Imagine: in 2001 Steven Spielberg released his science fiction movie called “Artificial Intelligence”. Now, just 17 years later, we’re talking about AI as an absolutely real part of our lives.
Inconceivable, isn’t it? Artificial intelligence programming is one of the hottest topics in the tech world today, and many influencers, from late, great Stephen Hawking to increasingly popular Elon Musk, both embrace the achievements of AI projects and warn us about the possible implications.
So how does this new technology influence the world around us? Should you be worried that some AI robot will steal your job any time soon? Let’s dive into it.
Both academic and industrial researchers have put a lot of effort into creating adaptable smart machines for all sorts of industrial processes. Many startups have caught the trend and are beginning to develop reinforcement learning algorithms for industrial robotics. The market for automation solutions also gets its fair share of saturation.
Reinforcement learning technologies by DeepMind were used by Google to reduce data center energy consumption. Another widely used solution is the use of long short term memory neural network to tune industrial equipment. Just a couple of years ago this was a job done exclusively by expert engineers.
Machine learning libraries are becoming even more accessible, but the variety of models and architectures causes a headache for many data scientists.
Several scientific groups have proposed using deep learning technologies to simplify the designing of complex AI architectures like long short term memory neural networks.
Google’s own AutoML, for instance, uses deep learning to generate computer vision and language architectures. Some try to apply deep q-learning to code correction or even replacing coders altogether in certain circumstances.
Artificial intelligence projects help us move from mass or group education to more personalized processes. The evolution of reinforcement learning algorithms can actually result in tutoring systems that customize and tune educational content for the needs of each student.
This, of course, leads to maximum efficiency, engagement, and inclusivity regardless of pre-existing conditions. The chances are, our kids’ schooling will look like nothing we had in our childhood.
Since healthcare is the industry with an insane amount of data processing needed for precise diagnosis, reinforcement learning algorithms find their respective niche here as well.
Many AI applications in medicine are dedicated to developing treatment programs and researching the causes of certain diseases. In future we’ll probably see more AI robots operating complex medical equipment, prescribing drugs, and performing clinical trials.
The AI’s ability to analyze, structure, and generate plausible texts is being put into use by many tech corporations nowadays. Just a couple of weeks ago we saw a demo of Google Duplex voice assistant basically passing the Turing test booking a restaurant table on the telephone.
We also see a huge wave of AI developers being acquired by corporations like Amazon, Apple, and Microsoft, because the arms race of voice assistants is reaching its peak.
When the memory networks will get seamlessly connected with deep learning algorithms, we might see a machine winning a Nobel Prize in Literature. If the award is not canceled due to some sex scandal again, of course.
Google, Facebook, and Microsoft are already using advanced artificial intelligence programming for the need of their advertising services.
Reinforcement learning algorithms are working to optimize content, targeting, and general strategy of ad campaigns and social media feeds around the world. The final call in configuring digital promotions is still in the hands of human professionals, but the benefits AI is offering are here to stay.
Since many professional stock exchange traders and bankers rely on quick, precise decisions in their professional routine, some AI-based market prediction tools wasted no time in hitting the Wall Street.
Besides that – not much progress so far. Just one more example worth mentioning is the trade execution system at JPMorgan Chase. The product uses reinforcement learning algorithms to hit the optimal deal price as quickly as possible.
This type of concern is easy to understand. Artificial intelligence jobs do take over routine work positions. But if we take a closer look at what’s been happening for the past 30 years, we’ll see that this process started getting pace long time ago, and the rise of AI robots is just another predictable step. The key solutions here are obvious:
Another important concept to understand here is the real-life difference between Big Data and Small. The machines are naturally better than us at operating huge data sets, and the majority of artificial intelligence jobs are created exactly in this area of operation.
But Small Data is equally as important as its larger counterpart, if not more important. And this is where a barista from your local coffee shop can beat advanced AI programming. Add emotional abilities and multitasking on top of that – and the future suddenly doesn’t seem so dark for humans.
No matter how quickly artificial intelligence projects are growing, the Skynet scenarios still don’t seem plausible for the foreseeable future. We have plenty of time to adapt and embrace new conditions of the AI-enhanced world. This incredible adaptability is what brought us to where we are right now, after all.
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