From Alan Turing to ChatGPT

Artificial intelligence, which has been, and is, developed by people, is the capability of machines to exhibit human skills like reasoning, learning, creativity and planning. It enables technical systems to perceive their environment, deal with what they perceive and solve problems.

A computer that collects data via sensors like a camera and microphone, for instance, or receives prepared data sets, such as statistics, processes the data and responds on the basis of that. Artificial intelligence systems have the capability of adapting their behaviour to some extent. They do this by analysing the outcomes of previous actions. Unlike their standard, ordinary counterparts, programmes powered by artificial intelligence can correct their own errors, learn from them and adapt to changing circumstances.

Artificial intelligence has come a long way since its beginnings in the mid twentieth century, making significant progress in a variety of fields, such as processing natural language, image recognition and data analysis, to name but three. But how did it all start?

The first concepts for artificial intelligence go back to the nineteen fifties. While Alan Turing was working for the University of Manchester, he published a paper entitled Computing Machinery and Intelligence. In it, he  proposed a test. Now known as the Turing test, it was designed to evaluate a machine’s ability to demonstrate intelligent behaviour indistinguishable from that of a human. That was in 1950.

The Dartmouth Summer Research Project on Artificial Intelligence, held in the USA at Dartmouth College in Hanover, New Hampshire  in 1956, is seen as the moment when AI as a field of science in its own right was born. It was then that John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon laid the foundations for the development of artificial intelligence, defining its basic goals and directions of research. 

The nineteen sixties and seventies were a period of intensive research and optimism about the potential of AI. The first expert systems, such as DENDRAL (1965) and MYCIN (1972), were created and they had the capability of diagnosing diseases on the basis of the data fed into them. Researchers focused on the development of algorithms and theories which could enable machines to solve problems and make decisions.

Despite the early enthusiasm, the nineteen seventies and eights saw the emergence of an AI winter. There were various reasons for this state of affairs, including overly high expectations, the limited capabilities of the hardware and problems with algorithms. Funding for research shrank and many scientists started turning towards other fields of study. However, even in the face of these obstacles, this was also the period when crucial theories and techniques like genetic algorithms, neural networks and fuzzy logic were born.

In the late nineteen eighties and early nineteen nineties, interest in AI flourished again. The development of computers and information technology made it possible to create more advanced systems. Another symbolic moment for AI came in 1997, when IBM’s chess-playing Deep Blue computer defeated world chess champion Garry Kasparov.

The noughties saw the development of AI forging ahead as a result of advances in data processing and machine learning (ML), along with growing computing power. During this period, technologies like speech recognition, machine translation and recommendation systems emerged and went into wide use in industry and daily life.

Artificial intelligence has been undergoing a real boom since 2010, largely owing to the development of deep learning (DL) and neural networks. AI systems are now capable of processing vast quantities of data, detecting patterns and making decisions with unprecedented precision. Algorithms like OpenAI’s ChatGPT can generate texts that are almost indistinguishable from writings produced by humans and, in 2016, AlphaGo, by Google DeepMind Technologies, defeated a world champion Go player.

Contemporary AI is used in numerous fields ranging from medicine and finance to transport and entertainment. Autonomous vehicles and voice assistants like Siri and Alexa, not to mention tools for analysing big data, are just a very few of the areas where AI plays a critical role. However, we can also spot earlier manifestations of AI in places where we wouldn’t expect to. One example of this is the T9 dictionary that we’re all so familiar with. 

One of the first uses of AI in everyday life came with the introduction of the T9 dictionary. ‘T9’ stands for ‘text on 9 keys. The dictionary, which appeared in the nineteen nineties, enabled cell phone users to write text messages more quickly by automatically suggesting words on the basis of the letters being entered. It used natural language processing (NLP) techniques and statistical language models to predict the text being entered by the cell phone user

T9 may not have been an advanced form of AI, but its introduction was a vital step towards more intelligent communication support systems. It demonstrated that automation and text prediction could significantly improve user convenience on mobile devices. And that inspired the development of more advanced AI technology.

AI is a regular presence in many aspects of daily life today. Here are just a few instances:

• voice assistants: Siri, Alexa and Google Assistant are smart assistants that use natural language processing;
• content recommendation: websites like Netflix, Spotify and YouTube use AI to analyse users’ preferences and recommend films, serials, music and videos they might enjoy;
• facial and image recognition: this technology is used for unlocking smartphones, on social media for tagging photos and in security systems;
• personalising adverts: AI analyses Internet users’ behaviour in order to adapt advertising content to their interests. This increases the efficacy of advertising campaigns;
• health: applications like Fitbit and Apple Health, which monitor physical activity, use AI to analyse data on the user’s health and suggest personalised fitness and diet plans.

With AI, businesses, like MakoLab, are able to analyse massive collections of data, providing a more informed basis for strategic decision-making. Examples here include:

• big data analytics: AI processes and analyses data from a range of sources, helping to identify trends and patterns which could have an impact on business strategies;
• predictive analytics: machine-learning algorithms predict future results such as product demand, consumer behaviour and market trends, facilitating better planning and responses to changes. 

