The top 7 AI trends to watch in 2023
Artificial intelligence (AI) is one of the most dynamic and disruptive fields of technology today. AI has the potential to transform various domains such as healthcare, manufacturing, retail, marketing, fintech, and more.
But what are the latest developments and innovations in AI that will shape the future of technology, business, and society in the upcoming years?
In this blog, we will explore some of the most important trends around the use of AI in 2023, based on expert predictions and market research.
Here are the top 10 AI trends to watch in 2023.
1. Rapid democratization of AI tech and research
AI will become more accessible and affordable for everyone, thanks to the growing availability of cloud-based platforms, open-source frameworks, no-code and low-code tools, and online education resources.
These will enable anyone, regardless of their technical skill level or budget, to create, deploy, and use AI solutions for various purposes.
This will also foster more innovation and collaboration in the AI community, as more people will be able to contribute to and benefit from AI research and development.
One of the main drivers of AI democratization is the availability of open-source frameworks and platforms that enable anyone to build and deploy AI models without writing complex code.
Some of the popular examples of such tools are TensorFlow, PyTorch, Keras, Scikit-learn, and ChatGPT.
These tools provide pre-built algorithms, libraries, and APIs that simplify the process of developing AI applications. They also support various languages, platforms, and devices, making them more accessible and versatile.
Another driver of AI democratization is the emergence of cloud-based AI services that offer high-performance computing and storage at affordable costs.
These services allow users to access and use AI models without installing or maintaining any hardware or software. Some of the leading providers of such services are Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and IBM Cloud.
These services offer various features such as data analytics, machine learning, natural language processing, computer vision, speech recognition, and more. They also provide scalability, security, and reliability for AI applications.
2. Generative AI taking it up a notch
Generative AI is a branch of AI that can create new content or data from scratch, such as images, videos, text, music, code, and more.
Generative AI has already shown impressive results in recent years, such as GPT-3 for natural language generation, DALL-E for image generation, and Jukebox for music generation.
In 2023, we can expect generative AI to become more advanced and realistic, as well as more diverse and creative. Generative AI will also find more applications in various domains, such as entertainment, education, art, design, gaming, and more.
One of the main applications of generative AI is natural language generation (NLG), which is the ability to create coherent and meaningful text from data or keywords. NLG can be used for various purposes, such as content creation, summarization, translation, dialogue, and more.
One of the most advanced examples of NLG is ChatGPT, a large language model developed by OpenAI that can generate realistic and engaging conversations on any topic. ChatGPT can also write code, lyrics, essays, and more.
Another application of generative AI is image generation, which is the ability to create realistic and novel images from text or sketches.
Image generation can be used for various purposes, such as art, design, gaming, education, and more.
One of the most impressive examples of image generation is DALL-E, another large language model developed by OpenAI that can generate images from text captions. DALL-E can create images of anything imaginable, such as a cat wearing a suit or a snail made of a harp.
3. Heightened AI industry regulation
AI poses various ethical and social challenges, such as privacy, security, bias, accountability, transparency, and human rights.
Therefore, we can expect more regulation and governance of the AI industry in 2023, both at the national and international levels. This will involve setting standards and guidelines for the development and use of AI systems, as well as enforcing compliance and accountability mechanisms.
One of the main drivers of AI regulation is the growing concern over the potential risks and harms of AI systems, especially those that are powerful and complex, such as generative AI tools.
These tools can create realistic and novel content or data from scratch, such as text, images, music, code, and more. However, they can also be used for malicious purposes, such as spreading misinformation, impersonating identities, or violating intellectual property rights.
Therefore, regulators are looking for ways to control the development and use of these tools, as well as to ensure their safety, quality, and ethics.
Another driver of AI regulation is the increasing demand for trust and confidence in AI systems among users and stakeholders.
As AI systems become more widespread and influential in various domains, such as healthcare, finance, law, or education, it becomes more important to ensure that they are fair, transparent, reliable, and accountable.
Therefore, regulators are looking for ways to ensure that AI systems are aligned with human values and principles, as well as to protect the rights and interests of users and affected parties.
