State of AI: AI Hype vs Reality

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State of AI: AI Hype vs Reality

State of AI: AI Hype vs Reality – AI is a hot topic these days, but have you ever been confused about whether it’s just AI hype versus the reality of AI? If you have, then stick around, ’cause that’s what we’re gonna discuss in this article.

Okay, AI is mired and hyped, especially marketing hype. And we’re hearing about the promise of AI more and more every day, and expectations have never been higher. Some people are also under the impression that AI may have already achieved human level intelligence, something we talked about in a previous article, where we talked about different categories of AI, one of which is called artificial general intelligence or AGI.

And this is partly due to what we see in, TV series and movies and comic books around science fiction type stuff, right? Like the “Ex-Machina,” the “Westworld,” the “Terminator series,” and so on. In my previous article, in addition to talking about artificial general intelligence or AGI, we also talked about artificial narrow intelligence or ANI, which is kind of what we have today.

All AI today is really just that. And we talked about how in ASI, it’s this idea of machines having better than human level intelligence, in such a way that they’re autonomous, self-learning, self-improving, self-directed and even multitasking. Now despite any of the hype or impressions otherwise, all of AI today could be considered weak AI or ANI. That just means that AI for the most part is sort of a one trick pony, and is used to solve highly specialized problems.

So when you’re creating, these machine learning models or AI models to do things like make predictions or, optimize something, you’re really working on one thing at a time and creating one model at a time for a certain, highly specialized task. Now you can combine these models to make something a bit more complex that does different things and gives the impression of multitasking. Great example of that would be something like an autonomous vehicle, for example.

So what that means is that AI is not all about killer robots and being a total job killer. At least not today, anyways, and hopefully never. Now AI will make some jobs obsolete, but a lot of studies and research also show that AI will create many new jobs, largely in the area of what they call human augmented intelligence. And that will be a topic of a future article. Now, all that being said, we definitely need to keep our eye on the potential impacts of, AI on society and on the workplace and the workforce and also the environment.

And it’s really important that we keep in mind as we move forward and things progress, this idea of ethical and responsible AI. So when I talk about AI hype as a concept, basically I’m describing that perception that,

AI may be at human level intelligence like AGI, or even beyond it like ASI, and also in combination with some of the marketing hype out there that kind of improperly sets expectations sometimes and impressions, it’s sort of generally over promises on AI. Also there’s this tendency for some people and companies to call things AI that really aren’t AI.

In a previous article, we defined AI as intelligence exhibited by machines. So that’s what I would call real AI, intelligence exhibited by machines. And that means that machines somehow learn, usually from data using machine learning algorithms, and then understand what they’ve learned, in the form, typically of models, AI or machine learning models that we create, and then use that understanding and learning to do things like make predictions, recommendations, optimize things, automate things and so on.

So unless that’s what happening, that’s really not AI. In addition to some of the hype we come across, when it comes to AI, there are also some misconceptions that I come across as well that I wanna talk about now. One of them is that AI is sort of easy and nowadays, there are solutions just right off the shelf, you can grab and plug in and bam you’re off and running.

And that you can expect to get big ROI and returns quickly. In reality, planning for and building AI solutions, is often very difficult, and there’s quite a talent shortage right now as well. So usually companies are best off sort of starting small and picking, individual smaller tasks that they can start demonstrating and building proficiency in AI and building out their competencies and capabilities, and then sort of work from there.

Additionally, while adoption rates are certainly increasing with AI, especially in the enterprise, there’s still quite a few barriers to entry. A big part of that is again, lack of talent, but also lack of understanding, training, funding, priorities, prioritization, executive level buy-in and so on. That’s something I talk about in my book, “AI For People And Business,” I created a model I call the AI Readiness Model, and a lot of these considerations on, sort of, this idea of being ready and being, developing AI maturity as well, I talk about it in length. Well, we’ve certainly talked a lot about AI hype and many of the misconceptions around AI in this article, and definitely let us know in the comments of any hype or misconceptions that you’ve come across out there in the real world.

All right, well there’s plenty of AI hype out there to navigate, but there’s also a lot of promise as well. Hopefully AI will have a significant and beneficial impact on both people and society. The key is to understand the difference between the hype of AI and the reality of AI, as things progress. And also to make sure that we use AI in an ethical and responsible way.

 

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