AI has evolved from ideas and theories into a technology that has reshaped software development. At Devōt, for instance, AI is a daily tool; for our developers, the choice is often GitHub Copilot or Codium AI.
It may seem that AI has experienced a boom in recent years, but would you guess that the concept of AI goes back to ancient myths?
There has always been a link between imagination and science. So, in this blog post, let's see how it all started, who the important (and should be more famous) scientists behind AI technology are, what are the good and the bad sides of AI software, and what is AI
History of artificial intelligence and AI solutions
AI - The story old as time
Artificial intelligence (AI) began long before the modern digital computer was invented. Believe it or not, one of the earlier examples of AI goes back to Greek methodology. It was said that Hephaestus (the story goes he created Pandora), the god of blacksmiths and metalworkers, had created automatons made of metal. These "mechanical servants" were, according to the myth, artificial women with god's knowledge.
Adrienne Mayor, a research scholar in the Department of Classics in the School of Humanities and Sciences, said:
“Our ability to imagine artificial intelligence goes back to the ancient times. Long before technological advances made self-moving devices possible, ideas about creating artificial life and robots were explored in ancient myths.”
Are these stories true or not? Who knows, but it really sounds interesting and showcases our fascination with artificial intelligence. If anything, it shows that humans have always wanted to create something more: machines that will act like them.
Can machines think?
In the 1940s and 1950s, the basic theories of AI were developed. Alan Turing, a British mathematician and logician, introduced the concept of a universal machine capable of performing any computable operation, now known as the Turing Machine (Imitation Game anyone?). This concept was an important theoretical construct for AI. Turing also proposed the "Turing Test" in 1950, a criterion of intelligence that has significantly influenced debates over AI. It was his question, "Can machines think?" that spurred the development of AI as a distinct field.
Who invented the future?
At the same time, other notable figures like Claude Shannon, the father of information theory, and John von Neumann, who developed the architecture for the series of computers that led to the modern digital computer, made significant contributions. Their work on how machines process information laid the ground for understanding how computers could potentially simulate human thinking.
Claude Shannon introduced information theory, which became fundamental in developing digital communications and computing. His theory explained how information is transmitted, stored, and processed, paving the way for more complex data handling in computing systems.
And no, this man who laid the foundation for communication infrastructure never won a Nobel Prize. There is a documentary about him, The Bit Player, that I immediately put on my watchlist. Considering that we will have a competition at the Job Fair about solving the Rubik's cube, it caught my eye that he invented a Rubik’s Cube solving machine!
John von Neumann developed the von Neumann architecture, which is still used in most computers today. This architecture describes a system where the computer's memory holds both the data and the program, allowing the machine to execute complex tasks efficiently.
In other words, it splits program code and data in memory, which speeds up and improves CPU instruction execution. Its simple design results in more durable, scalable, and affordable computing solutions, making it easier to update and modify software across various applications, from gaming to embedded systems.
From theory to early prototypes
The transition from theoretical concepts to tangible AI applications began to gain momentum in the 1950s and 1960s. One of the first major steps was the development of logic theorist programs by Allen Newell, Herbert A. Simon, and J.C. Shaw. This program, considered by many as the first AI software, was capable of solving puzzles using a decision tree method, setting the stage for more sophisticated AI systems.
In 1954, Newell was present at a talk given by AI pioneer Oliver Selfridge, who discussed pattern matching. During this event, Newell realized that the basic components of computers could collaborate to perform complex tasks, potentially even replicating human thought processes.
Nevell said in the book "The Machines Who Think":
“I had such a sense of clarity that this was a new path, and one I was going to go down. I haven’t had that sensation very many times. I’m pretty skeptical, and so I don’t normally go off on a toot, but I did on that one. Completely absorbed in it—without existing with the two or three levels consciousness so that you’re working, and aware that you’re working, and aware of the consequences and implications, the normal mode of thought. No. Completely absorbed for ten to twelve hours.”
In 1956, the Dartmouth Summer Research Project on Artificial Intelligence coined the term "Artificial Intelligence," bringing together researchers who believed that machine intelligence could be achieved through symbolic methods, including programming languages like LISP, developed by John McCarthy, which became crucial for future AI research.
During this era, AI research received significant funding and interest, leading to developing early prototypes in various domains. One such application was the development of ELIZA (giving normal names to AI was popular long before our AI chatbot Ante), a natural language processing computer program created by Joseph Weizenbaum in the mid-1960s. ELIZA used simple pattern-matching techniques to mimic human conversation but demonstrated the potential of AI in understanding and processing human language, a precursor to more advanced systems like today's virtual assistants.
