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July 22, 2023 - 7 minutes

Overcoming Challenges in Artificial Intelligence: Tips and Strategies

Overcoming challenges in artificial intelligence is crucial in effectively utilizing AI systems. 

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Artificial Intelligence

There’s no doubt that artificial intelligence (AI) is the future of online learning and data analysis: it’s slowly being integrated into every aspect of our lives. Services or tasks that previously had to be carried out by human intelligence can now be performed utilizing machine learning and computer systems. If done right, AI provides the opportunity to optimize tasks, improve organizational decision making processes, and enable creative problem solving. Staying up-to-date with AI systems may be met with unexpected hurdles; that said, knowing how to confront these challenges head on is necessary for fostering strong businesses. 

Artificial Intelligence: a Quick Summary

Artificial intelligence (AI) refers to the development of computer systems and machines that perform tasks that previously required human intelligence. This is done through the creation of intelligent agents that learn their environment, use reason, and make decisions to achieve specific goals. AI encompasses numerous subfields and techniques, including machine learning, natural language processing, computer vision, robotics, and expert systems. It’s quickly proving to have monumental impacts on numerous industries including healthcare, finance, transportation, and entertainment. 

The history of AI

Despite being a term that’s in greater circulation today, artificial intelligence is nothing new. It’s a science that’s been around for decades, dating back to the 1900s with the early introduction of robots and other machines that modeled human behavior. In 1949, Edmund Callus Berkeley published a book titled, “Giant Brains. Or Machines That Think” that analyzed the similarities between emerging computers and human brains. This research and investment in developing smart computers expanded in the 1950s. 

In 1955, John McCarthy coined the term “artificial intelligence” at a workshop, later stating that AI "is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." 

Since the 1950s, AI has only expanded. Especially with computers becoming more accessible, the potential for artificial intelligence to transform our daily tasks, streamline workflows, and optimize our computer systems continues to flourish. At this point in time, almost every tech company has embraced AI in one way or another and AI systems are continuously advancing. 

Applications of artificial intelligence 

Today our lives are totally intertwined with artificial intelligence. Whether it be Alexa turning on our lights, the predictive text features that help complete our sentences, or our increasing reliance on chatbots like Chat GPT to accelerate research and support users in everything from writing cover letters to break up messages, we’re no stranger to the ways AI mediates our lives. Artificial intelligence has proved useful across industries, here are a few notable applications: 

  1. Cybersecurity: today, cybersecurity professionals utilize AI to detect and prevent cyber threats through analyzing network traffic, noting patterns, and identifying suspicious activities. 

  2. Health care: artificial intelligence is used in medical imaging for analysis and diagnosis and can help identify abnormalities in X-rays, MRIs, and CT scans. Similarly, AI has proven useful in drug discovery, genomics research, and personalized medicine. 

  3. Finance: individuals who work in finance utilize AI for fraud detection, in assessing risk, and for algorithmic trading in the financial sector. Equally, chatbots have proven useful in servicing customers, answering questions, and providing automated investment recommendations. 

  4. Transportation: AI is being increasingly integrated into automobiles through the enhancement of self-driving cars, as well as the introduction of computer vision, sensor fusion, and other decision-making algorithms that aid drivers. AI has also helped to optimize traffic flow, predict maintenance needs, and support the management of supply chains. 

  5. Education: we hear both the stresses and the benefits that AI is having on education systems globally; of course there’s fear of students turning in AI-generated papers, but that does not mean these tools should not be utilized in educational settings. AI is utilized to personalize learning, facilitates adaptive tutoring systems, and can create intelligent assessment tools. This helps foster a more personalized learning path for individuals, allows one to target specific weaknesses, offers real-time feedback, and provides data-driven feedback to teachers. 

These are only a few examples of the applications of AI in our daily lives and the technology is only advancing. On one hand, AI is positively transforming lives and transforming the way we work from tech professionals, medical providers, and students alike. On the other hand, we must be aware of the challenges and potential for misinformation on the part of AI as we utilize these tools. The development of AI has sparked debate surrounding ethical concerns, privacy bias, and the impact that these chatbots will have on our job markets and society. 

Challenges in Artificial Intelligence

Artificial intelligence, like all fields of tech, is constantly developing and transforming in ways that can lead to challenges for every sector. The toughest challenges to overcome with artificial intelligence are the following.

Cybersecurity challenges

Cybersecurity challenges in AI stem from preexisting vulnerabilities associated with data and AI systems. This could lead to attacks on AI systems, compromising the quality of the information received through AI systems, and risking the privacy and integrity of personal data. Or at another extreme, individuals may deploy AI for malicious purposes like the generation of deep fake content or automating cyberattacks. In short, securing AI systems is crucial in securing reliable information, making strong data-informed decisions, and ensuring the protection and integrity of data. 

Ironhack’s tip: data security professionals must collaborate with cybersecurity professionals in order to create and protect AI systems. In order to do so effectively, tech professionals may need additional adversarial training, workshops in secure data handling, and expanded information on privacy preservation. This work must be ongoing and tech professionals must stay up-to-date on trends in data security and potential threats to AI systems

Digital pathology challenges

Digital pathology refers to the digitization and analysis of pathology slides utilizing AI. As opposed to viewing pathology slides through a microscope, these slides are digitized and converted into high-resolution images which allows pathologists to analyze and interpret the tissue in hand with computer-based tools. Despite revolutionizing this area of research, it’s a domain that faces challenges in everything from image quality standardization, annotation reliability, computational resource requirements, interpretability for pathologists, regulatory considerations, and workflow integration. 

Ironhack’s tip: in order to ensure high quality images, accurate annotations, and efficiency in computational processes, tech professionals must adhere to regulatory frameworks, integrate clinical workflows, and be ready to address ethical concerns. Strong collaboration between stakeholders is necessary in order to successfully confront challenges and realize the full potential of AI in digital pathology. 

Insufficient or low quality data

AI systems function are trained on a set of relevant data in order to learn patterns, identify trends, and create predictive models that aid decision making. If that data is limited, biased, or inaccurate, the AI system may not offer reliable recommendations. It’s a great challenge given that data is sometimes limited, hard to access, or simply does not yet exist. 

Ironhack’s tip: Data scientists must work to find high quality, accurate, and representative data. They must control for bias and modify the work according to outcomes to ensure the integrity of the recommendations and control for biases and discrimination.  

Outdated infrastructure

In order for AI systems to provide accurate and relevant recommendations and results, they need to process immense amounts of information in fractions of a second. That can only happen if the devices on which the AI systems operate are up-to-date, secure, and capable of providing the desired insights. 

Ironhack’s tip: if an organization is looking to utilize AI systems, they must be up-to-date on the latest tech trends, invest in infrastructure, tools, and applications that enable AI systems, and ensure that their tech professionals also possess the necessary skills and expertise to utilize these services. If the infrastructure is outdated, businesses are more vulnerable to some security threats and simply cannot secure the advantage that other organizations will secure. 

Ethical and legal implications

As AI is widely adopted, we see the rise of ethical concerns ranging from integrity of work, intellectual property concerns, privacy, security, and potential impacts on job security. Ensuring that AI is deployed in a responsible and ethical manner is a huge challenge for many organizations. 

Ironhack’s tip: in adapting to AI structures, make privacy and security a priority. Collaborate with cybersecurity professionals to ensure that data is protected and the information is properly secured. Stay up-to-date with legislation surrounding machine learning, data privacy, and AI generated content and establish your own organizational regulations, guidelines, and frameworks that ensure fairness, accountability, and transparency. 

In all, AI has the potential to transform our world through improving efficiency, accuracy, and consistency in all aspects of research and data analysis. However, there are endless challenges for those of us looking to utilize AI systems. In order to successfully confront these challenges, organizations must embrace collaboration, interdisciplinary research, and stay clued into the ongoing work on academics, policymakers, and industry trends and developments. 

If you’re interested in learning more about AI, data analysis, and machine learning, then look no further. Ironhack offers bootcamps in data analysis and cybersecurity that will help you hone your skills and expand your knowledge as a tech professional. 

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