perception


AI is Essentially "Artificial Perception"

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Human intelligence comes from an amazing duality of arriving at conclusions based on perception of patterns, and the contrary, conclusions based on very structured and rational decisions. Both forms are distinct, but complementary. Machine-based intelligence also comes in two forms: Deep learning based Artificial Intelligence interprets patterns in data to arrive at conclusions and hence, mimics the perception based intelligence of our brain whereas; and standard instruction-by-instruction computing (like in a PC) mimics the rational intelligence of our brain. I have always wondered why I can easily recall a song's tune, but not the words. I can remember a face better than the name.


29 Cutting Edge Applications of Artificial Intelligence - The Burnie Group

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Artificial Intelligence (AI) is the theory and development of computer systems that can perform tasks that normally require human intelligence. These tasks include visual perception, speech recognition, decision making, and language translation. Systems capable of performing such tasks are steadily transitioning from research laboratories into industry usage. AI technology is unique in that it is flexible in application. It can be used to improve processes, enhance interactions, and solve problems that, until recently, could only be performed by humans.


What AI-based Sentiment Analysis Can Tell Us About Fintech and Neobanks

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Over the past decade, fintech firms have set out to reinvent banking and financial services. One major market trend is the growth of the neobank, a new type of bank that is 100% digital. Instead of using physical branch networks, neobanks service customers using software and applications, allowing customers to transact on their mobile devices and providing accounts with much lower fees and more features. This trend to digitizing banking and the exchange of value is a natural progression of the information revolution to embrace digital. Fintech is an exciting market that continues to grow.


Unsupervised Learning of Object Keypoints for Perception and Control

Neural Information Processing Systems

The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods.


HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

Neural Information Processing Systems

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time.


Can artificial intelligence solve our societal issues?

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There are, largely speaking, two camps when it comes to artificial intelligence. On one hand, there's those who are wary of the negatives that have been reported on in the press and worry that the future isn't as bright as we'd hoped for. Then there's those who see the remarkable opportunity it offers and are working hard to mine its advantages. Undeniably, AI will shape society, it's unavoidable; history's not exactly littered with examples of technological advancements that have simply been rejected. While the tightrope of personal intrusion needs to be very carefully walked, it would be remiss of us to reject its benefits outright.


Why so many data scientists are leaving their jobs

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This quote is so apt. Many junior data scientists I know (this includes myself) wanted to get into data science because it was all about solving complex problems with cool new machine learning algorithms that make huge impact on a business. This was a chance to feel like the work we were doing was more important than anything we've done before. However, this is often not the case. In my opinion, the fact that expectation does not match reality is the ultimate reason why many data scientists leave.


Artificial Intelligence and machine learning

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The S.A.R.I., an acronym which stands for Automatic Image Recognition System, is a new system available to the State Police to counter criminal activity; exploiting the A.F.I.S. system, Automated Fingerprint Identification System which collects the fingerprints, personal data, photographs and biometric notations of the subjects under investigation, law enforcement agencies can count on an identification system with a database of more than 10 million data; in this way those who stain a crime can be identified more quickly and efficiently. In the United States, another system is used, the C.O.M.P.A.S. (Correctional Offender Management Profiling for Alternative Sanctions), which is an algorithm used by judges to calculate the probability of recidivism within two years of a crime. These are two concrete and recent examples of Artificial Intelligence. Artificial Intelligence, for Stuart Russell and Peter Norvig, authors of "Artificial Intelligence: A Modern Approach, Global Edition", means a "field of studies in which intelligent agents are designed and built". What would be the etymological meaning of the term?


Artificial Intelligence and machine learning

#artificialintelligence

The S.A.R.I., an acronym which stands for Automatic Image Recognition System, is a new system available to the State Police to counter criminal activity; exploiting the A.F.I.S. system, Automated Fingerprint Identification System which collects the fingerprints, personal data, photographs and biometric notations of the subjects under investigation, law enforcement agencies can count on an identification system with a database of more than 10 million data; in this way those who stain a crime can be identified more quickly and efficiently. In the United States, another system is used, the C.O.M.P.A.S. (Correctional Offender Management Profiling for Alternative Sanctions), which is an algorithm used by judges to calculate the probability of recidivism within two years of a crime. These are two concrete and recent examples of Artificial Intelligence. Artificial Intelligence, for Stuart Russell and Peter Norvig, authors of "Artificial Intelligence: A Modern Approach, Global Edition", means a "field of studies in which intelligent agents are designed and built". What would be the etymological meaning of the term?


Social artificial intelligence: intuitive or intrusive?

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The world is going bonkers over artificial intelligence and for all the right reasons. AI is almost at the helm of ruling the realm of marketing technology and it is here to stay for a long time to come. At the same time, B2B marketers are exploring and harnessing every possible tool, platforms, and techniques to tap to the most imperative business aspect, that is, customer experience and engagement. Here, social media plays a meaty role! The article will focus on probing the'ifs and buts' of combining social media marketing with artificial intelligence, popularly known as "social artificial intelligence," and will shed some light on how to add intuition to this union without being intrusive.