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Sea lion carcass being devoured by starfish off the coast of California winner in photo competition

Daily Mail - Science & tech

A haunting image showing a sea lion carcass being devoured by at least a dozen color starfish on the seafloor of Monterey Bay in California has won the'Aquatic Life' category in a photo competition. The eerie picture was captured by wildlife photographer David Slater, who submitted it to the California Academy of Science's Big Picture Competition. The bright orange, pink and blue starfish are bat stars - known scavengers of the ocean - which are turning the lifeless body into energy and nutrients that is returned to the marine food web. 'I knew this image was special when I first published it but words cannot even describe how I feel taking first place in such a prestigious contest,' Slater, who resides in Monterey, shared in an Instagram post. The Big Picture Competition includes several categories, all with a wildlife theme, and the grand prize winner is an image of bees swarming together in a mating ball.

Overstocking or Understocking leading you to losses?


Artificial Intelligence in Logistics involves using technology to automate complex tasks and unearthing previously unknown patterns in Supply chain processes or workflows, and the impact is game-changing and visible. AI systems also enable predictive analytics, which helps tackle operational challenges and disruptions to supply chains as well as the workforce. A constant challenge with manufacturing is the losses from overstocking or under-stocking inventories. Overstocking often leads to wastage and lower margins. Under-stocking can translate into losses in sales, revenue, and customers.

An Introductory Look on NumPy and Pandas


NumPy and Pandas are two significantly popular modules found in Python. Both modules are very popular to be main components of Machine Learning and Neural Networks studies. This article is taking these modules on board to summarize their features. Python is developed by Guido van Rossum and first released at the beginning of 90's as an open source programming language. With the increasing interest on Python, users contributed their work to the community.

Why Pattern Recognition?


Pattern Recognition, as the name suggests is "recognizing the patterns" in simple terms. We see flowers around us and we classify them into different categories based on the number of petals, color, etc, depending on the pattern. Similarly, machines can also try to identify patterns and classify them, right? Pattern Recognition is an important scientific discipline whose goal is to identify patterns, categorizing the objects into various classes or categories. These objects could be images or signal waveforms or any measures that need to be classified.

Artificial Intelligence and the future of the business consultant


Artificial intelligence has gained traction across various industries and as a result it's likely to disrupt and change many professions in the near future. McKinsey & Company estimates that as much as 45% of the tasks currently performed by people can be automated, and not only routine tasks, but also tasks which require knowledge capabilities. The consultant role requires skills such as negotiation, creativity, leadership and strategic thinking which are all intrinsic human capabilities that cannot easily be simulated. So it must be asked, how will artificial intelligence impact the role and responsibilities of consultants? Has AI become something organisations are actively using or will it still be a few years before organisations benefit from AI?

Summary: Few-Shot Object Detection with Fully Cross-Transformer


Object detection typically requires a large amount of label data and deep CNN[3] architecture which process the labeled data to learn the parameters of the model. Two popular object detection approaches are RCNN[5] and YOLO[4] which typically fall in this category. However, in general, real-world data suffers from a long-tail distribution where for the majority of categories only a small amount of data is available. Even if the data is available it's a tedious task to hand-labeled millions of images for training. An alternative approach to build an architecture that can learn from the small amount of data and yet perform equally well on unseen data.

How we describe the metaverse makes a difference – today's words could shape tomorrow's reality and who benefits from it


Quick, define the word "metaverse." Coined in 1992 by science fiction author Neal Stephenson, the relatively obscure term exploded in popularity during the COVID-19 pandemic, particularly after Facebook rebranded as Meta in October 2021. There are now myriad articles on the metaverse, and thousands of companies have invested in its development. Citigroup Inc. has estimated that by 2030 the metaverse could be a US$13 trillion market, with 5 billion users. From climate change to global connection and disability access to pandemic response, the metaverse has incredible potential.

How AI is assisting Coca-Cola in increasing supply chain purchasing


Artificial intelligence (AI) and machine learning tools have become indispensable to fuel procurement and sourcing efforts at the Atlanta-based global beverage leader, according to Brett Fultz, director of global analysis, global procurement and supply chain at Coca-Cola. For any company that manufactures or sells goods, buying and sourcing are integral functions of supply chain management. Sourcing, an early stage of the buying process, is about identifying and assessing potential suppliers of goods or services, negotiating terms, and selecting vendors. Procurement, however, goes further, and is about getting supplies and payment from suppliers who compete for business by submitting bids and negotiating contracts. But challenges abound in a supply chain landscape full of constraints and risks - from issues related to the COVID-19 pandemic and the war in Ukraine to climate change.

What machine learning can bring to credit risk management - Bobsguide


As credit markets continue to evolve, banks may take advantage of products which utilise machine learning – software which allows banks to anticipate risks more effectively. But should banks revise their credit risk management processes accordingly and employ these new solutions? According to McKinsey, AI and machine learning technologies could add up to $1 trillion in additional value to global banking every year. Financial institutions are using machine learning to make credit decisions more accurately and consistently while reducing risk, fraud, and costs. For example, Citi bank recently transformed its critical internal audit using machine learning--something that has contributed to high-quality credit decisions.