Artificial Intelligence in 2023: Importance, Trends, and Insights
Artificial intelligence (AI) has become a part of our daily lives. From ”Hey, Siri, set a meeting with Josh for noon on Wednesday” to “How could Netflix know that I'll like this movie?”. Even the Madrid government achieved a 25% decrease in annual energy consumption, analyzing information about passenger traffic, trains, and other subway-related rates with AI.
It's not surprising since about 80% of everyday devices are infused with some form of artificial intelligence, and 77% of people use AI-powered solutions. Moreover, financial reports state that we live through the AI revolution: the global AI market is predicted to grow to 309.6 billion USD by 2026 at a CAGR of 39.7%.?
AI will continue developing in 2023 but will face strict regulations. In today's article, we uncover the importance and trending applications of AI in 2023 along with a short implementation guide.
Why Is AI So Important in 2023?
Accuracy, cost-efficiency, and improved user experience are well-known AI pros. But they're pretty general and don't take into account the current situation. Today, we'll analyze the real value of AI in 2023:
- The world is still managing the challenges brought by 2020. Businesses need help transferring their activities to the digital environment since users have gotten used to fast and highly efficient online services. This tendency won't go anywhere in 2023, and AI is the best booster for replacing manual work, making delivery more accessible, and creating user-friendly digital products.
- The medical industry will need artificial intelligence too. The ongoing pandemic has brought out the necessity to enhance the productivity of aid systems and other related software for a better doctor and patient experience.
- The end of 2022 left thousands of people jobless. This problem is two-sided since businesses are now transforming non-automatic processes to automated ones, while potential employees need tools to search for new workplaces fast and conveniently. It looks like a sound premise for AI-powered product development.
- 212,485 cyber crimes were reported during only the first two months of 2022. For comparison, it's more than the total number in 2018. The bad news is that this figure tends to rise in 2023, but artificial intelligence is a real chance to improve the gloomy forecast.
7 Key AI Trends for 2023
We don’t know for sure what will happen in the tech world this year. But we can set a direction for development and transformation. Here you’ll find seven hottest artificial intelligence trends and ideas for their implementation in a particular business segment.
AI assistants?
Have you ever talked with a customer support representative or online store manager? These conversations keep valuable insights that help optimize? various business activities. Virtual AI assistants are a must for 2023 since they can analyze information about occuring issues based on voice recordings or text messages and with that, help continuously improve customer experience.?
The goal of any business is to meet (or even predict) customer needs. To make it possible, the technology can analyze the context, mood, semantic similarities, nuances of speech, and even accents. This information can be used to create scenarios for solving similar issues emerging in the future more effectively.?
For instance, your client asks to return their purchase. If there are? difficulties in the return process, AI analyzes the customer’s? call or message, connects the problem with its resolution, and solves similar problems more efficiently in the future.?
Content generation
Generative AI is an advanced tool for creating augmented content, be it images, texts, or even videos.
AI-powered solutions use several different sources and transfer-style learning (a machine learning method where we reuse a pre-trained model as the starting point for a model on a new task) to build the required content. Craiyon, for example,? generates images based on text descriptions.
AI already optimizes the marketer’s work, but we also expect it to assist media professionals in the future. One day, we'll watch movies created entirely by artificial intelligence and be able to recreate older movies in HD quality several times faster.
Explainable AI?
Explainable AI (XAI) is a set of tools that help humans understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. It's an analyzing set of tools and frameworks that help understand and characterize AI-based predictions.
Besides, explainable AI smoothes out the process of AI adoption. Managers and entrepreneurs get not only predictions but their grounding and justifications. It's not only ?what?? but also ?why??
In 2023, the demand for explainable AI will significantly increase in the healthcare and financial sectors. Medical workers would provide an AI-based prescription only on the basis of a strong justification, just as a bank worker can't reject a loan request without explanation.?
The biggest challenges of implementing explainable AI is ethical lapses. But this issue can be resolved with the help of thorough tests and audits conducted by legal and social structures.
Edge AI
Edge intelligence or edge AI is a compilation of artificial intelligence and edge computing. Hence, to understand edge AI, one needs to understand what edge computing is.
Let’s imagine an Apple Watch. It is an autonomous device exchanging data with other smart devices through the Internet of Things (IoT). Edge computing helps run the exchange without overstraining by moving data closer to the place of origin. So functions remain “on edge” and do not travel across the cloud storage. The “edge” can be a car, laptop, medical device – anything connected closely to the smart device.
Simply put, edge AI means applying intelligent algorithms in the edge computing environment. It provides secure storage and faster processing of massive amounts of content and business information, shopping records, and other big data outside the cloud.
Edge AI is likely to bring changes to several industries. In healthcare, sensitive patient information can be kept in one local storage, enabling medical organizations to conduct real-time analytics with no security threat. In the automotive sector, rapid data processing can help autonomous vehicles provide a safer driving service to their passengers.?
