” Artificial Intelligence will change the future”. You’re here to read this post so you probably know it already has. There is so much talk about how data science will change/have changed the way we shop, or how robotics is going to change medicine. In this week’s post, we’re here not to tell you how AI changes the world, but how AI itself is going to change in the coming years.
Continue reading to get acquainted with five important ideas and technologies that -in our opinion- will define the path of artificial intelligence in the next ten years.
Solving the climate challenge is the biggest challenge of this century for the planet earth and its inhabitants. Many scientists hope that AI can give material help in stopping the earth from getting warmer. However, the AI coin has two sides.
Artificial intelligence helps optimize the energy consumption of other industries. It helps scientists model climate change, its progress, and its effects on the environment. It makes power distribution systems more efficient with smart power grid design. and at the same time, it does computations at the cost of increased energy consumption, and we don’t know exactly how much fuel is consumed for creating the large ML models out there.
To maintain our planet, the path of AI must change in the coming years. We need to develop methods to quantify the carbon costs of large models and neural networks. Large tech companies must develop sustainable power infrastructures that maximally replace fossil fuels with renewable power grids. Designing more efficient GPU technology and Algorithms should be a concern again. We must think more about reusing the former large models than creating new ones from scratch. And at last, the AI community should collaborate more with climate experts.
Big data has been the phenomenon of the last decade. Large datasets consisting of millions of records that revolutionized the world of Artificial Intelligence.
less than a thousand records. Now, their opposite, the small data, is getting popularity too.
IBM researchers were the first to use the name small data for datasets with less than a thousand rows. Small data may belong to an individual, like their bank transactions, tweets, and even DNA subsequences. These datasets are too tiny to be analyzed using most regular statistical models. But AI has moved forward, and now we have methods that can produce the results we desire with fewer data.
In the last few years, Covid-19 brought up an unexpected environment across different markets and businesses, which made the former historical data and ML models less useful and in some cases, obsolete. Also, the establishment of new regulations for user privacy and safety is blocking the way to build big user datasets. This makes the small data play a more pivotal role in forming the path of ML in the near future.
Data Lineage Concerns
The quality of raw material, here data, is a major effective factor in the quality of the final product. A piece of data may come from a public database, which is made up of data extracted from multiple scientific papers, each with a different method and a different source, and some of them have been subject to alterations. The concept of data lineage or “data provenance” is about tracing the source of a piece of data, and the “how” and “why” of its current state.
The dataset’s lineage record is a type of metadata that includes the life history of each item in a dataset.
Data provenance techniques make the results of a data science project more reliable and explainable. They also make finding the root cause of problems much easier.
Intelligent Feature Engineering
Feature engineering is the process in which a data scientist designs the feature set for her database. A good feature set contains the fewest possible features that together, hold all the relevant information for training an ML model, and their values are standardized to prevent more bias in the resulting model. Many think that good feature engineering is the single most important step of problem-solving in data science. It has always been a challenging task for an experienced, sharp-minded data scientist to find the -nearly- optimum set of features to train a machine learning model. To lift some of this weight from the human engineer’s shoulder, the AI community is now moving towards automated feature engineering. Automated feature engineering aims to help the data scientist by automatically creating many features out of a dataset from which the best can be selected and used for training.
Graph databases are a new form of data storage that manages data in a more intuitive form, in contrast with the traditional, rigidly structured, relational databases. The idea behind Graph databases is based on mathematical graphs: where data objects are the nodes and their relationships are the edges. This enables graph databases to follow a more flexible, relation-oriented schema. This added connection layer makes graph databases very suitable to be used in machine learning.
Along with the above points, the fact that there have been over 28,000 peer-reviewed scientific publications about graph data science in recent years indicates that it is highly probable for graph databases to take over the ML world in the coming years.
Automated Feature Engineering