Artificial intelligence (AI) and machine learning (ML) are two of the most used terms that are not confined in a specific domain but have universal adoption. Owing to their exclusive properties to create new-edge tools/systems/softwares, AI and ML have taken over the technology and innovation domains.
There are countless astonishing ways machine learning and artificial intelligence are utilized in the background to affect our daily lives in areas like recommendation engines that suggest movies, products or services, voice assistants, speech recognition on smart devices, and much more.
No doubt businesses from diverse sectors such as SaaS, IT, streaming, e-commerce, gaming, e-learning, and others have adopted both AI and ML for various purposes—improving audience experience, marketing, boosting audience interaction, enhancing UI, and many more.
Statistics say AI-powered voice assistance will reach 8 billion by 2023, while AI can increase business productivity by 40%. And that’s not it; there are innumerable factors and applications that make AI and ML highly sought-after around the world.
In this blog I will analyze the top predictions for AI and ML in 2022 so that you know what to expect in the near future.
What Is Artificial Intelligence (AI)?
Artificial intelligence is a branch of computer science that helps create smart devices, solutions, and machines to execute tasks that require human intelligence.
For example, whenever you browse on your Netflix channel, you will find recommendations based on your previously watched movies, liked series, or the genre you search the most. This may feel as if a human is behind the list, but actually, it is the recommendation engine of Netflix that is equipped with AI to provide you with real time suggestions.
Simply put, AI emulates the intelligence of humans but is demonstrated by machines, tools, and software. In general, there are four main types of AI:
- Limited Memory AI: Self-driving cars with sensors that can identify traffic signals, steep roads, or people crossing roads
- Theory of Mind AI: A robot or system that can understand the mind of another similar robot or system
- Self-Aware AI: Robots with self-simulation
- Reactive AI: IBM’s Deep Blue, Netflix’s Recommendation Engine
What Is Machine Learning (ML)?
Machine learning is a branch of computer science and artificial intelligence that imitates human learning and improves capabilities by utilizing related data and through algorithms. ML is a subset of AI and based on the concept that smart systems can identify data patterns, can acquire knowledge from data, and can make decisions without requiring any significant human interaction.
As with AI, we can divide ML into four main types:
- Supervised: Linear regression for regression problems
- Semi-supervised: Text document classifiers
- Unsupervised: Hierarchical clustering
- Reinforcement: Autonomous cars
Now that you have the basic knowledge on AI and ML, let’s discuss the predictions for AI and ML in 2022.
Predictions for AI and ML in 2022: Everything You Need To Know
AI and ML are evolving fast and depend on a lot of factors such as their usability, demand, new innovations, inventions of new cutting-edge technologies, and others. However, on the basis of all these, I have listed the top predictions for AI and ML in 2022.
Wider Adoption of AI and ML
AI and ML are no longer just additional tools used by a handful of professionals, but are diversified across a broader range of businesses, industries, and fields. It is likely that this trend will continue, with even wider adoption of AI and ML on the horizon.
The applications of AI and ML will be everywhere, making it one of the most-discussed topics in 2022.
DevOps can be defined as a set of practices such as cultural philosophies and tools in order to combine software development and IT operations. One of the main goals of DevOps is to shorten the development cycle to deliver high software quality and higher efficiency.
With the introduction of ML, there will be a drastic shift in DevOps functionalities, operational structures, and outputs produced. Thus MLOps (Machine Learning DevOps) will take center stage while delivering advanced ML solutions along with a balanced view in the three main areas—people, process, and technology.
Such advanced DevOps equipped with ML will ensure delivery of the result in a more secure, scalable, automated, and robust way. In turn, there will be newer versions of the seven key areas of DevOps: configuration management, continuous integration, automated testing, infrastructure as code, continuous delivery, continuous deployment, and continuous monitoring.
From data ingestion to model monitoring, ML will improve everything while upgrading the experiments cycle including algorithm selection, feature engineering, parameter tuning, training, and validation.
Better Platform Support
As ML and AI are going to take over a majority of the sectors and the key functionalities of various domains, the requirement for more advanced platforms with better support will be high in demand.
For instance, AI-based tools like smart assistants, social media monitoring systems, automated financial investing tools, etc. require up-to-date, flexible platforms to perform seamlessly. As such tools are highly sought-after, the need for such platforms will also rise in 2022.
Furthermore, AI/ML/data science platforms will garner more popularity, and such platforms will also need easy integration with main/other platforms. No doubt this will ease the task of data analysis, processing, and building ML models to provide the required solutions in the respective platforms and domains.
As such, it will become pivotal to choose platforms according to the AI and ML operations/usage in 2022. For instance, some of the top AI platforms are Google AI Platform, TensorFlow, KAI, Microsoft Azure, Watson Studio, and Rainbird. Some of the best ML platforms include Amazon SageMaker, Microsoft Azure Machine Learning Studio, SAS, and TIBCO Software.
Not only will the demand for such platforms grow, but the requirement for better platform support will also surge further in 2022.
AI and ML work majorly on various data that needs to be accessed, analyzed, and processed. And with that comes the risk of data breach, cyber attacks, and other security concerns associated with data access.
With the rise in cybersecurity issues, the focus of the professionals operating in this field will be on secure data access. There will be even wider adoption of cybersecurity rules, protocols, and compliance to ensure the protection of digital data and sensitive information. And that goes for both AI and ML.
For instance, AI and ML are extensively used in fraud and anomaly detection. Fraud detection engines equipped with AI and ML can easily detect complex scam patterns, and their analytics dashboards can provide detailed insights to prevent cyber threats in advance. In 2022 data security will revolve around the area of data access when it comes to implementing AI and ML.
Growing Demand for Synthetic Data
Synthetic data is quite a common term in the world of artificial intelligence and machine learning. It refers to data that has the properties to replace real-time data.
Such data is generated by computer algorithms or simulations. Contrary to real-time data, synthetic data is generated in a digital world as an outcome of the computer programs or processes executed by a system, algorithm, or software. Such data is going to become more sought-after in the branches and applications of AI and ML in order to perform multiple tasks:
- Training ML models
- Validating mathematical models
- Creating data repositories
- Optimizing cost and privacy during training
In 2022 the rise in the usage of synthetic data will significantly change data modeling, training, and all the related activities in AI and ML.
Improved Internal AI Use Cases
Not only will AI be on-demand in multiple applications, but it will also improve the use cases (usage of AI in different areas or sectors such as inventing smart tools and systems) that are used internally in businesses, IT domains, and industrial sectors.
Some of the top internal AI use cases that will come to the limelight in 2022 and prevail further in the upcoming years are the following:
- Financial trading
- Market analysis and personalization
- Fraud detection
- Digital marketing
- Email marketing
- Finance and accounting
- Human resources
- Robotic process automation (RPA) for better productivity and efficiency
- Pre-sales, sales, and analytics automation
Simply put, in 2022, internal operations, use cases, and productivity will majorly depend on ML and AI paving new paths for industry leaders and stakeholders.
The AI and ML Trend Will Continue
Artificial intelligence and machine learning are going to be trending far beyond 2022. As the functionalities and advantages of AI and ML are making them reach to the peak of the increasing demand, they will both create long-lasting impact, while their significance will rise further.
As I showed you in this post, AI and ML hold unlimited potential that has already made their usage widespread. And with time, several factors will aid in making them even more popular: the requirement for smart new inventions, the introduction of cutting-edge technologies in the AI and ML field, the evolution of ML algorithms, and many more.
The day is not far when AI and ML will occupy even a more significant part of our daily activities and applications in all sectors around the globe.