Over the years, AI has moved from abstract theories about cognition and computation to practical experiments and prototypes demonstrating AI's potential to augment or mimic human intelligence.
Commercialization of AI
The shift to mainstream applications
The commercialization of AI began in the 1980s because of advancements in hardware and increasingly sophisticated algorithms. This period marked the beginning of AI's integration into mainstream applications, notably in industries such as manufacturing, where robotic process automation (RPA) started to revolutionize production lines. Another significant area was customer service, where AI began to power simple chatbots and interactive voice response systems, completely changing how businesses interact with customers.
During this time, expert systems, a form of AI designed to mimic the decision-making abilities of a human expert, became highly popular in sectors such as finance and healthcare. These systems were among the first AI applications to be adopted commercially, providing "intelligent" insights based on large sets of rules.
For example, in healthcare, early expert systems could suggest diagnoses and treatment plans based on patient data and a built-in database of medical knowledge.
As technology progressed, so did the capabilities of AI systems, leading to broader adoption in various business processes, from inventory management to fraud detection. These applications meant that the potential of AI is not just in automating routine tasks but also in providing valuable insights that could drive business growth and operational efficiency.
Rise of AI startups and investment trends
In the 2000s, AI development grew quickly with many new startups and large investments from venture capitalists. This era had many now-prominent AI companies that "pushed the boundaries" of what AI could achieve.
Investment trends during this time reflected confidence in AI's potential. Funding was channeled into areas like natural language processing, predictive analytics, and computer vision, powering innovations that would soon become integral to everyday technology. For example, AI developments in natural language processing led to more sophisticated and versatile virtual assistants, while advances in computer vision spurred innovations in sectors ranging from automotive (self-driving cars) to security (surveillance systems).
For example, Google's acquisition of DeepMind in 2014 for $500 million was a significant investment, but even before that, in 2006, Google started heavily investing in AI and machine learning with acquisitions like Neven Vision and the development of their own products like Google Translate.
How did AI-enhance software development
1. Boosted productivity
AI has impacted software development by improving developer productivity. AI-driven integrated development environments (IDEs), and code editors offer features like predictive coding and intelligent code completion. These features help software engineers by suggesting the next line of code or by completing algorithms based on common usage patterns and personal coding styles.
For example, tools like GitHub Copilot use contextual cues from the code to offer real-time suggestions.
2. Improved code quality and management
AI-powered tools analyze existing codebases to identify inconsistencies and areas for improvement, ensuring that the software not only runs efficiently but also adheres to coding standards.
This is important in large projects where maintaining a consistent coding style and architecture can be challenging. AI software development services often include automated code reviews and refactoring suggestions, which help maintain high standards of code quality and readability without human oversight.
3. Automated repetitive (and boring) tasks
One of AI's most welcomed impacts on software development is the automation of repetitive and mundane tasks. This includes everything from setting up development environments to managing database backups and even writing unit tests.
For instance, AI-driven bots can automatically generate API documentation or configure continuous integration/continuous deployment (CI/CD) pipelines, reducing the manual work required and minimizing human errors.
4. Personalized developer assistance
AI has personalized the software development experience by providing tailored assistance to developers. Through AI development services, tools are now capable of learning an individual developer's habits and adapting to their unique style and preferences.
For instance, virtual assistants in IDEs can suggest the most frequently used libraries or frameworks by the developer and can even alert them to new tools or libraries that might increase their productivity. Additionally, AI-powered educational platforms use adaptive learning techniques to help developers improve their coding skills by offering personalized learning experiences and challenges based on their proficiency level and career goals.
5. Switching between different programming languages
AI solutions in software development have optimized the process of switching between different programming languages. Advanced AI tools can automatically translate code from one language to another, making it easier for developers to manage projects that require multiple languages without needing deep expertise in each. This capability improves productivity and flexibility in AI software development and, at the end of the day, helps developers learn programming languages quickly.
6. Cuts down research time
AI reduces the time developers spend on research. By using AI-powered solutions for search and knowledge bases, developers can quickly find solutions, code snippets, and documentation.
The downsides of AI
1. When AI offsets the productivity it was supposed to bring
While AI has enhanced software development and other industries, it can also become a source of frustration. One common issue is when AI does not fully understand the context or subtleties of human interactions, leading to misinterpretations or incorrect outputs. For instance, AI-driven customer service tools (such as chatbots and virtual assistants) can sometimes provide irrelevant or generic responses. This can be frustrating if you are seeking specific assistance.
