The most significant decade of innovation and trends driven by these disruptive yet transformative technologies comes amid the AI and ML revolution. According to prophecies, the ongoing decade of 2020 will change humanity’s development. The scope of AI’s operation is clear from its effective capabilities in e-commerce, education, automotive, navigation, healthcare, and agriculture, and the list can go on for quite some time. Simply put, artificial intelligence refers to the deployment of algorithms and processes replicating human functioning and cognition, while machine learning is a subset of artificial intelligence.
With the proliferation of data and computing, artificial intelligence is set to rule the world of technology. The ongoing AI and machine learning trends will set the stage for some massive AI and ML innovations in the next three years.
This blog will show future trends in AI, Big Data, and Machine Learning.
Here’s a Look at Some of These New Trends:-
1. Small Data is the Future of AI
It’s a talk from yesterday, where we were learning big data analytics and tools for working with datasets that are too large or complex, and we’re already processing human data.
When I started my first ever job as a data analyst six months ago, I always thought that big data was what organizations worked with every day. However, I wouldn’t go into that completely today. Businesses are more interested in data with a manageable volume and format that makes it accessible, informative, and actionable.
Data big or small- stands on volume, speed, and variety pillars.
As I work and read about the new technology, I believe big data is approaching a plateau. More and more companies are maturing in collecting and using big data sets. While the conversation about What’s Next? has already begun, issues around the skills, budget, design principles, and resources to manage this big data are also at the fore. In 2022, more and more companies will begin to focus on automation using small data sets as the next phase of their data strategy.
2. Hyper Automation
Hyper automation brings a new era to the business domain, providing efficient operation for mundane tasks of repetitive processes. Hyper automation is an AI and ML trend that uses automatically generated learned algorithms and trained robotics to reduce human dependency and ensure accuracy, validity, and speed. With the expert help of AI and ML, business industries are set to automate several processes. A substantial increase in robotic process automation (RPA) can be expected in the next three years. According to Gartner, by 2024, organizations combining hyper-automation technologies with redesigned processes will reduce operational costs by up to 30%. This suggests a significant reduction in inter-operational expenses, enabling firms to invest in these resources. Hyper automation can be considered a critical trend in IT operations that can be leveraged to reveal valuable insights. Another reason for adopting this AI trend is the improvement in alignment between business and IT.
Our Learners Also Read: What Does a Data Scientist Do?
3. The Rise of Cloud Computing
Cloud Enterprise Solutions The pandemic has greatly accelerated the shift to cloud enterprise solutions for all data requirements. However, the real problem here is not the creation of the necessary data, but the lack of a secure space to collect, tag, clean, organize, format, and analyze this huge volume of data for insights. The solution to this urgent problem is a reliable cloud platform that can effectively store and protect the data it stores. The next few years will be crucial for the field of Data Science & Machine Learning as the war to build the most sustainable cloud ecosystems for enterprises continues.
It can collaborate on multiple clouds and joint cloud provider offerings, increasing time-to-market and strategies.
It is compatible with new and growing technologies and reduces processing times to improve operational efficiency.
Advice from a friendly neighbor: If you’re in a position to champion the data revolution in your company, it’s worth showing off your cloud migration – it’ll be a more considerable data engineering initiative, but now’s the time to pivot! From processing unpredictable real-time data at scale to faster end-user connections and data distribution, the power lies in the cloud.
4. Improved Low Code and No-Code Technology
Companies are starting to employ out-of-the-box foundation models as they apply AI in the business, significantly decreasing the time-to-value for AI solutions in areas like language, vision, and more. AI will have a big effect on how citizens evolve. With AI advancements in low-code technologies, anyone may become a citizen developer. Conversational AI will write code once citizen coders describe the issue they wish to solve in plain English.
2021 was also the year of LC/NC (low-code/no-code) technology, with many notable LC/NC ventures coming to the fore.
According to Forrester, the LC/NC market will increase from just $3.18 billion in 2017 to $21.2 billion in 2022.
As additional LC/NC AI solutions materialize, the same pattern in AI will be observed.
There is a shortage of skilled AI engineers in many organizations, so LC/NC solutions can help address this by offering simple interfaces to build increasingly complex AI systems.
5. Automated Machine Learning Will Advance
Data science is becoming more accessible because of the fascinating AI trend known as automated machine learning (or AutoML). Anyone can construct their own ML applications thanks to the platforms and tools made by autoML developers.
This can be helpful, especially for subject matter experts who may have knowledge and insights but frequently lack the coding skills required to use AI.
By making data cleaning simpler, it can also be helpful to data scientists. Since data scientists spend the majority of their time cleaning and preparing data, AutoML can assist in automating these tedious and repetitive operations.
AutoML also entails generating models, developing algorithms, and developing neural networks in addition to automating these repetitious activities.
6. Blockchain in Data Science
Using decentralized ledgers simplifies the management of large amounts of data.
Data scientists can perform analysis directly from their devices thanks to the decentralized nature of blockchain. Since the blockchain already tracks the origin of the data, data verification is more straightforward.
To prepare information for data analysis, data scientists must organize it centrally. This is still a time-consuming process that requires the efforts of data scientists. The problem can be effectively solved using blockchain technology.
7. Improved Natural Language Processing
Most businesses follow the latest trends and fruitful patterns that help their products/services/organizations. In this context, Natural Language Processing is most often incorporated to analyze data and identify these patterns and trends. This automated data analysis is excellent for getting reliable and meaningful information about Twitter Analytics, Customer Satisfaction Analytics, Customer Content Engagement Analytics, etc. The biggest and most valuable resource that firms have for strategy and company development is data. A function-based analysis is made possible by the use of traditional, automated, or scientific techniques for data cleaning, storing, and analysis. For more than ten years, Jigsaw has been creating technically solid and extremely engaging learning experiences using industry best practices. Industry professionals with years of experience and an in-depth understanding of the industry devised and delivered these seminars. Budding techies can use Jigsaw to find a variety of programs that can improve their specific areas of interest. They will undoubtedly benefit from extensive hands-on experience and robust pedagogy.
8. Advanced Data Analysis
Augmented analytics is a type of data analysis that automates the examination of large amounts of data by combining artificial intelligence, machine learning, and natural language processing. What used to be processed by data scientists is now automated to offer real-time information.
Businesses spend less time processing data and extracting insights from it. The result is also more accurate, leading to a better choice. AI, ML and NLP enable specialists to explore data and provide in-depth reporting and forecasting by helping with data preparation, data processing, analysis, and visualization. Through advanced analytics, data from inside and outside the company can be merged.
With the rise of visual data discovery tools in recent years, AI and machine learning capabilities are increasingly and directly implemented into analytics and BI systems to help business users rather than just data specialists. This brought together data, analytics, and machine learning when they were previously considered and managed separately. In the coming days, we will see more and more instances of advanced analytics.
Conclusion
2022 will be an exciting year for artificial intelligence and data science. However, these are only some of the ideas discussed in this article. The scope is vast, and we’ve only scratched the surface.
Moreover, like any other technology, AI and data science are changing daily. So let’s keep track of these trends to stay one step ahead. Let’s wait and see how AI and data science will evolve in the coming year.
