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: Our products are highly technical in nature, and in order to maintain and improve our market position, we depend on successful research and development activity to continue to improve our existing products and develop new products. We cannot be certain that we will have sufficient research and development capability to respond to changes in the industries in which we operate. These changes could include changes in the technological environment in which we currently operate, increased demand for new products or the development of alternatives to our products. For example, the development of lighter weight steel alloys has made the use of steel in gas cylinders a more competitive alternative to aluminum than it had been previously. In addition, our superformed aluminum components compete with new high-performance composite materials developed for use in the aerospace industry. In our efforts to develop and market new products and enhancements to our existing products, we may fail to identify new product opportunities or timely bring new products to market. We may also experience delays in completing development of, enhancements to or new versions of our products and product innovations may not achieve the market penetration or price stability necessary for profitability. In addition to benefiting from our research collaboration with universities, we spent $5.7 million, $6.4 million and $7.8 million in 2019, 2018 and 2017 respectively, on our own research and development activities. We expect to fund our future research and development expenditure requirements through operating cash flows and restricted levels of indebtedness, but if operating profit decreases, we may not be able to invest in research and development or continue to develop new products or enhancements.
Assistant: