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How a Memory Quirk of the Human Brain Can Galvanize AI

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Take a two-year-old that first learns to recognize a dog and a cat at home, then a horse and a sheep in a petting zoo. The kid will then also be able to tell apart a dog and a sheep, even if he can't yet articulate their differences. This ability comes so naturally to us it belies the complexity of the brain's data-crunching processes under the hood. To make the logical leap, the child first needs to remember distinctions between his family pets. When confronted with new categories--farm animals--his neural circuits call upon those past remembrances, and seamlessly incorporate those memories with new learnings to update his mental model of the world.


Andrew Stein, Software Engineer Waymo – Interview Series

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Andrew Stein is a Software Engineer who leads the perception team for Waymo, a name that stands for'New Way Forward in Mobility'. Waymo is an autonomous driving technology development company that is a subsidiary of Alphabet Inc, the parent company of Google. What initially attracted you to AI and robotics? I always liked making things that "did something" ever since I was very young. Arts and crafts could be fun, but my biggest passion was working on creations that were also functional in some way.


The Power of Machine Learning in Automated Video Redaction

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Many people are aware of AI or Artificial Intelligence and its meaning, especially in the way that it is often portrayed through movies. These movies are often exciting and captivate our imaginations. Machine learning, while similar to AI, is defined differently. A way to explain this in layman's terms is that AI is the breadth of knowledge contained and used by a system, while machine learning is the algorithms or processes in which the system gains the knowledge and assimilates it for future use. In human terms, AI would be all the information and knowledge you already have, while machine learning would be likened unto the steps you choose to acquire that knowledge, such as reading, observing, studying, or even making mistakes.


Getting AI to Learn Like a Baby is Goal of Self-Supervised Learning - AI Trends

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"Even with a lot of supervised data, AIs can't make the same kinds of generalizations that human children can," Gopnik said. "Their knowledge is much narrower and more limited, and they are easily fooled. Current AIs are like children with super-helicopter-tiger moms--programs that hover over the learner dictating whether it is right or wrong at every step. The helicoptered AI children can be very good at learning to do specific things well, but they fall apart when it comes to resilience and creativity. A small change in the learning problem means that they have to start all over again."


Amazon lets customers pay with their PALM using a recognition system

Daily Mail - Science & tech

Amazon unveiled a new palm recognition system at two of its Seattle stores that allows customers to pay for items with a simple wave of the hand. Called Amazon One, the technology creates a unique'palm signature' for each individual by gathering surface-area details and links it to a credit card. The device is being piloted at two Amazon Go locations, with more being added over the next few months. Along with making payments, the e-commerce giant sees its palm reading system being used for things like'presenting a loyalty card, entering a location like a stadium or badging into work.' Amazon has been working on the palm recognition system for quite some time, as last December the firm was awarded a patent for a'touchless scanning system' that identifies customers using hand recognition.


Overwatch, Call of Duty League teams can defer multimillion-dollar franchise fees due to covid-19

Washington Post - Technology News

Front offices around CDL and OWL agree that the goal is to return to live, in-person events whenever it's safe to do so. Activision Blizzard created both leagues to pioneer a city-based model, built like traditional sports leagues, and that's always been a selling point for franchise spots. Team presidents and owners see the attention to a local market as a surefire way to created a dedicated fan base with regional sponsors and packed stadiums.


XGBoost vs LightGBM on a High Dimensional Dataset

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I have recently completed a multi-class classification problem given as a take-home assignment for a data scientist position. It was a good opportunity to compare the two state-of-the-art implementations of gradient boosting decision trees which are XGBoost and LightGBM. Both algorithms are so powerful that they are prominent among the best performing machine learning models. The dataset contains over 60 thousand observations and 103 numerical features. The target variable contains 9 different classes.


Fabricius, a free machine learning-based Egyptian hieroglyphs translator

#artificialintelligence

Google's Arts & Culture division has a promising new app, called Fabricius, that uses machine learning to decode Egyptian hieroglyphics. The free app allows you to learn more about hieroglyphics, write them, and to decode them. Besides being a fun educational tool (and a way of sending coded love letters and hate mail), the program holds great promise for the study and instant translation of dead languages. In this video, an amateur Egyptology reviews the app and points out its strengths and its weaknesses.


AI Does Heavy Lifting Data Analysis for First Responders - Connected World

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The market for AI (artificial intelligence) technologies is going to expand tremendously in the next decade. Grand View Research says the global AI market will reach $733.7 billion by 2027, growing at a CAGR (compound annual growth rate) of 42.2%. One of the many sectors that will increasingly look to leverage AI technologies between now and 2027 (and beyond) is first response. In fact, in some cases, the first-response industry is already engaged in piloting AI technologies for use on the front lines. What AI-related innovations are to come, and how will they make first responders' jobs easier?


New Books and Resources for DSC Members

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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability.