Amazon announces 2 new ways it's using robots to assist employees and deliver for customers

We're thrilled to see how our technology is affecting Amazon's operations, from our newest robotic arms, Sparrow and Cardinal, to our first mobile robot that can operate on its own, Proteus. Currently, over 750,000 robots collaborate with human workers to complete extremely repetitive activities, freeing up human resources to better serve consumers.

We are very happy to announce that, in the midst of all of these endeavors, we have recently introduced a new robotic system to assist in fulfilling customer orders for holiday shopping this year. This new technology is called Sequoia, and it's currently in use at one of our fulfillment sites in Houston, Texas. 

Sequoia will help us delight consumers with better speed and increased accuracy for delivery estimates while also enhancing worker safety at our facilities by reinventing how we store and manage inventory at our sites. We can now identify and store goods at our fulfillment facilities up to 75% faster than we could before thanks to Sequoia. This helps vendors and buyers alike as we can put products for sale on Amazon.com more quickly. Sequoia also speeds up the order processing time through a fulfillment center by up to 25% after an order is placed, increasing the quantity of goods we can ship same-day or next-day and improving our shipment predictability.

Sequoia combines several robot technologies, including mobile robots, gantry systems, robotic arms, and a new ergonomic staff workstation, to containerize our inventory into totes, building on a number of research and development initiatives. The way the system operates is that mobile robots move containerized merchandise straight to a gantry, which is a tall structure with a platform that holds equipment that may be used to replenish totes or assign items to an employee for customers' orders to be picked out.

Workers get these totes at a recently created ergonomic workplace that enables them to complete all tasks in their power zone, which is the area between mid-thigh and mid-chest height. Employees will no longer need to frequently bend over or stoop to pick up orders from customers thanks to this method, supporting our

Machine learning helps Earth AI find high-grade molybdenum in unexpected place

 

The first artificial intelligence-based finding of a greenfield molybdenum deposit has been disclosed by clean energy metals exploration Earth AI.

The deposit was discovered close to Armidale, Australia's New South Wales. It is free, unlicensed terrain that is thought to be uninhabitable.

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However, Earth AI's founder and CEO, Roman Teslyuk, and his group had a suspicion. They therefore made the decision to develop and methodically examine a number of hypotheses. They tested a single hypothesis in each hole they bored.

They were able to locate high-grade ore after eight months, four holes drilled in the high Australian plateau during the winter, and numerous pieces of equipment lost to snow.

We dug four holes in the Northern Territory before this. Which leads us toAs a result, our success rate in locating economic-grade mineralization is now one in eight, a significant increase from the industry average of one in 200, Teslyuk told Mining.com.

MDC: Could you elaborate on the specifics of the discovery's process?

Tereza: Using local geology and geophysical data, our Mineral Targeting Platform is a geological deep learning solution that excels at locating mineral systems. It learns on almost all of the continent's known mineral possibilities and makes predictions about new systems based on this information.

In this instance, we had a "juicy target" on property that had been abandoned four times by majors and junior explorers who spent a lot of money on exploration but were unable to locate any mineral reserves. 

However, we licensed and committed.

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How does machine learning differ from traditional programming?

 

What distinguishes traditional programming from machine learning?

Machine learning has become a potent instrument in the rapidly changing field of technology, revolutionizing a number of different industries. However, what distinguishes it specifically from conventional programming? Let's examine the main distinctions between these two strategies.

Conventional programming entails giving a computer clear instructions on how to carry out a particular function. Coders create code that describes a set of actions that need to be executed. Because of the deterministic nature of this approach, the output is determined exclusively by the input and a predetermined set of rules. It's similar to following a recipe, where the results are always the same.

However, machine learning adopts a different strategy. It is an artificial intelligence application that makes possible.

systems that, without explicit programming, are able to learn from experience and get better. Machine learning algorithms examine enormous volumes of data to find patterns and generate predictions or judgments rather than depending on predetermined rules. This method is probabilistic in nature, with the outcome depending on statistical inference and the algorithm's capacity to extrapolate from the training set of data.

Adaptability is one of the main differences between machine learning and traditional programming. Conventional programs must be manually updated to reflect changes or new scenarios because they are static. Machine learning models, on the other hand, are able to adjust and get better over time when they come across new data. Machine learning systems can handle complicated and dynamic jobs more effectively because of their flexibility.

Q&A:

What does machine learning entail?

A subfield of artificial intelligence called machine learning gives systems the ability to learn from their experiences and get better without needing to be explicitly programmed.

What is the process of traditional programming?

A: In traditional programming, a computer is explicitly taught how to accomplish a task by creating code that describes the steps that need to be taken.

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Harnessing Machine Learning: Advancements in Tobacco Research and the Internet of Drones

 

Machine Learning (ML) has been a disruptive factor in many industries in recent years. This blog article explores two areas where machine learning (ML) is making great progress: Internet of Drones (IoD) and tobacco research. Despite their apparent differences, these domains are united by the advancement and creativity brought about by cutting-edge data analysis methods.

Machine Learning for Research on Tobacco:

A scoping assessment was carried out by Rui Fu and associates to assess the influence of machine learning on tobacco research. Their thorough analysis, which was published in the journal Tobacco Control, found 74 studies that used machine learning techniques. Four unique domains were identified from these studies:

1. ML-powered smoking cessation technology (n = 22)

2. Content analysis (n=32) of data on tobacco use on social media platforms

3. Classifying smokers using narrative clinical materials

4. Prediction of outcomes related to tobacco use based on administrative, survey, or clinical trial data (n=14)

This review demonstrates the enormous potential of machine learning to advance tobacco control initiatives and influence policy choices.

