AI trends in 2019

AI Trends in 2019

Since Artificial intelligence is on its way to surprise humans in every technological field, everyone is wondering what will be coming in 2020? Significant progress of artificial intelligence in our daily life routine is admirable either it is related to smart communication, health, smart cooking, smart home or smart driving. Artificial intelligence is everywhere!

Rise of artificial intelligence in industries in negotiable. For example, Lyft is integrating artificial intelligence to detect frauds and theft of credit cards and stolen money. Similarly, Walmart is using artificial intelligence models for increasing operational performances like improving audit processes and sales tax refunds. Further research shows that industries are investing more and more in AI and there is an observed increase of 21% in AI startups. It is enough to say that there is an ascending trajectory during 2019 in industries related to AI and it is a subject to a discussion where it would be in the next coming years.

Quantum computing

Quantum computer takes input in the form of qubits and performs complex mathematical operations related to quantum mechanics and physics. Quantum computers are assumed faster in computation than supercomputers. There are certain challenges like eliminating useless computations or maintaining synchronization between qubits. Researchers are more focused on the improvement of quantum computers to work best under a different set of inputs and minimize the error rate. As per Director of Joint Center for Quantum Information and Computer Science:

“Current error rates significantly limit the lengths of computations that can be performed; we’ll have to do a lot better if we want to do something interesting.”

The most appealing point is that quantum computers are improvising to do complex and unrealistic problems like climates variations, planets in the galaxy and human body ability to eradicate cancer.

Combination of AI and other trending technologies

2019 is leading towards a combination of AI with IoT and Blockchain to facilitate humans in a more surprising way. For example, the concept of a self-driving car is in vain without IoT. The working of a car is enabled using multiple sensors that collect real-time data; they are purely related to IoT and decision making is the power of AI. This car is modeled using deep learning algorithms for eye-tracking, path planning, driver monitoring, and language processing feature to interpret commands which help in decision making. This autonomous vehicle is able to communicate wirelessly with other vehicles on the road to avoid traffic mishaps.

Because AI and Blockchain are facing challenges with respect to security and privacy. Merging these two technologies to reduce security concerns is a considerable act. Blockchain is powerful in the decentralization of market places and provides aid to AI algorithms to work more transparently and efficiently. Startups are more focused on integrating AI with other technologies to create something more efficient and specific.

Face recognition

Face recognition working and efficiency is upgrading in 2019. Facial recognition is an application purely based on AI to identify a person using his digital image and face features. The renowned Face book is using Deep face application; using which you can tag family and friends in photos. In addition to that facial recognition is getting fame as the digital password for login purposes, advertising, payment processing, shopping and most importantly in biometric identification. Face recognition is working fine in Police departments and with law enforcement agencies to control crime rates in the country.

An upcoming trend in AI is its use in medical services – to proceed with clinical trials and medical diagnosis processes. One of a similar project is Openwater, a kind of prototype that is driving future devices to read images from our brains.

Learning reinforcement

Reinforcement learning is purely based upon experience-driven decision making opposite to supervised and unsupervised learning. In supervised learning labeled datasets are used and the end product is purely based on a given dataset. Whereas unsupervised learning is based upon linking between unlabeled data and clustering of that data.

Reinforcement learning is mostly involved in gaming algorithms; as these methods involve interactivity with the environment and achieving a goal by specifying moves the PC/user should make to win. Some trending uses of reinforcement learning are listed below:

  • Analyzing and discovering treatment procedures for chronic diseases such as diabetes, schizophrenia in the healthcare department
  • In the finance department “LOXM” is a program that is actively used in the stock market for executing trades. It is well known for receiving speedy client orders and their execution at the best cost.
  • Reinforcement learning is greatly used in the personalized teaching system in Higher education.

Biased data

As machine learning models are getting fame in decision making; similarly this topic is becoming important in social services. Different software is designed with a built-in tool that uses training data consciously or unconsciously to make a decision that is biased. For example, Amazon has an internal tool that is reportedly increasing bias toward the hiring of female staff. The usage of biased data in the application is increasing in 2019 and hence dealing with it is also getting complex.

The World Economic Forum emphasized on eliminating this biased machine learning and artificial intelligence results. This problem can be ignored by either active insertion of multiple inputs or analyzing risks of using biasness or by stabilizing speed and performance.

Neural networks

Artificial neural networks are used to copy human brain data in digital form- either it is in the form of text, sensory data and is processed for different purposes. Handwriting recognition is one of the most common practices in the digital world. While training data for this algorithm require a lot of data set with different handwritings to enable it to work efficiently.

There is an increased demand for neural networks in robotics which is genuinely improving the stock market, order fulfillment on time, medical services and so many other daily life complex processes.

Neural networks provide a fundamental basis in “deep learning” which is greatly implemented in image processing, natural language processing, and speech recognition. List of their usage is very long but shortly deep learning is helpful in the detection and cure of diseases like cancer, autonomous vehicles and fraud detection in large organizations. The invention of Alexa (Amazon) and Google home, multiple voice-enabled application which are implementing NLP algorithms are also a gift of deep learning.

Socio-economic model

As AI is getting prominent, humans are showing concern about their jobs.  This matter is getting serious as AI is assumed to replace humans in many departments and making processes automated. On the other hand, AI creates opportunities for new skills and jobs. For instance, if automation is removing certain jobs, there exists a certain need for a human resource such as customer representatives, teachers, caregivers and so on.

The balloon of AI is filling up with new trends and innovations. AI introduced GDPR to protect the privacy of our digital data. Artificial intelligence is continuously growing and 2020 will be a year of stun for technology holic persons. Let’s see what’s come next to automate human life.

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