The Future Of AI Is Upon Us, It’s Time To Have A Serious Look

Written By April Walker

You can’t begin any discussions about technology and enterprise today, without addressing the juggernaut of topics – artificial intelligence (AI). AI is without question a variable that every business leader, government entity, and global mission has line of sight and focus on. Audacious and bold predictions that the future of everything will be impacted and affected by the influence of artificial intelligence (AI), to some may seem theoretically premature, yet conceptually if you read the latest headlines and news reports, it’s certainly plausible. The acceleration and headways being made with this technology thus far depict that AI is and will in some measure or in its entirety be a part of everything that we as a global society will encounter. By all accounts the narrative is – there will be very few segments of our existence that will remain untouched by AI, and it will make everything better – does this sound like a dramatically utopian world is coming?  Maybe, but perhaps not too far-fetched. So, what exactly is meant by everything? Well, I am referring to how we navigate the world. Specifically, how we will live, learn, and even how we love. But let’s not get ahead of ourselves, I first want to provide context on a few fundamental terms before I explain my thoughts on these three areas. If you are curious about AI (as we all should be) – these are a few terms you should be familiar with, but know the AI list of terminologies is vast, so by no means should you consider this list exhaustive.  

As an experienced AI practitioner, architect, engineer, educator, and evangelist, I’m often asked: “Where should one begin their AI journey of discovery?” Agreeably, there is so much information available now that the thought of gaining knowledge and grasping all the concepts of AI could become overwhelming to even think about. Trust me, even the most disciplined, well-intentioned, voraciously well-read straight-A student would find it a daunting undertaking to absorb it all. My counsel – take it slow – there is no need to boil the ocean or overload the available bandwidth of precious time trying to fill your brain with every nuanced white paper, blog, or article written about this topic – except for mine of course! Attempts to do so would be fruitless anyway, this technology is evolving so quickly the notion that we’ll have to routinely expect to learn something new every day, appears to be the new normal. That said, you’d need to become a walking ‘Large Language Model (LLM)’ to comprehend it all – but don’t get distracted by the introduction by what may be this ‘new’ term – I’ll share more on LLMs in a minute – so keep reading!

A Brief History of AI

Simply put, AI can mimic human capabilities. A more refined definition equates to AI being a theory and development that computer systems can perform tasks normally required by human intelligence, i.e., such as communication, learning, and decision-making. These tasks can also include visual perception, speech recognition, and translation between languages, this represents the simulation of human intelligence processes by machines or computer systems. The birth of AI started between 1950-1956. Alan Turing, an English mathematician and computer scientist and often considered the father of modern computer science was famous for his work developing the first modern computers, decoding the encryption of German Enigma machines during the second world war, and detailing a procedure known as the Turing Test, forming the basis for artificial intelligence. He published “Computer Machinery and Intelligence” which proposed a test of machine intelligence called The Imitation Game. The actual field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956.

Terminologies and Definitions.

This next section should aid in deciphering and decoding this incredible technology and how it’s evolving in everyday use. Intentionally absent from this are concepts such as ethical AI, bias and inequality, societal and economic ramifications, and analysis of wealth creation and conversely wealth gaps.  AI has a direct impact on each of these areas and each concept will have material consequences for communities of color, thus, these topics merit comprehensive discussion and I will cover them in-depth in a future column, but for now, let’s start with the fundamentals.

1. Chatbot: A software application that is designed to imitate human conversation through text or voice commands. For example, when shopping online you may encounter a pop-up window on your screen asking if you need assistance with your purchase – thus you’ve experienced a chatbot!
2. Generative AI (GenAI): A type of technology that uses AI to create content, including text, video, code, and images. A generative AI system is trained using large amounts of data, so that it can find patterns for generating new content. The litany of benefits GenAI creates for industries presents compelling opportunities to improve consumer experiences and improve productivity for businesses. As a patient, customer, or client-facing assistant, access to information that is easily available and understandable alone demonstrates the significant potential for democratizing data, like never before.
3. Chat Generative Pre-Trained Transformer (ChatGPT): Developed by AI research company, Open AI, now on its most recent release GPT-4o (“o” for omni), ChatGPT is an AI chatbot technology that can process our natural human language and generate a response. OpenAI’s website reports this latest version is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, image, and video and generates any combination of text, audio, and image outputs. It can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is like human response times in a conversation. While it may be easy to confuse the difference between generative AI and ChatGPT, the two are summarily different. Where Generative AI’s focus is to generate ‘original’ responses, ChatGPT ‘mimics’ human conversation. Think of it this way, GenAI will create new content based upon inputs it receives and ChatGPT is what you would encounter during an exchange with online customer support.
4. Big Data: The power of AI is non-existent without data. Consider this, data is the fuel that AI needs to do its job!  Big Data refers to what is called the 3Vs – volume, velocity, and variety of data that artificial intelligence technologies are using to discover patterns and correlations hidden in massive collections of data.
5. Natural Language Processing (NLP): A type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices. A common example of an NLP can be found when querying voice assistant Siri for directions!
6. Machine Learning (ML):Many online and on-demand streaming platforms that provide content such as movies, documentaries, etc,  leverage ML algorithms to better understand and improve customer experience simply by using our viewing history.
7. Deep Learning (DL):If you are an avid connoisseur of platforms where you can contribute your own content or view those of others, and wondered how so many recommended videos appear for your viewing experience – well that’s DL at work as it leverages vast amounts of data of other viewers with similar viewing interests.
8. Neural Network: A method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. Have any of you had an experience of being a passenger in a self-driving car? Neural networks power self-driving vehicles – so the next time you are in San Francisco, take a ride!
9. Large Language Model (LLM): An AI model that has been trained on large amounts of text so that it can understand language and generate human-like text. The use of the word ‘trained’ here is important, hence this is the critical element of LLMs, as they are fed more data, they continue to learn and evolve. LLMs require an extensive training and fine-tuning process before they can deliver reliable and useful results. They are deep learning models that can perform natural language processing tasks, such as translation, summarization, and text classification. LLMs can also recognize images, solve math problems, and write code. LLMs, such as ChatGPT are now prevalent tools used in recruitment and hiring practices. Given the vast amount of data submitted by candidates seeking jobs, LLMs can make the task of shortlisting candidates a lot more efficient for HR professionals and reduce the time-to-select and hire by doing the tasks of parsing through CVs and matching them to desired or preferred criteria.

