VERSES Genius™ Agent Outperforms Leading AI Algorithms at Major Industry Benchmark
VERSES AI (VRSSF) has announced preliminary results of its Genius™ agent's performance in the 'Atari Challenge' AI benchmark test. The company's variant, dubbed 'Atari 10k,' demonstrated superior efficiency compared to leading Deep Reinforcement Learning (DRL) and Transformer algorithms, achieving comparable or better results while using:
- 90% less training data
- 96% faster training time
- 96% smaller model size
The Genius™ agents efficiently learned gameplay mechanics through interaction, without human supervision, by understanding cause-effect dynamics across multiple Atari games. This performance indicates potential applications in real-world scenarios where data may be or incomplete, such as financial analysis, medical applications, risk assessment, autonomous driving, and robotics.
VERSES AI (VRSSF) ha annunciato i risultati preliminari delle performance del suo agente Genius™ nel test di benchmark AI 'Atari Challenge'. La variante dell'azienda, soprannominata 'Atari 10k', ha dimostrato un'efficienza superiore rispetto ai principali algoritmi di Deep Reinforcement Learning (DRL) e Transformer, raggiungendo risultati comparabili o migliori utilizzando:
- il 90% in meno di dati di addestramento
- un tempo di addestramento più veloce del 96%
- una dimensione del modello più piccola del 96%
Gli agenti Genius™ hanno appreso in modo efficiente le meccaniche di gioco tramite interazione, senza supervisione umana, comprendendo le dinamiche causa-effetto in diversi giochi Atari. Questa performance indica potenziali applicazioni in scenari del mondo reale in cui i dati possono essere incompleti, come analisi finanziaria, applicazioni mediche, valutazione dei rischi, guida autonoma e robotica.
VERSES AI (VRSSF) ha anunciado los resultados preliminares del rendimiento de su agente Genius™ en la prueba de referencia de IA 'Atari Challenge'. La variante de la empresa, denominada 'Atari 10k', demostró una eficiencia superior en comparación con los principales algoritmos de Aprendizaje por Refuerzo Profundo (DRL) y Transformer, logrando resultados comparables o mejores mientras usaba:
- un 90% menos de datos de entrenamiento
- un 96% más rápido en el tiempo de entrenamiento
- un 96% más pequeño en tamaño del modelo
Los agentes Genius™ aprendieron de manera eficiente las mecánicas del juego a través de la interacción, sin supervisión humana, comprendiendo las dinámicas de causa-efecto en múltiples juegos de Atari. Este rendimiento indica aplicaciones potenciales en escenarios del mundo real donde los datos pueden ser incompletos, como análisis financiero, aplicaciones médicas, evaluación de riesgos, conducción autónoma y robótica.
VERSES AI (VRSSF)는 'Atari Challenge' AI 벤치마크 테스트에서 Genius™ 에이전트의 성능에 대한 예비 결과를 발표했습니다. 회사의 변형인 'Atari 10k'는 주요 심층 강화 학습(DRL) 및 Transformer 알고리즘에 비해 뛰어난 효율성을 보여주었으며, 다음과 같은 조건으로 비슷하거나 더 나은 결과를 달성했습니다:
- 훈련 데이터 90% 적게 사용
- 훈련 시간 96% 더 빠름
- 모델 크기 96% 더 작음
Genius™ 에이전트는 여러 Atari 게임에서 원인과 결과의 역학을 이해함으로써 인간의 감독 없이 상호 작용을 통해 게임 플레이 메커니즘을 효율적으로 학습했습니다. 이러한 성능은 재무 분석, 의료 응용, 위험 평가, 자율 주행 및 로봇 공학과 같은 데이터가 불완전할 수 있는 현실 세계 시나리오에서의 잠재적 응용을 나타냅니다.
VERSES AI (VRSSF) a annoncé les résultats préliminaires des performances de son agent Genius™ lors du test de référence AI 'Atari Challenge'. La variante de l'entreprise, surnommée 'Atari 10k', a démontré une efficacité supérieure par rapport aux principaux algorithmes d'apprentissage par renforcement profond (DRL) et de Transformer, atteignant des résultats comparables ou meilleurs tout en utilisant :
- 90% moins de données d'entraînement
- 96% de temps d'entraînement en moins
- 96% de taille de modèle en moins
Les agents Genius™ ont appris de manière efficace les mécaniques de jeu par interaction, sans supervision humaine, en comprenant les dynamiques de cause à effet dans plusieurs jeux Atari. Cette performance indique des applications potentielles dans des scénarios réels où les données peuvent être incomplètes, comme l'analyse financière, les applications médicales, l'évaluation des risques, la conduite autonome et la robotique.
