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If you will tell me precisely what it is that a machine cannot do, then I can always make a machine which will do just that.
John Von Neumann, 1948

Demand for Artificial Intelligence

How about endowing machines with intelligence and understanding, the unique attributes that always differentiate humans from machines? Scientists and AI experts like Nick Bostrom and Stuart J Russell have foreseen it - the myriad possibilities of making machines artificially intelligent. Despite warnings from eminent scientists like Stephen Hawkings or Elon Musk, AI is here to stay, whether as embodied Robots or as self-driving cars.
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AI has already set the chassis for the NextGen software solutions. It is enriching peoples’ lives with a blend of smart apps, intelligent computer systems, smart agents, and latest smart-sensor techniques offering real-time solutions anytime & anywhere.

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Intelligent agents form the core of AI which is run by machine learning algorithms and Python programming. As an AI engineer, you need a grasp of computer science, psychology, data science, math, and machine learning in prescribed proportions.
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AI Dream

Artificial Intelligence is an ambitious dream of making machines incorporate human intelligence - that means, training machines to behave as intelligently. Today, AI has emerged as a fascinating technology transforming the world around, making computers and machines progressively learn and acquire intelligent behaviour patterns. This also makes AI a rebel to the traditional control systems and theories that had been limited in their contexts and methodologies.
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Complex AI Algorithms

Complex AI algorithms and state-of-the-art deep neural networks optimization have moved ‘intelligence’ onto the devices. Efficient hardware encompassing heterogeneous compute blocks and deep learning software, complements the AI algorithms in making on-device intelligence become pervasive!

AI Turning Into a Booming Industry

AI has become a billion-dollar industry in less than a decade. The industry holds enormous possibilities for enriching visual perceptions, decision-making ability and to react and adapt to specific fields of work.
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AI has infused new algorithm-based vision systems, robots, specialized software and hardware. AI tools are all over the place speeding up work processes and providing precision in results while solving real-time challenges. Remember the popular AI-based assistants - Siri or Alexa?
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AI Applications

AI applications for face recognition, internet search, self-driven cars, robots, missiles, tumour/malignancy detection, data analytics, gaming consoles, machine learning and so on, are driving industries to keep pace with the inevitable changes that the future holds.

It needs no guessing why AI expert engineers are at an all-time high demand. Amazon has recently opened 1,000 postings for AI related jobs, while Google Corp has opened 500 postings in the United States alone. Corporate giants Microsoft and Sony, struck a strategic deal for making image sensor chips “to provide highly intuitive and user-friendly AI experiences.”

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Marvelous feats of Machine Learning is making AI hot! Very hot!!

AI has already set the chassis for the NextGen software solutions. It is enriching peoples’ lives with a blend of smart apps, intelligent computer systems, smart agents, and latest smart-sensor techniques offering real-time solutions anytime & anywhere. So, as the saying goes: Strike when the iron is hot!

AI is the hottest topic today with ‘intelligent agents’ forming the core of AI assisted by machine learning algorithms and Python programming. An AI engineer is equipped with a good grasp of computer science, psychology, data science, math, and machine learning in prescribed proportions.
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AI Revolutionizing the Future Cars

Every popular car maker in the world - Audi, Volvo, Tesla Motors, Mercedes-Benz, and General Motors, are testing self-driving vehicles.

The sophisticated AI algorithms are developed to offer enhanced experiences and new capabilities for car users. The AI technology offers an entirely new development paradigm where things exponentially improve by predicting beforehand what the car user needs. AI has pioneered in paving the way for “road-autonomy” by redefining the in-car experience, designing natural user interfaces, personalization and continuous driver awareness monitoring.
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The additional enhancements AI offers for autonomous cars are:

  • Customized user interfaces
  • Personalization of luxury features
  • Surround view perception
  • Sensor fusion
  • Path planner and finder
  • Decision making

As we zoom through downtown, highways and bylanes, what could be better than an intelligent car negotiating the traffic, the jams, confusing intersections, narrow lanes, bridges, rough, snowy or rainy weather, all by itself!! We love you AI!
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AI Augmenting Business Leadership

In this AI era, a successful business leader embraces innovation by exploring multiplicity of intelligences and not limiting themselves to academic excellence.

Data scientists can help organizations strategize by extracting the required business intelligence from the enormous amount of data generated everyday. Using the AI-based network, businesses can now track increase in sales, detect any type of fraud, improve customer experience, automate work processes, automate inventory and delivery management, all this based on AI-predictive models. AI helps bring about a paradigm shift in the thought process of the business leaders and enhance the development and execution of crucial business strategies in organizations.
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AI Enhancing Home Automation

The foundation of home automation products and devices be it interactive voice assistants or internet of things (IoT) sensor devices, is AI technology. It is essential to adopt AI for building intelligent conversational interfaces for devices and applications.

