This time last year, we engaged in some speculative ventures despite the stagnant economic environment, attempting to foresee what the future held.
Our predictions for 2023 encompassed the rise of multimodal chatbots, the competitive pressure faced by Big Tech from open-source startups (partially accurate as the open-source trend persisted, although companies like OpenAI and Google DeepMind remained prominent), and the transformative impact of AI on the pharmaceutical industry (still ongoing progress in AI-driven drug discovery, with the first AI-developed drugs yet to reach the market).
Fast forward to the present, and we are once again venturing into the realm of predictions.
While acknowledging the prevailing dominance of large language models, we anticipate a sharpening focus from regulators and stakeholders on the myriad challenges posed by AI, ranging from bias issues to ethical considerations, shaping the agenda for researchers, policymakers, and the general populace not just in 2024 but for the foreseeable future.
In addition to these broader trends, we have identified specific developments to monitor in 2024. Let’s delve into these insights and revisit our accuracy next year.
1. Tailored AI Experiences
Imagine having your personalized AI assistant! In the upcoming year, tech behemoths heavily invested in advanced AI technologies will face mounting pressure to demonstrate the commercial viability of their products. To achieve this, industry leaders like Google and OpenAI are pivoting towards democratizing AI through user-friendly platforms that empower individuals to customize robust language models and craft personalized chatbots tailored to their unique requirements—no coding expertise necessary. By launching web-based tools enabling users to become creators of AI software, these giants are democratizing the realm of conceptual AI development.
In 2024, conceptual AI is poised to transcend its niche appeal and become accessible to the average user, fostering a surge in experimentation with diverse AI applications. The latest AI models like GPT-4 and Gemini, boasting bidirectional capabilities encompassing text, images, and videos, hold the promise of unlocking a myriad of innovative applications. For instance, a real estate agent could seamlessly leverage AI to generate property descriptions by amalgamating text from previous listings, enhancing existing models, and incorporating multimedia elements with unparalleled ease.
Nevertheless, the efficacy of this democratization effort hinges on addressing inherent challenges. Language models often exhibit a propensity for generating erroneous content and are predisposed to biases. Moreover, the susceptibility of these models to intellectual property theft, especially in scenarios involving web-based interactions, remains a pressing concern that necessitates proactive mitigation strategies from technology firms to sustain user trust beyond the novelty phase.
Melissa Heikkilä
ENVATO | STEPHANIE ARNETT/MITTR
2. The Evolution of Relational AI towards Video
The rapid evolution of cutting-edge technologies often transitions from novelty to norm with astonishing speed. In 2022, the proliferation of realistic image-generating generative models captivated audiences worldwide, ushering in a new era of visual storytelling. Tools like OpenAI’s DALL-E, Stability AI’s Secure Diffusion, and Adobe Firefly inundated the internet with a captivating array of imagery, from surreal juxtapositions like the pope in Balenciaga to awe-inspiring artistic creations. However, this technological marvel also unveiled ethical quandaries, juxtaposing whimsical creations with instances of biased representations and counterfeit artistry.
The next frontier for innovation lies in text-to-video synthesis, heralding a paradigm shift in visual content creation. Anticipate a transformative wave sweeping across the landscape of multimedia content, redefining the boundaries of creativity and realism.
The potential of relational models in seamlessly blending multiple images to craft dynamic video sequences was initially marred by technical constraints, yielding sluggish and distorted outcomes. However, recent advancements have propelled this technology to new heights.
Companies like Runway, renowned for pioneering conceptual video models and co-developing Stable Diffusion, regularly introduce cutting-edge iterations of their tools. The latest offering, Gen-2, produces remarkably high-quality video snippets, rivaling the visual finesse of industry stalwarts like Pixar.
The burgeoning interest in relational AI extends beyond artistic endeavors, captivating the attention of major film studios like Paramount and Disney. Applications range from synchronizing lip movements with diverse language dubs to revolutionary storytelling formats. Noteworthy examples include the digitally rejuvenated portrayal of Harrison Ford in Indiana Jones and the Dial of Destiny in 2023, underscoring the transformative potential of AI in reshaping cinematic experiences.
