Challenges of Artificial Intelligence: Consequences and Solutions

This presentation summarize a small part of following paper that focus on challenges of AI.

(Saghiri, A.M., Vahidipour, S.M., Jabbarpour, M.R., Sookhak, M. and Forestiero, A., 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions. Applied Sciences, 12(8), p.4054.)

1. Challenges of Artificial Intelligence: Consequences and Solutions Ali Mohammad saghiri

2. agenda Motivations Different Types of Artificial Intelligence Challenges Challenges Consequences Internet of Things and Healthcare Potentials for research Suggested context and collaborators Conclusion References

3. Motivation

4. Motivation

5. Artificial intelligence ANI refers to intelligent systems that perform specific tasks like face recognition and games playing. AGI is used to describe agents whose intelligence is equivalent to that of humans and can be considered as HLI . ASI can be classified into three types: Speed ASI , collective ASI , and quality ASI , each with unique capabilities like superhuman speed and decision-making abilities beyond human capabilities. Saghiri, A.M., Vahidipour , S.M ., Jabbarpour , M.R., Sookhak , M. and Forestiero , A., 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions.  Applied Sciences ,  12 (8), p.4054 .

6. Finding new challenegs Reconsidering traditional concepts! How challenges may be appeared Saghiri, A.M., Vahidipour , S.M ., Jabbarpour , M.R., Sookhak , M. and Forestiero , A., 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions.  Applied Sciences ,  12 (8), p.4054 .

7. Challenges (Security) Security is a critical issue that has different dimensions Learning models may be hacked by malicious users In data-driven machine learning, developers might want to reverse-engineer the training data or learn how to develop a model that creates the desired output. Manipulating cross-over operators in a genetic algorithm may lead to different results in real-time applications. Adversarial machine learning can be considered as the first attempt to solve security problems in data driven algorithms. Security has close relationship with other challenges Explainable models Robustness Security Explainability Robustness

8. Challenges (Robustness) Robustness of an AI-based model refers to its stability after abnormal changes in input data caused by various factors. Malicious attacker Environmental noise Crash of other components of an AI-based system Traditional mechanisms like replication and multi-version programming might not work in intelligent systems Theory and concepts of robustness and reliability are in infancy, and new things would appear in this regard. Robustness Complexity

9. Challenges (Explainable) Explainable AI is an emerging field which refer to understanding and interpreting predictions made by machine learning models. Many applications in different domains, including healthcare, transportation, and military services. Many learning methods invest in non-explainable symbols to do tasks, in mission-critical tasks, we need to know the rationale behind decision-making in the intelligent system, and hence explainable AI can be useful. With such capability, humans may trust the decisions made by the models from different points of view, including bias and fairness challenges to mention a few. Explainability has close relationship with other challenges Fairness Accountability Trustworthiness Transparency Explainabiliy Fairness Accountability Trustworthiness Transparency

10. Challenges (Fairness) Bias in learning models can result in unfair decisions based on sensitive attributes such as race , gender , religion , etc. May be solved Data preprocessing step Manipulating the model after learning to attain fairness Imposing fairness constraints as a constraint to the main learning objective Massive amounts of data for training machines may lead to unfair learning systems Fairness has close relationship with other challenges Data Issues Fairness Data Issue

11. Challenges (Data Issues) A type of AI-based agent invests in data-driven methods to construct learning models. Cost of gathering, preparing, and cleaning the data Data incompleteness (or incomplete data) leads to inappropriate learning of algorithms and uncertainties during data analysis. Data heterogeneity , data insufficiency , imbalanced data , untrusted data, biased data , and data uncertainty are other data issues that may cause various difficulties in data-driven machine learning algorithms. Data Issues has close relationship with other challenges Privacy Energy Consumption Data Issue Energy Consumption Privacy

12. Challenges (energy consumption) Training machine learning model may lead to a high energy consumption. Deep learning models require a high computational power of GPUs . Mitigating the energy consumption problem intelligent agents can be addressed through four solutions: Investing in low-energy paradigms such as quantum computing , Finding mathematical frameworks for learning models with lower calculations, Sharing models among researchers, Using energy harvesting techniques

13. Challenges (Privacy) Users’ data is a crucial input for data-driven machine learning methods Data protection in the AI era can be viewed from two perspectives: data factors and human factors Preserving privacy in machine learning requires more effort and consideration, and federated learning is one such effort. Privacy has close relationship with other challenges Reproducibility Privacy Reproducibility

14. Challenges (Predictability) Whether the decision of an AI-based agent can be predicted in every situation or not Challenge is difficult to resolve due to unpredictability of agent behavior Reinforcement learning algorithms may contribute to unpredictability Chaos in mathematical and physical systems is a critical factor affecting predictability in AI-based agents Other issues, including ambiguity and paradox , may also contribute to unpredictability Unpredictability in AI-based agents may lead to subproblems in controllability, safety, accountability, and fairness. Predictability has close relationship with other challenges Controllability Safety Accountability Fairness Predictability Fairness Accountability Safety Controllability

15. Challenges (controllability) It is shown that this problem is not solvable considering safety issues and will be more severe by increasing the autonomy of AI-based agents. The halting problem is the problem of determining whether a computer program will finish running or continue to run forever. Alan Turing proved that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist. Some parts of AI control problems that can be reduced to halting problems that are not considered solvable problems. In the era of superintelligence , agents will be difficult to control for humans.

16. Other challenges Saghiri, A.M., Vahidipour , S.M ., Jabbarpour , M.R., Sookhak , M. and Forestiero , A., 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions.  Applied Sciences ,  12 (8), p.4054 .

17. Challenges Consequences AI has many applications and therefore its challenges leads to numerous problems! Peer to peer network Blockchain Challenge Consequence Lack of energy Training inaccurate models Lack of safety Learning models may hurt human Lack of explaiability The reason of detecting attack will be missing Lack of security A hacker may access to financial info by generating fake biometrics Lack of robustness The decisions(fork, configuration) may change by a little change in input Lack of controllability Ignoring users commands by network

18. Potentials for research Finding challenges New challenges cheating! Evolved challenges during transition to AGI Proposing solutions Vertical approach Finding an specific problem in a specific domain to solve( isolated solution may not work ) Security Robustness Energy Consumption Horizontal Approach Proposing solutions that consider the connections among challenges Solving security may lead to solve something for robustness in learning model Robustness Security

19. Conclusion A wide range of applications such as medical, educational, and military applications will use intelligent systems. We will see several learning models that may be used alone or with the cooperation of humans to solve problems. The number of challenges is increasing day by day. Ignoring challenges of AI may lead to several problems in near future. We summarized some of problems and general approaches for proposing solutions. This field has a high potential to gather funding and also organizing mega projects

20. References Saghiri, A.M., Vahidipour , S.M ., Jabbarpour , M.R., Sookhak , M. and Forestiero , A., 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions. Applied Sciences, 12(8), p.4054 . Jabbarpour , M.R., Saghiri, A.M. and Sookhak , M., 2021. A framework for component selection considering dark sides of artificial intelligence: a case study on autonomous vehicle. Electronics, 10(4), p.384 . Saghiri, A.M., 2020, April. A Survey on challenges in designing cognitive engines. In 2020 6th international conference on web research ( ICWR ) (pp. 165-171). IEEE. Saghiri, A.M., 2022. Cognitive Internet of Things: Challenges and Solutions. Artificial Intelligence-based Internet of Things Systems, pp.335 -362. "The Ethics of Artificial Intelligence", Stanford Encyclopedia of Philosophy. Retrieved from https://plato.stanford.edu/entries/ethics-ai/