The advent of the internet has helped technologies to develop at an exponential rate. Today as we live in the 21st century, Artificial Intelligence has become the most debated technology revolutionizing every sector of the business. AI is being widely used in various tasks to minimize human intervention. If you like to become an aspiring Artificial Intelligence Certified Professional, then knowing the technologies used in AI would help you to keep updated on the subject. In this blog, I’ll share with you the top 10 AI technologies in 2020 that will have a major impact in our lives.
The Popularity and Adoption Rate of AI Among Businesses
Artificial intelligence has turned to be a powerful driving force in a wide range of industries, helping people and businesses to create exciting, innovative products and services. AI has enabled businesses to make better informed decisions and to achieve key performance goals.
The following reports suggest that the market for Artificial Intelligence (AI) technologies is flourishing. According to the Gartner report, AI adoption rate has grown from 4% to 14% between the period 2018- 2019. According to the International Data Corporation (IDC) estimated report, the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020. Beyond the hype and the heightened media attention, there is a significant increase in the investment and adoption rate by numerous start-ups and enterprises. While, there is also a race to acquire smart AI start-ups by giant companies. So career in AI is very dominant now. The concepts of Artificial Intelligence can be learned with a Python Course.
Now, let’s check out the top trending Artificial Intelligence technologies now.
Top 10 AI Technologies
Natural Language Processing
Natural Language Processing, shortened as NLP, is the technology used to aid computers to understand the human’s natural language. This part of artificial intelligence technology lets computers deal with humans to interact in our own natural languages.
Using the NLP technology in AI, the ultimate objective is to read, decipher, understand, and make sense of the human language in a manner that is valuable.
To derive meaning from human languages, most NLP techniques rely on machine learning. Currently Natural Language Processing is used in report generation, customer service and summarizing business intelligence insights. Sample vendors who rely on NLP are Cambridge Semantics, Attivio, Lucidworks, Digital Reasoning, Narrative Science, SAS etc.
Some common Natural Language Processing application areas are:
- In Google Translate for Language translation applications
- To check grammatical accuracy of texts in Word Processors such as Microsoft Word and Grammarly.
- Used in Interactive Voice Response (IVR) applications to respond to certain users’ requests in call centers.
- In personal assistant applications such as OK Google, Cortana, Siri and Alexa.
Forrester calls Virtual Agents the “current darling of the media”. You might be familiar with the name “Alexa”, or the Amazon Alexa, a virtual assistant developed by Amazon in AI technology. From simple chatbots to advanced systems that can network with humans, virtual agents are a computer-generated intelligence that provides online customer assistance and service. They take the form of animated virtual characters that have human-like characteristics and lead discussions with customers and provide effective responses.
Some of the wider Virtual agents application areas are in websites when you want to make:
- Book an appointment
- Place an order
- Avail product information
Machine learning platforms are used to improve the functions of these virtual agents to provide better services. Some of the renowned companies that provide Virtual Agents are Microsoft, Google, Amazon, Apple, IBM, and Assist AI etc.
Speech Recognition technology is much more popular and widely used than it was earlier. Speech Recognition technology uses artificial intelligence to transcribe and transform human speech into format useful for computer applications. Speech Recognition is the ability of the machine/program to identify words and phrases in spoken language and then convert these into a machine-readable format.
Speech Recognition is currently used in interactive voice response systems and mobile applications. The most frequent applications of speech recognition within the enterprise include speech-to-text processing, call routing, voice search and voice dialing.
The technology is continuously updated for improvements as the current speech recognition software has a limited vocabulary of words and phrases. It may only identify these words and phrases if they are spoken very clearly. Some of the factors affecting computer speech recognition performance, include accent, pronunciation, pitch, volume and background noise. A more sophisticated software may have the ability to accept natural speech.
Nuance Communications, NICE, OpenText and Verint Systems are some of the companies offering speech recognition services.
Internet of Things (IoT)
IoT is a concept where devices such as cell phones, washing machines, coffee makers, lamps, headphones, etc. are connected to the internet via an on and off switch.
There are billions of physical devices around the world that are now connected to the internet, all collecting and sharing data. These physical devices are called IoT or The Internet of Things.
You can connect different manually operated objects to the internet by adding sensors to them. It will add up a level of digital intelligence to these devices and enable them to communicate real-time data without a human intervention. The Internet of Things is merging the digital and physical universes and making the world around us more smarter and more responsive through IoT devices.
IoT devices range from super-cheap computer chips and Smart security cameras to Smart speakers, Smartwatches, Smartphones, Smart Keychains, and Smartgates etc utilizing the ubiquity of wireless networks. Therefore, it’s possible to turn anything, from as small as a pill to something as big as an aeroplane, into a part of the IoT.
The interrelated computing devices are provided with unique identifiers (UIDs) to transfer data over networks without a human or computer interaction.
AI-powered and IoT-enabled applications along with predictive maintenance will enhance the user experience and add value to smart homes by making life more secure, comfortable, convenient, and safe.
Smart Homes are now a popular term which consist of a set of devices and services AI enabled to alleviate human burden. A Smart Home is a home that allows its inhabitants to smartly control all the connected devices via a system. These devices/appliances are normally inter-connected and to the internet that automatically respond to pre-set rules. It can be remotely accessed/ managed by mobile apps or a browser. It can even send alerts or messages to one or more users.
Smart Homes makes use of AI, and IoT (Internet of Things) devices such as connected sensors, lights, and meters to collect and analyze data. This data is used in making the effective use of home infrastructure, utilities and more to ease up the everyday life effectively and efficiently.
Smart Homes generally consists of devices and gadgets ranging from basic lighting, fans, Washing Machines, ACs, Refrigerators and even those electrical gadgets that exist within home structures like Pumps or Fire boards.
