System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: On September 15, 2020, the Company spun-off its specialty real estate holding business to an operating subsidiary and then pivot back to being a technology company. Going forward, the Company intends to focus its business model to operate and manage a portfolio of Electric Vehicles, Artificial Intelligence, Machine Learning and Robotics (“EV-AI-ML-R”) assets, businesses and operations in addition to its Power Controls, Battery Technology, Wireless Technology, and Residential utility meters and remote, mission-critical devices businesses in North America. The Company has been absent from the technology industry for more than ten years. Thus, in the Electric Vehicles, Artificial Intelligence, Machine Learning and Robotics industry, the Company is an early stage company. You must consider the risks and difficulties we face as an early stage company with limited operating history. If we do not successfully address these risks, our business, prospects, operating results and financial condition will be materially and adversely harmed. We have a very limited operating history on which investors can base an evaluation of our business, operating results and prospects. We intend to derive our revenues from sales of the electric vehicles, related sales of zero emission vehicle credits, and from electric powertrain development services and sales. However, there is no assurance that we could achieve this goal because we are new to this industry.
Assistant: