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Unpopular Opinion – Data Scientists Should Be More End-to-End - KDnuggets

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Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view. IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix. I find these definitions to be more prescriptive than I prefer. Instead, I have a simple (and pragmatic) definition: An end-to-end data scientist can identify and solve problems with data to deliver value.


Black tech organizations grow amid calls for racial justice

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Amazon applied science manager Dr. Nashlie Sephus has lived in New York City, Atlanta, Silicon Valley, and Seoul while pursuing her education and work in machine learning. She knows the look of a community that's thriving from technology and innovation, but she didn't see that growth happening in her hometown of Jackson, Mississippi. That's why last week she concluded an 18-month process by signing contracts to secure 12 acres of land that will be home to the Jackson Tech District. The Bean Path, a nonprofit organization created by Sephus, will operate a maker and innovation space on the land. There will also be restaurants and residential lofts spread across eight buildings, all located near the historically Black Jackson State University.


Unraveling Deep Learning Algorithms With Limited Data

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The study proposes an alternative repurposing technique for turning the weakness of deep neural networks into strengths. Deep learning has been an expanse of artificial intelligence, heavily researched by the data scientists in the past few areas. Experts are more curious about supplementing this technology in sectors where human skills perform mundane tasks. As it uses big data, which is garnered from various sources, makes patterns of this collected data and learn to perform a task without any supervision, it becomes data Hungry, which becomes a major challenge when the data is in scarcity. Apart from being hungry, two significant drawbacks presented with deep learning is the opacity and its shallowness.


Top 10 Fascinating Movies on Data Science, Machine Learning & AI

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Fairy tales are mostly fictions decorated with fantasy. But sci-fic movies are often taken into serious account. The reason behind this is that machines and robotics-related movies from the past are slowly turning to be a reality now. Henceforth, people are curious about what technologies movies might unravel to the mindsets of scientists. Familiar topics like advanced Artificial Intelligence (AI) systems, humanoid robots, self-driving cars, and a whole world full of digital growth is what the movie sector has portrayed so far.


Future of AI Part 2

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This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Some of the classical approaches to AI include (non-exhaustive list) Search algorithms such as Breath-First, Depth-First, Iterative Deepening Search, A* algorithm, and the field of Logic including Predicate Calculus and Propositional Calculus. Local Search approaches were also developed for example Simulated Annealing, Hill Climbing (see also Greedy), Beam Search and Genetic Algorithms (see below). Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. A non-exhaustive list of examples of techniques include Linear Regression, Logistic Regression, K-Means, k-Nearest Neighbour (kNN), Naive Bayes, Support Vector Machine (SVM), Decision Trees, Random Forests, XG Boost, Light Gradient Boosting Machine (LightGBM), CatBoost. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion.


How "green" is your Artificial Intelligence?

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Artificial intelligence (AI) systems face a set of conflicting goals: being accurate (consuming large amounts of computational power and electrical power) and being accessible (being lower in cost, less computationally intensive, and less power-hungry). Unfortunately, many of today's AI implementations are environmentally unsustainable. Improvements in AI energy efficiency will be driven by several factors, including more efficient algorithms, more efficient computing architectures, and more efficient components. It's necessary to measure and track the energy consumption of AI systems to identify any improvements in energy efficiency. One example of the increasing awareness of the importance of energy consumption in AI systems is having is reflected in the fact that the ULPMark (ultra-low power) benchmark line from EEMBC is now adding ML inference and developing a new benchmark, the ULPMark-ML.


Why Deep Learning DevCon Comes At The Right Time

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The Association of Data Scientists (ADaSci) recently announced Deep Learning DEVCON or DLDC 2020, a two-day virtual conference that aims to bring machine learning and deep learning practitioners and experts from the industry on a single platform to share and discuss recent developments in the field. Scheduled for 29th and 30th October, the conference comes at a time when deep learning, a subset of machine learning, has become one of the most advancing technologies in the world. From being used in the fields of natural language processing to making self-driving cars, it has come a long way. As a matter of fact, reports suggest that by 2024, the deep learning market is expected to grow at a CAGR of 25%. Thus, it can easily be established that the advancements in the field of deep learning have just initiated and got a long road ahead.


AutoML: Bridging the skills gap with machine learning

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Is there anything that can stop AI? As the novel Covid-19 pandemic forces the world to put on its brakes, AI technologies like machine learning – AutoML in particular – have been continuing to develop at break-neck speeds at the beginning of the new decade. Following a recent breakthrough by Google scientists at the start of a period of enforced lockdown, AutoML is seeing a wave of new progress in correlation with the explosion of big data, advanced analytics and predictive models. The increasing amount of viable data has meant that AI, machine learning (ML) and data science is undergoing reams of data and training that has served to boost the technology exponentially. AutoML in 2020, can perform data pre-processing, as well as Extraction, Transformation and Loading tasks (ETL).


NASA weighs mission to Venus after recent discovery of possible life

The Japan Times

Washington – NASA is considering approving by next April up to two planetary science missions from four proposals under review, including one to Venus that scientists involved in the project said could help determine whether or not that planet harbors life. An international research team on Monday described evidence of potential microbes residing in the harshly acidic Venusian clouds: traces of phosphine, a gas that on Earth is produced by bacteria inhabiting oxygen-free environments. It provided strong potential evidence of life beyond Earth. The U.S. space agency in February shortlisted four proposed missions that are now being reviewed by a NASA panel, two of which would involve robotic probes to Venus. One of those, called DAVINCI, would send a probe into the Venusian atmosphere.


Tech Behind Nasa's ML Model To Predict Hurricane Intensity

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With part of the world dealing with the adverse effects of hurricanes and intense tropical cyclones, it has become imperative for researchers and scientists to develop a way to predict and analyse these hurricane patterns. Thus in an attempt to forecast future hurricane intensity, scientists at NASA's Jet Propulsion Laboratory in Southern California have proposed a machine learning model that claims to predict rapid-intensification events of the future accurately. The critical factor in understanding the intensity of a hurricane is the wind speed. Traditionally it has been a challenge to predict the severity of storms or hurricanes while it's brewing. However, NASA's new ML model can improve the accuracy of the prediction and provide better results.