Introduction: Rise of Generative AI in 2024
2023 was a milestone year for generative AI.
While OpenAI’s ChatGPT and Google’s Gemini largely dominated the tech headlines, numerous open-source generative AI models were readily breaking into the mainstream, like Meta’s LLaMA, Hugging Face’s Bloom, and Mosaic-ML’s MPT-Series.
This increasing competition in the genAI industry has one significant benefit – better features and price competitiveness for businesses looking to profit from adopting the latest generative AI trends.
But what do all of these different GenAI models have in common?
They were excellent at responding to user queries, could research and analyze information, provide analytics and trends, and generate multimodal content from a few prompts. This led to the widespread use of generative AI in industries as diverse as manufacturing, healthcare, insurance, banking, retail, and FMCG.
This has led to a staggering rise in generative AI’s valuation. Currently valued at $130.2 billion in late 2023, this market is projected to rise with a staggering CAGR of 37.3% from 2023 to 2030. By the end of the decade, it is anticipated to reach a colossal $1,811.8 billion.
But generative AI is yet to reach its full potential.
The growing demand for generative AI has increasingly shed more light on it’s carbon footprint.
Researchers at Hugging Face and Carnegie Mellon University recently found that generating images using a standard AI model takes nearly the same amount of energy as fully charging one’s smartphone. As more businesses rush to implement generative AI in their business frameworks, energy consumption will be a factor that the industry will be keen to contain.
As such, while 2023 may have been the year for generative AI to go mainstream, 2024 will be the year it perfects itself as the ideal technological companion to enterprises and businesses.
We are talking about generative AI trends such as enhanced voice capabilities for digital assistants, custom chatbots that interact with employees and clients, as well as full coding support with minimal human interaction required.
Without further ado, let’s dive into our top 5 predictions for generative AI trends for 2024 and explore how they will reshape industries and the global economy.
Current Trends in Generative AI (H2)
Table of Content
- Trend 1: Generative AI Will Become More Regulated Due to Global Policies and Laws
- Trend 2: New Developments in Multimodal Models and What We Can Expect
- Trend 3: Industry-Wide Transformations Achieved by Generative AI
- Trend 4: Generative AI Wearables and Robots Are On The Rise!
- Trend 5: There Will Be A Greater Demand for High Quality Training Data
- Generative AI – The Good, The Bad and The Ugly
Trend 1: Generative AI Will Become More Regulated Due to Global Policies and Laws
In 2023, global AI policy and regulation took center stage, significantly influencing the trajectory of generative AI in the year ahead.
The European Union played a leading role by adopting the AI Act in December. This legislation introduces binding regulations and standards to guide the responsible development of AI, with a particular focus on high-risk AI applications.
The EU has enforced stringent regulations on risky AI applications
Notably, it sought to prohibit certain “unacceptable” uses of AI, such as the deployment of facial recognition technology by law enforcement in public spaces.
These regulations will exert a considerable impact on AI developers and companies operating within the EU, which may prompt EU companies to work in other countries where the laws are less stringent.
On a similar note, but with far fewer restrictions, the White House in the United States issued an executive order aimed at advancing AI policy. It provided government agencies with the flexibility to tailor AI regulations to their respective sectors, fostering a more adaptable regulatory environment for AI technologies.
Lawsuits and Deepfakes – Who is Responsible for AI Content?
While the executive order in the US is significantly less restrictive than the AI Act of the European Union, that has not stopped a record-number of legal disputes in the US in 2023 between AI companies and a large number of publications, artists, and writers.
The publications and creators have alleged that AI companies such as OpenAI and Google have accessed their content without proper consent or compensation and used the content to train generative AI models.
Google and OpenAI have been charged with multiple lawsuits due to their use of online data
These allegations form the basis for one of the central issues present in the generative AI industry today – who owns the content created by generative AI, and how can one differentiate it from human-made content?
One noteworthy policy proposal gaining momentum to combat this very issue of ownership is the adoption of watermarks for AI-generated content.
These concealed markers in text and images enable computers to identify AI-generated content, facilitating the detection of plagiarism and combating disinformation.
This is especially important due to the widespread use of AI-generated deepfakes that are blurring the lines between reality and fiction. This may have terrible consequences for the US, owing to the upcoming presidential elections, where deepfake videos of politicians can be used to shift voter intent and spread misinformation and fake news.
As we venture into 2024, the ramifications of these policy changes and emerging AI-related threats will become increasingly evident as AI companies work hard to balance the potential of their generative AI models.
Read More – Best Generative AI Tools For Businesses in 2024
Trend 2: New Developments in Multimodal Models and What We Can Expect
Recent studies have showcased significant advancements in Large Language Models (LLMs), with a notable 15% improvement in their efficiency in natural language understanding tasks compared to their predecessors.
But that still does not solve the problem of unpredictable AI outputs that most generative AI models are guilty of.
To tackle the unpredictability of generative models, researchers and organizations are actively working on influencing their behavior positively. OpenAI, for example, employs reinforcement learning from human feedback to guide models like ChatGPT toward more desirable responses.
The AI lab Anthropic has demonstrated how simple natural language instructions can steer large language models to reduce toxicity in their outputs.
But perhaps the most interesting development to watch out for in LLM models in 2024 is the rise and success of multimodal models that can handle text, audio, and visual inputs and outputs.
This will be a game-changer for the industry as it will allow the generative AI models to engage in real-time voice conversations and carry out complex instructions from voice-based commands. This will also lead to more industry-based use cases of generative AI trends, as various industries, such as banking and financial services, can take advantage of multimodal models for customer service, policy drafting, and fraud detection.
But in order for this to happen, AI companies will need powerful and energy efficient AI chips to power their models. Fortunately for businesses, the AI chip industry has been capable of delivering high quality chips for generative AI models.
The Rising AI Chip Market Led by Nvidia
The rise of multimodal models would not have been possible without the rise of sufficiently powerful AI chips in the market.
These chips have found widespread use across the automotive, healthcare, defense, IT, and telecommunications sectors. The global AI chip market is projected to reach $83.25 billion by 2027, exhibiting a remarkable CAGR of 35% during 2019-2027.
Nvidia and Intel are amongst the key players globally in manufacturing AI chips
North America leads in this market, while Asia-Pacific emerges as the fastest-growing region. Nvidia leads the race as the most prominent AI chip manufacturer, with AMD, Intel, and IBM not too far behind.
AI chips’ advantages include super-high bandwidth memory, speedy computation, and parallel, faster processing. This makes it especially attractive for generative AI models whose performance is heavily dependent on their hardware.
This will enable generative AI to carry out resource-extensive tasks such as image processing and video generation on-the-go.
Read More – Google’s Gemini Pro vs. OpenAI’s GPT-4: A Detailed Review
Generative AI Can Now Create Stunning Videos!
Another hotly anticipated generative AI trend in 2024 is the rise of generative-AI powered videos.
GenAI startup Runaway’s Gen-2 model is already capable of creating stunning short videos based on user prompts.
There is already talk of large movie studios, such as Paramount and Disney, adopting generative AI for video production and animations. Similarly, Google’s new VideoPoetAI is able to generate consistent motion across videos based on the recent examples shared by researchers online.
The use cases of generative AI-powered video are numerous. Across industries, such as healthcare and manufacturing, businesses can use these new video capabilities to create customized animations related to safety procedures and instructions and showcase digital processes.
Video GPT has revolutionized video creation, enabling high-quality outputs from text prompts and enhancing creativity in media.
Trend 3: Industry-Wide Transformations Achieved by Generative AI
In 2024, GenAI will significantly transform mainstream industries, as highlighted in PwC’s 2024 AI Business Predictions.
With 73% of US companies already incorporating AI in some business areas, generative AI remains the most popular AI technology for 2024 due to its ease of use and benefits. This technology is not just automating tasks; it’s fundamentally altering business processes, increasing productivity, and leading automation processes.
AI and Machine Learning Are Transforming Healthcare
AI’s impact on healthcare has been very impactful, spanning treatment, research, drug discovery, and diagnosis
Gartner reports that around half of US-based healthcare providers plan to deploy AI-assisted tools like Robotic Process Automation (RPA) in their facilities by the end of 2023. AI is also assisting in diagnosis, with 38% of health providers using computer systems for this purpose.
AI’s revolution in drug discovery, however, is what’s really grabbing the headlines. It is set to cross $4 billion by 2027 at a remarkable CAGR of 45.7, with researchers already claiming that enhanced machine learning and AI-designed customized drugs can save billions of dollars and decades of research for pharmaceutical companies.
In healthcare, radiology is another vertical that is expected to gain the most in 2024 from recent machine learning and generative AI trends.
The Association of American Medical Colleges (AAMC) anticipates a potential deficit of up to 41,900 radiologists in the United States. This is largely due to burnout (noted by 60 percent of surveyors) from excessive bureaucratic tasks.
Generative AI can help reduce radiologist burnout by assisting in tasks like image reconstruction, denoising, and artifact reduction, yielding clearer, more accurate diagnostics.
