Certified Associate in Artificial Intelligence – Artificial Neural Networks
A global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise.
- H. James Wilson, Paul R. Daugherty, and Nicola Morini-Bianzino
March 23, 2017
The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur.
It can be a distressing picture.
But here’s what we’ve been overlooking: Many new jobs will also be created — jobs that look nothing like those that exist today.
In Accenture PLC’s global study of more than 1,000 large companies already using or testing AI and machine-learning systems, we identified the emergence of entire categories of new, uniquely human jobs. These roles are not replacing old ones. They are novel, requiring skills and training that have no precedents. (Accenture’s study, “How Companies Are Reimagining Business Processes With IT,” will be published this summer.)
A category of new jobs will need human workers to teach AI systems how they should perform — and it is emerging rapidly. At one end of the spectrum, trainers help natural-language processors and language translators make fewer errors. At the other end, they teach AI algorithms how to mimic human behaviors.
Customer service chatbots, for example, need to be trained to detect the complexities and subtleties of human communication. Yahoo Inc. is trying to teach its language processing system that people do not always literally mean what they say. Thus far, Yahoo engineers have developed an algorithm that can detect sarcasm on social media and websites with an accuracy of at least 80%.
Companies that deploy advanced AI systems will need a cadre of employees who can explain the inner workings of complex algorithms to nontechnical professionals. For example, algorithm forensics analysts would be responsible for holding any algorithm accountable for its results. When a system makes a mistake or when its decisions lead to unintended negative consequences, the forensics analyst would be expected to conduct an “autopsy” on the event to understand the causes of that behavior, allowing it to be corrected.
Another category of jobs include – sustainers — who will help ensure that AI systems are operating as designed and that unintended consequences are addressed with the appropriate urgency.
Upon completion of the course, participants should be able to:
- Understand and demonstrate knowledge of artificial neural networks
- Building a Perceptron based classifier
- Constructing a single layer neural network
- Constructing a multilayer neural network
- Building a vector quantizer
- Analyzing sequential data using recurrent neural networks
- Visualizing characters in an Optical Character Recognition (OCR) database
- Building an Optical Character Recognition (OCR) engine
Who should attend
This course will help, programmers, application developers and software engineers pick the right strategy for developing cross-platform web applications that run on a variety of desktop computers as well as mobile devices. The primary audience is developers who need to learn how to develop mobile applications
Associate certificate in Machine Learning, Non-linear supervised learning algorithms
Mix of Instructor-led, case study driven and hands-on for select phases
H/w, S/w Reqd
A modern Mac running the current or previous generation of Mac OS
24 Hours (2 days Instructor led + 8 hours online learning)
- Course Name: Certified Associate in Artificial Intelligence – Artificial Neural Networks
- Location: Singapore
- Duration: 2 days classroom + 8 hours online
- Exam Time: 60 minutes
- Course Price: Call for price
- Minimum requirements: Foundational Certificate in Programming
|#||Topic||Method of Delivery|
Chapter 1 – Building a neural network
Layers of neurons
N-dimensional input data
Training a neural network
Chapter 2 – Building a Perceptron based classifier
Linear function to make a decision
Bias of the neuron
Constructing a single layer neural network
Constructing a multilayer neural network
Chapter 3 – Building a vector quantizer
Analyzing sequential data using recurrent neural networks
Chapter 4 – Optical Character Recognition (OCR)
Visualizing characters in an Optical Character
Building an Optical Character Recognition engine
|Online Self paced|
- Certificate Title: Certified Associate in Artificial Intelligence – Artificial Neural Networks
- Certificate Awarding Body: ITPACS
Information Technology Professional Accreditations and Certifications Society (ITPACS) is a non-profit organization focused on improving technology skills for the future. ITPACS offers associate level, professional level and leader certifications across 6 domains including data science, web development, mobile development, cyber security, IoT and blockchain. Applicants have to go through a exam eligibility process demonstrating their experience.
The Associate certification is catered to individuals with less than 1 year working experience in the field. This is ideal for newcomers starting out in the profession or those seeking to make an entry into the profession. Applicants are required to have completed the application process prior to taking the exam.
- Exam Format: Closed-book format.
Questions: 30 multiple choice questions, coding exercises
Passing Score: 65%
Exam Duration: 60 minutes
- Exam needs to be taken within 12 months from the exam voucher issue date
Artificial Intelligence - Other Associate Certifications
- Reinforcement Learning
- Classifiers and regressors
- Ensemble Learning
- Detecting Patterns with Unsupervised Learning
- Building Recommender Systems
- Logic Programming
- Heuristic Search Techniques
- Natural Language Processing
- Building A Speech Recognizer
- Object Detection and Tracking
- Artificial Neural Networks
- Deep Learning with Convolutional Neural Networks