This article delves into the landscape of artificial intelligence (AI) as a whole, exploring the origin, transformative trends, and emerging technologies shaping this dynamic field, as well as its connection to the Kondratieff Wave theory and its relevance to technological development. The research questions driving this inquiry encompass the brief history of AI (artificial intelligence), the identification of predominant trends, the assessment of technological advancements, and the implications of these developments on various sectors in 2024.

THE HISTORICAL PATH OF ARTIFICIAL INTELLIGENCE

The field of study of artificial intelligence encompasses the development of computers, computer software, and the algorithms that make them capable of intelligent behavior. The concept of artificial intelligence has been around for centuries, with early ideas about thinking machines appearing in mythology, fiction, and philosophy. However, the scientific pursuit of artificial intelligence as we know it began in the mid-20th century.

Here is a brief journey down the memory lane of AI:

1940s–1950s: Foundations During this epoch, the groundwork for AI was laid by mathematicians and philosophers who pondered the nature of human thought and the potential for machines to replicate it. Alan Turing, a British mathematician, developed the Turing Test in the 1950s to determine if a machine could exhibit intelligent behavior indistinguishable from that of a human. The design of the Universal Turing Machine laid the groundwork for all modern computers.

1956: The Birth of AI: The term “artificial intelligence” was first coined by John McCarthy, who, along with Marvin Minsky, Claude Shannon, and Nathaniel Rochester, organized the Dartmouth Conference in 1956. This conference is often considered the official birth of the field.

1960s: Early Enthusiasm During this period, researchers made significant advances in developing AI programs that could solve algebra problems, prove theorems in geometry, and understand natural language to some extent. An example of an AI program developed during this era was “General Problem Solver” (GPS), developed by Allen Newell and Herbert A. Simon at the RAND Corporation and Carnegie Mellon University. It was among the earliest attempts to create a universal problem-solving mechanism that could handle various tasks.

1970s: AI Winter: The limitations of early AI programs became apparent, leading to reduced funding and interest in the field. This period is often referred to as the “AI Winter,” a term used to describe times when hype surrounding AI led to disappointment and subsequent funding cuts.

1980s: Resurgence and Expert Systems: AI experienced a resurgence in the 1980s, thanks in part to the development of expert systems, which are programs that mimic the decision-making abilities of a human expert. The Japanese government’s Fifth Generation Computer Systems project spurred significant global interest and investment in AI.

1990s: The Rise of Machine Learning: The focus of AI research shifted towards machine learning, with an emphasis on developing algorithms that could learn from data. This shift was driven by the availability of more data and increases in computational power.

2000s: Big Data and Advanced Algorithms: The explosion of the internet and the digitization of information led to the Big Data era. AI systems, particularly those using deep learning, began to make significant strides in image and speech recognition.

2010s: AI Goes Mainstream: AI applications became part of everyday life. Virtual assistants, such as Siri and Alexa, autonomous vehicles, and recommendation systems are just a few examples. The victory of IBM’s Watson on the game show Jeopardy! and Google DeepMind’s AlphaGo defeating a world-champion Go player were symbolic milestones of AI’s capabilities.

2020s: Ethical and Societal Implications: As AI technology continues to advance, there is increasing focus on the ethical and societal implications of AI, including issues of privacy, bias, job displacement, and the need for regulation to ensure safe and beneficial outcomes.

TECHNOLOGICAL ADVANCEMENT AND THE KONDRATIEFF WAVE THEORY 

The Kondratieff Wave theory proposes long economic cycles (40–60 years) marked by technological revolutions driving growth. It illustrates how transformative technologies, like the Industrial Revolution or digital era, coincide with economic upswings, shaping industries and societies. This theory links economic phases to bursts of innovation, offering insights into when and how major technological shifts occur within the broader economic cycle.

The theory’s relevance to technological development lies in its recognition of long-term cycles that influence the trajectory of technological progress. It suggests that technological innovations tend to occur in clusters during specific phases of the economic cycle, driving both economic expansion and societal transformation.

Understanding these cycles can offer insights into the timing and nature of technological advancements, guiding predictions about when certain technologies might reach maturity or widespread adoption. It also emphasizes the interconnectedness between economic cycles, technological innovations, and societal progress, providing a framework to comprehend the evolution of technology within broader economic contexts.

CURRENT STATE OF AI IN 2023

Gen AI tools have exhibited fast growth in the recent period. A third of the respondents of a survey carried out by Mckinsey said their organizations already used gen AI tools regularly in a single business area, less than one year after the appearance of most such tools. We see here another example of the way AI has gone from “not our department” to “all hands on deck.” About 25 percent of C-Suite leaders are using gen AI tools in their jobs, and about 26 percent of respondents at organizations that use AI said it is already part of board-level discussions. Although, some other forty percent of respondents reported that their firms would be making more investments into general applications due to advances in generative AI.

