Top 10 Artificial Intelligence Trends in 2023

As we move into the 21st century, Artificial Intelligence (AI) is becoming an increasingly important field of study, and Artificial Intelligence (AI) will continue to evolve and grow in popularity. According to a recent report by MarketsandMarkets, the Artificial Intelligence (AI) market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2023. This rapid growth is due to the increasing demand for AI-powered solutions across industries. Following are the top 10 Artificial Intelligence trends that will dominate in 2023. 

Top 10 Artificial Intelligence Trends

1. Contactless Autonomous Shopping  

2. AI TRiSM (Trust, Risk, and Security Management) 

3. Adaptive AI 

4. AIOps 

5. Large Language Model  

6. Military Weapons (Weaponized AI) 

7. Generative AI 

8. AI in Courtroom (Legal/Ethical AI)  

9. Sustainable AI 

10. No code ML / AI 

Contactless Autonomous Shopping  

The space of shopping is becoming more and more digital. Customers seek new and improved in-store experiences prioritizing speed and safety. 

Following are some of the most significant changes and trends in the retail market in the past few years: 

• 82% of small businesses have adjusted how they operate their business post-pandemic. 

• 65% of consumers prefer to use contactless payments. 
• 85% of customers expect digital payment options when they shop in person. 
• 75% of small business owners expect consumers to choose contactless payments. 

The ultimate goal of contactless shopping solutions is to create an autonomous and frictionless customer experience that matches the convenience of online shopping. 

Computer vision is a game-changing technology that makes completely autonomous shopping experiences possible. With sensors and cameras powered by AI, shoppers pick items off a shelf and build a ‘virtual’ shopping cart.

Following are the reasons to use Computer Vision (AI) for contactless autonomous shopping: - 

1. Increased Customer Satisfaction 

2. Reduced Costs  

3. Increased Sales 

4. Increased Productivity  

AI TRiSM (Trust, Risk, and Security Management)

AI TRiSM is a framework that supports AI model governance, fairness, reliability, privacy, and data protection. It comprises techniques, solutions, and processes for model interpretability and explainability, privacy, model operations, and adversarial attack resistance for enterprises and customers.  

Poor implementation of AI technology will increase exposure to threats and vulnerabilities. It may cause data privacy breaches that lead to various nasty impacts, including financial loss, reputational loss, and harm to both the technology and the consumers. AI TRiSM ensures that every AI adoption in organizations comes with a trustworthy and reliable AI model governance framework, enabling them to reduce potential risks more effectively. 

AI TRiSM stands for Trust, Risk, and Security Management. With AI TRiSM, you can more easily identify risks and potential threats and then take steps to mitigate them. 

Once users start operationalizing their AI models, security and privacy concerns are the number one barrier to AI model implementation. AI is a diverse and powerful tool that solves countless problems in today’s world.  

1. The recommended videos on YouTube? It’s AI.  

2. The recommended routes on Google or Apple maps? It’s AI.  

3. Fraud analysis, self-driving cars, accepting or rejecting loans, and job applications? You guessed it, it’s AI.  

As AI is a powerful tool, we must understand there are chances of its failure too. And in the case of Artificial Intelligence, failure could mean millions in reputational, legal, or financial losses. 

That’s why AI TRiSM is essential – to prevent such errors.  

Adaptive AI

Unlike traditional AI systems, Adaptive Artificial Intelligence (AI) can revise its code to adjust for real-world changes that weren’t known when the code was first written. Adaptive AI can learn and adapt over time by learning from new data and previous experiences and it can constantly improve its performance. Organizations that build adaptability and resilience into design in this way can react more quickly and effectively to disruptions. 

Flexibility and adaptability are now vital, as many businesses have learned during the recent Covid outbreak. 

Adaptive AI combines a set of methods (i.e., agent-based design) and AI techniques (i.e., reinforcement learning) to enable systems to adjust their learning practices and behaviors to adapt to changing real-world circumstances while in production.  

• Adaptive AI creates a superior and faster user experience by adapting to changing real-world circumstances. 

