Emerging Artificial Intelligence and Machine Learning Trends

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Artificial Intelligence and Machine Learning

The already accelerated pace of technology adoption has received an extraordinary push from the current COVID-19 pandemic. More and more businesses are joining the digital transformation wave to ensure business continuity amidst the coronavirus situation. Technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Cloud Computing, etcetera are leading the path to innovations.

Both Ai and ML have become an integral part of modern world applications. From state-of-the-art quantum computing systems and ultra-modern personal assistance tools to language processing and consumer electronics, AI and ML are finding their way into diverse streams in modern times. 

Ever since their genesis, they have both been continuously assisting humans in numerous ways. They are providing key solutions to businesses to help them achieve crucial goals by deriving actionable insights from their data. They are the force behind the novel, innovative, and customer-centric products and services that present-day businesses are able to build.

The Internation Data Corporation (IDC) has forecasted a robust growth of 12.3 percent for the worldwide AI market in 2020, even amidst testing times of the novel coronavirus. It expects global revenue for the AI market to reach about $156.5 billion. The market includes hardware, software, and other AI services.

As we prepare to bid adieu to a turbulent 2020, let’s have a look at some of the most prominent AI and ML trends.

AI and ML for Cybersecurity Tools

Cybersecurity is one of the most important aspects of the global business landscape. A trivial appearing malware or Ransome attack can lead to an absolute shutdown of a business. It is the reason why cybersecurity strategies and applications need regular updates. 

Today, AI and ML technology find their greater involvement in cybersecurity systems to help organizations achieve enhanced security. Modern developers are increasingly utilizing AI and machine learning for their cybersecurity applications for corporate as well as home security. These applications safeguard business data and resources from varied security threats.

Developers are building efficient AI algorithms that are capable of recognizing patterns for identifying potential threats. These threats could be a suspicious Internet Protocol (IP) address or prospective data breach. 

The corporate cybersecurity tools, powered by AI, collect an organization’s transactional data, keep a track of its digital activities, access its communication networks and websites to recognize possible security threats. They also gather data from external public sources to ensure the security and protection of a business’s critical information.

As far as home security is concerned, AI aims to power self-learning smart home systems in the coming future. These systems promise to learn the practices and preferences of the residents for smartly identifying attempts of intrusion.

The increasing contribution of AI and ML in Hyperautomation

Hyperautomation is also known as ‘intelligent process automation’ and ‘digital process automation’. It augments human capabilities and automates complex tasks. It utilizes technologies like artificial intelligence, machine learning, robotic process automation (RPA), process mining, natural language processing, and decision management to create progressively automated business processes.

Developers have been utilizing static applications and software packages to achieve automation until the recent past. Today, with the rise of AI and ML, automated business processes are able to attune to varying business circumstances and respond to unanticipated situations, without manual intervention.

Artificial intelligence, together with deep learning (a subset of ML), helps in creating automated processes that automatically improve with time. The processes utilize learning models and algorithms to learn from the data generated by the system.

The Convergence of AI, ML, and Internet of Things (IoT)

Internet of Things, popularly known as IoT, is one of the most important technological developments of the 21st century. Simply put, IoT is a system of physical devices embedded with the latest sensors and software applications. It utilizes internet connectivity to make everyday devices smart by enabling data transmission and automation.

From a smart wearable device to a complex smart city project, IoT is the backbone of all. It helps realize the concept of modern smart homes where all devices are able to communicate with each other and perform automated tasks without human intervention.

AI and machine learning are increasingly being employed to build smarter and more secure IoT devices. The network of IoT devices and sensors collect large volumes of data. This data is exactly what AI and ML require to work successfully. 

AI-powered IoT, alternatively termed as AIoT (Artificial Intelligence of Things) promises to revolutionize industrial automation. IoT networks can be set up in an industrial setting to collect performance and operational data. This data can be further fed to the AI algorithms to achieve better production system performance and higher business efficiencies. It can also help to predict the maintenance requirements of various machines in the manufacturing plant. That way, the convergence of AI, ML, and IoT has huge potential going forward.

Disciplined AI Developments via AI Engineering

“One of Gartner’s researches reveals that only 53 percent of AI projects are able to achieve production from the prototype stage.”

More often than not, businesses recognize AI as a set of isolated, specialized algorithms and not a mainstream DevOps process. This leads to failure in obtaining the aimed results. In order to attain the hoped-for performance, interpretability, scalability, and reliability of AI systems, businesses need to espouse them as conventional processes. This can be done by incorporating a strong AI engineering strategy.

A disciplined AI engineering strategy utilizes components of ModelOps, DataOps, and DevOps to help make AI an integral part of the normal DevOps process. This helps AI systems achieve their full potential and deliver expected results. Therefore, a robust AI engineering program is critical to efficient AI developments and is one of the top strategic trends to keep our eyes on in the future.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks creates fresh data with the help of existing data to generate new, resembling products. Many people misunderstand it as a simple copying phenomenon, which is far from the truth. GANs are capable of creating synthetic pictures of human faces that resemble real humans.

Nvidia developed an application called ‘This Person Does Not Exist’ for generating random photos of fictional persons. It was an excellent demonstration of what GAN technology is capable of achieving.

GAN is a machine learning network based on two neural network models. It is the responsibility of one model, called the generator, to create fresh data samples, while the other, named discriminator, determines the resemblance of the generated data with the real one. The two models compete with each other, with the generator trying to deceive the discriminator.

While its ability to create completely fabricated yet realistic pictures is vulnerable to misuse, the positive applications are immensely beneficial. In the future, we can expect GAN to assist police departments in creating perfect criminal sketches. It can also prove to be an important tool for quality control and other inspection-based tasks.

Ethical Questions Pertaining to AI

While artificial intelligence promises of potentially smarter and better tomorrow, it has some areas of concern as well. The recent protests against racial discrimination have led firms like IBM, Amazon, and Microsoft to decide to limit the application of facial recognition tools by investigation agencies until regulatory federal laws pertaining to the use of the technology are defined.

The AI-powered tools can be greatly misused by internet fraudsters and cybercriminals. Therefore, it would be interesting to see what regulations do the law enforcement agencies bring in to ensure transparent, accountable, and ethical utilization of AI for a better future, preventing its misuse to as much extent as possible.

Conclusion

The changing needs of modern businesses make way for innovations and smarter solutions. Both AI and ML are the backbone of the modern wave of innovation. It would be exciting to witness how these technologies shape the future of humankind in general and businesses in particular.