Bloomberg reports about Amazon working on creating a domestic assistant robot Vesta.
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Machine Learning

Deploying machine learning algorithms, scientists and engineers have developed functionalities like wake word detection, automatic speech recognition, natural language understanding, contextual reasoning, dialog management, question answering, converting text-to-speech and so on. Alexa is revolutionizing daily conveniences like playing music, switching between TV channels, searching information, controlling smart home, to much more by accessing computing resources available via Amazon Web Services (AWS) and its large-scale heterogeneous data resources.

The role of AI in home automation is about exploring the use of contextual information for adapting language models (LMs) to each user agent interaction. Automatic Speech Recognition (ASR) systems are key to such interactions. Building language models (LMs) for ASR systems is to learn models that can be trained to predict the conditional word probabilities given the context of the previous words.
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AI for Capturing Cyber Threat Intelligence

AI-powered cyber security solutions are predicted to touch US$11,047 million by the year 2025. The perfect combination of AI and machine learning capabilities of cyber security, accelerates the detection of malware and network intrusion over large networks with higher speed and accuracy. Threat detection being a very specialized function, enabling AI-powered machines to learn from past cyber threats and gradually adapt to complex data, offer better security solutions against complex cyber threats we face today.

AI enables organizations to gather pre-reconnaissance data (including anomalies, botnet and phishing detection) that can be used in procuring cyber threat intelligence. Gathering of threat intelligence is the first line of defence in the cyber security infrastructure, followed by the reactive security systems such as intrusion detection systems (IDSs) and mitigation techniques.
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AI bringing about a paradigm shift in businesses

Most global businesses have started reaping the benefits of adopting the AI technology in their products and services. According to a UN report, “as much as two-thirds of all jobs in the developing countries could be affected by significant automation”.

Recently, a Chinese chess grandmaster, Ke Jie was defeated squarely by a stronger AI AlphaGo player.

From Netflix using AI cognitive capabilities to power movie recommendations, Google’s AlphaGo that can ‘think’ with advanced reasoning and deductive skills to beat humans at a game, Amazon being able to predict the buyer’s choices, AI assisting media planners buy advertisements, copywriters using AI to optimize their tasks, mobile becoming the all-pervasive AI platform, to AI emulating human behaviour, there is absolutely no business that has not been untouched by the AI revolution!
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Bringing AI to the Masses

The prevalent use of mobile phones and gadgets around the globe has led to having the latest technology available on your fingertips! The reach mobiles enjoy helps it to transform into a high-performance platform capable of supporting intelligent devices with super computing power. Integrating AI design frameworks and customer networks with innovative machine learning algorithms and software onto the mobile platform is easier owing to the technical advancements.

Our RCAI course also equips the students with hands-on experience by training them to build a basic search agent to explore adversarial search and learn how to program intelligent agents for games and logic problem solving. The additional activities will also include game creation and introduction to machine learning working on linear regression.
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Artificial intelligence is driving the greatest disruption to our global economy since industrialization, and Microsoft is an amazing partner as we develop solutions to empower companies and workers to meet that disruption head on.
Jake Schwartz, CEO and co-founder of GA

Intelligent Networks

AI offers an intelligent network system run by knowledge agents, logical agents and complex algorithms, which is the integral safety vault for cybersecurity.
The Telekom Innovation Labs of Israel did extensive research on AI and suggested a twin-fork approach of applying AI to enhance cybersecurity:

1. Building a global intelligence network to track any cyberthreat across regions or countries

2. Invest in R&D to improve and enhance data privacy processes

AI Global Education

Microsoft Corp and the global education providing company General Assembly (GA), recently launched a joint venture to bridge the global AI skills gap by upskilling and reskilling approximately a 15,000 strong workforce over a three-year period to develop an industry-accredited credentials for Artificial Intelligence Skills.
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AI Policing for Public Safety

The technique of ‘predictive policing’ using AI ensures law enforcement to prevent a crime from happening. With increase in the neural network applications, there is more accurate data capture and useful information processing that benefits policing to provide better public security.
Using AI, crimes can be predicted based on the following information captured:

  • Predicting location and period of the crime occurrence
  • Identification and prediction of potential criminals
  • Profile matching of a potential criminal
  • Mapping areas and demographics which could bear the brunt of the crime
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For accuracy in predictions, the AI system acquires and analyzes huge volumes of relevant data including historical crime data, call records, economic and geographic information. Based on the analysis accurate predictive models are created to help law enforcement agencies.
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Stunning Doctor Robots

If you thought robots manage to assist doctors in analysing and lending a helping hand during diagnosis, update yourselves! Robots are well poised to replace surgeons in the operation theatres! Further, robots are capable of training and assisting junior doctors and surgeons to do their job well.

As per the Lancet Commission, more than five billion people cannot access safe surgery, across the word. To meet this challenge and save 17 million lives every year, "we need to train 2.2 million extra surgeons," according to cancer specialist virtual reality surgery pioneer Shafi Ahmed.
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What you’ll need to know

The RCAI Engineer certification course is designed such that upon the course completion, the RCAI Engineer is up and ready for a productive role in the AI projects. Therefore, the RCAI certification requires the learner to have a basic knowledge of some of the related subjects including basics of Python programming language, calculus & linear algebra and probability.

