9senses on artificial intelligence
What is AI?
When most of us at 9senses began working with what is now labeled Artificial Intelligence, we didn’t use that term. Back then, we were talking about non-linear computing, fuzzy logic, heuristics, machine learning, among others.
Today, many people think that AI makes computers as smart as humans. In reality, computer software is still far away from reaching that level, but it today is able to emulate and even surpass human capabilities in specific fields, particularly those that require the processing of large amounts of information or the generation of output from a large data pool. We would like to instill a bit of clarity here, at the cost of taking some of the magic of AI away, as did Joseph Weizenbaum, the legendary creator of Eliza:
“What I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”
Joseph Weizenbaum (1923-2008), Inventor of Eliza
But what is AI really? We have asked two conversational AI systems about their definition of Artificial Intelligence and came back with quite divergent answers. Click to see what AI has to say on AI
After noticing how divergent the statements of those two AI systems were, we are no longer afraid of creating our own answer. We at 9senses define AI as “a computer system that is able to react to an event it has never experienced before in a meaningful way that is adequate to that event.” This ability clearly distinguishes it from traditional computer logic where each event (or combination of events) has only one defined reaction. We explicitly stay away from comparing it with humans, because in some areas, computers are still eons away from reaching our abilities, while in others, they massively outperform us.
Key Fields of AI
There are various key AI technology areas, here is one of many ways to break it down:
Natural Language Processing (NLP)
Being able to communicate with humans is one of the most recent key AI developments that helps interact with computers, for example in customer-facing IT.
Machine Learning
Finding patterns in large datasets and drawing conclusions, for example in customer or operational data, is something computers can do much better than any human.
Image Processing
Finding items and differences in still or moving imagery is something that computers excel at, for example when it comes to surveillance, irregularity detection or simply – counting.
Robotics
Creating autonomous systems that can perform physical actions, like driving a vehicle based on controlling equipment using sensor input and logic, is a key field of AI, albeit still a difficult one.
Unless you’re curious about the history of AI and our more philosophical views, you can now easily skip the rest of this page and move to what we can offer you.
ChatGPT answering “What is AI?”
Artificial Intelligence (AI) is the field of computer science focused on creating systems that can perform tasks that normally require human intelligence. AI enables machines to learn from experience, adapt to new inputs, and perform tasks like problem-solving, language understanding, perception, and decision-making.
AI can be broken down into several types and subfields:
- Narrow AI (or Weak AI) – AI systems designed for specific tasks, like voice recognition (e.g., Siri, Alexa), image recognition, or playing a game of chess. They excel at a narrow set of tasks but lack general awareness.
- General AI (or Strong AI) – Hypothetical AI that could perform any intellectual task a human can do, with the ability to reason, plan, and understand complex concepts. This level of AI does not yet exist.
- Machine Learning – A subset of AI that uses algorithms and statistical models to allow computers to learn from and make decisions based on data. For instance, machine learning is used in recommendation systems and predictive analytics.
- Deep Learning – A further subset of machine learning that uses artificial neural networks, inspired by the human brain, to analyze data and make decisions. Deep learning is commonly used in advanced image recognition, language processing, and autonomous driving.
- Natural Language Processing (NLP) – The branch of AI that focuses on enabling machines to understand, interpret, and respond to human language in a meaningful way.
AI is increasingly used in fields like healthcare, finance, automotive, education, and customer service, transforming how we interact with technology in everyday life.
Gemini (Google) answering “What is AI?”
Artificial intelligence (AI) is a branch of computer science that aims to create intelligent agents, which are systems that can reason, learn, and act autonomously. AI encompasses a wide range of techniques and technologies, including machine learning, natural language processing, and computer vision.
Key Concepts in AI:
- Machine Learning: This involves training algorithms on large datasets to make predictions or decisions without being explicitly programmed.
- Natural Language Processing (NLP): This enables computers to understand and process human language, allowing for tasks like language translation and sentiment analysis.
- Computer Vision: This focuses on teaching computers to interpret and understand visual information from the world, such as images and videos.
Applications of AI:
AI is being used in a variety of fields, including:
- Healthcare: AI is used to develop new drugs, analyze medical images, and provide personalized treatment plans.
- Finance: AI is used for fraud detection, algorithmic trading, and customer service chatbots.
- Autonomous Vehicles: AI is used to develop self-driving cars and trucks.
- Entertainment: AI is used to create realistic computer graphics and generate personalized recommendations.
History of AI
1816: Fiction
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The idea of “automatons” acting “intelligent” is much older than computers themselves. For example, in ETA Hoffmann’s “The Sandman”, published in 1816, a beautiful girl named Olimpia is introduced. She dances and sings beautifully, but only speaks a few words. In fact, she is an automaton, created by physics professor Spalanzani.
1940s: The Turing Test
With the appearance of the first computers in the 1940s, the fascination with the technology quickly led to the idea that they could soon perform as intelligently as humans. In 1949, British mathematician and computer scientist Alan Turing devised a test to evaluate when a computer would be able to emulate a human conversation convincingly. It took 65 years for the first simulation to narrowly pass.
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1966: Eliza
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When American psychologist Joseph Weizenbaum created chat program “Eliza” in 1966, simulating the dialogue with a psychologist, it was meant like a playful first attempt at processing natural speech. Even though the logic behind it was very simple, many people considered it intelligent and expected computers to be able to speak like humans soon.
1980s: Machine Learning
With increasingly powerful computers and larger storage capabilities that were able to handle large datasets, the first successful machine learning approaches were introduced. They were based on the ability to autonomously find patterns in data, relate them back to certain events and conditions and suggest or take action.
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1990s: Playing Chess (and Winning)
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In 1997, IBM’s Deep Blue supercomputer won its first match against acting chess campion Garry Kasparov. While mostly driven by sheer power which helped build its game on computing more than 200 million positions a second, it was using machine learning elements (heuristics and minimax optimization techniques) mid-game, which can be considered AI.
2000s: Seeing and Knowing
While optical character recognition (OCR) had been developed long ago, computers became capable of “seeing” in the late 20th and the early 21st century. This is when the first face and object recognition systems were developed. By now, AI is able to routinely identify people and objects and also understand what they are doing.
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2010s: Solving Complex Problems
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The previous decade was the era where all previous efforts in making computers act “intelligently” came together, and where many breakthroughs shifted public attention towards the term “Artificial Intelligence” again, after it had been rarely used since the 1970s. By 2010, normal desktop and laptop computers were strong enough to perform AI tasks.
2020s: Listening and Speaking
Finally, conversational AI is able to have conversations with humans based on Large Language Models that have been released. Those models are routinely able to pass the Turing Test, which means that they are able to understand and communicate back in natural language.
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