AI, ML & Deep Learning โ€“ Differences Explained by Experts

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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) โ€“ these are the three trending buzzwords that have created a great hype over the Internet and other media platforms for some time now. Irrespective of whether people hold a sound knowledge of the data science or not, everyone is actively making their own statements explaining the differences between these technologies, which thereby creating a mysterious situation for the newbies and laymen to understand the true differences between them. To make the things easy, this article will initially explain "what AI, ML, and DL are?", and later discusses the key differences between them.


Machine Learning: The Real Business Intelligence

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Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. But with the advancement of artificial intelligence and--more importantly--machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices. Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence.


Machine Learning: The Real Business Intelligence

#artificialintelligence

Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. But with the advancement of artificial intelligence and--more importantly--machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices. Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence.


Imitation of Intelligence : Exploring Artificial Intelligence! - Home

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What is the difference between "calculate" and "compute"? But this is something out of curiosity about the crux of what we are going to go through. So, the calculation involves an arithmetic process. Computation is involved in the implementation of non-arithmetic steps of the algorithm which actually brings things up to the calculation. You got the idea where I am going with this right?


Shaping the Future of A.I.

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One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google's DeepMind beat the world's best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM's Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today's human jobs in the years ahead. Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more. As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics.