Introducing Artificial Intelligence

Artificial intelligence (AI), using millions of bits, stored on servers all over the world, is reshaping industries by automating complex tasks, saving time, and personalizing customer experiences. In this blog, we’ll explore AI’s functionality, everyday applications, and its potential in business, as well as dangers and impacts on the future of humanity. 

How We Got Here: AI From Turing To Tomorrow

Artificial intelligence was the thought behind everything computer-related. Humans built computers to do the math for us, later to simplify and accelerate work processes. Artificial intelligence (AI) does nothing else. AI is based on the idea that we can mechanize human thought, the work our brain does. 

Alan Turing, one of the remarkable minds at Bletchley Park during World War II, made significant strides in cryptography, famously helping to crack the Enigma code. In 1950, he published Computer Machinery and Intelligence, where he introduced the idea of "machine intelligence." Just three years later, computer scientist Arthur Samuel developed a program to play checkers, and by 1956, a Dartmouth lecturer coined the term “artificial intelligence.” This marked the beginning of a field that would revolutionize computer science and technology, ultimately leading to smartphones and other innovations that simplify daily life.

Progress in AI continued, supported by new neurological insights, as researchers at MIT and Stanford explored how to make AI functional. In the 1980s, the field saw substantial advancements with the emergence of "deep learning"—a subset of machine learning (ML) driven by neural networks, which we will discuss in detail later. These neural networks have become central to AI’s development.

While the 1990s saw a decline in AI advancements, the field revived in the 2000s and 2010s with the development of more sophisticated algorithms, which found applications in areas such as social media. In 2011, Apple introduced Siri, the “intelligent” assistant familiar to all Apple users. Building on the original idea of machines performing human thinking, significant research continued, leading to the launch of OpenAI's GPT-3 in 2020—a large language model, though not the first of its kind. In 2021, OpenAI also released DALL-E, capable of creating images from provided prompts.

Today, robots and chatbots have become something normal, illustrating how AI seamlessly integrates into daily life. At its core, AI is driven by algorithms—code that teaches machines to learn and adapt. By 2025, these algorithms have grown more complex than ever before. In the next sections, we’ll explore the key components and types of AI, providing essential context for the rest of this article. 

Basics Defined: How Do ML, LLM, NN, and GenAI play into each other?

Neural Network

A neural network is a model in artificial intelligence that teaches programs to process data, inspired by the way the human brain works. It consists of different layers of nodes (artificial neurons). We have an input layer, several hidden layers that are trained for computations, and finally an output layer, for which different results are accompanied by a calculated probability. Through this, the result that most likely matches the expectancy is provided. The neurons in this model reconnect every time and leverage machine learning (ML) to constantly improve their work. 

Generative AI (GenAI)

Services like ChatGPT count as generative AI - we use them to create new content, images, texts and such. These AI models combine the other aspects discussed in this part, identifying patterns, using neural networks, and training themselves with large amounts of data. 

Machine Learning (ML)

The first step towards AI is machine learning (ML). It leverages statistical algorithms to learn the hidden patterns and relationships in a dataset. Amounts of data whose equivalents in paper would fill the libraries of the worlds are required for ML to understand these patterns in the data, to teach machines how to learn and perform tasks without direct instructions. Deep learning is considered a category of ML, dealing with more complex algorithms and models. 

Deep Learning

In deep learning, multiple layers of neural networks come together to build a complex form of machine learning (ML). These layers and the process involved when they analyze, categorize, and improve, all aim to have a result as aptly as possible. 

Large Language Model (LLM)

Large language models, as the name suggests, are trained on large amounts of data. They perform natural language processing (NLP), which allows the programs to understand, interpret, and manipulate human language. The datasets given can be texts from the internet. Through fine-tuning processes and deep learning, LLMs get better at translating, answering questions, generating texts. Our friend ChatGPT is a LLM. Complicating the process, LLM has to deal with many inaccurate information available out there, precisely being, why we should be careful when relying too much on these daily helpers. It is tough for them to be perfect. 

