I recently read an article by Kai-Fung Lee (Chairman & CEO of Sinovation Ventures & former head of Google China) in the Fortune magazine on the four waves of AI (Artificial Intelligence). AI is the pursuit of performing tasks usually reserved for human cognition: recognizing patterns, predicting outcomes clouded by uncertainty, and making complex decisions. After a long “AI winter” we have seen the resurgence of its power only in the last few years. This is due to the availability of huge amounts of data (its fuel) and tons of affordable computing power in the form of GPU (Graphical Processing Unit) and ASIC (Google has an ASIC hardware called TPU or Tensor Processing Unit). With deep learning and the data explosion as catalysts, AI has moved from the era of discovery to the era of implementation. For now, at least, the center of gravity has shifted from elite research laboratories to real-world applications. In essence, deep learning and big data have boosted AI onto a new plateau. In the article Kai-Fung Lee describes four waves of AI that is highlighted below.
- Wave 1 – Internet AI – Powered by the huge amount of data flowing through the web, Internet AI leverages the fact that users automatically label data as we browse: buying vs. not buying, clicking vs. not clicking. These cascades of labeled data build a detailed profile of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform, or to maximize revenue or profit.
- Wave 2 – Business AI – Here, algorithms can be trained on proprietary data sets ranging from customer purchases to machine maintenance records to complex business processes—and ultimately lead managers to improved decision-making. An algorithm, for example, might study many thousands of bank loans and repayment rates, and learn if one type of borrower is a hidden risk for default or, alternatively, a surprisingly good, but overlooked, lending prospect. Medical researchers, similarly, can use deep-learning algorithms to digest enormous quantities of data on patient diagnoses, genomic profiles, resultant therapies, and subsequent health outcomes and perhaps discover a worthy personalized treatment protocol that would have otherwise been missed.
- Wave 3 – Perception AI – As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy. This includes speech interfaces (from Alexa and Siri to future super smart assistants that remember everything for you) as well as computer-vision applications—from face recognition to manufacturing quality inspection.
- Wave 4 – Autonomous AI – This is the most difficult one and integrates all previous waves. Autonomous AI gives machines the ability to sense and respond to the world around them, to move intuitively, and to manipulate objects as easily as a human can. Included in this wave are autonomous vehicles that can “see” the environment around them; figuring out what they correlate to (stop signs); and then using that information to make decisions (applying pressure to the brake in order to slowly stop the vehicle). In the area of robotics, such advanced AI algorithms will be applied to industrial applications (automated assembly lines and warehouses), commercial tasks (dishwashing and fruit-harvesting robots), and eventually consumer ones too.
Kai-Fung Lee says, “Because AI can be programmed to maximize profitability or replace human labor, it adds immediate value to the economy. AI is fast, accurate, works around-the-clock, doesn’t complain, and can be applied to many tasks, with substantial economic benefit. How substantial? PwC estimates that the technology will contribute about $16 trillion to worldwide GDP by 2030”.
AI with its subset branches of ML/DL (Machine Learning/Deep Learning) is entering the mainstream in a big way: ML used for areas like predictive/prescriptive analytics and DL for areas like facial recognition, speech recognition, and language translation.
from: Jnan Dash’s Weblog
via Jnan Dash
Source: The four waves of AI Via Business Advice.
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