The first companies that were able to integrate AI solutions were also the first ones willing to try out new possibilities offered by general artificial intelligence while a group addressed as high performing AI organizations are witnessing maximum benefits with their older artificial intelligence models compared to others as far as adoption of enhanced Artificial General Intelligence is concerned.

In the tech world, 2023 will perhaps be remembered as the year that generative AI went mainstream. From computer code, to artwork, to essays, generative AI systems can quickly create a range of content which, while not perfect, has become an essential tool in some industries and professions.

AI TRENDS AND PREDICTIONS FOR 2024

In 2024, the field of Artificial Intelligence will experience new shifts to higher planes of possibilities that will further disrupt the current structure and work culture of organizations and professionals across other industries.

Here are some predictions :

THE SHIFT OF WHITE COLLAR WORK

There will be an endemic upshot of companies that will start delivering some of the productivity gains we have been eagerly looking for. There are other groups of people who will be affected, especially those who have been largely spared by the computer revolution in the past three decades such as knowledge workers. The jobs of creative professionals, lawyers and finance professions among others will change significantly over this year. If this inevitable change is embraced, then definitely it should make jobs better and allow us to do things we could not have done before. However, automation can only go so far – perhaps because it simply augments, expands and extends rather than replace human capabilities. 

THE EVOLUTION OF LARGE LANGUAGE MODELS

The term “large language model” (and its abbreviation LLM) frequently used as a tag for “any advanced AI model” will evolve as AI becomes increasingly multi-modal, this term will become increasingly imprecise and unhelpful.

The emergence of multi-modal AI has been one of the defining themes in AI in 2023. Many of today’s leading generative AI models incorporate text, images, 3-D, audio, video, music, physical action, and more. They are far more than just language models.

In 2024, as models become increasingly multidimensional, so, too, will the terms that we use to describe them. An example is Vision-language models (VLM), a type of machine-learning model that can process both visual information and natural language. 

THE RAPID RISE OF PROACTIVE AI AGENTS

AI agents are advanced systems that exhibit autonomy, proactivity and the ability to act independently. In 2024, there is going to be a shift from Reactive AI to Proactive AI Agents.

For example, in an area of environmental monitoring, a proactive AI Agent can be seen at work; it could then undergo training in data collection and analysis, and proceed to initiate preventive measures in real-time when signs are detected that a fire is going to start. Another instance, a financial AI agent could actively manage an investment portfolio using adaptive strategies that react to changing market conditions in real time.

A combination of Agentic AI and Multi-modal AI will give birth to new opportunities. Previously, an application designed to identify the contents of an uploaded image would have required a custom trained Model as well as the skill-set for deploying the application, but with the aggregation of Agentic and Multi-Modal AI Models, the same set of tasks could be achieved with natural language prompting only.

OPEN SOURCE AI

Since 2020, there weren’t many open source generative models available, and when they were, they frequently performed worse than proprietary solutions like ChatGPT. But in 2023, the field got much bigger, with strong open-source competitors including Mistral AI’s Mixtral models and Meta’s Llama 2. By giving smaller, less resource-rich entities access to advanced AI models and technologies that were previously unattainable, this might change the dynamics of the AI ecosystem in 2024. 

This makes access simple and pretty democratic for all, and it’s excellent for research and experimentation, especially for enthusiasts and entry level professionals.

AI REGULATIONS

2024 is looking to be a landmark year for AI regulation given the ethics, security, and data privacy concerns; laws, policies, and industry frameworks are fast changing both domestically and internationally. In the upcoming year, organizations will need to remain aware and flexible because changing compliance regulations may have a big impact on global operations around the use and deployment of Artificial Intelligence.

Emphasis on how to manage the potential proliferation of the use of shadow AI by both technical and non-technical employees (use of AI within an organization without explicit approval or oversight from the IT department. ) within large organizations.
Shadow AI typically occurs when Employees leverage Artificial Intelligence for quick solutions to a problem without going through IT reviews or approval. The implication is the potential leak of a company’s trade secrets or information into a public-facing LLM (Large Language Model) which exposes sensitive information to third parties.

In 2024, Organizations will be faced with the need to include risk examination and risk assessment tools that identify potential threats posed to them through the adoption of AI within their structures.

The coming year promises a higher leap in innovation and adoption of Artificial Intelligence by more individuals, enthusiasts, professionals and organizations across industries. We will see more use cases emerge as AI systems become more Multi-Modal. At the center of this evolution will be a moving attempt to establish frameworks that will regulate the power and use of Artificial Intelligence across board.