VERSES AI (VRSSF) hat die vorläufigen Ergebnisse der Leistung seines Genius™ Agenten im 'Atari Challenge' AI Benchmark-Test veröffentlicht. Die Variante des Unternehmens, 'Atari 10k' genannt, zeigte eine überlegene Effizienz im Vergleich zu führenden Deep Reinforcement Learning (DRL) und Transformer-Algorithmen, indem sie vergleichbare oder bessere Ergebnisse mit:
- 90% weniger Trainingsdaten
- 96% schnellerer Trainingszeit
- 96% kleinerer Modellgröße
Die Genius™ Agenten lernten effizient die Spielmechanik durch Interaktion, ohne menschliche Aufsicht, indem sie die Ursache-Wirkungs-Dynamik in mehreren Atari-Spielen verstanden. Diese Leistung weist auf potenzielle Anwendungen in realen Szenarien hin, in denen Daten unvollständig sein oder fehlen können, wie z.B. Finanzanalysen, medizinische Anwendungen, Risikobewertungen, autonomes Fahren und Robotik.
- Achieved superior performance metrics using 90% less data than competitors
- Demonstrated 96% faster training time compared to current AI standards
- Achieved 96% reduction in model size while maintaining performance
- Successfully operated across multiple games without specific tuning
- Demonstrated autonomous learning without human supervision
- Product is still in research phase, not yet commercialized
- Further work needed to productize research into Genius™
Demonstrating High Performance in “Atari Challenge” with
VANCOUVER, British Columbia, Jan. 22, 2025 (GLOBE NEWSWIRE) -- VERSES AI Inc. (CBOE:VERS) (OTCQB:VRSSF) ("VERSES'' or the "Company”), a cognitive computing company specializing in next-generation intelligent systems, has shared preliminary results on its variant of the “Atari 100K Challenge” a leading AI industry benchmark, where software agents must learn gameplay proficiency, on their own, using a limited amount of training data.
The results, which can be found in a blog post on the Company’s website titled “Mastering Atari Games with Natural Intelligence,” showcase how agents powered by GeniusTM, a toolkit for developing intelligent agents, were able to match or exceed the performance of state-of-the-art Deep Reinforcement Learning (DRL) and Transformer algorithms using
“The Atari Challenge represents more than just machines playing games. Video games are a proxy for the complex dynamic systems all around us,” said Gabriel René, founder and CEO of VERSES. “We need AI to help us to better navigate the complexity and uncertainty of the real-world; yet state-of-the-art AI algorithms remain too unreliable, inefficient and unexplainable. While there is more work to be done to productize this research into Genius, we believe these early results and the research behind them signal a historical shift towards developing smarter, safer and more scalable AI.”
The Atari 100k benchmark has been used to test an agent's ability to excel in three critical areas: interactivity, generalization, and efficiency. VERSES’ variant, known as “Atari 10k,” tests for these same capabilities, using only 1/10th of the sample data. This increase in efficiency reduces the need to rely on large datasets and compute architectures, and indicates applicability to real-world problems where data can be sparse, incomplete, noisy, and where learning may need to occur in real time. Genius agents have demonstrated these capabilities across multiple Atari games by efficiently learning about the objects and physical mechanics of the game environments through interaction, on their own, without the need for human supervision. By learning the hidden cause-effect dynamics in the games, the agents were able to better predict outcomes and select optimal actions to outperform the state-of-the-art models even across multiple games with different objects, dynamics and goals. In contrast, DRL and Transformer architectures must rely on substantial training data and compute as well as bespoke tuning to ‘fit’ with the gameplay mechanics.
“By aligning with how intelligence has evolved in nature and how energy efficient biological systems must act to preserve their existence, we believe that the architecture and methods that underwrite Genius offer a sustainable and scalable means to building ecosystems of authentic, autonomous and agentic intelligence,“ said Karl Friston, VERSES Chief Scientist.
