Challenges of Artificial Intelligence: Consequences and Solutions
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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.)
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Challenges of Artificial Intelligence: Consequences and Solutions
- 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 Artificial intelligence has had a great impact on every fields. Gartner predicts the business value created by AI will reach $3.9T in 2022. Most articles in the literature focus on the extraordinary capabilities of AI
- 4. MOTIVATION AI has many applications • Internet of Things: Smart city • Computer networks: Adaptive networks and cognitive networks • Software engineering: programming, test The fundamental challenges of AI inherit all of its applications • Energy • Safety • Fairness The definitions of challenges • change as AI evolve to • Artificial General Intelligence (AGI) • Artificial Super intelligence (ASI) Without appropriate solutions we may face hacking or failure
- 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 Explainabilit y 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 Explainabili y 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 Predictabilit y 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/
Editor's Notes
- An article from Forbes stated that Gartner predicts the business value created by AI will reach $3.9 trillion dollars in 2022. Meanwhile, IDC forecasts worldwide spending on cognitive and AI systems will reach $77.6 billion dollars in 2022. https://www.forbes.com/sites/louiscolumbus/2019/03/27/roundup-of-machine-learning-forecasts-and-market-estimates-2019/?sh=529bac47695a
- Security is a critical issue that needs to be considered in designing intelligent systems. Every piece of software, including learning systems, may be hacked by malicious users. Security challenges in AI may bring several new challenges that cannot be covered in this paper. 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. Adversarial machine learning can be considered as the first attempt to solve some security problems in machine learning. AI algorithms have been utilized by attackers to organize attacks, so AI-based defense mechanisms must be applied to enhance the security of AI-based systems. HLI-based agents pose an even more significant security challenge because humans may not be able to organize defense mechanisms faster than those agents. AI algorithms have been utilized by attackers to organize attacks, so AI-based defense mechanisms must be applied to enhance the security of AI-based systems.
- The robustness of an AI-based model refers to its stability after abnormal changes in input data caused by various factors. The cause of changes may be a malicious attacker, environmental noise, or a crash of other components of an AI-based system. A robust model has a higher priority in deployment, among several models with similar performance. Traditional mechanisms like replication and multi-version programming might not work in intelligent systems, and this field is in its early stage. Some works discuss the difference between accuracy and robustness of a learning model. Theory and concepts of robustness and reliability are in infancy, and new things would appear in this regard. HLI-based agents may face the challenge of weak robustness in unreliable machine learning models. An HLI with weak robustness is error-prone in practice.
- Explainable AI is an emerging field with many applications in different domains, including healthcare, transportation, and military services. A set of tools and processes may be used to bring explainability to a learning model. 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. This means that explainability may determine some solutions for other challenges, such as fairness and trustworthiness. Many learning methods invest in non-explainable symbols to do tasks, but in many situations, including mission-critical tasks, we need to know the rationale behind decision-making in the intelligent system, and hence explainable AI can be useful. Recent developments and applications in explainable learning algorithms in practice have been summarized in [68,69]. This challenge becomes more critical when a human agent is replaced with an HLI-based agent to do a critical task in healthcare, military, or other mission-critical situations. In these situations, any sort of decision must be explainable.
- Training an HLI-based agent may be challenging without appropriate datasets and theories The capability to consider sensitive attributes may be vital for an HLI-based agent in certain situations.
- Iterative learning processes in deep learning algorithms result in high energy consumption Deep learning is preferred for designing HLI-based agents due to its high accuracy and similarity to human decision-making High computational power, particularly GPUs, is required for deep learning models, making them costly to train and develop from financial and energy perspectives HLI-based agents may use a predefined plan for learning multiple models concurrently, which requires high computational power Mitigating the energy consumption problem in HLI-based 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, and using energy harvesting techniques Energy harvesting techniques can be used to recover wasted energy if there is no way to decrease the load of computations.
- Challenge: whether the decision of an AI-based agent can be predicted in every situation or not Consequence: ability to control and ensure safety/trust of future AI agents is uncertain 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 HLI-based agents may lead to subproblems in safety, trust, accountability, and fairness.
- 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 are not considered solvable problems. In the era of superintelligence, agents will be difficult to control for humans. Controllability has many dimensions, and few papers focus on this problem in a specific domain such as safety. Controllability has four types: explicit, implicit, delegated, and aligned. 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. Because of the assumed properties of HLI-based agents, machines may be uncontrollable in some situations.
- Smart Healthcare is the use of technology and data analytics to improve healthcare delivery and patient outcomes. It includes wearable devices, telemedicine, and electronic health records, among other digital tools. Smart Healthcare aims to provide more accessible, cost-effective, and personalized care.
- Challenges of AI were analyzed with a particular focus on HLI Popularities of challenges were not similar to each other Some challenges, such as security and fairness, are more popular than other challenges, such as energy and complexity Brief descriptions were given for each challenge to understand their nature Connections and combinations among challenges, and also their evolutions, were studied Some well-known challenges such as the curse of dimensionality were left out because of their popularity in the AI domain During the evolution from ANI to ASI, all challenges may get new dimensions as we discover more information about our environment during interaction with intelligent systems Some challenges such as the monopoly of corporations during the AI era, as well as widespread job loss, were not in the scope of this paper because the focus was mainly on computer science These challenges can be considered in future works