Similarly, the latest generative AI trends in healthcare are expected to help in creating comprehensive medical histories for cancer patients, helping create personalized cancer treatment plans for patients. This is achieved by utilizing machine learning and generative AI to pinpoint pertinent data, anomalies, and patterns within patient profiles.
AI-Powered Automotive Market Size Expands
The self-driving car market is on a trajectory from 20.3 million in 2021 to a staggering 13.7 billion by 2030, with a projected 10% of vehicles being driverless by 2030. Fully automated cars are expected to contribute significantly, amounting to $13.7 billion by 2030, with robo-taxis being a prominent use case.
Source: Wayve
Wayve, Waabo, and Ghost, among others, are the startups at the forefront of a revolutionary approach to self-driving AI.
They employ a single, comprehensive model to oversee vehicle control instead of relying on multiple smaller models for distinct driving functions. This has allowed them to perform on nearly the same levels as self-driving giants like Cruise and Waymo.
Generative AI models, when used in conjunction with self-driving cars, can result in fully autonomous cars that respond, interact, and learn from human inputs and voice commands.
Furthermore, innovative research like the one published as a paper in Communications Engineering has led to the creation of an ultra-high speed signal processor that can analyze up to 400,000 real time video images. This has incredible potential to further increase the efficiency and safety of AI-powered self-driving vehicles.
Generative AI’s Impact on Various Industry Verticals
According to Accenture, the manufacturing industry is expected to gain a remarkable $3.78 trillion from AI by 2035. Generative AI is expected to create design blueprints, lead predictive analytics, and drive the Industry 4.0 vision for the manufacturing industry.
The banking industry is also set to benefit, with AI projected to enhance revenue by $1 billion in the next three years.
Some of the prominent generative AI trends and use cases in banking include fraud detection, content generation for banking schemes and policies, as well as customized chatbots to answer customer queries.
Similarly, the telecommunications AI market, valued at nearly $2.5 billion in 2022, is growing at a rapid CAGR of 46.8%, with telecom companies hoping to release their generative AI-powered digital assistants in the coming years.
Trend 4: Generative AI Wearables and Robots Are On The Rise!
Wearable AI Market and the Rising Demand for AI Assistants
One of the most eagerly anticipated generative AI trends for 2024 is Wearable AI, a term that describes AI-based gadgets worn on the human body. Products like Alexa, Apple Watch, and Fitbits have already captured market share in this vertical.
According to Global Market Insights, the wearable AI market is expected to reach an impressive $180 billion by 2025. The healthcare industry had the most to gain from this surge, as digital healthcare is gradually adopting AI-wearable technology to better understand patients and their symptoms.
Source: Humane
At the forefront is Humane’s AI Pin, a $699 device that reimagines wearable interfaces. It uses a Snapdragon processor, voice control, gestures, and a camera with a built-in projector.
The AI Pin is a direct line to generative AI functionalities, especially OpenAI’s GPT-4, offering a range of features from voice messaging to real-time translation. This device is a revolutionary step towards more natural, intuitive interactions with AI.
But there’s more to discuss. The wearables sector in 2024 will be buzzing with AI-powered smart glasses from tech giants like Meta, Google, and Microsoft.
These companies are exploring wearables that understand and respond intelligently to the environment. For instance, Snapchat’s Spectacles are venturing into new realms with ChatGPT’s object-recognition software, hinting at a future where wearables are not just informative but contextually intelligent.
Similarly, Meta’s latest iteration of Ray-Ban Smart Glasses, featuring live streaming and AI assistant functionalities, exemplifies the blurring of lines between the digital and physical worlds.
On a similar note, the adoption of digital voice assistants is surging, with 8.4 billion in use globally from 2019 to 2024. While a majority of these are for personal use, industry-focused digital assistants (both voice and text) are already being produced for customer service and employee welfare across industries.
Multifunctional Robots That Adapt To Real-Time Tasks
Robots and human interaction are reaching an all-time high with the development of new self-learning AI-powered robotic technologies
A revolutionary new open-source system called Dobb-E is changing the robotics industry by teaching robots basic household tasks within 20 minutes.
Trained on data from real homes, Dobb-E empowers you to guide your robot through simple actions like opening an air fryer, closing a door, or even straightening a cushion. This breakthrough tackles a major roadblock in robotics: the lack of diverse training data.
No longer confined to pre-programmed routines, robots equipped with Dobb-E can adapt and learn alongside us, paving the way for truly helpful assistants. As it has been with the recent generative AI trends, these robots can communicate with humans in real-time and convey information through genAI interfaces.
