Master the concepts of ChatGPT to boost your skills, improve your productivity, and uncover new opportunities with our new ChatGPT Fundamental Course.
- 3 Hours
- 45 Lessons
Google Bard AI
Learn about the fundamentals of Bard AI, its evolution, common tools, and business use cases with our new Google Bard AI Course.
- 3 Hours
- 40 Lessons
AI For Business
Familiarize yourself with the capabilities of AI and its applications to improve your skills using AI in your business with the new AI For Business Course.
- 3 Hours
- 44 Lessons
Ethics of Artificial Intelligence
Understand the importance of ethics in AI, ethical frameworks, principles, and challenges with our new AI Ethics Course.
- 3 Hours
- 35 Lessons
AI & Fintech Masterclass
Explore the synergy between AI and Fintech in this masterclass, delving into how artificial intelligence is reshaping the financial industry for innovation, efficiency, and customer-centric services.
- 1.5 Hours
- 11 Lessons
Certified Web 3.0 Professional (CW3P)™
Certified Enterprise Blockchain Professional (CEBP)™
Certified Enterprise Blockchain Architect (CEBA)™
Generative AI Skill Path FAQs
The Generative AI skill path includes 5 courses on different topics, such as ChatGPT Fundamentals, Google Bard AI, AI for Business, Ethics of AI, and AI & Fintech Masterclass.
Yes, 101 Blockchains offers expert support with the Generative AI skill path. Generative AI is a new concept for many learners, and we understand the difficulty of beginners trying to find their way through generative AI applications. That is why we offer fast and practical solutions to provide our learners with a seamless learning experience.
The Generative AI skill path does not impose any specific prerequisites on learners interested in pursuing this skill path. On the other hand, prior awareness of AI fundamentals alongside the foundations of generative AI could serve valuable advantages.
The top concerns regarding the use of generative AI revolve around the possibilities of inaccuracy in their results. Generative AI could also disrupt many existing business models alongside offering new options for plagiarism. The applications of generative AI also deal with challenges such as impersonation of identities and possibilities for generating fake news.
The strategies for removing biases from generative AI models include fine-tuning, refining the diverse and unbiased training data, and developing fairness-aware models. You should ensure that the training data represents different perspectives and reduces inherent biases. It is also important to create fairness-aware models with techniques such as re-sampling, adversarial training, and fairness constraints. Another proven technique for removing bias in generative AI models is bias monitoring and evaluation. The continuous monitoring and evaluation of outputs of a generative AI model could help in identifying and resolving bias-related issues.
The Generative AI skill path offers dedicated training courses to help you familiarize yourself with the applications of generative AI tools. On top of it, the Generative AI skill path also offers practical demos for using ChatGPT, Google Bard, AI for Business and AI Ethics. You can become Generative AI Research Scientist and earn an average annual salary of $100,000 to $150,000.
The Generative AI skill path is a recommended training resource for anyone interested in learning about generative AI and its applications (no prior AI skills or knowledge are required). However, it is important to note the specific target groups that can make the most of the Generative AI skill path. The skill path is ideal for people interested in learning about ChatGPT and chatbot applications. It is also a suitable choice for content creators, business owners, entrepreneurs, senior executives, and managers.
One of the common questions we get regarding generative AI revolves around the ways in which it differs from traditional AI. Generative AI is capable of producing new content, designs, synthetic data, and chat responses. On the other hand, traditional AI focuses on the detection of patterns, analytics, decision-making, and data classification tasks. Generative AI utilizes neural network techniques and relies on prompt engineering to address different tasks.
Generative AI models can become more energy efficient by using different strategies such as model compression, adaptive computation, and hardware optimization. For example, model compression could help in reducing the size of AI models and computation complexity. Similarly, the efficiency of algorithms and training techniques could also play a crucial role in lowering energy consumption.
You can use different types of metrics to evaluate the outputs from generative AI models. Popular automatic metrics such as the BLEU score can help in measuring different factors in the output generated with generative AI models. In addition, you can also opt for human evaluation based on different factors such as relevance, fluency, creativity, and coherence of the content.