Technologies you need to know about for Artificial Intelligence

Artificial intelligence (AI) is perhaps the secret ingredient to every major advancement in the fourth industrial revolution.

From virtual assistants like Apple’s Siri to Google’s self-driving vehicles, to even biometrics and speech recognition programs, there’s no end to the application of AI.

Based on mimicking the human thought process, this disruptive technology has successfully infiltrated every facet of our lives. According to a recent publication from Brooklyn University, AI will play an important role in the future of most sectors, from Economics to Politics, and even to Crime.

This disruptive technology, however, is still evolving despite massive adoption in various industries. Yes, most AI technologies are still based on algorithms that respond based on pre-set user behaviours, which limits its true purpose. Therefore, there’s a need to improve the algorithms that make up the neural network of every AI technology out there. No doubt, the drive has spurred the innovation and subsequent adoption of technologies or software to transform the landscape of AI.

To help you navigate this landscape better, I’ve collated prominent AI technologies with the most potential to effect change. Therefore, you will not only become familiar with the nuances but also explore real-life use cases of these technologies.

Machine Learning

Machine learning is a dominant aspect of AI that focuses on the ability of machines to learn and make accurate decisions by using large amounts of input data. This technology utilises the vast amount of data gleaned from a myriad of IoT devices. It is able to utilise these vast amounts of data to perform tasks such as visual perceptions and speech recognition, which are considered to require human-level intelligence.

Unlike other AI technologies, machine learning utilises a combination of algorithms and data. Although much emphasis is placed on its data usage, its uniqueness lies in the ability to learn patterns and automatically create new and dynamic data. Furthermore, machine learning creates a feedback loop which enables it to produce more models without requiring additional resources.

It’s no surprise that mega-corporations such as Coca-Cola and Heineken have taken advantage of this technology to improve their operations, advertising, customer and marketing service. For instance, Coca-Cola leveraged machine learning to launch the Cherry Sprite. By collecting vast amounts of data gleaned from their soda dispensaries, this company was able to identify a vast market for the Cherry Sprite!

Natural Language Processing

By 2025, the global AI market is projected to hit a record high of $60 billion. Do you know the interesting part? A large percentage of this figure is expected to be derived from Natural Language Processing (NLP) technologies. From Amazon’s Alexa to Google’s Assistant, this speech-to-text technology is fast-becoming a constant in every aspect of society. It’s an advanced form of AI that helps machines to understand and, perhaps, even communicate with human speech. Let’s use Amazon’s Alexa as a case study: Alexa’s designers were able to take NLP a step further. Alexa uses a multi-layer communication system that spans across audio cues, screen, Alexa’s voice and apps. In addition to this, it utilises the Hidden Markov Models (HMM) to understand human language and the context in which it’s used.

Therefore, NLP technology is able to break down human language into parts of speech via a sequence of coded grammar regulations in order to understand the context of the language. Besides its massive application as a virtual assistant, this piece of technology is also used in data mining and fraud detection.

Biometrics

Passwords are highly vulnerable. In fact, they are often regarded as the weakest security link in an organisation. For this reason, Biometrics was developed to ensure a natural interaction between machines and humans by utilising fail-proof authentication criteria such as DNA, fingerprint, dental structure, and facial structures.

More so, it provides a faster form of identification than magnetic strips or passwords. It’s no surprise that a survey conducted on 4000 customers, revealed a 52% preference for biometric methods over traditional security protocols. Therefore, companies like Samsung and Apple have taken advantage of this AI technology to garner more subscribers for their products. No doubt, biometric systems are of indispensable value in various sectors. For instance, Government agencies use biometric systems in voter registration, ePassport, National ID, and Border Control. In addition to this, it offers a safe and more efficient method of identifying their citizens without requesting physical ID tags at all times.

Business Decision Management Framework

Companies take advantage of the vast repertoire of data at their disposal to make informed decisions to connect more with their target audience. No doubt, these decisions become more accurate when infused with AI. The effect of AI on decision management is immensely felt in eCommerce, insurance, and financial marketing trading.

This business decision management framework incorporates the design, building, and management of automated systems for better decision making. Companies are able to use it to manage their supplier, employee, and customer interactions in a bid to boost operational decisions. More so, mega-corporations like Amazon have taken this further by offering AI-inspired Decision Management services via their Amazon Web Services (AWS) Partner Network to companies and individuals alike. Frameworks such as the AWS Network enables businesses to connect with their target audience via up-to-date technical, business, and market support.

Robotic Process Automation

Here’s another AI technology currently revolutionising the workforce of most industries. In fact, commonplace for companies to employ the technology in areas where human labour is considered as expensive or inefficient. Robotic Process Automation (RPA) is a non-intrusive technology which leverages on existing infrastructure without creating a disruption to the system.

Concisely, this technology focuses on reducing cost without undermining efficiency or productivity. RPA robots can mimic many human user actions such as moving applications, fill in forms, copy and paste data, and extract data from documents. It’s no surprise that mega-corporations like PWC and IBM have integrated RPA to reduce cost while improving scalability, control, and quality. In fact, according to IBM’s analysis report, companies that use RPA for paying accounts, process invoices at a faster rate of 43% as compared to non-RPA users.

Furthermore, such companies had a 40% cost reduction in their operations. Mind you, RPA technology solely relies on algorithms. Therefore, it is incapable of creating new experiences from its operations. This mode of operation is different from machine learning or biometric applications such as Apple’s Face ID, which utilises a combination of algorithms and data to create a new dynamic feedback loop.

Takeaways

As mentioned earlier, AI is constantly evolving due to an influx of new and disruptive technologies. These technologies are not only expanding the landscape of AI but also increasing our understanding of how the brain works. With enough research and Innovations, man will learn how to improve the neural network which is the core of every AI technology.