• Broadening decision-making capabilities and flexibility happen while implementing decision intelligence capabilities. 
• IT leaders must re-engineer various processes to build adaptive AI systems that can learn and change their behaviors based on circumstances. 

There are many potential applications for adaptive AI. For example 

1. It is use to improve the accuracy of predictions made by a financial forecasting system. 

2. It is use to develop more effective marketing strategies by constantly learning from customer data. 

AIOps 

The AIOps refers to using Artificial Intelligence (AI) for IT operations. AIOps is a set of algorithms and tools that collect data from the entire IT environment, including different monitoring systems, log files, and other IT data sources. It aims to automate the detection and resolution of IT issues, thereby reducing the need for human intervention. 

AIOps has become a vital function within enterprise IT strategies as organizations implement intelligent automation by leveraging AI and machine learning to collect and analyze massive amounts of data, apply reasoning and problem solving, remove noise and prescribe best actions for ITOps. AIOps tools combine data from various sources, including monitoring tools and logs. 

Following are the stages of the AIOps Process: 

1. Data Ingestion 

2. Data Storage 

3. Data Analysis 

4. Knowledge Discovery 

5. Interaction & Visualization 

Key AIOps Benefits: 

1. Simplified troubleshooting  

2. Increased efficiency  

3. More data-driven decisions  

4. More accurate predictions  

5. Improvement in customer satisfaction  

Large Language Model  

Large language models have become increasingly popular in AI and NLP. These models can learn from large amounts of data and can be used for various tasks, such as natural language understanding, text generation, and machine translation. GPT3, T5, Turing-NLP, and Megatron-Turing LG are some of the latest Large Language Model with billions of parameters.

There are several benefits to using large language models.

1. First, they can learn from a large amount of data, which is important for tasks such as machine translation, where a large amount of data is available.

2. Second, they can be use for various tasks, including natural language understanding, text generation, and machine translation. 

3. Finally, they can be use to improve the performance of other models, such as those that are used for Named Entity Recognition.

One of the challenges with large language models is that they can be expensive to train. However, recent advancements in the field of AI have made it possible to train these models on a variety of different hardware, including GPUs and TPUs. 

Military Weapons (Weaponized AI) 

The military has always been at the cutting edge of technology. From the first rifles and cannons to today’s missiles and fighter jets, the military has always sought to weaponize the latest and most remarkable technologies. Now, the military is turning its attention to Artificial Intelligence. Weaponized AI is seen as the next big thing in military technology, and the race is on to develop the most effective and deadly AI weapons. 

The potential of AI weapons is enormous. They could be use to autonomously target and destroy enemy targets without the need for human pilots or soldiers. They could also be use to create new and novel weapons, such as intelligent missiles that could seek out and destroy targets without missing. 

The following listed five AI tools used in modern warfare: 

1. Lethal Autonomous Weapons (LAWS) 

2. AI-Enabled Drones 

3. AI-Powered Killer Robots 

4. Integrated Speech Solution 

5. AI-Based Landmine Detection System 

Generative AI 

Generative AI is a type of AI that focuses on generating new data. These models are trained on a dataset and then used to generate new data similar to the training data. For example, if you want to generate images of objects that don’t exist in the real world, generative AI can be use to create them. 

Generative AI is also often use for data augmentation. Data augmentation is the process of adding additional data to a dataset to improve the performance of an AI model. By using generative AI to create new data, we can improve the performance of our models. 

Tools like DALL-E, CHAT by OpenAI, Imagen, and Stable Diffusion generate original text-to-image are some recent examples of generative AI.  

Generative AI will not only improve digital product quality but, by 2025, will also account for 10% of all data produced — compared to a current 1%. 

Following things which Generative AI will do in the coming future  

• Corporate Tools will Integrate Creative AI At Scale 

• AI Generative Video Matures for Production 
• The Prompt is the New Search 
• Media Agencies will Embrace Generative AI 

For example, Microsoft is currently integrating DALL-E2 with its Designer and Image Creator applications.  