Manufacturing and logistics companies can especially consider using such robots to aid in factories and shipping facilities, ensuring there are no safety hazards for human workers.
DeepMind’s Robotcat and RT-X Are Not Far Behind
However, Dobb-E, built in partnership with NYU researchers and Meta, isn’t the only player in the AI arms race for versatile robots. In June 2023, DeepMind released Robocat, a self-learning marvel that masters various robot arms through trial and error.
Soon later, in October, DeepMind followed up with the release of RT-X and a massive training data set, a joint effort by DeepMind and 33 university labs. And it’s not just DeepMind; leading research teams like RAIL (Robotic Artificial Intelligence and Learning) at Berkeley are also pushing the boundaries of adaptability by working in a similar direction.
This surge in open-source data and general-purpose AI models unlocks exciting possibilities for robots that can learn on the fly, adapt to diverse environments, and become true partners in our daily lives.
However, a consistent problem in building these robotic and AI models has been a lack of data. Generative AI requires truly large datasets of text and images in order to fine-tune itself. The question, therefore, is do we have enough data to train our AI models?
Trend 5: There Will Be A Greater Demand for High Quality Training Data
While generative AI models have been largely successful in most areas, they have occasionally exhibited tendencies to hallucinate (AI models tend to fabricate information or content) and have shown concerning biases, particularly regarding gender and ethnicity.
Moreover, different language models have been found to produce text with varying political biases, sparking ethical concerns.
But these concerns can be largely connected to the training data that was used to train the LLM models.
The stable diffusion algorithm, which powers numerous AI image-generation applications like DALL-E, Lensa, and Midjourney, underwent training using the extensive LIAON-5B dataset. This dataset contained a staggering 5.8 billion image-text pairs!
When an algorithm is trained with insufficient data, it tends to generate inaccurate or low-quality results. This is why AI companies are always on the lookout for high quality data that they can use to train their AI models.
Businesses are increasingly realizing that the quality and consistency of their training data can make or break their generative AI models. As such, 2024 will see a far greater demand for quality data as more businesses create customized generative AI models for their business use cases.
High-Quality Data is Running Out – Can Synthetic Data be the Answer?
According to a study conducted by researchers at the AI forecasting firm Epoch AI, there is a looming possibility that AI companies might exhaust their reservoirs of high-quality textual training data as early as 2026.
Similarly, they also projected that low-quality language data would be depleted sometime between 2030 and 2050, while low-quality image data might run out between 2030 and 2060.
This generative AI trend will dominate much of 2024, with businesses focused on coming up with alternative sources of high-quality data for training generative AI models
One theory to combat this problem is using existing LLM models to create synthetic data that newer LLM models can train on. While these are still early days to test the large-scale efficiency of AI training other AI models, the possibility remains a useful one.
Synthetic data is becoming an increasingly viable option for data-starved companies that need large datasets to train their generative AI models
Numerous projects are already utilizing synthetic content, often sourced from data-generating services like Mostly AI. This practice is expected to become increasingly prevalent as high quality data online is exhausted.
Some AI companies, however, are considering a unique stance of digitizing pre-internet content and training their AI models on it. News Corp, a major news content owner with much of its content restricted behind paywalls, recently revealed negotiations with AI developers for content agreements.
These agreements may compel AI companies to pay for their training data, shifting away from the historical practice of scraping data freely from the internet.
This is a trend that may become more prevalent in 2024 as more publishing companies file legal cases against AI companies for unauthorized use of their content.
Generative AI – The Good, The Bad and The Ugly
With all the generative trends we have discussed so far, the technology seems to be on the right track. While 2023 may have been a whirlwind of various technological breakthroughs, 2024 will be the year of course-corrections and ethically working with generative AI.
An optimistic take would be that the technology is becoming better with every passing year. As more awareness and regulations come in, AI companies are being held accountable for the outputs of their models.
This ensures businesses follow ethical and compliant methods of working with their generative AI models.
But a pessimistic perspective on generative AI’s relatively short span of success would be that it’s moving too fast for humans and governments to catch up.
As Stephen Hawking famously said, “The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate.”
While this may seem far-fetched now, AI-led job loss is already an ugly reality for thousands of employees across the globe. Businesses are expected to spend a significant portion of 2024 trying to maintain the delicate balance between innovation and ethical compliance for generative AI implementation.
But as is the case with all emerging technologies, challenges are often followed by opportunities. And generative AI has enough opportunities for global businesses in 2024.
