Technology

Evolution of AI and Machine Learning

Since artificial intelligence (AI) started making waves across the globe, it has become integral to our digital lives and impacted the way we live our physical lives. Over the years, AI has evolved into a powerful technology that enterprises are leveraging to unearth new business insights and hidden patterns for strategic decision-making. AI is so powerful that it is believed to have given rise to the 4th industrial revolution which brought a complete shift to the way humans live their lives with automation gaining more attention. The focus is on productivity and AI has become the vehicle that enterprises are using to automate repetitive tasks leaving humans to channel their energies on innovation. Artificial intelligence is a key technology of industry 4.0 alongside other emerging technologies like robotics, augmented reality, virtual reality, the Internet of Things (IoT), and big data that integrate with people and organizations.

The current trend is hefty investments in AI research with software revenues across the globe projected at more than $100 billion by 2025. As this field advances, AI and machine learning professionals face the greatest challenge of keeping up with the latest trends through AI/ML conferences, blogs, and AI ML Bootcamp.

Often, AI and ML are used interchangeably. However, machine learning is a subset of artificial intelligence with other categories like deep learning.

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What is artificial intelligence?

Artificial intelligence is a bench of computer science that programs machines that are capable of mimicking human intelligence in real-world environments. Enterprises leverage the power of AI to gather, manage, and analyze large volumes of data to draw inferences, improve workflow efficiency, increase productivity, and mitigate risks.

AI-powered machines are programmed to perform tasks such as reasoning, decision-making, learning, creativity, and planning to support essential business needs like process automation, information and extraction through data analysis, as well as customer engagement. Renowned AI applications include voice assistants like Amazon’s Alexa and Apple’s Siri.

What is machine learning?

Machine learning is a subset of AI. It is a branch of AI which uses algorithms to learn insights and hidden patterns from historical data, gradually improving to make accurate predictions of outcomes without being explicitly programmed to do so. Machine learning models are built using training data and then become accurate over time when exposed to historical data and through experience. These algorithms have found widespread applications in healthcare, medical research and development, speech recognition, computer vision, spam filtering, and more.

Where it all began; the history of AI and Machine Learning

The field of AI and machine learning has a deep history in statistics, philosophy, and military science. It also draws from cognitive science and psychology as it borders heavily on simulating human intelligence. AI invention can be traced back to the years before the 50s when the research project, “Dartmouth” was undertaken to explore problem-solving and symbolic techniques. Around the same time, interest had already gained momentum in machine learning, a branch of AI, when neurophysiologist Warren McCulloch and his mathematician mate, Walter Pitts presented a neural network model with a foundation in mathematics at the same conference at Dartmouth College.

The conference that had attracted like-minded researchers would be the onset of symbolic reasoning, automata, and neural networks. This field was named artificial intelligence. The term machine learning was, in 1959, coined by Arthur Samuel, an expert in computer gaming and AI who defined ML as “the field of study that gives computers the ability to learn without being explicitly programmed”.

The Dartmouth research project would later, in 1960, attract the attention of the United States Department of Defense who considered programming computers to simulate human intelligence. The same year, a paper “programs with common sense” that describes systems that evolved the intelligence of human order was authored by McCarthy.

The Dartmouth conference laid out the following six main AI and ML design objectives:

  1. Teach machines to reason to perform certain mental tasks
  2. Present knowledge to and enable machines to interact with the world just like humans
  3. Train machines to plan and navigate the real world
  4. Empower machines to interpret natural language, conversation, and place speech within the context
  5. Train machines on human perception
  6. Train machines on general intelligence including emotional intelligence, creativity, and intuition.

This marked the beginning of a revolution of boundless limits that would completely shift the way humans lived their lives. A bottleneck that prevented the take-off of this promising technology was the limited capacity of computers to store commands. The evolution of AI and machine learning picked up momentum in the 90s with the invention of the internet. Today, AI and machine learning are the future as the focus turns on big data.

Where are we today with Artificial intelligence and machine learning?

The 21st century brought with it some serious innovations apart from the internet. Thanks to higher GPU and cloud computing power as well as higher storage capacity, today’s computers can ingest, process, and analyze massive volumes of data fast. This capacity matches the current velocity, volumes, and variety with which data is currently being generated.  Read more about Scope of Artificial Intelligence in Financial Services.

The key contributing factors to AI/ML advancements include:

  • The massive generation and wide accessibility of big data and the need to leverage this data
  • Development of open-source tools, frameworks, and languages that not only made it possible to explore these fields but also scale its capabilities and applications in enterprise-wide operations
  • The development of AI and ML training institutions both online and offline to fill a growing skills gap and spur research and innovation
  • The emergence of AI-as-a-Service enables advancements through research, development, prototyping, further exploration, and the invention of powerful AI/ML solutions
  • The emergence of machine learning subfields like deep learning has brought a whole new phase in the evolution of AI and machine learning

AI/ML has found wide application in the following areas:

  • Healthcare in faster research, development, and evaluation of the effectiveness of drugs, vaccines, and treatments. AI has also contributed significantly to the detection, cure, and prevention of diseases and outbreaks.
  • In retail, the development of recommender systems to improve customer/user experience
  • In finance, for fraud detection, credit rating, and mitigation of risks
  • Natural language processing applications in different industries for translation, speech recognition, and chatbots
  • Image and video processing are used in security for surveillance, video conferencing applications like Zoom, as well as in the entertainment industry.
  • Intelligent process automation that combines robotic process automation and artificial intelligence to automate repetitive rule-based processes to enhance efficiency and productivity.
  • Cybersecurity where machine learning models are being developed to detect unusual activity and patterns in systems and processes to prevent attacks, threats, and risks.
  • Customer relationship management to customize marketing campaigns and offer personalized services, products, and product recommendations for improved customer experience.

The future of AI

The future of AI is big. Chatbots, virtual assistants, language translation software, and other AI/ML applications are still evolving. We can expect that autonomous vehicles, a technology that is still infant for safety reasons, will gain traction and actually increase rather than posing a threat to safety. The world of AI/ML is headed to general AI away from narrow AI where computers will be empowered to handle all tasks beyond the human cognitive ability. Think about robots and their ability to handle human activities in their (humans) absence. Not just that, robots will possess emotional intelligence.

Finally, with all the advancements taking place in this field, there is growing awareness of both the positive and negative impact of artificial intelligence on humans and the environment around them. There is a rising demand for ethical AI which focuses on acceptable ethics and value.

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