AI-based frictionless shopping
Have you ever wondered if information about customer behavior in a physical store could be useful for business? Previous shopping experience and other information collected and analyzed by AI open up new horizons for the retail industry.
Let us consider the following example of AI-powered frictionless shopping based on computer vision (CV) and edge AI. Customers enter a store, scan a QR code via a special app, through which they have a payment method connected. Then customers simply select the items they want and walk out of the store with them. This flow is called Just Walk Out (JWO) technology . But wait, what exactly has just happened?
Our complex system detected all the goods that customers put in their shopping cart using smart cameras and special sensors on the shelves. This way, the system knows what goods a buyer picked, so payments are withdrawn automatically through the app.
Buyers will finally forget about long queues and regularly receive personalized recommendations for their next purchase. The information for process improvement will be obtained through video analytics, real-time insights, and integration with past purchase histories.
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Cybersecurity
In 2023, more companies are planning to invest in measures preventing business data leaks and cyber attacks, which are mainly caused by accessing corporate servers from personal devices. How can algorithms help with that?
AI-automated security models monitor large-scale networks and databases to evaluate risks and provide relevant recommendations for security improvement. This approach helps automate manual work and detect threats promptly.?
Federated learning
Federated learning is a direct result of the latest AI achievements in cybersecurity. It is a new way to train decentralized ML models across several edge devices, like common smartphones or more complex medical tools. The big advantage of this method is that devices continuously train a model but do not send the data to a central server, in contrast to traditional ML methods.
Plus, homomorphic encryption allows sharing information between a client and a server with no risks to privacy. This type of encryption allows users to perform computations on its encrypted data without first decrypting it.
Manufacturing companies can use federated learning models to develop predictive maintenance models for equipment. Predictive maintenance can face some barriers such as customers who do not want to share their personal data or exporting data problems from different countries/sites. Federated learning can handle these challenges by using local datasets.
Peer-to-peer (P2P) lending is one of the drivers of financial technology (FinTech). FL can improve the implementation of P2P, whereby lenders will have better information on the ability of a borrower to pay back the loan for the lenders. And this is also a case of credit analysis, whereby traditional banks (like P2P lenders) can assess the creditworthiness of a specific borrower through their data without needing the borrower's data to leave their phone.
It brings efficiency and speed when determining credit scores for borrowers because there's no need to clone the borrower's data onto a central server for an ML algorithm to perform the analysis before the credit report gets generated on the device. Instead, the ML algorithm runs the credit analysis on the borrower's device.
Google has already launched a beta version of the federated learning framework called Tensorflow Federated .??
How to Prepare for Implementing AI Innovations in Your Business?
In this part of the article, we share specific steps to set the stage for your AI solution.
1. Decide why you need AI in the first place
There must be something troubling you in business operations. Take your time and consider all of the recent workflow issues. Ask yourself these questions:
- What departments have been struggling with issues the most?
- What results do you expect to achieve after implementing AI??
- Set well-defined project goals based on qualitative and quantitative metrics.
2. Asses resources
Thoroughly assess available budget and professional capabilities. It will allow you to decide whether to buy a ready-made AI tool or develop a custom solution from scratch with the help of the in-house specialists or reliable AI vendor. You also have an option to collaborate with your business partner and outsource the required workforce from them.
3. Get the data ready
Most businesses keep their data unstructured , i.e., without a particular order. But order is essential to train AI algorithms, so make sure to structure your data, clean it up, and allocate a dedicated storage for it.
4. Clarify and test your idea
Everything is set for the start, but here comes the trickiest part. An AI solution is not a magic wand to solve existing issues right away. Our advice is to start small:
- Don’t use all of your data straight away. Start by testing one data set with an AI algorithm.
- Explore its pros and cons.
- Evaluate how close you are to your goal.
After this, you can expand your efforts and use all available assets.
5.? Continuously analyze the results
Every achievement should be measured to check whether it brought you desired results. Was your goal to speed up the work of the development team? See how much time they spent completing their tasks before and after AI transformation.?
We advise you to analyze and compare the results after each project stage. This way, you will see what step to take next for successful project delivery.
For a comprehensive guide to integrating artificial intelligence and decision-making with use cases, visit our blog post .?
In Conclusion
As we can see from trends and overall statistics, artificial intelligence is not just exciting technology anymore. It brings impact in each business sector.?
Spotify increases user satisfaction by leveraging user data, such as playlists or listening history, to predict which song can become your new favorite. As a result of the latest collaboration with IBM , McDonald's has automated drive-through chains, which brought excellent outcomes. Now the company is automating all of its restaurants.?
Your business idea can become another successful example in the list. And our R&D Center specializing in Artificial Intelligence and Machine Learning will be there to help. Read more about our AI expertise and request a quote now. Let's bring innovations to the world together!