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As AI technology has blossomed, our definition of the term ‘artificial intelligence’ has developed. But at its core it remains unchanged – a computer’s ability to complete tasks that we normally assume require a human mind.
One of the first assessments of AI, the Turing Test, was a simple embodiment of this: could a human agent have a conversation with this machine and not distinguish it from a human?
The closer AI gets to human intelligence, the more powerful it seems. And its most important breakthroughs have been driven by exactly this goal.
So what is artificial intelligence?
For many of us, if we stop and ask, ‘What is Artificial Intelligence?’ we find ourselves lacking a clear definition. Well, one useful definition is: a machine’s capability to think rationally, solve problems, and make decisions without the supervision of a human mind.
Those conditions are also traits of human intelligence.
In other words, we can think of artificial intelligence as the ability to think like a human without human supervision.
How is artificial intelligence becoming more human-like?
Natural language processing (NLP) has been a vital advance and underpins many of the most impressive AI innovations in recent years.
NLP is how a computer makes sense of human speech and text. It turns a computer from a data processing machine into something that can respond to you.
This might seem in some ways superficial, but ‘The Power of Natural Language Processing’ really can’t be overstated. In the past, it was generally accepted that computers were better at data-driven decision-making, but not cognitive and creative tasks. NLP changed that.
It made OpenAI’s ChatGPT-3 such a mind-blowing breakthrough, allowing it to write articles for major newspapers, and paved the way for AI-powered translation tools like DeepL.
Given how powerful this technology is becoming, the question is no longer ‘Has ChatGPT Changed Our Lives’ (it has), but how could it be helping you on a day-to-day basis?
How deep is the similarity?
It’s easy to think these similarities are superficial. Chatbots are built to seem human, but behind the scenes, they’re very different from human conversations.
But the similarities with human intelligence are certainly not skin deep.
Google and other tech companies have been pursuing semantic search for many years, and recent breakthroughs in this area have played a big part in driving AI forward.
Search engines have more data and processing power than the human mind, but don’t traditionally sort through it in the same way – they match terms in the question to a database.
By contrast, the human mind unpacks what a question means. That’s what semantic search does. This allows Google to return results that don’t have matching terms but still answer your question, but also use images and videos in your searches.
Is AI really like us?
These breakthroughs have led us to anthropomorphize AI more than ever, talking about its understanding of the world, of us, and even of itself.
But this can be dangerous, as it can lead us to overlook the limitations of the tech. It’s ‘Why Semantic Search is a Better Term Than Understanding for AI’ – because it recognizes what the tech is doing (searching based on meaning) and what it is not (understanding the experience of being in the world).
On the other hand, it’s important to recognize problematic elements of human intelligence that AI is inheriting. One of the reasons ‘The Ethical Considerations of AI Bias in Data-Driven Decision Making’ are so numerous is that we often overlook our own biases and when we spot them, dismiss them as human imperfection.
Last year, ChatGPT beat the Turing Test and now we’re looking for new ways to assess AI. But what is clear is that the closer AI gets to human intelligence, the more useful it seems
If you want to know ‘How AI is Reshaping the Workforce’, and how it could impact your work, you should start thinking about which tasks require a truly human understanding of people and the world.
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