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According to the May 2023 report by Challenger, Gray & Christmas Inc, US-based employers cut 88,000 jobs in this one month alone. Of these layoffs, Artificial Intelligence (AI) took over 4,000 jobs; or from another perspective, AI was responsible for 5% of the ensuing unemployment. Having read this, I am sure you have heard this question over the past few months, “Will A.I. take MY job?”
Everyone is wondering this, these days and for each person wondering about it, 10 articles have been written, answering the question from all possible angles; from a YES to a NO and everything else in between. This is because despite being experts, most pundits just have a 50/50 shot of being right, only a few points above guesswork.
The real issue isn’t who has the correct answer, or whether the job you do is replaceable by AI or not. Those are just excuses to avoid the inevitable. The major focus has to be on preparing for the possibility that yes, AI will take your job (and mine as well). Inevitably, to rephrase my earlier statement, someone who knows how to work with the AI, and knows its nuances, will take our jobs.
Hence, being prepared is the only way. To get you ready, SlideTeam has created this Comprehensive Training Curriculum on Artificial Intelligence in the form of a PowerPoint Presentation Deck. Trainers can download the training module with a click here. We finish the training with our signature slides on key takeaways from the training and a quiz to test what has been learnt.
In addition to having you in sync with the AI world, this training module has the following benefits to the trainer, the trainees, and the business:
- Enhanced Decision-Making: By educating employees about AI, companies can empower their workforce to make better decisions. AI techniques can help analyze large volumes of data, identify patterns, and extract valuable insights. When employees understand how AI works and its potential applications, they can leverage AI tools and techniques to make data-driven decisions, improve productivity, and uncover new business opportunities. This knowledge equips employees with the ability to harness AI to enhance their decision-making capabilities, leading to improved efficiency and competitiveness.
- Process Automation and Efficiency: AI has the potential to automate repetitive and mundane tasks, freeing up employees’ time to focus on more complex and strategic activities. By educating employees about AI, companies can foster a culture of innovation and automation. Employees learn how to identify processes and tasks that can be automated using AI technologies such as machine learning, natural language processing, and robotic process automation. This knowledge enables them to streamline workflows, reduce errors, and improve operational efficiency, ultimately saving time and resources.
- Innovation and Competitive Advantage: AI is transforming industries, and companies that embrace AI technologies have the potential to gain a competitive edge. By educating employees about AI concepts, applications, and emerging trends, companies can foster a culture of innovation and creativity. Employees who understand AI can identify areas where AI can be applied to drive innovation, optimize processes, and create new products or services. This knowledge empowers employees to think critically and strategically about AI, leading to the development of novel solutions that can differentiate the company from its competitors.
Access our Comprehensive Training Courses on AI Here
Access our Comprehensive Training Courses on AI Here
Session I: Introduction to Artificial Intelligence
Trainees are greeted to the first instruction to this workshop with a slide on the learning outcomes of the course, where we introduce them to AI. We begin the session with what AI is, go into its history and dive into its classification, on the two main basis of functionality and capability.
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On functionality, AI is classified into reactive machines, limited theory, theory of mind, etc. Seen from the perspective of capabilities, AI is classified into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). Access our learning modules for the slides that shed light on these classifications to have an understanding of these that goes beyond theory.
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Next, we devote some time to the importance of AI and how it differs from human intelligence on criteria, such as time efficiency, multitasking, decision-making, etc. We deconstruct the building blocks of an AI system such as the Input, Edge, Cloud, etc., and round-off the first session with slides to discuss trends and statistics in AI and the associated web of industries and business that have grown around it. Check our professionally designed artificial intelligence ppt now.
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Session II: Applications of Artificial Intelligence
AI is no more just a mere, wacky Sci-Fi theory, as evidenced by Siri, Google, or Alexa on your phone or in the latest addition, ChatGPT and Mid-Journey. Have you written your work emails with AI or made your favorite celebrity into a cute half rabbit half-man creature yet? If not, there is value to this tinkering that you may be missing.
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Applications of AI include marketing, finance, defense & military, telecommunication, sales, healthcare, automobile industry, and many more. Earmarked slide(s) explain in exquisite detail expert-curated content, relevant to each of these 18 applications.
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To understand these applications in greater detail, download this comprehensive training from the link below.
Session III: Core Concepts of Machine Learning
Machine Learning or ML is a significant part of AI. It’s what makes AI so ‘learned’ (note, we have not used the word knowledgeable) and ‘life-like.’ In this part of the training, we present key concepts that create the architecture of Machine Learning (ML). Beginning with an overview, we take the trainees through the history of ML, before diving deep into the ML algorithms. With a primary division into supervised, unsupervised, and reinforcement ML algorithms, we explain these in detail, along with their sub-categories. Some of these sub-divisions include Regression Models, Decision Trees, Clustering, Association Rule, and other kinds of rules.
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With the categories explored, we move to the importance of ML and the steps involved in its execution. These include data collection, data preparation, choosing a model, training the model, and such a chain continues till the point, where we get to making predictions. The Session III ends with a discussion on advantages, disadvantages, and the future of ML.
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Session IV: Use Cases of ML
With our commitment to providing a hands-on training experience, we choose Session IV to present the trainees with the use case of ML. This is important as many of these use cases are part of our day-to-day lives.
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We present four types of use cases:
- Translation: This category includes software like Google Translate, Microsoft Translate, and Facebook Translator as examples. We also present the limitations of these software.
- Medical Diagnosis: Diagnosing people online is a novel, yet real application of ML. In these slides, we discuss the objectives, benefits, and the applications of medical diagnosis using ML. Finally, we note some organizations that are using this application such as Google Health, medX, and more.
- Imagine Recognition: Opening your phone or laptop with your face ID is already a common use-case these days. In this part, we discuss how this function works, its ML models, and its application for face analysis and animal monitoring.
- Speech Recognition: Plenty of daily devices have the speech to text feature. In this section, we answer how this system works, its key features, algorithms, and a particular use case of the IBM company.
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With these use cases well-understood, we jump right into deep learning.
Session V: Deep Learning in a Nutshell
Let’s immerse the audience in Deep Learning in this session. We begin with an introduction to this concept, with a visual that will stay with the trainees for life. We move on to the importance and working of Deep Learning. We distinguish DL even further by comparing it with ML.
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The slides that follow emphasize the functions (mathematical) that run DL. These include Sigmoid Activation, tan-h, ReLu, etc. each function is described in one slide with its corresponding graph for a visual learning experience.
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We advance to the DL Process and begin its explanation with its working, deep neural networks, DL technique, creating DL models, and more. With an understanding of the workings of Deep Learning, we scan its advantages such as excellent use of unstructured data, end of dependence on function engineering, and more. Following these benefits, we get down to its on-ground applications with real potential, such as adding sound to silent movies, adding color to B&W images, pixel restoration, etc. Each application gets a dedicated slide to explore it in great depth.
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We complete this panel with a study of the limitations of deep learning and venture into the next session, where we get out heads around natural language processing and related concepts.
Session VI: Fundamentals of NLP, NLU, & NLG
Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU) are three crucial systems that make AI work. With a step-by-step approach, we ensure that the audience comprehends these concepts. Starting with NLP, we understand what it is and its techniques. We also have slides to go into its working and its importance to a business.
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The steps in NLP are presented and detailed in the slides that follow. These include: Lexical Analysis, Syntactical Analysis, Discourse Integration, etc. We finish NLP with a slide on its applications.
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For NLG, we present its concept, working, and applications. Some applications include creating chatbots, lead nurturing emails, SMS, etc. We engage with NLG as well, taking a glimpse at its advantages to a business.
