As we know, “data” is a new power and companies around the world are trying to harness that power in their businesses, be it in healthcare or in the Cybersecurity field, in movie or product recommendations, Business analytics, banking sectors, facial recognition and other areas, people are experimenting with machine learning to move forward ... or just in order to stay up-to-date with the current emerging trends and technologies.
Top and Emerging Machine learning trends in 2021:
1. Health Care: As per a McKinsey report, around 50% of the American population has a chronic illness and 80% of health care costs are spent on treatment. Recommended Examples of AI in Healthcare are:
*Cancer Diagnosis Using AI: Pathologists use AI in healthcare to make a more accurate diagnosis. The aim can be achieved by collecting data for different types of cancer such as histopathological cancer, breast cancer, cervical cancer and further using this data to build a predictive model such as PathAI that will help patients diagnose the disease more accurately.
*Development of new drugs with artificial intelligence: Biopharmaceutical companies are faced with the challenge of overcoming the problem of high attrition rates in drug development. The biopharmaceutical industry is working in close coordination with the artificial intelligence industry to address these challenges.
Atomwise, for example, is the first deep learning technology to discover new small molecules. It has been involved in the development of potential new drugs for 27 diseases and works with top institutions such as Harvard University and Stanford University, as well as many biopharmaceutical companies.
*Enhanced healthcare with AI: The number of patients worldwide is increasing every day. To analyze and process the data about each and every patient, automated systems, as well as methodologies, are needed. In this concern Enhanced healthcare services can be rendered with the help of artificial intelligence.
2. Finance: Nowadays the whole world is going digital, and our money too is getting transformed from cash to digital currency. Total digital payments transaction values were around $ 4.406 trillion in 2020 and this would increase to around $ 8.266 trillion by 2024.
All of these transactions are efficiently stored and processed. We can use this transaction data to improve the financial industry.
Top Examples of Artificial Intelligence in Finance
*Trading: With machine learning algorithms, trading operations can be carried out autonomously. We can tweet attributes such as price, volume, time, but also sentiment or weather data in order to develop a machine learning system that outperforms the market. The algorithm can learn in real-time and adapt to changes to make more accurate predictions. Eg, Kayrros is a data analytics firm that assists market participants in identifying investments in a better manner.
*Detection of fraud: The use of digital payments also harbors many risks. In 2018, $ 24.26 billion was lost to fraudulent transactions worldwide. Machine learning is ideal for effectively combating deceptive financial factors. The UK company AimBrain uses deep learning to combat new account fraud and account acquisition threats.
The model could be used to train the data and mark each transaction whether it was a fraud or not. We can then use metrics like precision and recovery to fit a model to our risk profile and adjust our false positives and false negatives predictions.
*Banking: Banks are increasingly using machine learning for customer service, asset modeling, risk prediction, risk prevention, and investing.
3. GAN: Generative Adversarial Networks, abbreviated as GAN, is basically a method to generative modelling that utilises deep learning methods such as CNN. GAN involves using a model to generate new, similar-looking data based on the data our network has been trained on, e.g. such as pictures. GANs can be used to generate data sets of images, human faces, cartoon characters, translate text to image, translate text from image, generate 3D objects, and other things. There are several other use-cases as far as GANs are concerned, but not all are good for sake of the society.
In 2017 the demonstration of the creation of realistic images of human faces took place. But don't let the miracles you perform with your data fool you. We will soon have to grapple with the adverse social impact deepfake imaging can have.
4. Reinforced Learning: Reinforced Learning (abbreviated as RL) is extremely appealing as it feels like we're watching and learning each day. For example, suppose you have a newly adopted dog that is only a month old. To teach your dog what is good and what is bad, a reward system maybe used. If the dog obeys you, you give him a cookie, and if he doesn't, you end up scolding him.
The Artificial Intelligence agents never received explicit directions regarding how to play; they learnt everything by themselves.
Eventually, AI agents learnt to manipulate and acclimatize to their environment:
• The agents in hiding learned, for example, the way of building small fortresses and barricades.
• In response, Seekers learned how to use ramps to climb walls and find out those hiding.
Open Artificial Intelligence techniques can also be improvised to other Artificial Intelligence scenarios by harnessing the potential of multi-agent competition, which is indeed one of the most amazing outcomes of the application of this technology.