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Is AI there yet?

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It's a cold winter day in Detroit, but the sun is shining bright. Robert Williams decided to spend some quality time rolling on his house's front loan with his two daughters. Suddenly, police officers appeared from nowhere and brought to an abrupt halt a perfect family day. Robert was ripped from the arms of his crying daughters without an explanation, and cold handcuffs now gripped his hands. The police took him away in no time! His family were left shaken in disbelief at the scene which had unfolded in front of their eyes. What followed for Robert were 30 long hours in police custody.



ML Scaling Requires Upgraded Data Management Plan

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Successful data strategies are built on a foundation of meticulous data management, creating enterprise architectures that "democratize" data access and usage, yielding measurable results from machine learning platforms. The reality, according to an examination of the emerging "AI organization," is that few data-driven organizations are able to deliver on their data strategy. A survey commissioned by Databricks and conducted by MIT Technology Review Insights found that a mere 13 percent of those polled actually achieve measurable business results. MIT Technology Review Insights said it polled 351 CDOs, chief analytics officers as well as CIOs, CTOs and senior technology executives. It also interviewed several other senior technology leaders.


Create 3D Models from Images! AI and Game Development, Design…

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What you see below is someone carefully creating a scene for a video game. It takes many hours of work by a professional just for a single object like this one. How cool would it be to take a picture of an object on the internet, let's say a car, and automatically have the 3D object in less than a second ready to insert in your game? Well, imagine that within a few seconds, you can even animate this car, making the wheels turn, flashing the lights, etc. Would you believe me if I told you that an AI could already do that? If video games weren't enough, this new application works for any 3D scene you are working on, illustrations, movies, architecture, design, and more!


EETimes - ReRAM Machine Learning Embraces Variability

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TORONTO--Sometimes a problem can become its own solution. For CEA-Leti scientists, it means that traits of resistive-RAM (ReRAM) devices that have been previously considered as "non-ideal" may be the answer to overcoming barriers to developing ReRAM-based edge-learning systems, as outlined in a recent Nature Electronics publication titled "In-situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling." It describes how RRAM, or memristor, technology can be used to create intelligent systems that learn locally at the edge, independent of the cloud. Thomas Dalgaty, a CEA-Leti scientist at France's Université Grenoble, explained how the team were able to navigate the intrinsic non-idealities of ReRAM technology--the learning algorithms used in current ReRAM-based edge approaches cannot be reconciled with device programming randomness, or variability, among others. In a telephone interview with EE Times, he said the solution was to implement a Markov Chain Monte Carlo (MCMC) sampling learning algorithm in a fabricated chip that acts as a Bayesian machine-learning model, which actively exploited memristor randomness. For the purposes of the research, Dalgaty said it's important to clearly define what is meant by an edge system.


Software Development Engineer (Big Data and ML) - FinTech

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Are you interested in building high-performance, globally scalable Financial systems that support Amazon's current and future growth? Are you seeking an environment where you can drive innovation? Does the prospect of working with top engineering talent get you charged up? If so, Amazon's Finance Technology (FinTech) is for you! As a Software Development Engineer in FinTech Treasury, you will build real time financial calculation engines and reports to assess Amazon's exposure to financial risks, and use Machine Learning (ML) to forecast cash balances across the company.


ContinualAI Releases Avalanche: An End-to-End Library for Continual Learning

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Albert Einstein once said that "wisdom is not a product of schooling, but the lifelong attempt to acquire it." Centuries of human progress have been built on our brains' ability to continually acquire, fine-tune and transfer knowledge and skills. Such continual learning however remains a long-standing challenge in machine learning (ML), where the ongoing acquisition of incrementally available information from non-stationary data often leads to catastrophic forgetting problems. Gradient-based deep architectures have spurred the development of continual learning in recent years, but continual learning algorithms are often designed and implemented from scratch with different assumptions, settings, and benchmarks, making them difficult to compare, port, or reproduce. Now, a research and development team from ContinualAI with researchers from KU Leuven, ByteDance AI Lab, University of California, New York University and other institutions has proposed Avalanche, an end-to-end library for continual learning based on PyTorch.


Intelligent edge computing and management

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We have a vision of a Network Compute Fabric where the lines between networking and computing disappear. On the journey there, edge cloud computing provides a critical stepping-stone where computing is pushed very close to where it is needed. This distribution of computing capabilities in the network creates new challenges for its management and operation. We argue that a data-centric approach that extensively uses artificial intelligence (AI) and machine learning (ML) technologies to realize specific management functions is a good candidate to tackle these challenges. As can be seen in Figure 1, edge computing services can be provided through compute/storage resources at different locations in a network, such as on-premises at a customer/enterprise site (industrial control, for example) or at access and local/regional sites (telco operators, for example).


Deep Learning vs Machine Learning

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Machine Learning and Deep Learning are concepts that are often overlapping. There can be a slight confusion between the terms, and thus, let us look at Machine learning vs Deep learning, and understand the similarities and differences between the same. Machine learning uses a set of algorithms to analyse and interpret data, learn from it, and based on the learnings, make best possible decisions. On the other hand, Deep learning structures the algorithms into multiple layers in order to create an "artificial neural network". This neural network can learn from the data and make intelligent decisions on its own.


Solving 'barren plateaus' is the key to quantum machine learning

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Many machine learning algorithms on quantum computers suffer from the dreaded "barren plateau" of unsolvability, where they run into dead ends on optimization problems. This challenge had been relatively unstudied--until now. Rigorous theoretical work has established theorems that guarantee whether a given machine learning algorithm will work as it scales up on larger computers. "The work solves a key problem of useability for quantum machine learning. We rigorously proved the conditions under which certain architectures of variational quantum algorithms will or will not have barren plateaus as they are scaled up," said Marco Cerezo, lead author on the paper published in Nature Communications today by a Los Alamos National Laboratory team.