The Exponential Rise of Artificial Intelligence
There’s No Turning Back
Artificial Intelligence is the technology of the future. This statement is evident in the current advances in the fields of Big Data, Knowledge Acquisition and Representation, Machine Learning and Fuzzy logic based expert control systems. Over the past several years, we have seen the spread of automation in various fields, including the automobile, construction and manufacturing industries. This year alone, self-driving cars, computer simulations that beat humans in challenges such as chess, and robot assistance are growing popular. Since the invention of the programmable digital computer in the 1940s, the fields of Machine Learning, Data Acquisition and Representation have witnessed an exponential rise. This report discusses the rise of Artificial Intelligence by placing it within an American Revolution context.
The Law of Accelerating Returns
Theorized by futurist Ray Kurzweil, this law states that the growth of Information Technology is an exponential one, rather than linear. According to Kurzweil, “fundamental measures of information technology follow predictable and exponential trajectories.” (Baer, 2015) This law follows Moore’s Law, coined by Intel co-founder, Gordon Moore, who stated that computer processing power will double every two years. Judging by the evolution of computers, the exponential rise in Information Technology has held true. Since the invention of the Personal Computer, devices have reduced in size and increased their processing power exponentially. Artificial Intelligence, which currently has the most significant disciplines has also shown potential in following an exponential development path.
The Origins of Artificial Intelligence
From being an imagination of science fiction authors and movies, Artificial Intelligence (A.I.) has come a long way since the dawn of computing. While having been theorized since the middle ages, it wasn’t until the 20th century that the A.I. revolution started taking shape. Alan Turing, a British mathematician, is credited as having invented machine knowledge acquisition and learning. Turing, therefore, invented the computer age. In fact, the ‘Turing Test’, named after the late mathematician, determines a machine’s ability to ‘think’. (Appel, 2014) What followed was a rush in computing and algorithms, which led to the invention of the computer, and later the personal computer. Several decades after the proliferation of the internet and personal computers, scientists and investors have started to notice the potential of Artificial Intelligence and its implications for business, combat, entertainment and manufacturing.
The Present Status of Artificial Intelligence
With the rapid development of computer algorithms comes machine learning. Artificial Intelligence technology is able to learn from the data it gathers. The more data that a machine gathers, the higher the accuracy in knowledge representation and prediction. For a long time, Artificial Intelligence only existed in scientific publications and research laboratories. The proliferation of the internet has, however, seen A.I. move from theory to commercial avenues. (Urban, 2015) Simple applications included targeted advertisements and auto respond emails. Recently, A.I. has found application in various fields of manufacturing, construction, automotive and entertainment.
Artificial Intelligence in Business Management
The modern business world reaps great benefits from the advancement of intelligent computer systems. These advancements mainly impact customer-facing facets of business such as consumer support, marketing automation and demand management. Businesses have also recorded boosts in Enterprise Resource Planning (ERP), Order Management, and Supply chain optimization. (Khalid & Madhusudhan, 2012) A.I. has reduced customer acquisition costs for business by automating lead generation. Artificially Intelligent systems have also helped businesses optimize timing, thereby allowing them to cease opportunities when they arise, especially for seasonal businesses. Automation in check-ins and check-outs has made these processes efficient, resulting in overall customer satisfaction.
The industrial revolution led to the creation of mass production. While this streamlined production and reduces the cost, it brought with it one massive inefficiency: inflexibility. Wrangles within the energy have also increased production costs, further necessitating cost reduction methods. Resource wastage in manufacturing facilities is a result of unexpected equipment failures/downtimes, poor capacity planning/demand forecasting, supply chain bottlenecks and unsafe workplace processes. (Renzi, 2014) Manufacturing companies also suffer due to a shrinking labor market since the younger generations are not interested in production work. Artificial intelligence is a simple answer to all these issues. A.I. introduces adaptive manufacturing, predictive equipment maintenance, automated quality control, and demand driven production which mitigate the problems currently facing the manufacturing industry. Artificial Intelligence could give investors in the manufacturing industry a guaranteed Return on Investment (R.O.I).
A.I. in the Automobile Industry
Most vehicle manufacturers have capitalized on the recent advances in A.I. technology to improve the driving experience. The most popular adaptations are assisted driving and, in the future, driverless cars. A combination of powerful microcomputers, Global Positioning Systems (GPS) and advances in A.I. have facilitated the development of vehicles that guide drivers using weather and traffic data. (Hengstler, 2016) Companies like google and Tesla motors dedicate huge chunks of their budget to trying to realize autonomous cars. Tesla is confident that all its cars released post 2017 are capable of the ‘driverless’ experience, with the only roadblocks being regulations and software validation.
