Recent Advancements in Artificial Intelligince and Machine Learning Flashcards
is a subset of machine learning
that uses artificial neural networks to model
complex patterns in large datasets.
Deep learning
is an area of machine learning where an agent
learns to make decisions by interacting with an
environment and receiving feedback in the form
of rewards or penalties.
Reinforcement Learning (RL)
is a field of AI that enables computers to
understand, interpret, and generate human
language.
Natural Language Processing (NLP)
allows businesses and
individuals to store, process, and analyze data
using remote servers accessed via the internet,
rather than local on-premises servers.
Cloud Computing
involves using
advanced tools and algorithms to process and
analyze this data to uncover hidden patterns,
correlations, and insights.
Big Data Analytics
refers to extremely large datasets that
are difficult to manage and analyze using
traditional tools.
Big Data
Services like Amazon S3 and Google
Cloud Storage provide nearly infinite storage for
organizations, which can scale up or down based on
demand.
Scalable Storage
Tools like Google Docs and
Microsoft Office 365 leverage cloud computing for real-time
collaboration and remote work.
Real-Time Collaboration
Cloud providers like AWS, Microsoft
Azure, and Google Cloud now offer serverless architectures,
where developers can deploy applications without
managing server infrastructure. This reduces costs and
operational complexity.
Serverless Computing
A growing trend where computation is
performed closer to the data source (e.g., IoT devices) to
reduce latency and bandwidth usage.
Edge Computing
Cloud platforms provide AI/ML services like
AWS SageMaker, Azure ML, and Google AI, allowing
businesses to deploy machine learning models without
deep expertise.
AI as a Service
Frameworks like Apache Hadoop and
Apache Spark have become widely adopted for processing
large datasets. Spark, in particular, allows for fast, in-memory
data processing, making it highly efficient for big data analytics.
Hadoop and Spark
Cloud-based data lakes like Amazon Redshift and
Google BigQuery enable organizations to store both structured
and unstructured data in its native form, allowing for more
flexible and cost-effective data analysis.
Data Lakes
With advances in machine learning and AI,
predictive analytics tools are becoming more sophisticated,
helping organizations predict future trends, customer behavior,
and market changes.
Predictive Analytics
Big data analytics is used for predictive modeling in
healthcare, helping predict disease outbreaks, patient
outcomes, and drug discovery.
Healthcare
Retailers use big data to optimize supply chain
management, analyze customer buying patterns, and provide
personalized marketing.
Retail
Financial institutions use big data to detect fraud,
assess credit risk, and optimize trading strategies.
Finance
Cloud services can scale storage and computing resources dynamically
based on demand, making it easier to handle varying data loads.
Scalability
Organizations can pay for only the resources they use, avoiding the
high capital expenses of traditional data centers.
Cost Efficiency
Teams can collaborate on data analysis in real-time from different
locations.
Collaboration
Cloud platforms provide integrated tools for
running machine learning models on big data, making it easier to extract insights
and predictions.
AI and Machine Learning Integration
The rise of transformer architectures, particularly for
NLP tasks, revolutionized language understanding and generation.
Transformer Models
Neural networks are increasingly used for generative
tasks, such as creating images, audio, and even text.
Generative AI
Deep learning has driven breakthroughs in image
recognition
Computer Vision
Deep learning is used in medical diagnostics
(e.g., detecting cancer from radiology scans).
Healthcare
Deep learning models help cars
perceive their surroundings and make decisions in real-
time.
Autonomous Vehicles
AI-generated art, music, and writing are
becoming more mainstream.
Content Creation
Google’s DeepMind made headlines when
AlphaGo defeated human champions in the game of Go using RL.
Later, AlphaZero demonstrated even more powerful learning
capabilities by mastering Go, chess, and Shogi without human
input, learning purely through self-play.
AlphaGo and AlphaZero
is increasingly being applied to autonomous robotics,
where robots learn to perform tasks like grasping objects,
navigating spaces, or playing sports by trial and error in simulated
environments.
Robotics
RL is used in supply chain optimization,
automated trading, and smart grid management.
Industry Applications
AI agents are becoming superhuman in
complex games like Go, Chess, Dota 2, and
StarCraft.
Game AI
RL helps in training robots for real-world
tasks like assembly, pick-and-place, or navigating
complex environments.
Robotics
These models can perform various tasks, such as translation,
summarization, and text generation, by fine-tuning on specific
datasets.
Pre-trained Language Models
conversational
agents like Siri, Alexa, and ChatGPT have become more natural
and responsive, significantly improving customer service
automation.
Conversational AI
Models like DeepSpeech and Whisper have
improved the accuracy and robustness of speech-to-text
applications, enabling better voice control interfaces and
transcription services.
Speech Recognition
AI-powered virtual assistants (e.g., Alexa,
Google Assistant) have improved in understanding and
interacting with users.
Virtual Assistants
NLP is used to analyze customer
sentiment from reviews, social media, and feedback surveys.
Sentiment Analysis
Applications like Google Translate
leverage NLP to provide real-time, accurate translations.
Language Translation
