The Undeniable Evolution of AI and Machine Learning

The Undeniable Evolution of AI and Machine Learning

AI is evolving rapidly. But how does it affect us?

Once upon a time, the idea of interacting with Artificial Intelligence (AI) was a far off and potentially terrifying concept. Images of killer robot armies and a world run by cyborgs stealing our jobs often came to mind. But, with recent developments, a future with AI actually seems plausible and much closer than we humans think.

Rather than the typical image of a human-like being, types of AI are already in use throughout the world. From the virtual assistant feature in iPhones, SIRI, to self-driving cars and intuitive chatbots, the reality of AI is in the here and now. And it’s more advanced than you think.

Types of AI and functions

Artificial Intelligence

The basic function of Artificial Intelligence is to create or simulate intelligence in computers and machines to function in an intelligent manner and complete tasks. While a lot of people are afraid that AI will replace human jobs, the main appeal for businesses to implement AI is to eliminate time-consuming work through automation. Allowing humans to focus on the creative and high-level thinking aspect of work, without the distraction of mundane tasks.

Machine learning

Machine learning is the idea that a machine can be presented with a data set and be able to make sense of it. The aim is to give computers the ability to identify and comprehend patterns in data, without being explicitly programmed. Machine learning is a subset AI that helps to interpret data and make decisions with as little human intervention as possible.

Machine learning algorithms are applied to systems like Netflix and Spotify. The data collected from these platforms means machine learning algorithms can make highly educated guesses about what you might want to watch or listen to next.

One of the most prevalent uses of machine learning is in SMS spam detection. Machine learning algorithms are applied to easily classify whether a text message is spam or not. Studies show that these algorithms have proved to be 99.4% accurate when filtering spam from non-spam text messages.

Machine algorithms typically associate specific features with spam in SMS, including:

  • Mathematical symbols such as ‘+’, ‘-’, and ‘/’
  • Presence of URLs
  • Special symbols such as ‘$’, ‘*’, ‘!’, and ‘#’
  • Words in uppercase
  • Presence of a mobile number

Instances of SMS scams such as spoofing and smishing are increasing. This is why it’s more important than ever to trust verified SMS gateways and to comply with A2P messaging regulations.

Related: What do smishing, spoofing, and social engineering have in common?

Conversational AI

The essential function of Conversational AI is to automate communication, usually within a business setting for personalized customer interactions.

Some of the most common forms of conversational AI include:

  • Automated messaging via OTT apps such as WhatsApp
  • Voice-based assistants such as Amazon Alexa
  • Chatbot computer programs that conduct conversations via text

These interactions are all based on natural language processing (NLP). The process of understanding human communication in written and spoken forms. Machine learning is implemented in NLP to assist a machine’s understanding of the human language and its various nuances. NLP also means machines can learn to respond in a way that makes sense to the human on the other end.

Where AI development currently sits

In the age of ‘big data’, humans can no longer process or handle the infinite waves of information now available to us. This is where AI is highly advantageous and useful, especially in industries such as tech, banking, and digital marketing. The application of artificial intelligence allows for a higher capacity of data and information to be processed efficiently and with greater accuracy.

Marketing and Communications

Machine learning and AI have already proved successful in digital marketing. Not least for its ability to interpret patterns and trends in consumer behavior with accuracy and efficiency. Implementing AI in marketing can aid in the prediction of sales forecasting and ad optimization. It also has the expansive ability to sift through data, providing information to assist in executing marketing and business strategies.

AI is especially useful in the customization of mobile marketing. With 64% of consumers expecting a more personalized shopping experience, AI is helpful in refining and creating a valuable customer journey and unique customer experience (CX).

Whether a business is looking to automate its bulk SMS API or personalize mobile advertisements, AI is key in revolutionizing communication strategies. With an increased demand for eCommerce, it has become even more imperative to utilize machine learning and AI in online interactions. AI chatbots have become increasingly useful for customer support via SMS due to its highly intuitive algorithms. As an AI software that simulates a conversation in natural language, chatbots recognize SMS keywords and triggers and respond to important queries automatically. Giving human-like responses with accurate information and relevant solutions.

Related: 'Why Customer Experience (CX) is Vital to your Marketing Strategy in 2020'

Tech companies

Tech giants like Google, Microsoft, and Amazon are leading the charge in their use of AI. These companies gather large amounts of data and with the help of AI use it to conduct predictive business models rather than old-school reactive models. Data insights provided by AI allow tech companies to quickly create products and reshape current product developments using real-time, predictive analysis.

Self-driving cars

Self-driving cars are perhaps one of the most well-known forms of advanced AI. Some forms of automated driver assistance are already in use globally, and have become the norm in many new vehicles. Features such as auto-correction sensors when turning or braking, and automated driving for self-parking cars, are available from the likes of Ford, Volkswagen, Holden, Lexus, and Audi.

While completely autonomous self-driving cars aren’t yet in use, AI-assisted driving is popular in situations of high human error such as:

  • Vehicle control in emergencies
  • Sudden braking
  • Monitoring car blind-spots
  • Detecting traffic signals and lane boundaries

Timeline forecasts have been made by a number of industry professionals, including Tesla Founder, Elon Musk, and former Uber CEO, Travis Kalanick. Travis Kalanick stated that by 2030 Uber’s fleet will be completely driverless. The driverless service would reportedly be so prevalent and inexpensive it would render personal cars obsolete. Elon Musk also estimated that this could be achieved by 2023, with an additional two or three years for regulatory approval. “We will be able to achieve true autonomous driving where you could literally get in the car, go to sleep and wake up at your destination”, Musk predicted.


Banking and the broader finance industry recognizes the potential of AI to enhance, streamline, and secure business processes. A survey for financial service professionals determined that 80% of banks are highly conscious of AI and Digital Intelligence’s potential benefits. These banks acknowledged how such technology can boost operations, reduce costs, and improve CX, as well as overall customer satisfaction.

AI and machine learning are already being implemented within financial institutions for:

  • Fraud detection
  • Processing payments
  • Account verification
  • Support Services

Compliance and fraud prevention

One of AI’s key purposes within the banking industry is its ability to quickly detect and therefore prevent fraudulent transactions and activity. AI provides the scale and speed required to interpret fraudulent activity and its complexities. Using predictive analytics and machine learning techniques, AI is able to quickly discern discrepancies in large-scale data networks.

Payment processing

Using NLP, AI can easily identify problems with declined payments immediately. Issues such as missing signatures on checks, incorrect payment amounts, or other contradictory information can be easily detected, where it would possibly be missed by human error.


The use of chatbots within the banking industry assists in the seamless identification and authentication of customers. This ensures that all accounts are verified before proceeding with online requests, such as updating personal information or contact details.

Personalization of customer interactions

By emulating ‘real’ employees, chatbots and voice assistants can provide a tailored customer experience when offering support and suggestions. This can involve personalized recommendations and relevant, in-depth insights into a customer’s account activity.

Can AI show empathy?

As societal norms shift in the digital age, there seems to be a natural progression towards communicating with machines more, and more. As AI continues to evolve, it undoubtedly alters digital communication as we know it. Communication will become faster, streamlined, and more personal—even if these interactions might be with machines.

Chatbots are one of the most ubiquitous forms of AI in current existence. However, recent advances in learning algorithms and NLP could mean chatbots are able to communicate with empathy and emotional intelligence. Similar to how a human would interact.

Facebook’s new bot, Blender, is the social media platform’s most empathetic chatbot to date. Blender was ‘trained’ using various data sets including 1.5 billion comments from Reddit message boards. It was further refined using data sets containing question-and-answer conversations and ‘emotional’ dialogues between real people. With exposure to diverse data sets, Blender “has the ability to assume a persona, discuss nearly any topic, and show empathy,” according to Facebook.

Other forms of AI have excelled in executing pre-programmed commands and tasks, like booking a flight or fetching order information. However, the real art of conversational AI lies in its ability to maintain intelligent and personable discussions. This can only occur when AI systems ‘understand’ the broad context of conversations and how various topics can relate to one another. With AI chatbots such as Facebook’s Blender, researchers are well on their way to advancing away from bots that simply mimic behavior. Bots that can now recognize subtle changes in conversational tone and adjust its approach accordingly, much like a human. Facebook has also chosen to release the open-source coding for Blender so that the community can continue to build on its progress.

What does the future of AI look like?

A post-apocalyptic future run entirely by intelligent machinery is not as likely as sci-fi films make it out to be. Humans maintain the ability to outperform AI in relationship building and original thought processes, and we should embrace the potential of machinery. After all, the main aim of implementing AI is to surpass human capacity in performing the mundane, repetitive, and time-consuming tasks that we don’t want to do.

We are already experiencing kinds of mass automation across a number of industries. The benefits of such automation are apparent—54% of executives state that AI solutions have already increased productivity in their businesses.

Regarding the future of AI, the possibilities are endless and what comes next remains to be seen.

AI in healthcare

Perhaps the most exciting potential of AI is within the medical industry. There are a number of studies that suggest AI can perform “as well or better than humans at key healthcare tasks, such as diagnosing disease.”

Machine Learning methods have been shown to “improve the accuracy of cancer susceptibility, recurrence, and survival predictions.” Fundamental factors in the early diagnosis and prognosis in cancer research. A 2019 report also suggested that due to rapid advances in AI for imaging analysis, it is likely most radiology and pathology images will be examined by machines at “some point”.

Medical professionals need not fear. While AI may have superior accuracy when it comes to imaging analysis and diagnosis, many patients prefer to receive important health information from an empathetic, qualified professional. AI systems may enhance efforts in patient care and treatment, but without the complexity of uniquely human traits such as empathy and seeing the ‘big picture’, it will not replace humans in the medical industry on a large scale.

While studies of AI in the medical industry have shown promising results, their implementation within daily medical care is not yet in the near future. Challenges such as regulation approval, clinician education, and public and private funding prevent the widespread adoption of such technology. With this in mind, the limited use of AI in clinical practice can be expected within the next five years, and implemented more extensively within 10 years.

Quantum machine learning (QML)

The benefits of machine learning as an application of AI mean machines can interpret and use data sets. However, something bigger may be on the horizon—quantum machine learning. Using quantum computers that are designed to handle large amounts of data, quantum machine learning and algorithms could extend the capabilities of traditional computing.

With the ability to complete multiple calculations with multiple inputs simultaneously, Google’s quantum computer claims to calculate 100 million times faster than any system today. “Quantum machine learning can be more efficient than classic machine learning”, said quantum algorithm researcher, Samuel Fernández Lorenzo. “Quantum computers are exponentially faster than classical computers … Such a fast system is critical to process the monumental amount of data that businesses generate on a daily basis”.

Industries such as pharmaceuticals, science, and finance are generating more and more data. At some point, this will require the powerful processing tools offered by quantum computing which allows for the quick analysis and integration of large datasets. Furthermore, access to such extensive data allows the machine learning and AI capabilities of a quantum computer to adapt, develop, and transform in accordance.

Tech giants Google, IBM, and Microsoft are currently looking into the many possibilities of quantum machine learning. However, the technology and infrastructure behind quantum computing are still evolving. While it has been a trending topic, before this technology can enter the mainstream, more research is required. Particularly, in the fundamental requirements necessary for QML algorithms used to process information in quantum computing.