AI Automation

AI automation is the leveraging of machine learning, and natural language processing to analyze large amounts of data. The idea is that AI takes over repetitive tasks and constantly improves itself to provide you with more accurate research and thought-through recommendations. 

How to Go About AI in Business: Do’s, Don’ts, and a Changing Job Market

First up, AI can be a great tool for analyzing masses of data, saving you and your team time. The processes outlined above indicate to you how AI works in this regard. It is important to note, though, that we should not be too optimistic about the accuracy AI tools like ChatGPT provide. 

Never use artificial intelligence for facts, if you can rely on facts provided by multiple human sources. To utilize AI and make the best of it, do the research yourself, make sure this data is accurate and only bring AI into the game when it comes to dealing with the data. That is where its strength lies. 

Viewed from a different angle, AI tools employed by, for example, Google with their “overview” function, currently hurt the SEO of companies trying to promote their content. 

On the other hand, AI tools like the “overview” provided by Google’s Gemini actively hurt the SEO of companies trying to promote their content. Also, AI keeps selling its interesting ideas to users when, for instance, stating that eating stones might be a clever idea (see the BBC for more information: https://www.bbc.com/news/articles/cd11gzejgz4o - this is real!). 

Another advantage are AI management tools. Several options to handle emails and calendars rise in popularity and seem to be worth it. One of the popular ones is Motion. This fee-based platform can manage your calendar, (re-)schedule meetings and respect your free time all in one.

It might also be worth it to consider data management tools. These, combined with AI’s ability to analyze and organize millions of information, assist you and your company with anomaly detections, storing and quality checks. When employing such a system, you might however build dependency on larger corporations in the IT market that are handling your data. 

Finally, we have long gotten used to chatbots in customer service. And while it can be good for efficient product suggestions to customers or answering easy questions, you also take away the trust factor when working with AI. it can seriously hurt your customer relationships. 

Affecting Jobs Around the World

That AI will take over most of our jobs has developed into some kind of common sense. That is why various studies and reports try to paint a different picture, to provide us with data to take away some of the fear associated with AI. In the following, we are looking at Deloitte's State of Generative AI in the Enterprise Q3 2024 Report, research from the University of San Diego, the World Economic Forum 2020 Future of Jobs Report, and the Chicago Booth Review. We will investigate how AI is affecting the economy, the job market and how it will do so in the future. 

According to Deloitte, organizations see early benefits in efficiency, productivity and cost reduction when it comes to generative AI. Many of them, on the other hand, do not have specific KPIs (key performance indicators) to measure how effective AI really is. Researchers from Chicago Booth expect an exponential rise in the effect of AI on companies. 

But what truly bothers employees is the thought of their job being replaced. Some experts on the field state that, while this will happen, a series of new job opportunities will be created by AI, therefore simply following the pattern that any kind of advancement has. The University of Oxford predicted, pessimistic, that 47% of US jobs would be automated by AI between 2013 and 2033. A more recent study by Goldman Sachs suggests that generative AI will impact around 300 million full-time jobs globally. As a result, adaptability gets increasingly important in the job market. The WEF (World Economic Forum) believes that creativity, analytical and strategic thinking, problem solving, as well as social skills and a broad understanding of technology are required to stay competitive in this day and age. 

A Compact Overview: Do’s For Employers

• Use AI tools for the analysis of given data and focus on accuracy 

• Leverage AI tools for time management, scheduling, and prioritization

• Evaluate AI vendors for compliance and data security

• Implement strict data governance policies

• Use AI selectively for fact-finding to protect SEO and brand credibility

• Encourage ethical and responsible use of customer data

• Automate routine tasks to free up employee time for strategic work

• Ensure chatbots can escalate issues to human agents as needed

• Inform customers when they’re interacting with AI

• Invest in regular AI training for employees (we got you covered for a Beginner’s Guide on effective prompting down below)

