When we talk about advancements, the world has come a long way. Around a hundred years ago, people were highly dependent on manual execution. Even the simple tasks like arithmetic operations were too long and monotonous. To overcome this difficulty, various technologies were introduced, that were capable of executing complex calculations. These technologies grew and were implemented in various fields like hospitals, businesses, defence, etc.
No matter how proficient these machines were, the world found out the lack of ‘intelligence’ in these technologies. Computers, for instance, are precise, consistent, and a zillion times faster than the human brain, but they were considered ‘dumb machines.’
The concept of artificial intelligence is far superior to any other concept. It aims to make machines learn and respond on their own. Although the term ‘AI’ has been around us for more than five decades, it was in the second decade, when the world started realizing its importance.
Artificial Intelligence (AI) has totally changed the way we used to think about technology. It is changing the various aspects of our life at a much faster rate. The market for artificial intelligence is also touching the sky. In 2017, the AI market was valued at USD 16.06 billion, and it is estimated to reach USD 190.61 billion by 2025.
Nowadays, more and more companies have started investing in and adopting artificial intelligence technology. According to the Narrative Science survey, 39 percent of the companies were already using AI, which, by 2018, rose to 62 percent. Many existing technologies have a direct or indirect use of artificial intelligence. To understand better, let’s have a look at the top 9 hot artificial intelligence technologies:
This is the fastest growing field and the next-generation of machine learning. This form of machine learning replicates the neural circuits of the human brain to process data and creates patterns for decision making. Just like how we learn from experience, the deep learning algorithm repeatedly performs a task, every time it is modified to enhance the outcome. The technology is called ‘deep learning’ because there are different deep layers in the neutral level that permit learning. Any problem that requires ‘thought’ to find out is an issue that deep learning can solve. Since deep learning algorithms require tons of data to learn from, this growth in data production is one reason why deep learning skills have increased in recent years. Facial recognition, Chat and service bots, Personalization shopping and entertainment are some of the examples of deep learning technology.
Conventionally, the power of computing is connected with the number of CPUs and cores per processing unit. When Wintel started entering the data center during the 90s, application performance and database were directly corresponding to the number of RAM and CPUs available. While these factors are crucial in achieving the optimal quality of business applications, a new processor began to gain attention and i.e GPU (Graphics Processing Unit). When we think about GPUs, many of us think about video cards that were designed for graphic-intensive games. But in the AI generation, graphic designs by GPUs have found a new position that makes them as significant as CPUs. Today, companies have started investing in AI to speed up the next generation of applications. GPUs and appliances are specifically designed and architected to competently run AI-oriented computational jobs. IBM, Alluviate, Google are some of the AI-optimized hardware focused companies.
Natural Language Generation:
Sometimes even after writing the right words and sequences, it gets tricky for businesses to convey the appropriate message. This is even more difficult for a machine that processes information in a different way than the human brain does. For years to come, solving this issue has been the prime focus of the Natural Language Generation’s (NLG) growing field. NLG has begun to manifest in many areas of our lives. At present, it is being used in customer service to make reports and market summaries.
Biometric technology refers to the technology that is used to recognize an individual based on a few characteristics of their biology, for example, fingerprints. In fact, fingerprints are one of the first biometric technologies that have been categorized loosely under digital forensics. Companies all over the world are taking full advantage of this technology. In Florida, Walt Disney World Park uses wearable technologies and finger-printing to make a memorable experience for visitors. After receiving the customized wristbands and placing fingers on the biometric scanner at the main entrance, visitors enjoyed personalized services throughout their stay. The technology is currently being used mostly for market research.
Virtual Agents, also known as an intelligent virtual agent, is an animated, human-like graphical chatbot that is widely used in smart home managers, customer service, and support. This technology has simplified the way employees, for instance, interact with support desks. It communicates with users, answers their questions, and works 24*7. The virtual agents interact with users, either verbally or in a written form. Apple, Google, Amazon, Microsoft, IBM are some of the companies that are providing virtual agents.
Just like a name, Speech Recognition is a technology that, by using artificial intelligence, converts human speech into a computer-accessible format. Today, almost every smartphone has this feature. According to reports, the market size for speech recognition is expected to grow USD 31.82 billion by 2025. Apple, Google, Facebook, Microsoft, and Amazon are the top tech companies that are providing this technology on various devices through services such as Siri, Amazon Eco, and Google Home.
Text Analytics and NLP:
The proliferation of unstructured text data has made a momentous growth in the size and range of data. Today, for making sense of such a huge data collection, companies are dependent on technologies such as Text Analytics and NLP. To unlock the business value, both these technologies hold the key within these huge data sets. NLP is concerned with making natural language accessible to computers, whereas, Text Analytics is related to extract constructive information from text sources. Currently, NLP and Text Analytics are used for fraud detection systems, security systems, and a huge variety of automated assistants.
To interpret and convert data into predictive models, modern decision management systems rely heavily on artificial intelligence capabilities. In the long run, these models help companies to take essential and successful decisions. These systems are extensively used in a huge number of enterprise-level applications. Such applications offer automated decision-making potential to any organization or person using it.
Robotic Process Automation:
Robotic Process Automation is a type of artificial intelligence where robots copy the job of humans to perform organizational jobs. This technology is used in situations where hiring people to perform a particular task is expensive. Many companies nowadays, have started using this technology to run their businesses quicker, more efficient and productive.
Artificial Intelligence has successfully set its milestones in almost every industry. And its trend is not going to slow down anytime soon in the near future. If you want to implement any of these technologies in your business, it would be great to read the case studies and learn from the experiences of other companies. If you have any questions, don’t hesitate to ask in the comments section below.