AI Trends of Next Two Years

The 12 AI Trends That Will Dominate the next two years.

In this article, I’ll be discussing the top 12 AI trends that will dominate 2024 and 2025. These are not just important they're revolutionary. From generative tools that can create art, movies and music to powerful leaps in Computing hardware, talent, and open source AI LLM’s growth, the landscape of the next two years is poised to alter the way we live, work and interact.

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Let’s begin with Number 1: Skills enhancements.

Skill enhancement is paramount in the AI age. A prevailing sentiment is that AI itself may not entirely eliminate jobs, but individuals adept at leveraging AI capabilities could undoubtedly outperform those who fail to adapt, thus gaining a substantial competitive advantage.

It's crucial to comprehend how generative AI technologies can influence your specific career path and to cultivate the expertise necessary for selecting and utilizing the appropriate AI tools for various tasks. Forward-thinking organizations with a long-term vision will undoubtedly take proactive measures to facilitate this transition for their workforce by integrating generative AI into their educational and training initiatives.

If your current employer isn't making such efforts, the good news is that a wealth of educational resources is readily available online, many of which are complementary or even free. By immersing yourself in these resources, you'll not only enhance your skillset but also position yourself as a more attractive candidate for employment with organizations that embrace technological advancements and prioritize continuous learning and development.

Furthermore, actively seeking opportunities to collaborate with Gen-AI systems can provide invaluable hands-on experience, allowing you to develop a deeper understanding of their capabilities, limitations, and ethical considerations. This practical knowledge, coupled with a strong theoretical foundation, will enable you to effectively leverage AI as a powerful tool, rather than perceiving it as a threat to your career.

Number 2: The Rise of No-Code and Low-Code Developers.

According to a recent forecast by Gartner, a staggering 80 percent of Software and related tools, products and services could be developed by non-IT professionals starting in 2024.

The proliferation of ‘low-code’ and ‘no-code’ platforms has significantly lowered the barrier to entry into the technology development arena, enabling a democratization of tech innovation.

This trend is further propelled by advancements in generative AI technologies such as GPT4, Claude 3, Gemini 1.5, Mixtral 8x7B, among others which facilitate quick and efficient development of software applications even by those who lack specialized technical skills. Rather than signaling the end of traditional IT roles, this shift is expected to open up a plethora of opportunities for a more diverse group of innovators, including business technologists, data scientists, and citizen developers.

These individuals, although not formally trained in tech, are increasingly finding themselves involved in the customization or creation of tech solutions in their day-to-day work. For instance, a marketing professional might leverage a no-code platform like Bubble.io or Appian to build a custom web application for a promotional campaign. Similarly, a financial analyst could use tools like Tableau or Microsoft Power BI to create interactive data visualizations and dashboards, without writing a single line of code.

Furthermore, the rise of generative AI assistants like GitHub Copilot and Amazon CodeWhisperer is empowering even traditional software developers to enhance their productivity and efficiency by automating tedious coding tasks, suggesting code snippets, and generating boilerplate code.

This democratization of technology development is not only fostering innovation across various industries but also enabling organizations to rapidly adapt to changing market conditions and customer demands by leveraging the collective creativity and domain expertise of their diverse workforce.

Number 3: The AI Regulatory Landscape

The Emerging AI Regulatory Landscape will start reshaping how AI is developed and Deployed. While lawmakers have historically struggled to keep pace with the rapid evolution of technology, the transformative power of artificial intelligence is now capturing their attention more seriously. As AI systems become increasingly sophisticated and pervasive, concerns over potential risks such as job displacement, privacy violations, algorithmic bias, and misuse of AI for nefarious purposes are prompting governments worldwide to take action.

China has already enacted specific regulations, such as laws that outlaw the creation of deepfakes without explicit consent. Meanwhile, other major regions, including the European Union, the United States, and India, are actively working on formulating comprehensive regulatory frameworks to govern the development and use of AI technologies.

The United Kingdom is currently considering a legislative proposal, the Artificial Intelligence Act, anticipated to be enacted in 2024. This proposed legislation aims to establish a risk-based approach to AI regulation, with stricter rules for high-risk applications like recruitment, credit scoring, and critical infrastructure management.

Similarly, the European Union is expected to have a comprehensive AI Act ready by early 2025. The proposed EU AI Act seeks to create a harmonized regulatory framework across member states, categorizing AI systems based on their perceived risk levels and imposing varying degrees of compliance obligations accordingly.

Each of these legal initiatives aims to strike a delicate balance between safeguarding the public from potential AI-induced risks while still fostering an environment conducive to technological innovation and business growth. Conversations about how to delineate these boundaries and address complex ethical considerations surrounding AI will likely be a significant component of political discussions throughout the year 2024 and beyond.

As the AI regulatory landscape continues to evolve, businesses and organizations will need to closely monitor these developments and adapt their AI strategies and practices accordingly. Failure to comply with emerging regulations could result in hefty fines, legal liabilities, and reputational damage, underscoring the importance of responsible AI development and deployment.

Number 4: Incorporating AI into Customer Service.

In the realm of customer service, the operational landscape is often populated by routine, repetitive actions that are occasionally interrupted by complex and urgent situations requiring specialized attention. This type of setting is particularly well-suited for the introduction of artificial intelligence technologies aimed at automating standard procedures.

By leveraging AI, human employees are freed up to focus on scenarios that particularly demand the human element. AI can effectively manage the first layer of customer engagement through phone calls or chat interfaces, devise individualized solutions for commonly encountered issues, and also compile comprehensive reports and summaries detailing various customer interactions.

For instance, AI-powered virtual assistants can handle routine inquiries about product information, order tracking, or account details, while escalating more complex cases to human agents. Conversational AI chatbots can provide 24/7 support, reducing wait times and improving customer satisfaction. Additionally, AI-driven sentiment analysis tools can analyze customer interactions and identify potential areas of concern or opportunities for improvement.

According to recent research by Boston Consulting Group, a significant 95 percent of decision-makers in the customer service industry anticipate that within the next three years, their customer interactions will likely be facilitated, at least in part, by AI-driven bots or virtual assistants.

This trend is further exemplified by industry leaders such as Amazon, which has integrated its Alexa virtual assistant into its customer service operations, enabling customers to track orders, update payment methods, and even initiate returns using voice commands. Similarly, banks like JPMorgan Chase and Capital One have deployed AI-powered chatbots to handle routine banking inquiries and transactions.

As AI technologies continue to evolve and become more sophisticated, their integration into customer service operations will become increasingly seamless, enabling organizations to deliver more personalized, efficient, and cost-effective support experiences.

Number 5: Transcending Text

The Next Wave of Multimodal Generative AI for Creative Visuals, Animation, Movies, and Music.

The chatbots and image creation tools that captivated and occasionally unsettled us throughout 2023 will soon be overshadowed by the subsequent wave of multimodal generative AI technologies, capable of producing sophisticated creative outputs across various modalities.

Regenerative tools for video and music production are already emerging, and these tools will continue to advance, becoming more potent and easier to navigate as the technology matures. We will witness their integration into platforms for creativity and work efficiency, much like how ChatGPT has been incorporated this year. For instance, companies like Runway, Pika Labs, OpenAI, Google and Anthropic are actively developing AI models that can generate high-quality videos and animation sequences from textual descriptions or basic sketches.

Innovative applications in the realm of generative design and voice modulation software are also on the horizon. Imagine being able to create a realistic 3D model of a product or architectural design simply by describing it in natural language. Or envision an AI-powered voice cloning tool that can mimic the voice and speech patterns of any individual, opening up new possibilities for audio production, accessibility, and language learning.

However, as these capabilities become more widespread, the ability to distinguish between what's genuine and what's machine-generated will soon become a crucial skill to have in our analytical arsenal. Techniques such as digital watermarking, provenance tracking, and AI-assisted authentication will likely emerge to combat the potential misuse of these technologies for deepfakes or other nefarious purposes.

Moreover, the ethical considerations surrounding the ownership and attribution of AI-generated creative works will need to be addressed. Debates around intellectual property rights, fair compensation for human creators, and the potential impact on various creative industries will undoubtedly intensify as these multimodal generative AI tools become more accessible and influential.