AI in Courtroom (Legal/Ethical AI)  

The adoption of Artificial Intelligence (AI) computers is destined to bring about a revolution in the legal profession and industry. 

There are a few key considerations when it comes to AI in courtrooms. 

• First, AI can help to automate repetitive work such as data entry and case management. This can free up time for lawyers to focus on more complex tasks. 
• Second, AI can assist in the analysis of legal documents. This can reduce the time needed to review cases and make decisions. 
• Third, and perhaps most controversially, AI can be use to predict the outcome of cases. This is where the potential for bias and abuse comes into play. If AI is correctly calibrated, it could make fair and discriminatory decisions. This real concern must be addressed before AI can be used in courtrooms. 

Overall, using AI in courtrooms is a complex issue with many legal and ethical implications. 

The followings are the number of advancements of AI in legal tech:   

• Large-scale searching and updating capabilities 

• Bulk analytics that can capture relevant data on contract risk, performance, and efficiency 
• AI that can rewrite portions of text under intense negotiations, increasing the likelihood of contracts being executed successfully. 
• AI-guided contract drafting based on analytics, best practices, and attorney inputs 

Sustainable AI 

AI systems are compute-intensive: the AI lifecycle often requires long-running training jobs, hyperparameter searches, inference jobs, and other costly computations. They also require massive amounts of data that might be moved over the wire and specialized hardware to operate effectively especially large-scale AI systems. All of these activities require electricity — which has a carbon cost. 

Sustainable AI is about using Artificial Intelligence environmentally and socially responsibly. This means creating algorithms that don’t gobble up resources, don’t perpetuate bias, and don’t cause unintended harm. 

There are many different ways to approach sustainable AI. 

• One approach is to design AI systems that are more efficient in using resources such as power and data. 
• Another method is to create more transparent and accountable systems so we can understand and trust them. 

All companies will be under pressure to reduce their carbon footprint and minimize their environmental impact in 2023. AI can be a driver of sustainability in other industries and areas of operation, too – for example, computer vision is use in conjunction with satellite imagery to identify deforestation and illegal logging activity in the rainforests, as well as unlawful fishing activity that impacts biodiversity in the oceans. 

Following are the few approaches for Sustainable AI 

• Elevating smaller models — Using pruning, quantization, compression, and distillation techniques.  
• Alternate deployment strategies 

• Carbon-efficiency and carbon-awareness 

No code ML / AI 

Currently, we are living in an age where technology is advancing at a rapid pace. Each day, new and innovative products are releasing that make our lives easier. In recent years one such area that has seen a lot of development is machine learning. 

No code ML / AI is the process of using machine learning algorithms without writing any code. This can be done using a graphical interface or by using a library of pre-built functions. No code ML / AI is becoming increasingly popular as it allows people with no coding experience to build complex machine learning models. 

There are many benefits to using no-code ML / AI. It allows you to build models quickly and easily without writing code. This means you can focus on the data and the results rather than the code. It also makes it easier to collaborate with others, as they can use the same interface to build and test models. 

There are some drawbacks to using no code ML / AI, however. The biggest one is that it can be challenging to debug models, as you cannot see the code. This can make it hard to find and fix errors. Additionally, no code ML / AI can be less flexible than traditional coding, as you are limited to the options provided by the library or interface you use. 

No code ML / AI use cases  

• Finance: Automating customer onboarding and loan application approval based on specific criteria and customer risk levels. 

• Marketing: A model can be created to identify the patterns in text, image or audio and analyzes sales transcripts. 
• Healthcare:  Tools to revenue cycle management or optimize billing systems, for example, via robotic process automation (RPA), mobile solutions or chatbots. 

Summary: 

Artificial Intelligence (AI) will continue to grow in popularity; the AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2023. This rapid growth is due to the increasing demand for AI-powered solutions. The future of AI is shrouded in potential but fraught with uncertainty. AI is being applied in a widening range of domains, such as finance, healthcare, transportation, Security, Legal, and manufacturing. AI TRiSM, Adaptive AI, AIOps, and Generative AI are some of the trends that will be in 2023.