ML in Quitting Smoking:

Machine learning applications have demonstrated potential in offering tailored interventions in the field of smoking cessation. ML algorithms have the capability to customize tactics to enhance the probability of stopping by examining individual smoking behaviors and aspects that contribute to the success of cessation. These technological advancements not only empower individuals but also improve the overall effectiveness of smoking cessation programs.

Examining Social Media Content: More info

Will Artificial Intelligence Replace Architects?

 

Will architects be replaced in their positions by artificial intelligence? Thomas Lane claims that AI may automate up to 37% of the work that engineers and architects normally do in the May 2023 issue of Building magazine. However, it is likely that mundane and less creative jobs will be the focus of this automation, freeing up professionals to focus on more creative and strategic aspects of their work.

The same is true of AI tools—just as Revit and 3D software did not replace architects, but rather changed their workflows. AI is about to change the landscape of architecture by bringing with it new duties like AI management in addition to current ones.

Early in 2023, the volume of photos produced by AI systems like Midjourney has left manyarchitects thinking about the ramifications. While there's a common fear that artificial intelligence will become omnipotent, architects are curious in AI and actively investigating its potential integration into their work in an effort to understand its potential uses in their industry.

It seems unlikely that AI will soon completely replace architects. The architectural scene is changing quickly, and while new applications will always emerge, our understanding of AI's potential and limitations is increasingly becoming more apparent. A clearer knowledge of how AI might influence and revolutionize our professional activities is being shaped by this growing awareness.

Until AI emerges victorious in a competition for architectural design, we have nothing to fear.

More Info: https://www.archdaily.com/1007802/will-artificial-intelligence-replace-architects

OnPassive Chief Marketing Officer Mohammad Nazzal On The Power Of Artificial Intelligence To Boost Your Business

 

Even though there are still a number of concerns around the application of artificial intelligence (AI), it is evident that those who adopt this technology first stand to gain a competitive advantage over others.

We recently had a conversation about best practices for incorporating AI technology, how it may boost marketing campaigns, and how OnPassive's array of AI products can help companies of all sizes with Mohammad Nazzal, CMO of OnPassive.

Nazzal advises beginning the process of incorporating AI technology into your company by determining a particular use case—such as enhancing customer satisfaction, productivity, or efficiency—where AI may assist your enterprise. Watch the video to hear Nazzal's full message!

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Top 8 Python data science books for comprehensive learning

Top 8 Python data science books for comprehensive learning

In the ever-evolving field of data science, Python has emerged as a powerhouse programming language. Its versatility, vast library ecosystem, and ease of use make it the top choice for data scientists and analysts. Whether you are a beginner looking to start your journey or an experienced practitioner seeking to expand your knowledge, there are several excellent Python data science books available. In this article, we’ll explore 8 of the best Python data science books to help you master this exciting field.

1. “Python for Data Analysis” by Wes McKinney

Wes McKinney’s “Python for Data Analysis” is a timeless classic in the data science community. It covers essential Python libraries like pandas and NumPy, providing hands-on guidance for data manipulation, analysis, and visualization. This book is a must-read for anyone looking to become proficient in data wrangling and exploratory data analysis.
2. “Data Science for Business” by Foster Provost and Tom Fawcett

Understanding the business aspects of data science is crucial, and “Data Science for Business” offers precisely that. This book teaches you how to apply data science techniques to solve real-world business problems. It’s an ideal resource for professionals aiming to bridge the gap between data science and business strategy.
3. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

Machine learning is a cornerstone of data science, and Aurélien Géron’s book is a fantastic guide to the subject. It covers essential machine learning concepts, algorithms, and tools like Scikit-Learn, Keras, and TensorFlow. With practical examples and exercises, this book helps you build and train machine learning models effectively.
4. “Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili

“Python Machine Learning” is a comprehensive book that delves deep into the world of machine learning using Python. It covers a wide range of topics, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is an excellent choice for those looking to advance their machine-learning skills.
5. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

For those interested in the cutting-edge field of deep learning, “Deep Learning” is an authoritative resource. Authored by three leading experts, this book provides an in-depth understanding of neural networks, deep learning architectures, and their applications. It’s a must-read for aspiring deep-learning practitioners.
6. “Python for Data Science Handbook” by Jake VanderPlas

“Python for Data Science Handbook” by Jake VanderPlas is a comprehensive guide that covers the essential tools and techniques for data science in Python. It explores libraries like Matplotlib, Seaborn, and Scikit-Learn, offering practical insights and code examples. This book is suitable for both beginners and experienced data scientists.
7. “Practical Statistics for Data Scientists” by Andrew Bruce and Peter Bruce

Statistics is the foundation of data science, and “Practical Statistics for Data Scientists” equips you with the statistical knowledge necessary for effective data analysis. It covers topics like probability, hypothesis testing, and regression analysis, providing practical examples and exercises to reinforce your learning.
8. “Data Science from Scratch” by Joel Grus

If you’re eager to learn data science from the ground up, “Data Science from Scratch” is an excellent choice. Joel Grus takes you on a journey through essential data science concepts and tools using Python. This book is perfect for beginners who want to build a strong foundation in data science.