Number 6: Rise of AI Ethicists

The Emergence and Rapid Advancements of AI come with Major Transformative Powers. This transformative power of AI is so immense that it becomes crucial to ensure its ethical development and application, aiming to reduce the scope for potential harm.

We're already acquainted with some of the high-risk elements, such as inherent biases, lack of transparency, the risk of eliminating human employment opportunities, and the ever-present uncertainty that it will always remain within control. In the upcoming year of 2024, there will be a sustained emphasis on addressing these concerns and maintaining alertness for new challenges that may arise.

The role of AI ethicists will experience a surge in necessity as enterprises strive to showcase their commitment to ethical protocols and the implementation of effective safety measures. However, one major concern that warrants attention is how AI will impact third-world nations and cultures.

This concern arises from the fact that the overwhelming majority of training data and corpora in state-of-the-art large language models (LLMs), including multimodal LLMs, are in English and Chinese languages. How will generative AI impact those cultures, languages, and customs? Will they disappear and be replaced by a 'cookie-cutter' language and culture? Many nations, such as Japan, Norway, Sweden, Denmark, Italy, and Spain, have dwindling birth rates. What will happen to these nations and cultures if LLMs sidestep them?

Furthermore, many third-world nations are not included in the fast-paced digital revolution. The digital divide is worsening, and how will generative AI impact these nations and cultures? There is very little debate on this subject, and ethics, values, and norms are currently very 'Western-biased.' 2024 could be the year when these discussions open up more broadly.

Addressing these concerns will require a concerted effort from the AI community, policymakers, and cultural organizations. Initiatives to promote language diversity in AI training data, support for local language models, and measures to bridge the digital divide will be crucial. Additionally, ethical frameworks and guidelines must be developed with a more inclusive and global perspective, considering the unique challenges and cultural nuances of different regions.

Number 7: Enhancing Workplace Abilities:

Gaining insights into how we can elevate our human cognitive abilities and practical skills to perform our occupational tasks more swiftly, proficiently, and with greater safety will emerge as a critical professional asset by 2024. For legal professionals, this augmented capability can facilitate rapid analysis of applicable case laws or hasten the creation of legal documents, such as contracts, briefs, and pleadings. Medical personnel will find it beneficial for expediting the compilation of patient records, interpreting medical imaging, or even assisting with diagnosis and treatment planning.

Software developers can employ these enhanced skills to accelerate the coding process and concurrently make the testing and debugging phases more efficient, potentially reducing the time-to-market for new software products. For academics and students, it offers a way to systematically organize study notes, facilitate research efforts, and even generate drafts of academic papers or dissertations.

For those currently without employment, it becomes a helpful tool for crafting an effective resume, composing persuasive job application letters, and preparing for interviews by simulating potential questions and practicing responses. Additionally, in fields like marketing and advertising, these capabilities can aid in generating compelling copy, crafting targeted messaging, and analyzing consumer data for insights.

Moreover, in the realm of creative industries, such as film, music, and design, enhanced cognitive abilities can foster ideation, facilitate storyboarding, and even assist in the generation of preliminary drafts or concepts. The possibilities are vast, and those who embrace these augmented capabilities will undoubtedly gain a competitive edge in their respective fields, enabling them to work more efficiently, accurately, and innovatively.

Number 8: New AI Career Paths

New Career Paths in AI will emerge for those eager to participate in the AI Sector. Those who have no formal education or background in AI or Computer Science will find new opportunities with Generative-AI.

There's encouraging news for this group. A plethora of new job positions will be making their appearance in 2024, beyond the fundamental roles of engineers and technicians who construct these systems. We will also witness the emergence of new positions such as prompt engineers, who formulate the prompts and directives that guide AI software behavior, and AI managers, responsible for overseeing and coordinating teams of virtual assistants or AI-powered systems.

Additionally, there will be a rising demand for AI project coordinators, responsible for managing the integration of AI solutions into existing workflows and ensuring seamless collaboration between human and artificial intelligence components. AI training specialists, tasked with curating and maintaining high-quality training data for machine learning models, will also be in high demand, as will AI ethics advisors, who will play a crucial role in ensuring the responsible development and deployment of AI technologies.