2. Hallucinating and fabricating facts
One significant issue is AI models' tendency to "hallucinate" or fabricate facts. This occurs when AI systems generate information that is not grounded in reality, often due to training on noisy or misleading data.
In software development, tools like AI-powered code completers can suggest inappropriate code snippets when they misinterpret the developer's intent, potentially leading to errors or confusion.
Moreover, AI applications that generate content, such as automated reporting tools or content creators, can produce outputs that are oddly phrased or factually incorrect if not properly supervised. All this can disrupt the workflow and require additional time to correct.
3. Low-quality data
The effectiveness of AI models heavily depends on the quality of the data they are trained on. In many existing systems, the data available can often be of low quality—unstructured, outdated, or biased—which significantly impairs the performance of AI applications.
4. AI models can suffer from dementia
An interesting phenomenon in AI systems is akin to dementia (or at least that's what I call it). After several prompts, an AI might not recall everything you've told it, showing the limits of its memory and learning process.
5. Dependency and overreliance on technology
Another significant concern with the widespread use of AI in software development and business operations is the growing dependency on this technology. Overreliance on AI can lead to degrading human skills, as routine tasks are delegated to intelligent systems, potentially leaving a skills gap in critical thinking and problem-solving. For example, if AI tools handle most troubleshooting and problem identification, new developers may find it challenging to perform these tasks without AI assistance.
This dependency is also a risk from a business continuity perspective. Companies heavily invested in AI-driven processes may find themselves at a disadvantage when faced with AI system failures or cyber-attacks targeting AI systems. The impact of such events could be severe if there isn't a sufficient understanding or capability to work independently of these AI systems.
Moreover, an overreliance on AI can lead to complacency in decision-making. Predictive analytics and AI-driven decision-making tools can make business operations more efficient but relying too heavily on AI suggestions without human oversight can result in missed opportunities for innovation or failure to catch errors that a human would recognize.
Real-world examples of AI failures
Notable AI mistakes and their impact
1. Self-driving car resulted in fatal accident
One of the most publicized incidents involved a self-driving car developed by an AI development company. In 2018, a test vehicle failed to recognize a pedestrian crossing the street, resulting in a fatal accident. This tragedy highlighted the limitations of AI in dealing with unpredictable real-world scenarios and the need for safety standards.
2. The rise of fake news
Another notable mistake occurred with social media, more specifically Facebook, where an AI algorithm designed to create engaging news feeds ended up spreading fake news and polarizing content during critical times, such as elections.
3. The case of unsafe advice in the healthcare sector
In the healthcare sector, IBM's Watson for Oncology was designed to revolutionize cancer treatment by providing customized treatment recommendations. However, the system struggled to make accurate suggestions outside of its training set, often offering irrelevant or unsafe advice.
Learning from mistakes - what failures teach us
Each AI failure provides valuable lessons that can drive the technology forward. The autonomous vehicle incident increased scrutiny and regulatory measures to ensure the safety of AI implementations in critical applications.
The spread of misinformation by AI algorithms on social media platforms has taught developers the importance of ethical AI design. It has spurred advancements in natural language understanding and the development of filters that detect and minimize biased or harmful content. Now, every artificial intelligence development company should be more aware of the social responsibilities of deploying AI in public domains.
The shortcomings of AI in healthcare, like those experienced by IBM Watson, showcase the necessity of combining AI solutions with human expertise. AI is not a replacement for professional judgment but rather a tool that must be integrated thoughtfully and carefully, with ongoing evaluations and adjustments based on real-world performance and feedback.
Creativity will never be replaced by AI capabilities
Despite the advancements in AI, human creativity and touch are irreplaceable. AI does not operate independently; it relies heavily on human input and creativity to function effectively and achieve meaningful outcomes. We are not at the mercy of AI; rather, it is AI that cannot exist without our direction and innovation.
Also, people have different perceptions when someone says AI. Deep learning and machine learning are not synonymous. Deep learning is a subset of machine learning involving more complex algorithms that mimic the human brain's neural networks. However, what most developers encounter in AI software development is just scratching the surface of machine learning's full potential (at least for now).
And, final words: AI software development services need a balanced approach to AI integration. The right AI software development company will know how to embrace AI while maintaining human oversight. If you want to integrate AI into your business and boost your bottom line, contact us and stay ahead of the competition.
