Machine learning, artificial intelligence, virtual assistants and more, the definition of AI has changed so much over the years. According to Search Engine Watch, the concept of AI has changed at least 20 times over the last 20 years. It seems like AI is constantly evolving.
The good news is that AI is actually not a totally new concept, it’s just so new and it’s constantly evolving that it’s impossible to give you an exact definition.
We will likely never see a machine that is completely intelligent. Machines will never be able to think and see on their own, at least that’s not how humans are, so we will never truly know how an AI machine thinks. However, when dealing with machines today, a machine is still capable of doing a lot of tasks on their own and AI is certainly making it much easier to implement technology that would have once been difficult and costly to implement.
According to Forbes, AI has the potential to make a huge impact on businesses across a wide variety of industries. From sms marketing solutions (such as HeyMarket) to self driving trucks, AI is the way to go! And regardless of industry, AI is getting very close to fulfilling its promise, that’s good news.
Machine Learning
Machines are becoming more powerful, both physically and mentally, and artificial intelligence is making that much easier to accomplish. All machines are machines, so there is no way that machines will ever be able to totally replace humans, but they can certainly make a big difference in what we do. Let’s take autonomous vehicles for example. Self-driving trucks, which were a fantasy a couple of decades ago, can now be provided by the likes of Torc Robotics or similar companies, in order to make deliveries of goods in bulk to different businesses. This can save a lot of manpower and can be cost-effect for the business in the long run. Similarly, a lot of businesses are starting to learn about machine learning and other AI techniques, and if your business isn’t using it, it might be a good idea to get involved, but it’s not something you can start just anywhere.
AI and Machine Learning
AI has a lot of different definitions, but according to Search Engine Watch, machine learning is an approach to how AI is implemented in software. Contact center automation is a great example of this. Machine learning allows AI to learn without being explicitly programmed, meaning AI will only learn if it receives input from humans. This is a big deal because without people monitoring the system, it won’t necessarily learn how to be a better program. You will only learn what has happened to it.
Let’s say you have a machine learning algorithm that detects different viruses that are affecting different animals and when that happens, you know that the algorithm is receiving feedback from the humans that are working with it, you know that that’s the information it needs to be able to evolve. The best way to help the machine learn is to show it real-world data about the animals that are experiencing the virus, so that it can learn how to detect the virus itself.
That’s just one example, but machine learning is a huge deal because it’s easier to do it that way, it’s faster and it can do much more than it could before.
Furthermore, machine learning has been used to develop fraud detection software to reduce fraud losses. Businesses have traditionally used rule-based systems to prevent fraudulent payments. While rules are still an important part of fraud detection and prevention tools, using them alone has caused some issues in the past. An analysis of the collected data with appropriate methods, proper classification of the data, and fraud measures in parallel with tracking user movements revealed the necessity of machine learning systems as the amount of data that organizations needed to examine increased. At this point, fraud detection using machine learning may provide many advantages to businesses over the traditional approach, such as a lower number of false-positive cases, faster fraud detection, and analyzing normal and abnormal activities. Machine Learning for Internal Fraud Detection has basically eliminated the heavy data analysis responsibility on fraud detection teams. According to Search Engine Watch, machine learning is similar to artificial neural networks, which means AI has achieved something that it wasn’t capable of before. Artificial neural networks are the backbone of AI.