Conversely, for those who do possess technical skills, a range of novel employment opportunities awaits, particularly in specialized areas like AI engineering, where professionals will be responsible for designing, developing, and optimizing AI algorithms and architectures. DevOps management for AI systems, involving the continuous integration, deployment, and monitoring of AI applications, will also emerge as a critical role.

Furthermore, the field of AI cybersecurity will gain prominence, with professionals tasked with identifying and mitigating potential vulnerabilities in AI systems, as well as developing robust defense mechanisms against adversarial attacks and data poisoning attempts.

Number 9: The convergence of AI, blockchain, and metaverse technologies.

2024 will be the year that these three powerful technologies fuse together. This powerful combination has the potential to enable transparent and secure global commerce, as well as a thriving user-empowered creative economy. The fusion of AI, blockchain, and metaverse technologies in 2024 will pave the way for a transparent and secure ecosystem for commerce and a user-driven creative economy. AI, with its powerful language models and generative capabilities, will empower users to create high-quality content, including virtual worlds, digital twins, stories, music, and movies, which can be showcased on the metaverse. Blockchain will provide a trusted and immutable ledger for storing fingerprints of AI models, ensuring transparency, accountability, and compliance with regulations. It will also enable tokenization, secure ownership, and monetization of user-generated content through non-fungible tokens (NFTs) and decentralized payment rails. The metaverse will serve as a virtual marketplace for this AI-driven, user-created content, fostering novel business models and experiences. Together, these technologies will facilitate secure transactions, protect intellectual property rights, and enable decentralized governance and identity management, ultimately empowering users and fostering a thriving, transparent, and self-regulated digital economy.

Number 10: AI’s dominating role in Cybersecurity

In 2024, AI will play a crucial role in augmenting the efforts of human cybersecurity teams as they strive to keep pace with the ever-expanding and dynamic attack surface. With the increasing complexity and volume of cyber threats, traditional human-centric approaches are no longer sufficient, and AI provides much-needed assistance in analysis, threat identification, and incident response.

AI systems will be leveraged to identify and prioritize risks, instantly detect malware on networks, guide incident response efforts, and detect intrusions before they occur. This human-machine partnership will push the boundaries of cybersecurity knowledge, enabling teams to more effectively reduce breach risks and improve overall security posture.

Specifically, AI will be used to rapidly analyze millions of events and identify various types of threats, ranging from malware exploiting zero-day vulnerabilities to risky behavior that could lead to phishing attacks or malicious code downloads. These self-learning systems will continuously gather data from enterprise information systems, analyze it, and correlate patterns across billions of signals relevant to the attack surface, enabling the identification of new and evolving attack vectors.

As cyber-attacks continue to evolve, and the proliferation of devices escalates, managing cybersecurity posture is no longer a human-scale problem. AI-based tools will become increasingly essential for cybersecurity teams to reduce breach risks by providing real-time monitoring, improving threat detection, and prompting action to stay ahead of malicious actors. Consequently, AI-based cybersecurity posture management systems are expected to gain widespread adoption as organizations recognize their ability to automate threat detection and predict cyberattacks with unmatched precision, risk and improve their security posture efficiently and effectively.

Number 11: Accelerated Research in Quantum-Enhanced AI

While it may not have an immediate influence on everyone, Quantum Computing is gradually revealing its value in the AI landscape, distinguished for its ability to rapidly process specific tasks that are computationally intensive. Quantum Computing utilizes qubits, which intriguingly can exist in multiple states simultaneously, unlike traditional computer bits that are restricted to being either a one or a zero.

This distinct capability substantially amplifies their efficiency when compared to conventional algorithms, particularly for complex challenges like optimization, where the objective is to ascertain the most effective combinations from a wide range of variables – something commonly addressed in machine learning. As we navigate through 2024, we anticipate further strides in incorporating Quantum Computing to fuel increasingly intricate and larger-scale neural network designs and algorithms.

For instance, quantum algorithms could revolutionize fields like drug discovery and materials science, where identifying optimal molecular structures and configurations is a computationally demanding task. By leveraging quantum parallelism, these algorithms can explore vast combinatorial spaces more efficiently, potentially leading to breakthroughs in the development of new medicines or advanced materials.