Beyond the silver screen, the commercialization of deepfake technology for marketing and educational purposes is gaining traction. Companies like Synthesia offer tools capable of seamlessly transforming static performances into dynamic avatars capable of delivering scripted content, with a significant adoption rate among Fortune 100 enterprises.
As stakeholders navigate this burgeoning landscape, critical questions emerge regarding the ethical implications and artistic evolution catalyzed by these technological advancements. The creative landscape stands on the cusp of a profound metamorphosis, as the boundaries between reality and artistry blur in the realm of AI-driven content creation.
Will Douglas Sky
3. Proliferation of AI-Generated Election Propaganda
With a record voter turnout projected for the upcoming 2024 elections, the specter of AI-generated election propaganda and deepfakes looms large on the political horizon. Incidents of political candidates leveraging AI-generated content to discredit adversaries have already surfaced, exemplifying the weaponization of AI in shaping public discourse. From fabricated endorsements to malicious disinformation campaigns, the proliferation of AI-generated propaganda poses a formidable challenge to electoral integrity.
The ease of creating sophisticated deepfakes, once a domain reserved for tech-savvy individuals, has been democratized by generative AI, rendering the distinction between authentic and manipulated content increasingly blurred. Instances of AI-generated imagery infiltrating mainstream media platforms, masquerading as genuine visual narratives, underscore the urgency of combating the dissemination of falsified information.
As the battle against AI-generated misinformation intensifies, stakeholders grapple with the imperative of developing robust mechanisms to detect and counteract synthetic content effectively. Watermarking solutions like Google DeepMind’s SynthID offer a glimpse into potential mitigation strategies, yet the efficacy of such interventions remains contingent upon proactive vigilance and swift responses from social media platforms.
The impending electoral landscape stands at a crossroads, where the convergence of AI and political propaganda necessitates a concerted effort to safeguard the sanctity of democratic processes against the encroaching tide of synthetic narratives.
Melissa Heikkilä
STEPHANIE ARNETT/MITTR | ENVATO’S ISTOCK
4. Versatile Task-Oriented Robotics
The realm of robotics is witnessing a paradigm shift towards multifunctional robots, mirroring the strategic pivot observed in the domain of generative AI.
In a departure from the conventional approach of deploying specialized models for distinct tasks, the trend towards unified, monolithic models is gaining momentum across diverse domains. Just as bidirectional models like GPT-4 and Google DeepMind’s Gemini revolutionized language processing by accommodating text and image inputs seamlessly, a similar paradigm shift is underway in the realm of robotics.
By harnessing the principles of fine-tuning—enabling a single model to excel at a spectrum of tasks ranging from culinary feats to logistical challenges—researchers are spearheading a new era of versatile robotics. Notable advancements include DeepMind’s Robocat, an innovative robotic system adept at mastering diverse control tasks through iterative learning methodologies, underscoring the potential for unified robotic platforms to transcend traditional task-specific limitations.
Collaborative initiatives like the RT-X project, a collaborative endeavor involving 33 academic laboratories, exemplify the collective drive towards developing general-purpose robotic frameworks capable of addressing a myriad of real-world challenges. The quest for data abundance remains a critical frontier, as the transition towards unified robotic models necessitates extensive datasets akin to the vast repositories fueling conceptual AI advancements.
Pioneering endeavors led by visionaries like Lerrel Pinto from New York University, who champions data-driven learning methodologies through community engagement initiatives, underscore the transformative potential of democratizing access to training datasets. Industry titans like Meta’s Ego4D are also contributing to this data democratization drive, fostering a collaborative ecosystem conducive to training the next generation of task-oriented robots.
As the trajectory of robotics converges with the ethos of unified AI models, the landscape of autonomous systems stands poised for a paradigm shift towards versatile, task-oriented robotics capable of navigating multifaceted challenges with unparalleled dexterity.
Will Douglas Sky