“By Applying the Free Energy Principle, the Active Inference framework, and Bayesian Machine Learning, we are working on developing an efficient and generalized architecture that allows intelligent agents to learn and develop expertise across any domain,“ said Hari Thiruvengada, VERSES Chief Technology Officer. “The reasoning, planning, and online learning capabilities that allow a Genius Agent to engage in dynamic gameplay is extremely relevant to applications such as classification, recommendations, prediction, and decision-making across a variety of industries, including financial, medical, risk analysis, autonomous driving, robotics and more.”
For a more detailed description and videos of gameplay visit our blog at verses.ai.
About VERSES
VERSES is a cognitive computing company building next-generation intelligent software systems modeled after the wisdom and genius of Nature. Designed around first principles found in science, physics and biology, our flagship product, Genius, currently in beta testing phase, is a suite of tools for machine learning practitioners to model complex dynamic systems and generate autonomous intelligent agents that continuously reason, plan, and learn. Imagine a Smarter World that elevates human potential through technology inspired by Nature. Learn more at verses.ai, LinkedIn, and X.
On behalf of the Company
Gabriel René, Founder & CEO, VERSES AI Inc.
Press Inquiries: press@verses.ai
Investor Relations Inquiries
U.S., Matthew Selinger, Partner, Integrous Communications, mselinger@integcom.us 415-572-8152
Canada, Leo Karabelas, President, Focus Communications, info@fcir.ca 416-543-3120
Cautionary Note Regarding Forward-Looking Statements
When used in this press release, the words "estimate", "project", "belief", "anticipate", "intend", "expect", "plan", "predict", "may" or "should" and the negative of these words or such variations thereon or comparable terminology are intended to identify forward-looking statements and information. Although VERSES believes, in light of the experience of their respective officers and directors, current conditions and expected future developments and other factors that have been considered appropriate, that the expectations reflected in the forward-looking statements and information in this press release are reasonable, undue reliance should not be placed on them because the parties can give no assurance that such statements will prove to be correct. The forward-looking statements and information in this press release include, among other things, statements regarding the fact that machine intelligence holds the potential of helping the public better navigate the complexity and uncertainty of the real-world; that Genius will be a viable, smarter, safer and more sustainable alternative to current mainstream AI; that the Company is developing an efficient and generalized architecture that allows intelligent agents to learn and develop expertise; the Company’s research and development plans; and the capabilities and benefits of Genius.
There are risks and uncertainties that may cause actual results to differ materially from those contemplated in those forward-looking statements and information. In making the forward-looking statements in this news release, the Company has applied various material assumptions, including, but not limited to, that the Company’s expectations regarding the capabilities and performance of Genius will prove to be accurate; that the Company will have the necessary funds and other resources required to carry out its business plans including the development and commercialization of Genius; that machine intelligence will be able to help the public in the manner expected; that Genius will prove to be a viable, smarter, and more sustainable option to mainstream AI; and that the Company’s development plans will remain unchanged.
By their nature, forward-looking statements involve known and unknown risks, uncertainties and other factors which may cause our actual results, performance or achievements, or other future events, to be materially different from any future results, performance or achievements expressed or implied by such forward-looking statements. There are a number of important factors and risks that could cause VERSES' actual results to differ materially from those indicated or implied by forward-looking statements and information. Such factors and risks may include, among other things, unanticipated costs; changes in legislation, political instability, and general economic factors impacting the Company’s development and commercialization plans and the Company’s business generally; that the Company’s expectations regarding the capabilities and performance of Genius will prove to be inaccurate; that the Company will fail to raise the necessary capital or fail to acquire or maintain the resources required to carry out its business plans including the development and commercialization of Genius; that machine intelligence will fail to help the public in the manner expected; that Genius will fail to be a viable, smarter, and more sustainable option to mainstream AI; and that the Company’s development plans will change. VERSES cautions that the foregoing list of material factors is not exhaustive. When relying on VERSES’ forward-looking statements and information to make decisions, investors and others should carefully consider the foregoing factors and other uncertainties and potential events. VERSES has assumed that the material factors referred to in the previous paragraph will not cause such forward-looking statements and information to differ materially from actual results or events. However, the list of these factors is not exhaustive and is subject to change and there can be no assurance that such assumptions will reflect the actual outcome of such items or factors. The forward-looking information contained in this press release represents the expectations of VERSES as of the date of this press release and, accordingly, are subject to change after such date. VERSES does not undertake to update this information at any particular time except as required in accordance with applicable laws.
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