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With an introduction to NLU, we contrast it with NLP and jump right into its use cases such as, automatic ticket routing, automated reasoning, etc. With specific discussions on their uses, we move on to the importance of NLP and the factors to consider when choosing business and industrial solutions, where NLP serves as the key ingredient.
We ensure clarity in this novel and interesting topic, with a number of slides dedicated to comparing AI concepts such as ML, DL, AI, etc. with each other.
This is but a glimpse into the extensive content-ready collection of slides in this exhaustive training module. Download now with a click on the link below.
Session VII: Hybrid Artificial Intelligence: Machines as Creative Partners
People have been using ChatGPT now for some amazing and mind-blowing applications as well as Mid-Journey to have their imagination turn into reality. In this session, we focus on how AI boosts our creative endeavors in the form of a Hybrid Model.
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We begin with an overview that classifies the hybrid model into symbolic and non-symbolic and understand the pros and cons of this model. We dive right into neural networks; what they are, the layers within, the ‘neurons’, and the functions that make these neural networks work.
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We spend some slides on Backpropagation and Artificial Neural Networks and discuss their advantages and disadvantages.
It is time well-spent in mentoring the trainees on Convoluted Neural Networks, Autoencoders, Variational Autoencoders, Feedforward Neural Networks, Recurrent Neural Networks, and finally Mixture Density Networks.
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To gain access to these slides and concepts, the training module is just a click away!
Session VIII: Essentials of Successful AI Strategy for Business
The final session is your one stop-shop for AI implementation in business operations. In this section, we present slides with relevant graphs, charts, and tables to ensure you and your team are at ease when integrating business with AI.
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We have slides for AI strategies for business outcomes, building an AI strategy, roadmap for building a viable AI strategy, five-step implementation plan, and much more.
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This section will take you from theory to hands on experience to application/use.
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Download our comprehensive training module now to take your business to the future.
FAQs on Artificial Intelligence
What are four types of AI?
The four types of AI are:
- Reactive Machines: These AI systems operate solely on the basis of the current input without any memory or past experience. They don’t possess the ability to learn or adapt. Examples include chess-playing computers that evaluate the current board state to make the next move.
- Limited Memory: These AI systems can store and retrieve past experiences to make decisions. Self-driving cars often use limited memory to recognize and respond to objects based on previous encounters.
- Theory of Mind: This level of AI is hypothetical and involves understanding emotions, beliefs, intentions, and thoughts of others to interact more intelligently. Currently, this level of AI is still under development.
- Self-Awareness: This is the highest level of AI, where machines possess consciousness and self-awareness, on a par with human beings. Such AI systems do not exist yet.
Who is the father of AI?
The term "father of AI" is often attributed to John McCarthy. He is a computer scientist who coined the term "artificial intelligence" in 1956 during the Dartmouth Conference. McCarthy's work laid the foundation for the development of AI as a field of study.
What are the 5 big ideas in AI?
The five big ideas in AI, as highlighted by computer scientist Stuart Russell and Google's DeepMind co-founder Demis Hassabis, are:
- Systems that think rationally: Developing AI systems capable of reasoning, logic, and rational decision-making.
- Systems that act humanely: Creating AI systems that mimic human behavior, understanding natural language, and recognizing objects.
- Systems that think like humans: Building AI systems that can understand, learn, and generalize from experiences.
- Systems that act rationally: Designing AI systems that maximize expected utility based on available information, even if their behavior does not resemble human-like thinking.
- Systems that learn from data: Developing AI systems capable of learning from large datasets and improving performance over time.
Is Siri artificial intelligence?
Siri, a tool that Apple acquired in 2010, can be considered a form of artificial intelligence. Siri uses NLP and ML techniques to understand and respond to user queries or commands. It can perform tasks like setting reminders, making appointments, answering questions, and controlling smart devices. While Siri is a limited form of AI, it demonstrates elements of intelligent behavior by interpreting and responding to user inputs.
