1 / 8

Machine Learning The Powerhouse of AI Explained

Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors.<br>

CIOLook
Télécharger la présentation

Machine Learning The Powerhouse of AI Explained

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MACHINE LEARNING: THE POWERHOUSE OF AI EXPLAINED pitch deck by : CioLook

  2. TABLE OF CONTENT Artificial Intelligence (AI) and Machine Learning (ML) Definition of Machine Learning How Machine Learning Algorithms Learn From Data Opportunities With Machine Learning:

  3. HOW MACHINE LEARNING ALGORITHMS LEARN FROM DATA Machine Learning (ML) algorithms learn from data in a manner somewhat akin to how humans learn from experience. The process starts when an algorithm is given a dataset, often referred to as training data. This data is typically labelled, meaning each data point or example is paired with a corresponding output or answer.

  4. ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have revolutionized the technology landscape, becoming integral in various sectors. Artificial Intelligence, at its core, refers to the simulation of human intelligence processes by machines, particularly computer systems. In other words, AI aims to create systems that can perform tasks that would ordinarily require human intelligence, such as recognizing speech or making decisions.

  5. OPPORTUNITIES WITH MACHINE LEARNING: 1 2 3 Cross-Sector Advancements: Machine Learning paves the way for improvements across diverse sectors such as healthcare, finance, transportation, leading to more precise diagnoses, smarter investments, and efficient logistics. Data-Driven Decision Making: By learning from large datasets, ML enables more informed, data-driven decision-making, leading to optimized outcomes. Task Automation: ML can automate various complex tasks, potentially increasing efficiency and productivity across industries.

  6. CHALLENGES WITH MACHINE LEARNING: 1 2 3 Data Privacy and Security: ML models require large amounts of data, which raises concerns about data privacy and security. Opacity (‘Black Box’ issue): The decision-making process of ML algorithms can be complex and opaque, posing problems for transparency and accountability. Job Displacement: The potential for job displacement due to automation, driven by ML, is a concern requiring careful societal and policy considerations.

  7. THE FUTURE OF MACHINE LEARNING The future of Machine Learning (ML) is set to be exciting and transformative. With advancements in computational power and the availability of vast amounts of data, ML models are expected to become more sophisticated and accurate. The rise of explainable AI aims to address the ‘black box’ problem, ensuring transparency in ML-driven decisions. Techniques like federated learning could allow ML models to learn from a plethora of devices while preserving data privacy.

  8. Thank You READ MORE : CLICK HERE

More Related