Inside cowboy Charles Jesse "Buffalo" Jones's get-rich-quick scheme to restore the plains 100 years ago. By 1888, Charles Jesse "Buffalo" Jones had succeeded in crossbreeding a buffalo with cow, a hybrid he claimed would be as tasty as beef and as hardy as buffalo. Breakthroughs, discoveries, and DIY tips sent every weekday. The "cattalo" was a homely creature--stocky and shaggy, with a slight buffalo's hump and a cow's docile face. Charles "Buffalo" Jones invented the cow-buffalo hybrid in 1888.

Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models turn data into representations of knowledge and beliefs about the world, are poorly understood. Insights into these processes could pave the way for developing LMs with knowledge representations that are more consistent, robust, and complete. To facilitate studying these questions, we present LMEnt, a suite for analyzing knowledge acquisition in LMs during pretraining. LMEnt introduces: (1) a knowledge-rich pretraining corpus, fully annotated with entity mentions, based on Wikipedia, (2) an entity-based retrieval method over pretraining data that outperforms previous approaches by as much as 80.4%, and (3) 12 pretrained models with up to 1B parameters and 4K intermediate checkpoints, with comparable performance to popular open-sourced models on knowledge benchmarks. Together, these resources provide a controlled environment for analyzing connections between entity mentions in pretraining and downstream performance, and the effects of causal interventions in pretraining data. We show the utility of LMEnt by studying knowledge acquisition across checkpoints, finding that fact frequency is key, but does not fully explain learning trends. We release LMEnt to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, and learning dynamics.huggingface.co/LMEnt

Cryptocurrencies are highly volatile financial instruments with more and more new retail investors joining the scene with each passing day. Bitcoin has always proved to determine in which way the rest of the cryptocurrency market is headed towards. As of today Bitcoin has a market dominance of close to 50 percent. Bull and bear phases in cryptocurrencies are determined based on the performance of Bitcoin over the 50 Day and 200 Day Moving Averages. The aim of this paper is to foretell the performance of bitcoin in the near future by employing predictive algorithms. This predicted data will then be used to calculate the 50 Day and 200 Day Moving Averages and subsequently plotted to establish the potential bull and bear phases.

Today we are presenting our paper with @hemant_pt about ranking multilingual help requests on social media during disasters at #ICWSM Full paper: https://t.co/fBphALS7fj

Roman V. Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. There he was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London, College of London. In 2008 Dr. Yampolskiy accepted an assistant professor position at the Speed School of Engineering, University of Louisville, KY.

Confusion Matrix is mainly used machine language and deep learning field to evaluate the perfomance of model,by showing all its predicted value in a table. It is mainly used to evaluate classification model(classifier) by calculating precision,recall and f1 score using confusion matrix. As name suggest it create confusion in terminalogy which is used in table.when In the dog vs cat example we have designed and trained model to predict two values which is either dog or cat . To create confusion matrix we need to take one item at a time .In the our case we are taking first dog then we will create four values that is TP,TN,FP,FN.

An AI tool has predicted 540 mammalian species that are most likely to spread covid-19 using information about where they live and aspects of their biology. According to the model, mink, Sunda pangolins and bats are among the top 10 per cent of species most likely to spread covid-19, which matches results from lab experiments. The SARS-CoV-2 coronavirus, which causes covid-19, invades human and animal cells by engaging the ACE2 protein on host cells with its spike protein. This step is required to infect an animal and be transmitted to other hosts. Distinct species have different versions of the protein, so understanding how well their ACE2 protein binds to the coronavirus spike protein can help scientists predict which animals are most likely to spread covid-19.

One robot's skinny leg, which relies on computer vision to detect recyclables, plucks a hunk of blue plastic off a conveyor belt, while the other's grabs a piece of an old water bottle. The machine then places those bits into sorting bins using a vacuum gripper. For the nation's 600-plus recycling facilities, which process some 67 million tons of waste, these leggy robots from AMP Robotics are one answer to the current bottlenecks facing the industry. Even before Covid-19 struck, AMP Robotics was starting to gain traction. But as boxes from home deliveries piled up at recycling centers and hiring--already a tough proposition--got even tougher as workers feared getting ill, AMP's business boomed.

Researchers at the University of Buffalo have received a $316,000 grant from the Defense Advanced Research Projects Agency (DARPA), an agency funded by the US Department of Defense, to develop an artificial intelligence capable of controlling swarms of up to 250 drones. To created the experimental AI, scientists from the university's Artificial Intelligence Institute will study video game players as they pilot autonomous swarms of digital military units in real time strategy games like StarCraft, Stellaris, and Company of Heroes. The team will collect data on how the players react to a wide variety of different tactical challenges as well as watching how they react to unexpected changes in the terrain or terms of battle. Researchers at the University of Buffalo's Artificial Intelligence Institute will study the way video game players make choices in real time strategy games like StarCraft and Company of Heroes to develop an AI that can control swarms of up to 250 drones'We don't want the AI system just to mimic human behavior; we want it to form a deeper understanding of what motivates human actions,' University of Buffalo's Souma Chowdhury told the school's news site. 'That's what will lead to more advanced AI.' The team will also collect a range of biometric data from the players, through eyetracking software and electroencephalograms, which monitors brain activity while they play.

The National Science Foundation and Amazon, the grant's joint funders, have partnered on a program called Fairness in Artificial Intelligence (FAI) that aims to address bias and build trustworthy computational systems that can contribute to solving the biggest challenges facing modern societies. Over the course of three years, the UB researchers will collaborate with the Hillside Family of Agencies (Rochester, N.Y.), one of the oldest family and youth nonprofit human services organizations in the country, and a youth advisory council made up of individuals who have recently aged out of foster care, to develop the tool. They will also consult with national experts across specializations to inform this complex work. Researchers will use data from the Administration on Children and Families' (ACF) federally mandated National Youth in Transition Database (NYTD) and input from collaborators to inform their predictive model. Each state participates in NYTD to report the experiences and services used by youth in foster care.