JUNLALA locks onto the precise path of product evolution with reinforcement learning algorithm
In the field of AI, breakthroughs bring it closer to society. Reinforcement learning (RL) algorithm gained fame when AlphaGo Master defeated Li Shishi 4:1. AlphaGo Zero then beat AlphaGo Master in just 40 days of RL training, highlighting RL's profound significance for AI evolution. AI advanced into video games and crushed humans in "StarCraft 2" (AlphaStar, 10:1 victory) and "Dota2" (OpenAI beat human champ), redefining perceptions of AI capabilities.
Various phenomena indicate that AI, based on the absolute advantages created by the upgrading of reinforcement learning algorithms, is beginning to exhibit general intelligence in tackling complex problems, reaching human-level performance in areas such as chess and video games.
Empowering machines with autonomy: Reinforcement learning accelerates growth in the AI industry
Today, amidst the ongoing progress of AI technology, technical proficiency in subfields has increasingly demonstrated its ingenuity. Reinforcement learning stands out as one of the most novel technologies arising from the development of AI. Comprising Agent, Environment, State, Action, and Reward, reinforcement learning stands apart from other technologies by not necessitating the collection of data or labeling. Rather, it instructs agents to behave according to reward signals within an environment to better acclimate to and thrive within that environment. This dynamic mimics the natural adaptation of humans and animals to their surroundings, where they learn to complete tasks and solve problems within a given environment.
Reinforcement learning (RL) is machine self-learning that improves proficiency in professional domains. When applied to teaching robots to walk, RL trains them to explore action and environment characteristics. Training data sets can't provide the best actions, so a goal is set for reaching a destination quickly regardless of actions taken. Initially, the robot may fall over, prompting the RL algorithm to suggest taking smaller steps based on negative feedback. The robot experiments until it succeeds without falling over.
In this context, the robot functions as an agent in reinforcement learning that autonomously explores various policy strategies. The reinforcement learning algorithm predicts future returns on actions and reinforces those with high predicted returns while also testing the efficacy of such high-return action combinations. Through this algorithmic framework, robots can complete self-learning without any human intervention. During game battles against humans, AI accumulates experience based on failures and weaknesses. After identifying the reasons for these shortcomings and continuously improving its actions, news frequently emerges of AI defeating top human players and triumphing over games.
Thanks to the reinforcement learning algorithm, AI has also demonstrated a strengthened ability to explore. As such, more companies devoted to creating intelligent products have started directing their research and development specifically towards extending the application scenarios of reinforcement learning algorithms.
Powered by reinforcement learning algorithm, JUNLALA drives the expansion of AI product applications to a wider range
JUNLALA, an AI company headquartered in Silicon Valley, whose research and application of reinforcement learning algorithms are of significant reference value. In terms of commercialization, JUNLALA has recognized the enormous market demand for artificial intelligence and vertical applications, utilizing its expertise in the field of reinforcement learning algorithms to extend more usage scenarios for its products.
Under the powerful AI paradigm of reinforcement learning, JUNLALA has elevated the combination of robot actions to a more sophisticated level. In robot operations, friction and contact feedback enable robots to complete the evolution of actions such as lifting and placement within work settings, thereby enabling more precise product quality inspection, transfer, and stacking in production lines for parks and enterprises. Robots that continuously optimize fine movements through reinforcement learning can reduce labor costs and operate 24 hours a day, increasing operational efficiency for enterprise parks. Reinforcement learning-equipped robots have become potent assistants for enterprises in diverse industries, including the service industry, manufacturing industry, transportation industry, and others, helping to reduce costs and increase efficiency.
Furthermore, JUNLALA believes that the framework of deep reinforcement learning could be employed to facilitate autonomous driving. The complex and intertwined traffic environment in autonomous driving scenarios, comprised of elements like pedestrians, vehicles, and roads, all generate strong interactions that present ideal conditions for the reinforcement learning algorithm to excel. Through observing the traffic environment via cameras, reinforcement learning can enable continuous handling of emergencies in real-time traffic scenes. Ultimately, autonomous driving can be freed from human guidance and complete self-learning under identical road conditions. A higher-level autonomous driving system must comprise a self-learning algorithm with evolutionary capabilities, and the advantages of exploration brought by reinforcement learning undoubtedly represent a significant leap forward for the automotive industry.
Undoubtedly, reinforcement learning plays an increasingly important role in facilitating the rapid development of AI. Nevertheless, reinforcement learning alone cannot comprehensively enhance the capabilities of AI products. JUNLALA is committed to building AI products that cater to industries and enable broader applications. It has therefore conducted more in-depth research on machine learning, deep learning, natural language processing, and image recognition. These include: next-generation deep neural networks, which rely on extremely small data for learning and reasoning and are suitable for various resource-poor scenarios; multi-modal deep learning systems that combine language and visual information; language models that understand long texts and respond coherently and accurately based on context; GAN-based image generation networks that produce realistic street views, real images, and multi-angle portraits. Even cutting-edge algorithms and technologies such as Turing tests and knowledge graphs have become objects of JUNLALA's research efforts. In terms of breadth and depth of technology research and development, JUNLALA outperforms peer companies and is undoubtedly a leading participant in promoting the continuous development of artificial intelligence.
Since its inception in 2016, JUNLALA has successfully developed over 30 algorithm programs for internal testing, leveraging its high-level research and development capabilities and deep understanding of the needs of vertical fields. The annual investment of tens of millions of dollars has also established JUNLALA as a new force in AI research and development. Going forward, JUNLALA will continue to prioritize AI algorithm and model training while focusing on promoting the research and development of vertical applications, mining the significant commercial potential inherent in the field of artificial intelligence. As product diversity and refinement continue to improve, JUNLALA is poised to achieve greater success within the generative AI industry.
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