Living with AI

The concepts illustrated here depict the sheer magnitude of how today and well into the future AI influence will impact how we navigate the world. Living with and in the world of AI shouldn’t cause fear but should embolden a global perspective that significant opportunities to improve the human condition exists.  Imagine a future that consists of robots powered by AI that can clean your home, run your errands, baby-sit your children – okay perhaps that last one maybe taking things a bit too far, but is it really?! I digress. From healthcare, finance, manufacturing, to entertainment, AI is the catalyst of the future that will forever change all aspects of human engagement, experiences, and interaction that we have with one another, and increasingly our connection with that of machines. Vastly improving many of these circumstances, but some could argue creating more isolation, disconnectedness, and a diminishing sense of security in the very same way. The cautionary lesson will be to ensure that amid this rapid innovation we don’t forget that while humans create this technology, it is our humanity that will determine its most powerful use.

AI Advancement on How and What We Learn.

Traditional methods of learning that required sitting in an actual classroom with a teacher at the helm, thank goodness, have not been entirely replaced with the likes of online learning, (we still love our face-to-face interaction with teachers and the classroom experience). Today, a growing population of students reaching the farthest corners of the most impoverished communities globally can now obtain education through various means of virtual tools and resources. Artificial intelligence, by far, presents the most compelling and feasibly game-changing effect on how education could become accessible to a much larger audience than ever before. The dilemma – one must ponder, is this the universal goal, if not, why not? As a competitive advantage, every country and its leaders should be embracing the notion that competitive advantage lies with those whose communities are the most educated, innovative, and skilled – to disregard this as a non-societal imperative, is, well enormously and dangerously shortsighted.

Recognizing the growing economic precarity, business leaders are grappling with how to upskill their workforce for the future.  The advent of GenAI is helping to free-up time-consuming and often mundane tasks in the workplace and as a result allowing employees to focus their time on other things and enterprise leaders to rethink and prioritize the most critical

needs of their businesses and to drive growth. So, what does that mean in terms of the skills needed for the future – it will require a talent pool that is versed in the application of AI, digital literacy, and data analysis, to name just a few. The good news is that there are ample resources available to learn about AI and in many cases these training courses are available at no cost. Online education platforms such as edX and Coursera each provide self-paced options and offer a myriad of specializations and preparation for technical certifications.

The final theme – Love and AI.

Candidly conjuring up images of romantic interludes isn’t exactly easily invoked in your mind when thinking about love and AI as conduits for achieving connection, but as strange as it may sound, there do exist plausible intersections between the two. Stay with me – it will make a lot more sense to some of you naysayers, very soon. Now I’m not suggesting that AI would serve as the proverbial wing man or woman, but [it] could in the very near future become the data-infused ‘dating agent’ that leverages profile data to assist with identifying viable matches on dating apps for example and could even be used by these very same apps to safeguard subscribers against potential risks – the benefits and use cases are endless. That said, privacy is always a big concern. Sharing of personal information is rampant and inescapable in the world of AI, striking the right balance between how much to relinquish and whom it is given should always be at the forefront of our minds. Trust is key! Knowing that companies entrusted with your data have unwavering policies and practices for protecting your personal information is evidenced through their willingness to be held accountable and responsible for storing and using this data. These companies should relentlessly pursue measures to maintain our privacy at all costs – this diligence should resemble the same approach to consider when choosing a soul mate or…. just allow AI to do it for you!

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April Walker is a digital transformation expert, technologist, board member, published author, industry thought-leader, and C-suite advisor. A career spanning more than 30 years in STEM, April has held leadership roles in both the public and private sectors. She has been an executive leading global organizations and served as a catalyst for driving change, direction, and developing enterprise-wide and global strategies for digital transformation, technology, and innovation for Fortune 500 companies including Microsoft, MetLife, and NBCUniversal, to name a few, and today, Aprilserves as Salesforce’s Senior Vice President of Customer Success for North America.