Machine Learning Platforms
Computers have proved that they can also learn, and can be incredibly intelligent. Machine learning is a branch of artificial intelligence and a subdiscipline of computer science. The major goal of Machine Learning is to develop techniques that allow computers to learn.
Machine learning platforms are gaining more and more traction every day by providing APIs (application programming interface), algorithms, development and training tools, applications, big data and other machines.
Mainly used for prediction or classification these Machine Learning Platforms are currently used in a wide range of enterprise applications. H2O, Apache PredictionIO, Eclipse Deeplearning4j, Accord.NET Framework, Microsoft, ai-one, IBM’s Watson platform, and TensorFlow etc are few of the Machine Learning Platforms for Developers to seamlessly integrate the power of ML into daily tasks. Popular companies which are focused in machine learning include Amazon, Google, H2O.ai, Microsoft, SAS, Fractal Analytics, Skytree and Leverton etc.
Image Recognition features are gaining momentum with its wide application in the ecommerce industry. Image Recognition is a computer vision technique which allows machines to detect and interpret an object or feature in a video or digital image into a category. AI is increasingly used on top of this technology. AI helps search and social media platforms for photos and compare them to a wide array of data sets to finalize the ones that would be most relevant during image searches. Image recognition can be used to analyze clients, and their opinions, verify users based on their faces, diagnose diseases, and detect license plates.
Often referred to as “image classification” or “image labeling”, the image recognition task is a foundational component in solving many computer vision-based machine learning problems.
Chatbots revolutionised the way humans interact with computers/machines. It is the most promising and advanced artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone.
From a technological point of view Chatbots represent only the natural evolution of a Question Answering system leveraging Natural Language Processing (NLP).
Chatbot responds based on the input given by a user. Chatbot applications help streamline interactions between people and services, thereby enhancing the customer experience. At the same time, these chatbots offer companies new opportunities to reduce the cost of customer service by improving the customer engagement process and operational efficiency.
To be successful, a chatbot solution should be able to effectively perform both of these tasks (customer engagement process and operational efficiency). Regardless of the kind of approach and the platform used for chatbot technology, here human support plays a key role in training, configuring and optimizing the chatbot system.
Deep Learning Platforms
Deep learning is the new big trend and an algorithmic approach in the fastest growing field of machine learning. A set of algorithms here use artificial neural networks to learn in multi-levels, corresponding to different levels of abstraction.
The concept of Neural networks are inspired by our understanding of the biology of our brains and the interconnections between the neurons. Whereas our brain neurons can connect to any other neurons within a certain physical distance, the artificial neural networks in deep learning have discrete layers, connections, and directions of data propagation.
Some of the applications of deep learning are image recognition/Optical character recognition, automatic speech recognition, Natural Language Processing (NLP), and classification/clustering/prediction of almost any entity that can be sensed & digitized.
Some of the Deep learning platform services providers and suppliers include MathWorks, Peltarion, Deep Instinct, Saffron Technology, Ersatz Labs, Fluid AI, Sentient Technologies, Leverton etc. This is why learning a programming language is important.
Augmented reality (AR) is one of the biggest technology trends right now which seems to be altering the perception of the real-world environment. If you have played Pokemon Go and Snapchat lens, you have already had your trial on AR. Augmented Reality is getting only bigger and bigger as there are latest releases on AR ready smartphones and other devices. And these devices are fast becoming accessible around the world. Right now, it’s making its way into retail stores, furnishing your new home and even make-up selection.
The advanced AR technologies use the information present in the world around us as the subject of interaction and digital manipulation. To make AR a reality you’ll have to wear special goggles to understand and visualize the finished project even before its completion.
Augmented Reality may seem to be like for example, you could see the dogs mingling with their cartoon counterparts, and kids playing a soccer game on the playground could be seen kicking past an alien spacecraft. It’s a combination of real-world and digital augmentation overlaid on it.
Augmented reality is in fact used in a variety of ways from shopping apps that let you try on clothes without even leaving home and as Snapchat lenses, in apps that help you find your car in a crowded parking lot.
Main reasons for the vast popularity of AI
More computing power:
Building AI models and implementing it requires heavy computing power and the use of complex neural networks. The invention of Graphic Processing Units or GPU has made it possible. This enables us to perform high-level computations and implement complex algorithms.
We’ve been generating an immeasurable amount of data over the years. Such data needs to be analyzed and processed by using Machine Learning algorithms and other AI techniques.
Over the past years, developers have managed to develop state of the art algorithms in the successful implementation of Deep Neural Networks.
AI has gained so much popularity with a massive increase in the demand for AI-based systems that tech giants like Facebook, Netflix, Tesla, and few others have started investing in it.
The growth of Artificial Intelligence is adding to the economy at an accelerated pace. So if you’re looking for AI based job opportunities, this is the right time to get into this field.
What are the career benefits of an AI course?
Artificial intelligence is growing at an exponential rate and it is becoming a powerful driving force in a wide range of industries. The number of people signing up for AI Courses are also increasing day-by-day. AI is helping people and businesses to create exciting, innovative products and services while enabling them to make more informed business decisions and to achieve key performance goals.
According to a report by Datamation, the average salary of an AI engineer in the US is $171,715 and the Gartner Report suggests, by 2022 the AI market will grow at a CAGR of 53.25 per cent, and it is estimated by 2020 jobs in the AI field will reach to 2.3 million.
We are now living in a world where machines mimic human intelligence, and thanks to the innovations in AI. As AI has already become an influential factor in our lives, you may want to become an expert in AI and learn more about AI certifications. Today, many business benefits can be gained from AI technologies, but many companies still haven’t fully understood how to adopt them. I hope this blog on Top 10 Artificial Intelligence Technologies in AI can help you to collect some basic understanding on these technologies.