Most vehicle manufacturers are reserved when it comes to fully incorporating A.I. into their vehicles, but most companies have introduced driver-assist features which improve safety. Some of these features include: collision avoidance, automatic braking, control traffic alerts, weather alerts, pedestrian and cyclist warning and intelligent cruise control systems.
Cloud-hosted Intelligence and the Internet of Things have also found their way inside modern automobiles. (Lee, 2015) Cloud-hosted intelligence offers big data access and analytics, fast information processing and centralized connectivity which make it perfect for deployment in the car manufacturing industry. A major example of cloud-based automotive support is the collaboration between IBM’s Watson system and General Motors to develop an extension of the OnStar system, which will include A.I. features. Soon, most cars will have internet connectivity, and will therefore be part of the Internet of Things (IoT). IoT has a major impact on the automobile industry, including:
- Vehicle firmware updates through internet connections.
- Fleet management and improved safety.
- IoT based processes could improve manufacturing quality and efficiency.
- Improved emergency response since sensors inside vehicles could detect and report on status of the occupants.
- The technology could improve diagnosis and schedule of repairs by sending performance data directly to dealers.
A.I. in Medicine
Deep learning algorithms have made tremendous advances in various branches of medicine including cardiology, radiology, ophthalmology and pathology. Researchers have trained software to detect ailments such as Tuberculosis and cancer in X-rays and other specimen. These advances arise from improvements in deep learning and image processing software. A.I. has one distinct advantage over humans since humans are more likely to compromise accuracy due to fatigue and objectivity. (Park, 2017) Neural networks have proven to better than humans at discerning change in diabetes level by observing multiple images of patients’ retinas.
A.I. in Entertainment
Data collection and processing in the entertainment industry has made it easier for companies to personalize content for targeted markets. Deep learning in predictive analytics has enabled content developers to judge consumers’ tastes in music, movies and television shows. (Chen, 2017) YouTube, a popular video streaming site has benefited greatly from advances in data analytics and processing to ensure viewers access content that fits their preferences. Machine Learning has also helped automate various processes in the entertainment industry, including creating music playlists for radio stations, audio and video sync with very little room for error. Other areas of the entertainment industry impacted by A.I. include: audio transcription, content fingerprinting, visual recognition, optimization of video for streaming, Virtual Reality and Augmented reality (VR/AR).
Other disciplines benefiting from A.I. include: finance, marketing, aviation, (Stark, 2017) telecommunication, education among others.
The Evolution of A.I.
Since the invention of the transistor, computer technologies have witnessed a tremendous and unprecedented growth. The growth of the computing industry shows an exponential curve, and future developments are expected to grow even faster. The period between the invention of the computer and that of the personal computer spans about three decades. (Jones, 2015) After the invention of the personal computer, the computing industry picked up the pace, and the next three decades saw the invention of the internet, the cellphone, and the smartphone. Computing devices have shrunk tremendously, while their processing power has increased tenfold. The past decade has seen developments in the fields of Virtual Reality, Robot Assisted Medicine, Global Positioning Systems, Deep Learning and Machine learning. Thus, judging by the past, it is true that A.I. technology has followed an exponential growth curve.
The Law of Accelerating Returns, the Future of A.I. and the Singularity
A quick look at the history of A.I. and computing algorithms has shown that the technology experiences exponential growth. Ray Kurzweil, in his book ‘The Age of Spiritual Machines’ proposed the ‘Law of Accelerating Returns’ as the reason for this growth. The law suggests that the evolution of various systems is exponential. Moore’s law could, thus, be a technological extrapolation of Kurzweil’s law. The law of accelerating returns explains this trend. Advancements in technology allow us to work with ease when creating newer technologies. For example, the internet has empowered researchers worldwide by granting access to information and communication channels, thus developers get the right tools and feedback for their work. (Kurzweil, 2016) Therefore, the more advancements we make, the faster we make improvements on existing technologies.
