If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning (ML) algorithms identify patterns in data. Their major strength is the desired capability to find and discriminate classes in training data, and to use those insights to make predictions for new, unseen data. In the era of "big data", a large quantity of data is available, with all sorts of variables. The general assumption is that the more data is used, the more precise the algorithm and its predictions become. When using a large amount of data, it clearly contains many correlations.
A pair of researchers, one with Gymnasium of Johannes Kepler, the other with the University of California, Berkley, has developed an artificial intelligence (AI) application capable of determining whether a video clip of a famous person is genuine or a deepfake. In their paper published in Proceedings of the National Academy of Sciences, Matyáš Boháček and Hany Farid describe training their AI system to recognize unique body movements of certain individuals to discern whether a video was real or not. As deepfake technology has grown more sophisticated, it has become more difficult to determine whether a video is genuine. In the realm of public figures, such videos can become problematic. Such was the case when parties in Russia created a recent deepfake video of Ukraine president Volodymyr Zelenskyy saying things that he did not actually say--a video that was reportedly created to help the Russian government convince its citizenry of Russian state propaganda regarding the invasion of Ukraine.
Blockchain technology can be used in secure and transparent data management by providing a decentralized ledger for recording transactions. This eliminates the need for intermediaries, reducing the risk of data breaches and cyber-attacks. The cryptographic algorithms used in blockchain ensure the integrity and immutability of the data, making it resistant to tampering or unauthorized changes. The decentralized nature of the technology also allows for increased transparency, as all participants in the network have access to the same information. In addition, blockchain can be used to implement smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. This further enhances the security and transparency of the data management process.
Using NIR Spectroscopy and don't want to pay for a calibration abo or a subscription based software/service? If you would like Pay per calibration, then CalibrationModel is the solution for you. "Near infrared spectroscopy for blend uniformity monitoring: An innovative qualitative application based on the coefficient of determination" LINK "Research on the secondary structure and hydration water around human serum albumin induced by ethanol with infrared and near-infrared spectroscopy" LINK "Point-of-Care Using Vis-NIR Spectroscopy for White Blood Cell Count Analysis" LINK "Rapid determination of viscosity and viscosity index of lube base oil based on near-infrared spectroscopy and new transformation formula" LINK "A recognition method of mushroom mycelium varieties based on near-infrared spectroscopy and deep learning model" LINK "Fast and nondestructive discrimination of fresh tea leaves at different altitudes based on near infrared spectroscopy and various chemometrics methods" LINK "Detection of early collision and compression bruises for pears based on hyperspectral imaging technology" LINK "Hyperspectral Imaging based Detection of PVC during Sellafield Repackaging Procedures" LINK "Study on the detection of apple soluble solids based on fractal theory and hyperspectral imaging technology" LINK "Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques" LINK "Sensors: Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression" LINK "Foods: Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method" LINK "Analysis of physio-chemical properties of solution grown third order nonlinear optical single crystal: 1, 4-oxazinanium nitrate for photonic applications" LINK "A novel composite colorimetric sensor array for quality characterization of shrimp paste based on indicator displacement assay and etching of silver nanoprisms" LINK "Research on weed identification method in rice fields based on UAV remote sensing" LINK "Flexible Microspectrometers Based on Printed Perovskite Pixels with Graded Bandgaps" spectrometers miniaturization LINK "Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification" LINK "Biomedicines: Fourier Transform Infrared Spectroscopy Reveals Molecular Changes in Blood Vessels of Rats Treated with Pentadecapeptide BPC 157" LINK "Electrochromic Tungsten Oxide Nanofilms and Ionic Liquid Based Ion Conductor for Smart Windows Development: Preparation, Characterization and …" LINK
Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results--millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Data scientists are in demand: the U.S. Bureau of Labor Statistics predicts that the employment of data scientists will grow 36 percent by 2031,1 much faster than the average for all occupations. Data scientists are also some of the highest-paid job roles, so data scientists need to quickly show their value by getting to real results as quickly, safely, and accurately as possible.
Have you ever heard of Midjourney? Well, in case you did not know, it's a standalone research laboratory responsible for developing an AI program of the same name. This program generates pictures based on textual descriptions, similar to OpenAI's DALL-E and Stable Diffusion. You probably don't know a lot about this, but today my aim is to change that. According to the company's founder (David Holz) the company was already profitable in August 2022.
BERT and RoBERTa require that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations ( 65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. Sentence-BERT (SBERT), presents a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. SBERT adds a pooling operation to the output of BERT / RoBERTa to derive a fixed sized sentence embedding.
A host of financial reports and documents provide information about the operations, cash flows, current and future financial position of companies. This information is used by the readers and analysts to make critical decisions, involving millions/billions of dollars. Such analysis demands advanced expertise in finance and involves performing complex numerical reasoning. Sentiment analysis models are able to predict the sentiment/emotion of events that have impact on the companies performance. A Question Answering system helps in getting answers to questions during financial analysis to aid while making decisions.
There are many different ways to solve mathematical optimization problems. You can use greedy algorithms, constraint programming, mixed integer programming, genetic algorithms, local search, and others. Depending on the size and type of the problem, and the solution quality desired, one technique may work better than the other. This post gives an overview of different heuristics for solving discrete optimization problems. First, I explain the three components you need to describe an optimization problem mathematically.
According to a recent study, machine learning could aid in the creation of new metal types with advantageous characteristics like resistance to rust and high temperatures. A variety of industries could benefit from this; for instance, spacecraft could be improved with metals that function well at lower temperatures, while boats and submarines could benefit from corrosion-resistant metals. Currently, attempts to produce new metals are mostly conducted in laboratories by scientists. Typically, they begin with one well-known element, such as iron, which is readily available and malleable, and then add one or two more to examine how it affects the base material. Trial & error is a hard process that invariably produces more failures than successful outcomes.