Rapid democratization of AI tech and research

Artificial intelligence (AI) is one of the most dynamic and disruptive fields of technology, constantly evolving and creating new possibilities for businesses and society. In this blogpost, we will explore some of the major trends that are shaping the AI landscape in 2023, based on the latest research and insights from various sources.

Rapid democratization of AI tech and research

One of the key drivers of AI innovation and adoption is the democratization of AI tools and platforms, which makes it easier for anyone to access, use, and create AI solutions. According to a recent Forbes article1, an ever-growing number of apps put AI functionality at the fingers of anyone, regardless of their level of technical skill. These apps range from simple predictive text suggestions to sophisticated data visualization and analysis tools. Moreover, there are also no-code and low-code platforms that enable users to create, test, and deploy AI-powered solutions using simple drag-and-drop or wizard-based interfaces. Examples include SwayAI, used to develop enterprise AI applications, and Akkio, which can create prediction and decision-making tools1.

The democratization of AI also extends to the research domain, where open-source frameworks, datasets, and models are widely available and shared among the AI community. For instance, Hugging Face is a platform that hosts thousands of pre-trained natural language processing (NLP) models that can be easily downloaded and fine-tuned for various tasks. Similarly, TensorFlow Hub is a repository of reusable machine learning components that can be integrated into different applications. These resources lower the barriers to entry for AI research and development, and foster collaboration and innovation.

Generative AI taking it up a notch

Another major trend in AI is the emergence and growth of generative AI, which refers to the ability of AI systems to generate novel and realistic content, such as images, text, audio, or video. Generative AI is powered by advanced deep learning techniques, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers. These techniques enable AI systems to learn from large amounts of data and produce outputs that are indistinguishable from human-made ones.

Generative AI has many potential applications across various domains, such as entertainment, education, healthcare, marketing, and security. For example, generative AI can be used to create realistic virtual characters and environments for gaming and simulation, generate personalized learning content and feedback for students, synthesize medical images and diagnoses for diagnosis and treatment, create engaging and relevant ads and campaigns for customers, and enhance biometric authentication and verification systems.

According to a McKinsey Global Survey on the state of AI in 20232, generative AI is already widely used in at least one business function by one-third of the respondents. Moreover, 40 percent of the respondents say their organizations will increase their investment in AI overall because of advances in generative AI2. The survey also reveals that gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work.

Heightened AI industry regulation

As AI becomes more pervasive and powerful, it also raises various ethical, social, legal, and security challenges that need to be addressed by appropriate regulation. Some of the issues that arise from the use of AI include privacy breaches, bias and discrimination, accountability and liability, transparency and explainability, human dignity and autonomy, safety and reliability, and social impact.

In 2023, we can expect more efforts from governments, regulators, industry bodies, civil society organizations, and academia to establish standards, guidelines, frameworks, principles, and best practices for responsible and trustworthy AI. For instance, the European Commission has proposed a new regulation on artificial intelligence3, which aims to ensure that AI systems are aligned with EU values and fundamental rights. The regulation categorizes AI systems into four levels of risk: unacceptable risk (such as social scoring or mass surveillance), high risk (such as critical infrastructure or biometric identification), limited risk (such as chatbots or deepfakes), and minimal risk (such as spam filters or video games). Depending on the level of risk, different requirements apply for providers and users of AI systems3.

Similarly, the US Federal Trade Commission (FTC) has issued a blog post4 warning businesses against selling or using biased or deceptive AI. The FTC advises businesses to follow three basic principles: be transparent about how they use AI; ensure that their data sources are representative; and test their algorithms for accuracy and fairness4. The FTC also reminds businesses that they are subject to existing laws that prohibit unfair or deceptive practices or discrimination based on protected classes4.

Increased collaboration between humans and AI

The final trend that we will discuss is the increased collaboration between humans and AI systems, which can enhance the capabilities and outcomes of both parties. Human-AI collaboration can take various forms, such as human-in-the-loop, human-on-the-loop, or human-out-of-the-loop. These forms indicate the degree of human involvement and oversight in the AI system’s operation and decision making.

Human-AI collaboration can also be characterized by different modes of interaction, such as cooperation, coordination, or competition. These modes indicate the nature and goal of the interaction between humans and AI systems. For example, cooperation implies that humans and AI systems work together toward a common goal; coordination implies that humans and AI systems work independently but share information or resources; and competition implies that humans and AI systems work against each other to achieve opposing goals.

Human-AI collaboration can bring various benefits, such as improved efficiency, accuracy, creativity, and satisfaction. For example, human-AI collaboration can enable faster and better diagnosis and treatment in healthcare; more innovative and personalized learning in education; more effective and engaging communication in marketing; and more fun and immersive entertainment in gaming.

Conclusion AI is a rapidly evolving field that offers tremendous opportunities and challenges for businesses and society. In 2023, we can expect to see some of the major trends that are shaping the AI landscape, such as the democratization of AI tech and research; the emergence and growth of generative AI; the heightened regulation of the AI industry; the increased emphasis on explainable AI; and the enhanced collaboration between humans and AI. These trends will have significant implications for how we develop, use, and govern AI systems in the future.