Natural Language Processing

1. Curate, annotate, and refine datasets to train the bot on various intents, entities, and dialogues. 2. Work with natural language processing (NLP) models, refine algorithms, and adjust training parameters to enhance the bot’s performance. 3. Craft conversation flows, responses, and scenarios that are natural, engaging, and aligned with user expectations. 4. Continuously monitor the bot's performance, identify areas for improvement, and implement updates to improve accuracy and user experience. 5. Analyse user interactions and feedback to fine-tune the bot's responses and functionality. 6. Work closely with developers, UX designers, and other stakeholders to integrate the bot into various platforms and ensure it meets business objectives.

1. collect and analyse data from various sources to extract meaningful insights and inform business decisions. 2. design and create visualisations and dashboards to present data trends and findings to stakeholders. 3. clean, preprocess, and organise data to ensure accuracy and readiness for analysis. 4. perform statistical analysis using tools like Excel and Python to uncover patterns, correlations, and trends in data sets. 5. collaborate with cross-functional teams to understand data requirements and deliver actionable insights. 6. automate data collection and analysis processes to streamline operations and improve efficiency. 7. conduct data quality assessments and implement data cleansing techniques to maintain data integrity. 8. utilise machine learning and predictive modeling techniques to forecast trends and outcomes. 9. stay updated on data analysis techniques and tools to enhance analytical capabilities. 10. communicate findings and recommendations to non-technical stakeholders in a clear and understandable manner.

1. Develop optimized and secure prompts to ensure AI systems generate 2 Identify and mitigate vulnerabilities in AI language models by testing for prompt injection attacks and other adversarial inputs. 2. Conduct risk assessments for language models and AI systems to evaluate exposure to prompt injection attacks and recommend improvements. 3. Develop and run adversarial prompt scenarios to probe for weaknesses in AI model responses, with the goal of enhancing the system’s resilience against malicious prompts. 4. Work closely with AI developers, machine learning engineers, and security teams to integrate prompt protection mechanisms in AI models. 5. Assist in refining the training data and fine-tuning AI models to ensure that they handle complex, ambiguous, or malicious prompts effectively. 6. Ethical AI and Safety Standards: Ensure that the design and implementation of prompt strategies align with ethical guidelines and safety standards, avoiding harmful or unintended outputs. 7. Automated Defense Systems: Design and develop automated systems that can detect and respond to potential prompt injection attacks in real-time 8. Continuous Improvement and Monitoring: Monitor deployed AI systems for signs of prompt injection vulnerabilities, providing continuous improvement updates as new attack vectors emerge. 9. Research and Innovation in AI Security: Stay updated with the latest research in AI safety, prompt engineering, and adversarial attacks, and apply innovative solutions to enhance system security. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/v1/2020.acl-main.463 Wallace, E., Feng, S., Kandpal, N., Singh, S., & Gardner, M. (2019). Universal adversarial triggers for attacking and analyzing NLP. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2153-2162. https://doi.org/10.18653/v1/D19-1221 Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019). Defending against neural fake news. Advances in Neural Information Processing Systems, 32, 9051–9062. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. Goodfellow, I., McDaniel, P., & Papernot, N. (2018). Making machine learning robust against adversarial inputs. Communications of the ACM, 61(7), 56-66. https://doi.org/10.1145/3134599