AI Trends to Watch in 2024
January 18, 2024Introduction
Over the past decade, artificial intelligence (AI) has seen explosive growth, catalyzed by the availability of big datasets and computing power. From beating humans at complex games like Go to advancing robotics and language capabilities, AI is impacting nearly every industry. 2024 is set to accelerate this progress even further across major fields like generative models, multimodal learning, robotics and AI safety.
The 2010s saw neural networks eclipse traditional rules-based AI approaches using hand-crafted features and heuristics. Advances like AlexNet, word embeddings and AlphaGo proved neural networks’ superiority at perception, language understanding and strategy. The 2020s build on this with transformers scaling to hundreds of billions of parameters, mastering text, images, code and more. 2024 will take this trend even further with models expected to gain reasoning, communication skills and causality.
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Another key theme is transfer learning - utilizing knowledge gained from large multi-task training to quickly master new tasks using less data. In 2024, transfer learning will enable customizable models which users can easily re-train for their own applications. Combining modalities is also rising, with multimodal models accepting text, images, speech, sensor data and more as integrated inputs. This allows more flexible real-world applications.
On the robotics front, cheaper sensors and simulation are enabling rapid progress in physical capability. 2024 may see the first household robots able to safely navigate and manipulate previously unseen environments. Critical advances are also expected in AI safety, auditing and alignment research to ensure reliable, ethical systems.
Overall 2024 promises dramatic improvements in what AI can do including generating photos, texts or robotic behaviors which humans cannot distinguish from reality. The societal impacts of such technologies inspire both awe and concern. This article summarizes the key innovations experts anticipate in 2024 and what they might mean for AI’s future and our own. Understanding these trends can help technologists steer progress toward benefit rather than harm as this extraordinarily disruptive technology continues advancing faster than many deemed possible.
Generative AI
Generative AI models like DALL-E 2, GPT-4 and PaLM are creating ultra-realistic synthetic content ranging from images to prose, poems, computer code and more. Trained on vast datasets, these models can now conjure creative outputs rivaling human quality on demand.
OpenAI’s DALL-E 2 stunned observers by rendering strikingly vivid images from text captions. The detail, coherence and photorealism show a new level of visual generative mastery. For businesses, graphic designers or social media, the customizable images have enormous potential. Social causes could also benefit - medical textbooks augmented with synthesized diagrams for instance. However, risks like deep fakes necessitate safeguards against misuse.
In text, 2024 will likely see fine-tuned versions of GPT-4 which temper its inaccuracies and risky responses using techniques like chain-of-thought prompting or reinforcement learning. Fine tuned GPT-4 offers enticing use cases like automatically generating marketing copy, fluidly conversing as AI assistants or even accelerated drug discovery considering proteins’ 3D structures. Still unchecked language risks like persuasive misinformation spread compel mitigation research into truth and ethics-preserving systems.
PaLM’s code generation capabilities point to AI dramatically enhancing developer productivity. Beyond auto completing code, PaLM can comment functions or convert natural language requests into runnable programs. This could expand coding accessibility to non-experts and increase software engineers’ output multiple-fold according to OpenAI. However, properly integrating AI coding assistants presents challenges in understanding context which projects in 2024 must overcome.
Overall generative models’ advancing coherence and capabilities promise to transform nearly every knowledge domain. Yet ensuring this prolific creativity serves the collective good rather than wreaks havoc necessitates deliberate ethical safeguarding and policy ahead of technological timelines. Prioritizing this now in 2024 and beyond is imperative considering AI’s accelerating progress exponentially amplifying both benefit and harm potential.
Key model milestones anticipated in 2024 include GPT versions demonstrating rudimentary reasoning chains in response to "why” questions plus elementary factual consistency. Spatial reasoning allowing text-to-3D-scene rendering could also emerge along with better conveying human tone and personality when conversing. As models grow more well-rounded and persuasive, correctly aligning their goals with social priorities through transparency and oversight grows urgent. How humanity responds now likely determines whether this historic innovation uplifts or undermines coming generations across economics, education, governance, security and beyond.
Transfer Learning and Multimodal Models
Transfer learning is revolutionizing AI by allowing models to build on extensive general knowledge gains to achieve mastery of new tasks with limited additional data. In 2024, transferability will strengthen significantly - enabling users without extensive compute to re-train customizable models for their own needs simply by providing small domain-specific datasets.
For example, clinics could record just 50 x-rays of localized injuries to overlay deep medical imaging insights into radiologists’ diagnostic process. Retailers might quickly adapt inventory robots to smoothly grasp thousands more products with only a few hundred examples. As little as 500 lines labeled code could teach programming assistants to imbue projects with custom style guidelines.
This democratized efficiency promises to make AI radically more accessible and impactful. Progress by 2024 includes standardized protocols simplifying re-training for non-experts and techniques like batch normalization further easing model tune-ability. Continual learning allowing users to incrementally update systems with new data without losing past knowledge will also come into sharper focus.
Multimodal processing integrating different data types like text, images and speech is also rising fast. Unifying these inputs and outputs allows more flexible real-world applications versus handling a single modality.
Examples include conversational agents seamlessly discussing images shared mid-dialogue or joint text-video machine learning accurately assessing complex domains like surgical procedures or manufacturing quality assurance. Architectures expected to advance integration capabilities in 2024 include graph networks representing different modalities as nodes & edges and tensor fusion directly mixing signals for tighter symbiosis.
Key multimodal use cases on the horizon range from augmented reality interfaces overlaying visual data points onto real environments to emotion recognition combining facial and audio cues for empathetic assistance. Diagnostic systems marrying scans, tests and medical histories will also progress. Output-wise, multimedia generation blending coherent images, text and audio could enable next-level entertainment experiences and compelling educational simulations.
Together transfer learning and multimodality promise greatly expanded utility and accessibility of AI technology for solving multifaceted real-world problems. But increased independence and autonomy of systems also warrants continued vigilance around reliability and security to ensure effectiveness of human oversight. Getting governance right early in this stage of assimilation into everyday processes is critical.
Robotics and Embodied AI
Robots that learn entirely through self-supervised interaction with the physical world are making rapid advances. By exploring environments and building experiential understanding of how their actions affect objects and obstacles, systems are steadily acquiring capabilities previously requiring immense human data supervision.
In manipulation, robotic hands equipped with touch sensors now autonomously squeeze, prod and grasp thousands of household items - discovering how to pick up previously unseen mugs, tools or toys simply via trial and error. Sim-to-real transfer is also progressing fast, with simulation accelerating grasping mastery tremendously before applying learnings to physical systems. For example Meta’s self-supervised robotics approach surpassed 70-80% successful grasps across over 1,200 novel objects absent any human grasping demonstrations or annotations.
Navigation-wise, quadrupeds like ANYmal and digit legs are growing adept at teaching themselves to climb steps, leap gaps and traverse objects only encountered during deployment. Optical flow, proprioception and other sensory feedback train autopilot mode reactively avoiding obstacles through clutter. This bypasses tedious manual mapping or path programming, enabling direct environmental walkthroughs to bootstrap navigation capability.
Sensing enhancements like thermal imaging are also being integrated both furthering scene understanding and safer interaction when contact risks limbs. Closed-loop self-supervision combining all domains allows systems to attempt tasks, gauge failure modes through errors, then take corrective actions to incrementally improve wholly unassisted.
With simulations accelerating robotic learning further, automating entire pipelines from mining real-world environmental data and modeling domain randomness to policy transfers onto physical systems will compound progress. Key 2024 milestones include advanced mobile manipulation unlocking robots coordinating arms and bases to heft bulky packages through congested spaces. Visually navigating unfamiliar apartments or offices while avoiding dynamic obstacles and properly using human appliances also appears achievable this year.
Ultimately self-supervised robots promise massive productivity gains to warehouses, labs, machine shops and other sites facing labor shortages or requiring human-unfriendly environments be regularly monitored or maintained. But again oversight and safeguards must keep pace with automation capabilities to ensure efficacy and orthogonality to human values as these systems’ autonomy expands. Getting governance ahead rather than perpetually behind technological timelines remains imperative for smooth assimilation across industries and workforces over transitioning decades.
AI Safety and Policy
As AI systems grow more capable and autonomous, intensifying focus on safety and policy aims to ensure innovations positively transform society. On the technical front, 2024 expects milestones in formal verification mathematically proving neural networks behave correctly and alignment initiatives targeting models inheriting human priorities.
Swarm verification stands to demonstrate sigma-level certainty of 10,000+ parameter autonomous drones avoiding collisions or correctly classifying restricted airspace, enabling mass coordination. And simplified preference learning techniques show promise for customizable assistants absorbing ~100 ranked demonstrations then inferring users’ goals and constraints for personalized support in domains like art curation or essay writing.
Bolstering such technical assuredness, new governmental initiatives guide trustworthy development and integration. For example, the 2021 European AI Act draft encompasses assessments classifying high-risk systems like China’s social credit scoring network which must establish human oversight under forthcoming enactment.
Rights-respecting principles also highlight data privacy, avoiding monopolistic centralization and other priorities balancing innovation with caution moving forward.
The IEEE global initiative towards prioritizing the public interest using AI similarly flags issues like transparency requirements currently lacking in commercial offerings. Extending existing technical standards on safety-critical systems to guide best practices for powerful AI may also emerge through working groups releasing initial frameworks for comment in 2024.
Overall flags include ethically handling biases around race, gender and disabilities while ensuring legibility of automated decisions impacting people's lives. Growing recognition that AI cannot develop unfettered shines promisingly through intensifying multi-national receptiveness towards responsible innovation guidelines through targeted policymaking.
Yet more is needed considering slender margins of critical oversight narrowing as exponential technological change outpaces comparatively glacial legal processes. Vigilance must pressure development timelines respecting human values rather than racing unsafe systems to market or warfare theaters.
With AI progress risking such outsized societal consequences from work automation to information warfare and human rights, asserting global wisdom over national or corporate interests promoting short-sighted gains grows increasingly urgent each breakthrough year. The cooperation and courage civilization demonstrates this decade may profoundly influence whether later generations remember this point as an uplifting or devastating turning. Our actions choose which path AI’s unfolding promises tread.
Concluding Thoughts
The staggering pace of AI innovation forecast over the next couple years heralds both enormous benefits and risks for society. Mastering capabilities like realistic media synthesis, transferable knowledge and embodied robotics promises to transform industries from creative arts to manufacturing, healthcare, transportation and more. Yet the same technological leaps also necessitate urgent improvement of safety systems and policy guiding ethical development before uncontrolled consequences irreversibly harden.
Synthesizing the covered trends, 2024 expects generative models producing increasingly coherent, multipurpose content rivaling human output while transfer learning multiplies use cases through easy retraining. Concurrently robotics is actualizing capacities long resigned to distant decades through self-supervised physical learning. Accentuating productivity, personalized services and economic access promises rising living standards for many.
But unchecked risks like information hazards eroding public trust or sudden labor market disruption without safety nets spark turmoil. Similar to climate change, advancement without foresight strands humanity struggling to mitigate threats rather than proactively channeling progress toward equitable benefit.
Collective prioritization of the positive impacts within reach through international collaboration, safety-conscious development incentives and policy steering innovation in line with humanity’s shared values is thus imperative. The responsibility now borne by leaders across technical and governance spheres is historic in scope. Their actions over ongoing years stand to either compound existing inequalities through accumulated advantage or lift billions through compassionately transitioning economic gains into inclusive opportunity – determining trajectories of coming generations during this pivotal window of influence over AI’s first steps. With progress accelerating, adaptable leaders must guide systems ethically maturing before blind capability outstrips wisdom. Our shared future turns on recognizing AI as a civilizational challenge necessitating unity, not isolationism.