Artificial Intelligence is one of the most exciting and expanding fields in science and technology today.
While the concept of AI was construed back in 1956, the subject as a whole took off only recently in the past decade. The development of electronics, computer science & programming, and data networking led to the creation of extremely powerful data processing & handling devices—just what AI and its research required.
Today, supercomputers, distributed computing systems, and GPU data processing now makes it possible for AI models to analyze scores of data and learn insightful things. The year 2020 witnessed and is still seeing massive developments & implementation of AI in several real-world scenarios. The enormous potential of AI models is undeniable, and the practices of harnessing this potential will only increase in the future.
Top 10 AI Trends in 2021
Here are 10 of the most striking AI applications in the real world that are expected to become a raging trend shortly.
1. Digital Workers
Utilizing the finest in cognitive science and machine learning, the concept of digital workers is groundbreaking in the field of process automation and AI.
According to IBM, automated digital workforces will soon be working alongside human employees across different sectors. With skills and capabilities that go beyond robotic process automation, digital workers are software-based labors who can carry out complex processes meaningfully.
- Companies like IBM are offering their service to help businesses get their own digital workforce.
- With the ability to perform skillful labor with impunity, digital workers will be able to complete a combination of different tasks in a rational & human manner.
- Amalgamating RPA, machine learning and human supervision, digital workers will soon automate large parts of an operation or process in several economic sectors.
2. Deep Learning Applications
A subset of machine learning and artificial intelligence, deep learning algorithms follow the design and function of the human nervous system.
Deep learning uses artificial neural networks to learn and achieve possible outcomes. The field uses brain simulations to make AI learning algorithms better and easier to use. Andrew Ng, the brains behind Google Brain and large-scale implementation of deep learning tech in Google’s services, calls ANNs the best shot at building real AI.
Here’s a list of some of the applications of deep learning:
- Generation of image captions
- Electronic games
- Adding sounds to silent movies
- Natural Language Generation
- Colorization of images
- Object Classification in Photos
The increased dependency on online platforms and computer science made businesses across different sectors look into innovative means of overcoming disruptions to their operations. Companies are embracing AI, and deep learning is expected to enhance the quality of AI applications manifold.
3. Edge Machine Learning and AI-Enabled IoT
IoT, the Internet of Things is the virtual network existing amongst the billions of connected devices in homes, factories, workplaces, cities, etc. A multitude of sensors gathers data from users & the environment and processes it on a single device or shares it with multiple devices on a network.
- Machine learning models allow for a more in-depth and more thorough analysis of data in an IoT. These models process all the data and extract insightful knowledge to make better predictions & decisions.
- As more and more devices become a part of the Internet-of-Things, network congestions will be inevitable. This is where edge computing comes into the picture as edge devices will be able to reduce latency and uncomplicated the network structure.
- Edge machine learning performs data analysis closer to the data source and helps associated systems make better decisions much more quickly.
Edge machine learning, coupled with IoT is slowly paving the way for the Internet of Conscious Things.
4. Personalized Conversational AI
One of the most commonly implemented applications of AI in recent years are chatbots and conversational assistants.
AI bots are now able to carry out natural, contextual and thoughtful conversations. Most chatbots can currently respond to simple questions and carry out specific hardwired tasks. AI bots take things a notch further.
AI conversational assistants exhibit different levels of maturity according to their design:
- Level 1: At this level, chatbots acts as a regular notification assistant and deliver pre-built responses.
- Level 2: In this case, AI bots can answer FAQs and also follow-up with other suggestions.
- Level 3: These bots can engage in a back and forth conversation. They can interact much more flexibly, can handle abrupt queries and under the context of a conversation.
- Level 4: Machine Learning enabled bots can now recognize users well at this level. It can make personalized suggestions and act in a much more proactive manner.
- Level 5& beyond: Contextual AI assistants at this level can carry out more than just intelligent conversations. It will be able to handle other tasks and operations besides interacting with users.
Natural Language Processing, Natural Language Understanding, and Natural Language Generation lie at the heart of most AI-enabled chatbots.
5. Automated Machine Learning
The year 2020 is witnessing tremendous advances in the field of AI. One such advancement is the automation of the deployment process of a machine-learning model, starting from development & training to implementation and support.
AutoML will allow scientists, developers and the like to identify & deploy an appropriate end-to-end ML pipeline for any problem. Megacorporations such as Microsoft are now offering automated machine learning services via their Azure Machine Learning platform.
Automated machine learning hides the technicalities and the mathematics involved by automating tasks like:
- Data mining and cleaning
- Feature selections
- Model selections
- Parameter selections
Automated Machine Learning is revolutionizing the revolutionary field of AI. Soon, Auto ML may make even data scientists redundant. Businesses would be able to implement machine-learning models as a service, without worrying about the complexities involved.
6. Quantum AI
The intersection and amalgamation of AI and quantum computing were inevitable. Tech giants such as Google are researching on quantum AI with a focus on using quantum computing to enhance & accelerate computational processes of machine learning.
The focus areas of Google’s Quantum AI research are:
- Superconducting quantum bit or qubit processors
- Developing a quantum circuit
- Quantum simulations of physical systems
- Quantum neural networks and Quantum Machine Learning
7. AI-enabled Chips
Processors capable of performing machine learning specific computations will soon become widely available. 2020 witnessedprocessor-developing giants Intel integrate AI abilities right into the instruction set architecture of a processor itself.
10th and 11th Generation Intel Core processors implement AI-enabled architectures for :
- Image deepscaling
- AI deblur
- AI accelerated HD graphics generation
- AI-assisted audio processing
Also called AI accelerators, these specialized processors are designed to boost AI applications like machine learning, machine visions, and neural networks.
8. Enterprise AI
As businesses are implementing AI more and more into their operations, researchers and data scientists are now developing AI applications capable of enterprise-wide operations.
Enterprise AI is a particular category of software applications that harness AI techniques to transform operations. Enterprise AI platforms by DataRobot, an AI application development company, allow for the deployment of end-to-end machine learning pipeline models, which would enable companies to turn data into value.
9. AI in Cybersecurity
It was only a matter of time before AI found its way into cyber-security. Smart technology is now playing a pivotal role in preventing digital intrusions. AI can be designed to track any penetrations or suspicious activities and can raise alarms just in time.
According to IBM, AI-enabled smarter cybersecurity will learn from data resources, gather insightful knowledge, make intelligent inferences, and augment the analysis of & response to threats.
10. AI in Manufacturing
The manufacturing sectors implementing scalable AI applications increasingly. In a recently published article, Capgemini offered an insight into several such utilization across different manufacturing domains.
Here are a few examples:
- General Motors is working with Autodesk to implement ML design algorithms to overcome constraints and optimize the design process.
- Nokia is using an ML-enabled video application for its production process.
- The Danone Group is a multinational food-products manufacturer that use ML systems to make demand forecasts accurately.
- BMW uses AI to evaluate design images from its production for quality control purposes.
2020 was a year of firsts. From the widespread disruptions in human society due to a global pandemic to the consequential large-scale implementations of electronics, computer science, and AI across several sectors, the year is witnessed so many landmark events. Specific AI and technological trends have been established that will change the future of humanity forever!
Author-Bio: Olaila Lee is a project manager at a reputed company in USA. She also offers assignment help to students at MyAssignmenthelp.com. Olaila likes to bake cookies in her free time.