There was a time when we heard terms like Artificial Intelligence and Machine Learning only in sci-fi movies. But today, technological advances have brought us to a point where businesses across verticals are not only talking about, but also implementing artificial intelligence and machine learning in everyday scenarios. AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI has evolved from being a research topic to being at the early stages of enterprise adoption.
Dr. Parshotam S. Manhas We're entering a new world in which data may be more important than software -Tim O'Reilly Data Science is the technology that has emerged out as one of the most popular fields of 21st Century due to the onset of Artificial Intelligence and Deep Learning. Data science employs scientific methodologies, processes, algorithms and systems to extract knowledge and useful insights across structured and unstructured data in various forms. It is in fact an empirical concept to amalgam statistics, data analysis, machine learning and their related methods to analyze actual phenomena with data. Data is considered as a'fourth paradigm' of science after empirical, theoretical, computational science and everything about science is changing because of the impact of information technology and the humongous data explosion. Data scientists work as decision makers and are mainly responsible for analyzing and handling a large amount of data. Data science makes use of several statistical procedures ranging from data transformations, data modeling, statistical operations to machine learning modeling.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
When I hear news about "AI" these days, what is often meant are methods for pattern recognition and approximations of complex functions, most importantly in the form of Machine Learning. It is true that we have seen impressive applications of Machine Learning systems in a number of different industries such as product personalization, fraud detection, credit risk modeling, insurance pricing, medical image analysis, or self-driving cars. What is the origin of intelligent behavior? Intelligent behavior is the capability of using one's knowledge about the world to make decisions in novel situations: people act intelligently if the use what they know to get what they want. The premise of AI research is that this type of intelligence is fundamentally computational in nature, and that we can therefore find ways to replicate it in machines.
With the development of mobile social networks, more and more crowdsourced data are generated on the Web or collected from real-world sensing. The fragment, heterogeneous, and noisy nature of online/offline crowdsourced data, however, makes it difficult to be understood. Traditional content-based analyzing methods suffer from potential issues such as computational intensiveness and poor performance. To address them, this paper presents CrowdMining. In particular, we observe that the knowledge hidden in the process of data generation, regarding individual/crowd behavior patterns (e.g., mobility patterns, community contexts such as social ties and structure) and crowd-object interaction patterns (flickering or tweeting patterns) are neglected in crowdsourced data mining. Therefore, a novel approach that leverages implicit human intelligence (implicit HI) for crowdsourced data mining and understanding is proposed. Two studies titled CrowdEvent and CrowdRoute are presented to showcase its usage, where implicit HIs are extracted either from online or offline crowdsourced data. A generic model for CrowdMining is further proposed based on a set of existing studies. Experiments based on real-world datasets demonstrate the effectiveness of CrowdMining.
Alan Turing is considered the father of artificial intelligence, and rightfully so. Marrying mathematical study with computer science, Turing was the first to contend that computers could think like humans, and he pioneered the concept of machines that could perform tasks on par with human experts – a bedrock concept of modern AI computer science to this day. Given the intense interest in AI of recent years, Turing is more famous now than he was at the time of his death, 15 days shy of his 42nd birthday in 1954. I'm constantly amazed by Turing's prescience in laying the theoretical groundwork for what he called thinking computers, those that exhibit intelligent behavior equal to or indistinguishable from that of a human. However, Turing's work occurred more than 65 years ago, and -- give the guy a break -- while several of his predictions are uncannily on the mark, he wasn't able to foresee all the advances that are shaping life in 2019.
Microsoft has announced two new cloud services to help administrators detect and manage threats to their systems. The first, Azure Sentinel, is very much in line with other cloud services: it's dependent on machine learning to sift through vast amounts of data to find a signal among all the noise. The second, Microsoft Threat Experts, is a little different: it's powered by humans, not machines. Azure Sentinel is a machine learning-based Security Information and Event Management that takes the (often overwhelming) stream of security events--a bad password, a failed attempt to elevate privileges, an unusual executable that's blocked by anti-malware, and so on--and distinguishes between important events that actually deserve investigation and mundane events that can likely be ignored. Sentinel can use a range of data sources.
Since the appearance of the first primates on earth around 55 million years ago, the brain evolution has progressed following a rather flat linear progression. Then, around 2 million years ago, while the evolutionary line leading to hominins finally became distinct and the Homo Habilis was walking its first steps, the growth rate of its cranial capacity suddenly began to increase exponentially from around 500cm3 to 1.330cm3 of the modern human brain. Along with its growth in size, brain kept increasing in the number of neurons it contained: from an estimated 40 to 50 billion neurons for Homo Habilis to the 86 billion of a modern adult human. And neurons are heavily involved in determining general information processing capacity (IPC), as reflected by general intelligence. The new'extra' portion of the brain that our ancestors gained, the neocortex, together with the high availability of neurons, is what makes us so special by giving us extraordinary cognitive abilities including feelings, language, thinking, planning, and personality.
The use of artificial intelligence (AI) and cognitive intelligence is being used to transform cyber security and aid security analysts' identity threats more accurately. This is word from global firm IBM, which released results this week from an online survey of 150 federal IT managers familiar with their department's current cyber security capabilities and future strategies. Titled "The Federal Cyber AI IQ Test," the survey found that federal IT managers see cyber security as the single biggest opportunity for AI in the federal government. "Only 21% say they are'very comfortable' with the idea of using AI for cyber security today. Feds are roughly split regarding the ideal adoption pace for AI - 46% want to be first, 48% are afraid to take the risk.