Artificial intelligence is one of the most popular and powerful tools nowadays. But it’s also an area of myths and misunderstandings. In a typical situation there are 2 reasons for integrating AI in your product or infrastructure.
The first reason is around streamlining processes that can help your clients, or your own business, generate more profit. The second reason is around PR and growing hype.
This article is to help you choose the best strategy for the implementation of AI into your business by breaking down the myths and shedding light on what’s real and what’s fantasy.
Artificial intelligence: history and classification
When working with tasks, first of all, we need to understand what exactly we should do to complete it before concentrating on the task itself. But for a better understanding of AI, we will start with the classification in historical order.
1. Symbolic AI is developed like standard software and nowadays is not considered AI. You might also hear the phrase “expert system”, the second and the most common name for symbolic AI. It’s programmed as a set of rules and algorithms. Symbolic AI is a really powerful tool that can solve a lot of issues, but in this article, we won’t cover it deeper as a part of AI because there are a lot of sources already online that describe expert systems.
2. Machine learning algorithms use different approaches. Developers don’t program software as usual. The usual approach is based on human knowledge about some processes to build the algorithms. So the system will get input and based on algorithms return the output. The ML system will get input data and output data and based on it generate algorithms. ML-based solutions have a simple algorithm (in comparison with DML) and aren’t really popular, but they also have a set of tasks that provide better results than other approaches (for example, the Random Forest algorithm).
3. Deep Machine learning (DML) is the next step after ML. In general, it uses the same approaches as ML but other mathematical and algorithmic structure-neural networks. DML is often called AI nowadays.
It’s really important to understand what you might need, whether that’s DML, ML, or Symbolic AI approaches. Each has its own benefits associated with solving specific tasks.
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The main purpose of DML (ML)
I won’t go into the difference between DML and ML. It’s better to delegate ML algorithms to ML specialists. This article is an overview and is not for ML specialists.
The main purpose of DML (ML) is finding relation and dependence from a provided dataset, the closest analog we can get in statistics is multifactor regression analysis. A system based on data without the support of the developer in classic format will find relations in the provided dataset and after will make predictions, or calculate some important params.
DML (ML) is only able to evaluate relations in big datasets but can’t implement other human-based work. The main value here is that it can process, correlate, and make predictions based on data much faster than a human can.
DML (ML) does not imitate the human mind. It simply understands neural networks like a mathematical model. Also, DML (ML) can’t create something new or predict unpredictable things (things that have never happened before or are not related to a forecastable formula) but can combine something that has already been created and make predictions based on tons of collected data better than humans or classic algorithms.
Types of common tasks
Let’s go over an overview of the tasks that can be solved with DML (ML). But keep in mind, I won’t cover algorithmic bases here (regression, classification, prediction, etc.). I’m going to focus on practical application.
1. Recognizing. One of the most common tasks DML(ML) solves is object recognition. That’s the ability to recognize people or things in videos and pictures, track those objects, detect the condition of those objects, and more. This also includes text recognition and handwriting.
2. Classifying. Another process that DML (ML) is really good at is sorting and analyzing objects and sorting them into categories. A good example of this is sorting website traffic by gender, age, geographical location, etc.
3. Predicting and Forecasting. DML (ML) systems based on datasets (LSTM for example) can predict the value of parameters in a dataset in the future. For example, the value of KPIs in the future, cost of property, currency exchange rate, etc.
4. Suggesting and Recommending. After reading a dataset, DML (ML) can make suggestions on one or more parameters. For example, making a diagnosis according to the information found in a medical analysis. Another very practical application for DML (ML) is the ability to recommend objects based on search results or history. For example, if you buy a bed online, you might also get offered to buy sheets or pillows.
5. Getting dependency on the dataset. For example, splitting voice and noise in the audio which was recorded with 2 mics. For such a type of task, we don’t need a trained dataset.
6. Generating. DML (ML) can also generate content or even speak on a specific topic. However, this requires that the system be trained with datasets and information related to the topic in deep detail. The same thing can be applied to generating images, music, and more based on the processed datasets.
Data in DML (ML)
The key to success in solving DML (ML)-related tasks is data. The FAANG company achieved really fantastic results with DML (ML). And the most important thing to consider here is the data. It seems strange to say, but it’s not about the DML (ML) specialists, algorithms, or hardware. Certainly, those are all really important because, without algorithms or hardware, we can process big volumes of data. But nothing is possible without the data, no matter how great the algorithms or specialists are.
For building a powerful DML (ML) system, you need to know how to work with big data. Big data is a dataset that can be processed or stored in one single machine. The ability to create a distributed data store, build data collectors, data transformers, and data pipelines before we start working on neural networks is crucial.
It’s really important to think about how to get enough data before starting to work on a DML (ML) task or project. The collection of data can take more time and resources than rating DML (ML) networks and building data pipelines. Before we here at Computools start developing a neural network for a client’s needs, we build a separate project for data collecting and processing (by a human operator).
Artificial intelligence is a really powerful tool, it can solve a lot of practical tasks. It’s perfect for image/video recognition and getting relationship data out of big datasets. But AI is not magic and can’t do new things or fully emulate human activity. Also, DML (ML) algorithms require big datasets and aren’t accurate without sufficient data to read and process.
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