In order to boost operational effectiveness and reduce costs, numerous business processes can be automated by AI by way of:

• robotic process automation (RPA): AI automates routine administrative tasks like invoice processing, inventory management and client/customer service, freeing staff up to focus on more valuable activities;
• chatbots and virtual assistants: these tools automate client/customer service operations, answering queries, solving issues and processing orders.

AI supports marketing activities, making it possible to create personalised campaigns and optimise strategies through:

• recommendation systems: here, algorithms analyse client/customer preferences in order to recommend products and services;
• targeted advertising: AI analyses demographic and behavioural data so as to tailor adverts to the relevant audience groups, increasing campaign effectiveness.

AI optimises supply chain management, improving efficiency and reducing costs:

• route optimisation: AI analyses data on traffic, the weather and other factors to plan the most efficient delivery routes;
• inventory management: algorithms predict demand and manage inventory, minimising excess stock and product shortages.

Looking back on the development of artificial intelligence over recent years and analysing it allows us to predict that, in the future, AI will steadily become omnipresent in various aspects of business, from human resource management to advanced market analysis. Companies will be investing in AI technology with growing frequency with a view to taking their competitiveness and innovativeness to new levels.

The development of more advanced AI systems, such as artificial general intelligence (AGI), is expected to have a tremendous impact on business. AGI will be capable of carrying out a wide range of humanlike tasks, opening up new prospects for automation and innovation.

AI will increasingly be integrated with other technologies like the Internet of things, blockchain and robotics, creating solutions that are smarter and more comprehensive. Smart factories, autonomous vehicles and advanced data management systems are just three examples of those kinds of solutions.

AI will be capable of offering an even more personalised client/customer experience, tailoring products and services to their individual needs and preferences. This could include smart recommendations, personalised adverts and a more engaged interaction with clients and customers.

AI will be developing towards a better understanding of clients’ and customers’ emotions and moods, equipping companies for more empathetic and effective interactions. Analyses of tone of voice, facial expressions and natural language mean that communications can be better shaped to clients’ and customers’ needs.

AI will play a vital role in increasing transparency and trust in a business. One example of this is through the development of advanced audit and regulatory compliance systems. The AI technology will have the capability of automatically monitoring and reporting activities, providing more transparency and ensuring compliance with the applicable law.

The AI-based automation of creative processes such as graphic design, content creation and video production will become increasingly sophisticated. This will enable companies to work faster on creating innovative products and marketing campaigns.

AI is already playing a key role in transforming the business landscape and its significance will grow in the future. From data analysis and decision-making to automated processes and personalised client and customer experiences, it offers a wide range of uses that can upgrade a company’s effectiveness, efficiency and competitiveness. As the technology evolves, new opportunities and challenges will emerge, moulding the future of business and society. 

From its origins in machine learning and the T9 dictionary to today’s sophisticated systems, artificial intelligence has had a significant impact on everyday life. And it is not only business that will be benefitting from it. Solutions like smart homes, smart cars and devices for measuring the status of people’s health make it easier to function and enhance quality of life at the same time. The massive interest in AI and the cost-effectiveness of its development suggest that progress in the field could well happen faster than we expect.

However rapidly things change, MakoLab won’t be taken by surprise, though. Artificial intelligence is a tool that we’ve been using for years and we always have our finger on the pulse. Our sustained experience, our pioneering intelligence amplified approach and our work to stay ahead of the curve enable us to create, develop and deploy solutions that are tailored to our clients’ needs and empower them to adapt to the demands of constant market changes.

If you are considering introducing AI-based tools into your company, then contact us and put your project in our experts’ hands.

What is machine learning? 

Machine learning, commonly referred to as ML, is a sub-discipline of artificial intelligence which is based around creating algorithms and statistical models that enable computers to ‘learn’ from data. Unlike traditional software, where the rules are hand coded by programmers, machine learning involves computers learning the rules from the data and experiences provided.

What is blockchain?

Blockchain is a decentralised, distributed database that facilitates the secure, transparent and immutable registration of transactions and information. This technology operates on the principle of a chain of blocks, where every block contains a set of transactions and is cryptographically linked to the previous block.

What is the IoT?

IoT is the abbreviation of ‘Internet of things'. This is the concept of networking physical objects that are equipped with electronics, software and Internet connectivity, enabling them to connect with the Internet and communicate with each other. The IoT is also the process of connecting objects to both the Internet and a digital network of electronic devices connecting those objects. The process is carried out using tools such as sensors, embedded systems, sensor networks, cloud data processing technologies and so on.

What is deep learning?

Deep learning, often referred to as DL, is a sub-discipline of machine learning. It focuses on using multilayered neural networks to analyse and process data. An advanced technique, it allows computers to learn representations of complex data like images, sounds and texts automatically, without the need for hand-crafted features.

What are neural networks?

Neural networks are computer algorithms inspired by the structure and functioning of biological brains. Designed to recognise patterns and solve problems by processing data rather like human thought does, they are a vital element in the field of artificial intelligence and machine learning.

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7. P. Paoli, History of AI: from Alan Turing to John McCarthy, the first definition of Artificial Intelligence, Pigro.ai