Wearables and the Internet of Things (IoT) may give the impression that its all about the sensors, hardware, communication middleware, network and data but the real value (and company valuation) is in insights. In this article, we explore artificial intelligence (AI) and machine learning that are becoming indispensable tools for insights, views on AI, and a practical playbook on how to make AI part of your organizations core, defensible strategy. <\/p>\n
Before we proceed, lets first define the terms. Otherwise, we risk commingling marketing terms like Big Data and not addressing the actual fields. <\/p>\n
Artificial Intelligence: The field of artificial intelligence is the study and design of intelligent agents able to perform tasks that require human intelligence, such as visual perception, speech recognition, and decision-making. In order to pass the Turing test, intelligence must be able to reason, represent knowledge, plan, learn, communicate in natural language and integrate all these skills towards a common goal. <\/p>\n
Machine Learning: The subfield of machine learning grew out of the effort of building artificial intelligence. Under the learning trait of AI, machine learning is the subfield that learns and adapts automatically through experience. It focuses on prediction, based on known properties learned from the training data. The origin of machine learning can be traced back to the development of neural network model and later to the decision tree method. Supervised and unsupervised learning algorithms are used to predict the outcome based on the data. <\/p>\n
Data Mining: The field of data mining grew out of Knowledge Discovery in Databases (KDD), where data mining represents the analysis step of the KDD process. Data mining focuses on the discovery of previously unknown properties in the data. It originated from research on efficient algorithm for mining association rules in large databases, which then spurred other research on discovering patterns and more efficient mining algorithms. Machine learning and data mining overlap in many ways. Data mining uses many machine learning methods, but often with a slightly different goal in mind. The difference between machine learning and data mining is that in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge while in KDD the key task is the discovery of previously unknown knowledge. Unlike machine learning, in KDD, supervised methods cannot be used due to the unavailability of training data. <\/p>\n
Though perhaps not explicitly stated, you will find that some at your work hold sci-fi views of AI that could hamper proactive exploration of AI and machine learning within your organization. AI, for some, bring images of HAL 9000 from A Space Odyssey or more recent films such as Her and The Machine. <\/p>\n
Many futurists have speculated about the future of artificial intelligence that could rival or exceed human intelligence. One of those futurists is Ray Kurzweil, a recipient of the prestigious National Medal of Technology and Innovation honor. <\/p>\n
In The Singularity is Near, Kurzweil elaborates on the singularity hypothesis. Kurzweil predicts that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity. During this period, he predicts human life will be irreversibly transformed and humans will transcend the limitations of our biological bodies and brain. <\/p>\n
Kurzweil claims that machines will pass the Turing AI test by 2029, and that around 2045, the pace of change will be so astonishingly quick that we wont be able to keep up, unless we enhance our own intelligence by merging with the intelligent machines we are creating. He further claims that humans will be a hybrid of biological and non-biological intelligence that becomes increasingly dominated by its non-biological component. Kurzweil envisions nanobots inside our bodies that fight against infections and cancer, replace organs, and improve memory and cognitive abilities. Eventually our bodies will contain so much augmentation that we will be able to alter our physical manifestation at will. <\/p>\n
The artificial general intelligence (AGI) or strong AI community, though varying widely in timeframe to reach singularity, are in consensus that its plausible, with most mainstream AI researchers doubting that progress will be rapid. <\/p>\n
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Wearing Your Intelligence: How to Apply Artificial Intelligence in Wearables and IoT<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Wearables and the Internet of Things (IoT) may give the impression that its all about the sensors, hardware, communication middleware, network and data but the real value (and company valuation) is in insights. In this article, we explore artificial intelligence (AI) and machine learning that are becoming indispensable tools for insights, views on AI, and a practical playbook on how to make AI part of your organizations core, defensible strategy. Before we proceed, lets first define the terms. Continue reading