4. More emphasis on explainable AI
As AI systems become more complex and powerful, it becomes more difficult to understand how they make decisions and why they behave in certain ways.
This poses challenges for trust, accountability, and fairness, especially when AI systems are used for high-stakes applications such as healthcare, finance, or law.
Therefore, we can expect more emphasis on explainable AI in 2023, which is a branch of AI that aims to make AI systems more transparent and interpretable for humans. Explainable AI will also enable users to provide feedback and correct errors or biases in AI systems.
One of the main challenges of AI regulation is the diversity and complexity of AI systems, which make it difficult to apply a one-size-fits-all approach.
Therefore, regulators are looking for ways to tailor AI regulation according to the specific use cases, domains, and risks involved.
For instance, the European Commission has proposed a risk-based framework for AI regulation, which classifies AI systems into four categories: unacceptable, high-risk, limited-risk, and minimal-risk.
Each category has different requirements and obligations for AI developers and users.
Similarly, the US National Telecommunications and Information Administration has requested public comments on how to develop a risk-based approach for AI governance.
5. Increased collaboration between humans and AI
AI is not meant to replace humans, but to augment and complement their capabilities. In 2023, we will see more examples of human-AI collaboration, where humans and AI systems work together to achieve better outcomes than either could alone.
For instance, human-AI teams can leverage the strengths of both parties, such as human creativity and intuition and AI speed and accuracy.
Human-AI collaboration can also enhance learning and innovation, as humans can teach AI systems new skills and knowledge, and AI systems can provide humans with insights and suggestions. One of the main applications of human-AI collaboration is in mental health support, where AI can help humans provide more empathic and effective conversations to those seeking help.
For instance, a recent study showed that an AI-in-the-loop agent called HAILEY can provide just-in-time feedback to peer supporters on an online platform, helping them respond more empathically to support seekers.
The study found that human-AI collaboration led to a significant increase in conversational empathy and self-efficacy among peer supporters, as well as positive feedback from support seekers.
6. Growth of social commerce
Social commerce is the use of social media platforms to sell products or services directly to consumers.
Social commerce can leverage various features of social media, such as live streaming, stories, shoppable posts, chatbots, or influencers. Social commerce can provide a more engaging and convenient shopping experience for customers, as well as a more effective marketing channel for businesses.
In 2023, we will see more expansion of social commerce, as more social media platforms integrate e-commerce functionalities and more businesses adopt conversational AI tools to facilitate social commerce.
One of the main drivers of social commerce is the growing number of social media users in India, which is expected to reach around 448 million in 2023.
Social media users spend an average of three hours per day on social media platforms, which provides a huge opportunity for businesses to reach and convert them into customers.
Social commerce also caters to the preferences and behaviors of Indian consumers, who value trust, convenience, and personalization.
Social commerce enables consumers to discover new products, get recommendations from peers or influencers, and interact with brands in a more natural and authentic way.
7. Expansion of AI-enabled drug discovery
AI-enabled drug discovery is the use of AI techniques and tools to accelerate and improve the process of discovering new drugs or repurposing existing ones.
AI-enabled drug discovery can help reduce the time, cost, and risk involved in drug development, which is typically a long and complex process that involves multiple stages such as target identification, lead optimization, preclinical testing, clinical trials, and regulatory approval.
AI-enabled drug discovery can also help address some of the major challenges and opportunities in the pharmaceutical industry, such as rare diseases, personalized medicine, and pandemic response.
One of the main drivers of AI-enabled drug discovery is the availability of large amounts of data and computational power that can enable AI systems to analyze and learn from complex biological and chemical information.
AI systems can also leverage various techniques such as natural language processing, computer vision, machine learning, deep learning, reinforcement learning, generative models, and explainable AI to perform various tasks such as literature mining, data integration, and drug design.
AI is one of the most dynamic and disruptive fields of technology today, and it will continue to shape the future of technology, business, and society in the upcoming years.
AI is one of the most dynamic and disruptive fields of technology today, and it will continue to shape the future of technology, business, and society in the upcoming years.