Even when artificial intelligence is becoming a cornerstone of current business, many businesses are still struggling to get started.
Those who watch what companies run by artificial intelligence like Amazon, Microsoft and Google Cloud those who do may worry that they do not have deep pockets or the best trained staff 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 would be in good company – the global AI market is expected to grow $ 93.5 billion in 2021 to $ 641.3 billion in 2028
The ideal first steps for those companies that want to grow their business are the pursuit of three of the most common applications – chatbots, image classification and price forecasting.
See also: Top artificial intelligence software
1) All chatbot calls: AI chats on the rise
Chatbots are AI-powered customer service agents. Ask a chat bot question and it will check in multiple systems to give the customer an answer.
While chatbots used to struggle to win the favor of consumers, today they help improve customer service and satisfaction, as well as save companies significant money. Juniper Research projects that chatbots save businesses up to $ 8 billion a year.
Ping Ann, a major financial services provider based in China, was an early pioneer in the use of chatbots. Using AI to develop and train conversational chatbots with higher levels of understanding and accuracy, it is able to address millions of customer inquiries per day – providing not only significant cost savings, but just as importantly, the ability to improve customer service by reduced waiting in the call center times.
Basic areas for conversational AI applications
- Automatic speech recognition, or ASR, works when we talk to virtual assistants in our homes or on our phones so they can convert text to type.
- Natural language processing, or NLPASR takes it one step further and is used to build applications to ensure seamless human-technology interactions.
- Voice synthesis speech conversion allows the chatbot to answer the client’s question.
Implementing a successful chatbot requires speed, accuracy, adaptive speech and language – and must be scalable so that it can handle hundreds or thousands of customer requests if needed.
Sounds simple, so what’s the problem? This is not a one-time process. Developing accurate and fast software requires constant tuning, which can seriously burden data science teams if they do everything manually. Fortunately, there are a growing number of software tools that can reduce the time it takes to develop a powerful chatbot – what used to take months can now be done in days.
Teams can also develop the skills to build a chatbot before embarking on building one from scratch, with pre-trained models available to provide a starting start.
2) See the full picture with image classification
Computer vision, also known as image classification, is the process of grouping and sorting images using AI to increase accuracy, improve safety, and accelerate new projects. For example, travel planning or traffic light time – all cases require real-time information and solutions based on ever-changing data points. Computer vision helps the physical world to meet the virtual world.
The implementation of image classification requires a trained AI model that is ready to perform output workloads in order to make predictions.
These three stages of segmentationclassification and detection are collected while the system draws conclusions – in milliseconds only.
- A typical image classification system involves image segmentation.
- The parts of the image are classified into categories.
- All detected anomalies are marked on the operators.
Medical imaging, autonomous vehicles and traffic control systems are three areas in which image classification helps industries improve security, safety and precision. To achieve these goals, the conclusion of AI must work quickly, achieve accurate results and retrain regularly.
Businesses can develop skills to build an image classification system in hosted laboratories that explore how to create end-to-end workflows and implement the model in production when it is time to output.
See also: What is data visualization
3) Understanding why price forecasting is key
In almost every industry, commodity prices are becoming increasingly difficult to predict due to unforeseen events related to the pandemic, politics and extreme weather conditions.
As these variables continue to change, AI-driven price forecasting can help businesses overcome challenges to bring stability to operations and help maximize profitability.
AI price forecasting models estimate a number of data points that vary depending on the application:
- The travel price forecasting model can take into account the time of day, time and area of geolocation routing.
- A model for forecasting future wheat prices may include data on seasonal demand, weather and political activities.
Training in AI model for price forecasting includes fundamental data science work, including preparation of data for processing. In the example of travel sharing, building a price forecasting model would include estimating data sets, including data set collection points, descent points, fare amount, number of passengers, travel demand, and possibly even time .
Again, price forecasting models need access to large data sets that need to be processed quickly before the information becomes outdated and outdated. Accuracy and efficiency require accelerated calculation to ensure that the forecast will reach the target. If accelerated data science is a new workload for your business, labs can help teams hone their skills quickly.
See also: Technical forecasts for 2022: cloud, data, cybersecurity, AI and more
Launch your first AI project
So where can a company start its AI journey? Developing the skills to perform these and other key AI workloads should not be costly or require a return to academia.
Businesses wishing to expand their artificial intelligence capabilities can invest in the skills of their existing teams or upgrade their skills in various virtual tests and sponsored by the company or third-country training laboratories around the world.
A good, hands-on lab experience will allow users to see, understand and test the types of AI applications that could be most useful for a particular industry. AI can have a huge impact on almost any industry or organization. This includes developing a new, time-saving chatbot for an airline reservation system, an image classification application that speeds up warehousing operations, or price forecasting models that save the food retail industry billions of dollars.
Although the value of AI in business is high, testing a few AI application ideas can be free. So, take the time now to decide where you want to start and take advantage of one of the many free virtual labs available around the world to start your journey.
About the author:
Justin Boytano is vice president of Enterprise and Edge Computing NVIDIA.
https://www.eweek.com/big-data-and-analytics/getting-started-ai/