• Maintain open communication with employees about AI’s role in their work

A Compact Overview: Don'ts For Employees

• Do not rely solely on AI-driven insights; human oversight is needed

• Do not allow unrestricted access to customer data through AI systems

• Do not over-rely on chatbots for complex or sensitive customer issues

• Do not implement AI without assessing its productivity impact

• Do not replace the human touch entirely in areas needing empathy and judgment

Beginner’s Guide to Prompting With AI

Working with AI often requires clever prompting to get the best result for your problem. To achieve these results, start with context. Specify purpose, audience and tone when ChatGPT is supposed to draft an email. Be specific. Another way to manipulate AI is asking it to take on a certain “role”. 

Further, make sure to explicitly state your expected length and format, such as bullet points. It might even be good to narrow down the options AI has by providing it with “do” and “don’t” instructions. 

Additionally, sharing examples of a certain structure or writing style can guide AI to mirror these. 

And finally, be aware of AI’s current limitations, as well as its flaws. Careful reading over any generated products is advised. 

Dangers, Human Cost, the Environment, and a Dystopian Future

The Factor of Human Cost: How Your Automated Email Builds on Workers in Kenya

The idea of a digital age has created many misconceptions. One of them is the belief that everything is just magically saved in the clouds. In reality, networks like artificial intelligence run on millions of servers all around the world. And while machine learning brings these systems forward, helping them optimize themselves, any kind of artificial intelligence would be nothing without data. For this purpose, large amounts of low-paid workers are employed in countries like Kenya or the Philippines. Mark Graham, Professor of Internet Geography at the Oxford Internet Institute, looked into the hidden human cost of AI. All the work we think is done by machines - annotation of data, content moderation, and the training of ML algorithms - are conducted by these people who are “forced to work like robots”.

Since AI operates on a global scale, these jobs are often located in countries where the labor is cheaper, and the regulations are limited. Regulations would get these companies to move the jobs to different countries, therefore not having an impact on better conditions for the workers.

How Data Centers Affect Our Environment

“Current estimates suggest that data centers account for 3% of global electricity consumption, with predictions indicating a rise to a potential 10% by 2030” (Dataversity). The construction and maintenance process of data centers in which the AI servers are stored are increasingly becoming a problem for our environment. The training of a LLM, for example, can sometimes make up for the emissions comparable to 60 flights between London and New York. 

A(I) Dystopian Future

Much has been written and thought about how AI will not only shape the job market but reshape the way our planet, and our societies work. In his 2018 non-fiction book 21 Lessons For The 21 Century, historian Yuval Noah Harari explains that many traditional jobs may vanish, requiring people to develop skills that are adaptable. More so, in his view, the future job market will shift toward a collaboration between humans and AI, where humans manage and control machines while they are doing the work. Harari also points out the danger of monopolies controlling the majority of AI data, thereby creating a two class society of those on top and ordinary humans. The spreading of fake news will also grow, according to Harari, with the growth of generative AI.

All of these possible effects of AI on our future lead to three conclusions, not just for Harari but for us as humans when considering the situation: Firstly, we, as society, have to develop a strong position on how we want to face ethical and moral dilemmas created by AI, for example when it comes to data privacy and regulating AI. Since machines have taken over manual labor and AI is replacing the cognitive abilities of humans, what remains is our values on how far we want to go with this progress. Second of all, states Harari, we will have to revolutionize our education system, prioritizing adaptability, critical thinking and emotional resilience. And finally, we will have to define how important our autonomy as humans, cognitively, is to us.

Conclusion

Humanity has created a system incomparable to anything else. It is said that artificial intelligence has the power to overtake the world - at least to reshape it. Since Alan Turing, computer scientists have worked on programming artificial intelligence to take away many cognitive burdens off our shoulders. When employed correctly, it can indeed be a good tool for businesses and business owners to automate certain tasks. Nevertheless, to be able to deal with AI, we also have to understand the price that comes with it. Being aware of the negative effects that AI has, preparing oneself for a future dominated by AI, is only wise.