world conference on
Artificial Intelligence and Machine Learning
Theme: Shaping the Future: Transformative Impact of Artificial Intelligence and Machine Learning
Artificial Intelligence 2026
- 23-25 March 2026
- Tokyo, Japan
Our Event Start in
About
Artificial Intelligence 2026
The Artificial Intelligence and Machine Learning Conference will be held from March 23-25, 2026, in the innovative city of Tokyo, Japan. This prominent event will bring together experts, researchers, industry leaders, and policymakers from around the globe to explore the transformative impact of AI and ML on various sectors and their pivotal role in shaping the future.
With the theme “Shaping the Future: Transformative Impact of Artificial Intelligence and Machine Learning,” the conference will focus on the latest advancements in AI and ML technologies, practical applications, and ethical considerations. Discussions will address key issues such as AI-driven innovations, automation, ethical AI development, and the integration of AI and ML across industries, including healthcare, finance, education, and transportation. Topics will include deep learning, natural language processing, computer vision, AI ethics, data privacy, and real-time analytics.
The conference will feature a variety of sessions, including keynote lectures, panel discussions, workshops, and poster exhibitions designed to foster innovation and collaboration in the AI and ML landscape. Participants will have the opportunity to network, share insights, and discuss strategies to address the opportunities and challenges presented by these transformative technologies.
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The Topics to be Discussed
Machine Learning & Deep Learning
Machine Learning (ML): ML is a branch of AI where systems learn from data to recognize patterns and make decisions without explicit programming. It includes supervised, unsupervised, and reinforcement learning, with applications in finance, healthcare, and marketing. Deep Learning (DL): DL, a subset of ML, uses multi-layered neural networks to model complex data patterns. It excels in tasks like image and speech recognition, driving breakthroughs in AI fields like computer vision and NLP.
Neural Networks & Computer Vision
Neural Networks: Inspired by the human brain, neural networks consist of layers of neurons that process data and learn patterns by adjusting connections. They power deep learning and are used in tasks like image and speech recognition. Computer Vision: A branch of AI that enables machines to interpret visual data from images and videos. It drives applications like facial recognition, medical imaging, and autonomous vehicles by mimicking human vision.
Natural Language Processing (NLP)
Natural Language Processing (NLP): Natural Language Processing (NLP) is a field of Artificial Intelligence that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. NLP involves tasks like text classification, sentiment analysis, machine translation, and named entity recognition.
Chatbots & Generative AI
Chatbots: AI-powered tools that simulate conversations using Natural Language Processing (NLP). They can be rule-based or AI-driven, automating tasks in customer service, healthcare, and finance. Generative AI: AI that creates new content—text, images, music—by learning from data. Examples include GPT for text and GANs for images, revolutionizing content creation and innovation.
Reinforcement Learning & Predictive Analytics
Reinforcement Learning (RL): A machine learning approach where an agent learns by interacting with an environment, receiving rewards or penalties. Used in robotics, gaming (e.g., AlphaGo), and autonomous systems. Predictive Analytics: Uses machine learning and statistical methods to analyze past data and predict future outcomes. Applied in finance, healthcare, and marketing for forecasting trends and risks.
AI Ethics & AI Algorithms
AI Ethics: Focuses on fairness, transparency, and accountability in AI to prevent bias, protect privacy, and ensure ethical decision-making in areas like healthcare and law enforcement. AI Algorithms: Mathematical models that power AI, enabling tasks like pattern recognition and decision-making. Examples include decision trees, neural networks, and reinforcement learning.
Meet Our Committee Members
Meet Our Speakers
Andrew Matin
Head of Food and Drink, AMRC Cymru, United Kingdom
Andrew Matin
Head of Food and Drink, AMRC Cymru, United Kingdom
Andrew Matin
Head of Food and Drink, AMRC Cymru, United Kingdom
Andrew Matin
Head of Food and Drink, AMRC Cymru, United Kingdom
Registrations
Delegate
- Welcome kit with conference material
- Access to All Talks + Posters
- Opportunity schedule B2B meeting with Attendees
- Certificate of Participation
- Logo and profile placement on Conference Website
- Conference picture and videos distributed
Speaker
- Welcome kit with conference material
- Access to All Talks + Posters
- Opportunity to deliver a poster or oral presentation
- Logo and profile placement on Conference Website
- Certificate of Participation
- Conference picture and videos distributed
Student
- Welcome kit with conference material
- Access to All Talks + Posters
- Opportunity to deliver a poster or oral presentation
- Certificate of Participation
- Logo and profile placement on Conference Website
- Conference picture and videos distributed
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