airline reservation system, or an image sorting application that speeds up warehouse operations, or saving money in food retail. Billions of dollars in price prediction models, these could have a huge impact.

While AI is highly valuable in the enterprise, testing some AI application ideas may be free. So take some time now to evaluate where you want to start and start your journey by taking advantage of the many free virtual labs around the world.

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