+44 7459 302492
info@uplatz.com
AI Fundamentals
-----------------------------------------------------------------------
Course Objective
At the end of this workshop delegates will:
- Gain a greater understanding of the tools used in AI
- Be able to identify the hardware required to optimise an AI solution
- Have a greater insight into Data Analysis and Analytics within AI
- Be equipped to realise the benefits of Machine Learning and Deep learning
Target Audience
The workshop is aimed at the individuals charged with implementing an AI solution.
-----------------------------------------------------------------------
AI Fundamentals
AI Tools
Data Science Languages
The Role of Python in Artificial Intelligence
Python Libraries for Artificial Intelligence
- NumPy: NumPy the computing library for Python.
- SciPy: SciPy is an advanced library containing algorithms that are used for data science
- scikit-learn: scikit-learn is Python's main machine learning library
- NLTK: Library for natural language processing
- TensorFlow: TensorFlow is Google's neural network library used for implementing deep learning artificial intelligence
Understanding the Role of Algorithms
- Planning and branching
- Local search and heuristics
Using expert systems
Hardware
- Standard Hardware
- Von Neumann bottleneck
- Single points of failure
- Tasking and multitasking
Specialised Hardware
- Graphic Processing Units (GPUs)
- Why are GPU’s suited to this field?
- Application Specific Integrated Circuits (ASICs):
- Field Programmable Gate Arrays (FPGAs):
- Specialized Sensors
Data Powers AI
- What is Data Science?
- Big Data
- Data Structures and Formats
- Data Sources
- Data Storage
Data Quality and Readiness
- Data quality and readiness is key to a successful implementation. intelligence is based on knowledge and data is the raw material
- Balance
- Representative
- Completeness
- Clean Data
Predictive Analytics
- Regression
- Classification
Data Analysis for AI
- Transforming: Changes the data’s appearance
- Cleansing: Fixes imperfect data.
- Inspecting: Validates the data.
- Modelling: Discovers the relationship between the elements present in data.
Define Machine Learning
How machine learning works
What are the benefits of machine learning?
- Automation:
- Fraud detection:
- Customer service:
- Resource scheduling:
- Resource scheduling:
- Safety systems:
- Machine efficiency:
Learning Models
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Machine learning approaches
- Naïve Bayes:
- Bayesian networks graph:
- Decision trees:
Enhancing AI with Deep Learning
- Simple neural networks
- The strength of the connection between neurons in the network
- Continuous learning using online learning
- Reusable solutions using transfer learning
- End-to-end learning
-------------------------------------------------------------------------------------------------------
AI Interview Questions & Answers
-------------------------------------------------------------------------------------------------------1) What is Artificial Intelligence?
Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.
2) What is an artificial intelligence Neural Networks?
Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.
3) What are the various areas where AI (Artificial Intelligence) can be used?
Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
4) Which is not commonly used programming language for AI?
Perl language is not commonly used programming language for AI.
5) What is Prolog in AI?
In AI, Prolog is a programming language based on logic.
6) Give an explanation on the difference between strong AI and weak AI?
Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.
7) Mention the difference between statistical AI and Classical AI?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
8) What is alternate, artificial, compound and natural key?
Alternate Key: Excluding primary keys all candidate keys are known as Alternate Keys.
Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence. This is known as artificial key.
Compound Key: When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.
Natural Key: Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.
9) What does a production rule consist of?
The production rule comprises of a set of rule and a sequence of steps.
10) Which search method takes less memory?
The “depth first search” method takes less memory.
11) Which is the best way to go for Game playing problem?
Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.
12) A* algorithm is based on which search method?
A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.
13) What does a hybrid Bayesian network contain?
A hybrid Bayesian network contains both a discrete and continuous variables.
14) What is agent in artificial intelligence?
Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. Agent includes Robots, Programs, and Humans etc.
15) What does Partial order or planning involve?
In partial order planning , rather than searching over possible situation it involves searching over the space of possible plans. The idea is to construct a plan piece by piece.
16) What are the two different kinds of steps that we can take in constructing a plan?
a) Add an operator (action)
b) Add an ordering constraint between operators
17) Which property is considered as not a desirable property of a logical rule-based system?
“Attachment” is considered as not a desirable property of a logical rule based system.
18) What is Neural Network in Artificial Intelligence?
In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, process the data and gives the output based on the algorithm and empirical data.
19) When an algorithm is considered completed?
An algorithm is said completed when it terminates with a solution when one exists.
20) What is a heuristic function?
A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
21) What is the function of the third component of the planning system?
In a planning system, the function of the third component is to detect when a solution to problem has been found.
22) What is “Generality” in AI?
Generality is the measure of ease with which the method can be adapted to different domains of application.
23) What is a top-down parser?
A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.
24) Mention the difference between breadth first search and best first search in artificial intelligence?
These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.
25) What are frames and scripts in “Artificial Intelligence”?
Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered. Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.
26) What are the different types of AI?
- Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory.
Example: Deep Blue - Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
- Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
- Self Aware AI: AIs that posses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
- Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual assistants like Siri.
- Artificial General Intelligence (AGI): Also known as strong AI. An example is the Pillo robot that answers questions related to health.
- Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.
27) How is Machine Learning related to Artificial Intelligence?
Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.
28) Explain the commonly used Artificial Neural Networks.
Feedforward Neural Network
- The simplest form of ANN, where the data or the input travels in one direction.
- The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.
Convolutional Neural Network
- Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
- Mainly used for signal and image processing
Recurrent Neural Network(RNN) – Long Short Term Memory
- Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
- Here, you let the neural network to work on the front propagation and remember what information it needs for later use
- This way each neuron will remember some information it had in the previous time-step.
Autoencoders
- These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
- The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
- Typically for the purpose of dimensionality reduction and for learning generative models of data.
29) Where To Find Specific Information On Search Bots?
Check out ALICE and ELIZA bots are very good ...and we can get more info on how to build in respective websites.
30) What Is A Chatterbot?
chatterbot is a game.
31) What Is Relational Knowledge?
It is a knowledge representation scheme in which facts are represented as a set of relations. For example knowledge about players can be represented using a relation called “player” having three fields: player name, height and weight. This form of knowledge representation provides weak inferential capabilities when used as standalone but are useful as an input for sophisticated inferential procedures.
32) What Is Inheritable Knowledge?
It is a knowledge representation scheme in which knowledge is represented using objects, their attributes and corresponding value of the attributes. The relation between different objects is defined using a “isa” property. For example if two entities “Adult male” and “Person” are represented as objects then the relation between the two is that Adult male “isa” person.
33) What is FOPL stands for and explain its role in Artificial Intelligence?
FOPL stands for First Order Predicate Logic, Predicate Logic provides
a) A language to express assertions about certain “World”
b) An inference system to deductive apparatus whereby we may draw conclusions from such assertion
c) A semantic based on set theory
34) What does the language of FOPL consists of?
a) A set of constant symbols
b) A set of variables
c) A set of predicate symbols
d) A set of function symbols
e) The logical connective
f) The Universal Quantifier and Existential Qualifier
g) A special binary relation of equality
35) For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?
In online search, it will first take action and then observes the environment.
36) Which search algorithm will use a limited amount of memory in online search?
RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.
37) In ‘Artificial Intelligence’ where you can use the Bayes rule?
In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.
38) For building a Bayes model how many terms are required?
For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.
39) While creating Bayesian Network what is the consequence between a node and its predecessors?
While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
40) To answer any query how the Bayesian network can be used?
If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.
