Currently, machine learning and artificial intelligence are popular and pioneering domains in computer science. Future scope of data science is bright and every day scientists are touching new horizons of innovation and pushing forward the definition of what’s possible. Let’s explore the current trends that are ongoing in this field.
What are machine learning and artificial intelligence?
The following diagram will clear up the relationship between the two fields:
Image source: towardsdatascience.com
Thus, machine learning is really a subset of artificial intelligence.
The latter is concerned with fashioning machines to think, reason, and act like humans. To make decisions like a human. Machine learning, on the other hand, is an application of artificial intelligence which is concerned with the development of computer programs that can use data and learn for themselves. Thus, where AI aims for intelligence/ wisdom, machine learning aims for knowledge.
The latest in machine learning and AI
Shifting away from supervised learning methods
Previously, efforts centered around supervised learning algorithms that predicted future events by applying knowledge gained in the past to new data through the use of labeled examples. Now, the focus is shifting to other domains like semi-supervised learning, active learning, domain adaptation, and generative models. New models like the neural rendering model were developed to combine prediction and generation. This happened in a single network and encouraged semi-supervised learning where both labeled and unlabeled data is used for training.
Deep learning finds new applications
Scientists have now expanded the applications of deep learning to include material sciences, protein engineering, high-energy physics, control systems, and earthquake predictions. Learning was combined with domain knowledge and constraints.
AI getting better at emotion detection
The University of Alberta has developed a technology that can detect the depressive language in social media posts with greater accuracy and with much less need for data. Past deep learning experiments and attempts to detect depressive language were expensive and tedious. The University’s research, headed by Nawshad Farruque, reduces the need for large amounts of data.
He has fed a lot of examples taken from depression forums to teach the model how to truly recognize the depressive language. He’s also working to acquire suicide notes and love letters with similar language to foster more accuracy in the results.
With this work, Farraque hopes to detect depression as soon as possible so the affected can be pointed to the required resources. One day, he hopes, it can be built into Twitter’s self-harm and suicide policy and improve Facebook’s existing depression algorithms.
Machine learning is being used for art preservation
Natural Language Processing
In the Netherlands, researchers at the TU delft are working to digitally reconstruct artworks using machine learning methods. They’ve developed a convolutional neural network (CNN) to reconstruct a faded Vincent Van Gogh drawing on paper. For training the model, they used a dataset that contained different quality reproductions of the original drawing. These reproductions were made at different times during the past century.
Although efforts are focusing on preserving and reconstructing artworks, the playing field can be expanded to include degraded images and documents as well. Also, the model has only utilized visual information as of now. In the future, researchers are working to factor in chemical information as well, thereby increasing complexity but also improving the model’s performance and results.
Machine learning is being used for age estimation
In another almost-superhuman feat, researchers at the University of Kwazulu-Natal, in South Africa developed a convolutional neural network to estimate people’s age. This is done by taking their images in random, real-life environments. In the past, this age estimation was done by photographing people in controlled environments like a lab or photography studio. With the shift in modus operandi, the results have also shifted for the better.
Improvement in accuracy came out to be 8.6% better than the previous best results.
Maturation of AI education
Owing to both popularity and nature, AI and ML education are heavily in demand. Online learning platforms like upGrad are minting this with specialized university-taught online courses for everyone. This has led to an increase in the interest and adoption of AI and ML- both personally and professionally.
The emergence of Machine Learning in the cloud
Taking machine learning to the cloud will make it easy for companies to experiment and push the boundaries of machine learning capabilities. It is not always easy to implement and scale-up machine learning projects with existing hardware and software. Taking machine learning to the cloud is not only democratizing it but also opening up opportunities for many enterprises to become AI and ML-driven. If you’d like to make the most of this new next big thing, then our Advanced Certification in Machine Learning in the Cloud course is the way to go.
Scandals also increase
Artificial intelligence and machine learning are powerful tools. And with power, comes responsibility. In an ideal world, everyone would be striving to use these tools for the betterment of humankind, but we do not live in an ideal world.
For example, Cambridge Analytica is held accused os using personal information from people’s Facebook profiles to build a system that targeted US voters. Based on their psychological profile, the system showed personalized political advertisements. A former Facebook manager has also warned that information about hundreds of millions of users might be in the hands of private companies without the users knowing about it.
Due to Facebook’s involvement and previous concerns about its data security policy, the case is not going to be forgotten very easily. It might also increase people’s paranoia about data sharing on the internet and the unethical side of data-driven technologies.
The above 7 developments encompass the direction that AI and ML are headed in as a whole. Specific developments will vary but at their root, they will all signify progress, advancement, questions about privacy, and the power of technology. If you’re interested in working on things like training an agent to play tic tac toe, train a chatbot, etc. you should check our Advanced Certification in Machine Learning and Cloud course from upGrad and IIT-Madras.