With the accelerated growth in the field of AI, the market demands professionals with software knowledge and skills to handle AI applications. Keeping this employment demand in mind, the governments of the US and Europe have approved of several job titles with appropriate remuneration and job benefits.
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Rocheston Certified Artificial Intelligence Engineer

This certificate will open many doors for you. AI Engineers are the most sought after professionals in the tech industry today.
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Rocheston can help you in your amazing journey as an Artificial Intelligence expert with access to its exhaustive course materials, interactive sessions and innovative training solutions. Organizations are focussing on various business applications of AI on the enterprise level and that is exactly where Rocheston would want you to fit in!

Rocheston Certified Artificial Intelligence Course Outline

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View Course Outline Here

What’s in it for you?

A peek into our thoughtfully designed syllabus:

Module 1 - Introduction to AI

  • What is AI?
  • History of AI
  • AI applications

Module 2 - Agents and Environments

Intelligent Agents:

  • Rational agents
  • PEAS
  • Single vs multiagents
  • Learning agents
  • Simple reflex agents
  • Model-based reflex agents
  • Goal-based agents
  • Utility-based agents

Intelligent Environments:

  • Deterministic vs stochastic environments
  • Episodic vs sequential environments
  • Known vs unknown
  • Static vs dynamic
  • Discrete vs continuous

Module 3 - Problem Solving Types

Anatomy of a problem

  • Initial state
  • Set of actions
  • Transition model
  • Goal test
  • Path cost
  • State space

Types of problems:

  • Toy problem
  • Real-world problem
  • 8-queens problem
  • Protein design – assembly sequencing
  • Route finding

Module 4 - Problem Solving by Searching

  • Types of searches:
  • Uniformed search
  • Informed search
  • Breadth-first search
  • Depth-first search
  • Depth-limited search
  • Iterative deepening search

Compare Informed and Uninformed Search Strategies

Module 5 - Problem Solving Algorithms

  • Local search
  • Hill-climbing search
  • Simulated annealing
  • Genetic algorithm

Module 6 Gaming

  • Game elements
  • Game tree
  • Search tree
  • Pruning and its types
  • Optimal decision making
  • Imperfect real-time decision making
  • Stochastic games
  • Variety of game programs

Module 7 Knowledge and Logic Agents

  • Knowledge agents
  • Logic agents
  • The Wumpus world

Module 8 Propositional Logic

  • Syntax and semantics
  • Theorem proof
  • SAT problem solving
  • Propositional model checking

Module 9 First-Order Logic

  • Syntax and semantics
  • Applying first-order logic
  • Knowledge engineering procedure

Module 10 Scheduling & Planning in the Real-World

  • Hierarchical task network (HTN)
  • Classical planning
  • Planning graphs
  • Contingent plans
  • Online planning agent
  • Multiagent planning

Module 11 Ontological Engineering

Upper ontology
  • Conceptualization
  • Categories and objects
  • Events and processes
  • Event calculus

Module 12 Decision Theory

  • Handling uncertainty
  • Uncertain reasoning approaches
  • Probability theory
  • Utility theory
  • Bayes’ rule

Module 13 Probability Theory

  • Bayesian networks
  • Dynamic Bayesian networks
  • Polytrees
  • Markov chain
  • Monte Carlo algorithm
  • Probability models

Module 14 Utility Theory

  • Multiattribute utility theory
  • Decision networks
  • Expert systems

Module 15 Game Theory for Decision Making

  • Single-move games
  • Repeated games
  • Sequential games
  • Nash equilibrium

Module 16 Machine Learning

  • Deep learning
  • Supervised Learnin
  • Unsupervised Learning
  • Reinforced Learning
  • Ensemble Learning
  • Computational Learning
  • Neural Networks
  • Decision Trees

Module 17 Natural Language Processing

  • Introduction to NLP
  • Information extraction
  • Deep Semantic Similarity Models (DSSM)
  • Deep reinforcement learning in NLP
  • Vision-Language Multimodal Intelligence

Module 18 Speech Recognition in AI

  • Fundamentals of Speech Recognition
  • Machine translation systems
  • Acoustic Modeling and Labeling
  • Decoding Acoustic Features into Speech

Module 19 Image Processing in AI

  • Image formation
  • Object recognition (by appearance and structural information)
  • 3D image reconstruction

Module 20 Robotics

  • Robot hardware
  • Robotic software architectures

Module 21 Integration of IoT with AI

  • Amazon’s Alexa
  • Science of Google Home
  • Apple Homekit

Module 22 AI Toolkits

  • Microsoft Cognitive Toolkit
  • PyTorch
  • Tensorflow

Module 23 Prime AI Applications

  • Agriculture
  • Call Centres
  • Technical Support
  • Energy and Mining
  • Healthcare
  • Intellectual Property
  • IT Service Management
  • Manufacturing
  • Software development
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