2022 will be a critical year as artificial intelligence (AI) and machine learning (ML) continue to stride along the path to turning themselves into the most disruptive yet transformative technology ever developed. According to Google CEO, Sundar Pichai, the impact of AI would be even more significant than that of fire or electricity on the development of humans as a species. While this may be an ambitious claim, AI’s potential is very clear from the way it has been used to explore space, tackle climate change, and develop cancer treatments.
Today we will look ahead to the new year to see where AI and ML are headed to learn about the way these technologies will be used in 2022.
Intelligent Process Automation
In line with the latest technology trends, companies are looking for intelligent automation tools to solve business problems and increase productivity, efficiency, and accuracy for the benefit of the organization. One of these tools – intelligent process automation (IPA) – combines robotic process automation (RPA) and artificial intelligence (AI) technologies to provide fast end-to-end automation of business processes and accelerate digital transformation. In RPA, computer software robots perform repetitive digital tasks based on rules driven by structured data. However, many business processes now rely on large amounts of unstructured data or generate such data in real-time. IPA allows you to automate processes using machine learning, analytical capabilities, and cognitive technologies such as computer vision, natural language processing (NLP), and fuzzy logic. The volume of IPA implementation is expected to grow in the near future, with large-scale growth expected in some industries.
A lot of the IPA concepts also apply to areas of data quality since a lot of the data coming in is unstructured. Companies are looking for smarter ways of increasing the health of their data while eliminating a lot of the manual processes that come with it. This is where DataGroomr can offer a lot of value since machine learning does the lion’s share of the deduplication work for you and eliminates time-consuming processes like setting complex rules.
Combining Machine Learning with the IoT
This is one of the most talked-about and long-awaited trends. Thanks to the development of 5G, internet speeds will be fast enough to allow the devices to not only respond quickly but also be able to transmit and receive more information. IoT (Internet of Things) technology allows several devices to be connected to a single network via the internet. Every year, the percentage of production and the volume of production of the internet of things is growing. The main essence of their work is related to the collection of data, which will be analyzed and studied to maximize the provision of useful information. This parameter is key to determining the importance of machine learning.
The use of IoT projects covers a large number of different areas. It can be ecology, medicine, education, trade, IT-sphere, and much more. This is one of the main reasons why the use of IoT in conjunction with AI and ML is expected to grow in the coming years. In fact, by 2023, the global AI in the embedded IoT devices market will approach $26.2 billion and the IoT will represent 83% of the entire AI chipsets market.
Also, the combination of AI and the IoT will help to maximize cybersecurity. New technologies can contain a large number of errors that can lead to data breaches. Since all elements of the internet of things are directly connected to the devices, it is necessary to analyze the possibility of external threats and eliminate them at their early stages. This is where AI can provide IT security professionals with better tools to combat various threats. We explore this further in the next trend.
AI Will Play a Greater Role in Cybersecurity
With data becoming more valuable than ever, there is no shortage of cybercriminals looking for new ways to get their hands on it. One of the drawbacks of recent AI developments is that hackers can use them to access confidential information. Therefore, an important AI trend is the development of technologies for recognizing and reporting common types of attacks. Likewise, antivirus software, such as Bitdefender, is being developed with the help of AI, as this technology can help prevent devastating consequences from the threat of malware.
For businesses, AI-powered cybersecurity tools can also collect data from a company’s own communications networks, digital activities, transaction processing systems, websites, and external open sources of information. These tools then run algorithms to identify patterns and detect or predict threatening activity, potential data leaks, and more. Since criminals are constantly creating new malware and ways to illegally obtain data, this trend can be expected to continue in the future.
AI for AI
If you have not yet heard about AI for AI, it involves using AI to automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models. At a certain level, AI can develop its own algorithms to solve problems, increase efficiency and provide a person with useful research data.
The use of automated AI will allow even non-specialists to apply AI algorithms and methods. One example is Google’s AutoML, a tool that makes it easier to create machine learning models and makes the technology available to a wider audience. Such tools can be as customizable as necessary, even without detailed knowledge of the complex machine learning workflow. While this type of development is in its infancy, automated AI is showing exponential growth and is an important AI trend.
Machine Learning Will Become More Accessible
Out-of-the-box and pre-trained models will make machine learning available to more developers. This means that more services will use machine learning technologies, and they will play even greater roles in our lives. Along with the emergence of very complex methods, the trend is also developing for lightweight machine learning models that run on user devices.
There are three reasons why developers are trying to move some of the machine learning from servers to devices:
- Better protection of personal data – If the “smart” speaker itself recognizes your voice, it will send only a text request to the server, and the leakage of biometric data will be impossible in principle.
- Faster and more resistant to bad networks – For example, during a Zoom or Teams call, a neural network on a smartphone detects when you are talking and transmits only your speech, blocking the uninformative background noise.
- Cheaper service infrastructure – This helps developers to implement new features, thinking primarily about the quality of the product and not about what there are enough servers for.
Start Using AI & ML Projects to Generate Value for Your Business
According to a report from McKinsey, more and more companies are using AI and ML to generate value and, increasingly, this value is in the form of revenues. A small contingent of respondents coming from a variety of industries attribute 20% or more of their organizations’ earnings before interest and taxes (EBIT) to AI. We can expect this number to increase as technology develops and becomes more mainstream.
If you are looking to leverage ML to streamline tedious and mundane processes involved in deduping your Salesforce environment, consider using DataGroomr to help resolve all data cleaning issues. DataGroomr is the only app on the Salesforce AppExchange to use machine learning to dedupe your records, which eliminates a lot of the manual work involved when using rule-based apps. In addition to deduplication, it will also help you validate your customer contact information such as emails, phone numbers, and addresses.
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