Unleashing the Potential of Artificial Intelligence: A Primer
Ever wonder how Artificial Intelligence (AI) would describe itself? I did; this is what it said.
Artificial Intelligence (AI) refers to the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. AI systems are designed to perceive and understand their environment, reason, learn from data, and make decisions or take actions to achieve specific goals.
Google Bard -
Artificial intelligence (AI) is a branch of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. AI research has been highly successful in developing effective techniques for solving a wide range of problems, from game playing to medical diagnosis.
Bing AI Chat -
Artificial intelligence (AI) is intelligence demonstrated by computers, as opposed to intelligence displayed by humans or by other animals. "Intelligence" encompasses the ability to learn and to reason, to generalize, and to infer meaning. It is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
Before diving deeper, let's discuss a few milestones AI has willed itself through over the years.
In The Beginning
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Alan Turing was crucial in developing theoretical computer science and computer machinery. He introduced the Turing Test in his 1950 paper "Computer Machinery and Intelligence." The paper stated that we could consider a machine intelligent if it could carry out a conversation with a human in an indistinguishable manner.
There have been more noteworthy individuals who have played critical roles in the development and propagation of AI. Here are a few of their names:
- John McCarthy - Coined the term "artificial intelligence" and developed the LISP programming language, foundational in AI research.
- Marvin Minsky - Co-founder of the MIT AI Lab, worked on neural networks, robotics, and the Perceptron.
- Allen Newell - Developed the Logic Theorist program and proposed the "Physical Symbol System Hypothesis" with Herbert A. Simon.
- Herbert A. Simon - Collaborated with Newell on problem-solving and received the Nobel Prize in Economics for AI and cognitive psychology contributions.
- Arthur Samuel - Pioneered machine learning with the first self-learning checkers program, showcasing the potential for machine improvement through experience.
Since 1950, the advancement of AI has seen its fair share of ups and downs. Popular culture has taken on the concepts around AI and presented them through comic books, magazines, TV shows, and movies.
You may have heard of Knight Rider, The Terminator, 2001: A Space Odyssey, and a cult classic, The Matrix - which took AI to another level. My favorite is JARVIS from Iron Man.
In a significant turning point in 1996, Garry Kasparov, a chess Grandmaster, played against IBM's Deep Blue computer in a six-game chess match. Deep Blue won and set a milestone in the applications and development of AI.
IBM advanced the next stage in AI development through its AI platform, Watson, which has been developing since the mid-2000s. The suite of technologies available as part of Watson are natural language processing, machine learning algorithms, and data analytics.
Watson has even been on the TV game show Jeopardy in 2011. Demonstrating its capabilities on the game show, it set another milestone in the growing adoption and comfort around AI and its capabilities.
The Potential of AI
To think about the potential of AI and what it could be ten years down the line is a giant leap for anyone to imagine.
When the first smartphone with Google Maps, the T-Mobile G1, was released in 2008, running Android, nobody thought billions of people would be navigating with it. Google Maps uses a form of AI to determine optimal routes based on historical and current traffic trends, among other things.
Unsurprisingly, they announced that AI would offer users immersive map experiences at their 'Everything Google' conference in May 2023. Imagine getting an in-depth aerial view of a national park before you go to visit.
Let's discuss a few applications of AI and where we see it making the most impact.
The first concern with AI has always been the sanctity of job security and employment opportunities. Some advocates say that AI would create jobs, as AI is nowhere near able to replace a human being.
Not to be confused with automation, which has already impacted the business world since the 18th and 19th centuries. Employment opportunities have increased even with rapid technological advances in the case of automating production, logistics, and storage of goods.
The distinctive factor that sets people apart from machines is skill sets. Machines have a specific job they were made for, and that's their only skill. People adapt and learn and can do it repeatedly; something machines do not have the intelligence to do.
Yes, some jobs can become less dependent on human interaction, but the chances of a machine dominating an industry seem like science fiction today.
An article published in 2019 from IOT News states that Singapore sees its rate of innovation doubling due to the adoption of AI. Another recent study supporting that claim estimated the entire Southeast Asia zone could add a GDP of US $1 trillion due to AI adoption by 2030.
That doesn't look like AI is taking away your job.
AI can perform any task taught to it with data, which it then learns about more precisely as it interacts with it. Training an AI model involves feeding it with labelled or unlabeled data, which it then uses as a basis for learning and providing predictions or decisions.
The question arises - Does an AI model understand the difference between data that is harmless and data that can potentially place a person in an uneasy (physically or emotionally) or harmful situation?
