WILL MACHINES TAKE OVER THE WORLD?
We are disruptively swinging into a new industrial revolution called the Fourth Industrial Revolution. Industry 4.0 as it is fondly called is a trend toward information transparency, technical assistance, decentralized decisions, and interconnectivity between man and machine. The integration of this revolution operates through processes like cognitive computing, the Internet of Things (IoT), cyber-physical systems, and cloud computing.
A significant aspect of the fourth industrial revolution is the pursuit of more intelligent machines pushed by technologies like Artificial Intelligence, Advanced Robotics, and Machine Learning. So far, there has been significant success in this pursuit with the advent of humanoid robots like Sophia and Ameca, which is currently the world’s most advanced humanoid robot. Some interesting features about Ameca are its great smile, it can detect people, and react to objects such as when a finger is held in front of its face. Its movement and emotions are more lifelike than any other robot.
Are emotions necessary in these robots?
“Early research has underpinned the fact that adding emotional elements to human-computer interactions is crucial and can enrich their relationship. The study of robots in service environments expands the knowledge of consumers’ experiences and paves the way for potential applications of service robots. Notwithstanding the ongoing debates and controversial issues related to safety and security, some stakeholders in the service industries have envisioned anthropomorphic/humanoid robots as the next contemporary social practice. For instance, revealed that consumers present a higher degree of trustworthiness towards robots displaying positive emotions as opposed to its neutral counterpart.” — Science Direct
However, Robots are highly logical; they are created and given intelligence through clear thought-out code that commands for highly specific actions. Robots could weigh the pros and cons of human existence, and based on humanity’s current actions, it is likely that AI would find us to be harmful to earth that they would try to eradicate.
“If robots were to feel emotions, society would need to consider their rights as living beings, which could be detrimental to humanity. It is unjust and cruel to deny a living, caring thing certain treatments and activities. Therefore, robots with emotions and specific desires would be a severe weight on our society. Robots are designed to aid humans, and purely that. If AI had emotions, they would have certain needs beyond what is needed for their basic function. If robots were to suddenly need food or fuel, or leisure time, or certain amenities, there would be a noticeable cost to society. Furthermore, if the individual desires of AI conflicted with that of humans, the consequences could be severe. Robots, enhanced with personal desires and emotion, could seek to destroy humanity if they felt that humanity’s existence was negatively affecting the earth.” — Kirsten Miler
This brings me to Elon Musk’s humanoid robot, Optimus which was announced last year on Tesla’s AI day. The goal is to create a machine that could drive labor costs down. Unlike Sophia and Ameca, he didn’t talk about simulating emotions and expressions in Optimus. He said:
“It’s intended to be friendly, of course, and navigate through a world of humans, and eliminate dangerous, repetitive and boring tasks. It should be able to, you know, please go to the store and get me the following groceries, that kind of thing.”
Even though in the past he has severally warned against the dangerous potentials of Artificial Intelligence. His top concern is about DeepMind of AI. He further said:
“As you see Optimus develop, everyone’s going to make sure it’s safe. No Terminator stuff or that kind of thing.”
As exciting as this technology is, the world is worried about a time when machine intelligence transcends ours. In this recurring debate, some visualize a Terminator-like world, or the machines pulling a Decepticon on us, while some have a fancier vision, where robots do the stressful tasks while humans enjoy more relaxing activities.
Other milestones in Intelligent Machines include self-driving cars, cognitive computing engines, deep neural networks, knowledge engineering, and conversational AI among others.
Cognitive computing: is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. A fine example is IBM Watson’s victory over champion players in a TV game show, Jeopardy in 2011 and winning a $1 million price. It was a significant milestone because this was a computer beating humans at a language-based creative-thinking game which is new compared to the mathematical proficiency that we are used to.
Deep Learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. The deep neural network does not only work according to the algorithm but also can predict a solution for a task and make conclusions using its previous experience. It has gained application in fields like computer vision, speech recognition, natural language processing, and bioinformatics, among others. In the past, algorithms have shown their proficiency in recognizing and describing a library of 1,000 images — declaring that machines are now outperforming humans. Researchers at Image Net say that the accuracy rate of the winning algorithm increased from 71.8% to 97.3% — promoting researchers to declare that computers could identify objects in visual data more accurately than humans. It is safe to say machines see better than humans.
“Researchers at Stanford and Google including Jeff Dean and Andrew Ng publish their paper Building High-Level Features Using Large Scale Unsupervised Learning, building on previous research into multilayer neural nets known as deep neural networks. They singled out the fact that their system had become highly competent at recognizing pictures of cats. IT describes a model which would enable an artificial network to be built containing around one billion connections. It also conceded that while this was a significant step towards building an “artificial brain,” there was still some way to go — with neurons in a human brain thought to be joined by a network of around 10 trillion connectors.” — Forbes
Knowledge Engineering: is a field of artificial intelligence (AI) that tries to emulate the judgment and behavior of a human expert in a given field. It transfers human knowledge into a database that can serve as a basis for enabling AI applications to perform human-like reasoning.
Conversational AI: It is artificial brainpower that makes machines capable of understanding, processing, and responding to human language. Examples are chatbots or virtual agents like Alexa and Siri.
The advancement in technology is gradually getting to a point where data mining is absolutely easy and the algorithms needed to find, index, and process all that information continue to improve. You could be searching for things on google and the next minute Instagram is suggesting these products to you. Machines and devices can now collect and analyze data on their own. Where do we draw the line as the advancement in more intelligent machines is ongoing? What do we do about biases in data and algorithms?
“The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues. We can balance innovation with basic human values by improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.” — Brookings Report.
Another worrisome question is the impact of Machine intelligence on jobs. Will it create more jobs or take them?
“Technology will eventually make work obsolete. Our big problems are going to be figuring out how to survive the transition, then figuring out what to do with all that free time.” – John Hauer
When we reach our breaking points, technology comes through for us. PwC predicts that by the mid-2030s, up to 30% of jobs could be automated. CBS News reports machines could replace 40% of the world’s workers within 15 to 25 years. While the advancement in technology will contribute to the growing number of jobs lost to automation, one thing is for certain: There are still many industries and workflows that need humans, for example, surgery, lawyers, psychiatrists, human resource managers, scientists, and writers like me.
“You can kill it with labor, or you can kill it with technology.”
I don’t think man and machine can be separated anymore, given most people's relationship with their phones, other gadgets, and machines. We are moving into a world of “singularity” between man and machine and eventually one might surpass the other and the outcome of the future — machines pulling a Decepticon or creating a heaven-like world for us — solely depends on us. One thing is evident, though, machines make our lives easier and better. So if sometime in the future machine take over most of our jobs, it is time to start planning our transition and the key question is “what are we going to use all our free time to do?”.