Furthermore, quantum computing could enhance the training and inference processes of massive language models (LLMs) and multimodal AI systems. The ability to perform certain calculations exponentially faster than classical computers could enable the training of even larger and more sophisticated models, potentially leading to improved performance in tasks like natural language processing, image recognition, and multimodal understanding.

It will be interesting to see how Quantum Computing's parallelism potential can give rise to multimodal, massive LLMs that can process and generate content across various modalities, such as text, images, audio, and video, with unprecedented accuracy and efficiency.

However, it's important to note that the integration of quantum computing and AI is still in its nascent stages, and significant challenges, such as noise and error correction, need to be addressed before we can fully realize the potential of quantum-enhanced AI.

Number 12: Early Sparks of Artificial General Intelligence (or AGI)

AGI is a term used to describe an AI when it develops the ability to understand and reason at the same level as a human being. In theory, AGI is anticipated to understand, reason, and perform intellectual tasks comparable to human abilities. In other words, an AGI would be able to perform any intellectual task that a human could.

The Quest for AGI is one of the most challenging and ambitious goals in the field of AI. While we have witnessed remarkable advancements in narrow AI systems, achieving AGI requires a confluence of several key developments and breakthroughs across various domains of AI technology.

The next two years, that is, 2024 and 2025 may actually provide the initial Sparks for AGI. This assumption is grounded by some recent key developments. Here are these developments that could potentially lay the foundation for AGI:

1. Mixture of Experts (M O E) architectures: M O E models, like those used in GPT4 and Mixtral-8x7B allow for the specialization of different components (experts) within a single model, enabling more efficient handling of diverse tasks and modalities. This could be a step towards AGI systems that can seamlessly integrate and leverage multiple specialized capabilities.

2. Q-Star and A-Star Algorithms: These are advanced AI algorithms that could potentially contribute to the development of AGI. Q-Star is a reinforcement learning algorithm that can perform math, while A-Star is a pathfinding and graph traversal algorithm for pathfinding and route planning. Both could potentially be integrated into larger architectures and frameworks that combine various AI techniques in the Quest for AGI.

3. Autonomous AI Agents: The development of AI systems that can autonomously learn, reason, and act in complex environments without constant human supervision is a crucial step towards AGI. Advances in reinforcement learning, hierarchical reinforcement learning, and multi-agent systems could contribute to this goal.

4. Larger Context Windows: Current language models are limited by their finite context window size, which restricts their ability to capture and maintain long-term dependencies and coherence. Innovations that allow for larger context windows or alternative architectures that can effectively model long-range dependencies could enhance the reasoning capabilities of AI systems.

5. Synthetic Training Data: As the demand for diverse and high-quality training data increases, the ability to generate synthetic data using generative models could provide a scalable solution for training more robust and adaptable AI systems.

6. Multimodal Capabilities: AGI systems would need to seamlessly integrate and reason across multiple modalities, such as text, images, audio, and video. Advancements in multimodal AI models, like those being developed by companies like Anthropic and Google, could be crucial building blocks for AGI.

7. Advanced Computer Vision and Robotics: Integrating advanced computer vision capabilities, sensory perception, and 3D reasoning with language models could pave the way for embodied AGI systems that can effectively interact with and navigate the physical world.

While these individual developments are significant in their own right, the true "sparks" of AGI may lie in the synergistic combination of these technologies. For instance, the integration of multimodal language models with advanced robotics and computer vision capabilities could potentially lead to embodied AGI systems with human-like intellectual and physical capabilities.

However, it's important to note that achieving AGI is not just a technological challenge but also involves addressing complex philosophical, ethical, and societal considerations. Issues such as machine consciousness, value alignment, and the potential risks and implications of AGI must be carefully examined and addressed alongside technological advancements.

While the path to AGI remains uncertain, the continued progress in these key areas, combined with interdisciplinary collaboration and responsible development, could bring us closer to achieving AGI.

In conclusion, in the next two years, we may reach an inflection point and ignite the engine of innovation. We may see the positive feedback loop in AI technology mature and lead to exponential growth. AI will start to accelerate it’s own innovations, inventions and further development. We’ll see a lot of new AI created by existing AI forming an ever increasing larger and larger circle of advanced AI innovations and inventions.