The more we keep making these advancements, the more likely we are to create even more powerful A.I.. Currently, there is a computer that could beat the world’s greatest at chess. Deep Learning and Machine Learning not only create A.I. that is capable of assisting humans, but could also replicate themselves. In the future, we shall have A.I. that is able to give rise to more advanced ‘species’ of A.I.. This trend could go on to a point where we achieve Singularity- a super intelligent A.I. with aspects far beyond human understanding. (Kurzweil, 2016) Many authors, scientists and notable personalities share this sentiment, with most predicting the date of the singularity to be between 2040 and 2100.
The Potential of A.I.: The Good and the Bad
While some critics assert that no machine will ever rival human intelligence, futurists posit that we are already living in the age of artificial intelligence. Advances in A.I. have drawn mixed reactions from the scientific and literary communities.
Coined by John McCarthy in 1955, the term ‘Artificial Intelligence’ promised a future where machines could work alongside humans to help ease the burden of labor. As seen in current trends, A.I. has helped humanity make great strides in the fields of medicine, aviation, space exploration, education, entertainment and transport among others. The future of A.I. is promising, with experts suggesting the invention of nanobots that could help in fighting pathogens, safe driverless vehicles, unmanned commercial flights and cloud-based teaching assistance. (Hsieh, 2017)
Critics of the evolution, such as Tesla’s Elon Musk and scientist Stephen Hawking are skeptical about the pace with which we are developing the technology. (Metz, 2017) They consider uncontrolled development could trigger human-machine conflict, and assistance technologies could also be detrimental to the human growth curve.
Artificial Intelligence has come a long way, with a tremendous exponential growth curve. Advances in the technology have impacted other fields, including manufacturing, medicine, communication, entertainment, transport and education. While these advances are beneficial to human existence, there is the need to control the development and proliferation of this technology. Controlled development will result in a healthier, cleaner yet safer future existence for humanity.
Baer, Drake. “Google’s Genius Futurist Has One Theory That He Says Will Rule The Future — And It’s A Little Terrifying.” Business Insider. N.P., 2017. Web. 19 Nov. 2017.
Chen, Brian. “Apple Postpones Release Of Homepod Speaker.” Nytimes.com. N.P., 2017. Web. 19 Nov. 2017.
H, Madhusudhan, and Khalid Nazim. “A Comparative Study on Different A.I. Techniques towards Performance Evaluation in RRM (Radar Resource Management).” International Journal of Advanced Research in Artificial Intelligence 1.5 (2012): 59-72. Web. 16 Nov. 2017.
Hengstler, Monika, Ellen Enkel, and Selina Duelli. “Applied Artificial Intelligence and Trust—the Case of Autonomous Vehicles and Medical Assistance Devices.” Technological Forecasting and Social Change 105 (2016): 105-120. Web.
Hsieh, Paul. “A.I. in Medicine: Rise of the Machines.” Forbes.com. N.P., 2017. Web. 18 Nov. 2017.
Kurzweil, Ray. “The Singularity Is Near.” Ethics and Emerging Technologies (2016): 393. Print.
Lee, In, and Kyoochun Lee. “The Internet of Things (IoT): Applications, Investments, and Challenges for Enterprises.” Business Horizons 58.4 (2015): 431-440. Web.
Metz, Cade. “Building A.I. That Can Build A.I.” Nytimes.com. N.P., 2017. Web. 21 Nov. 2017.
Park, Alice. “How Robots Are Changing The Way You See A Doctor.” Time. N.P., 2017. Web. 17 Nov. 2017.
Renzi, C. et al. “A Review on Artificial Intelligence Applications to the Optimal Design of Dedicated and Reconfigurable Manufacturing Systems.” The International Journal of Advanced Manufacturing Technology 72.1-4 (2014): 403-418. Web.
STARK, SAMANTHA et al. “Could Robotic Birds Lead To Safer Air Travel?” NYTimes.com – Video. N.P., 2017. Web. 28 Nov. 2017.
Tim, Jones. Artificial Intelligence: A Systems Approach: A Systems Approach. Jones & Bartlett Learning, 2015. Print.
Turing, Alan Mathison, Andrew W Appel, and Solomon Feferman. Alan Turing’s Systems of Logic. Princeton: Princeton University Press, 2014. Print.
Urban, Tim. “The Artificial Intelligence Revolution: Part 2 – Wait But Why.” Wait But Why. N.P., 2015. Web. 17 Nov. 2017.
Urban, Tim. “The Artificial Intelligence Revolution: Part 1 – Wait But Why.” Wait But Why. N.P., 2015. Web. 17 Nov. 2017.
Zuoyue Wang, Ph.D. UCSD
History of American Science and Technology
Leave a Reply