Some of the immediate areas where this would have an impact are:
- When utilized in hiring and loan approval processes, AI algorithms have demonstrated a bias towards gender, race, and other attributes.
- Autonomous vehicles have caused accidents by not recognizing complex traffic situations, resulting in injuries.
- The healthcare industry has had some setbacks with AI giving incorrect treatment and diagnosis.
As a primer, here are some roles AI can help with to ensure a safer future for everyone.
- Building Regulations: Construction is perhaps the most important industry where AI can impact lives. Natural disasters like earthquakes are deadly on their own, and if you factor in buildings constructed without the foresight to withstand them, the loss is immeasurable. The poor quality of concrete and non-conformity of building codes have been attributed to the catastrophic failure of many residential buildings; this was the unfortunate case in the earthquake that struck Turkey in February 2003. With a proper system implemented, AI can help ensure that safety regulations are met and adhered to.
- Workplace Safety: AI can also help implement and monitor standards in the workplace, especially in infrastructure, mechanical production, and plants that process hazardous materials. Even though most of these adhere to strict guidelines, there is always room for improvement and increasing efficiency without sacrificing quality. A recent example would be Oracle buying Newmetrix in 2022, a company that used AI to monitor safety at construction and build sites.
- Wearable AI: Companies like IBM, Strongarm Technologies, and others are already providing devices that monitor personnel at their workplaces, where they interact in jobs that are physically demanding. Data collected helps them avert risks and achieve operational efficiency. With further development, AI can enhance other operations factors, including time management, workflow, production cycles, and shipping.
Positioning AI to monitor and assist in building safer work environments are just a few examples of how organizations can use this technology.
Various companies have implemented AI-driven systems in their automation processes to streamline operations, improve customer service, and innovate.
Google, Amazon, Netflix, IBM, Uber are just some of the most well-known companies on the list. AI has driven significant aspects of their business to improve in areas such as predictive search, warehouse robots, recommendation engines, automated solutions platforms, and route optimization, among other uses.
But how else can AI assist in automation? How far can it be taken?
The natural response has been chatbots, robotics, process optimization, and others. All these apply to workforce-oriented tasks, but people spend most of their time at home.
The home automation market has been active for quite a few years, from unlocking your front door with an app and recipe suggestions based on food inventory to digital assistants that control your lighting and connected devices. Challenges remain, with infrastructure and data privacy being the primary concerns.
Analysis (Predictive and Prescriptive)
This branch of applications deals more with the movement of goods, services, commerce, health, travel, and climate.
History repeats itself, but the impact of human activity has created a future for which there is no precedent. The technological advances made to enhance our way of life are no secret, and they can be linked to various health ailments, ozone layer concerns, climate change, weather patterns, deforestation, and more.
Can AI help us, help ourselves?
With data, some historical precedent, and an analytical engine that can predict and prescribe a solution, AI could offer us one. To create a sustainable future by balancing the economics of change and its potential impact.
AI has already made strides in assisting with relief efforts; when an earthquake hit Nepal in 2015, AIDR (Artificial Intelligence for Disaster Relief) monitored the movement of affected people through tweets, providing timely relief. Applications like these can help predict outcomes and offer solutions to reduce impact while providing employment opportunities and a healthy advancement ecosystem.
Subfields Within AI
Machine Learning - This field deals with allowing computers to learn how to interact with each other without being programmed explicitly for it. These algorithms make predictions, classify data, and identify patterns.
Deep Learning - A subset of machine learning, deep learning uses artificial neural networks to learn how to perform tasks like image recognition, speech, and natural language processing.
Natural Language Processing - NLP algorithms perform tasks associated with text analysis, machine translation, and question answering.
Robotics - AI in robotics controls them, programs their movements, and provides instructions on interacting with their environment.
Reasoning - This subset is generally applied in finance, medicine, law, and engineering. The aim is to replicate knowledge and reason, as an average person would, to enable decision-making.
Ethics - Addressing the ethical considerations and responsible use of AI. Concerns around ethics in AI center around bias, privacy, and transparency, along with the impact it can have on society as a whole.
Challenges with AI
In summary, we can address AI's main challenges with ongoing research, the development of ethical frameworks, and responsible governance to ensure a safe and transparent future for AI to co-exist with us.
Any emerging technology must have a vast network of individuals from various backgrounds, ethnicities, and social structures to represent the data. Because that ultimately defines our humanity, and AI should have that as its foundation.
A future in which AI represents humanity from all walks of life would stand as one of the most intelligent achievements by human beings.
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