1997 was a pivotal year in the world of Artificial Intelligence (AI) as it was the first time a machine managed to defeat a world champion in chess. Deep Blue was an IBM Supercomputer and, after losing to Garry Kasparov 4-2 in 1996, had learned and improved and came back to defeat him in 1997 after a hard-fought battle.
Machine learning (ML) and AI use complicated algorithms to learn and process information. These algorithms mimic the human brain, and just like a human child learns, they can also be taught through data and experience.
The ability of a computer to play chess is something that can be programmed. The number of moves and sequences are all simple and straightforward. However, a Grand Master in chess looks many steps ahead and plans out actions based on different strategies. Machines are better at processing and storing data than we are. Transforming them from simple storage and calculating devices into intelligent ones requires the use of training data.
Training data is simply a set of information provided to machines to teach and educate them. In this way, computers learn the difference between cats and dogs, for example. By providing a computer algorithm with examples of each, they gradually learn over time the specific distinguishing characteristics to look out for. As you continue refining the samples, you can train them on differences between different breeds, further improving their abilities.
Bad data can be catastrophic. If the data is mislabeled, it can have far-reaching consequences. If instead of classifying cats and dogs, you were looking at people and pets; mixing these up could be quite impactful.
Consider a security company with AI-enabled cameras for at home. Most people set their units to ignore the movements of their pets, especially when sleeping. If the camera mistook a person for a pet; however, it could fail to alert the homeowner of a potential intruder. This can be disastrous and can have a significant impact on health and safety of the users.
Training data is critical within machine learning, and the data must be accurate. With training data, quality, quantity and variety are all important factors. AI and machines learn from the data they receive.
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Data itself can generally be classified in two different ways: structured data and unstructured data. Structured data is generally information that is labeled and categorized and can be found in databases. Unstructured data, however, has no pre-defined definition or model.
Even if your algorithm has access to well-structured data, it might not be the right data for its needs. It is essential to ensure that your algorithm is learning from information that will guide it in the right direction. Data used in ML is generally split into three different criteria.
Think of training data as a textbook your AI is learning from. It is something that will be used many times and will be continually referred to. This is the data that your model will continuously rely on and should include the bulk of the criteria you measure against.
Machines learn not just from reviewing information but also by learning from mistakes. This is where validation data comes into play, and this data type can help programmers determine how accurate a model is. Also, validation data can be used to fine-tune the model to improve its overall capabilities.
Just like students take tests at the end of their school year, AI and ML models need similar validation. This step is critical in ensuring and understanding the accuracy of the model. Testing data can only be introduced at the final stages as earlier introduction will invalidate the training.
While the data itself can be categorized in the three types mentioned, they do share some commonalities. Generally speaking, the data will be formatted in pairs where one set is the input information, and the second labeled set corresponds to specific answers. Labels, however, do not need to be restricted to just one field. Properly formatted data can be categorized with multiple fields to educate the algorithm better.
Different AI and ML systems cannot be trained with the same data. While inputs for the different systems might be the same, the outputs would be different, and using the same data set, would end up skewing the results in a specific direction. Each algorithm requires uniquely created and formatted data to ensure that learning is efficient and optimized.
There is no specific right number when it comes to the amount of data your model requires. Data scientists generally agree that more information is better than less, but the amount varies depending on what you are attempting to accomplish. Simply put, the more complex the task, the greater the amount of data required.
Training data is available, but at times finding it can be difficult. This is primarily for two different reasons. In one case, the data available has been created for a specific purpose, which does not match your requirements. Alternatively, the data available is too generic and, again, is not useful for your purpose.
However, it is possible to find data that can be tagged. Doing this will ensure that you have the right dataset available for your algorithms, which can save significant time in the future. This data is available through some public sources and also from training data providers. Training data providers are more expensive but can help save time.
Accurate training data is critical for success. Students that are provided with a textbook that is missing pages or with incorrect information are unlikely to do well. The same situation applies to AI and ML systems provided with inaccurate or faulty information.
Today’s computers and laptops are significantly more potent in terms of capability and can defeat Deep Blue without trouble. However, getting the machines to that state took time and continuous effort. Each iteration built on the successes and failures of previous generations. If you think about it, this is very similar to how we all learn, too.
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Dieser Artikel wurde am 18.May 2020 von humangrid geschrieben.
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