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  • Melissa Sims

We Only Fear That Which We Don’t Understand

The Rapid Evolution of Artificial Intelligence


Artificial Intelligence (AI) has evolved from a mere concept in science fiction to a dynamic, real-world tool reshaping our everyday lives. It’s natural to have some apprehension regarding it’s speed of advancement, but as long as we can use it mindfully, we can probably let go of the scary theories about it taking over the world.


Recently, Democratic voters in New Hampshire received an unusual call: “Joe Biden” was calling to tell them not to vote in the primary and instead “save” their vote for the November election. The call was found to be a robocall, with a deep fake voice imitation of Joe Biden. That’s a bit alarming. And that’s not all - there have been many instances where Taylor Swift’s likeness was used in some explicit photos that were also deep fakes. With the nefarious use of AI in the past few years, many people are worried that all of those science fiction movies may not be such fiction after all.


It is normal to be nervous about AI, but I think a lot of that stems from not actually understanding it. Let’s take a little history lesson on where AI started, and where it could be headed. The concept of intelligent machines dates back to ancient myths, but the modern journey of AI began in the mid-20th century. The 1950s witnessed the first AI programs, which could play checkers or solve algebra problems. A landmark moment was in 1956, when John McCarthy coined the term "Artificial Intelligence" at the Dartmouth Conference, marking the official birth of the field.


In the 1950s through the 1970s, foundational work was laid in AI. Alan Turing's famous Turing Test, proposed in 1950, still serves as a criterion for intelligence in machines. Early AI research focused on problem-solving and symbolic methods. The first AI programs, like the Logic Theorist and ELIZA, demonstrated problem-solving and natural language processing, albeit in a primitive form.


The initial optimism in AI led to inflated expectations, followed by disappointment and reduced funding. During the late 70s through the 1990s, progress slowed, but the field continued to evolve. The development of machine learning algorithms in the 1980s, especially neural networks, marked a significant shift in AI research, laying the groundwork for modern AI.


We are now seeing a massive forward shift in AI, fueled by increased computational power, vast amounts of data, and advances in machine learning, particularly deep learning. AI applications have become more sophisticated, ranging from speech recognition (like Siri and Alexa) to game-playing AI (like DeepMind's AlphaGo).


There are several types of AI, but what’s most important to understand is that the only AI that is not theory, is Narrow (or Weak) AI. The name is a little deceiving, as there’s nothing really weak about how incredible the technology is. Most current AI applications fall into this category. These systems are designed for specific tasks, like language translation or image recognition, and operate under a constrained set of guidelines. They do not possess consciousness or general intelligence. An example of this would be Siri or Alexa, and surprisingly, even with all of its capability, Chat GPT. But, even as you can see in the AI generated picture, Narrow AI can’t even get words right on an image.



General (or Strong) AI is the concept of a machine with general intelligence that can understand, learn, and apply its intelligence to a wide range of problems, much like a human. This type of AI remains theoretical and is a subject of ongoing research. Super AI is another concept. This form of AI would surpass human intelligence in all aspects - problem-solving, creativity, and social skills. Like Strong AI, it remains a concept rather than a reality.


Currently, today's AI advancements are predominantly in machine learning and deep learning. Machine learning algorithms enable systems to learn and improve from experience without being explicitly programmed. Deep learning, a subset of machine learning, involves neural networks with many layers, allowing for complex pattern recognition.


AI applications are diverse. In healthcare, AI assists in diagnostics and personalized medicine. In business, it's used for customer service and data analysis. In everyday life, we see AI in smartphones, smart homes, and even in autonomous vehicles. We’ve been using some form of AI for years, but it has just recently become a hot topic due to privacy concerns.


Experts predict that the future of AI needs a lot of parameters and controls for privacy and ethics. AI is currently learning from human input, which has a natural bias. Yet there are so many opportunities for AI to be a significant part of our lives in incredibly positive ways.


AI in Healthcare: The use of AI in diagnosing diseases and in drug discovery is expected to grow significantly.


Autonomous Vehicles: Expect further advancements in self-driving technology, though full autonomy might still be a few years away.


AI in Entertainment: Personalized content recommendations and AI-generated content will become more sophisticated.


Advancements in Natural Language Processing: NLP will continue to advance, making interactions with AI more seamless and natural.


Quantum AI: The integration of quantum computing with AI could lead to significant breakthroughs, although this is still in the experimental phase.


AI in Education: Customized learning experiences, driven by AI, will become more prevalent in educational settings.


The journey of AI has been nothing short of remarkable. From its early days of symbolic processing to the current era of machine learning and deep learning, AI has continually evolved, integrating more deeply into our lives. While predictions for the future of AI are varied, one thing is certain: AI will continue to advance, bringing both challenges and opportunities. As we stand on the brink of potential breakthroughs like Strong AI and Quantum AI, the next year promises to be an exciting one for the field of artificial intelligence.


Yet it also brings significant changes for humans. We may not be living Minority Report in real life yet, but many of those advancements are coming. We just aren’t yet to the point that machines are thinking. They still need humans to operate, and in fact, many experts don’t even think it’s possible to have Super AI.


I encourage you to try it. Fear isn’t going to be helpful, and AI is here to stay. I use it daily, for help with content, driving my car, using my phone - even creating meal plans! If you choose to use AI, be sure that you are checking it for accuracy, making sure that anything you share is double checked for copyright/misuse, and that whatever it is isn’t harming another human being. If we can use AI mindfully, it can help us in so many ways. The key is to not fear it, but seek to understand it.


Teresa and I sat down recently on the podcast to chat about AI. Watch here:




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